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marek
2015-10-15 17:07:34 +02:00
parent da44352544
commit 9ebff446fa
239 changed files with 375645 additions and 0 deletions
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ICP/ICP.vcxproj
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ICP/ICP.vcxproj.filters
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LiveScan.sln
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LiveScanClient/KinectClient.vcxproj
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<Filter>Header Files</Filter>
</ClInclude>
<ClInclude Include="..\include\LiveScanClient\kinectCapture.h">
<Filter>Header Files</Filter>
</ClInclude>
<ClInclude Include="..\include\LiveScanClient\liveScanClient.h">
<Filter>Header Files</Filter>
</ClInclude>
<ClInclude Include="..\include\LiveScanClient\marker.h">
<Filter>Header Files</Filter>
</ClInclude>
<ClInclude Include="..\include\LiveScanClient\utils.h">
<Filter>Header Files</Filter>
</ClInclude>
</ItemGroup>
<ItemGroup>
<ClCompile Include="..\src\socketCS.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="..\src\LiveScanClient\calibration.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="..\src\LiveScanClient\filter.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="..\src\LiveScanClient\iCapture.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="..\src\LiveScanClient\imageRenderer.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="..\src\LiveScanClient\iMarker.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="..\src\LiveScanClient\kinectCapture.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="..\src\LiveScanClient\liveScanClient.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="..\src\LiveScanClient\marker.cpp">
<Filter>Source Files</Filter>
</ClCompile>
<ClCompile Include="..\src\LiveScanClient\utils.cpp">
<Filter>Source Files</Filter>
</ClCompile>
</ItemGroup>
<ItemGroup>
<Image Include="app.ico">
<Filter>Resource Files</Filter>
</Image>
</ItemGroup>
<ItemGroup>
<ResourceCompile Include="LiveScanClient.rc">
<Filter>Resource Files</Filter>
</ResourceCompile>
</ItemGroup>
</Project>
LiveScanClient/LiveScanClient.rc
+112
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// Microsoft Visual C++ generated resource script.
//
#include "resource.h"
#define APSTUDIO_READONLY_SYMBOLS
/////////////////////////////////////////////////////////////////////////////
//
// Generated from the TEXTINCLUDE 2 resource.
//
#define APSTUDIO_HIDDEN_SYMBOLS
#include "windows.h"
#undef APSTUDIO_HIDDEN_SYMBOLS
/////////////////////////////////////////////////////////////////////////////
#undef APSTUDIO_READONLY_SYMBOLS
/////////////////////////////////////////////////////////////////////////////
// English (United States) resources
#if !defined(AFX_RESOURCE_DLL) || defined(AFX_TARG_ENU)
LANGUAGE LANG_ENGLISH, SUBLANG_ENGLISH_US
#pragma code_page(1252)
/////////////////////////////////////////////////////////////////////////////
//
// Icon
//
// Icon with lowest ID value placed first to ensure application icon
// remains consistent on all systems.
IDI_APP ICON "app.ico"
/////////////////////////////////////////////////////////////////////////////
//
// Dialog
//
IDD_APP DIALOGEX 0, 0, 643, 415
STYLE DS_SETFONT | DS_FIXEDSYS | WS_MINIMIZEBOX | WS_CLIPCHILDREN | WS_CAPTION | WS_SYSMENU
EXSTYLE WS_EX_CONTROLPARENT | WS_EX_APPWINDOW
CAPTION "LiveScanClient"
CLASS "LiveScanClientAppDlgWndClass"
FONT 8, "MS Shell Dlg", 400, 0, 0x1
BEGIN
CONTROL "",IDC_VIDEOVIEW,"Static",SS_BLACKFRAME,0,0,641,360
DEFPUSHBUTTON "Connect",IDC_BUTTON_CONNECT,282,388,90,12
EDITTEXT IDC_IP,387,387,118,13,ES_AUTOHSCROLL
LTEXT "",IDC_STATUS,0,402,642,11,SS_SUNKEN
DEFPUSHBUTTON "Show depth",IDC_BUTTON_SWITCH,282,373,90,12
LTEXT "Server IP address:",IDC_STATIC,389,374,82,10
END
/////////////////////////////////////////////////////////////////////////////
//
// DESIGNINFO
//
#ifdef APSTUDIO_INVOKED
GUIDELINES DESIGNINFO
BEGIN
IDD_APP, DIALOG
BEGIN
RIGHTMARGIN, 642
BOTTOMMARGIN, 413
END
END
#endif // APSTUDIO_INVOKED
#ifdef APSTUDIO_INVOKED
/////////////////////////////////////////////////////////////////////////////
//
// TEXTINCLUDE
//
1 TEXTINCLUDE
BEGIN
"resource.h\0"
END
2 TEXTINCLUDE
BEGIN
"#define APSTUDIO_HIDDEN_SYMBOLS\r\n"
"#include ""windows.h""\r\n"
"#undef APSTUDIO_HIDDEN_SYMBOLS\r\n"
"\0"
END
3 TEXTINCLUDE
BEGIN
"\r\n"
"\0"
END
#endif // APSTUDIO_INVOKED
#endif // English (United States) resources
/////////////////////////////////////////////////////////////////////////////
#ifndef APSTUDIO_INVOKED
/////////////////////////////////////////////////////////////////////////////
//
// Generated from the TEXTINCLUDE 3 resource.
//
/////////////////////////////////////////////////////////////////////////////
#endif // not APSTUDIO_INVOKED
LiveScanClient/app.ico
BIN
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After

Width:  |  Height:  |  Size: 12 KiB

LiveScanClient/resource.h
+24
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//{{NO_DEPENDENCIES}}
// Microsoft Visual C++ generated include file.
//
#define IDI_APP 100
#define IDD_APP 100
#define IDC_VIDEOVIEW 1000
#define IDC_STATUS 1001
#define IDC_BUTTON_CONNECT 1003
#define IDC_IP 1006
#define IDC_BUTTON_SWITCH 1013
#define IDC_STATUS2 1014
#define IDC_STATIC -1
// Next default values for new objects
//
#ifdef APSTUDIO_INVOKED
#ifndef APSTUDIO_READONLY_SYMBOLS
#define _APS_NO_MFC 1
#define _APS_NEXT_RESOURCE_VALUE 101
#define _APS_NEXT_COMMAND_VALUE 32771
#define _APS_NEXT_CONTROL_VALUE 1015
#define _APS_NEXT_SYMED_VALUE 102
#endif
#endif
LiveScanClient/stdafx.h
+44
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//------------------------------------------------------------------------------
// <copyright file="stdafx.h" company="Microsoft">
// Copyright (c) Microsoft Corporation. All rights reserved.
// </copyright>
//------------------------------------------------------------------------------
// include file for standard system and project includes
#pragma once
#ifndef WIN32_LEAN_AND_MEAN
#define WIN32_LEAN_AND_MEAN // Exclude rarely-used stuff from Windows headers
#endif
// Windows Header Files
#include <windows.h>
#include <Shlobj.h>
// Direct2D Header Files
#include <d2d1.h>
#pragma comment (lib, "d2d1.lib")
#ifdef _UNICODE
#if defined _M_IX86
#pragma comment(linker,"/manifestdependency:\"type='win32' name='Microsoft.Windows.Common-Controls' version='6.0.0.0' processorArchitecture='x86' publicKeyToken='6595b64144ccf1df' language='*'\"")
#elif defined _M_X64
#pragma comment(linker,"/manifestdependency:\"type='win32' name='Microsoft.Windows.Common-Controls' version='6.0.0.0' processorArchitecture='amd64' publicKeyToken='6595b64144ccf1df' language='*'\"")
#else
#pragma comment(linker,"/manifestdependency:\"type='win32' name='Microsoft.Windows.Common-Controls' version='6.0.0.0' processorArchitecture='*' publicKeyToken='6595b64144ccf1df' language='*'\"")
#endif
#endif
// Safe release for interfaces
template<class Interface>
inline void SafeRelease(Interface *& pInterfaceToRelease)
{
if (pInterfaceToRelease != NULL)
{
pInterfaceToRelease->Release();
pInterfaceToRelease = NULL;
}
}
LiveScanServer/App.config
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<?xml version="1.0" encoding="utf-8" ?>
<configuration>
<startup>
<supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.5" />
</startup>
</configuration>
LiveScanServer/KinectServer.cs
+429
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Threading;
using System.Net.Sockets;
using System.Net;
using System.ComponentModel;
namespace KinectServer
{
public delegate void SocketListChangedHandler(List<KinectSocket> list);
public class KinectServer
{
Socket oServerSocket;
bool bServerRunning = false;
KinectSettings oSettings;
object oClientSocketLock = new object();
object oFrameRequestLock = new object();
List<KinectSocket> lClientSockets = new List<KinectSocket>();
public event SocketListChangedHandler eSocketListChanged;
public int nClientCount
{
get
{
int nClients;
lock (oClientSocketLock)
{
nClients = lClientSockets.Count;
}
return nClients;
}
}
public List<AffineTransform> lCameraPoses
{
get
{
List<AffineTransform> cameraPoses = new List<AffineTransform>();
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
cameraPoses.Add(lClientSockets[i].oCameraPose);
}
}
return cameraPoses;
}
}
public List<AffineTransform> lWorldTransforms
{
get
{
List<AffineTransform> worldTransforms = new List<AffineTransform>();
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
worldTransforms.Add(lClientSockets[i].oWorldTransform);
}
}
return worldTransforms;
}
set
{
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
lClientSockets[i].oWorldTransform = value[i];
}
}
}
}
public bool bAllCalibrated
{
get
{
bool allCalibrated = true;
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
if (!lClientSockets[i].bCalibrated)
{
allCalibrated = false;
break;
}
}
}
return allCalibrated;
}
}
public KinectServer(KinectSettings settings)
{
this.oSettings = settings;
}
private void SocketListChanged()
{
if (eSocketListChanged != null)
{
eSocketListChanged(lClientSockets);
}
}
public void StartServer()
{
if (!bServerRunning)
{
oServerSocket = new Socket(SocketType.Stream, ProtocolType.Tcp);
oServerSocket.Blocking = false;
IPEndPoint endPoint = new IPEndPoint(IPAddress.Any, 48001);
oServerSocket.Bind(endPoint);
oServerSocket.Listen(10);
bServerRunning = true;
Thread listeningThread = new Thread(this.ListeningWorker);
listeningThread.Start();
Thread receivingThread = new Thread(this.ReceivingWorker);
receivingThread.Start();
}
}
public void StopServer()
{
if (bServerRunning)
{
bServerRunning = false;
oServerSocket.Close();
lock (oClientSocketLock)
lClientSockets.Clear();
}
}
public void CaptureSynchronizedFrame()
{
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
lClientSockets[i].CaptureFrame();
}
//Wait till frames captured
bool allGathered = false;
while (!allGathered)
{
allGathered = true;
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
if (!lClientSockets[i].bFrameCaptured)
{
allGathered = false;
break;
}
}
}
}
}
public void Calibrate()
{
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
lClientSockets[i].Calibrate();
}
}
}
public void SendSettings()
{
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
lClientSockets[i].SendSettings(oSettings);
}
}
}
public void SendCalibrationData()
{
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
lClientSockets[i].SendCalibrationData();
}
}
}
public void GetStoredFrame(List<List<byte>> lFramesRGB, List<List<Single>> lFramesVerts)
{
lFramesRGB.Clear();
lFramesVerts.Clear();
lock (oFrameRequestLock)
{
//Request frames
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
lClientSockets[i].RequestStoredFrame();
}
//Wait till frames received
bool allGathered = false;
while (!allGathered)
{
allGathered = true;
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
if (!lClientSockets[i].bStoredFrameReceived)
{
allGathered = false;
break;
}
}
}
}
//Store received frames
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
lFramesRGB.Add(new List<byte>(lClientSockets[i].lFrameRGB));
lFramesVerts.Add(new List<Single>(lClientSockets[i].lFrameVerts));
}
}
}
}
public void GetLatestFrame(List<List<byte>> lFramesRGB, List<List<Single>> lFramesVerts)
{
lFramesRGB.Clear();
lFramesVerts.Clear();
lock (oFrameRequestLock)
{
//Request frames
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
lClientSockets[i].RequestLastFrame();
}
//Wait till frames received
bool allGathered = false;
while (!allGathered)
{
allGathered = true;
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
if (!lClientSockets[i].bLatestFrameReceived)
{
allGathered = false;
break;
}
}
}
}
//Store received frames
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
lFramesRGB.Add(new List<byte>(lClientSockets[i].lFrameRGB));
lFramesVerts.Add(new List<Single>(lClientSockets[i].lFrameVerts));
}
}
}
}
public void ClearStoredFrames()
{
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
lClientSockets[i].ClearStoredFrames();
}
}
}
private void ListeningWorker()
{
while (bServerRunning)
{
try
{
Socket newClient = oServerSocket.Accept();
//we do not want to add new clients while a frame is being requested
lock (oFrameRequestLock)
{
lock (oClientSocketLock)
{
lClientSockets.Add(new KinectSocket(newClient));
lClientSockets[lClientSockets.Count - 1].SendSettings(oSettings);
lClientSockets[lClientSockets.Count - 1].eChanged += new SocketChangedHandler(SocketListChanged);
if (eSocketListChanged != null)
{
eSocketListChanged(lClientSockets);
}
}
}
}
catch (SocketException)
{
}
System.Threading.Thread.Sleep(100);
}
if (eSocketListChanged != null)
{
eSocketListChanged(lClientSockets);
}
}
private void ReceivingWorker()
{
System.Timers.Timer checkConnectionTimer = new System.Timers.Timer();
checkConnectionTimer.Interval = 1000;
checkConnectionTimer.Elapsed += delegate(object sender, System.Timers.ElapsedEventArgs e)
{
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
if (!lClientSockets[i].SocketConnected())
{
lClientSockets.RemoveAt(i);
if (eSocketListChanged != null)
{
eSocketListChanged(lClientSockets);
}
continue;
}
}
}
};
checkConnectionTimer.Start();
while (bServerRunning)
{
lock (oClientSocketLock)
{
for (int i = 0; i < lClientSockets.Count; i++)
{
byte[] buffer = lClientSockets[i].Receive(1);
while (buffer.Length != 0)
{
if (buffer[0] == 0)
{
lClientSockets[i].bFrameCaptured = true;
}
else if (buffer[0] == 1)
{
lClientSockets[i].ReceiveCalibrationData();
}
//stored frame
else if (buffer[0] == 2)
{
lClientSockets[i].ReceiveFrame();
lClientSockets[i].bStoredFrameReceived = true;
}
//last frame
else if (buffer[0] == 3)
{
lClientSockets[i].ReceiveFrame();
lClientSockets[i].bLatestFrameReceived = true;
}
buffer = lClientSockets[i].Receive(1);
}
}
}
Thread.Sleep(10);
}
checkConnectionTimer.Stop();
}
}
}
LiveScanServer/KinectSettings.cs
+93
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace KinectServer
{
[Serializable]
public class KinectSettings
{
public float[] aMinBounds = new float[3];
public float[] aMaxBounds = new float[3];
public bool bFilter = false;
public int nFilterNeighbors = 10;
public float fFilterThreshold = 0.1f;
public BindingList<MarkerPose> lMarkerPoses = new BindingList<MarkerPose>();
public int nNumICPIterations = 10;
public int nMaxNumRefineIters = 10;
public bool bMergeScansForSave = false;
public KinectSettings()
{
aMinBounds[0] = -5f;
aMinBounds[1] = -5f;
aMinBounds[2] = -5f;
aMaxBounds[0] = 5f;
aMaxBounds[1] = 5f;
aMaxBounds[2] = 5f;
}
public List<byte> ToByteList()
{
List<byte> lData = new List<byte>();
byte[] bTemp = new byte[sizeof(float) * 3];
Buffer.BlockCopy(aMinBounds, 0, bTemp, 0, sizeof(float) * 3);
lData.AddRange(bTemp);
Buffer.BlockCopy(aMaxBounds, 0, bTemp, 0, sizeof(float) * 3);
lData.AddRange(bTemp);
if (bFilter)
lData.Add(1);
else
lData.Add(0);
bTemp = BitConverter.GetBytes(nFilterNeighbors);
lData.AddRange(bTemp);
bTemp = BitConverter.GetBytes(fFilterThreshold);
lData.AddRange(bTemp);
bTemp = BitConverter.GetBytes(lMarkerPoses.Count);
lData.AddRange(bTemp);
for (int i = 0; i < lMarkerPoses.Count; i++)
{
bTemp = new byte[sizeof(float) * 9];
Buffer.BlockCopy(lMarkerPoses[i].pose.R, 0, bTemp, 0, sizeof(float) * 9);
lData.AddRange(bTemp);
bTemp = new byte[sizeof(float) * 3];
Buffer.BlockCopy(lMarkerPoses[i].pose.t, 0, bTemp, 0, sizeof(float) * 3);
lData.AddRange(bTemp);
bTemp = BitConverter.GetBytes(lMarkerPoses[i].id);
lData.AddRange(bTemp);
}
return lData;
}
}
}
LiveScanServer/KinectSocket.cs
+246
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@@ -0,0 +1,246 @@
// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Net.Sockets;
namespace KinectServer
{
public delegate void SocketChangedHandler();
public class KinectSocket
{
Socket oSocket;
byte[] byteToSend = new byte[1];
public bool bFrameCaptured = false;
public bool bLatestFrameReceived = false;
public bool bStoredFrameReceived = false;
public bool bCalibrated = false;
//The pose of the sensor in the scene (used by the OpenGLWindow to show the sensor)
public AffineTransform oCameraPose = new AffineTransform();
//The transform that maps the vertices in the sensor coordinate system to the world corrdinate system.
public AffineTransform oWorldTransform = new AffineTransform();
public string sSocketState;
public List<byte> lFrameRGB = new List<byte>();
public List<Single> lFrameVerts = new List<Single>();
public event SocketChangedHandler eChanged;
public KinectSocket(Socket clientSocket)
{
oSocket = clientSocket;
sSocketState = oSocket.RemoteEndPoint.ToString() + " Calibrated = false";
}
public void CaptureFrame()
{
bFrameCaptured = false;
byteToSend[0] = 0;
SendByte();
}
public void Calibrate()
{
bCalibrated = false;
sSocketState = oSocket.RemoteEndPoint.ToString() + " Calibrated = false";
byteToSend[0] = 1;
SendByte();
UpdateSocketState();
}
public void SendSettings(KinectSettings settings)
{
List<byte> lData = settings.ToByteList();
byte[] bTemp = BitConverter.GetBytes(lData.Count);
lData.InsertRange(0, bTemp);
lData.Insert(0, 2);
if (SocketConnected())
oSocket.Send(lData.ToArray());
}
public void RequestStoredFrame()
{
byteToSend[0] = 3;
SendByte();
bStoredFrameReceived = false;
}
public void RequestLastFrame()
{
byteToSend[0] = 4;
SendByte();
bLatestFrameReceived = false;
}
public void SendCalibrationData()
{
int size = 1 + 2 * (9 + 3) * sizeof(float);
byte[] data = new byte[size];
int i = 0;
data[i] = 5;
i++;
Buffer.BlockCopy(oWorldTransform.R, 0, data, i, 9 * sizeof(float));
i += 9 * sizeof(float);
Buffer.BlockCopy(oWorldTransform.t, 0, data, i, 3 * sizeof(float));
i += 3 * sizeof(float);
Buffer.BlockCopy(oCameraPose.R, 0, data, i, 9 * sizeof(float));
i += 9 * sizeof(float);
Buffer.BlockCopy(oCameraPose.t, 0, data, i, 3 * sizeof(float));
i += 3 * sizeof(float);
if (SocketConnected())
oSocket.Send(data);
}
public void ClearStoredFrames()
{
byteToSend[0] = 6;
SendByte();
}
public void ReceiveCalibrationData()
{
bCalibrated = true;
byte[] buffer = Receive(sizeof(int) * 1);
//currently not used
int markerId = BitConverter.ToInt32(buffer, 0);
buffer = Receive(sizeof(float) * 9);
Buffer.BlockCopy(buffer, 0, oWorldTransform.R, 0, sizeof(float) * 9);
buffer = Receive(sizeof(float) * 3);
Buffer.BlockCopy(buffer, 0, oWorldTransform.t, 0, sizeof(float) * 3);
buffer = Receive(sizeof(float) * 9);
Buffer.BlockCopy(buffer, 0, oCameraPose.R, 0, sizeof(float) * 9);
buffer = Receive(sizeof(float) * 3);
Buffer.BlockCopy(buffer, 0, oCameraPose.t, 0, sizeof(float) * 3);
UpdateSocketState();
}
public void ReceiveFrame()
{
lFrameRGB.Clear();
lFrameVerts.Clear();
int nToRead;
byte[] buffer = new byte[1024];
while (oSocket.Available == 0)
{
if (!SocketConnected())
return;
}
oSocket.Receive(buffer, 4, SocketFlags.None);
//string result = System.Text.Encoding.UTF8.GetString(buffer);
//n_to_read = Int32.Parse(result);
nToRead = BitConverter.ToInt32(buffer, 0);
if (nToRead == 0)
return;
buffer = new byte[nToRead];
int nAlreadyRead = 0;
while (nAlreadyRead != nToRead)
{
while (oSocket.Available == 0)
{
if (!SocketConnected())
return;
}
nAlreadyRead += oSocket.Receive(buffer, nAlreadyRead, nToRead - nAlreadyRead, SocketFlags.None);
}
int point_size = 3 + 3 * 4;
int n_vertices = nToRead / point_size;
for (int i = 0; i < n_vertices; i++)
{
for (int j = 0; j < 3; j++)
{
lFrameRGB.Add(buffer[i * point_size + j]);
lFrameVerts.Add(BitConverter.ToSingle(buffer, i * point_size + j * 4 + 3));
}
}
}
public byte[] Receive(int nBytes)
{
byte[] buffer;
if (oSocket.Available != 0)
{
buffer = new byte[Math.Min(nBytes, oSocket.Available)];
oSocket.Receive(buffer, nBytes, SocketFlags.None);
}
else
buffer = new byte[0];
return buffer;
}
public bool SocketConnected()
{
bool part1 = oSocket.Poll(1000, SelectMode.SelectRead);
bool part2 = (oSocket.Available == 0);
if (part1 && part2)
{
return false;
}
else
{
return true;
}
}
private void SendByte()
{
oSocket.Send(byteToSend);
}
public void UpdateSocketState()
{
if (bCalibrated)
sSocketState = oSocket.RemoteEndPoint.ToString() + " Calibrated = true";
else
sSocketState = oSocket.RemoteEndPoint.ToString() + " Calibrated = false";
if (eChanged != null)
eChanged();
}
}
}
LiveScanServer/LiveScanServer.csproj
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<?xml version="1.0" encoding="utf-8"?>
<Project ToolsVersion="12.0" DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
<Import Project="$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props" Condition="Exists('$(MSBuildExtensionsPath)\$(MSBuildToolsVersion)\Microsoft.Common.props')" />
<PropertyGroup>
<Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
<Platform Condition=" '$(Platform)' == '' ">AnyCPU</Platform>
<ProjectGuid>{78422B2C-0EF1-4A28-A40E-2A7C4A9204B1}</ProjectGuid>
<OutputType>WinExe</OutputType>
<AppDesignerFolder>Properties</AppDesignerFolder>
<RootNamespace>KinectServer</RootNamespace>
<AssemblyName>LiveScanServer</AssemblyName>
<TargetFrameworkVersion>v4.5</TargetFrameworkVersion>
<FileAlignment>512</FileAlignment>
</PropertyGroup>
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' ">
<PlatformTarget>AnyCPU</PlatformTarget>
<DebugSymbols>true</DebugSymbols>
<DebugType>full</DebugType>
<Optimize>false</Optimize>
<OutputPath>..\bin\</OutputPath>
<DefineConstants>DEBUG;TRACE</DefineConstants>
<ErrorReport>prompt</ErrorReport>
<WarningLevel>4</WarningLevel>
</PropertyGroup>
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|AnyCPU' ">
<PlatformTarget>AnyCPU</PlatformTarget>
<DebugType>pdbonly</DebugType>
<Optimize>true</Optimize>
<OutputPath>..\bin\</OutputPath>
<DefineConstants>TRACE</DefineConstants>
<ErrorReport>prompt</ErrorReport>
<WarningLevel>4</WarningLevel>
</PropertyGroup>
<ItemGroup>
<Reference Include="OpenTK, Version=1.1.0.0, Culture=neutral, PublicKeyToken=bad199fe84eb3df4, processorArchitecture=MSIL" />
<Reference Include="OpenTK.Compatibility, Version=1.1.0.0, Culture=neutral, PublicKeyToken=bad199fe84eb3df4, processorArchitecture=MSIL" />
<Reference Include="OpenTK.GLControl, Version=1.1.0.0, Culture=neutral, PublicKeyToken=bad199fe84eb3df4, processorArchitecture=MSIL" />
<Reference Include="System" />
<Reference Include="System.Core" />
<Reference Include="System.Xml.Linq" />
<Reference Include="System.Data.DataSetExtensions" />
<Reference Include="Microsoft.CSharp" />
<Reference Include="System.Data" />
<Reference Include="System.Deployment" />
<Reference Include="System.Drawing" />
<Reference Include="System.Windows.Forms" />
<Reference Include="System.Xml" />
</ItemGroup>
<ItemGroup>
<Compile Include="MainWindowForm.cs">
<SubType>Form</SubType>
</Compile>
<Compile Include="MainWindowForm.Designer.cs">
<DependentUpon>MainWindowForm.cs</DependentUpon>
</Compile>
<Compile Include="KinectServer.cs" />
<Compile Include="KinectSettings.cs" />
<Compile Include="KinectSocket.cs" />
<Compile Include="OpenGLWindow.cs" />
<Compile Include="Program.cs" />
<Compile Include="Properties\AssemblyInfo.cs" />
<Compile Include="SettingsForm.cs">
<SubType>Form</SubType>
</Compile>
<Compile Include="SettingsForm.Designer.cs">
<DependentUpon>SettingsForm.cs</DependentUpon>
</Compile>
<Compile Include="Utils.cs" />
<EmbeddedResource Include="MainWindowForm.resx">
<DependentUpon>MainWindowForm.cs</DependentUpon>
</EmbeddedResource>
<EmbeddedResource Include="Properties\Resources.resx">
<Generator>ResXFileCodeGenerator</Generator>
<LastGenOutput>Resources.Designer.cs</LastGenOutput>
<SubType>Designer</SubType>
</EmbeddedResource>
<Compile Include="Properties\Resources.Designer.cs">
<AutoGen>True</AutoGen>
<DependentUpon>Resources.resx</DependentUpon>
</Compile>
<EmbeddedResource Include="SettingsForm.resx">
<DependentUpon>SettingsForm.cs</DependentUpon>
</EmbeddedResource>
<None Include="Properties\Settings.settings">
<Generator>SettingsSingleFileGenerator</Generator>
<LastGenOutput>Settings.Designer.cs</LastGenOutput>
</None>
<Compile Include="Properties\Settings.Designer.cs">
<AutoGen>True</AutoGen>
<DependentUpon>Settings.settings</DependentUpon>
<DesignTimeSharedInput>True</DesignTimeSharedInput>
</Compile>
</ItemGroup>
<ItemGroup>
<None Include="App.config" />
</ItemGroup>
<Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
<!-- To modify your build process, add your task inside one of the targets below and uncomment it.
Other similar extension points exist, see Microsoft.Common.targets.
<Target Name="BeforeBuild">
</Target>
<Target Name="AfterBuild">
</Target>
-->
</Project>
LiveScanServer/MainWindowForm.Designer.cs
Generated
+225
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namespace KinectServer
{
partial class MainWindowForm
{
/// <summary>
/// Required designer variable.
/// </summary>
private System.ComponentModel.IContainer components = null;
/// <summary>
/// Clean up any resources being used.
/// </summary>
/// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
protected override void Dispose(bool disposing)
{
if (disposing && (components != null))
{
components.Dispose();
}
base.Dispose(disposing);
}
#region Windows Form Designer generated code
/// <summary>
/// Required method for Designer support - do not modify
/// the contents of this method with the code editor.
/// </summary>
private void InitializeComponent()
{
this.btStart = new System.Windows.Forms.Button();
this.btCalibrate = new System.Windows.Forms.Button();
this.btRecord = new System.Windows.Forms.Button();
this.lClientListBox = new System.Windows.Forms.ListBox();
this.statusStrip1 = new System.Windows.Forms.StatusStrip();
this.statusLabel = new System.Windows.Forms.ToolStripStatusLabel();
this.recordingWorker = new System.ComponentModel.BackgroundWorker();
this.txtSeqName = new System.Windows.Forms.TextBox();
this.btRefineCalib = new System.Windows.Forms.Button();
this.OpenGLWorker = new System.ComponentModel.BackgroundWorker();
this.savingWorker = new System.ComponentModel.BackgroundWorker();
this.updateWorker = new System.ComponentModel.BackgroundWorker();
this.btShowLive = new System.Windows.Forms.Button();
this.btSettings = new System.Windows.Forms.Button();
this.refineWorker = new System.ComponentModel.BackgroundWorker();
this.lbSeqName = new System.Windows.Forms.Label();
this.statusStrip1.SuspendLayout();
this.SuspendLayout();
//
// btStart
//
this.btStart.Location = new System.Drawing.Point(12, 12);
this.btStart.Name = "btStart";
this.btStart.Size = new System.Drawing.Size(95, 23);
this.btStart.TabIndex = 0;
this.btStart.Text = "Start server";
this.btStart.UseVisualStyleBackColor = true;
this.btStart.Click += new System.EventHandler(this.btStart_Click);
//
// btCalibrate
//
this.btCalibrate.Location = new System.Drawing.Point(12, 70);
this.btCalibrate.Name = "btCalibrate";
this.btCalibrate.Size = new System.Drawing.Size(95, 23);
this.btCalibrate.TabIndex = 2;
this.btCalibrate.Text = "Calibrate";
this.btCalibrate.UseVisualStyleBackColor = true;
this.btCalibrate.Click += new System.EventHandler(this.btCalibrate_Click);
//
// btRecord
//
this.btRecord.Location = new System.Drawing.Point(12, 126);
this.btRecord.Name = "btRecord";
this.btRecord.Size = new System.Drawing.Size(95, 23);
this.btRecord.TabIndex = 4;
this.btRecord.Text = "Start recording";
this.btRecord.UseVisualStyleBackColor = true;
this.btRecord.Click += new System.EventHandler(this.btRecord_Click);
//
// lClientListBox
//
this.lClientListBox.FormattingEnabled = true;
this.lClientListBox.Location = new System.Drawing.Point(113, 12);
this.lClientListBox.Name = "lClientListBox";
this.lClientListBox.Size = new System.Drawing.Size(219, 108);
this.lClientListBox.TabIndex = 5;
//
// statusStrip1
//
this.statusStrip1.Items.AddRange(new System.Windows.Forms.ToolStripItem[] {
this.statusLabel});
this.statusStrip1.Location = new System.Drawing.Point(0, 187);
this.statusStrip1.Name = "statusStrip1";
this.statusStrip1.Size = new System.Drawing.Size(344, 22);
this.statusStrip1.TabIndex = 6;
this.statusStrip1.Text = "statusStrip1";
//
// statusLabel
//
this.statusLabel.Name = "statusLabel";
this.statusLabel.Size = new System.Drawing.Size(0, 17);
//
// recordingWorker
//
this.recordingWorker.WorkerSupportsCancellation = true;
this.recordingWorker.DoWork += new System.ComponentModel.DoWorkEventHandler(this.recordingWorker_DoWork);
this.recordingWorker.RunWorkerCompleted += new System.ComponentModel.RunWorkerCompletedEventHandler(this.recordingWorker_RunWorkerCompleted);
//
// txtSeqName
//
this.txtSeqName.Location = new System.Drawing.Point(113, 155);
this.txtSeqName.MaxLength = 40;
this.txtSeqName.Name = "txtSeqName";
this.txtSeqName.Size = new System.Drawing.Size(106, 20);
this.txtSeqName.TabIndex = 7;
this.txtSeqName.Text = "noname";
//
// btRefineCalib
//
this.btRefineCalib.Location = new System.Drawing.Point(12, 97);
this.btRefineCalib.Name = "btRefineCalib";
this.btRefineCalib.Size = new System.Drawing.Size(95, 23);
this.btRefineCalib.TabIndex = 11;
this.btRefineCalib.Text = "Refine calib";
this.btRefineCalib.UseVisualStyleBackColor = true;
this.btRefineCalib.Click += new System.EventHandler(this.btRefineCalib_Click);
//
// OpenGLWorker
//
this.OpenGLWorker.DoWork += new System.ComponentModel.DoWorkEventHandler(this.OpenGLWorker_DoWork);
this.OpenGLWorker.RunWorkerCompleted += new System.ComponentModel.RunWorkerCompletedEventHandler(this.OpenGLWorker_RunWorkerCompleted);
//
// savingWorker
//
this.savingWorker.WorkerSupportsCancellation = true;
this.savingWorker.DoWork += new System.ComponentModel.DoWorkEventHandler(this.savingWorker_DoWork);
this.savingWorker.RunWorkerCompleted += new System.ComponentModel.RunWorkerCompletedEventHandler(this.savingWorker_RunWorkerCompleted);
//
// updateWorker
//
this.updateWorker.WorkerSupportsCancellation = true;
this.updateWorker.DoWork += new System.ComponentModel.DoWorkEventHandler(this.updateWorker_DoWork);
//
// btShowLive
//
this.btShowLive.Location = new System.Drawing.Point(12, 154);
this.btShowLive.Name = "btShowLive";
this.btShowLive.Size = new System.Drawing.Size(95, 23);
this.btShowLive.TabIndex = 12;
this.btShowLive.Text = "Show live";
this.btShowLive.UseVisualStyleBackColor = true;
this.btShowLive.Click += new System.EventHandler(this.btShowLive_Click);
//
// btSettings
//
this.btSettings.Location = new System.Drawing.Point(12, 41);
this.btSettings.Name = "btSettings";
this.btSettings.Size = new System.Drawing.Size(95, 23);
this.btSettings.TabIndex = 13;
this.btSettings.Text = "Settings";
this.btSettings.UseVisualStyleBackColor = true;
this.btSettings.Click += new System.EventHandler(this.btSettings_Click);
//
// refineWorker
//
this.refineWorker.DoWork += new System.ComponentModel.DoWorkEventHandler(this.refineWorker_DoWork);
this.refineWorker.RunWorkerCompleted += new System.ComponentModel.RunWorkerCompletedEventHandler(this.refineWorker_RunWorkerCompleted);
//
// lbSeqName
//
this.lbSeqName.AutoSize = true;
this.lbSeqName.Location = new System.Drawing.Point(113, 136);
this.lbSeqName.Name = "lbSeqName";
this.lbSeqName.Size = new System.Drawing.Size(88, 13);
this.lbSeqName.TabIndex = 14;
this.lbSeqName.Text = "Sequence name:";
//
// MainWindowForm
//
this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
this.ClientSize = new System.Drawing.Size(344, 209);
this.Controls.Add(this.lbSeqName);
this.Controls.Add(this.btSettings);
this.Controls.Add(this.btShowLive);
this.Controls.Add(this.btRefineCalib);
this.Controls.Add(this.txtSeqName);
this.Controls.Add(this.statusStrip1);
this.Controls.Add(this.lClientListBox);
this.Controls.Add(this.btRecord);
this.Controls.Add(this.btCalibrate);
this.Controls.Add(this.btStart);
this.FormBorderStyle = System.Windows.Forms.FormBorderStyle.FixedSingle;
this.MaximizeBox = false;
this.Name = "MainWindowForm";
this.Text = "LiveScanServer";
this.FormClosing += new System.Windows.Forms.FormClosingEventHandler(this.Form1_FormClosing);
this.statusStrip1.ResumeLayout(false);
this.statusStrip1.PerformLayout();
this.ResumeLayout(false);
this.PerformLayout();
}
#endregion
private System.Windows.Forms.Button btStart;
private System.Windows.Forms.Button btCalibrate;
private System.Windows.Forms.Button btRecord;
private System.Windows.Forms.ListBox lClientListBox;
private System.Windows.Forms.StatusStrip statusStrip1;
private System.ComponentModel.BackgroundWorker recordingWorker;
private System.Windows.Forms.TextBox txtSeqName;
private System.Windows.Forms.Button btRefineCalib;
private System.ComponentModel.BackgroundWorker OpenGLWorker;
private System.ComponentModel.BackgroundWorker savingWorker;
private System.ComponentModel.BackgroundWorker updateWorker;
private System.Windows.Forms.Button btShowLive;
private System.Windows.Forms.Button btSettings;
private System.ComponentModel.BackgroundWorker refineWorker;
private System.Windows.Forms.ToolStripStatusLabel statusLabel;
private System.Windows.Forms.Label lbSeqName;
}
}
LiveScanServer/MainWindowForm.cs
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
using System;
using System.Collections.Generic;
using System.Runtime.InteropServices;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Threading;
using System.Windows.Forms;
using System.Globalization;
using System.Runtime.Serialization;
using System.IO;
using System.Net;
using System.Net.Sockets;
using System.Timers;
namespace KinectServer
{
public partial class MainWindowForm : Form
{
[DllImport("ICP.dll")]
static extern float ICP(IntPtr verts1, IntPtr verts2, int nVerts1, int nVerts2, float[] R, float[] t, int maxIter = 200);
KinectServer oServer;
//Those three variables are shared with the OpenGLWindow class and are used to exchange data with it.
//Vertices from all of the sensors
List<float> lAllVertices = new List<float>();
//Color data from all of the sensors
List<byte> lAllColors = new List<byte>();
//Sensor poses from all of the sensors
List<AffineTransform> lAllCameraPoses = new List<AffineTransform>();
bool bServerRunning = false;
bool bRecording = false;
bool bSaving = false;
//Live view open or not
bool bLiveViewRunning = false;
System.Timers.Timer oStatusBarTimer = new System.Timers.Timer();
KinectSettings oSettings = new KinectSettings();
//The live view window class
OpenGLWindow oOpenGLWindow;
public MainWindowForm()
{
//This tries to read the settings from "settings.bin", if it failes the settings stay at default values.
try
{
IFormatter formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter();
Stream stream = new FileStream("settings.bin", FileMode.Open, FileAccess.Read);
oSettings = (KinectSettings)formatter.Deserialize(stream);
stream.Close();
}
catch(Exception)
{
}
oServer = new KinectServer(oSettings);
oServer.eSocketListChanged += new SocketListChangedHandler(UpdateListView);
InitializeComponent();
}
private void Form1_FormClosing(object sender, FormClosingEventArgs e)
{
//The current settings are saved to a files.
IFormatter formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter();
Stream stream = new FileStream("settings.bin", FileMode.Create, FileAccess.Write);
formatter.Serialize(stream, oSettings);
stream.Close();
oServer.StopServer();
}
//Starts the server
private void btStart_Click(object sender, EventArgs e)
{
bServerRunning = !bServerRunning;
if (bServerRunning)
{
oServer.StartServer();
btStart.Text = "Stop server";
}
else
{
oServer.StopServer();
btStart.Text = "Start server";
}
}
//Opens the settings form
private void btSettings_Click(object sender, EventArgs e)
{
SettingsForm form = new SettingsForm();
form.oSettings = oSettings;
form.oServer = oServer;
form.Show();
}
//Performs recording which is synchronized frame capture.
//The frames are downloaded from the clients and saved once recording is finished.
private void recordingWorker_DoWork(object sender, DoWorkEventArgs e)
{
oServer.ClearStoredFrames();
int nCaptured = 0;
BackgroundWorker worker = (BackgroundWorker)sender;
while (!worker.CancellationPending)
{
oServer.CaptureSynchronizedFrame();
nCaptured++;
SetStatusBarOnTimer("Captured frame " + (nCaptured).ToString() + ".", 5000);
}
}
private void recordingWorker_RunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e)
{
//After recording has been terminated it is time to begin saving the frames.
//Saving is downloading the frames from clients and saving them locally.
bSaving = true;
btRecord.Text = "Stop saving";
btRecord.Enabled = true;
savingWorker.RunWorkerAsync();
}
//Opens the live view window
private void OpenGLWorker_DoWork(object sender, DoWorkEventArgs e)
{
bLiveViewRunning = true;
oOpenGLWindow = new OpenGLWindow();
//The variables below are shared between this class and the OpenGLWindow.
lock (lAllVertices)
{
oOpenGLWindow.vertices = lAllVertices;
oOpenGLWindow.colors = lAllColors;
oOpenGLWindow.cameraPoses = lAllCameraPoses;
oOpenGLWindow.settings = oSettings;
}
oOpenGLWindow.Run();
}
private void OpenGLWorker_RunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e)
{
bLiveViewRunning = false;
updateWorker.CancelAsync();
}
private void savingWorker_DoWork(object sender, DoWorkEventArgs e)
{
int nFrames = 0;
string outDir = "out" + "\\" + txtSeqName.Text + "\\";
DirectoryInfo di = Directory.CreateDirectory(outDir);
BackgroundWorker worker = (BackgroundWorker)sender;
//This loop is running till it is either cancelled (using the btRecord button), or till there are no more stored frames.
while (!worker.CancellationPending)
{
List<List<byte>> lFrameRGBAllDevices = new List<List<byte>>();
List<List<float>> lFrameVertsAllDevices = new List<List<float>>();
oServer.GetStoredFrame(lFrameRGBAllDevices, lFrameVertsAllDevices);
nFrames++;
int nVerticesTotal = 0;
for (int i = 0; i < lFrameRGBAllDevices.Count; i++)
{
nVerticesTotal += lFrameVertsAllDevices[i].Count;
}
//This indicates that there are no more stored frames.
if (nVerticesTotal == 0)
break;
List<byte> lFrameRGB = new List<byte>();
List<Single> lFrameVerts = new List<Single>();
SetStatusBarOnTimer("Saving frame " + (nFrames).ToString() + ".", 5000);
for (int i = 0; i < lFrameRGBAllDevices.Count; i++)
{
lFrameRGB.AddRange(lFrameRGBAllDevices[i]);
lFrameVerts.AddRange(lFrameVertsAllDevices[i]);
//This is ran if the frames from each client are to be placed in separate files.
if (!oSettings.bMergeScansForSave)
{
string outputFilename = outDir + "\\" + nFrames.ToString().PadLeft(5, '0') + i.ToString() + ".ply";
saveToPly(outputFilename, lFrameVertsAllDevices[i], lFrameRGBAllDevices[i]);
}
}
//This is ran if the frames from all clients are to be placed in a single file.
if (oSettings.bMergeScansForSave)
{
string outputFilename = outDir + "\\" + nFrames.ToString().PadLeft(5, '0') + ".ply";
saveToPly(outputFilename, lFrameVerts, lFrameRGB);
}
}
}
private void savingWorker_RunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e)
{
oServer.ClearStoredFrames();
bSaving = false;
//If the live view window was open, we need to restart the UpdateWorker.
if (bLiveViewRunning)
RestartUpdateWorker();
btRecord.Enabled = true;
btRecord.Text = "Start recording";
btRefineCalib.Enabled = true;
btCalibrate.Enabled = true;
}
//Continually requests frames that will be displayed in the live view window.
private void updateWorker_DoWork(object sender, DoWorkEventArgs e)
{
List<List<byte>> lFramesRGB = new List<List<byte>>();
List<List<Single>> lFramesVerts = new List<List<Single>>();
BackgroundWorker worker = (BackgroundWorker)sender;
while (!worker.CancellationPending)
{
Thread.Sleep(1);
oServer.GetLatestFrame(lFramesRGB, lFramesVerts);
//Update the vertex and color lists that are common between this class and the OpenGLWindow.
lock (lAllVertices)
{
lAllVertices.Clear();
lAllColors.Clear();
lAllCameraPoses.Clear();
for (int i = 0; i < lFramesRGB.Count; i++)
{
lAllVertices.AddRange(lFramesVerts[i]);
lAllColors.AddRange(lFramesRGB[i]);
lAllCameraPoses.Add(oServer.lCameraPoses[i]);
}
}
//Notes the fact that a new frame was downloaded, this is used to estimate the FPS.
if (oOpenGLWindow != null)
oOpenGLWindow.CloudUpdateTick();
}
}
//Performs the ICP based pose refinement.
private void refineWorker_DoWork(object sender, DoWorkEventArgs e)
{
if (oServer.bAllCalibrated == false)
{
SetStatusBarOnTimer("Not all of the devices are calibrated.", 5000);
return;
}
//Download a frame from each client.
List<List<float>> lAllFrameVertices = new List<List<float>>();
List<List<byte>> lAllFrameColors = new List<List<byte>>();
oServer.GetLatestFrame(lAllFrameColors, lAllFrameVertices);
//Initialize containers for the poses.
List<float[]> Rs = new List<float[]>();
List<float[]> Ts = new List<float[]>();
for (int i = 0; i < lAllFrameVertices.Count; i++)
{
float[] tempR = new float[9];
float[] tempT = new float[3];
for (int j = 0; j < 3; j++)
{
tempT[j] = 0;
tempR[j + j * 3] = 1;
}
Rs.Add(tempR);
Ts.Add(tempT);
}
//Use ICP to refine the sensor poses.
//This part is explained in more detail in our article (name on top of this file).
float error = 1;
float oldError = 1;
bool firstPass = true;
int iter = 0;
while (true)
{
if (iter > oSettings.nMaxNumRefineIters)
break;
error = 0;
for (int i = 0; i < lAllFrameVertices.Count; i++)
{
List<float> otherFramesVertices = new List<float>();
for (int j = 0; j < lAllFrameVertices.Count; j++)
{
if (j == i)
continue;
otherFramesVertices.AddRange(lAllFrameVertices[j]);
}
float[] verts1 = otherFramesVertices.ToArray();
float[] verts2 = lAllFrameVertices[i].ToArray();
IntPtr pVerts1 = Marshal.AllocHGlobal(otherFramesVertices.Count * sizeof(float));
IntPtr pVerts2 = Marshal.AllocHGlobal(lAllFrameVertices[i].Count * sizeof(float));
Marshal.Copy(verts1, 0, pVerts1, verts1.Length);
Marshal.Copy(verts2, 0, pVerts2, verts2.Length);
error += ICP(pVerts1, pVerts2, otherFramesVertices.Count / 3, lAllFrameVertices[i].Count / 3, Rs[i], Ts[i], oSettings.nNumICPIterations);
Marshal.Copy(pVerts2, verts2, 0, verts2.Length);
lAllFrameVertices[i].Clear();
lAllFrameVertices[i].AddRange(verts2);
}
if (firstPass)
{
firstPass = false;
}
else
{
if (Math.Abs(oldError - error) / error < 0.01)
break;
}
oldError = error;
iter++;
}
//Update the calibration data in client machines.
List<AffineTransform> worldTransforms = oServer.lWorldTransforms;
for (int i = 0; i < worldTransforms.Count; i++)
{
float[] tempT = new float[3];
float[,] tempR = new float[3, 3];
for (int j = 0; j < 3; j++)
{
for (int k = 0; k < 3; k++)
{
tempT[j] += Ts[i][k] * worldTransforms[i].R[k, j];
}
worldTransforms[i].t[j] += tempT[j];
}
for (int j = 0; j < 3; j++)
{
for (int k = 0; k < 3; k++)
{
for (int l = 0; l < 3; l++)
{
tempR[j, k] += Rs[i][l * 3 + j] * worldTransforms[i].R[l, k];
}
worldTransforms[i].R[j, k] = tempR[j, k];
}
}
}
oServer.lWorldTransforms = worldTransforms;
oServer.SendCalibrationData();
}
private void refineWorker_RunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e)
{
//Re-enable all of the buttons after refinement.
btRefineCalib.Enabled = true;
btCalibrate.Enabled = true;
btRecord.Enabled = true;
}
//This is used for: starting/stopping the recording worker, stopping the saving worker
private void btRecord_Click(object sender, EventArgs e)
{
if (oServer.nClientCount < 1)
{
SetStatusBarOnTimer("At least one client needs to be connected for recording.", 5000);
return;
}
//If we are saving frames right now, this button stops saving.
if (bSaving)
{
btRecord.Enabled = false;
savingWorker.CancelAsync();
return;
}
bRecording = !bRecording;
if (bRecording)
{
//Stop the update worker to reduce the network usage (provides better synchronization).
updateWorker.CancelAsync();
recordingWorker.RunWorkerAsync();
btRecord.Text = "Stop recording";
btRefineCalib.Enabled = false;
btCalibrate.Enabled = false;
}
else
{
btRecord.Enabled = false;
recordingWorker.CancelAsync();
}
}
private void btCalibrate_Click(object sender, EventArgs e)
{
oServer.Calibrate();
}
private void btRefineCalib_Click(object sender, EventArgs e)
{
if (oServer.nClientCount < 2)
{
SetStatusBarOnTimer("To refine calibration you need at least 2 connected devices.", 5000);
return;
}
btRefineCalib.Enabled = false;
btCalibrate.Enabled = false;
btRecord.Enabled = false;
refineWorker.RunWorkerAsync();
}
private void saveToPly(string filename, List<Single> vertices, List<byte> colors)
{
int nVertices = vertices.Count / 3;
System.IO.StreamWriter file = new System.IO.StreamWriter(filename);
//PLY file header is written here.
file.WriteLine("ply\nformat ascii 1.0");
file.WriteLine("element vertex " + nVertices.ToString());
file.WriteLine("property float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header");
//Vertex and color data are written here.
for (int j = 0; j < vertices.Count / 3; j++)
{
string s = "";
for (int k = 0; k < 3; k++) s += vertices[j * 3 + k].ToString(CultureInfo.InvariantCulture) + " ";
for (int k = 0; k < 3; k++) s += colors[j * 3 + k].ToString(CultureInfo.InvariantCulture) + " ";
file.WriteLine(s);
}
file.Close();
}
void RestartUpdateWorker()
{
if (!updateWorker.IsBusy)
updateWorker.RunWorkerAsync();
}
private void btShowLive_Click(object sender, EventArgs e)
{
RestartUpdateWorker();
//Opens the live view window if it is not open yet.
if (!OpenGLWorker.IsBusy)
OpenGLWorker.RunWorkerAsync();
}
private void SetStatusBarOnTimer(string message, int milliseconds)
{
statusLabel.Text = message;
oStatusBarTimer.Stop();
oStatusBarTimer = new System.Timers.Timer();
oStatusBarTimer.Interval = milliseconds;
oStatusBarTimer.Elapsed += delegate(object sender, System.Timers.ElapsedEventArgs e)
{
oStatusBarTimer.Stop();
statusLabel.Text = "";
};
oStatusBarTimer.Start();
}
//Updates the ListBox contaning the connected clients, called by events inside KinectServer.
private void UpdateListView(List<KinectSocket> socketList)
{
List<string> listBoxItems = new List<string>();
for (int i = 0; i < socketList.Count; i++)
listBoxItems.Add(socketList[i].sSocketState);
lClientListBox.DataSource = listBoxItems;
}
}
}
LiveScanServer/MainWindowForm.resx
+138
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@@ -0,0 +1,138 @@
<?xml version="1.0" encoding="utf-8"?>
<root>
<!--
Microsoft ResX Schema
Version 2.0
The primary goals of this format is to allow a simple XML format
that is mostly human readable. The generation and parsing of the
various data types are done through the TypeConverter classes
associated with the data types.
Example:
... ado.net/XML headers & schema ...
<resheader name="resmimetype">text/microsoft-resx</resheader>
<resheader name="version">2.0</resheader>
<resheader name="reader">System.Resources.ResXResourceReader, System.Windows.Forms, ...</resheader>
<resheader name="writer">System.Resources.ResXResourceWriter, System.Windows.Forms, ...</resheader>
<data name="Name1"><value>this is my long string</value><comment>this is a comment</comment></data>
<data name="Color1" type="System.Drawing.Color, System.Drawing">Blue</data>
<data name="Bitmap1" mimetype="application/x-microsoft.net.object.binary.base64">
<value>[base64 mime encoded serialized .NET Framework object]</value>
</data>
<data name="Icon1" type="System.Drawing.Icon, System.Drawing" mimetype="application/x-microsoft.net.object.bytearray.base64">
<value>[base64 mime encoded string representing a byte array form of the .NET Framework object]</value>
<comment>This is a comment</comment>
</data>
There are any number of "resheader" rows that contain simple
name/value pairs.
Each data row contains a name, and value. The row also contains a
type or mimetype. Type corresponds to a .NET class that support
text/value conversion through the TypeConverter architecture.
Classes that don't support this are serialized and stored with the
mimetype set.
The mimetype is used for serialized objects, and tells the
ResXResourceReader how to depersist the object. This is currently not
extensible. For a given mimetype the value must be set accordingly:
Note - application/x-microsoft.net.object.binary.base64 is the format
that the ResXResourceWriter will generate, however the reader can
read any of the formats listed below.
mimetype: application/x-microsoft.net.object.binary.base64
value : The object must be serialized with
: System.Runtime.Serialization.Formatters.Binary.BinaryFormatter
: and then encoded with base64 encoding.
mimetype: application/x-microsoft.net.object.soap.base64
value : The object must be serialized with
: System.Runtime.Serialization.Formatters.Soap.SoapFormatter
: and then encoded with base64 encoding.
mimetype: application/x-microsoft.net.object.bytearray.base64
value : The object must be serialized into a byte array
: using a System.ComponentModel.TypeConverter
: and then encoded with base64 encoding.
-->
<xsd:schema id="root" xmlns="" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:msdata="urn:schemas-microsoft-com:xml-msdata">
<xsd:import namespace="http://www.w3.org/XML/1998/namespace" />
<xsd:element name="root" msdata:IsDataSet="true">
<xsd:complexType>
<xsd:choice maxOccurs="unbounded">
<xsd:element name="metadata">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" />
</xsd:sequence>
<xsd:attribute name="name" use="required" type="xsd:string" />
<xsd:attribute name="type" type="xsd:string" />
<xsd:attribute name="mimetype" type="xsd:string" />
<xsd:attribute ref="xml:space" />
</xsd:complexType>
</xsd:element>
<xsd:element name="assembly">
<xsd:complexType>
<xsd:attribute name="alias" type="xsd:string" />
<xsd:attribute name="name" type="xsd:string" />
</xsd:complexType>
</xsd:element>
<xsd:element name="data">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
<xsd:element name="comment" type="xsd:string" minOccurs="0" msdata:Ordinal="2" />
</xsd:sequence>
<xsd:attribute name="name" type="xsd:string" use="required" msdata:Ordinal="1" />
<xsd:attribute name="type" type="xsd:string" msdata:Ordinal="3" />
<xsd:attribute name="mimetype" type="xsd:string" msdata:Ordinal="4" />
<xsd:attribute ref="xml:space" />
</xsd:complexType>
</xsd:element>
<xsd:element name="resheader">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
</xsd:sequence>
<xsd:attribute name="name" type="xsd:string" use="required" />
</xsd:complexType>
</xsd:element>
</xsd:choice>
</xsd:complexType>
</xsd:element>
</xsd:schema>
<resheader name="resmimetype">
<value>text/microsoft-resx</value>
</resheader>
<resheader name="version">
<value>2.0</value>
</resheader>
<resheader name="reader">
<value>System.Resources.ResXResourceReader, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089</value>
</resheader>
<resheader name="writer">
<value>System.Resources.ResXResourceWriter, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089</value>
</resheader>
<metadata name="statusStrip1.TrayLocation" type="System.Drawing.Point, System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
<value>155, 17</value>
</metadata>
<metadata name="recordingWorker.TrayLocation" type="System.Drawing.Point, System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
<value>435, 17</value>
</metadata>
<metadata name="OpenGLWorker.TrayLocation" type="System.Drawing.Point, System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
<value>579, 17</value>
</metadata>
<metadata name="savingWorker.TrayLocation" type="System.Drawing.Point, System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
<value>702, 19</value>
</metadata>
<metadata name="updateWorker.TrayLocation" type="System.Drawing.Point, System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
<value>828, 19</value>
</metadata>
<metadata name="refineWorker.TrayLocation" type="System.Drawing.Point, System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
<value>958, 19</value>
</metadata>
</root>
LiveScanServer/OpenGLWindow.cs
+560
View File
@@ -0,0 +1,560 @@
// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Globalization;
using OpenTK;
using OpenTK.Graphics;
using OpenTK.Input;
enum ECameraMode
{
CAMERA_NONE, CAMERA_TRACK, CAMERA_DOLLY, CAMERA_ORBIT
}
namespace KinectServer
{
class OpenGLWindow : GameWindow
{
int PointCount;
int LineCount;
VertexC4ubV3f[] VBO;
float PointSize = 0.0f;
ECameraMode CameraMode = ECameraMode.CAMERA_NONE;
static float KEYBOARD_MOVE_SPEED = 0.01f;
static float MOUSE_ORBIT_SPEED = 0.30f; // 0 = SLOWEST, 1 = FASTEST
static float MOUSE_DOLLY_SPEED = 0.2f; // same as above...but much more sensitive
static float MOUSE_TRACK_SPEED = 0.003f; // same as above...but much more sensitive
float g_heading;
float g_pitch;
float dx = 0.0f;
float dy = 0.0f;
Vector2 MousePrevious = new Vector2();
Vector2 MouseCurrent = new Vector2();
float[] cameraPosition = new float[3];
float[] targetPosition = new float[3];
public List<float> vertices = new List<float>();
public List<byte> colors = new List<byte>();
public List<AffineTransform> cameraPoses = new List<AffineTransform>();
public KinectSettings settings = new KinectSettings();
DateTime tFPSUpdateTimer = DateTime.Now;
int nTickCounter = 0;
// this struct is used for drawing
struct VertexC4ubV3f
{
public byte R, G, B, A;
public Vector3 Position;
public static int SizeInBytes = 16;
}
uint VBOHandle;
/// <summary>Creates a 800x600 window with the specified title.</summary>
public OpenGLWindow()
: base(800, 600)
{
this.VSync = VSyncMode.Off;
MouseUp += new EventHandler<MouseButtonEventArgs>(OnMouseButtonUp);
MouseDown += new EventHandler<MouseButtonEventArgs>(OnMouseButtonDown);
MouseMove += new EventHandler<MouseMoveEventArgs>(OnMouseMove);
MouseWheel += new EventHandler<MouseWheelEventArgs>(OnMouseWheelChanged);
KeyDown += new EventHandler<KeyboardKeyEventArgs>(OnKeyDown);
cameraPosition[0] = 0;
cameraPosition[1] = 0;
cameraPosition[2] = 1.0f;
targetPosition[0] = 0;
targetPosition[1] = 0;
targetPosition[2] = 0;
}
public void CloudUpdateTick()
{
nTickCounter++;
}
void OnKeyDown(object sender, KeyboardKeyEventArgs e)
{
// e.Key
}
/// <summary>Load resources here.</summary>
/// <param name="e">Not used.</param>
protected override void OnLoad(EventArgs e)
{
base.OnLoad(e);
Version version = new Version(GL.GetString(StringName.Version).Substring(0, 3));
Version target = new Version(1, 5);
if (version < target)
{
throw new NotSupportedException(String.Format(
"OpenGL {0} is required (you only have {1}).", target, version));
}
GL.ClearColor(.1f, 0f, .1f, 0f);
GL.Enable(EnableCap.DepthTest);
// Setup parameters for Points
GL.PointSize(PointSize);
GL.Enable(EnableCap.PointSmooth);
GL.Hint(HintTarget.PointSmoothHint, HintMode.Nicest);
// Setup VBO state
GL.EnableClientState(EnableCap.ColorArray);
GL.EnableClientState(EnableCap.VertexArray);
GL.GenBuffers(1, out VBOHandle);
// Since there's only 1 VBO in the app, might aswell setup here.
GL.BindBuffer(BufferTarget.ArrayBuffer, VBOHandle);
GL.ColorPointer(4, ColorPointerType.UnsignedByte, VertexC4ubV3f.SizeInBytes, (IntPtr)0);
GL.VertexPointer(3, VertexPointerType.Float, VertexC4ubV3f.SizeInBytes, (IntPtr)(4 * sizeof(byte)));
PointCount = 0;
LineCount = 12;
VBO = new VertexC4ubV3f[PointCount + 2 * LineCount];
}
protected override void OnUnload(EventArgs e)
{
GL.DeleteBuffers(1, ref VBOHandle);
}
/// <summary>
/// Called when your window is resized. Set your viewport here. It is also
/// a good place to set up your projection matrix (which probably changes
/// along when the aspect ratio of your window).
/// </summary>
/// <param name="e">Contains information on the new Width and Size of the GameWindow.</param>
protected override void OnResize(EventArgs e)
{
GL.Viewport(0, 0, Width, Height);
GL.MatrixMode(MatrixMode.Projection);
Matrix4 p = Matrix4.CreatePerspectiveFieldOfView(MathHelper.PiOver4, Width / (float)Height, 0.1f, 50.0f);
GL.LoadMatrix(ref p);
GL.MatrixMode(MatrixMode.Modelview);
Matrix4 mv = Matrix4.LookAt(Vector3.UnitZ, Vector3.Zero, Vector3.UnitY);
GL.LoadMatrix(ref mv);
}
void OnMouseWheelChanged(object sender, MouseWheelEventArgs e)
{
dy = e.Delta * MOUSE_DOLLY_SPEED;
cameraPosition[2] -= dy;
//if (cameraPosition[2] < 0)
// cameraPosition[2] = 0;
}
void OnMouseMove(object sender, MouseMoveEventArgs e)
{
MouseCurrent.X = e.Mouse.X;
MouseCurrent.Y = e.Mouse.Y;
// Now use mouse_delta to move the camera
switch (CameraMode)
{
case ECameraMode.CAMERA_TRACK:
dx = MouseCurrent.X - MousePrevious.X;
dx *= MOUSE_TRACK_SPEED;
dy = MouseCurrent.Y - MousePrevious.Y;
dy *= MOUSE_TRACK_SPEED;
cameraPosition[0] -= dx;
cameraPosition[1] += dy;
//targetPosition[0] -= dx;
//targetPosition[1] += dy;
break;
case ECameraMode.CAMERA_DOLLY:
dy = MouseCurrent.Y - MousePrevious.Y;
dy *= MOUSE_DOLLY_SPEED;
cameraPosition[2] -= dy;
// if (cameraPosition[2] < 0)
// cameraPosition[2] = 0;
break;
case ECameraMode.CAMERA_ORBIT:
dx = MouseCurrent.X - MousePrevious.X;
dx *= MOUSE_ORBIT_SPEED;
dy = MouseCurrent.Y - MousePrevious.Y;
dy *= MOUSE_ORBIT_SPEED;
g_heading += dx;
g_pitch += dy;
break;
}
MousePrevious.X = MouseCurrent.X;
MousePrevious.Y = MouseCurrent.Y;
}
void OnMouseButtonUp(object sender, MouseButtonEventArgs e)
{
CameraMode = ECameraMode.CAMERA_NONE;
}
void OnMouseButtonDown(object sender, MouseButtonEventArgs e)
{
switch (e.Button)
{
case MouseButton.Left:
CameraMode = ECameraMode.CAMERA_ORBIT;
break;
case MouseButton.Middle:
CameraMode = ECameraMode.CAMERA_DOLLY;
break;
case MouseButton.Right:
CameraMode = ECameraMode.CAMERA_TRACK;
break;
}
MousePrevious.X = Mouse.X;
MousePrevious.Y = Mouse.Y;
}
protected override void OnUpdateFrame(FrameEventArgs e)
{
if ((DateTime.Now - tFPSUpdateTimer).Seconds >= 1)
{
double FPS = nTickCounter / (DateTime.Now - tFPSUpdateTimer).TotalSeconds;
this.Title = "FPS: " + string.Format("{0:F}", FPS);
tFPSUpdateTimer = DateTime.Now;
nTickCounter = 0;
}
var keyboard = OpenTK.Input.Keyboard.GetState();
if (keyboard[Key.Escape])
{
Exit();
}
if (keyboard[Key.Plus])
{
PointSize += 0.1f;
GL.PointSize(PointSize);
}
if (keyboard[Key.Minus])
{
if (PointSize != 0)
PointSize -= 0.1f;
GL.PointSize(PointSize);
}
if (keyboard[Key.W])
cameraPosition[2] -= KEYBOARD_MOVE_SPEED;
if (keyboard[Key.A])
cameraPosition[0] -= KEYBOARD_MOVE_SPEED;
if (keyboard[Key.S])
cameraPosition[2] += KEYBOARD_MOVE_SPEED;
if (keyboard[Key.D])
cameraPosition[0] += KEYBOARD_MOVE_SPEED;
lock (vertices)
{
lock (settings)
{
PointCount = vertices.Count / 3;
//bounding box
LineCount = 12;
//markers
LineCount += settings.lMarkerPoses.Count * 3;
//cameras
LineCount += cameraPoses.Count * 3;
VBO = new VertexC4ubV3f[PointCount + 2 * LineCount];
for (int i = 0; i < PointCount; i++)
{
VBO[i].R = (byte)colors[i * 3];
VBO[i].G = (byte)colors[i * 3 + 1];
VBO[i].B = (byte)colors[i * 3 + 2];
VBO[i].A = 255;
VBO[i].Position.X = vertices[i * 3];
VBO[i].Position.Y = vertices[i * 3 + 1];
VBO[i].Position.Z = vertices[i * 3 + 2];
}
int iCurLineCount = 0;
iCurLineCount += AddBoundingBox(PointCount + 2 * iCurLineCount);
for (int i = 0; i < settings.lMarkerPoses.Count; i++)
{
iCurLineCount += AddMarker(PointCount + 2 * iCurLineCount, settings.lMarkerPoses[i].pose);
}
for (int i = 0; i < cameraPoses.Count; i++)
{
iCurLineCount += AddCamera(PointCount + 2 * iCurLineCount, cameraPoses[i]);
}
}
}
}
/// <summary>
/// Called when it is time to render the next frame. Add your rendering code here.
/// </summary>
/// <param name="e">Contains timing information.</param>
protected override void OnRenderFrame(FrameEventArgs e)
{
GL.Clear(ClearBufferMask.ColorBufferBit | ClearBufferMask.DepthBufferBit);
GL.PushMatrix();
GL.MatrixMode(MatrixMode.Modelview);
GL.Translate(-cameraPosition[0], -cameraPosition[1], -cameraPosition[2]);
GL.Rotate(g_pitch, 1.0f, 0.0f, 0.0f);
GL.Rotate(g_heading, 0.0f, 1.0f, 0.0f);
// Tell OpenGL to discard old VBO when done drawing it and reserve memory _now_ for a new buffer.
// without this, GL would wait until draw operations on old VBO are complete before writing to it
GL.BufferData(BufferTarget.ArrayBuffer, (IntPtr)(VertexC4ubV3f.SizeInBytes * (PointCount + 2 * LineCount)), IntPtr.Zero, BufferUsageHint.StreamDraw);
// Fill newly allocated buffer
GL.BufferData(BufferTarget.ArrayBuffer, (IntPtr)(VertexC4ubV3f.SizeInBytes * (PointCount + 2 * LineCount)), VBO, BufferUsageHint.StreamDraw);
GL.DrawArrays(BeginMode.Points, 0, PointCount);
GL.DrawArrays(BeginMode.Lines, PointCount, 2 * LineCount);
GL.PopMatrix();
SwapBuffers();
}
private int AddBoundingBox(int startIdx)
{
int nLinesBeingAdded = 12;
//2 points per line
int nPointsToAdd = 2 * nLinesBeingAdded;
for (int i = startIdx; i < startIdx + nPointsToAdd; i++)
{
VBO[i].R = 255;
VBO[i].G = 255;
VBO[i].B = 0;
VBO[i].A = 0;
}
int n = 0;
//bottom vertices
//first vertex
AddLine(startIdx + n, settings.aMinBounds[0], settings.aMinBounds[1], settings.aMinBounds[2],
settings.aMaxBounds[0], settings.aMinBounds[1], settings.aMinBounds[2]);
n += 2;
AddLine(startIdx + n, settings.aMinBounds[0], settings.aMinBounds[1], settings.aMinBounds[2],
settings.aMinBounds[0], settings.aMaxBounds[1], settings.aMinBounds[2]);
n += 2;
AddLine(startIdx + n, settings.aMinBounds[0], settings.aMinBounds[1], settings.aMinBounds[2],
settings.aMinBounds[0], settings.aMinBounds[1], settings.aMaxBounds[2]);
n += 2;
//second vertex
AddLine(startIdx + n, settings.aMaxBounds[0], settings.aMinBounds[1], settings.aMinBounds[2],
settings.aMaxBounds[0], settings.aMaxBounds[1], settings.aMinBounds[2]);
n += 2;
AddLine(startIdx + n, settings.aMaxBounds[0], settings.aMinBounds[1], settings.aMinBounds[2],
settings.aMaxBounds[0], settings.aMinBounds[1], settings.aMaxBounds[2]);
n += 2;
//third vertex
AddLine(startIdx + n, settings.aMaxBounds[0], settings.aMinBounds[1], settings.aMaxBounds[2],
settings.aMaxBounds[0], settings.aMaxBounds[1], settings.aMaxBounds[2]);
n += 2;
AddLine(startIdx + n, settings.aMaxBounds[0], settings.aMinBounds[1], settings.aMaxBounds[2],
settings.aMinBounds[0], settings.aMinBounds[1], settings.aMaxBounds[2]);
n += 2;
//fourth vertex
AddLine(startIdx + n, settings.aMinBounds[0], settings.aMinBounds[1], settings.aMaxBounds[2],
settings.aMinBounds[0], settings.aMaxBounds[1], settings.aMaxBounds[2]);
n += 2;
//top vertices
//fifth vertex
AddLine(startIdx + n, settings.aMinBounds[0], settings.aMaxBounds[1], settings.aMinBounds[2],
settings.aMaxBounds[0], settings.aMaxBounds[1], settings.aMinBounds[2]);
n += 2;
AddLine(startIdx + n, settings.aMinBounds[0], settings.aMaxBounds[1], settings.aMinBounds[2],
settings.aMinBounds[0], settings.aMaxBounds[1], settings.aMaxBounds[2]);
n += 2;
//sixth vertex
AddLine(startIdx + n, settings.aMaxBounds[0], settings.aMaxBounds[1], settings.aMaxBounds[2],
settings.aMaxBounds[0], settings.aMaxBounds[1], settings.aMinBounds[2]);
n += 2;
AddLine(startIdx + n, settings.aMaxBounds[0], settings.aMaxBounds[1], settings.aMaxBounds[2],
settings.aMinBounds[0], settings.aMaxBounds[1], settings.aMaxBounds[2]);
n += 2;
return nLinesBeingAdded;
}
private int AddMarker(int startIdx, AffineTransform pose)
{
int nLinesBeingAdded = 3;
//2 points per line
int nPointsToAdd = 2 * nLinesBeingAdded;
for (int i = startIdx; i < startIdx + nPointsToAdd; i++)
{
VBO[i].R = 255;
VBO[i].G = 0;
VBO[i].B = 0;
VBO[i].A = 0;
}
int n = 0;
float x0 = pose.t[0];
float y0 = pose.t[1];
float z0 = pose.t[2];
float x1 = 0.1f;
float y1 = 0.1f;
float z1 = 0.1f;
float x2 = pose.R[0, 0] * x1;
float y2 = pose.R[1, 0] * x1;
float z2 = pose.R[2, 0] * x1;
x2 += pose.t[0];
y2 += pose.t[1];
z2 += pose.t[2];
AddLine(startIdx + n, x0, y0, z0, x2, y2, z2);
n += 2;
x2 = pose.R[0, 1] * y1;
y2 = pose.R[1, 1] * y1;
z2 = pose.R[2, 1] * y1;
x2 += pose.t[0];
y2 += pose.t[1];
z2 += pose.t[2];
AddLine(startIdx + n, x0, y0, z0, x2, y2, z2);
n += 2;
x2 = pose.R[0, 2] * z1;
y2 = pose.R[1, 2] * z1;
z2 = pose.R[2, 2] * z1;
x2 += pose.t[0];
y2 += pose.t[1];
z2 += pose.t[2];
AddLine(startIdx + n, x0, y0, z0, x2, y2, z2);
n += 2;
return nLinesBeingAdded;
}
private int AddCamera(int startIdx, AffineTransform pose)
{
int nLinesBeingAdded = 3;
//2 points per line
int nPointsToAdd = 2 * nLinesBeingAdded;
for (int i = startIdx; i < startIdx + nPointsToAdd; i++)
{
VBO[i].R = 0;
VBO[i].G = 255;
VBO[i].B = 0;
VBO[i].A = 0;
}
int n = 0;
float x0 = pose.t[0];
float y0 = pose.t[1];
float z0 = pose.t[2];
float x1 = 0.1f;
float y1 = 0.1f;
float z1 = 0.1f;
float x2 = pose.R[0, 0] * x1;
float y2 = pose.R[1, 0] * x1;
float z2 = pose.R[2, 0] * x1;
x2 += pose.t[0];
y2 += pose.t[1];
z2 += pose.t[2];
AddLine(startIdx + n, x0, y0, z0, x2, y2, z2);
n += 2;
x2 = pose.R[0, 1] * y1;
y2 = pose.R[1, 1] * y1;
z2 = pose.R[2, 1] * y1;
x2 += pose.t[0];
y2 += pose.t[1];
z2 += pose.t[2];
AddLine(startIdx + n, x0, y0, z0, x2, y2, z2);
n += 2;
x2 = pose.R[0, 2] * z1;
y2 = pose.R[1, 2] * z1;
z2 = pose.R[2, 2] * z1;
x2 += pose.t[0];
y2 += pose.t[1];
z2 += pose.t[2];
AddLine(startIdx + n, x0, y0, z0, x2, y2, z2);
n += 2;
return nLinesBeingAdded;
}
private void AddLine(int startIdx, float x0, float y0, float z0,
float x1, float y1, float z1)
{
VBO[startIdx].Position.X = x0;
VBO[startIdx].Position.Y = y0;
VBO[startIdx].Position.Z = z0;
VBO[startIdx + 1].Position.X = x1;
VBO[startIdx + 1].Position.Y = y1;
VBO[startIdx + 1].Position.Z = z1;
}
}
}
LiveScanServer/Program.cs
+22
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@@ -0,0 +1,22 @@
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using System.Windows.Forms;
namespace KinectServer
{
static class Program
{
/// <summary>
/// The main entry point for the application.
/// </summary>
[STAThread]
static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new MainWindowForm());
}
}
}
LiveScanServer/Properties/AssemblyInfo.cs
+36
View File
@@ -0,0 +1,36 @@
using System.Reflection;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;
// General Information about an assembly is controlled through the following
// set of attributes. Change these attribute values to modify the information
// associated with an assembly.
[assembly: AssemblyTitle("KinectServer")]
[assembly: AssemblyDescription("")]
[assembly: AssemblyConfiguration("")]
[assembly: AssemblyCompany("")]
[assembly: AssemblyProduct("KinectServer")]
[assembly: AssemblyCopyright("Copyright © 2015")]
[assembly: AssemblyTrademark("")]
[assembly: AssemblyCulture("")]
// Setting ComVisible to false makes the types in this assembly not visible
// to COM components. If you need to access a type in this assembly from
// COM, set the ComVisible attribute to true on that type.
[assembly: ComVisible(false)]
// The following GUID is for the ID of the typelib if this project is exposed to COM
[assembly: Guid("c781e249-f1ed-41af-89ab-4dbc1dde8bfd")]
// Version information for an assembly consists of the following four values:
//
// Major Version
// Minor Version
// Build Number
// Revision
//
// You can specify all the values or you can default the Build and Revision Numbers
// by using the '*' as shown below:
// [assembly: AssemblyVersion("1.0.*")]
[assembly: AssemblyVersion("1.0.0.0")]
[assembly: AssemblyFileVersion("1.0.0.0")]
LiveScanServer/Properties/Resources.Designer.cs
Generated
+71
View File
@@ -0,0 +1,71 @@
//------------------------------------------------------------------------------
// <auto-generated>
// This code was generated by a tool.
// Runtime Version:4.0.30319.34014
//
// Changes to this file may cause incorrect behavior and will be lost if
// the code is regenerated.
// </auto-generated>
//------------------------------------------------------------------------------
namespace KinectServer.Properties
{
/// <summary>
/// A strongly-typed resource class, for looking up localized strings, etc.
/// </summary>
// This class was auto-generated by the StronglyTypedResourceBuilder
// class via a tool like ResGen or Visual Studio.
// To add or remove a member, edit your .ResX file then rerun ResGen
// with the /str option, or rebuild your VS project.
[global::System.CodeDom.Compiler.GeneratedCodeAttribute("System.Resources.Tools.StronglyTypedResourceBuilder", "4.0.0.0")]
[global::System.Diagnostics.DebuggerNonUserCodeAttribute()]
[global::System.Runtime.CompilerServices.CompilerGeneratedAttribute()]
internal class Resources
{
private static global::System.Resources.ResourceManager resourceMan;
private static global::System.Globalization.CultureInfo resourceCulture;
[global::System.Diagnostics.CodeAnalysis.SuppressMessageAttribute("Microsoft.Performance", "CA1811:AvoidUncalledPrivateCode")]
internal Resources()
{
}
/// <summary>
/// Returns the cached ResourceManager instance used by this class.
/// </summary>
[global::System.ComponentModel.EditorBrowsableAttribute(global::System.ComponentModel.EditorBrowsableState.Advanced)]
internal static global::System.Resources.ResourceManager ResourceManager
{
get
{
if ((resourceMan == null))
{
global::System.Resources.ResourceManager temp = new global::System.Resources.ResourceManager("KinectServer.Properties.Resources", typeof(Resources).Assembly);
resourceMan = temp;
}
return resourceMan;
}
}
/// <summary>
/// Overrides the current thread's CurrentUICulture property for all
/// resource lookups using this strongly typed resource class.
/// </summary>
[global::System.ComponentModel.EditorBrowsableAttribute(global::System.ComponentModel.EditorBrowsableState.Advanced)]
internal static global::System.Globalization.CultureInfo Culture
{
get
{
return resourceCulture;
}
set
{
resourceCulture = value;
}
}
}
}
LiveScanServer/Properties/Resources.resx
+117
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@@ -0,0 +1,117 @@
<?xml version="1.0" encoding="utf-8"?>
<root>
<!--
Microsoft ResX Schema
Version 2.0
The primary goals of this format is to allow a simple XML format
that is mostly human readable. The generation and parsing of the
various data types are done through the TypeConverter classes
associated with the data types.
Example:
... ado.net/XML headers & schema ...
<resheader name="resmimetype">text/microsoft-resx</resheader>
<resheader name="version">2.0</resheader>
<resheader name="reader">System.Resources.ResXResourceReader, System.Windows.Forms, ...</resheader>
<resheader name="writer">System.Resources.ResXResourceWriter, System.Windows.Forms, ...</resheader>
<data name="Name1"><value>this is my long string</value><comment>this is a comment</comment></data>
<data name="Color1" type="System.Drawing.Color, System.Drawing">Blue</data>
<data name="Bitmap1" mimetype="application/x-microsoft.net.object.binary.base64">
<value>[base64 mime encoded serialized .NET Framework object]</value>
</data>
<data name="Icon1" type="System.Drawing.Icon, System.Drawing" mimetype="application/x-microsoft.net.object.bytearray.base64">
<value>[base64 mime encoded string representing a byte array form of the .NET Framework object]</value>
<comment>This is a comment</comment>
</data>
There are any number of "resheader" rows that contain simple
name/value pairs.
Each data row contains a name, and value. The row also contains a
type or mimetype. Type corresponds to a .NET class that support
text/value conversion through the TypeConverter architecture.
Classes that don't support this are serialized and stored with the
mimetype set.
The mimetype is used for serialized objects, and tells the
ResXResourceReader how to depersist the object. This is currently not
extensible. For a given mimetype the value must be set accordingly:
Note - application/x-microsoft.net.object.binary.base64 is the format
that the ResXResourceWriter will generate, however the reader can
read any of the formats listed below.
mimetype: application/x-microsoft.net.object.binary.base64
value : The object must be serialized with
: System.Serialization.Formatters.Binary.BinaryFormatter
: and then encoded with base64 encoding.
mimetype: application/x-microsoft.net.object.soap.base64
value : The object must be serialized with
: System.Runtime.Serialization.Formatters.Soap.SoapFormatter
: and then encoded with base64 encoding.
mimetype: application/x-microsoft.net.object.bytearray.base64
value : The object must be serialized into a byte array
: using a System.ComponentModel.TypeConverter
: and then encoded with base64 encoding.
-->
<xsd:schema id="root" xmlns="" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:msdata="urn:schemas-microsoft-com:xml-msdata">
<xsd:element name="root" msdata:IsDataSet="true">
<xsd:complexType>
<xsd:choice maxOccurs="unbounded">
<xsd:element name="metadata">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" />
</xsd:sequence>
<xsd:attribute name="name" type="xsd:string" />
<xsd:attribute name="type" type="xsd:string" />
<xsd:attribute name="mimetype" type="xsd:string" />
</xsd:complexType>
</xsd:element>
<xsd:element name="assembly">
<xsd:complexType>
<xsd:attribute name="alias" type="xsd:string" />
<xsd:attribute name="name" type="xsd:string" />
</xsd:complexType>
</xsd:element>
<xsd:element name="data">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
<xsd:element name="comment" type="xsd:string" minOccurs="0" msdata:Ordinal="2" />
</xsd:sequence>
<xsd:attribute name="name" type="xsd:string" msdata:Ordinal="1" />
<xsd:attribute name="type" type="xsd:string" msdata:Ordinal="3" />
<xsd:attribute name="mimetype" type="xsd:string" msdata:Ordinal="4" />
</xsd:complexType>
</xsd:element>
<xsd:element name="resheader">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
</xsd:sequence>
<xsd:attribute name="name" type="xsd:string" use="required" />
</xsd:complexType>
</xsd:element>
</xsd:choice>
</xsd:complexType>
</xsd:element>
</xsd:schema>
<resheader name="resmimetype">
<value>text/microsoft-resx</value>
</resheader>
<resheader name="version">
<value>2.0</value>
</resheader>
<resheader name="reader">
<value>System.Resources.ResXResourceReader, System.Windows.Forms, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089</value>
</resheader>
<resheader name="writer">
<value>System.Resources.ResXResourceWriter, System.Windows.Forms, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089</value>
</resheader>
</root>
LiveScanServer/Properties/Settings.Designer.cs
Generated
+30
View File
@@ -0,0 +1,30 @@
//------------------------------------------------------------------------------
// <auto-generated>
// This code was generated by a tool.
// Runtime Version:4.0.30319.34014
//
// Changes to this file may cause incorrect behavior and will be lost if
// the code is regenerated.
// </auto-generated>
//------------------------------------------------------------------------------
namespace KinectServer.Properties
{
[global::System.Runtime.CompilerServices.CompilerGeneratedAttribute()]
[global::System.CodeDom.Compiler.GeneratedCodeAttribute("Microsoft.VisualStudio.Editors.SettingsDesigner.SettingsSingleFileGenerator", "11.0.0.0")]
internal sealed partial class Settings : global::System.Configuration.ApplicationSettingsBase
{
private static Settings defaultInstance = ((Settings)(global::System.Configuration.ApplicationSettingsBase.Synchronized(new Settings())));
public static Settings Default
{
get
{
return defaultInstance;
}
}
}
}
LiveScanServer/Properties/Settings.settings
+7
View File
@@ -0,0 +1,7 @@
<?xml version='1.0' encoding='utf-8'?>
<SettingsFile xmlns="http://schemas.microsoft.com/VisualStudio/2004/01/settings" CurrentProfile="(Default)">
<Profiles>
<Profile Name="(Default)" />
</Profiles>
<Settings />
</SettingsFile>
LiveScanServer/SettingsForm.Designer.cs
Generated
+569
View File
@@ -0,0 +1,569 @@
namespace KinectServer
{
partial class SettingsForm
{
/// <summary>
/// Required designer variable.
/// </summary>
private System.ComponentModel.IContainer components = null;
/// <summary>
/// Clean up any resources being used.
/// </summary>
/// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
protected override void Dispose(bool disposing)
{
if (disposing && (components != null))
{
components.Dispose();
}
base.Dispose(disposing);
}
#region Windows Form Designer generated code
/// <summary>
/// Required method for Designer support - do not modify
/// the contents of this method with the code editor.
/// </summary>
private void InitializeComponent()
{
this.lbMerge = new System.Windows.Forms.Label();
this.chMerge = new System.Windows.Forms.CheckBox();
this.lbICPIters = new System.Windows.Forms.Label();
this.txtICPIters = new System.Windows.Forms.TextBox();
this.grClient = new System.Windows.Forms.GroupBox();
this.grMarkers = new System.Windows.Forms.GroupBox();
this.lbX2 = new System.Windows.Forms.Label();
this.btRemove = new System.Windows.Forms.Button();
this.txtOrientationZ = new System.Windows.Forms.TextBox();
this.txtId = new System.Windows.Forms.TextBox();
this.lbY2 = new System.Windows.Forms.Label();
this.txtOrientationY = new System.Windows.Forms.TextBox();
this.lbId = new System.Windows.Forms.Label();
this.lbZ2 = new System.Windows.Forms.Label();
this.txtOrientationX = new System.Windows.Forms.TextBox();
this.btAdd = new System.Windows.Forms.Button();
this.lbTranslation = new System.Windows.Forms.Label();
this.lbOrientation = new System.Windows.Forms.Label();
this.txtTranslationZ = new System.Windows.Forms.TextBox();
this.txtTranslationX = new System.Windows.Forms.TextBox();
this.lisMarkers = new System.Windows.Forms.ListBox();
this.txtTranslationY = new System.Windows.Forms.TextBox();
this.grBounding = new System.Windows.Forms.GroupBox();
this.lbMin = new System.Windows.Forms.Label();
this.txtMaxZ = new System.Windows.Forms.TextBox();
this.txtMaxY = new System.Windows.Forms.TextBox();
this.txtMinX = new System.Windows.Forms.TextBox();
this.txtMaxX = new System.Windows.Forms.TextBox();
this.txtMinY = new System.Windows.Forms.TextBox();
this.lbMax = new System.Windows.Forms.Label();
this.txtMinZ = new System.Windows.Forms.TextBox();
this.lbZ = new System.Windows.Forms.Label();
this.lbY = new System.Windows.Forms.Label();
this.lbX = new System.Windows.Forms.Label();
this.grFiltering = new System.Windows.Forms.GroupBox();
this.txtFilterNeighbors = new System.Windows.Forms.TextBox();
this.chFilter = new System.Windows.Forms.CheckBox();
this.lbFilterNeighbors = new System.Windows.Forms.Label();
this.lbFilterDistance = new System.Windows.Forms.Label();
this.txtFilterDistance = new System.Windows.Forms.TextBox();
this.grServer = new System.Windows.Forms.GroupBox();
this.txtRefinIters = new System.Windows.Forms.TextBox();
this.lbOuterIters = new System.Windows.Forms.Label();
this.grClient.SuspendLayout();
this.grMarkers.SuspendLayout();
this.grBounding.SuspendLayout();
this.grFiltering.SuspendLayout();
this.grServer.SuspendLayout();
this.SuspendLayout();
//
// lbMerge
//
this.lbMerge.AutoSize = true;
this.lbMerge.Location = new System.Drawing.Point(6, 49);
this.lbMerge.Name = "lbMerge";
this.lbMerge.Size = new System.Drawing.Size(69, 13);
this.lbMerge.TabIndex = 22;
this.lbMerge.Text = "Scan saving:";
//
// chMerge
//
this.chMerge.AutoSize = true;
this.chMerge.Location = new System.Drawing.Point(91, 48);
this.chMerge.Name = "chMerge";
this.chMerge.Size = new System.Drawing.Size(86, 17);
this.chMerge.TabIndex = 23;
this.chMerge.Text = "merge scans";
this.chMerge.UseVisualStyleBackColor = true;
this.chMerge.CheckedChanged += new System.EventHandler(this.chMerge_CheckedChanged);
//
// lbICPIters
//
this.lbICPIters.AutoSize = true;
this.lbICPIters.Location = new System.Drawing.Point(6, 26);
this.lbICPIters.Name = "lbICPIters";
this.lbICPIters.Size = new System.Drawing.Size(86, 13);
this.lbICPIters.TabIndex = 24;
this.lbICPIters.Text = "Num of ICP iters:";
//
// txtICPIters
//
this.txtICPIters.Location = new System.Drawing.Point(91, 23);
this.txtICPIters.Name = "txtICPIters";
this.txtICPIters.Size = new System.Drawing.Size(38, 20);
this.txtICPIters.TabIndex = 25;
this.txtICPIters.TextChanged += new System.EventHandler(this.txtICPIters_TextChanged);
//
// grClient
//
this.grClient.Controls.Add(this.grMarkers);
this.grClient.Controls.Add(this.grBounding);
this.grClient.Controls.Add(this.grFiltering);
this.grClient.Location = new System.Drawing.Point(12, 12);
this.grClient.Name = "grClient";
this.grClient.Size = new System.Drawing.Size(632, 212);
this.grClient.TabIndex = 43;
this.grClient.TabStop = false;
this.grClient.Text = "KinectClient settings";
//
// grMarkers
//
this.grMarkers.Controls.Add(this.lbX2);
this.grMarkers.Controls.Add(this.btRemove);
this.grMarkers.Controls.Add(this.txtOrientationZ);
this.grMarkers.Controls.Add(this.txtId);
this.grMarkers.Controls.Add(this.lbY2);
this.grMarkers.Controls.Add(this.txtOrientationY);
this.grMarkers.Controls.Add(this.lbId);
this.grMarkers.Controls.Add(this.lbZ2);
this.grMarkers.Controls.Add(this.txtOrientationX);
this.grMarkers.Controls.Add(this.btAdd);
this.grMarkers.Controls.Add(this.lbTranslation);
this.grMarkers.Controls.Add(this.lbOrientation);
this.grMarkers.Controls.Add(this.txtTranslationZ);
this.grMarkers.Controls.Add(this.txtTranslationX);
this.grMarkers.Controls.Add(this.lisMarkers);
this.grMarkers.Controls.Add(this.txtTranslationY);
this.grMarkers.Location = new System.Drawing.Point(271, 19);
this.grMarkers.Name = "grMarkers";
this.grMarkers.Size = new System.Drawing.Size(353, 185);
this.grMarkers.TabIndex = 45;
this.grMarkers.TabStop = false;
this.grMarkers.Text = "Calibration markers";
//
// lbX2
//
this.lbX2.AutoSize = true;
this.lbX2.Location = new System.Drawing.Point(230, 19);
this.lbX2.Name = "lbX2";
this.lbX2.Size = new System.Drawing.Size(14, 13);
this.lbX2.TabIndex = 49;
this.lbX2.Text = "X";
//
// btRemove
//
this.btRemove.Location = new System.Drawing.Point(233, 127);
this.btRemove.Name = "btRemove";
this.btRemove.Size = new System.Drawing.Size(92, 23);
this.btRemove.TabIndex = 59;
this.btRemove.Text = "Remove marker";
this.btRemove.UseVisualStyleBackColor = true;
this.btRemove.Click += new System.EventHandler(this.btRemove_Click);
//
// txtOrientationZ
//
this.txtOrientationZ.Location = new System.Drawing.Point(305, 38);
this.txtOrientationZ.Name = "txtOrientationZ";
this.txtOrientationZ.Size = new System.Drawing.Size(38, 20);
this.txtOrientationZ.TabIndex = 48;
this.txtOrientationZ.TextChanged += new System.EventHandler(this.txtOrientationZ_TextChanged);
//
// txtId
//
this.txtId.Location = new System.Drawing.Point(217, 93);
this.txtId.Name = "txtId";
this.txtId.Size = new System.Drawing.Size(38, 20);
this.txtId.TabIndex = 58;
this.txtId.TextChanged += new System.EventHandler(this.txtId_TextChanged);
//
// lbY2
//
this.lbY2.AutoSize = true;
this.lbY2.Location = new System.Drawing.Point(274, 19);
this.lbY2.Name = "lbY2";
this.lbY2.Size = new System.Drawing.Size(14, 13);
this.lbY2.TabIndex = 50;
this.lbY2.Text = "Y";
//
// txtOrientationY
//
this.txtOrientationY.Location = new System.Drawing.Point(261, 38);
this.txtOrientationY.Name = "txtOrientationY";
this.txtOrientationY.Size = new System.Drawing.Size(38, 20);
this.txtOrientationY.TabIndex = 47;
this.txtOrientationY.TextChanged += new System.EventHandler(this.txtOrientationY_TextChanged);
//
// lbId
//
this.lbId.AutoSize = true;
this.lbId.Location = new System.Drawing.Point(132, 96);
this.lbId.Name = "lbId";
this.lbId.Size = new System.Drawing.Size(54, 13);
this.lbId.TabIndex = 57;
this.lbId.Text = "Marker id:";
//
// lbZ2
//
this.lbZ2.AutoSize = true;
this.lbZ2.Location = new System.Drawing.Point(317, 19);
this.lbZ2.Name = "lbZ2";
this.lbZ2.Size = new System.Drawing.Size(14, 13);
this.lbZ2.TabIndex = 51;
this.lbZ2.Text = "Z";
//
// txtOrientationX
//
this.txtOrientationX.Location = new System.Drawing.Point(217, 38);
this.txtOrientationX.Name = "txtOrientationX";
this.txtOrientationX.Size = new System.Drawing.Size(38, 20);
this.txtOrientationX.TabIndex = 46;
this.txtOrientationX.TextChanged += new System.EventHandler(this.txtOrientationX_TextChanged);
//
// btAdd
//
this.btAdd.Location = new System.Drawing.Point(135, 127);
this.btAdd.Name = "btAdd";
this.btAdd.Size = new System.Drawing.Size(92, 23);
this.btAdd.TabIndex = 56;
this.btAdd.Text = "Add marker";
this.btAdd.UseVisualStyleBackColor = true;
this.btAdd.Click += new System.EventHandler(this.btAdd_Click);
//
// lbTranslation
//
this.lbTranslation.AutoSize = true;
this.lbTranslation.Location = new System.Drawing.Point(132, 68);
this.lbTranslation.Name = "lbTranslation";
this.lbTranslation.Size = new System.Drawing.Size(62, 13);
this.lbTranslation.TabIndex = 52;
this.lbTranslation.Text = "Translation:";
//
// lbOrientation
//
this.lbOrientation.AutoSize = true;
this.lbOrientation.Location = new System.Drawing.Point(132, 41);
this.lbOrientation.Name = "lbOrientation";
this.lbOrientation.Size = new System.Drawing.Size(61, 13);
this.lbOrientation.TabIndex = 45;
this.lbOrientation.Text = "Orientation:";
//
// txtTranslationZ
//
this.txtTranslationZ.Location = new System.Drawing.Point(305, 65);
this.txtTranslationZ.Name = "txtTranslationZ";
this.txtTranslationZ.Size = new System.Drawing.Size(38, 20);
this.txtTranslationZ.TabIndex = 55;
this.txtTranslationZ.TextChanged += new System.EventHandler(this.txtTranslationZ_TextChanged);
//
// txtTranslationX
//
this.txtTranslationX.Location = new System.Drawing.Point(217, 65);
this.txtTranslationX.Name = "txtTranslationX";
this.txtTranslationX.Size = new System.Drawing.Size(38, 20);
this.txtTranslationX.TabIndex = 53;
this.txtTranslationX.TextChanged += new System.EventHandler(this.txtTranslationX_TextChanged);
//
// lisMarkers
//
this.lisMarkers.FormattingEnabled = true;
this.lisMarkers.Location = new System.Drawing.Point(6, 41);
this.lisMarkers.Name = "lisMarkers";
this.lisMarkers.Size = new System.Drawing.Size(120, 108);
this.lisMarkers.TabIndex = 43;
//
// txtTranslationY
//
this.txtTranslationY.Location = new System.Drawing.Point(261, 65);
this.txtTranslationY.Name = "txtTranslationY";
this.txtTranslationY.Size = new System.Drawing.Size(38, 20);
this.txtTranslationY.TabIndex = 54;
this.txtTranslationY.TextChanged += new System.EventHandler(this.txtTranslationY_TextChanged);
//
// grBounding
//
this.grBounding.Controls.Add(this.lbMin);
this.grBounding.Controls.Add(this.txtMaxZ);
this.grBounding.Controls.Add(this.txtMaxY);
this.grBounding.Controls.Add(this.txtMinX);
this.grBounding.Controls.Add(this.txtMaxX);
this.grBounding.Controls.Add(this.txtMinY);
this.grBounding.Controls.Add(this.lbMax);
this.grBounding.Controls.Add(this.txtMinZ);
this.grBounding.Controls.Add(this.lbZ);
this.grBounding.Controls.Add(this.lbY);
this.grBounding.Controls.Add(this.lbX);
this.grBounding.Location = new System.Drawing.Point(9, 19);
this.grBounding.Name = "grBounding";
this.grBounding.Size = new System.Drawing.Size(249, 91);
this.grBounding.TabIndex = 46;
this.grBounding.TabStop = false;
this.grBounding.Text = "Bounding box";
//
// lbMin
//
this.lbMin.AutoSize = true;
this.lbMin.Location = new System.Drawing.Point(8, 38);
this.lbMin.Name = "lbMin";
this.lbMin.Size = new System.Drawing.Size(65, 13);
this.lbMin.TabIndex = 13;
this.lbMin.Text = "Min bounds:";
//
// txtMaxZ
//
this.txtMaxZ.Location = new System.Drawing.Point(181, 62);
this.txtMaxZ.Name = "txtMaxZ";
this.txtMaxZ.Size = new System.Drawing.Size(38, 20);
this.txtMaxZ.TabIndex = 23;
this.txtMaxZ.TextChanged += new System.EventHandler(this.txtMaxZ_TextChanged);
//
// txtMaxY
//
this.txtMaxY.Location = new System.Drawing.Point(137, 62);
this.txtMaxY.Name = "txtMaxY";
this.txtMaxY.Size = new System.Drawing.Size(38, 20);
this.txtMaxY.TabIndex = 22;
this.txtMaxY.TextChanged += new System.EventHandler(this.txtMaxY_TextChanged);
//
// txtMinX
//
this.txtMinX.Location = new System.Drawing.Point(93, 35);
this.txtMinX.Name = "txtMinX";
this.txtMinX.Size = new System.Drawing.Size(38, 20);
this.txtMinX.TabIndex = 14;
this.txtMinX.TextChanged += new System.EventHandler(this.txtMinX_TextChanged);
//
// txtMaxX
//
this.txtMaxX.Location = new System.Drawing.Point(93, 62);
this.txtMaxX.Name = "txtMaxX";
this.txtMaxX.Size = new System.Drawing.Size(38, 20);
this.txtMaxX.TabIndex = 21;
this.txtMaxX.TextChanged += new System.EventHandler(this.txtMaxX_TextChanged);
//
// txtMinY
//
this.txtMinY.Location = new System.Drawing.Point(137, 35);
this.txtMinY.Name = "txtMinY";
this.txtMinY.Size = new System.Drawing.Size(38, 20);
this.txtMinY.TabIndex = 15;
this.txtMinY.TextChanged += new System.EventHandler(this.txtMinY_TextChanged);
//
// lbMax
//
this.lbMax.AutoSize = true;
this.lbMax.Location = new System.Drawing.Point(8, 65);
this.lbMax.Name = "lbMax";
this.lbMax.Size = new System.Drawing.Size(68, 13);
this.lbMax.TabIndex = 20;
this.lbMax.Text = "Max bounds:";
//
// txtMinZ
//
this.txtMinZ.Location = new System.Drawing.Point(181, 35);
this.txtMinZ.Name = "txtMinZ";
this.txtMinZ.Size = new System.Drawing.Size(38, 20);
this.txtMinZ.TabIndex = 16;
this.txtMinZ.TextChanged += new System.EventHandler(this.txtMinZ_TextChanged);
//
// lbZ
//
this.lbZ.AutoSize = true;
this.lbZ.Location = new System.Drawing.Point(193, 16);
this.lbZ.Name = "lbZ";
this.lbZ.Size = new System.Drawing.Size(14, 13);
this.lbZ.TabIndex = 19;
this.lbZ.Text = "Z";
//
// lbY
//
this.lbY.AutoSize = true;
this.lbY.Location = new System.Drawing.Point(150, 16);
this.lbY.Name = "lbY";
this.lbY.Size = new System.Drawing.Size(14, 13);
this.lbY.TabIndex = 18;
this.lbY.Text = "Y";
//
// lbX
//
this.lbX.AutoSize = true;
this.lbX.Location = new System.Drawing.Point(106, 16);
this.lbX.Name = "lbX";
this.lbX.Size = new System.Drawing.Size(14, 13);
this.lbX.TabIndex = 17;
this.lbX.Text = "X";
//
// grFiltering
//
this.grFiltering.Controls.Add(this.txtFilterNeighbors);
this.grFiltering.Controls.Add(this.chFilter);
this.grFiltering.Controls.Add(this.lbFilterNeighbors);
this.grFiltering.Controls.Add(this.lbFilterDistance);
this.grFiltering.Controls.Add(this.txtFilterDistance);
this.grFiltering.Location = new System.Drawing.Point(9, 116);
this.grFiltering.Name = "grFiltering";
this.grFiltering.Size = new System.Drawing.Size(249, 88);
this.grFiltering.TabIndex = 45;
this.grFiltering.TabStop = false;
this.grFiltering.Text = "Filtering";
//
// txtFilterNeighbors
//
this.txtFilterNeighbors.Location = new System.Drawing.Point(93, 39);
this.txtFilterNeighbors.Name = "txtFilterNeighbors";
this.txtFilterNeighbors.Size = new System.Drawing.Size(38, 20);
this.txtFilterNeighbors.TabIndex = 20;
this.txtFilterNeighbors.TextChanged += new System.EventHandler(this.txtFilterNeighbors_TextChanged);
//
// chFilter
//
this.chFilter.AutoSize = true;
this.chFilter.Location = new System.Drawing.Point(93, 16);
this.chFilter.Name = "chFilter";
this.chFilter.Size = new System.Drawing.Size(100, 17);
this.chFilter.TabIndex = 18;
this.chFilter.Text = "filtering enabled";
this.chFilter.UseVisualStyleBackColor = true;
this.chFilter.CheckedChanged += new System.EventHandler(this.chFilter_CheckedChanged);
//
// lbFilterNeighbors
//
this.lbFilterNeighbors.AutoSize = true;
this.lbFilterNeighbors.Location = new System.Drawing.Point(8, 42);
this.lbFilterNeighbors.Name = "lbFilterNeighbors";
this.lbFilterNeighbors.Size = new System.Drawing.Size(67, 13);
this.lbFilterNeighbors.TabIndex = 19;
this.lbFilterNeighbors.Text = "N neighbors:";
//
// lbFilterDistance
//
this.lbFilterDistance.AutoSize = true;
this.lbFilterDistance.Location = new System.Drawing.Point(8, 68);
this.lbFilterDistance.Name = "lbFilterDistance";
this.lbFilterDistance.Size = new System.Drawing.Size(73, 13);
this.lbFilterDistance.TabIndex = 21;
this.lbFilterDistance.Text = "Max distance:";
//
// txtFilterDistance
//
this.txtFilterDistance.Location = new System.Drawing.Point(93, 65);
this.txtFilterDistance.Name = "txtFilterDistance";
this.txtFilterDistance.Size = new System.Drawing.Size(38, 20);
this.txtFilterDistance.TabIndex = 22;
this.txtFilterDistance.TextChanged += new System.EventHandler(this.txtFilterDistance_TextChanged);
//
// grServer
//
this.grServer.Controls.Add(this.txtRefinIters);
this.grServer.Controls.Add(this.lbOuterIters);
this.grServer.Controls.Add(this.lbMerge);
this.grServer.Controls.Add(this.txtICPIters);
this.grServer.Controls.Add(this.chMerge);
this.grServer.Controls.Add(this.lbICPIters);
this.grServer.Location = new System.Drawing.Point(12, 230);
this.grServer.Name = "grServer";
this.grServer.Size = new System.Drawing.Size(632, 78);
this.grServer.TabIndex = 44;
this.grServer.TabStop = false;
this.grServer.Text = "KinectServer settings";
//
// txtRefinIters
//
this.txtRefinIters.Location = new System.Drawing.Point(271, 23);
this.txtRefinIters.Name = "txtRefinIters";
this.txtRefinIters.Size = new System.Drawing.Size(38, 20);
this.txtRefinIters.TabIndex = 27;
this.txtRefinIters.TextChanged += new System.EventHandler(this.txtRefinIters_TextChanged);
//
// lbOuterIters
//
this.lbOuterIters.AutoSize = true;
this.lbOuterIters.Location = new System.Drawing.Point(135, 26);
this.lbOuterIters.Name = "lbOuterIters";
this.lbOuterIters.Size = new System.Drawing.Size(139, 13);
this.lbOuterIters.TabIndex = 26;
this.lbOuterIters.Text = "Max num of refinement iters:";
//
// SettingsForm
//
this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
this.ClientSize = new System.Drawing.Size(656, 319);
this.Controls.Add(this.grServer);
this.Controls.Add(this.grClient);
this.FormBorderStyle = System.Windows.Forms.FormBorderStyle.FixedSingle;
this.MaximizeBox = false;
this.Name = "SettingsForm";
this.Text = "Settings";
this.Load += new System.EventHandler(this.SettingsForm_Load);
this.grClient.ResumeLayout(false);
this.grMarkers.ResumeLayout(false);
this.grMarkers.PerformLayout();
this.grBounding.ResumeLayout(false);
this.grBounding.PerformLayout();
this.grFiltering.ResumeLayout(false);
this.grFiltering.PerformLayout();
this.grServer.ResumeLayout(false);
this.grServer.PerformLayout();
this.ResumeLayout(false);
}
#endregion
private System.Windows.Forms.Label lbMerge;
private System.Windows.Forms.CheckBox chMerge;
private System.Windows.Forms.Label lbICPIters;
private System.Windows.Forms.TextBox txtICPIters;
private System.Windows.Forms.GroupBox grClient;
private System.Windows.Forms.GroupBox grServer;
private System.Windows.Forms.TextBox txtRefinIters;
private System.Windows.Forms.Label lbOuterIters;
private System.Windows.Forms.GroupBox grFiltering;
private System.Windows.Forms.TextBox txtFilterNeighbors;
private System.Windows.Forms.CheckBox chFilter;
private System.Windows.Forms.Label lbFilterNeighbors;
private System.Windows.Forms.Label lbFilterDistance;
private System.Windows.Forms.TextBox txtFilterDistance;
private System.Windows.Forms.GroupBox grMarkers;
private System.Windows.Forms.Label lbX2;
private System.Windows.Forms.Button btRemove;
private System.Windows.Forms.TextBox txtOrientationZ;
private System.Windows.Forms.TextBox txtId;
private System.Windows.Forms.Label lbY2;
private System.Windows.Forms.TextBox txtOrientationY;
private System.Windows.Forms.Label lbId;
private System.Windows.Forms.Label lbZ2;
private System.Windows.Forms.TextBox txtOrientationX;
private System.Windows.Forms.Button btAdd;
private System.Windows.Forms.Label lbTranslation;
private System.Windows.Forms.Label lbOrientation;
private System.Windows.Forms.TextBox txtTranslationZ;
private System.Windows.Forms.TextBox txtTranslationX;
private System.Windows.Forms.ListBox lisMarkers;
private System.Windows.Forms.TextBox txtTranslationY;
private System.Windows.Forms.GroupBox grBounding;
private System.Windows.Forms.Label lbMin;
private System.Windows.Forms.TextBox txtMaxZ;
private System.Windows.Forms.TextBox txtMaxY;
private System.Windows.Forms.TextBox txtMinX;
private System.Windows.Forms.TextBox txtMaxX;
private System.Windows.Forms.TextBox txtMinY;
private System.Windows.Forms.Label lbMax;
private System.Windows.Forms.TextBox txtMinZ;
private System.Windows.Forms.Label lbZ;
private System.Windows.Forms.Label lbY;
private System.Windows.Forms.Label lbX;
}
}
LiveScanServer/SettingsForm.cs
+285
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using System.Globalization;
namespace KinectServer
{
public partial class SettingsForm : Form
{
public KinectSettings oSettings;
public KinectServer oServer;
public SettingsForm()
{
InitializeComponent();
}
private void SettingsForm_Load(object sender, EventArgs e)
{
txtMinX.Text = oSettings.aMinBounds[0].ToString(CultureInfo.InvariantCulture);
txtMinY.Text = oSettings.aMinBounds[1].ToString(CultureInfo.InvariantCulture);
txtMinZ.Text = oSettings.aMinBounds[2].ToString(CultureInfo.InvariantCulture);
txtMaxX.Text = oSettings.aMaxBounds[0].ToString(CultureInfo.InvariantCulture);
txtMaxY.Text = oSettings.aMaxBounds[1].ToString(CultureInfo.InvariantCulture);
txtMaxZ.Text = oSettings.aMaxBounds[2].ToString(CultureInfo.InvariantCulture);
chFilter.Checked = oSettings.bFilter;
txtFilterNeighbors.Text = oSettings.nFilterNeighbors.ToString();
txtFilterDistance.Text = oSettings.fFilterThreshold.ToString(CultureInfo.InvariantCulture);
lisMarkers.DataSource = oSettings.lMarkerPoses;
chMerge.Checked = oSettings.bMergeScansForSave;
txtICPIters.Text = oSettings.nNumICPIterations.ToString();
txtRefinIters.Text = oSettings.nMaxNumRefineIters.ToString();
}
void UpdateClients()
{
oServer.SendSettings();
}
void UpdateMarkerFields()
{
if (lisMarkers.SelectedIndex >= 0)
{
MarkerPose pose = oSettings.lMarkerPoses[lisMarkers.SelectedIndex];
float X, Y, Z;
pose.GetOrientation(out X, out Y, out Z);
txtOrientationX.Text = X.ToString(CultureInfo.InvariantCulture);
txtOrientationY.Text = Y.ToString(CultureInfo.InvariantCulture);
txtOrientationZ.Text = Z.ToString(CultureInfo.InvariantCulture);
txtTranslationX.Text = pose.pose.t[0].ToString(CultureInfo.InvariantCulture);
txtTranslationY.Text = pose.pose.t[1].ToString(CultureInfo.InvariantCulture);
txtTranslationZ.Text = pose.pose.t[2].ToString(CultureInfo.InvariantCulture);
txtId.Text = pose.id.ToString(CultureInfo.InvariantCulture);
}
else
{
txtOrientationX.Text = "";
txtOrientationY.Text = "";
txtOrientationZ.Text = "";
txtTranslationX.Text = "";
txtTranslationY.Text = "";
txtTranslationZ.Text = "";
txtId.Text = "";
}
}
private void txtMinX_TextChanged(object sender, EventArgs e)
{
Single.TryParse(txtMinX.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out oSettings.aMinBounds[0]);
UpdateClients();
}
private void txtMinY_TextChanged(object sender, EventArgs e)
{
Single.TryParse(txtMinY.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out oSettings.aMinBounds[1]);
UpdateClients();
}
private void txtMinZ_TextChanged(object sender, EventArgs e)
{
Single.TryParse(txtMinZ.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out oSettings.aMinBounds[2]);
UpdateClients();
}
private void txtMaxX_TextChanged(object sender, EventArgs e)
{
Single.TryParse(txtMaxX.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out oSettings.aMaxBounds[0]);
UpdateClients();
}
private void txtMaxY_TextChanged(object sender, EventArgs e)
{
Single.TryParse(txtMaxY.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out oSettings.aMaxBounds[1]);
UpdateClients();
}
private void txtMaxZ_TextChanged(object sender, EventArgs e)
{
Single.TryParse(txtMaxZ.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out oSettings.aMaxBounds[2]);
UpdateClients();
}
private void chFilter_CheckedChanged(object sender, EventArgs e)
{
oSettings.bFilter = chFilter.Checked;
UpdateClients();
}
private void txtFilterNeighbors_TextChanged(object sender, EventArgs e)
{
Int32.TryParse(txtFilterNeighbors.Text, out oSettings.nFilterNeighbors);
UpdateClients();
}
private void txtFilterDistance_TextChanged(object sender, EventArgs e)
{
Single.TryParse(txtFilterDistance.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out oSettings.fFilterThreshold);
UpdateClients();
}
private void txtICPIters_TextChanged(object sender, EventArgs e)
{
Int32.TryParse(txtICPIters.Text, out oSettings.nNumICPIterations);
}
private void txtRefinIters_TextChanged(object sender, EventArgs e)
{
Int32.TryParse(txtRefinIters.Text, out oSettings.nMaxNumRefineIters);
}
private void chMerge_CheckedChanged(object sender, EventArgs e)
{
oSettings.bMergeScansForSave = chMerge.Checked;
}
private void btAdd_Click(object sender, EventArgs e)
{
lock (oSettings)
oSettings.lMarkerPoses.Add(new MarkerPose());
lisMarkers.SelectedIndex = oSettings.lMarkerPoses.Count - 1;
UpdateMarkerFields();
UpdateClients();
}
private void btRemove_Click(object sender, EventArgs e)
{
if (oSettings.lMarkerPoses.Count > 0)
{
oSettings.lMarkerPoses.RemoveAt(lisMarkers.SelectedIndex);
lisMarkers.SelectedIndex = oSettings.lMarkerPoses.Count - 1;
UpdateMarkerFields();
UpdateClients();
}
}
private void lisMarkers_SelectedIndexChanged(object sender, EventArgs e)
{
UpdateMarkerFields();
}
private void txtOrientationX_TextChanged(object sender, EventArgs e)
{
if (lisMarkers.SelectedIndex >= 0)
{
MarkerPose pose = oSettings.lMarkerPoses[lisMarkers.SelectedIndex];
float X, Y, Z;
pose.GetOrientation(out X, out Y, out Z);
Single.TryParse(txtOrientationX.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out X);
pose.SetOrientation(X, Y, Z);
UpdateClients();
}
}
private void txtOrientationY_TextChanged(object sender, EventArgs e)
{
if (lisMarkers.SelectedIndex >= 0)
{
MarkerPose pose = oSettings.lMarkerPoses[lisMarkers.SelectedIndex];
float X, Y, Z;
pose.GetOrientation(out X, out Y, out Z);
Single.TryParse(txtOrientationY.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out Y);
pose.SetOrientation(X, Y, Z);
UpdateClients();
}
}
private void txtOrientationZ_TextChanged(object sender, EventArgs e)
{
if (lisMarkers.SelectedIndex >= 0)
{
MarkerPose pose = oSettings.lMarkerPoses[lisMarkers.SelectedIndex];
float X, Y, Z;
pose.GetOrientation(out X, out Y, out Z);
Single.TryParse(txtOrientationZ.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out Z);
pose.SetOrientation(X, Y, Z);
UpdateClients();
}
}
private void txtTranslationX_TextChanged(object sender, EventArgs e)
{
if (lisMarkers.SelectedIndex >= 0)
{
float X;
MarkerPose pose = oSettings.lMarkerPoses[lisMarkers.SelectedIndex];
Single.TryParse(txtTranslationX.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out X);
pose.pose.t[0] = X;
UpdateClients();
}
}
private void txtTranslationY_TextChanged(object sender, EventArgs e)
{
if (lisMarkers.SelectedIndex >= 0)
{
float Y;
MarkerPose pose = oSettings.lMarkerPoses[lisMarkers.SelectedIndex];
Single.TryParse(txtTranslationY.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out Y);
pose.pose.t[1] = Y;
UpdateClients();
}
}
private void txtTranslationZ_TextChanged(object sender, EventArgs e)
{
if (lisMarkers.SelectedIndex >= 0)
{
float Z;
MarkerPose pose = oSettings.lMarkerPoses[lisMarkers.SelectedIndex];
Single.TryParse(txtTranslationZ.Text, NumberStyles.Any, CultureInfo.InvariantCulture, out Z);
pose.pose.t[2] = Z;
UpdateClients();
}
}
private void txtId_TextChanged(object sender, EventArgs e)
{
if (lisMarkers.SelectedIndex >= 0)
{
int id;
MarkerPose pose = oSettings.lMarkerPoses[lisMarkers.SelectedIndex];
Int32.TryParse(txtId.Text, out id);
pose.id = id;
UpdateClients();
}
}
}
}
LiveScanServer/SettingsForm.resx
+120
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<?xml version="1.0" encoding="utf-8"?>
<root>
<!--
Microsoft ResX Schema
Version 2.0
The primary goals of this format is to allow a simple XML format
that is mostly human readable. The generation and parsing of the
various data types are done through the TypeConverter classes
associated with the data types.
Example:
... ado.net/XML headers & schema ...
<resheader name="resmimetype">text/microsoft-resx</resheader>
<resheader name="version">2.0</resheader>
<resheader name="reader">System.Resources.ResXResourceReader, System.Windows.Forms, ...</resheader>
<resheader name="writer">System.Resources.ResXResourceWriter, System.Windows.Forms, ...</resheader>
<data name="Name1"><value>this is my long string</value><comment>this is a comment</comment></data>
<data name="Color1" type="System.Drawing.Color, System.Drawing">Blue</data>
<data name="Bitmap1" mimetype="application/x-microsoft.net.object.binary.base64">
<value>[base64 mime encoded serialized .NET Framework object]</value>
</data>
<data name="Icon1" type="System.Drawing.Icon, System.Drawing" mimetype="application/x-microsoft.net.object.bytearray.base64">
<value>[base64 mime encoded string representing a byte array form of the .NET Framework object]</value>
<comment>This is a comment</comment>
</data>
There are any number of "resheader" rows that contain simple
name/value pairs.
Each data row contains a name, and value. The row also contains a
type or mimetype. Type corresponds to a .NET class that support
text/value conversion through the TypeConverter architecture.
Classes that don't support this are serialized and stored with the
mimetype set.
The mimetype is used for serialized objects, and tells the
ResXResourceReader how to depersist the object. This is currently not
extensible. For a given mimetype the value must be set accordingly:
Note - application/x-microsoft.net.object.binary.base64 is the format
that the ResXResourceWriter will generate, however the reader can
read any of the formats listed below.
mimetype: application/x-microsoft.net.object.binary.base64
value : The object must be serialized with
: System.Runtime.Serialization.Formatters.Binary.BinaryFormatter
: and then encoded with base64 encoding.
mimetype: application/x-microsoft.net.object.soap.base64
value : The object must be serialized with
: System.Runtime.Serialization.Formatters.Soap.SoapFormatter
: and then encoded with base64 encoding.
mimetype: application/x-microsoft.net.object.bytearray.base64
value : The object must be serialized into a byte array
: using a System.ComponentModel.TypeConverter
: and then encoded with base64 encoding.
-->
<xsd:schema id="root" xmlns="" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:msdata="urn:schemas-microsoft-com:xml-msdata">
<xsd:import namespace="http://www.w3.org/XML/1998/namespace" />
<xsd:element name="root" msdata:IsDataSet="true">
<xsd:complexType>
<xsd:choice maxOccurs="unbounded">
<xsd:element name="metadata">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" />
</xsd:sequence>
<xsd:attribute name="name" use="required" type="xsd:string" />
<xsd:attribute name="type" type="xsd:string" />
<xsd:attribute name="mimetype" type="xsd:string" />
<xsd:attribute ref="xml:space" />
</xsd:complexType>
</xsd:element>
<xsd:element name="assembly">
<xsd:complexType>
<xsd:attribute name="alias" type="xsd:string" />
<xsd:attribute name="name" type="xsd:string" />
</xsd:complexType>
</xsd:element>
<xsd:element name="data">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
<xsd:element name="comment" type="xsd:string" minOccurs="0" msdata:Ordinal="2" />
</xsd:sequence>
<xsd:attribute name="name" type="xsd:string" use="required" msdata:Ordinal="1" />
<xsd:attribute name="type" type="xsd:string" msdata:Ordinal="3" />
<xsd:attribute name="mimetype" type="xsd:string" msdata:Ordinal="4" />
<xsd:attribute ref="xml:space" />
</xsd:complexType>
</xsd:element>
<xsd:element name="resheader">
<xsd:complexType>
<xsd:sequence>
<xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
</xsd:sequence>
<xsd:attribute name="name" type="xsd:string" use="required" />
</xsd:complexType>
</xsd:element>
</xsd:choice>
</xsd:complexType>
</xsd:element>
</xsd:schema>
<resheader name="resmimetype">
<value>text/microsoft-resx</value>
</resheader>
<resheader name="version">
<value>2.0</value>
</resheader>
<resheader name="reader">
<value>System.Resources.ResXResourceReader, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089</value>
</resheader>
<resheader name="writer">
<value>System.Resources.ResXResourceWriter, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089</value>
</resheader>
</root>
LiveScanServer/Utils.cs
+95
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@@ -0,0 +1,95 @@
// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
using System;
namespace KinectServer
{
[Serializable]
public class AffineTransform
{
public float[,] R = new float[3, 3];
public float[] t = new float[3];
public AffineTransform()
{
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
if (i == j)
R[i, j] = 1;
else
R[i, j] = 0;
}
t[i] = 0;
}
}
}
[Serializable]
public class MarkerPose
{
public AffineTransform pose = new AffineTransform();
public int id = -1;
public MarkerPose()
{
UpdateRotationMatrix();
}
public void SetOrientation(float X, float Y, float Z)
{
r[0] = X;
r[1] = Y;
r[2] = Z;
UpdateRotationMatrix();
}
public void GetOrientation(out float X, out float Y, out float Z)
{
X = r[0];
Y = r[1];
Z = r[2];
}
private void UpdateRotationMatrix()
{
float radX = r[0] * (float)Math.PI / 180.0f;
float radY = r[1] * (float)Math.PI / 180.0f;
float radZ = r[2] * (float)Math.PI / 180.0f;
float c1 = (float)Math.Cos(radZ);
float c2 = (float)Math.Cos(radY);
float c3 = (float)Math.Cos(radX);
float s1 = (float)Math.Sin(radZ);
float s2 = (float)Math.Sin(radY);
float s3 = (float)Math.Sin(radX);
//Z Y X rotation
pose.R[0, 0] = c1 * c2;
pose.R[0, 1] = c1 * s2 * s3 - c3 * s1;
pose.R[0, 2] = s1 * s3 + c1 * c3 * s2;
pose.R[1, 0] = c2 * s1;
pose.R[1, 1] = c1 * c3 + s1 * s2 * s3;
pose.R[1, 2] = c3 * s1 * s2 - c1 * s3;
pose.R[2, 0] = -s2;
pose.R[2, 1] = c2 * s3;
pose.R[2, 2] = c2 * c3;
}
private float[] r = new float[3];
}
}
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include/ICP/icp.h
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#include <stdio.h>
#include <vector>
#include "nanoflann.h"
#if defined(ICP_DLL_EXPORTS) // inside DLL
# define ICP_API __declspec(dllexport)
#else // outside DLL
# define ICP_API __declspec(dllimport)
#endif
using namespace std;
struct Point3f
{
float X, Y, Z;
};
// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
struct PointCloud
{
std::vector<Point3f> pts;
// Must return the number of data points
inline size_t kdtree_get_point_count() const { return pts.size(); }
// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
inline float kdtree_distance(const float *p1, const size_t idx_p2, size_t /*size*/) const
{
const float d0 = p1[0] - pts[idx_p2].X;
const float d1 = p1[1] - pts[idx_p2].Y;
const float d2 = p1[2] - pts[idx_p2].Z;
return d0*d0 + d1*d1 + d2*d2;
}
// Returns the dim'th component of the idx'th point in the class:
// Since this is inlined and the "dim" argument is typically an immediate value, the
// "if/else's" are actually solved at compile time.
inline float kdtree_get_pt(const size_t idx, int dim) const
{
if (dim == 0) return pts[idx].X;
else if (dim == 1) return pts[idx].Y;
else return pts[idx].Z;
}
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
// Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
// Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX>
bool kdtree_get_bbox(BBOX& /*bb*/) const { return false; }
};
extern "C" ICP_API float __stdcall ICP(Point3f *verts1, Point3f *verts2, int nVerts1, int nVerts2, float *R, float *t, int maxIter = 10);
include/LiveScanClient/calibration.h
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
#pragma once
#include "stdafx.h"
#include "marker.h"
#include "utils.h"
vector<float> RotatePoint(vector<float> &point, std::vector<std::vector<float>> &R);
vector<float> InverseRotatePoint(vector<float> &point, std::vector<std::vector<float>> &R);
struct MarkerPose
{
int markerId;
float R[3][3];
float t[3];
};
class Calibration
{
public:
vector<float> worldT;
vector<vector<float>> worldR;
int iUsedMarkerId;
vector<float> cameraT;
vector<vector<float>> cameraR;
vector<MarkerPose> markerPoses;
bool bCalibrated;
Calibration();
~Calibration();
bool Calibrate(RGB *pBuffer, Point3f *pCameraCoordinates, int cColorWidth, int cColorHeight);
private:
IMarker *pDetector;
int nSampleCounter;
int nRequiredSamples;
vector<vector<Point3f>> marker3DSamples;
void Procrustes(MarkerInfo &marker, vector<Point3f> &markerInWorld, vector<float> &markerT, vector<vector<float>> &markerR);
bool GetMarkerCorners3D(vector<Point3f> &marker3D, MarkerInfo &marker, Point3f *pCameraCoordinates, int cColorWidth, int cColorHeight);
};
include/LiveScanClient/filter.h
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
#pragma once
#include <vector>
#include "nanoflann.h"
#include "utils.h"
struct KNNeighborsResult
{
std::vector<size_t> neighbors;
std::vector<float> distances;
float kDistance;
};
struct PointCloud
{
std::vector<Point3f> pts;
// Must return the number of data points
inline size_t kdtree_get_point_count() const { return pts.size(); }
// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
inline float kdtree_distance(const float *p1, const size_t idx_p2, size_t /*size*/) const
{
const float d0 = p1[0] - pts[idx_p2].X;
const float d1 = p1[1] - pts[idx_p2].Y;
const float d2 = p1[2] - pts[idx_p2].Z;
return d0*d0 + d1*d1 + d2*d2;
}
// Returns the dim'th component of the idx'th point in the class:
// Since this is inlined and the "dim" argument is typically an immediate value, the
// "if/else's" are actually solved at compile time.
inline float kdtree_get_pt(const size_t idx, int dim) const
{
if (dim == 0) return pts[idx].X;
else if (dim == 1) return pts[idx].Y;
else return pts[idx].Z;
}
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
// Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
// Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX>
bool kdtree_get_bbox(BBOX& /*bb*/) const { return false; }
};
typedef nanoflann::KDTreeSingleIndexAdaptor<nanoflann::L2_Simple_Adaptor<float, PointCloud>, PointCloud, 3> kdTree;
void filter(std::vector<Point3f> &vertices, std::vector<RGB> &colors, int k = 10, float maxDist = 0.01);
include/LiveScanClient/iCapture.h
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
#pragma once
#include "utils.h"
class ICapture
{
public:
ICapture();
~ICapture();
virtual bool Initialize() = 0;
virtual bool AcquireFrame() = 0;
virtual void MapDepthFrameToCameraSpace(Point3f *pCameraSpacePoints) = 0;
virtual void MapColorFrameToCameraSpace(Point3f *pCameraSpacePoints) = 0;
virtual void MapDepthFrameToColorSpace(Point2f *pColorSpacePoints) = 0;
virtual void MapColorFrameToDepthSpace(Point2f *pDepthSpacePoints) = 0;
bool bInitialized;
int nColorFrameHeight, nColorFrameWidth;
int nDepthFrameHeight, nDepthFrameWidth;
UINT16 *pDepth;
RGB *pColorRGBX;
};
include/LiveScanClient/iMarker.h
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
#pragma once
#include "utils.h"
//struktura przechowywuj¹ca wszystkie dane markera
typedef struct MarkerStruct
{
int id;
//po³o¿enie naro¿ników markera w obrazie
std::vector<Point2f> corners;
//te same punkty w uk³adzie wspó³rzêdnych markera
std::vector<Point3f> points;
MarkerStruct()
{
id = -1;
}
MarkerStruct(int id, std::vector<Point2f> corners, std::vector<Point3f> points)
{
this->id = id;
this->corners = corners;
this->points = points;
}
} MarkerInfo;
class IMarker
{
public:
IMarker() {};
//znajduje wszystkie markery w obrazie i zapisuje je w zmiennej markers
virtual bool GetMarker(RGB *img, int height, int width, MarkerInfo &marker) = 0;
};
include/LiveScanClient/imageRenderer.h
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//------------------------------------------------------------------------------
// <copyright file="ImageRenderer.h" company="Microsoft">
// Copyright (c) Microsoft Corporation. All rights reserved.
// </copyright>
//------------------------------------------------------------------------------
// Manages the drawing of image data
#pragma once
#include <d2d1.h>
class ImageRenderer
{
public:
/// <summary>
/// Constructor
/// </summary>
ImageRenderer();
/// <summary>
/// Destructor
/// </summary>
virtual ~ImageRenderer();
/// <summary>
/// Set the window to draw to as well as the video format
/// Implied bits per pixel is 32
/// </summary>
/// <param name="hWnd">window to draw to</param>
/// <param name="pD2DFactory">already created D2D factory object</param>
/// <param name="sourceWidth">width (in pixels) of image data to be drawn</param>
/// <param name="sourceHeight">height (in pixels) of image data to be drawn</param>
/// <param name="sourceStride">length (in bytes) of a single scanline</param>
/// <returns>indicates success or failure</returns>
HRESULT Initialize(HWND hwnd, ID2D1Factory* pD2DFactory, int sourceWidth, int sourceHeight, int sourceStride);
/// <summary>
/// Draws a 32 bit per pixel image of previously specified width, height, and stride to the associated hwnd
/// </summary>
/// <param name="pImage">image data in RGBX format</param>
/// <param name="cbImage">size of image data in bytes</param>
/// <returns>indicates success or failure</returns>
HRESULT Draw(BYTE* pImage, unsigned long cbImage);
private:
HWND m_hWnd;
// Format information
UINT m_sourceHeight;
UINT m_sourceWidth;
LONG m_sourceStride;
// Direct2D
ID2D1Factory* m_pD2DFactory;
ID2D1HwndRenderTarget* m_pRenderTarget;
ID2D1Bitmap* m_pBitmap;
/// <summary>
/// Ensure necessary Direct2d resources are created
/// </summary>
/// <returns>indicates success or failure</returns>
HRESULT EnsureResources();
/// <summary>
/// Dispose of Direct2d resources
/// </summary>
void DiscardResources();
};
include/LiveScanClient/kinectCapture.h
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
#pragma once
#include "stdafx.h"
#include "ICapture.h"
#include "Kinect.h"
#include "utils.h"
class KinectCapture : public ICapture
{
public:
KinectCapture();
~KinectCapture();
bool Initialize();
bool AcquireFrame();
void MapDepthFrameToCameraSpace(Point3f *pCameraSpacePoints);
void MapColorFrameToCameraSpace(Point3f *pCameraSpacePoints);
void MapDepthFrameToColorSpace(Point2f *pColorSpacePoints);
void MapColorFrameToDepthSpace(Point2f *pDepthSpacePoints);
private:
ICoordinateMapper* pCoordinateMapper;
IKinectSensor* pKinectSensor;
IMultiSourceFrameReader* pMultiSourceFrameReader;
};
include/LiveScanClient/liveScanClient.h
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
#pragma once
#include "resource.h"
#include "ImageRenderer.h"
#include "SocketCS.h"
#include "calibration.h"
#include "utils.h"
#include "KinectCapture.h"
#include <thread>
#include <mutex>
class LiveScanClient
{
public:
LiveScanClient();
~LiveScanClient();
static LRESULT CALLBACK MessageRouter(HWND hWnd, UINT uMsg, WPARAM wParam, LPARAM lParam);
LRESULT CALLBACK DlgProc(HWND hWnd, UINT uMsg, WPARAM wParam, LPARAM lParam);
int Run(HINSTANCE hInstance, int nCmdShow);
bool m_bSocketThread;
private:
Calibration calibration;
bool m_bCalibrate;
bool m_bFilter;
ICapture *pCapture;
int m_nFilterNeighbors;
float m_fFilterThreshold;
bool m_bCaptureFrame;
bool m_bConnected;
bool m_bConfirmCaptured;
bool m_bConfirmCalibrated;
bool m_bShowDepth;
SocketClient *m_pClientSocket;
std::vector<float> m_vBounds;
std::vector<Point3f> m_vLastFrameVertices;
std::vector<RGB> m_vLastFrameRGB;
std::vector<std::vector<Point3f>> m_vGatheredVertices;
std::vector<std::vector<RGB>> m_vGatheredRGBPoints;
HWND m_hWnd;
INT64 m_nLastCounter;
double m_fFreq;
INT64 m_nNextStatusTime;
DWORD m_nFramesSinceUpdate;
Point3f* m_pCameraSpaceCoordinates;
Point2f* m_pColorCoordinates;
// Direct2D
ImageRenderer* m_pDrawColor;
ID2D1Factory* m_pD2DFactory;
RGB* m_pDepthRGBX;
void UpdateFrame();
void ProcessColor(RGB* pBuffer, int nWidth, int nHeight);
void ProcessDepth(const UINT16* pBuffer, int nHeight, int nWidth);
bool SetStatusMessage(_In_z_ WCHAR* szMessage, DWORD nShowTimeMsec, bool bForce);
void HandleSocket();
void SocketThreadFunction();
void StoreFrame(Point3f *vertices, Point2f *mapping, RGB *color);
void ShowFPS();
void ReadIPFromFile();
void WriteIPToFile();
};
include/LiveScanClient/marker.h
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
#pragma once
#include "stdafx.h"
#include <opencv2/opencv.hpp>
#include "utils.h"
#include "IMarker.h"
using namespace std;
class MarkerDetector : public IMarker
{
public:
MarkerDetector();
bool GetMarker(RGB *img, int height, int width, MarkerInfo &marker);
private:
int nMarkerCorners;
vector<cv::Point2f> vPts;
int nMinSize;
int nMaxSize;
int nThreshold;
double dApproxPolyCoef;
double dMarkerFrame;
bool bDraw;
bool GetMarker(cv::Mat &img, MarkerInfo &marker);
bool OrderCorners(vector<cv::Point2f> &corners);
int GetCode(cv::Mat &img, vector<cv::Point2f> points, vector<cv::Point2f> corners);
void CornersSubPix(vector<cv::Point2f> &corners, vector<cv::Point> contour, bool order);
cv::Point2f GetIntersection(cv::Vec4f lin1, cv::Vec4f lin2);
void GetMarkerPoints(vector<Point3f> &pts);
void GetMarkerPointsForWarp(vector<cv::Point2f> &pts);
double GetMarkerArea(MarkerInfo &marker);
};
include/LiveScanClient/utils.h
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// Copyright (C) 2015 Marek Kowalski (M.Kowalski@ire.pw.edu.pl), Jacek Naruniec (J.Naruniec@ire.pw.edu.pl)
// License: MIT Software License See LICENSE.txt for the full license.
// If you use this software in your research, then please use the following citation:
// Kowalski, M.; Naruniec, J.; Daniluk, M.: "LiveScan3D: A Fast and Inexpensive 3D Data
// Acquisition System for Multiple Kinect v2 Sensors". in 3D Vision (3DV), 2015 International Conference on, Lyon, France, 2015
// @INPROCEEDINGS{Kowalski15,
// author={Kowalski, M. and Naruniec, J. and Daniluk, M.},
// booktitle={3D Vision (3DV), 2015 International Conference on},
// title={LiveScan3D: A Fast and Inexpensive 3D Data Acquisition System for Multiple Kinect v2 Sensors},
// year={2015},
// }
#pragma once
#include "stdafx.h"
#include <stdio.h>
#include <string>
#include <vector>
enum INCOMING_MESSAGE_TYPE
{
MSG_CAPTURE_FRAME,
MSG_CALIBRATE,
MSG_RECEIVE_SETTINGS,
MSG_REQUEST_STORED_FRAME,
MSG_REQUEST_LAST_FRAME,
MSG_RECEIVE_CALIBRATION,
MSG_CLEAR_STORED_FRAMES
};
enum OUTGOING_MESSAGE_TYPE
{
MSG_CONFIRM_CAPTURED,
MSG_CONFIRM_CALIBRATED,
MSG_STORED_FRAME,
MSG_LAST_FRAME
};
typedef struct Point3f
{
Point3f()
{
this->X = 0;
this->Y = 0;
this->Z = 0;
}
Point3f(float X, float Y, float Z)
{
this->X = X;
this->Y = Y;
this->Z = Z;
}
float X;
float Y;
float Z;
} Point3f;
typedef struct Point2f
{
Point2f()
{
this->X = 0;
this->Y = 0;
}
Point2f(float X, float Y)
{
this->X = X;
this->Y = Y;
}
float X;
float Y;
} Point2f;
typedef struct RGB
{
BYTE rgbBlue;
BYTE rgbGreen;
BYTE rgbRed;
BYTE rgbReserved;
} RGB;
Point3f RotatePoint(Point3f &point, std::vector<std::vector<float>> &R);
Point3f InverseRotatePoint(Point3f &point, std::vector<std::vector<float>> &R);
include/nanoflann.h
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include/opencv/cv.h
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OLD_CV_H__
#define __OPENCV_OLD_CV_H__
#if defined(_MSC_VER)
#define CV_DO_PRAGMA(x) __pragma(x)
#define __CVSTR2__(x) #x
#define __CVSTR1__(x) __CVSTR2__(x)
#define __CVMSVCLOC__ __FILE__ "("__CVSTR1__(__LINE__)") : "
#define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (__CVMSVCLOC__ _msg))
#elif defined(__GNUC__)
#define CV_DO_PRAGMA(x) _Pragma (#x)
#define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (_msg))
#else
#define CV_DO_PRAGMA(x)
#define CV_MSG_PRAGMA(_msg)
#endif
#define CV_WARNING(x) CV_MSG_PRAGMA("Warning: " #x)
//CV_WARNING("This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module")
#include "opencv2/core/core_c.h"
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/tracking.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/flann/flann.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/legacy/compat.hpp"
#if !defined(CV_IMPL)
#define CV_IMPL extern "C"
#endif //CV_IMPL
#if defined(__cplusplus)
#include "opencv2/core/internal.hpp"
#endif //__cplusplus
#endif // __OPENCV_OLD_CV_H_
include/opencv/cv.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OLD_CV_HPP__
#define __OPENCV_OLD_CV_HPP__
//#if defined(__GNUC__)
//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
//#endif
#include <cv.h>
#endif
include/opencv/cvaux.h
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OLD_AUX_H__
#define __OPENCV_OLD_AUX_H__
//#if defined(__GNUC__)
//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
//#endif
#include "opencv2/core/core_c.h"
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/tracking.hpp"
#include "opencv2/video/background_segm.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/legacy/legacy.hpp"
#include "opencv2/legacy/compat.hpp"
#include "opencv2/legacy/blobtrack.hpp"
#include "opencv2/contrib/contrib.hpp"
#endif
/* End of file. */
include/opencv/cvaux.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OLD_AUX_HPP__
#define __OPENCV_OLD_AUX_HPP__
//#if defined(__GNUC__)
//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
//#endif
#include <cvaux.h>
#endif
include/opencv/cvwimage.h
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///////////////////////////////////////////////////////////////////////////////
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to
// this license. If you do not agree to this license, do not download,
// install, copy or use the software.
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2008, Google, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation or contributors may not be used to endorse
// or promote products derived from this software without specific
// prior written permission.
//
// This software is provided by the copyright holders and contributors "as is"
// and any express or implied warranties, including, but not limited to, the
// implied warranties of merchantability and fitness for a particular purpose
// are disclaimed. In no event shall the Intel Corporation or contributors be
// liable for any direct, indirect, incidental, special, exemplary, or
// consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
#ifndef __OPENCV_OLD_WIMAGE_HPP__
#define __OPENCV_OLD_WIMAGE_HPP__
#include "opencv2/core/wimage.hpp"
#endif
include/opencv/cxcore.h
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OLD_CXCORE_H__
#define __OPENCV_OLD_CXCORE_H__
//#if defined(__GNUC__)
//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
//#endif
#include "opencv2/core/core_c.h"
#include "opencv2/core/core.hpp"
#endif
include/opencv/cxcore.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OLD_CXCORE_HPP__
#define __OPENCV_OLD_CXCORE_HPP__
//#if defined(__GNUC__)
//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
//#endif
#include <cxcore.h>
#endif
include/opencv/cxeigen.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OLD_EIGEN_HPP__
#define __OPENCV_OLD_EIGEN_HPP__
#include "opencv2/core/eigen.hpp"
#endif
include/opencv/cxmisc.h
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#ifndef __OPENCV_OLD_CXMISC_H__
#define __OPENCV_OLD_CXMISC_H__
#include "opencv2/core/internal.hpp"
#endif
include/opencv/highgui.h
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OLD_HIGHGUI_H__
#define __OPENCV_OLD_HIGHGUI_H__
#include "opencv2/core/core_c.h"
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/highgui/highgui.hpp"
#endif
include/opencv/ml.h
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OLD_ML_H__
#define __OPENCV_OLD_ML_H__
#include "opencv2/core/core_c.h"
#include "opencv2/core/core.hpp"
#include "opencv2/ml/ml.hpp"
#endif
include/opencv2/calib3d/calib3d.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_CALIB3D_HPP__
#define __OPENCV_CALIB3D_HPP__
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#ifdef __cplusplus
extern "C" {
#endif
/****************************************************************************************\
* Camera Calibration, Pose Estimation and Stereo *
\****************************************************************************************/
typedef struct CvPOSITObject CvPOSITObject;
/* Allocates and initializes CvPOSITObject structure before doing cvPOSIT */
CVAPI(CvPOSITObject*) cvCreatePOSITObject( CvPoint3D32f* points, int point_count );
/* Runs POSIT (POSe from ITeration) algorithm for determining 3d position of
an object given its model and projection in a weak-perspective case */
CVAPI(void) cvPOSIT( CvPOSITObject* posit_object, CvPoint2D32f* image_points,
double focal_length, CvTermCriteria criteria,
float* rotation_matrix, float* translation_vector);
/* Releases CvPOSITObject structure */
CVAPI(void) cvReleasePOSITObject( CvPOSITObject** posit_object );
/* updates the number of RANSAC iterations */
CVAPI(int) cvRANSACUpdateNumIters( double p, double err_prob,
int model_points, int max_iters );
CVAPI(void) cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst );
/* Calculates fundamental matrix given a set of corresponding points */
#define CV_FM_7POINT 1
#define CV_FM_8POINT 2
#define CV_LMEDS 4
#define CV_RANSAC 8
#define CV_FM_LMEDS_ONLY CV_LMEDS
#define CV_FM_RANSAC_ONLY CV_RANSAC
#define CV_FM_LMEDS CV_LMEDS
#define CV_FM_RANSAC CV_RANSAC
enum
{
CV_ITERATIVE = 0,
CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation"
CV_P3P = 2 // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem"
};
CVAPI(int) cvFindFundamentalMat( const CvMat* points1, const CvMat* points2,
CvMat* fundamental_matrix,
int method CV_DEFAULT(CV_FM_RANSAC),
double param1 CV_DEFAULT(3.), double param2 CV_DEFAULT(0.99),
CvMat* status CV_DEFAULT(NULL) );
/* For each input point on one of images
computes parameters of the corresponding
epipolar line on the other image */
CVAPI(void) cvComputeCorrespondEpilines( const CvMat* points,
int which_image,
const CvMat* fundamental_matrix,
CvMat* correspondent_lines );
/* Triangulation functions */
CVAPI(void) cvTriangulatePoints(CvMat* projMatr1, CvMat* projMatr2,
CvMat* projPoints1, CvMat* projPoints2,
CvMat* points4D);
CVAPI(void) cvCorrectMatches(CvMat* F, CvMat* points1, CvMat* points2,
CvMat* new_points1, CvMat* new_points2);
/* Computes the optimal new camera matrix according to the free scaling parameter alpha:
alpha=0 - only valid pixels will be retained in the undistorted image
alpha=1 - all the source image pixels will be retained in the undistorted image
*/
CVAPI(void) cvGetOptimalNewCameraMatrix( const CvMat* camera_matrix,
const CvMat* dist_coeffs,
CvSize image_size, double alpha,
CvMat* new_camera_matrix,
CvSize new_imag_size CV_DEFAULT(cvSize(0,0)),
CvRect* valid_pixel_ROI CV_DEFAULT(0),
int center_principal_point CV_DEFAULT(0));
/* Converts rotation vector to rotation matrix or vice versa */
CVAPI(int) cvRodrigues2( const CvMat* src, CvMat* dst,
CvMat* jacobian CV_DEFAULT(0) );
/* Finds perspective transformation between the object plane and image (view) plane */
CVAPI(int) cvFindHomography( const CvMat* src_points,
const CvMat* dst_points,
CvMat* homography,
int method CV_DEFAULT(0),
double ransacReprojThreshold CV_DEFAULT(3),
CvMat* mask CV_DEFAULT(0));
/* Computes RQ decomposition for 3x3 matrices */
CVAPI(void) cvRQDecomp3x3( const CvMat *matrixM, CvMat *matrixR, CvMat *matrixQ,
CvMat *matrixQx CV_DEFAULT(NULL),
CvMat *matrixQy CV_DEFAULT(NULL),
CvMat *matrixQz CV_DEFAULT(NULL),
CvPoint3D64f *eulerAngles CV_DEFAULT(NULL));
/* Computes projection matrix decomposition */
CVAPI(void) cvDecomposeProjectionMatrix( const CvMat *projMatr, CvMat *calibMatr,
CvMat *rotMatr, CvMat *posVect,
CvMat *rotMatrX CV_DEFAULT(NULL),
CvMat *rotMatrY CV_DEFAULT(NULL),
CvMat *rotMatrZ CV_DEFAULT(NULL),
CvPoint3D64f *eulerAngles CV_DEFAULT(NULL));
/* Computes d(AB)/dA and d(AB)/dB */
CVAPI(void) cvCalcMatMulDeriv( const CvMat* A, const CvMat* B, CvMat* dABdA, CvMat* dABdB );
/* Computes r3 = rodrigues(rodrigues(r2)*rodrigues(r1)),
t3 = rodrigues(r2)*t1 + t2 and the respective derivatives */
CVAPI(void) cvComposeRT( const CvMat* _rvec1, const CvMat* _tvec1,
const CvMat* _rvec2, const CvMat* _tvec2,
CvMat* _rvec3, CvMat* _tvec3,
CvMat* dr3dr1 CV_DEFAULT(0), CvMat* dr3dt1 CV_DEFAULT(0),
CvMat* dr3dr2 CV_DEFAULT(0), CvMat* dr3dt2 CV_DEFAULT(0),
CvMat* dt3dr1 CV_DEFAULT(0), CvMat* dt3dt1 CV_DEFAULT(0),
CvMat* dt3dr2 CV_DEFAULT(0), CvMat* dt3dt2 CV_DEFAULT(0) );
/* Projects object points to the view plane using
the specified extrinsic and intrinsic camera parameters */
CVAPI(void) cvProjectPoints2( const CvMat* object_points, const CvMat* rotation_vector,
const CvMat* translation_vector, const CvMat* camera_matrix,
const CvMat* distortion_coeffs, CvMat* image_points,
CvMat* dpdrot CV_DEFAULT(NULL), CvMat* dpdt CV_DEFAULT(NULL),
CvMat* dpdf CV_DEFAULT(NULL), CvMat* dpdc CV_DEFAULT(NULL),
CvMat* dpddist CV_DEFAULT(NULL),
double aspect_ratio CV_DEFAULT(0));
/* Finds extrinsic camera parameters from
a few known corresponding point pairs and intrinsic parameters */
CVAPI(void) cvFindExtrinsicCameraParams2( const CvMat* object_points,
const CvMat* image_points,
const CvMat* camera_matrix,
const CvMat* distortion_coeffs,
CvMat* rotation_vector,
CvMat* translation_vector,
int use_extrinsic_guess CV_DEFAULT(0) );
/* Computes initial estimate of the intrinsic camera parameters
in case of planar calibration target (e.g. chessboard) */
CVAPI(void) cvInitIntrinsicParams2D( const CvMat* object_points,
const CvMat* image_points,
const CvMat* npoints, CvSize image_size,
CvMat* camera_matrix,
double aspect_ratio CV_DEFAULT(1.) );
#define CV_CALIB_CB_ADAPTIVE_THRESH 1
#define CV_CALIB_CB_NORMALIZE_IMAGE 2
#define CV_CALIB_CB_FILTER_QUADS 4
#define CV_CALIB_CB_FAST_CHECK 8
// Performs a fast check if a chessboard is in the input image. This is a workaround to
// a problem of cvFindChessboardCorners being slow on images with no chessboard
// - src: input image
// - size: chessboard size
// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called,
// 0 if there is no chessboard, -1 in case of error
CVAPI(int) cvCheckChessboard(IplImage* src, CvSize size);
/* Detects corners on a chessboard calibration pattern */
CVAPI(int) cvFindChessboardCorners( const void* image, CvSize pattern_size,
CvPoint2D32f* corners,
int* corner_count CV_DEFAULT(NULL),
int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE) );
/* Draws individual chessboard corners or the whole chessboard detected */
CVAPI(void) cvDrawChessboardCorners( CvArr* image, CvSize pattern_size,
CvPoint2D32f* corners,
int count, int pattern_was_found );
#define CV_CALIB_USE_INTRINSIC_GUESS 1
#define CV_CALIB_FIX_ASPECT_RATIO 2
#define CV_CALIB_FIX_PRINCIPAL_POINT 4
#define CV_CALIB_ZERO_TANGENT_DIST 8
#define CV_CALIB_FIX_FOCAL_LENGTH 16
#define CV_CALIB_FIX_K1 32
#define CV_CALIB_FIX_K2 64
#define CV_CALIB_FIX_K3 128
#define CV_CALIB_FIX_K4 2048
#define CV_CALIB_FIX_K5 4096
#define CV_CALIB_FIX_K6 8192
#define CV_CALIB_RATIONAL_MODEL 16384
/* Finds intrinsic and extrinsic camera parameters
from a few views of known calibration pattern */
CVAPI(double) cvCalibrateCamera2( const CvMat* object_points,
const CvMat* image_points,
const CvMat* point_counts,
CvSize image_size,
CvMat* camera_matrix,
CvMat* distortion_coeffs,
CvMat* rotation_vectors CV_DEFAULT(NULL),
CvMat* translation_vectors CV_DEFAULT(NULL),
int flags CV_DEFAULT(0),
CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria(
CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON)) );
/* Computes various useful characteristics of the camera from the data computed by
cvCalibrateCamera2 */
CVAPI(void) cvCalibrationMatrixValues( const CvMat *camera_matrix,
CvSize image_size,
double aperture_width CV_DEFAULT(0),
double aperture_height CV_DEFAULT(0),
double *fovx CV_DEFAULT(NULL),
double *fovy CV_DEFAULT(NULL),
double *focal_length CV_DEFAULT(NULL),
CvPoint2D64f *principal_point CV_DEFAULT(NULL),
double *pixel_aspect_ratio CV_DEFAULT(NULL));
#define CV_CALIB_FIX_INTRINSIC 256
#define CV_CALIB_SAME_FOCAL_LENGTH 512
/* Computes the transformation from one camera coordinate system to another one
from a few correspondent views of the same calibration target. Optionally, calibrates
both cameras */
CVAPI(double) cvStereoCalibrate( const CvMat* object_points, const CvMat* image_points1,
const CvMat* image_points2, const CvMat* npoints,
CvMat* camera_matrix1, CvMat* dist_coeffs1,
CvMat* camera_matrix2, CvMat* dist_coeffs2,
CvSize image_size, CvMat* R, CvMat* T,
CvMat* E CV_DEFAULT(0), CvMat* F CV_DEFAULT(0),
CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria(
CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e-6)),
int flags CV_DEFAULT(CV_CALIB_FIX_INTRINSIC));
#define CV_CALIB_ZERO_DISPARITY 1024
/* Computes 3D rotations (+ optional shift) for each camera coordinate system to make both
views parallel (=> to make all the epipolar lines horizontal or vertical) */
CVAPI(void) cvStereoRectify( const CvMat* camera_matrix1, const CvMat* camera_matrix2,
const CvMat* dist_coeffs1, const CvMat* dist_coeffs2,
CvSize image_size, const CvMat* R, const CvMat* T,
CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2,
CvMat* Q CV_DEFAULT(0),
int flags CV_DEFAULT(CV_CALIB_ZERO_DISPARITY),
double alpha CV_DEFAULT(-1),
CvSize new_image_size CV_DEFAULT(cvSize(0,0)),
CvRect* valid_pix_ROI1 CV_DEFAULT(0),
CvRect* valid_pix_ROI2 CV_DEFAULT(0));
/* Computes rectification transformations for uncalibrated pair of images using a set
of point correspondences */
CVAPI(int) cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2,
const CvMat* F, CvSize img_size,
CvMat* H1, CvMat* H2,
double threshold CV_DEFAULT(5));
/* stereo correspondence parameters and functions */
#define CV_STEREO_BM_NORMALIZED_RESPONSE 0
#define CV_STEREO_BM_XSOBEL 1
/* Block matching algorithm structure */
typedef struct CvStereoBMState
{
// pre-filtering (normalization of input images)
int preFilterType; // =CV_STEREO_BM_NORMALIZED_RESPONSE now
int preFilterSize; // averaging window size: ~5x5..21x21
int preFilterCap; // the output of pre-filtering is clipped by [-preFilterCap,preFilterCap]
// correspondence using Sum of Absolute Difference (SAD)
int SADWindowSize; // ~5x5..21x21
int minDisparity; // minimum disparity (can be negative)
int numberOfDisparities; // maximum disparity - minimum disparity (> 0)
// post-filtering
int textureThreshold; // the disparity is only computed for pixels
// with textured enough neighborhood
int uniquenessRatio; // accept the computed disparity d* only if
// SAD(d) >= SAD(d*)*(1 + uniquenessRatio/100.)
// for any d != d*+/-1 within the search range.
int speckleWindowSize; // disparity variation window
int speckleRange; // acceptable range of variation in window
int trySmallerWindows; // if 1, the results may be more accurate,
// at the expense of slower processing
CvRect roi1, roi2;
int disp12MaxDiff;
// temporary buffers
CvMat* preFilteredImg0;
CvMat* preFilteredImg1;
CvMat* slidingSumBuf;
CvMat* cost;
CvMat* disp;
} CvStereoBMState;
#define CV_STEREO_BM_BASIC 0
#define CV_STEREO_BM_FISH_EYE 1
#define CV_STEREO_BM_NARROW 2
CVAPI(CvStereoBMState*) cvCreateStereoBMState(int preset CV_DEFAULT(CV_STEREO_BM_BASIC),
int numberOfDisparities CV_DEFAULT(0));
CVAPI(void) cvReleaseStereoBMState( CvStereoBMState** state );
CVAPI(void) cvFindStereoCorrespondenceBM( const CvArr* left, const CvArr* right,
CvArr* disparity, CvStereoBMState* state );
CVAPI(CvRect) cvGetValidDisparityROI( CvRect roi1, CvRect roi2, int minDisparity,
int numberOfDisparities, int SADWindowSize );
CVAPI(void) cvValidateDisparity( CvArr* disparity, const CvArr* cost,
int minDisparity, int numberOfDisparities,
int disp12MaxDiff CV_DEFAULT(1) );
/* Reprojects the computed disparity image to the 3D space using the specified 4x4 matrix */
CVAPI(void) cvReprojectImageTo3D( const CvArr* disparityImage,
CvArr* _3dImage, const CvMat* Q,
int handleMissingValues CV_DEFAULT(0) );
#ifdef __cplusplus
}
//////////////////////////////////////////////////////////////////////////////////////////
class CV_EXPORTS CvLevMarq
{
public:
CvLevMarq();
CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria=
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
bool completeSymmFlag=false );
~CvLevMarq();
void init( int nparams, int nerrs, CvTermCriteria criteria=
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
bool completeSymmFlag=false );
bool update( const CvMat*& param, CvMat*& J, CvMat*& err );
bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm );
void clear();
void step();
enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 };
cv::Ptr<CvMat> mask;
cv::Ptr<CvMat> prevParam;
cv::Ptr<CvMat> param;
cv::Ptr<CvMat> J;
cv::Ptr<CvMat> err;
cv::Ptr<CvMat> JtJ;
cv::Ptr<CvMat> JtJN;
cv::Ptr<CvMat> JtErr;
cv::Ptr<CvMat> JtJV;
cv::Ptr<CvMat> JtJW;
double prevErrNorm, errNorm;
int lambdaLg10;
CvTermCriteria criteria;
int state;
int iters;
bool completeSymmFlag;
};
namespace cv
{
//! converts rotation vector to rotation matrix or vice versa using Rodrigues transformation
CV_EXPORTS_W void Rodrigues(InputArray src, OutputArray dst, OutputArray jacobian=noArray());
//! type of the robust estimation algorithm
enum
{
LMEDS=CV_LMEDS, //!< least-median algorithm
RANSAC=CV_RANSAC //!< RANSAC algorithm
};
//! computes the best-fit perspective transformation mapping srcPoints to dstPoints.
CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints,
int method=0, double ransacReprojThreshold=3,
OutputArray mask=noArray());
//! variant of findHomography for backward compatibility
CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints,
OutputArray mask, int method=0, double ransacReprojThreshold=3);
//! Computes RQ decomposition of 3x3 matrix
CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ,
OutputArray Qx=noArray(),
OutputArray Qy=noArray(),
OutputArray Qz=noArray());
//! Decomposes the projection matrix into camera matrix and the rotation martix and the translation vector
CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix,
OutputArray rotMatrix, OutputArray transVect,
OutputArray rotMatrixX=noArray(),
OutputArray rotMatrixY=noArray(),
OutputArray rotMatrixZ=noArray(),
OutputArray eulerAngles=noArray() );
//! computes derivatives of the matrix product w.r.t each of the multiplied matrix coefficients
CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B,
OutputArray dABdA,
OutputArray dABdB );
//! composes 2 [R|t] transformations together. Also computes the derivatives of the result w.r.t the arguments
CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1,
InputArray rvec2, InputArray tvec2,
OutputArray rvec3, OutputArray tvec3,
OutputArray dr3dr1=noArray(), OutputArray dr3dt1=noArray(),
OutputArray dr3dr2=noArray(), OutputArray dr3dt2=noArray(),
OutputArray dt3dr1=noArray(), OutputArray dt3dt1=noArray(),
OutputArray dt3dr2=noArray(), OutputArray dt3dt2=noArray() );
//! projects points from the model coordinate space to the image coordinates. Also computes derivatives of the image coordinates w.r.t the intrinsic and extrinsic camera parameters
CV_EXPORTS_W void projectPoints( InputArray objectPoints,
InputArray rvec, InputArray tvec,
InputArray cameraMatrix, InputArray distCoeffs,
OutputArray imagePoints,
OutputArray jacobian=noArray(),
double aspectRatio=0 );
//! computes the camera pose from a few 3D points and the corresponding projections. The outliers are not handled.
enum
{
ITERATIVE=CV_ITERATIVE,
EPNP=CV_EPNP,
P3P=CV_P3P
};
CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints,
InputArray cameraMatrix, InputArray distCoeffs,
OutputArray rvec, OutputArray tvec,
bool useExtrinsicGuess=false, int flags=ITERATIVE);
//! computes the camera pose from a few 3D points and the corresponding projections. The outliers are possible.
CV_EXPORTS_W void solvePnPRansac( InputArray objectPoints,
InputArray imagePoints,
InputArray cameraMatrix,
InputArray distCoeffs,
OutputArray rvec,
OutputArray tvec,
bool useExtrinsicGuess = false,
int iterationsCount = 100,
float reprojectionError = 8.0,
int minInliersCount = 100,
OutputArray inliers = noArray(),
int flags = ITERATIVE);
//! initializes camera matrix from a few 3D points and the corresponding projections.
CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints,
InputArrayOfArrays imagePoints,
Size imageSize, double aspectRatio=1. );
enum { CALIB_CB_ADAPTIVE_THRESH = 1, CALIB_CB_NORMALIZE_IMAGE = 2,
CALIB_CB_FILTER_QUADS = 4, CALIB_CB_FAST_CHECK = 8 };
//! finds checkerboard pattern of the specified size in the image
CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize,
OutputArray corners,
int flags=CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE );
//! finds subpixel-accurate positions of the chessboard corners
CV_EXPORTS bool find4QuadCornerSubpix(InputArray img, InputOutputArray corners, Size region_size);
//! draws the checkerboard pattern (found or partly found) in the image
CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize,
InputArray corners, bool patternWasFound );
enum { CALIB_CB_SYMMETRIC_GRID = 1, CALIB_CB_ASYMMETRIC_GRID = 2,
CALIB_CB_CLUSTERING = 4 };
//! finds circles' grid pattern of the specified size in the image
CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize,
OutputArray centers, int flags=CALIB_CB_SYMMETRIC_GRID,
const Ptr<FeatureDetector> &blobDetector = new SimpleBlobDetector());
//! the deprecated function. Use findCirclesGrid() instead of it.
CV_EXPORTS_W bool findCirclesGridDefault( InputArray image, Size patternSize,
OutputArray centers, int flags=CALIB_CB_SYMMETRIC_GRID );
enum
{
CALIB_USE_INTRINSIC_GUESS = CV_CALIB_USE_INTRINSIC_GUESS,
CALIB_FIX_ASPECT_RATIO = CV_CALIB_FIX_ASPECT_RATIO,
CALIB_FIX_PRINCIPAL_POINT = CV_CALIB_FIX_PRINCIPAL_POINT,
CALIB_ZERO_TANGENT_DIST = CV_CALIB_ZERO_TANGENT_DIST,
CALIB_FIX_FOCAL_LENGTH = CV_CALIB_FIX_FOCAL_LENGTH,
CALIB_FIX_K1 = CV_CALIB_FIX_K1,
CALIB_FIX_K2 = CV_CALIB_FIX_K2,
CALIB_FIX_K3 = CV_CALIB_FIX_K3,
CALIB_FIX_K4 = CV_CALIB_FIX_K4,
CALIB_FIX_K5 = CV_CALIB_FIX_K5,
CALIB_FIX_K6 = CV_CALIB_FIX_K6,
CALIB_RATIONAL_MODEL = CV_CALIB_RATIONAL_MODEL,
// only for stereo
CALIB_FIX_INTRINSIC = CV_CALIB_FIX_INTRINSIC,
CALIB_SAME_FOCAL_LENGTH = CV_CALIB_SAME_FOCAL_LENGTH,
// for stereo rectification
CALIB_ZERO_DISPARITY = CV_CALIB_ZERO_DISPARITY
};
//! finds intrinsic and extrinsic camera parameters from several fews of a known calibration pattern.
CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints,
InputArrayOfArrays imagePoints,
Size imageSize,
CV_OUT InputOutputArray cameraMatrix,
CV_OUT InputOutputArray distCoeffs,
OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
int flags=0, TermCriteria criteria = TermCriteria(
TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON) );
//! computes several useful camera characteristics from the camera matrix, camera frame resolution and the physical sensor size.
CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix,
Size imageSize,
double apertureWidth,
double apertureHeight,
CV_OUT double& fovx,
CV_OUT double& fovy,
CV_OUT double& focalLength,
CV_OUT Point2d& principalPoint,
CV_OUT double& aspectRatio );
//! finds intrinsic and extrinsic parameters of a stereo camera
CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints,
InputArrayOfArrays imagePoints1,
InputArrayOfArrays imagePoints2,
CV_OUT InputOutputArray cameraMatrix1,
CV_OUT InputOutputArray distCoeffs1,
CV_OUT InputOutputArray cameraMatrix2,
CV_OUT InputOutputArray distCoeffs2,
Size imageSize, OutputArray R,
OutputArray T, OutputArray E, OutputArray F,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6),
int flags=CALIB_FIX_INTRINSIC );
//! computes the rectification transformation for a stereo camera from its intrinsic and extrinsic parameters
CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1,
InputArray cameraMatrix2, InputArray distCoeffs2,
Size imageSize, InputArray R, InputArray T,
OutputArray R1, OutputArray R2,
OutputArray P1, OutputArray P2,
OutputArray Q, int flags=CALIB_ZERO_DISPARITY,
double alpha=-1, Size newImageSize=Size(),
CV_OUT Rect* validPixROI1=0, CV_OUT Rect* validPixROI2=0 );
//! computes the rectification transformation for an uncalibrated stereo camera (zero distortion is assumed)
CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2,
InputArray F, Size imgSize,
OutputArray H1, OutputArray H2,
double threshold=5 );
//! computes the rectification transformations for 3-head camera, where all the heads are on the same line.
CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1,
InputArray cameraMatrix2, InputArray distCoeffs2,
InputArray cameraMatrix3, InputArray distCoeffs3,
InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3,
Size imageSize, InputArray R12, InputArray T12,
InputArray R13, InputArray T13,
OutputArray R1, OutputArray R2, OutputArray R3,
OutputArray P1, OutputArray P2, OutputArray P3,
OutputArray Q, double alpha, Size newImgSize,
CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags );
//! returns the optimal new camera matrix
CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs,
Size imageSize, double alpha, Size newImgSize=Size(),
CV_OUT Rect* validPixROI=0, bool centerPrincipalPoint=false);
//! converts point coordinates from normal pixel coordinates to homogeneous coordinates ((x,y)->(x,y,1))
CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst );
//! converts point coordinates from homogeneous to normal pixel coordinates ((x,y,z)->(x/z, y/z))
CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst );
//! for backward compatibility
CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst );
//! the algorithm for finding fundamental matrix
enum
{
FM_7POINT = CV_FM_7POINT, //!< 7-point algorithm
FM_8POINT = CV_FM_8POINT, //!< 8-point algorithm
FM_LMEDS = CV_FM_LMEDS, //!< least-median algorithm
FM_RANSAC = CV_FM_RANSAC //!< RANSAC algorithm
};
//! finds fundamental matrix from a set of corresponding 2D points
CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2,
int method=FM_RANSAC,
double param1=3., double param2=0.99,
OutputArray mask=noArray());
//! variant of findFundamentalMat for backward compatibility
CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2,
OutputArray mask, int method=FM_RANSAC,
double param1=3., double param2=0.99);
//! finds coordinates of epipolar lines corresponding the specified points
CV_EXPORTS_W void computeCorrespondEpilines( InputArray points,
int whichImage, InputArray F,
OutputArray lines );
CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2,
InputArray projPoints1, InputArray projPoints2,
OutputArray points4D );
CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2,
OutputArray newPoints1, OutputArray newPoints2 );
template<> CV_EXPORTS void Ptr<CvStereoBMState>::delete_obj();
/*!
Block Matching Stereo Correspondence Algorithm
The class implements BM stereo correspondence algorithm by K. Konolige.
*/
class CV_EXPORTS_W StereoBM
{
public:
enum { PREFILTER_NORMALIZED_RESPONSE = 0, PREFILTER_XSOBEL = 1,
BASIC_PRESET=0, FISH_EYE_PRESET=1, NARROW_PRESET=2 };
//! the default constructor
CV_WRAP StereoBM();
//! the full constructor taking the camera-specific preset, number of disparities and the SAD window size
CV_WRAP StereoBM(int preset, int ndisparities=0, int SADWindowSize=21);
//! the method that reinitializes the state. The previous content is destroyed
void init(int preset, int ndisparities=0, int SADWindowSize=21);
//! the stereo correspondence operator. Finds the disparity for the specified rectified stereo pair
CV_WRAP_AS(compute) void operator()( InputArray left, InputArray right,
OutputArray disparity, int disptype=CV_16S );
//! pointer to the underlying CvStereoBMState
Ptr<CvStereoBMState> state;
};
/*!
Semi-Global Block Matching Stereo Correspondence Algorithm
The class implements the original SGBM stereo correspondence algorithm by H. Hirschmuller and some its modification.
*/
class CV_EXPORTS_W StereoSGBM
{
public:
enum { DISP_SHIFT=4, DISP_SCALE = (1<<DISP_SHIFT) };
//! the default constructor
CV_WRAP StereoSGBM();
//! the full constructor taking all the necessary algorithm parameters
CV_WRAP StereoSGBM(int minDisparity, int numDisparities, int SADWindowSize,
int P1=0, int P2=0, int disp12MaxDiff=0,
int preFilterCap=0, int uniquenessRatio=0,
int speckleWindowSize=0, int speckleRange=0,
bool fullDP=false);
//! the destructor
virtual ~StereoSGBM();
//! the stereo correspondence operator that computes disparity map for the specified rectified stereo pair
CV_WRAP_AS(compute) virtual void operator()(InputArray left, InputArray right,
OutputArray disp);
CV_PROP_RW int minDisparity;
CV_PROP_RW int numberOfDisparities;
CV_PROP_RW int SADWindowSize;
CV_PROP_RW int preFilterCap;
CV_PROP_RW int uniquenessRatio;
CV_PROP_RW int P1;
CV_PROP_RW int P2;
CV_PROP_RW int speckleWindowSize;
CV_PROP_RW int speckleRange;
CV_PROP_RW int disp12MaxDiff;
CV_PROP_RW bool fullDP;
protected:
Mat buffer;
};
//! filters off speckles (small regions of incorrectly computed disparity)
CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal, int maxSpeckleSize, double maxDiff,
InputOutputArray buf=noArray() );
//! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify())
CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2,
int minDisparity, int numberOfDisparities,
int SADWindowSize );
//! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm
CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost,
int minDisparity, int numberOfDisparities,
int disp12MaxDisp=1 );
//! reprojects disparity image to 3D: (x,y,d)->(X,Y,Z) using the matrix Q returned by cv::stereoRectify
CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity,
OutputArray _3dImage, InputArray Q,
bool handleMissingValues=false,
int ddepth=-1 );
CV_EXPORTS_W int estimateAffine3D(InputArray src, InputArray dst,
OutputArray out, OutputArray inliers,
double ransacThreshold=3, double confidence=0.99);
}
#endif
#endif
include/opencv2/contrib/contrib.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_CONTRIB_HPP__
#define __OPENCV_CONTRIB_HPP__
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#ifdef __cplusplus
/****************************************************************************************\
* Adaptive Skin Detector *
\****************************************************************************************/
class CV_EXPORTS CvAdaptiveSkinDetector
{
private:
enum {
GSD_HUE_LT = 3,
GSD_HUE_UT = 33,
GSD_INTENSITY_LT = 15,
GSD_INTENSITY_UT = 250
};
class CV_EXPORTS Histogram
{
private:
enum {
HistogramSize = (GSD_HUE_UT - GSD_HUE_LT + 1)
};
protected:
int findCoverageIndex(double surfaceToCover, int defaultValue = 0);
public:
CvHistogram *fHistogram;
Histogram();
virtual ~Histogram();
void findCurveThresholds(int &x1, int &x2, double percent = 0.05);
void mergeWith(Histogram *source, double weight);
};
int nStartCounter, nFrameCount, nSkinHueLowerBound, nSkinHueUpperBound, nMorphingMethod, nSamplingDivider;
double fHistogramMergeFactor, fHuePercentCovered;
Histogram histogramHueMotion, skinHueHistogram;
IplImage *imgHueFrame, *imgSaturationFrame, *imgLastGrayFrame, *imgMotionFrame, *imgFilteredFrame;
IplImage *imgShrinked, *imgTemp, *imgGrayFrame, *imgHSVFrame;
protected:
void initData(IplImage *src, int widthDivider, int heightDivider);
void adaptiveFilter();
public:
enum {
MORPHING_METHOD_NONE = 0,
MORPHING_METHOD_ERODE = 1,
MORPHING_METHOD_ERODE_ERODE = 2,
MORPHING_METHOD_ERODE_DILATE = 3
};
CvAdaptiveSkinDetector(int samplingDivider = 1, int morphingMethod = MORPHING_METHOD_NONE);
virtual ~CvAdaptiveSkinDetector();
virtual void process(IplImage *inputBGRImage, IplImage *outputHueMask);
};
/****************************************************************************************\
* Fuzzy MeanShift Tracker *
\****************************************************************************************/
class CV_EXPORTS CvFuzzyPoint {
public:
double x, y, value;
CvFuzzyPoint(double _x, double _y);
};
class CV_EXPORTS CvFuzzyCurve {
private:
std::vector<CvFuzzyPoint> points;
double value, centre;
bool between(double x, double x1, double x2);
public:
CvFuzzyCurve();
~CvFuzzyCurve();
void setCentre(double _centre);
double getCentre();
void clear();
void addPoint(double x, double y);
double calcValue(double param);
double getValue();
void setValue(double _value);
};
class CV_EXPORTS CvFuzzyFunction {
public:
std::vector<CvFuzzyCurve> curves;
CvFuzzyFunction();
~CvFuzzyFunction();
void addCurve(CvFuzzyCurve *curve, double value = 0);
void resetValues();
double calcValue();
CvFuzzyCurve *newCurve();
};
class CV_EXPORTS CvFuzzyRule {
private:
CvFuzzyCurve *fuzzyInput1, *fuzzyInput2;
CvFuzzyCurve *fuzzyOutput;
public:
CvFuzzyRule();
~CvFuzzyRule();
void setRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1);
double calcValue(double param1, double param2);
CvFuzzyCurve *getOutputCurve();
};
class CV_EXPORTS CvFuzzyController {
private:
std::vector<CvFuzzyRule*> rules;
public:
CvFuzzyController();
~CvFuzzyController();
void addRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1);
double calcOutput(double param1, double param2);
};
class CV_EXPORTS CvFuzzyMeanShiftTracker
{
private:
class FuzzyResizer
{
private:
CvFuzzyFunction iInput, iOutput;
CvFuzzyController fuzzyController;
public:
FuzzyResizer();
int calcOutput(double edgeDensity, double density);
};
class SearchWindow
{
public:
FuzzyResizer *fuzzyResizer;
int x, y;
int width, height, maxWidth, maxHeight, ellipseHeight, ellipseWidth;
int ldx, ldy, ldw, ldh, numShifts, numIters;
int xGc, yGc;
long m00, m01, m10, m11, m02, m20;
double ellipseAngle;
double density;
unsigned int depthLow, depthHigh;
int verticalEdgeLeft, verticalEdgeRight, horizontalEdgeTop, horizontalEdgeBottom;
SearchWindow();
~SearchWindow();
void setSize(int _x, int _y, int _width, int _height);
void initDepthValues(IplImage *maskImage, IplImage *depthMap);
bool shift();
void extractInfo(IplImage *maskImage, IplImage *depthMap, bool initDepth);
void getResizeAttribsEdgeDensityLinear(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
void getResizeAttribsInnerDensity(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
void getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh);
bool meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth);
};
public:
enum TrackingState
{
tsNone = 0,
tsSearching = 1,
tsTracking = 2,
tsSetWindow = 3,
tsDisabled = 10
};
enum ResizeMethod {
rmEdgeDensityLinear = 0,
rmEdgeDensityFuzzy = 1,
rmInnerDensity = 2
};
enum {
MinKernelMass = 1000
};
SearchWindow kernel;
int searchMode;
private:
enum
{
MaxMeanShiftIteration = 5,
MaxSetSizeIteration = 5
};
void findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth);
public:
CvFuzzyMeanShiftTracker();
~CvFuzzyMeanShiftTracker();
void track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass = MinKernelMass);
};
namespace cv
{
class CV_EXPORTS Octree
{
public:
struct Node
{
Node() {}
int begin, end;
float x_min, x_max, y_min, y_max, z_min, z_max;
int maxLevels;
bool isLeaf;
int children[8];
};
Octree();
Octree( const vector<Point3f>& points, int maxLevels = 10, int minPoints = 20 );
virtual ~Octree();
virtual void buildTree( const vector<Point3f>& points, int maxLevels = 10, int minPoints = 20 );
virtual void getPointsWithinSphere( const Point3f& center, float radius,
vector<Point3f>& points ) const;
const vector<Node>& getNodes() const { return nodes; }
private:
int minPoints;
vector<Point3f> points;
vector<Node> nodes;
virtual void buildNext(size_t node_ind);
};
class CV_EXPORTS Mesh3D
{
public:
struct EmptyMeshException {};
Mesh3D();
Mesh3D(const vector<Point3f>& vtx);
~Mesh3D();
void buildOctree();
void clearOctree();
float estimateResolution(float tryRatio = 0.1f);
void computeNormals(float normalRadius, int minNeighbors = 20);
void computeNormals(const vector<int>& subset, float normalRadius, int minNeighbors = 20);
void writeAsVrml(const String& file, const vector<Scalar>& colors = vector<Scalar>()) const;
vector<Point3f> vtx;
vector<Point3f> normals;
float resolution;
Octree octree;
const static Point3f allzero;
};
class CV_EXPORTS SpinImageModel
{
public:
/* model parameters, leave unset for default or auto estimate */
float normalRadius;
int minNeighbors;
float binSize;
int imageWidth;
float lambda;
float gamma;
float T_GeometriccConsistency;
float T_GroupingCorespondances;
/* public interface */
SpinImageModel();
explicit SpinImageModel(const Mesh3D& mesh);
~SpinImageModel();
void setLogger(std::ostream* log);
void selectRandomSubset(float ratio);
void setSubset(const vector<int>& subset);
void compute();
void match(const SpinImageModel& scene, vector< vector<Vec2i> >& result);
Mat packRandomScaledSpins(bool separateScale = false, size_t xCount = 10, size_t yCount = 10) const;
size_t getSpinCount() const { return spinImages.rows; }
Mat getSpinImage(size_t index) const { return spinImages.row((int)index); }
const Point3f& getSpinVertex(size_t index) const { return mesh.vtx[subset[index]]; }
const Point3f& getSpinNormal(size_t index) const { return mesh.normals[subset[index]]; }
const Mesh3D& getMesh() const { return mesh; }
Mesh3D& getMesh() { return mesh; }
/* static utility functions */
static bool spinCorrelation(const Mat& spin1, const Mat& spin2, float lambda, float& result);
static Point2f calcSpinMapCoo(const Point3f& point, const Point3f& vertex, const Point3f& normal);
static float geometricConsistency(const Point3f& pointScene1, const Point3f& normalScene1,
const Point3f& pointModel1, const Point3f& normalModel1,
const Point3f& pointScene2, const Point3f& normalScene2,
const Point3f& pointModel2, const Point3f& normalModel2);
static float groupingCreteria(const Point3f& pointScene1, const Point3f& normalScene1,
const Point3f& pointModel1, const Point3f& normalModel1,
const Point3f& pointScene2, const Point3f& normalScene2,
const Point3f& pointModel2, const Point3f& normalModel2,
float gamma);
protected:
void defaultParams();
void matchSpinToModel(const Mat& spin, vector<int>& indeces,
vector<float>& corrCoeffs, bool useExtremeOutliers = true) const;
void repackSpinImages(const vector<uchar>& mask, Mat& spinImages, bool reAlloc = true) const;
vector<int> subset;
Mesh3D mesh;
Mat spinImages;
std::ostream* out;
};
class CV_EXPORTS TickMeter
{
public:
TickMeter();
void start();
void stop();
int64 getTimeTicks() const;
double getTimeMicro() const;
double getTimeMilli() const;
double getTimeSec() const;
int64 getCounter() const;
void reset();
private:
int64 counter;
int64 sumTime;
int64 startTime;
};
CV_EXPORTS std::ostream& operator<<(std::ostream& out, const TickMeter& tm);
class CV_EXPORTS SelfSimDescriptor
{
public:
SelfSimDescriptor();
SelfSimDescriptor(int _ssize, int _lsize,
int _startDistanceBucket=DEFAULT_START_DISTANCE_BUCKET,
int _numberOfDistanceBuckets=DEFAULT_NUM_DISTANCE_BUCKETS,
int _nangles=DEFAULT_NUM_ANGLES);
SelfSimDescriptor(const SelfSimDescriptor& ss);
virtual ~SelfSimDescriptor();
SelfSimDescriptor& operator = (const SelfSimDescriptor& ss);
size_t getDescriptorSize() const;
Size getGridSize( Size imgsize, Size winStride ) const;
virtual void compute(const Mat& img, vector<float>& descriptors, Size winStride=Size(),
const vector<Point>& locations=vector<Point>()) const;
virtual void computeLogPolarMapping(Mat& mappingMask) const;
virtual void SSD(const Mat& img, Point pt, Mat& ssd) const;
int smallSize;
int largeSize;
int startDistanceBucket;
int numberOfDistanceBuckets;
int numberOfAngles;
enum { DEFAULT_SMALL_SIZE = 5, DEFAULT_LARGE_SIZE = 41,
DEFAULT_NUM_ANGLES = 20, DEFAULT_START_DISTANCE_BUCKET = 3,
DEFAULT_NUM_DISTANCE_BUCKETS = 7 };
};
typedef bool (*BundleAdjustCallback)(int iteration, double norm_error, void* user_data);
class CV_EXPORTS LevMarqSparse {
public:
LevMarqSparse();
LevMarqSparse(int npoints, // number of points
int ncameras, // number of cameras
int nPointParams, // number of params per one point (3 in case of 3D points)
int nCameraParams, // number of parameters per one camera
int nErrParams, // number of parameters in measurement vector
// for 1 point at one camera (2 in case of 2D projections)
Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras
// 1 - point is visible for the camera, 0 - invisible
Mat& P0, // starting vector of parameters, first cameras then points
Mat& X, // measurements, in order of visibility. non visible cases are skipped
TermCriteria criteria, // termination criteria
// callback for estimation of Jacobian matrices
void (CV_CDECL * fjac)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& A, Mat& B, void* data),
// callback for estimation of backprojection errors
void (CV_CDECL * func)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& estim, void* data),
void* data, // user-specific data passed to the callbacks
BundleAdjustCallback cb, void* user_data
);
virtual ~LevMarqSparse();
virtual void run( int npoints, // number of points
int ncameras, // number of cameras
int nPointParams, // number of params per one point (3 in case of 3D points)
int nCameraParams, // number of parameters per one camera
int nErrParams, // number of parameters in measurement vector
// for 1 point at one camera (2 in case of 2D projections)
Mat& visibility, // visibility matrix. rows correspond to points, columns correspond to cameras
// 1 - point is visible for the camera, 0 - invisible
Mat& P0, // starting vector of parameters, first cameras then points
Mat& X, // measurements, in order of visibility. non visible cases are skipped
TermCriteria criteria, // termination criteria
// callback for estimation of Jacobian matrices
void (CV_CDECL * fjac)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& A, Mat& B, void* data),
// callback for estimation of backprojection errors
void (CV_CDECL * func)(int i, int j, Mat& point_params,
Mat& cam_params, Mat& estim, void* data),
void* data // user-specific data passed to the callbacks
);
virtual void clear();
// useful function to do simple bundle adjustment tasks
static void bundleAdjust(vector<Point3d>& points, // positions of points in global coordinate system (input and output)
const vector<vector<Point2d> >& imagePoints, // projections of 3d points for every camera
const vector<vector<int> >& visibility, // visibility of 3d points for every camera
vector<Mat>& cameraMatrix, // intrinsic matrices of all cameras (input and output)
vector<Mat>& R, // rotation matrices of all cameras (input and output)
vector<Mat>& T, // translation vector of all cameras (input and output)
vector<Mat>& distCoeffs, // distortion coefficients of all cameras (input and output)
const TermCriteria& criteria=
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON),
BundleAdjustCallback cb = 0, void* user_data = 0);
public:
virtual void optimize(CvMat &_vis); //main function that runs minimization
//iteratively asks for measurement for visible camera-point pairs
void ask_for_proj(CvMat &_vis,bool once=false);
//iteratively asks for Jacobians for every camera_point pair
void ask_for_projac(CvMat &_vis);
CvMat* err; //error X-hX
double prevErrNorm, errNorm;
double lambda;
CvTermCriteria criteria;
int iters;
CvMat** U; //size of array is equal to number of cameras
CvMat** V; //size of array is equal to number of points
CvMat** inv_V_star; //inverse of V*
CvMat** A;
CvMat** B;
CvMat** W;
CvMat* X; //measurement
CvMat* hX; //current measurement extimation given new parameter vector
CvMat* prevP; //current already accepted parameter.
CvMat* P; // parameters used to evaluate function with new params
// this parameters may be rejected
CvMat* deltaP; //computed increase of parameters (result of normal system solution )
CvMat** ea; // sum_i AijT * e_ij , used as right part of normal equation
// length of array is j = number of cameras
CvMat** eb; // sum_j BijT * e_ij , used as right part of normal equation
// length of array is i = number of points
CvMat** Yj; //length of array is i = num_points
CvMat* S; //big matrix of block Sjk , each block has size num_cam_params x num_cam_params
CvMat* JtJ_diag; //diagonal of JtJ, used to backup diagonal elements before augmentation
CvMat* Vis_index; // matrix which element is index of measurement for point i and camera j
int num_cams;
int num_points;
int num_err_param;
int num_cam_param;
int num_point_param;
//target function and jacobian pointers, which needs to be initialized
void (*fjac)(int i, int j, Mat& point_params, Mat& cam_params, Mat& A, Mat& B, void* data);
void (*func)(int i, int j, Mat& point_params, Mat& cam_params, Mat& estim, void* data);
void* data;
BundleAdjustCallback cb;
void* user_data;
};
CV_EXPORTS_W int chamerMatching( Mat& img, Mat& templ,
CV_OUT vector<vector<Point> >& results, CV_OUT vector<float>& cost,
double templScale=1, int maxMatches = 20,
double minMatchDistance = 1.0, int padX = 3,
int padY = 3, int scales = 5, double minScale = 0.6, double maxScale = 1.6,
double orientationWeight = 0.5, double truncate = 20);
class CV_EXPORTS_W StereoVar
{
public:
// Flags
enum {USE_INITIAL_DISPARITY = 1, USE_EQUALIZE_HIST = 2, USE_SMART_ID = 4, USE_AUTO_PARAMS = 8, USE_MEDIAN_FILTERING = 16};
enum {CYCLE_O, CYCLE_V};
enum {PENALIZATION_TICHONOV, PENALIZATION_CHARBONNIER, PENALIZATION_PERONA_MALIK};
//! the default constructor
CV_WRAP StereoVar();
//! the full constructor taking all the necessary algorithm parameters
CV_WRAP StereoVar(int levels, double pyrScale, int nIt, int minDisp, int maxDisp, int poly_n, double poly_sigma, float fi, float lambda, int penalization, int cycle, int flags);
//! the destructor
virtual ~StereoVar();
//! the stereo correspondence operator that computes disparity map for the specified rectified stereo pair
CV_WRAP_AS(compute) virtual void operator()(const Mat& left, const Mat& right, CV_OUT Mat& disp);
CV_PROP_RW int levels;
CV_PROP_RW double pyrScale;
CV_PROP_RW int nIt;
CV_PROP_RW int minDisp;
CV_PROP_RW int maxDisp;
CV_PROP_RW int poly_n;
CV_PROP_RW double poly_sigma;
CV_PROP_RW float fi;
CV_PROP_RW float lambda;
CV_PROP_RW int penalization;
CV_PROP_RW int cycle;
CV_PROP_RW int flags;
private:
void autoParams();
void FMG(Mat &I1, Mat &I2, Mat &I2x, Mat &u, int level);
void VCycle_MyFAS(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level);
void VariationalSolver(Mat &I1_h, Mat &I2_h, Mat &I2x_h, Mat &u_h, int level);
};
CV_EXPORTS void polyfit(const Mat& srcx, const Mat& srcy, Mat& dst, int order);
class CV_EXPORTS Directory
{
public:
static std::vector<std::string> GetListFiles ( const std::string& path, const std::string & exten = "*", bool addPath = true );
static std::vector<std::string> GetListFilesR ( const std::string& path, const std::string & exten = "*", bool addPath = true );
static std::vector<std::string> GetListFolders( const std::string& path, const std::string & exten = "*", bool addPath = true );
};
/*
* Generation of a set of different colors by the following way:
* 1) generate more then need colors (in "factor" times) in RGB,
* 2) convert them to Lab,
* 3) choose the needed count of colors from the set that are more different from
* each other,
* 4) convert the colors back to RGB
*/
CV_EXPORTS void generateColors( std::vector<Scalar>& colors, size_t count, size_t factor=100 );
/*
* Estimate the rigid body motion from frame0 to frame1. The method is based on the paper
* "Real-Time Visual Odometry from Dense RGB-D Images", F. Steinbucker, J. Strum, D. Cremers, ICCV, 2011.
*/
enum { ROTATION = 1,
TRANSLATION = 2,
RIGID_BODY_MOTION = 4
};
CV_EXPORTS bool RGBDOdometry( Mat& Rt, const Mat& initRt,
const Mat& image0, const Mat& depth0, const Mat& mask0,
const Mat& image1, const Mat& depth1, const Mat& mask1,
const Mat& cameraMatrix, float minDepth=0.f, float maxDepth=4.f, float maxDepthDiff=0.07f,
const std::vector<int>& iterCounts=std::vector<int>(),
const std::vector<float>& minGradientMagnitudes=std::vector<float>(),
int transformType=RIGID_BODY_MOTION );
/**
*Bilinear interpolation technique.
*
*The value of a desired cortical pixel is obtained through a bilinear interpolation of the values
*of the four nearest neighbouring Cartesian pixels to the center of the RF.
*The same principle is applied to the inverse transformation.
*
*More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
*/
class CV_EXPORTS LogPolar_Interp
{
public:
LogPolar_Interp() {}
/**
*Constructor
*\param w the width of the input image
*\param h the height of the input image
*\param center the transformation center: where the output precision is maximal
*\param R the number of rings of the cortical image (default value 70 pixel)
*\param ro0 the radius of the blind spot (default value 3 pixel)
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
* \a 0 means that the retinal image is computed within the inscribed circle.
*\param S the number of sectors of the cortical image (default value 70 pixel).
* Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
* \a 0 means that the parameter \a S is provided by the user.
*/
LogPolar_Interp(int w, int h, Point2i center, int R=70, double ro0=3.0,
int interp=INTER_LINEAR, int full=1, int S=117, int sp=1);
/**
*Transformation from Cartesian image to cortical (log-polar) image.
*\param source the Cartesian image
*\return the transformed image (cortical image)
*/
const Mat to_cortical(const Mat &source);
/**
*Transformation from cortical image to retinal (inverse log-polar) image.
*\param source the cortical image
*\return the transformed image (retinal image)
*/
const Mat to_cartesian(const Mat &source);
/**
*Destructor
*/
~LogPolar_Interp();
protected:
Mat Rsri;
Mat Csri;
int S, R, M, N;
int top, bottom,left,right;
double ro0, romax, a, q;
int interp;
Mat ETAyx;
Mat CSIyx;
void create_map(int M, int N, int R, int S, double ro0);
};
/**
*Overlapping circular receptive fields technique
*
*The Cartesian plane is divided in two regions: the fovea and the periphery.
*The fovea (oversampling) is handled by using the bilinear interpolation technique described above, whereas in
*the periphery we use the overlapping Gaussian circular RFs.
*
*More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
*/
class CV_EXPORTS LogPolar_Overlapping
{
public:
LogPolar_Overlapping() {}
/**
*Constructor
*\param w the width of the input image
*\param h the height of the input image
*\param center the transformation center: where the output precision is maximal
*\param R the number of rings of the cortical image (default value 70 pixel)
*\param ro0 the radius of the blind spot (default value 3 pixel)
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
* \a 0 means that the retinal image is computed within the inscribed circle.
*\param S the number of sectors of the cortical image (default value 70 pixel).
* Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
* \a 0 means that the parameter \a S is provided by the user.
*/
LogPolar_Overlapping(int w, int h, Point2i center, int R=70,
double ro0=3.0, int full=1, int S=117, int sp=1);
/**
*Transformation from Cartesian image to cortical (log-polar) image.
*\param source the Cartesian image
*\return the transformed image (cortical image)
*/
const Mat to_cortical(const Mat &source);
/**
*Transformation from cortical image to retinal (inverse log-polar) image.
*\param source the cortical image
*\return the transformed image (retinal image)
*/
const Mat to_cartesian(const Mat &source);
/**
*Destructor
*/
~LogPolar_Overlapping();
protected:
Mat Rsri;
Mat Csri;
vector<int> Rsr;
vector<int> Csr;
vector<double> Wsr;
int S, R, M, N, ind1;
int top, bottom,left,right;
double ro0, romax, a, q;
struct kernel
{
kernel() { w = 0; }
vector<double> weights;
int w;
};
Mat ETAyx;
Mat CSIyx;
vector<kernel> w_ker_2D;
void create_map(int M, int N, int R, int S, double ro0);
};
/**
* Adjacent receptive fields technique
*
*All the Cartesian pixels, whose coordinates in the cortical domain share the same integer part, are assigned to the same RF.
*The precision of the boundaries of the RF can be improved by breaking each pixel into subpixels and assigning each of them to the correct RF.
*This technique is implemented from: Traver, V., Pla, F.: Log-polar mapping template design: From task-level requirements
*to geometry parameters. Image Vision Comput. 26(10) (2008) 1354-1370
*
*More details can be found in http://dx.doi.org/10.1007/978-3-642-23968-7_5
*/
class CV_EXPORTS LogPolar_Adjacent
{
public:
LogPolar_Adjacent() {}
/**
*Constructor
*\param w the width of the input image
*\param h the height of the input image
*\param center the transformation center: where the output precision is maximal
*\param R the number of rings of the cortical image (default value 70 pixel)
*\param ro0 the radius of the blind spot (default value 3 pixel)
*\param smin the size of the subpixel (default value 0.25 pixel)
*\param full \a 1 (default value) means that the retinal image (the inverse transform) is computed within the circumscribing circle.
* \a 0 means that the retinal image is computed within the inscribed circle.
*\param S the number of sectors of the cortical image (default value 70 pixel).
* Its value is usually internally computed to obtain a pixel aspect ratio equals to 1.
*\param sp \a 1 (default value) means that the parameter \a S is internally computed.
* \a 0 means that the parameter \a S is provided by the user.
*/
LogPolar_Adjacent(int w, int h, Point2i center, int R=70, double ro0=3.0, double smin=0.25, int full=1, int S=117, int sp=1);
/**
*Transformation from Cartesian image to cortical (log-polar) image.
*\param source the Cartesian image
*\return the transformed image (cortical image)
*/
const Mat to_cortical(const Mat &source);
/**
*Transformation from cortical image to retinal (inverse log-polar) image.
*\param source the cortical image
*\return the transformed image (retinal image)
*/
const Mat to_cartesian(const Mat &source);
/**
*Destructor
*/
~LogPolar_Adjacent();
protected:
struct pixel
{
pixel() { u = v = 0; a = 0.; }
int u;
int v;
double a;
};
int S, R, M, N;
int top, bottom,left,right;
double ro0, romax, a, q;
vector<vector<pixel> > L;
vector<double> A;
void subdivide_recursively(double x, double y, int i, int j, double length, double smin);
bool get_uv(double x, double y, int&u, int&v);
void create_map(int M, int N, int R, int S, double ro0, double smin);
};
CV_EXPORTS Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
CV_EXPORTS Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
class CV_EXPORTS LDA
{
public:
// Initializes a LDA with num_components (default 0) and specifies how
// samples are aligned (default dataAsRow=true).
LDA(int num_components = 0) :
_num_components(num_components) {};
// Initializes and performs a Discriminant Analysis with Fisher's
// Optimization Criterion on given data in src and corresponding labels
// in labels. If 0 (or less) number of components are given, they are
// automatically determined for given data in computation.
LDA(const Mat& src, vector<int> labels,
int num_components = 0) :
_num_components(num_components)
{
this->compute(src, labels); //! compute eigenvectors and eigenvalues
}
// Initializes and performs a Discriminant Analysis with Fisher's
// Optimization Criterion on given data in src and corresponding labels
// in labels. If 0 (or less) number of components are given, they are
// automatically determined for given data in computation.
LDA(InputArrayOfArrays src, InputArray labels,
int num_components = 0) :
_num_components(num_components)
{
this->compute(src, labels); //! compute eigenvectors and eigenvalues
}
// Serializes this object to a given filename.
void save(const string& filename) const;
// Deserializes this object from a given filename.
void load(const string& filename);
// Serializes this object to a given cv::FileStorage.
void save(FileStorage& fs) const;
// Deserializes this object from a given cv::FileStorage.
void load(const FileStorage& node);
// Destructor.
~LDA() {}
//! Compute the discriminants for data in src and labels.
void compute(InputArrayOfArrays src, InputArray labels);
// Projects samples into the LDA subspace.
Mat project(InputArray src);
// Reconstructs projections from the LDA subspace.
Mat reconstruct(InputArray src);
// Returns the eigenvectors of this LDA.
Mat eigenvectors() const { return _eigenvectors; };
// Returns the eigenvalues of this LDA.
Mat eigenvalues() const { return _eigenvalues; }
protected:
bool _dataAsRow;
int _num_components;
Mat _eigenvectors;
Mat _eigenvalues;
void lda(InputArrayOfArrays src, InputArray labels);
};
class CV_EXPORTS_W FaceRecognizer : public Algorithm
{
public:
//! virtual destructor
virtual ~FaceRecognizer() {}
// Trains a FaceRecognizer.
CV_WRAP virtual void train(InputArrayOfArrays src, InputArray labels) = 0;
// Updates a FaceRecognizer.
CV_WRAP void update(InputArrayOfArrays src, InputArray labels);
// Gets a prediction from a FaceRecognizer.
virtual int predict(InputArray src) const = 0;
// Predicts the label and confidence for a given sample.
CV_WRAP virtual void predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) const = 0;
// Serializes this object to a given filename.
CV_WRAP virtual void save(const string& filename) const;
// Deserializes this object from a given filename.
CV_WRAP virtual void load(const string& filename);
// Serializes this object to a given cv::FileStorage.
virtual void save(FileStorage& fs) const = 0;
// Deserializes this object from a given cv::FileStorage.
virtual void load(const FileStorage& fs) = 0;
};
CV_EXPORTS_W Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
CV_EXPORTS_W Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
CV_EXPORTS_W Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8, double threshold = DBL_MAX);
enum
{
COLORMAP_AUTUMN = 0,
COLORMAP_BONE = 1,
COLORMAP_JET = 2,
COLORMAP_WINTER = 3,
COLORMAP_RAINBOW = 4,
COLORMAP_OCEAN = 5,
COLORMAP_SUMMER = 6,
COLORMAP_SPRING = 7,
COLORMAP_COOL = 8,
COLORMAP_HSV = 9,
COLORMAP_PINK = 10,
COLORMAP_HOT = 11
};
CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap);
CV_EXPORTS bool initModule_contrib();
}
#include "opencv2/contrib/retina.hpp"
#include "opencv2/contrib/openfabmap.hpp"
#endif
#endif
include/opencv2/contrib/detection_based_tracker.hpp
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#pragma once
#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(ANDROID)
#include <opencv2/core/core.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <vector>
class DetectionBasedTracker
{
public:
struct Parameters
{
int minObjectSize;
int maxObjectSize;
double scaleFactor;
int maxTrackLifetime;
int minNeighbors;
int minDetectionPeriod; //the minimal time between run of the big object detector (on the whole frame) in ms (1000 mean 1 sec), default=0
Parameters();
};
DetectionBasedTracker(const std::string& cascadeFilename, const Parameters& params);
virtual ~DetectionBasedTracker();
virtual bool run();
virtual void stop();
virtual void resetTracking();
virtual void process(const cv::Mat& imageGray);
bool setParameters(const Parameters& params);
const Parameters& getParameters();
typedef std::pair<cv::Rect, int> Object;
virtual void getObjects(std::vector<cv::Rect>& result) const;
virtual void getObjects(std::vector<Object>& result) const;
protected:
class SeparateDetectionWork;
cv::Ptr<SeparateDetectionWork> separateDetectionWork;
friend void* workcycleObjectDetectorFunction(void* p);
struct InnerParameters
{
int numLastPositionsToTrack;
int numStepsToWaitBeforeFirstShow;
int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown;
int numStepsToShowWithoutDetecting;
float coeffTrackingWindowSize;
float coeffObjectSizeToTrack;
float coeffObjectSpeedUsingInPrediction;
InnerParameters();
};
Parameters parameters;
InnerParameters innerParameters;
struct TrackedObject
{
typedef std::vector<cv::Rect> PositionsVector;
PositionsVector lastPositions;
int numDetectedFrames;
int numFramesNotDetected;
int id;
TrackedObject(const cv::Rect& rect):numDetectedFrames(1), numFramesNotDetected(0)
{
lastPositions.push_back(rect);
id=getNextId();
};
static int getNextId()
{
static int _id=0;
return _id++;
}
};
int numTrackedSteps;
std::vector<TrackedObject> trackedObjects;
std::vector<float> weightsPositionsSmoothing;
std::vector<float> weightsSizesSmoothing;
cv::CascadeClassifier cascadeForTracking;
void updateTrackedObjects(const std::vector<cv::Rect>& detectedObjects);
cv::Rect calcTrackedObjectPositionToShow(int i) const;
void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector<cv::Rect>& detectedObjectsInRegions);
};
namespace cv
{
using ::DetectionBasedTracker;
} //end of cv namespace
#endif
include/opencv2/contrib/hybridtracker.hpp
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//*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_HYBRIDTRACKER_H_
#define __OPENCV_HYBRIDTRACKER_H_
#include "opencv2/core/core.hpp"
#include "opencv2/core/operations.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/video/tracking.hpp"
#include "opencv2/ml/ml.hpp"
#ifdef __cplusplus
namespace cv
{
// Motion model for tracking algorithm. Currently supports objects that do not move much.
// To add Kalman filter
struct CV_EXPORTS CvMotionModel
{
enum {LOW_PASS_FILTER = 0, KALMAN_FILTER = 1, EM = 2};
CvMotionModel()
{
}
float low_pass_gain; // low pass gain
};
// Mean Shift Tracker parameters for specifying use of HSV channel and CamShift parameters.
struct CV_EXPORTS CvMeanShiftTrackerParams
{
enum { H = 0, HS = 1, HSV = 2 };
CvMeanShiftTrackerParams(int tracking_type = CvMeanShiftTrackerParams::HS,
CvTermCriteria term_crit = CvTermCriteria());
int tracking_type;
vector<float> h_range;
vector<float> s_range;
vector<float> v_range;
CvTermCriteria term_crit;
};
// Feature tracking parameters
struct CV_EXPORTS CvFeatureTrackerParams
{
enum { SIFT = 0, SURF = 1, OPTICAL_FLOW = 2 };
CvFeatureTrackerParams(int featureType = 0, int windowSize = 0)
{
feature_type = featureType;
window_size = windowSize;
}
int feature_type; // Feature type to use
int window_size; // Window size in pixels around which to search for new window
};
// Hybrid Tracking parameters for specifying weights of individual trackers and motion model.
struct CV_EXPORTS CvHybridTrackerParams
{
CvHybridTrackerParams(float ft_tracker_weight = 0.5, float ms_tracker_weight = 0.5,
CvFeatureTrackerParams ft_params = CvFeatureTrackerParams(),
CvMeanShiftTrackerParams ms_params = CvMeanShiftTrackerParams(),
CvMotionModel model = CvMotionModel());
float ft_tracker_weight;
float ms_tracker_weight;
CvFeatureTrackerParams ft_params;
CvMeanShiftTrackerParams ms_params;
int motion_model;
float low_pass_gain;
};
// Performs Camshift using parameters from MeanShiftTrackerParams
class CV_EXPORTS CvMeanShiftTracker
{
private:
Mat hsv, hue;
Mat backproj;
Mat mask, maskroi;
MatND hist;
Rect prev_trackwindow;
RotatedRect prev_trackbox;
Point2f prev_center;
public:
CvMeanShiftTrackerParams params;
CvMeanShiftTracker();
explicit CvMeanShiftTracker(CvMeanShiftTrackerParams _params);
~CvMeanShiftTracker();
void newTrackingWindow(Mat image, Rect selection);
RotatedRect updateTrackingWindow(Mat image);
Mat getHistogramProjection(int type);
void setTrackingWindow(Rect _window);
Rect getTrackingWindow();
RotatedRect getTrackingEllipse();
Point2f getTrackingCenter();
};
// Performs SIFT/SURF feature tracking using parameters from FeatureTrackerParams
class CV_EXPORTS CvFeatureTracker
{
private:
Ptr<Feature2D> dd;
Ptr<DescriptorMatcher> matcher;
vector<DMatch> matches;
Mat prev_image;
Mat prev_image_bw;
Rect prev_trackwindow;
Point2d prev_center;
int ittr;
vector<Point2f> features[2];
public:
Mat disp_matches;
CvFeatureTrackerParams params;
CvFeatureTracker();
explicit CvFeatureTracker(CvFeatureTrackerParams params);
~CvFeatureTracker();
void newTrackingWindow(Mat image, Rect selection);
Rect updateTrackingWindow(Mat image);
Rect updateTrackingWindowWithSIFT(Mat image);
Rect updateTrackingWindowWithFlow(Mat image);
void setTrackingWindow(Rect _window);
Rect getTrackingWindow();
Point2f getTrackingCenter();
};
// Performs Hybrid Tracking and combines individual trackers using EM or filters
class CV_EXPORTS CvHybridTracker
{
private:
CvMeanShiftTracker* mstracker;
CvFeatureTracker* fttracker;
CvMat* samples;
CvMat* labels;
Rect prev_window;
Point2f prev_center;
Mat prev_proj;
RotatedRect trackbox;
int ittr;
Point2f curr_center;
inline float getL2Norm(Point2f p1, Point2f p2);
Mat getDistanceProjection(Mat image, Point2f center);
Mat getGaussianProjection(Mat image, int ksize, double sigma, Point2f center);
void updateTrackerWithEM(Mat image);
void updateTrackerWithLowPassFilter(Mat image);
public:
CvHybridTrackerParams params;
CvHybridTracker();
explicit CvHybridTracker(CvHybridTrackerParams params);
~CvHybridTracker();
void newTracker(Mat image, Rect selection);
void updateTracker(Mat image);
Rect getTrackingWindow();
};
typedef CvMotionModel MotionModel;
typedef CvMeanShiftTrackerParams MeanShiftTrackerParams;
typedef CvFeatureTrackerParams FeatureTrackerParams;
typedef CvHybridTrackerParams HybridTrackerParams;
typedef CvMeanShiftTracker MeanShiftTracker;
typedef CvFeatureTracker FeatureTracker;
typedef CvHybridTracker HybridTracker;
}
#endif
#endif
include/opencv2/contrib/openfabmap.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
// This file originates from the openFABMAP project:
// [http://code.google.com/p/openfabmap/]
//
// For published work which uses all or part of OpenFABMAP, please cite:
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
//
// Original Algorithm by Mark Cummins and Paul Newman:
// [http://ijr.sagepub.com/content/27/6/647.short]
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
// [http://ijr.sagepub.com/content/30/9/1100.abstract]
//
// License Agreement
//
// Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
// Will Maddern [w.maddern@qut.edu.au], all rights reserved.
//
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OPENFABMAP_H_
#define __OPENCV_OPENFABMAP_H_
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include <vector>
#include <list>
#include <map>
#include <set>
#include <valarray>
namespace cv {
namespace of2 {
using std::list;
using std::map;
using std::multiset;
/*
Return data format of a FABMAP compare call
*/
struct CV_EXPORTS IMatch {
IMatch() :
queryIdx(-1), imgIdx(-1), likelihood(-DBL_MAX), match(-DBL_MAX) {
}
IMatch(int _queryIdx, int _imgIdx, double _likelihood, double _match) :
queryIdx(_queryIdx), imgIdx(_imgIdx), likelihood(_likelihood), match(
_match) {
}
int queryIdx; //query index
int imgIdx; //test index
double likelihood; //raw loglikelihood
double match; //normalised probability
bool operator<(const IMatch& m) const {
return match < m.match;
}
};
/*
Base FabMap class. Each FabMap method inherits from this class.
*/
class CV_EXPORTS FabMap {
public:
//FabMap options
enum {
MEAN_FIELD = 1,
SAMPLED = 2,
NAIVE_BAYES = 4,
CHOW_LIU = 8,
MOTION_MODEL = 16
};
FabMap(const Mat& clTree, double PzGe, double PzGNe, int flags,
int numSamples = 0);
virtual ~FabMap();
//methods to add training data for sampling method
virtual void addTraining(const Mat& queryImgDescriptor);
virtual void addTraining(const vector<Mat>& queryImgDescriptors);
//methods to add to the test data
virtual void add(const Mat& queryImgDescriptor);
virtual void add(const vector<Mat>& queryImgDescriptors);
//accessors
const vector<Mat>& getTrainingImgDescriptors() const;
const vector<Mat>& getTestImgDescriptors() const;
//Main FabMap image comparison
void compare(const Mat& queryImgDescriptor,
vector<IMatch>& matches, bool addQuery = false,
const Mat& mask = Mat());
void compare(const Mat& queryImgDescriptor,
const Mat& testImgDescriptors, vector<IMatch>& matches,
const Mat& mask = Mat());
void compare(const Mat& queryImgDescriptor,
const vector<Mat>& testImgDescriptors,
vector<IMatch>& matches, const Mat& mask = Mat());
void compare(const vector<Mat>& queryImgDescriptors, vector<
IMatch>& matches, bool addQuery = false, const Mat& mask =
Mat());
void compare(const vector<Mat>& queryImgDescriptors,
const vector<Mat>& testImgDescriptors,
vector<IMatch>& matches, const Mat& mask = Mat());
protected:
void compareImgDescriptor(const Mat& queryImgDescriptor,
int queryIndex, const vector<Mat>& testImgDescriptors,
vector<IMatch>& matches);
void addImgDescriptor(const Mat& queryImgDescriptor);
//the getLikelihoods method is overwritten for each different FabMap
//method.
virtual void getLikelihoods(const Mat& queryImgDescriptor,
const vector<Mat>& testImgDescriptors,
vector<IMatch>& matches);
virtual double getNewPlaceLikelihood(const Mat& queryImgDescriptor);
//turn likelihoods into probabilities (also add in motion model if used)
void normaliseDistribution(vector<IMatch>& matches);
//Chow-Liu Tree
int pq(int q);
double Pzq(int q, bool zq);
double PzqGzpq(int q, bool zq, bool zpq);
//FAB-MAP Core
double PzqGeq(bool zq, bool eq);
double PeqGL(int q, bool Lzq, bool eq);
double PzqGL(int q, bool zq, bool zpq, bool Lzq);
double PzqGzpqL(int q, bool zq, bool zpq, bool Lzq);
double (FabMap::*PzGL)(int q, bool zq, bool zpq, bool Lzq);
//data
Mat clTree;
vector<Mat> trainingImgDescriptors;
vector<Mat> testImgDescriptors;
vector<IMatch> priorMatches;
//parameters
double PzGe;
double PzGNe;
double Pnew;
double mBias;
double sFactor;
int flags;
int numSamples;
};
/*
The original FAB-MAP algorithm, developed based on:
http://ijr.sagepub.com/content/27/6/647.short
*/
class CV_EXPORTS FabMap1: public FabMap {
public:
FabMap1(const Mat& clTree, double PzGe, double PzGNe, int flags,
int numSamples = 0);
virtual ~FabMap1();
protected:
//FabMap1 implementation of likelihood comparison
void getLikelihoods(const Mat& queryImgDescriptor, const vector<
Mat>& testImgDescriptors, vector<IMatch>& matches);
};
/*
A computationally faster version of the original FAB-MAP algorithm. A look-
up-table is used to precompute many of the reoccuring calculations
*/
class CV_EXPORTS FabMapLUT: public FabMap {
public:
FabMapLUT(const Mat& clTree, double PzGe, double PzGNe,
int flags, int numSamples = 0, int precision = 6);
virtual ~FabMapLUT();
protected:
//FabMap look-up-table implementation of the likelihood comparison
void getLikelihoods(const Mat& queryImgDescriptor, const vector<
Mat>& testImgDescriptors, vector<IMatch>& matches);
//precomputed data
int (*table)[8];
//data precision
int precision;
};
/*
The Accelerated FAB-MAP algorithm, developed based on:
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942
*/
class CV_EXPORTS FabMapFBO: public FabMap {
public:
FabMapFBO(const Mat& clTree, double PzGe, double PzGNe, int flags,
int numSamples = 0, double rejectionThreshold = 1e-8, double PsGd =
1e-8, int bisectionStart = 512, int bisectionIts = 9);
virtual ~FabMapFBO();
protected:
//FabMap Fast Bail-out implementation of the likelihood comparison
void getLikelihoods(const Mat& queryImgDescriptor, const vector<
Mat>& testImgDescriptors, vector<IMatch>& matches);
//stucture used to determine word comparison order
struct WordStats {
WordStats() :
q(0), info(0), V(0), M(0) {
}
WordStats(int _q, double _info) :
q(_q), info(_info), V(0), M(0) {
}
int q;
double info;
mutable double V;
mutable double M;
bool operator<(const WordStats& w) const {
return info < w.info;
}
};
//private fast bail-out necessary functions
void setWordStatistics(const Mat& queryImgDescriptor, multiset<WordStats>& wordData);
double limitbisection(double v, double m);
double bennettInequality(double v, double m, double delta);
static bool compInfo(const WordStats& first, const WordStats& second);
//parameters
double PsGd;
double rejectionThreshold;
int bisectionStart;
int bisectionIts;
};
/*
The FAB-MAP2.0 algorithm, developed based on:
http://ijr.sagepub.com/content/30/9/1100.abstract
*/
class CV_EXPORTS FabMap2: public FabMap {
public:
FabMap2(const Mat& clTree, double PzGe, double PzGNe, int flags);
virtual ~FabMap2();
//FabMap2 builds the inverted index and requires an additional training/test
//add function
void addTraining(const Mat& queryImgDescriptors) {
FabMap::addTraining(queryImgDescriptors);
}
void addTraining(const vector<Mat>& queryImgDescriptors);
void add(const Mat& queryImgDescriptors) {
FabMap::add(queryImgDescriptors);
}
void add(const vector<Mat>& queryImgDescriptors);
protected:
//FabMap2 implementation of the likelihood comparison
void getLikelihoods(const Mat& queryImgDescriptor, const vector<
Mat>& testImgDescriptors, vector<IMatch>& matches);
double getNewPlaceLikelihood(const Mat& queryImgDescriptor);
//the likelihood function using the inverted index
void getIndexLikelihoods(const Mat& queryImgDescriptor, vector<
double>& defaults, map<int, vector<int> >& invertedMap,
vector<IMatch>& matches);
void addToIndex(const Mat& queryImgDescriptor,
vector<double>& defaults,
map<int, vector<int> >& invertedMap);
//data
vector<double> d1, d2, d3, d4;
vector<vector<int> > children;
// TODO: inverted map a vector?
vector<double> trainingDefaults;
map<int, vector<int> > trainingInvertedMap;
vector<double> testDefaults;
map<int, vector<int> > testInvertedMap;
};
/*
A Chow-Liu tree is required by FAB-MAP. The Chow-Liu tree provides an
estimate of the full distribution of visual words using a minimum spanning
tree. The tree is generated through training data.
*/
class CV_EXPORTS ChowLiuTree {
public:
ChowLiuTree();
virtual ~ChowLiuTree();
//add data to the chow-liu tree before calling make
void add(const Mat& imgDescriptor);
void add(const vector<Mat>& imgDescriptors);
const vector<Mat>& getImgDescriptors() const;
Mat make(double infoThreshold = 0.0);
private:
vector<Mat> imgDescriptors;
Mat mergedImgDescriptors;
typedef struct info {
float score;
short word1;
short word2;
} info;
//probabilities extracted from mergedImgDescriptors
double P(int a, bool za);
double JP(int a, bool za, int b, bool zb); //a & b
double CP(int a, bool za, int b, bool zb); // a | b
//calculating mutual information of all edges
void createBaseEdges(list<info>& edges, double infoThreshold);
double calcMutInfo(int word1, int word2);
static bool sortInfoScores(const info& first, const info& second);
//selecting minimum spanning egdges with maximum information
bool reduceEdgesToMinSpan(list<info>& edges);
//building the tree sctructure
Mat buildTree(int root_word, list<info> &edges);
void recAddToTree(Mat &cltree, int q, int pq,
list<info> &remaining_edges);
vector<int> extractChildren(list<info> &remaining_edges, int q);
};
/*
A custom vocabulary training method based on:
http://www.springerlink.com/content/d1h6j8x552532003/
*/
class CV_EXPORTS BOWMSCTrainer: public BOWTrainer {
public:
BOWMSCTrainer(double clusterSize = 0.4);
virtual ~BOWMSCTrainer();
// Returns trained vocabulary (i.e. cluster centers).
virtual Mat cluster() const;
virtual Mat cluster(const Mat& descriptors) const;
protected:
double clusterSize;
};
}
}
#endif /* OPENFABMAP_H_ */
include/opencv2/contrib/retina.hpp
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/*#******************************************************************************
** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
**
** By downloading, copying, installing or using the software you agree to this license.
** If you do not agree to this license, do not download, install,
** copy or use the software.
**
**
** HVStools : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab.
** Use: extract still images & image sequences features, from contours details to motion spatio-temporal features, etc. for high level visual scene analysis. Also contribute to image enhancement/compression such as tone mapping.
**
** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications)
**
** Creation - enhancement process 2007-2011
** Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France
**
** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr).
** Refer to the following research paper for more information:
** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book:
** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
**
** The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author :
** _take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper:
** ====> B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
** _take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions.
** ====> more informations in the above cited Jeanny Heraults's book.
**
** License Agreement
** For Open Source Computer Vision Library
**
** Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
**
** For Human Visual System tools (hvstools)
** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
**
** Third party copyrights are property of their respective owners.
**
** Redistribution and use in source and binary forms, with or without modification,
** are permitted provided that the following conditions are met:
**
** * Redistributions of source code must retain the above copyright notice,
** this list of conditions and the following disclaimer.
**
** * Redistributions in binary form must reproduce the above copyright notice,
** this list of conditions and the following disclaimer in the documentation
** and/or other materials provided with the distribution.
**
** * The name of the copyright holders may not be used to endorse or promote products
** derived from this software without specific prior written permission.
**
** This software is provided by the copyright holders and contributors "as is" and
** any express or implied warranties, including, but not limited to, the implied
** warranties of merchantability and fitness for a particular purpose are disclaimed.
** In no event shall the Intel Corporation or contributors be liable for any direct,
** indirect, incidental, special, exemplary, or consequential damages
** (including, but not limited to, procurement of substitute goods or services;
** loss of use, data, or profits; or business interruption) however caused
** and on any theory of liability, whether in contract, strict liability,
** or tort (including negligence or otherwise) arising in any way out of
** the use of this software, even if advised of the possibility of such damage.
*******************************************************************************/
#ifndef __OPENCV_CONTRIB_RETINA_HPP__
#define __OPENCV_CONTRIB_RETINA_HPP__
/*
* Retina.hpp
*
* Created on: Jul 19, 2011
* Author: Alexandre Benoit
*/
#include "opencv2/core/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support
#include <valarray>
namespace cv
{
enum RETINA_COLORSAMPLINGMETHOD
{
RETINA_COLOR_RANDOM, //!< each pixel position is either R, G or B in a random choice
RETINA_COLOR_DIAGONAL,//!< color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR...
RETINA_COLOR_BAYER//!< standard bayer sampling
};
class RetinaFilter;
/**
* @class Retina a wrapper class which allows the Gipsa/Listic Labs model to be used.
* This retina model allows spatio-temporal image processing (applied on still images, video sequences).
* As a summary, these are the retina model properties:
* => It applies a spectral whithening (mid-frequency details enhancement)
* => high frequency spatio-temporal noise reduction
* => low frequency luminance to be reduced (luminance range compression)
* => local logarithmic luminance compression allows details to be enhanced in low light conditions
*
* USE : this model can be used basically for spatio-temporal video effects but also for :
* _using the getParvo method output matrix : texture analysiswith enhanced signal to noise ratio and enhanced details robust against input images luminance ranges
* _using the getMagno method output matrix : motion analysis also with the previously cited properties
*
* for more information, reer to the following papers :
* Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
* Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
*
* The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author :
* _take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper:
* ====> B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
* _take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions.
* ====> more informations in the above cited Jeanny Heraults's book.
*/
class CV_EXPORTS Retina {
public:
// parameters structure for better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel
struct RetinaParameters{
struct OPLandIplParvoParameters{ // Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters
OPLandIplParvoParameters():colorMode(true),
normaliseOutput(true),
photoreceptorsLocalAdaptationSensitivity(0.7f),
photoreceptorsTemporalConstant(0.5f),
photoreceptorsSpatialConstant(0.53f),
horizontalCellsGain(0.0f),
hcellsTemporalConstant(1.f),
hcellsSpatialConstant(7.f),
ganglionCellsSensitivity(0.7f){};// default setup
bool colorMode, normaliseOutput;
float photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, hcellsTemporalConstant, hcellsSpatialConstant, ganglionCellsSensitivity;
};
struct IplMagnoParameters{ // Inner Plexiform Layer Magnocellular channel (IplMagno)
IplMagnoParameters():
normaliseOutput(true),
parasolCells_beta(0.f),
parasolCells_tau(0.f),
parasolCells_k(7.f),
amacrinCellsTemporalCutFrequency(1.2f),
V0CompressionParameter(0.95f),
localAdaptintegration_tau(0.f),
localAdaptintegration_k(7.f){};// default setup
bool normaliseOutput;
float parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k;
};
struct OPLandIplParvoParameters OPLandIplParvo;
struct IplMagnoParameters IplMagno;
};
/**
* Main constructor with most commun use setup : create an instance of color ready retina model
* @param inputSize : the input frame size
*/
Retina(Size inputSize);
/**
* Complete Retina filter constructor which allows all basic structural parameters definition
* @param inputSize : the input frame size
* @param colorMode : the chosen processing mode : with or without color processing
* @param colorSamplingMethod: specifies which kind of color sampling will be used
* @param useRetinaLogSampling: activate retina log sampling, if true, the 2 following parameters can be used
* @param reductionFactor: only usefull if param useRetinaLogSampling=true, specifies the reduction factor of the output frame (as the center (fovea) is high resolution and corners can be underscaled, then a reduction of the output is allowed without precision leak
* @param samplingStrenght: only usefull if param useRetinaLogSampling=true, specifies the strenght of the log scale that is applied
*/
Retina(Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0);
virtual ~Retina();
/**
* retreive retina input buffer size
*/
Size inputSize();
/**
* retreive retina output buffer size
*/
Size outputSize();
/**
* try to open an XML retina parameters file to adjust current retina instance setup
* => if the xml file does not exist, then default setup is applied
* => warning, Exceptions are thrown if read XML file is not valid
* @param retinaParameterFile : the parameters filename
* @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
*/
void setup(std::string retinaParameterFile="", const bool applyDefaultSetupOnFailure=true);
/**
* try to open an XML retina parameters file to adjust current retina instance setup
* => if the xml file does not exist, then default setup is applied
* => warning, Exceptions are thrown if read XML file is not valid
* @param fs : the open Filestorage which contains retina parameters
* @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
*/
void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true);
/**
* try to open an XML retina parameters file to adjust current retina instance setup
* => if the xml file does not exist, then default setup is applied
* => warning, Exceptions are thrown if read XML file is not valid
* @param newParameters : a parameters structures updated with the new target configuration
* @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
*/
void setup(RetinaParameters newParameters);
/**
* @return the current parameters setup
*/
Retina::RetinaParameters getParameters();
/**
* parameters setup display method
* @return a string which contains formatted parameters information
*/
const std::string printSetup();
/**
* write xml/yml formated parameters information
* @rparam fs : the filename of the xml file that will be open and writen with formatted parameters information
*/
virtual void write( std::string fs ) const;
/**
* write xml/yml formated parameters information
* @param fs : a cv::Filestorage object ready to be filled
*/
virtual void write( FileStorage& fs ) const;
/**
* setup the OPL and IPL parvo channels (see biologocal model)
* OPL is referred as Outer Plexiform Layer of the retina, it allows the spatio-temporal filtering which withens the spectrum and reduces spatio-temporal noise while attenuating global luminance (low frequency energy)
* IPL parvo is the OPL next processing stage, it refers to Inner Plexiform layer of the retina, it allows high contours sensitivity in foveal vision.
* for more informations, please have a look at the paper Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
* @param colorMode : specifies if (true) color is processed of not (false) to then processing gray level image
* @param normaliseOutput : specifies if (true) output is rescaled between 0 and 255 of not (false)
* @param photoreceptorsLocalAdaptationSensitivity: the photoreceptors sensitivity renage is 0-1 (more log compression effect when value increases)
* @param photoreceptorsTemporalConstant: the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame
* @param photoreceptorsSpatialConstant: the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel
* @param horizontalCellsGain: gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0
* @param HcellsTemporalConstant: the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors
* @param HcellsSpatialConstant: the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel, this value is also used for local contrast computing when computing the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular channel model)
* @param ganglionCellsSensitivity: the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 230
*/
void setupOPLandIPLParvoChannel(const bool colorMode=true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity=0.7, const float photoreceptorsTemporalConstant=0.5, const float photoreceptorsSpatialConstant=0.53, const float horizontalCellsGain=0, const float HcellsTemporalConstant=1, const float HcellsSpatialConstant=7, const float ganglionCellsSensitivity=0.7);
/**
* set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel
* this channel processes signals outpint from OPL processing stage in peripheral vision, it allows motion information enhancement. It is decorrelated from the details channel. See reference paper for more details.
* @param normaliseOutput : specifies if (true) output is rescaled between 0 and 255 of not (false)
* @param parasolCells_beta: the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
* @param parasolCells_tau: the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
* @param parasolCells_k: the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
* @param amacrinCellsTemporalCutFrequency: the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, tipicall value is 5
* @param V0CompressionParameter: the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 200
* @param localAdaptintegration_tau: specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
* @param localAdaptintegration_k: specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
*/
void setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta=0, const float parasolCells_tau=0, const float parasolCells_k=7, const float amacrinCellsTemporalCutFrequency=1.2, const float V0CompressionParameter=0.95, const float localAdaptintegration_tau=0, const float localAdaptintegration_k=7);
/**
* method which allows retina to be applied on an input image, after run, encapsulated retina module is ready to deliver its outputs using dedicated acccessors, see getParvo and getMagno methods
* @param inputImage : the input cv::Mat image to be processed, can be gray level or BGR coded in any format (from 8bit to 16bits)
*/
void run(const Mat &inputImage);
/**
* accessor of the details channel of the retina (models foveal vision)
* @param retinaOutput_parvo : the output buffer (reallocated if necessary), this output is rescaled for standard 8bits image processing use in OpenCV
*/
void getParvo(Mat &retinaOutput_parvo);
/**
* accessor of the details channel of the retina (models foveal vision)
* @param retinaOutput_parvo : the output buffer (reallocated if necessary), this output is the original retina filter model output, without any quantification or rescaling
*/
void getParvo(std::valarray<float> &retinaOutput_parvo);
/**
* accessor of the motion channel of the retina (models peripheral vision)
* @param retinaOutput_magno : the output buffer (reallocated if necessary), this output is rescaled for standard 8bits image processing use in OpenCV
*/
void getMagno(Mat &retinaOutput_magno);
/**
* accessor of the motion channel of the retina (models peripheral vision)
* @param retinaOutput_magno : the output buffer (reallocated if necessary), this output is the original retina filter model output, without any quantification or rescaling
*/
void getMagno(std::valarray<float> &retinaOutput_magno);
// original API level data accessors : get buffers addresses...
const std::valarray<float> & getMagno() const;
const std::valarray<float> & getParvo() const;
/**
* activate color saturation as the final step of the color demultiplexing process
* -> this saturation is a sigmoide function applied to each channel of the demultiplexed image.
* @param saturateColors: boolean that activates color saturation (if true) or desactivate (if false)
* @param colorSaturationValue: the saturation factor
*/
void setColorSaturation(const bool saturateColors=true, const float colorSaturationValue=4.0);
/**
* clear all retina buffers (equivalent to opening the eyes after a long period of eye close ;o)
*/
void clearBuffers();
/**
* Activate/desactivate the Magnocellular pathway processing (motion information extraction), by default, it is activated
* @param activate: true if Magnocellular output should be activated, false if not
*/
void activateMovingContoursProcessing(const bool activate);
/**
* Activate/desactivate the Parvocellular pathway processing (contours information extraction), by default, it is activated
* @param activate: true if Parvocellular (contours information extraction) output should be activated, false if not
*/
void activateContoursProcessing(const bool activate);
protected:
// Parameteres setup members
RetinaParameters _retinaParameters; // structure of parameters
// Retina model related modules
std::valarray<float> _inputBuffer; //!< buffer used to convert input cv::Mat to internal retina buffers format (valarrays)
// pointer to retina model
RetinaFilter* _retinaFilter; //!< the pointer to the retina module, allocated with instance construction
/**
* exports a valarray buffer outing from HVStools objects to a cv::Mat in CV_8UC1 (gray level picture) or CV_8UC3 (color) format
* @param grayMatrixToConvert the valarray to export to OpenCV
* @param nbRows : the number of rows of the valarray flatten matrix
* @param nbColumns : the number of rows of the valarray flatten matrix
* @param colorMode : a flag which mentions if matrix is color (true) or graylevel (false)
* @param outBuffer : the output matrix which is reallocated to satisfy Retina output buffer dimensions
*/
void _convertValarrayBuffer2cvMat(const std::valarray<float> &grayMatrixToConvert, const unsigned int nbRows, const unsigned int nbColumns, const bool colorMode, Mat &outBuffer);
/**
*
* @param inputMatToConvert : the OpenCV cv::Mat that has to be converted to gray or RGB valarray buffer that will be processed by the retina model
* @param outputValarrayMatrix : the output valarray
* @return the input image color mode (color=true, gray levels=false)
*/
bool _convertCvMat2ValarrayBuffer(const cv::Mat inputMatToConvert, std::valarray<float> &outputValarrayMatrix);
//! private method called by constructors, gathers their parameters and use them in a unified way
void _init(const Size inputSize, const bool colorMode, RETINA_COLORSAMPLINGMETHOD colorSamplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0);
};
}
#endif /* __OPENCV_CONTRIB_RETINA_HPP__ */
include/opencv2/core/core.hpp
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include/opencv2/core/cuda_devptrs.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_CORE_DEVPTRS_HPP__
#define __OPENCV_CORE_DEVPTRS_HPP__
#ifdef __cplusplus
#ifdef __CUDACC__
#define __CV_GPU_HOST_DEVICE__ __host__ __device__ __forceinline__
#else
#define __CV_GPU_HOST_DEVICE__
#endif
namespace cv
{
namespace gpu
{
// Simple lightweight structures that encapsulates information about an image on device.
// It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile
template <bool expr> struct StaticAssert;
template <> struct StaticAssert<true> {static __CV_GPU_HOST_DEVICE__ void check(){}};
template<typename T> struct DevPtr
{
typedef T elem_type;
typedef int index_type;
enum { elem_size = sizeof(elem_type) };
T* data;
__CV_GPU_HOST_DEVICE__ DevPtr() : data(0) {}
__CV_GPU_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {}
__CV_GPU_HOST_DEVICE__ size_t elemSize() const { return elem_size; }
__CV_GPU_HOST_DEVICE__ operator T*() { return data; }
__CV_GPU_HOST_DEVICE__ operator const T*() const { return data; }
};
template<typename T> struct PtrSz : public DevPtr<T>
{
__CV_GPU_HOST_DEVICE__ PtrSz() : size(0) {}
__CV_GPU_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr<T>(data_), size(size_) {}
size_t size;
};
template<typename T> struct PtrStep : public DevPtr<T>
{
__CV_GPU_HOST_DEVICE__ PtrStep() : step(0) {}
__CV_GPU_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr<T>(data_), step(step_) {}
/** \brief stride between two consecutive rows in bytes. Step is stored always and everywhere in bytes!!! */
size_t step;
__CV_GPU_HOST_DEVICE__ T* ptr(int y = 0) { return ( T*)( ( char*)DevPtr<T>::data + y * step); }
__CV_GPU_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)DevPtr<T>::data + y * step); }
__CV_GPU_HOST_DEVICE__ T& operator ()(int y, int x) { return ptr(y)[x]; }
__CV_GPU_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; }
};
template <typename T> struct PtrStepSz : public PtrStep<T>
{
__CV_GPU_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {}
__CV_GPU_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_)
: PtrStep<T>(data_, step_), cols(cols_), rows(rows_) {}
template <typename U>
explicit PtrStepSz(const PtrStepSz<U>& d) : PtrStep<T>((T*)d.data, d.step), cols(d.cols), rows(d.rows){}
int cols;
int rows;
};
typedef PtrStepSz<unsigned char> PtrStepSzb;
typedef PtrStepSz<float> PtrStepSzf;
typedef PtrStepSz<int> PtrStepSzi;
typedef PtrStep<unsigned char> PtrStepb;
typedef PtrStep<float> PtrStepf;
typedef PtrStep<int> PtrStepi;
#if defined __GNUC__
#define __CV_GPU_DEPR_BEFORE__
#define __CV_GPU_DEPR_AFTER__ __attribute__ ((deprecated))
#elif defined(__MSVC__) //|| defined(__CUDACC__)
#pragma deprecated(DevMem2D_)
#define __CV_GPU_DEPR_BEFORE__ __declspec(deprecated)
#define __CV_GPU_DEPR_AFTER__
#else
#define __CV_GPU_DEPR_BEFORE__
#define __CV_GPU_DEPR_AFTER__
#endif
template <typename T> struct __CV_GPU_DEPR_BEFORE__ DevMem2D_ : public PtrStepSz<T>
{
DevMem2D_() {}
DevMem2D_(int rows_, int cols_, T* data_, size_t step_) : PtrStepSz<T>(rows_, cols_, data_, step_) {}
template <typename U>
explicit __CV_GPU_DEPR_BEFORE__ DevMem2D_(const DevMem2D_<U>& d) : PtrStepSz<T>(d.rows, d.cols, (T*)d.data, d.step) {}
} __CV_GPU_DEPR_AFTER__ ;
typedef DevMem2D_<unsigned char> DevMem2Db;
typedef DevMem2Db DevMem2D;
typedef DevMem2D_<float> DevMem2Df;
typedef DevMem2D_<int> DevMem2Di;
template<typename T> struct PtrElemStep_ : public PtrStep<T>
{
PtrElemStep_(const DevMem2D_<T>& mem) : PtrStep<T>(mem.data, mem.step)
{
StaticAssert<256 % sizeof(T) == 0>::check();
PtrStep<T>::step /= PtrStep<T>::elem_size;
}
__CV_GPU_HOST_DEVICE__ T* ptr(int y = 0) { return PtrStep<T>::data + y * PtrStep<T>::step; }
__CV_GPU_HOST_DEVICE__ const T* ptr(int y = 0) const { return PtrStep<T>::data + y * PtrStep<T>::step; }
__CV_GPU_HOST_DEVICE__ T& operator ()(int y, int x) { return ptr(y)[x]; }
__CV_GPU_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; }
};
template<typename T> struct PtrStep_ : public PtrStep<T>
{
PtrStep_() {}
PtrStep_(const DevMem2D_<T>& mem) : PtrStep<T>(mem.data, mem.step) {}
};
typedef PtrElemStep_<unsigned char> PtrElemStep;
typedef PtrElemStep_<float> PtrElemStepf;
typedef PtrElemStep_<int> PtrElemStepi;
//#undef __CV_GPU_DEPR_BEFORE__
//#undef __CV_GPU_DEPR_AFTER__
namespace device
{
using cv::gpu::PtrSz;
using cv::gpu::PtrStep;
using cv::gpu::PtrStepSz;
using cv::gpu::PtrStepSzb;
using cv::gpu::PtrStepSzf;
using cv::gpu::PtrStepSzi;
using cv::gpu::PtrStepb;
using cv::gpu::PtrStepf;
using cv::gpu::PtrStepi;
}
}
}
#endif // __cplusplus
#endif /* __OPENCV_CORE_DEVPTRS_HPP__ */
include/opencv2/core/devmem2d.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/core/cuda_devptrs.hpp"
include/opencv2/core/eigen.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_CORE_EIGEN_HPP__
#define __OPENCV_CORE_EIGEN_HPP__
#ifdef __cplusplus
#include "opencv2/core/core_c.h"
#include "opencv2/core/core.hpp"
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4714 ) //__forceinline is not inlined
#pragma warning( disable: 4127 ) //conditional expression is constant
#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data
#endif
namespace cv
{
template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols>
void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, Mat& dst )
{
if( !(src.Flags & Eigen::RowMajorBit) )
{
Mat _src(src.cols(), src.rows(), DataType<_Tp>::type,
(void*)src.data(), src.stride()*sizeof(_Tp));
transpose(_src, dst);
}
else
{
Mat _src(src.rows(), src.cols(), DataType<_Tp>::type,
(void*)src.data(), src.stride()*sizeof(_Tp));
_src.copyTo(dst);
}
}
template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols>
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
{
CV_DbgAssert(src.rows == _rows && src.cols == _cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else if( src.cols == src.rows )
{
src.convertTo(_dst, _dst.type());
transpose(_dst, _dst);
}
else
Mat(src.t()).convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
else
{
Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
}
// Matx case
template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols>
void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
{
if( !(dst.Flags & Eigen::RowMajorBit) )
{
Mat _dst(_cols, _rows, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
transpose(src, _dst);
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
else
{
Mat _dst(_rows, _cols, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
Mat(src).copyTo(_dst);
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
}
template<typename _Tp>
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
{
dst.resize(src.rows, src.cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else if( src.cols == src.rows )
{
src.convertTo(_dst, _dst.type());
transpose(_dst, _dst);
}
else
Mat(src.t()).convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
else
{
Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
}
// Matx case
template<typename _Tp, int _rows, int _cols>
void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
{
dst.resize(_rows, _cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
Mat _dst(_cols, _rows, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
transpose(src, _dst);
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
else
{
Mat _dst(_rows, _cols, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
Mat(src).copyTo(_dst);
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
}
template<typename _Tp>
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
{
CV_Assert(src.cols == 1);
dst.resize(src.rows);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else
Mat(src.t()).convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
else
{
Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
}
// Matx case
template<typename _Tp, int _rows>
void cv2eigen( const Matx<_Tp, _rows, 1>& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
{
dst.resize(_rows);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
Mat _dst(1, _rows, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
transpose(src, _dst);
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
else
{
Mat _dst(_rows, 1, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
src.copyTo(_dst);
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
}
template<typename _Tp>
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
{
CV_Assert(src.rows == 1);
dst.resize(src.cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else
Mat(src.t()).convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
else
{
Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
}
//Matx
template<typename _Tp, int _cols>
void cv2eigen( const Matx<_Tp, 1, _cols>& src,
Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
{
dst.resize(_cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
Mat _dst(_cols, 1, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
transpose(src, _dst);
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
else
{
Mat _dst(1, _cols, DataType<_Tp>::type,
dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
Mat(src).copyTo(_dst);
CV_DbgAssert(_dst.data == (uchar*)dst.data());
}
}
}
#endif
#endif
include/opencv2/core/gpumat.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_GPUMAT_HPP__
#define __OPENCV_GPUMAT_HPP__
#ifdef __cplusplus
#include "opencv2/core/core.hpp"
#include "opencv2/core/cuda_devptrs.hpp"
namespace cv { namespace gpu
{
//////////////////////////////// Initialization & Info ////////////////////////
//! This is the only function that do not throw exceptions if the library is compiled without Cuda.
CV_EXPORTS int getCudaEnabledDeviceCount();
//! Functions below throw cv::Expception if the library is compiled without Cuda.
CV_EXPORTS void setDevice(int device);
CV_EXPORTS int getDevice();
//! Explicitly destroys and cleans up all resources associated with the current device in the current process.
//! Any subsequent API call to this device will reinitialize the device.
CV_EXPORTS void resetDevice();
enum FeatureSet
{
FEATURE_SET_COMPUTE_10 = 10,
FEATURE_SET_COMPUTE_11 = 11,
FEATURE_SET_COMPUTE_12 = 12,
FEATURE_SET_COMPUTE_13 = 13,
FEATURE_SET_COMPUTE_20 = 20,
FEATURE_SET_COMPUTE_21 = 21,
FEATURE_SET_COMPUTE_30 = 30,
FEATURE_SET_COMPUTE_35 = 35,
GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11,
SHARED_ATOMICS = FEATURE_SET_COMPUTE_12,
NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13,
WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30,
DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35
};
// Checks whether current device supports the given feature
CV_EXPORTS bool deviceSupports(FeatureSet feature_set);
// Gives information about what GPU archs this OpenCV GPU module was
// compiled for
class CV_EXPORTS TargetArchs
{
public:
static bool builtWith(FeatureSet feature_set);
static bool has(int major, int minor);
static bool hasPtx(int major, int minor);
static bool hasBin(int major, int minor);
static bool hasEqualOrLessPtx(int major, int minor);
static bool hasEqualOrGreater(int major, int minor);
static bool hasEqualOrGreaterPtx(int major, int minor);
static bool hasEqualOrGreaterBin(int major, int minor);
private:
TargetArchs();
};
// Gives information about the given GPU
class CV_EXPORTS DeviceInfo
{
public:
// Creates DeviceInfo object for the current GPU
DeviceInfo() : device_id_(getDevice()) { query(); }
// Creates DeviceInfo object for the given GPU
DeviceInfo(int device_id) : device_id_(device_id) { query(); }
std::string name() const { return name_; }
// Return compute capability versions
int majorVersion() const { return majorVersion_; }
int minorVersion() const { return minorVersion_; }
int multiProcessorCount() const { return multi_processor_count_; }
size_t sharedMemPerBlock() const;
void queryMemory(size_t& totalMemory, size_t& freeMemory) const;
size_t freeMemory() const;
size_t totalMemory() const;
// Checks whether device supports the given feature
bool supports(FeatureSet feature_set) const;
// Checks whether the GPU module can be run on the given device
bool isCompatible() const;
int deviceID() const { return device_id_; }
private:
void query();
int device_id_;
std::string name_;
int multi_processor_count_;
int majorVersion_;
int minorVersion_;
};
CV_EXPORTS void printCudaDeviceInfo(int device);
CV_EXPORTS void printShortCudaDeviceInfo(int device);
//////////////////////////////// GpuMat ///////////////////////////////
//! Smart pointer for GPU memory with reference counting. Its interface is mostly similar with cv::Mat.
class CV_EXPORTS GpuMat
{
public:
//! default constructor
GpuMat();
//! constructs GpuMatrix of the specified size and type (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
GpuMat(int rows, int cols, int type);
GpuMat(Size size, int type);
//! constucts GpuMatrix and fills it with the specified value _s.
GpuMat(int rows, int cols, int type, Scalar s);
GpuMat(Size size, int type, Scalar s);
//! copy constructor
GpuMat(const GpuMat& m);
//! constructor for GpuMatrix headers pointing to user-allocated data
GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP);
GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP);
//! creates a matrix header for a part of the bigger matrix
GpuMat(const GpuMat& m, Range rowRange, Range colRange);
GpuMat(const GpuMat& m, Rect roi);
//! builds GpuMat from Mat. Perfom blocking upload to device.
explicit GpuMat(const Mat& m);
//! destructor - calls release()
~GpuMat();
//! assignment operators
GpuMat& operator = (const GpuMat& m);
//! pefroms blocking upload data to GpuMat.
void upload(const Mat& m);
//! downloads data from device to host memory. Blocking calls.
void download(Mat& m) const;
//! returns a new GpuMatrix header for the specified row
GpuMat row(int y) const;
//! returns a new GpuMatrix header for the specified column
GpuMat col(int x) const;
//! ... for the specified row span
GpuMat rowRange(int startrow, int endrow) const;
GpuMat rowRange(Range r) const;
//! ... for the specified column span
GpuMat colRange(int startcol, int endcol) const;
GpuMat colRange(Range r) const;
//! returns deep copy of the GpuMatrix, i.e. the data is copied
GpuMat clone() const;
//! copies the GpuMatrix content to "m".
// It calls m.create(this->size(), this->type()).
void copyTo(GpuMat& m) const;
//! copies those GpuMatrix elements to "m" that are marked with non-zero mask elements.
void copyTo(GpuMat& m, const GpuMat& mask) const;
//! converts GpuMatrix to another datatype with optional scalng. See cvConvertScale.
void convertTo(GpuMat& m, int rtype, double alpha = 1, double beta = 0) const;
void assignTo(GpuMat& m, int type=-1) const;
//! sets every GpuMatrix element to s
GpuMat& operator = (Scalar s);
//! sets some of the GpuMatrix elements to s, according to the mask
GpuMat& setTo(Scalar s, const GpuMat& mask = GpuMat());
//! creates alternative GpuMatrix header for the same data, with different
// number of channels and/or different number of rows. see cvReshape.
GpuMat reshape(int cn, int rows = 0) const;
//! allocates new GpuMatrix data unless the GpuMatrix already has specified size and type.
// previous data is unreferenced if needed.
void create(int rows, int cols, int type);
void create(Size size, int type);
//! decreases reference counter;
// deallocate the data when reference counter reaches 0.
void release();
//! swaps with other smart pointer
void swap(GpuMat& mat);
//! locates GpuMatrix header within a parent GpuMatrix. See below
void locateROI(Size& wholeSize, Point& ofs) const;
//! moves/resizes the current GpuMatrix ROI inside the parent GpuMatrix.
GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright);
//! extracts a rectangular sub-GpuMatrix
// (this is a generalized form of row, rowRange etc.)
GpuMat operator()(Range rowRange, Range colRange) const;
GpuMat operator()(Rect roi) const;
//! returns true iff the GpuMatrix data is continuous
// (i.e. when there are no gaps between successive rows).
// similar to CV_IS_GpuMat_CONT(cvGpuMat->type)
bool isContinuous() const;
//! returns element size in bytes,
// similar to CV_ELEM_SIZE(cvMat->type)
size_t elemSize() const;
//! returns the size of element channel in bytes.
size_t elemSize1() const;
//! returns element type, similar to CV_MAT_TYPE(cvMat->type)
int type() const;
//! returns element type, similar to CV_MAT_DEPTH(cvMat->type)
int depth() const;
//! returns element type, similar to CV_MAT_CN(cvMat->type)
int channels() const;
//! returns step/elemSize1()
size_t step1() const;
//! returns GpuMatrix size:
// width == number of columns, height == number of rows
Size size() const;
//! returns true if GpuMatrix data is NULL
bool empty() const;
//! returns pointer to y-th row
uchar* ptr(int y = 0);
const uchar* ptr(int y = 0) const;
//! template version of the above method
template<typename _Tp> _Tp* ptr(int y = 0);
template<typename _Tp> const _Tp* ptr(int y = 0) const;
template <typename _Tp> operator PtrStepSz<_Tp>() const;
template <typename _Tp> operator PtrStep<_Tp>() const;
// Deprecated function
__CV_GPU_DEPR_BEFORE__ template <typename _Tp> operator DevMem2D_<_Tp>() const __CV_GPU_DEPR_AFTER__;
__CV_GPU_DEPR_BEFORE__ template <typename _Tp> operator PtrStep_<_Tp>() const __CV_GPU_DEPR_AFTER__;
#undef __CV_GPU_DEPR_BEFORE__
#undef __CV_GPU_DEPR_AFTER__
/*! includes several bit-fields:
- the magic signature
- continuity flag
- depth
- number of channels
*/
int flags;
//! the number of rows and columns
int rows, cols;
//! a distance between successive rows in bytes; includes the gap if any
size_t step;
//! pointer to the data
uchar* data;
//! pointer to the reference counter;
// when GpuMatrix points to user-allocated data, the pointer is NULL
int* refcount;
//! helper fields used in locateROI and adjustROI
uchar* datastart;
uchar* dataend;
};
//! Creates continuous GPU matrix
CV_EXPORTS void createContinuous(int rows, int cols, int type, GpuMat& m);
CV_EXPORTS GpuMat createContinuous(int rows, int cols, int type);
CV_EXPORTS void createContinuous(Size size, int type, GpuMat& m);
CV_EXPORTS GpuMat createContinuous(Size size, int type);
//! Ensures that size of the given matrix is not less than (rows, cols) size
//! and matrix type is match specified one too
CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, GpuMat& m);
CV_EXPORTS void ensureSizeIsEnough(Size size, int type, GpuMat& m);
CV_EXPORTS GpuMat allocMatFromBuf(int rows, int cols, int type, GpuMat &mat);
////////////////////////////////////////////////////////////////////////
// Error handling
CV_EXPORTS void error(const char* error_string, const char* file, const int line, const char* func = "");
////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////
inline GpuMat::GpuMat()
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
}
inline GpuMat::GpuMat(int rows_, int cols_, int type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if (rows_ > 0 && cols_ > 0)
create(rows_, cols_, type_);
}
inline GpuMat::GpuMat(Size size_, int type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if (size_.height > 0 && size_.width > 0)
create(size_.height, size_.width, type_);
}
inline GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if (rows_ > 0 && cols_ > 0)
{
create(rows_, cols_, type_);
setTo(s_);
}
}
inline GpuMat::GpuMat(Size size_, int type_, Scalar s_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0)
{
if (size_.height > 0 && size_.width > 0)
{
create(size_.height, size_.width, type_);
setTo(s_);
}
}
inline GpuMat::~GpuMat()
{
release();
}
inline GpuMat GpuMat::clone() const
{
GpuMat m;
copyTo(m);
return m;
}
inline void GpuMat::assignTo(GpuMat& m, int _type) const
{
if (_type < 0)
m = *this;
else
convertTo(m, _type);
}
inline size_t GpuMat::step1() const
{
return step / elemSize1();
}
inline bool GpuMat::empty() const
{
return data == 0;
}
template<typename _Tp> inline _Tp* GpuMat::ptr(int y)
{
return (_Tp*)ptr(y);
}
template<typename _Tp> inline const _Tp* GpuMat::ptr(int y) const
{
return (const _Tp*)ptr(y);
}
inline void swap(GpuMat& a, GpuMat& b)
{
a.swap(b);
}
inline GpuMat GpuMat::row(int y) const
{
return GpuMat(*this, Range(y, y+1), Range::all());
}
inline GpuMat GpuMat::col(int x) const
{
return GpuMat(*this, Range::all(), Range(x, x+1));
}
inline GpuMat GpuMat::rowRange(int startrow, int endrow) const
{
return GpuMat(*this, Range(startrow, endrow), Range::all());
}
inline GpuMat GpuMat::rowRange(Range r) const
{
return GpuMat(*this, r, Range::all());
}
inline GpuMat GpuMat::colRange(int startcol, int endcol) const
{
return GpuMat(*this, Range::all(), Range(startcol, endcol));
}
inline GpuMat GpuMat::colRange(Range r) const
{
return GpuMat(*this, Range::all(), r);
}
inline void GpuMat::create(Size size_, int type_)
{
create(size_.height, size_.width, type_);
}
inline GpuMat GpuMat::operator()(Range _rowRange, Range _colRange) const
{
return GpuMat(*this, _rowRange, _colRange);
}
inline GpuMat GpuMat::operator()(Rect roi) const
{
return GpuMat(*this, roi);
}
inline bool GpuMat::isContinuous() const
{
return (flags & Mat::CONTINUOUS_FLAG) != 0;
}
inline size_t GpuMat::elemSize() const
{
return CV_ELEM_SIZE(flags);
}
inline size_t GpuMat::elemSize1() const
{
return CV_ELEM_SIZE1(flags);
}
inline int GpuMat::type() const
{
return CV_MAT_TYPE(flags);
}
inline int GpuMat::depth() const
{
return CV_MAT_DEPTH(flags);
}
inline int GpuMat::channels() const
{
return CV_MAT_CN(flags);
}
inline Size GpuMat::size() const
{
return Size(cols, rows);
}
inline uchar* GpuMat::ptr(int y)
{
CV_DbgAssert((unsigned)y < (unsigned)rows);
return data + step * y;
}
inline const uchar* GpuMat::ptr(int y) const
{
CV_DbgAssert((unsigned)y < (unsigned)rows);
return data + step * y;
}
inline GpuMat& GpuMat::operator = (Scalar s)
{
setTo(s);
return *this;
}
template <class T> inline GpuMat::operator PtrStepSz<T>() const
{
return PtrStepSz<T>(rows, cols, (T*)data, step);
}
template <class T> inline GpuMat::operator PtrStep<T>() const
{
return PtrStep<T>((T*)data, step);
}
template <class T> inline GpuMat::operator DevMem2D_<T>() const
{
return DevMem2D_<T>(rows, cols, (T*)data, step);
}
template <class T> inline GpuMat::operator PtrStep_<T>() const
{
return PtrStep_<T>(static_cast< DevMem2D_<T> >(*this));
}
inline GpuMat createContinuous(int rows, int cols, int type)
{
GpuMat m;
createContinuous(rows, cols, type, m);
return m;
}
inline void createContinuous(Size size, int type, GpuMat& m)
{
createContinuous(size.height, size.width, type, m);
}
inline GpuMat createContinuous(Size size, int type)
{
GpuMat m;
createContinuous(size, type, m);
return m;
}
inline void ensureSizeIsEnough(Size size, int type, GpuMat& m)
{
ensureSizeIsEnough(size.height, size.width, type, m);
}
}}
#endif // __cplusplus
#endif // __OPENCV_GPUMAT_HPP__
include/opencv2/core/internal.hpp
+781
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@@ -0,0 +1,781 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* The header is for internal use and it is likely to change.
It contains some macro definitions that are used in cxcore, cv, cvaux
and, probably, other libraries. If you need some of this functionality,
the safe way is to copy it into your code and rename the macros.
*/
#ifndef __OPENCV_CORE_INTERNAL_HPP__
#define __OPENCV_CORE_INTERNAL_HPP__
#include <vector>
#include "opencv2/core/core.hpp"
#include "opencv2/core/types_c.h"
#if defined WIN32 || defined _WIN32
# ifndef WIN32
# define WIN32
# endif
# ifndef _WIN32
# define _WIN32
# endif
#endif
#if !defined WIN32 && !defined WINCE
# include <pthread.h>
#endif
#ifdef __BORLANDC__
# ifndef WIN32
# define WIN32
# endif
# ifndef _WIN32
# define _WIN32
# endif
# define CV_DLL
# undef _CV_ALWAYS_PROFILE_
# define _CV_ALWAYS_NO_PROFILE_
#endif
#ifndef FALSE
# define FALSE 0
#endif
#ifndef TRUE
# define TRUE 1
#endif
#define __BEGIN__ __CV_BEGIN__
#define __END__ __CV_END__
#define EXIT __CV_EXIT__
#ifdef HAVE_IPP
# include "ipp.h"
CV_INLINE IppiSize ippiSize(int width, int height)
{
IppiSize size = { width, height };
return size;
}
#endif
#ifndef IPPI_CALL
# define IPPI_CALL(func) CV_Assert((func) >= 0)
#endif
#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2)
# include "emmintrin.h"
# define CV_SSE 1
# define CV_SSE2 1
# if defined __SSE3__ || (defined _MSC_VER && _MSC_VER >= 1500)
# include "pmmintrin.h"
# define CV_SSE3 1
# endif
# if defined __SSSE3__ || (defined _MSC_VER && _MSC_VER >= 1500)
# include "tmmintrin.h"
# define CV_SSSE3 1
# endif
# if defined __SSE4_1__ || (defined _MSC_VER && _MSC_VER >= 1500)
# include <smmintrin.h>
# define CV_SSE4_1 1
# endif
# if defined __SSE4_2__ || (defined _MSC_VER && _MSC_VER >= 1500)
# include <nmmintrin.h>
# define CV_SSE4_2 1
# endif
# if defined __AVX__ || (defined _MSC_FULL_VER && _MSC_FULL_VER >= 160040219)
// MS Visual Studio 2010 (2012?) has no macro pre-defined to identify the use of /arch:AVX
// See: http://connect.microsoft.com/VisualStudio/feedback/details/605858/arch-avx-should-define-a-predefined-macro-in-x64-and-set-a-unique-value-for-m-ix86-fp-in-win32
# include <immintrin.h>
# define CV_AVX 1
# if defined(_XCR_XFEATURE_ENABLED_MASK)
# define __xgetbv() _xgetbv(_XCR_XFEATURE_ENABLED_MASK)
# else
# define __xgetbv() 0
# endif
# endif
#endif
#if (defined WIN32 || defined _WIN32) && defined(_M_ARM)
# include <Intrin.h>
# include "arm_neon.h"
# define CV_NEON 1
# define CPU_HAS_NEON_FEATURE (true)
#elif defined(__ARM_NEON__)
# include <arm_neon.h>
# define CV_NEON 1
# define CPU_HAS_NEON_FEATURE (true)
#endif
#ifndef CV_SSE
# define CV_SSE 0
#endif
#ifndef CV_SSE2
# define CV_SSE2 0
#endif
#ifndef CV_SSE3
# define CV_SSE3 0
#endif
#ifndef CV_SSSE3
# define CV_SSSE3 0
#endif
#ifndef CV_SSE4_1
# define CV_SSE4_1 0
#endif
#ifndef CV_SSE4_2
# define CV_SSE4_2 0
#endif
#ifndef CV_AVX
# define CV_AVX 0
#endif
#ifndef CV_NEON
# define CV_NEON 0
#endif
#ifdef HAVE_TBB
# include "tbb/tbb_stddef.h"
# if TBB_VERSION_MAJOR*100 + TBB_VERSION_MINOR >= 202
# include "tbb/tbb.h"
# include "tbb/task.h"
# undef min
# undef max
# else
# undef HAVE_TBB
# endif
#endif
#ifdef HAVE_EIGEN
# if defined __GNUC__ && defined __APPLE__
# pragma GCC diagnostic ignored "-Wshadow"
# endif
# include <Eigen/Core>
# include "opencv2/core/eigen.hpp"
#endif
#ifdef __cplusplus
namespace cv
{
#ifdef HAVE_TBB
typedef tbb::blocked_range<int> BlockedRange;
template<typename Body> static inline
void parallel_for( const BlockedRange& range, const Body& body )
{
tbb::parallel_for(range, body);
}
template<typename Iterator, typename Body> static inline
void parallel_do( Iterator first, Iterator last, const Body& body )
{
tbb::parallel_do(first, last, body);
}
typedef tbb::split Split;
template<typename Body> static inline
void parallel_reduce( const BlockedRange& range, Body& body )
{
tbb::parallel_reduce(range, body);
}
typedef tbb::concurrent_vector<Rect> ConcurrentRectVector;
typedef tbb::concurrent_vector<double> ConcurrentDoubleVector;
#else
class BlockedRange
{
public:
BlockedRange() : _begin(0), _end(0), _grainsize(0) {}
BlockedRange(int b, int e, int g=1) : _begin(b), _end(e), _grainsize(g) {}
int begin() const { return _begin; }
int end() const { return _end; }
int grainsize() const { return _grainsize; }
protected:
int _begin, _end, _grainsize;
};
template<typename Body> static inline
void parallel_for( const BlockedRange& range, const Body& body )
{
body(range);
}
typedef std::vector<Rect> ConcurrentRectVector;
typedef std::vector<double> ConcurrentDoubleVector;
template<typename Iterator, typename Body> static inline
void parallel_do( Iterator first, Iterator last, const Body& body )
{
for( ; first != last; ++first )
body(*first);
}
class Split {};
template<typename Body> static inline
void parallel_reduce( const BlockedRange& range, Body& body )
{
body(range);
}
#endif
// Returns a static string if there is a parallel framework,
// NULL otherwise.
CV_EXPORTS const char* currentParallelFramework();
} //namespace cv
#define CV_INIT_ALGORITHM(classname, algname, memberinit) \
static ::cv::Algorithm* create##classname() \
{ \
return new classname; \
} \
\
static ::cv::AlgorithmInfo& classname##_info() \
{ \
static ::cv::AlgorithmInfo classname##_info_var(algname, create##classname); \
return classname##_info_var; \
} \
\
static ::cv::AlgorithmInfo& classname##_info_auto = classname##_info(); \
\
::cv::AlgorithmInfo* classname::info() const \
{ \
static volatile bool initialized = false; \
\
if( !initialized ) \
{ \
initialized = true; \
classname obj; \
memberinit; \
} \
return &classname##_info(); \
}
#endif //__cplusplus
/* maximal size of vector to run matrix operations on it inline (i.e. w/o ipp calls) */
#define CV_MAX_INLINE_MAT_OP_SIZE 10
/* maximal linear size of matrix to allocate it on stack. */
#define CV_MAX_LOCAL_MAT_SIZE 32
/* maximal size of local memory storage */
#define CV_MAX_LOCAL_SIZE \
(CV_MAX_LOCAL_MAT_SIZE*CV_MAX_LOCAL_MAT_SIZE*(int)sizeof(double))
/* default image row align (in bytes) */
#define CV_DEFAULT_IMAGE_ROW_ALIGN 4
/* matrices are continuous by default */
#define CV_DEFAULT_MAT_ROW_ALIGN 1
/* maximum size of dynamic memory buffer.
cvAlloc reports an error if a larger block is requested. */
#define CV_MAX_ALLOC_SIZE (((size_t)1 << (sizeof(size_t)*8-2)))
/* the alignment of all the allocated buffers */
#define CV_MALLOC_ALIGN 16
/* default alignment for dynamic data strucutures, resided in storages. */
#define CV_STRUCT_ALIGN ((int)sizeof(double))
/* default storage block size */
#define CV_STORAGE_BLOCK_SIZE ((1<<16) - 128)
/* default memory block for sparse array elements */
#define CV_SPARSE_MAT_BLOCK (1<<12)
/* initial hash table size */
#define CV_SPARSE_HASH_SIZE0 (1<<10)
/* maximal average node_count/hash_size ratio beyond which hash table is resized */
#define CV_SPARSE_HASH_RATIO 3
/* max length of strings */
#define CV_MAX_STRLEN 1024
#if 0 /*def CV_CHECK_FOR_NANS*/
# define CV_CHECK_NANS( arr ) cvCheckArray((arr))
#else
# define CV_CHECK_NANS( arr )
#endif
/****************************************************************************************\
* Common declarations *
\****************************************************************************************/
#ifdef __GNUC__
# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x)))
#elif defined _MSC_VER
# define CV_DECL_ALIGNED(x) __declspec(align(x))
#else
# define CV_DECL_ALIGNED(x)
#endif
#ifndef CV_IMPL
# define CV_IMPL CV_EXTERN_C
#endif
#define CV_DBG_BREAK() { volatile int* crashMe = 0; *crashMe = 0; }
/* default step, set in case of continuous data
to work around checks for valid step in some ipp functions */
#define CV_STUB_STEP (1 << 30)
#define CV_SIZEOF_FLOAT ((int)sizeof(float))
#define CV_SIZEOF_SHORT ((int)sizeof(short))
#define CV_ORIGIN_TL 0
#define CV_ORIGIN_BL 1
/* IEEE754 constants and macros */
#define CV_POS_INF 0x7f800000
#define CV_NEG_INF 0x807fffff /* CV_TOGGLE_FLT(0xff800000) */
#define CV_1F 0x3f800000
#define CV_TOGGLE_FLT(x) ((x)^((int)(x) < 0 ? 0x7fffffff : 0))
#define CV_TOGGLE_DBL(x) \
((x)^((int64)(x) < 0 ? CV_BIG_INT(0x7fffffffffffffff) : 0))
#define CV_NOP(a) (a)
#define CV_ADD(a, b) ((a) + (b))
#define CV_SUB(a, b) ((a) - (b))
#define CV_MUL(a, b) ((a) * (b))
#define CV_AND(a, b) ((a) & (b))
#define CV_OR(a, b) ((a) | (b))
#define CV_XOR(a, b) ((a) ^ (b))
#define CV_ANDN(a, b) (~(a) & (b))
#define CV_ORN(a, b) (~(a) | (b))
#define CV_SQR(a) ((a) * (a))
#define CV_LT(a, b) ((a) < (b))
#define CV_LE(a, b) ((a) <= (b))
#define CV_EQ(a, b) ((a) == (b))
#define CV_NE(a, b) ((a) != (b))
#define CV_GT(a, b) ((a) > (b))
#define CV_GE(a, b) ((a) >= (b))
#define CV_NONZERO(a) ((a) != 0)
#define CV_NONZERO_FLT(a) (((a)+(a)) != 0)
/* general-purpose saturation macros */
#define CV_CAST_8U(t) (uchar)(!((t) & ~255) ? (t) : (t) > 0 ? 255 : 0)
#define CV_CAST_8S(t) (schar)(!(((t)+128) & ~255) ? (t) : (t) > 0 ? 127 : -128)
#define CV_CAST_16U(t) (ushort)(!((t) & ~65535) ? (t) : (t) > 0 ? 65535 : 0)
#define CV_CAST_16S(t) (short)(!(((t)+32768) & ~65535) ? (t) : (t) > 0 ? 32767 : -32768)
#define CV_CAST_32S(t) (int)(t)
#define CV_CAST_64S(t) (int64)(t)
#define CV_CAST_32F(t) (float)(t)
#define CV_CAST_64F(t) (double)(t)
#define CV_PASTE2(a,b) a##b
#define CV_PASTE(a,b) CV_PASTE2(a,b)
#define CV_EMPTY
#define CV_MAKE_STR(a) #a
#define CV_ZERO_OBJ(x) memset((x), 0, sizeof(*(x)))
#define CV_DIM(static_array) ((int)(sizeof(static_array)/sizeof((static_array)[0])))
#define cvUnsupportedFormat "Unsupported format"
CV_INLINE void* cvAlignPtr( const void* ptr, int align CV_DEFAULT(32) )
{
assert( (align & (align-1)) == 0 );
return (void*)( ((size_t)ptr + align - 1) & ~(size_t)(align-1) );
}
CV_INLINE int cvAlign( int size, int align )
{
assert( (align & (align-1)) == 0 && size < INT_MAX );
return (size + align - 1) & -align;
}
CV_INLINE CvSize cvGetMatSize( const CvMat* mat )
{
CvSize size;
size.width = mat->cols;
size.height = mat->rows;
return size;
}
#define CV_DESCALE(x,n) (((x) + (1 << ((n)-1))) >> (n))
#define CV_FLT_TO_FIX(x,n) cvRound((x)*(1<<(n)))
/****************************************************************************************\
Generic implementation of QuickSort algorithm.
----------------------------------------------
Using this macro user can declare customized sort function that can be much faster
than built-in qsort function because of lower overhead on elements
comparison and exchange. The macro takes less_than (or LT) argument - a macro or function
that takes 2 arguments returns non-zero if the first argument should be before the second
one in the sorted sequence and zero otherwise.
Example:
Suppose that the task is to sort points by ascending of y coordinates and if
y's are equal x's should ascend.
The code is:
------------------------------------------------------------------------------
#define cmp_pts( pt1, pt2 ) \
((pt1).y < (pt2).y || ((pt1).y < (pt2).y && (pt1).x < (pt2).x))
[static] CV_IMPLEMENT_QSORT( icvSortPoints, CvPoint, cmp_pts )
------------------------------------------------------------------------------
After that the function "void icvSortPoints( CvPoint* array, size_t total, int aux );"
is available to user.
aux is an additional parameter, which can be used when comparing elements.
The current implementation was derived from *BSD system qsort():
* Copyright (c) 1992, 1993
* The Regents of the University of California. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* 3. All advertising materials mentioning features or use of this software
* must display the following acknowledgement:
* This product includes software developed by the University of
* California, Berkeley and its contributors.
* 4. Neither the name of the University nor the names of its contributors
* may be used to endorse or promote products derived from this software
* without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
* OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
* HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
* OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
* SUCH DAMAGE.
\****************************************************************************************/
#define CV_IMPLEMENT_QSORT_EX( func_name, T, LT, user_data_type ) \
void func_name( T *array, size_t total, user_data_type aux ) \
{ \
int isort_thresh = 7; \
T t; \
int sp = 0; \
\
struct \
{ \
T *lb; \
T *ub; \
} \
stack[48]; \
\
aux = aux; \
\
if( total <= 1 ) \
return; \
\
stack[0].lb = array; \
stack[0].ub = array + (total - 1); \
\
while( sp >= 0 ) \
{ \
T* left = stack[sp].lb; \
T* right = stack[sp--].ub; \
\
for(;;) \
{ \
int i, n = (int)(right - left) + 1, m; \
T* ptr; \
T* ptr2; \
\
if( n <= isort_thresh ) \
{ \
insert_sort: \
for( ptr = left + 1; ptr <= right; ptr++ ) \
{ \
for( ptr2 = ptr; ptr2 > left && LT(ptr2[0],ptr2[-1]); ptr2--) \
CV_SWAP( ptr2[0], ptr2[-1], t ); \
} \
break; \
} \
else \
{ \
T* left0; \
T* left1; \
T* right0; \
T* right1; \
T* pivot; \
T* a; \
T* b; \
T* c; \
int swap_cnt = 0; \
\
left0 = left; \
right0 = right; \
pivot = left + (n/2); \
\
if( n > 40 ) \
{ \
int d = n / 8; \
a = left, b = left + d, c = left + 2*d; \
left = LT(*a, *b) ? (LT(*b, *c) ? b : (LT(*a, *c) ? c : a)) \
: (LT(*c, *b) ? b : (LT(*a, *c) ? a : c)); \
\
a = pivot - d, b = pivot, c = pivot + d; \
pivot = LT(*a, *b) ? (LT(*b, *c) ? b : (LT(*a, *c) ? c : a)) \
: (LT(*c, *b) ? b : (LT(*a, *c) ? a : c)); \
\
a = right - 2*d, b = right - d, c = right; \
right = LT(*a, *b) ? (LT(*b, *c) ? b : (LT(*a, *c) ? c : a)) \
: (LT(*c, *b) ? b : (LT(*a, *c) ? a : c)); \
} \
\
a = left, b = pivot, c = right; \
pivot = LT(*a, *b) ? (LT(*b, *c) ? b : (LT(*a, *c) ? c : a)) \
: (LT(*c, *b) ? b : (LT(*a, *c) ? a : c)); \
if( pivot != left0 ) \
{ \
CV_SWAP( *pivot, *left0, t ); \
pivot = left0; \
} \
left = left1 = left0 + 1; \
right = right1 = right0; \
\
for(;;) \
{ \
while( left <= right && !LT(*pivot, *left) ) \
{ \
if( !LT(*left, *pivot) ) \
{ \
if( left > left1 ) \
CV_SWAP( *left1, *left, t ); \
swap_cnt = 1; \
left1++; \
} \
left++; \
} \
\
while( left <= right && !LT(*right, *pivot) ) \
{ \
if( !LT(*pivot, *right) ) \
{ \
if( right < right1 ) \
CV_SWAP( *right1, *right, t ); \
swap_cnt = 1; \
right1--; \
} \
right--; \
} \
\
if( left > right ) \
break; \
CV_SWAP( *left, *right, t ); \
swap_cnt = 1; \
left++; \
right--; \
} \
\
if( swap_cnt == 0 ) \
{ \
left = left0, right = right0; \
goto insert_sort; \
} \
\
n = MIN( (int)(left1 - left0), (int)(left - left1) ); \
for( i = 0; i < n; i++ ) \
CV_SWAP( left0[i], left[i-n], t ); \
\
n = MIN( (int)(right0 - right1), (int)(right1 - right) ); \
for( i = 0; i < n; i++ ) \
CV_SWAP( left[i], right0[i-n+1], t ); \
n = (int)(left - left1); \
m = (int)(right1 - right); \
if( n > 1 ) \
{ \
if( m > 1 ) \
{ \
if( n > m ) \
{ \
stack[++sp].lb = left0; \
stack[sp].ub = left0 + n - 1; \
left = right0 - m + 1, right = right0; \
} \
else \
{ \
stack[++sp].lb = right0 - m + 1; \
stack[sp].ub = right0; \
left = left0, right = left0 + n - 1; \
} \
} \
else \
left = left0, right = left0 + n - 1; \
} \
else if( m > 1 ) \
left = right0 - m + 1, right = right0; \
else \
break; \
} \
} \
} \
}
#define CV_IMPLEMENT_QSORT( func_name, T, cmp ) \
CV_IMPLEMENT_QSORT_EX( func_name, T, cmp, int )
/****************************************************************************************\
* Structures and macros for integration with IPP *
\****************************************************************************************/
/* IPP-compatible return codes */
typedef enum CvStatus
{
CV_BADMEMBLOCK_ERR = -113,
CV_INPLACE_NOT_SUPPORTED_ERR= -112,
CV_UNMATCHED_ROI_ERR = -111,
CV_NOTFOUND_ERR = -110,
CV_BADCONVERGENCE_ERR = -109,
CV_BADDEPTH_ERR = -107,
CV_BADROI_ERR = -106,
CV_BADHEADER_ERR = -105,
CV_UNMATCHED_FORMATS_ERR = -104,
CV_UNSUPPORTED_COI_ERR = -103,
CV_UNSUPPORTED_CHANNELS_ERR = -102,
CV_UNSUPPORTED_DEPTH_ERR = -101,
CV_UNSUPPORTED_FORMAT_ERR = -100,
CV_BADARG_ERR = -49, //ipp comp
CV_NOTDEFINED_ERR = -48, //ipp comp
CV_BADCHANNELS_ERR = -47, //ipp comp
CV_BADRANGE_ERR = -44, //ipp comp
CV_BADSTEP_ERR = -29, //ipp comp
CV_BADFLAG_ERR = -12,
CV_DIV_BY_ZERO_ERR = -11, //ipp comp
CV_BADCOEF_ERR = -10,
CV_BADFACTOR_ERR = -7,
CV_BADPOINT_ERR = -6,
CV_BADSCALE_ERR = -4,
CV_OUTOFMEM_ERR = -3,
CV_NULLPTR_ERR = -2,
CV_BADSIZE_ERR = -1,
CV_NO_ERR = 0,
CV_OK = CV_NO_ERR
}
CvStatus;
#define CV_NOTHROW throw()
typedef struct CvFuncTable
{
void* fn_2d[CV_DEPTH_MAX];
}
CvFuncTable;
typedef struct CvBigFuncTable
{
void* fn_2d[CV_DEPTH_MAX*4];
} CvBigFuncTable;
#define CV_INIT_FUNC_TAB( tab, FUNCNAME, FLAG ) \
(tab).fn_2d[CV_8U] = (void*)FUNCNAME##_8u##FLAG; \
(tab).fn_2d[CV_8S] = 0; \
(tab).fn_2d[CV_16U] = (void*)FUNCNAME##_16u##FLAG; \
(tab).fn_2d[CV_16S] = (void*)FUNCNAME##_16s##FLAG; \
(tab).fn_2d[CV_32S] = (void*)FUNCNAME##_32s##FLAG; \
(tab).fn_2d[CV_32F] = (void*)FUNCNAME##_32f##FLAG; \
(tab).fn_2d[CV_64F] = (void*)FUNCNAME##_64f##FLAG
#ifdef __cplusplus
// < Deprecated
class CV_EXPORTS CvOpenGlFuncTab
{
public:
virtual ~CvOpenGlFuncTab();
virtual void genBuffers(int n, unsigned int* buffers) const = 0;
virtual void deleteBuffers(int n, const unsigned int* buffers) const = 0;
virtual void bufferData(unsigned int target, ptrdiff_t size, const void* data, unsigned int usage) const = 0;
virtual void bufferSubData(unsigned int target, ptrdiff_t offset, ptrdiff_t size, const void* data) const = 0;
virtual void bindBuffer(unsigned int target, unsigned int buffer) const = 0;
virtual void* mapBuffer(unsigned int target, unsigned int access) const = 0;
virtual void unmapBuffer(unsigned int target) const = 0;
virtual void generateBitmapFont(const std::string& family, int height, int weight, bool italic, bool underline, int start, int count, int base) const = 0;
virtual bool isGlContextInitialized() const = 0;
};
CV_EXPORTS void icvSetOpenGlFuncTab(const CvOpenGlFuncTab* tab);
CV_EXPORTS bool icvCheckGlError(const char* file, const int line, const char* func = "");
// >
namespace cv { namespace ogl {
CV_EXPORTS bool checkError(const char* file, const int line, const char* func = "");
}}
#define CV_CheckGlError() CV_DbgAssert( (cv::ogl::checkError(__FILE__, __LINE__, CV_Func)) )
#endif //__cplusplus
#endif // __OPENCV_CORE_INTERNAL_HPP__
include/opencv2/core/mat.hpp
+2619
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include/opencv2/core/opengl_interop.hpp
+284
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OPENGL_INTEROP_HPP__
#define __OPENCV_OPENGL_INTEROP_HPP__
#ifdef __cplusplus
#include "opencv2/core/core.hpp"
#include "opencv2/core/opengl_interop_deprecated.hpp"
namespace cv { namespace ogl {
/////////////////// OpenGL Objects ///////////////////
//! Smart pointer for OpenGL buffer memory with reference counting.
class CV_EXPORTS Buffer
{
public:
enum Target
{
ARRAY_BUFFER = 0x8892, //!< The buffer will be used as a source for vertex data
ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example)
PIXEL_PACK_BUFFER = 0x88EB, //!< The buffer will be used for reading from OpenGL textures
PIXEL_UNPACK_BUFFER = 0x88EC //!< The buffer will be used for writing to OpenGL textures
};
enum Access
{
READ_ONLY = 0x88B8,
WRITE_ONLY = 0x88B9,
READ_WRITE = 0x88BA
};
//! create empty buffer
Buffer();
//! create buffer from existed buffer id
Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false);
Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false);
//! create buffer
Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
//! copy from host/device memory
explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false);
//! create buffer
void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false) { create(asize.height, asize.width, atype, target, autoRelease); }
//! release memory and delete buffer object
void release();
//! set auto release mode (if true, release will be called in object's destructor)
void setAutoRelease(bool flag);
//! copy from host/device memory
void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false);
//! copy to host/device memory
void copyTo(OutputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false) const;
//! create copy of current buffer
Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const;
//! bind buffer for specified target
void bind(Target target) const;
//! unbind any buffers from specified target
static void unbind(Target target);
//! map to host memory
Mat mapHost(Access access);
void unmapHost();
//! map to device memory
gpu::GpuMat mapDevice();
void unmapDevice();
int rows() const { return rows_; }
int cols() const { return cols_; }
Size size() const { return Size(cols_, rows_); }
bool empty() const { return rows_ == 0 || cols_ == 0; }
int type() const { return type_; }
int depth() const { return CV_MAT_DEPTH(type_); }
int channels() const { return CV_MAT_CN(type_); }
int elemSize() const { return CV_ELEM_SIZE(type_); }
int elemSize1() const { return CV_ELEM_SIZE1(type_); }
unsigned int bufId() const;
class Impl;
private:
Ptr<Impl> impl_;
int rows_;
int cols_;
int type_;
};
//! Smart pointer for OpenGL 2D texture memory with reference counting.
class CV_EXPORTS Texture2D
{
public:
enum Format
{
NONE = 0,
DEPTH_COMPONENT = 0x1902, //!< Depth
RGB = 0x1907, //!< Red, Green, Blue
RGBA = 0x1908 //!< Red, Green, Blue, Alpha
};
//! create empty texture
Texture2D();
//! create texture from existed texture id
Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false);
Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false);
//! create texture
Texture2D(int arows, int acols, Format aformat, bool autoRelease = false);
Texture2D(Size asize, Format aformat, bool autoRelease = false);
//! copy from host/device memory
explicit Texture2D(InputArray arr, bool autoRelease = false);
//! create texture
void create(int arows, int acols, Format aformat, bool autoRelease = false);
void create(Size asize, Format aformat, bool autoRelease = false) { create(asize.height, asize.width, aformat, autoRelease); }
//! release memory and delete texture object
void release();
//! set auto release mode (if true, release will be called in object's destructor)
void setAutoRelease(bool flag);
//! copy from host/device memory
void copyFrom(InputArray arr, bool autoRelease = false);
//! copy to host/device memory
void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const;
//! bind texture to current active texture unit for GL_TEXTURE_2D target
void bind() const;
int rows() const { return rows_; }
int cols() const { return cols_; }
Size size() const { return Size(cols_, rows_); }
bool empty() const { return rows_ == 0 || cols_ == 0; }
Format format() const { return format_; }
unsigned int texId() const;
class Impl;
private:
Ptr<Impl> impl_;
int rows_;
int cols_;
Format format_;
};
//! OpenGL Arrays
class CV_EXPORTS Arrays
{
public:
Arrays();
void setVertexArray(InputArray vertex);
void resetVertexArray();
void setColorArray(InputArray color);
void resetColorArray();
void setNormalArray(InputArray normal);
void resetNormalArray();
void setTexCoordArray(InputArray texCoord);
void resetTexCoordArray();
void release();
void setAutoRelease(bool flag);
void bind() const;
int size() const { return size_; }
bool empty() const { return size_ == 0; }
private:
int size_;
Buffer vertex_;
Buffer color_;
Buffer normal_;
Buffer texCoord_;
};
/////////////////// Render Functions ///////////////////
//! render texture rectangle in window
CV_EXPORTS void render(const Texture2D& tex,
Rect_<double> wndRect = Rect_<double>(0.0, 0.0, 1.0, 1.0),
Rect_<double> texRect = Rect_<double>(0.0, 0.0, 1.0, 1.0));
//! render mode
enum {
POINTS = 0x0000,
LINES = 0x0001,
LINE_LOOP = 0x0002,
LINE_STRIP = 0x0003,
TRIANGLES = 0x0004,
TRIANGLE_STRIP = 0x0005,
TRIANGLE_FAN = 0x0006,
QUADS = 0x0007,
QUAD_STRIP = 0x0008,
POLYGON = 0x0009
};
//! render OpenGL arrays
CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255));
CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255));
}} // namespace cv::gl
namespace cv { namespace gpu {
//! set a CUDA device to use OpenGL interoperability
CV_EXPORTS void setGlDevice(int device = 0);
}}
namespace cv {
template <> CV_EXPORTS void Ptr<cv::ogl::Buffer::Impl>::delete_obj();
template <> CV_EXPORTS void Ptr<cv::ogl::Texture2D::Impl>::delete_obj();
}
#endif // __cplusplus
#endif // __OPENCV_OPENGL_INTEROP_HPP__
include/opencv2/core/opengl_interop_deprecated.hpp
+330
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@@ -0,0 +1,330 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_OPENGL_INTEROP_DEPRECATED_HPP__
#define __OPENCV_OPENGL_INTEROP_DEPRECATED_HPP__
#ifdef __cplusplus
#include "opencv2/core/core.hpp"
namespace cv
{
//! Smart pointer for OpenGL buffer memory with reference counting.
class CV_EXPORTS GlBuffer
{
public:
enum Usage
{
ARRAY_BUFFER = 0x8892, // buffer will use for OpenGL arrays (vertices, colors, normals, etc)
TEXTURE_BUFFER = 0x88EC // buffer will ise for OpenGL textures
};
//! create empty buffer
explicit GlBuffer(Usage usage);
//! create buffer
GlBuffer(int rows, int cols, int type, Usage usage);
GlBuffer(Size size, int type, Usage usage);
//! copy from host/device memory
GlBuffer(InputArray mat, Usage usage);
void create(int rows, int cols, int type, Usage usage);
void create(Size size, int type, Usage usage);
void create(int rows, int cols, int type);
void create(Size size, int type);
void release();
//! copy from host/device memory
void copyFrom(InputArray mat);
void bind() const;
void unbind() const;
//! map to host memory
Mat mapHost();
void unmapHost();
//! map to device memory
gpu::GpuMat mapDevice();
void unmapDevice();
inline int rows() const { return rows_; }
inline int cols() const { return cols_; }
inline Size size() const { return Size(cols_, rows_); }
inline bool empty() const { return rows_ == 0 || cols_ == 0; }
inline int type() const { return type_; }
inline int depth() const { return CV_MAT_DEPTH(type_); }
inline int channels() const { return CV_MAT_CN(type_); }
inline int elemSize() const { return CV_ELEM_SIZE(type_); }
inline int elemSize1() const { return CV_ELEM_SIZE1(type_); }
inline Usage usage() const { return usage_; }
class Impl;
private:
int rows_;
int cols_;
int type_;
Usage usage_;
Ptr<Impl> impl_;
};
template <> CV_EXPORTS void Ptr<GlBuffer::Impl>::delete_obj();
//! Smart pointer for OpenGL 2d texture memory with reference counting.
class CV_EXPORTS GlTexture
{
public:
//! create empty texture
GlTexture();
//! create texture
GlTexture(int rows, int cols, int type);
GlTexture(Size size, int type);
//! copy from host/device memory
explicit GlTexture(InputArray mat, bool bgra = true);
void create(int rows, int cols, int type);
void create(Size size, int type);
void release();
//! copy from host/device memory
void copyFrom(InputArray mat, bool bgra = true);
void bind() const;
void unbind() const;
inline int rows() const { return rows_; }
inline int cols() const { return cols_; }
inline Size size() const { return Size(cols_, rows_); }
inline bool empty() const { return rows_ == 0 || cols_ == 0; }
inline int type() const { return type_; }
inline int depth() const { return CV_MAT_DEPTH(type_); }
inline int channels() const { return CV_MAT_CN(type_); }
inline int elemSize() const { return CV_ELEM_SIZE(type_); }
inline int elemSize1() const { return CV_ELEM_SIZE1(type_); }
class Impl;
private:
int rows_;
int cols_;
int type_;
Ptr<Impl> impl_;
GlBuffer buf_;
};
template <> CV_EXPORTS void Ptr<GlTexture::Impl>::delete_obj();
//! OpenGL Arrays
class CV_EXPORTS GlArrays
{
public:
inline GlArrays()
: vertex_(GlBuffer::ARRAY_BUFFER), color_(GlBuffer::ARRAY_BUFFER), bgra_(true), normal_(GlBuffer::ARRAY_BUFFER), texCoord_(GlBuffer::ARRAY_BUFFER)
{
}
void setVertexArray(InputArray vertex);
inline void resetVertexArray() { vertex_.release(); }
void setColorArray(InputArray color, bool bgra = true);
inline void resetColorArray() { color_.release(); }
void setNormalArray(InputArray normal);
inline void resetNormalArray() { normal_.release(); }
void setTexCoordArray(InputArray texCoord);
inline void resetTexCoordArray() { texCoord_.release(); }
void bind() const;
void unbind() const;
inline int rows() const { return vertex_.rows(); }
inline int cols() const { return vertex_.cols(); }
inline Size size() const { return vertex_.size(); }
inline bool empty() const { return vertex_.empty(); }
private:
GlBuffer vertex_;
GlBuffer color_;
bool bgra_;
GlBuffer normal_;
GlBuffer texCoord_;
};
//! OpenGL Font
class CV_EXPORTS GlFont
{
public:
enum Weight
{
WEIGHT_LIGHT = 300,
WEIGHT_NORMAL = 400,
WEIGHT_SEMIBOLD = 600,
WEIGHT_BOLD = 700,
WEIGHT_BLACK = 900
};
enum Style
{
STYLE_NORMAL = 0,
STYLE_ITALIC = 1,
STYLE_UNDERLINE = 2
};
static Ptr<GlFont> get(const std::string& family, int height = 12, Weight weight = WEIGHT_NORMAL, Style style = STYLE_NORMAL);
void draw(const char* str, int len) const;
inline const std::string& family() const { return family_; }
inline int height() const { return height_; }
inline Weight weight() const { return weight_; }
inline Style style() const { return style_; }
private:
GlFont(const std::string& family, int height, Weight weight, Style style);
std::string family_;
int height_;
Weight weight_;
Style style_;
unsigned int base_;
GlFont(const GlFont&);
GlFont& operator =(const GlFont&);
};
//! render functions
//! render texture rectangle in window
CV_EXPORTS void render(const GlTexture& tex,
Rect_<double> wndRect = Rect_<double>(0.0, 0.0, 1.0, 1.0),
Rect_<double> texRect = Rect_<double>(0.0, 0.0, 1.0, 1.0));
//! render mode
namespace RenderMode {
enum {
POINTS = 0x0000,
LINES = 0x0001,
LINE_LOOP = 0x0002,
LINE_STRIP = 0x0003,
TRIANGLES = 0x0004,
TRIANGLE_STRIP = 0x0005,
TRIANGLE_FAN = 0x0006,
QUADS = 0x0007,
QUAD_STRIP = 0x0008,
POLYGON = 0x0009
};
}
//! render OpenGL arrays
CV_EXPORTS void render(const GlArrays& arr, int mode = RenderMode::POINTS, Scalar color = Scalar::all(255));
CV_EXPORTS void render(const std::string& str, const Ptr<GlFont>& font, Scalar color, Point2d pos);
//! OpenGL camera
class CV_EXPORTS GlCamera
{
public:
GlCamera();
void lookAt(Point3d eye, Point3d center, Point3d up);
void setCameraPos(Point3d pos, double yaw, double pitch, double roll);
void setScale(Point3d scale);
void setProjectionMatrix(const Mat& projectionMatrix, bool transpose = true);
void setPerspectiveProjection(double fov, double aspect, double zNear, double zFar);
void setOrthoProjection(double left, double right, double bottom, double top, double zNear, double zFar);
void setupProjectionMatrix() const;
void setupModelViewMatrix() const;
private:
Point3d eye_;
Point3d center_;
Point3d up_;
Point3d pos_;
double yaw_;
double pitch_;
double roll_;
bool useLookAtParams_;
Point3d scale_;
Mat projectionMatrix_;
double fov_;
double aspect_;
double left_;
double right_;
double bottom_;
double top_;
double zNear_;
double zFar_;
bool perspectiveProjection_;
};
inline void GlBuffer::create(Size _size, int _type, Usage _usage) { create(_size.height, _size.width, _type, _usage); }
inline void GlBuffer::create(int _rows, int _cols, int _type) { create(_rows, _cols, _type, usage()); }
inline void GlBuffer::create(Size _size, int _type) { create(_size.height, _size.width, _type, usage()); }
inline void GlTexture::create(Size _size, int _type) { create(_size.height, _size.width, _type); }
} // namespace cv
#endif // __cplusplus
#endif // __OPENCV_OPENGL_INTEROP_DEPRECATED_HPP__
include/opencv2/core/operations.hpp
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include/opencv2/core/types_c.h
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include/opencv2/core/version.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright( C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
//(including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort(including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
definition of the current version of OpenCV
Usefull to test in user programs
*/
#ifndef __OPENCV_VERSION_HPP__
#define __OPENCV_VERSION_HPP__
#define CV_VERSION_EPOCH 2
#define CV_VERSION_MAJOR 4
#define CV_VERSION_MINOR 8
#define CV_VERSION_REVISION 0
#define CVAUX_STR_EXP(__A) #__A
#define CVAUX_STR(__A) CVAUX_STR_EXP(__A)
#define CVAUX_STRW_EXP(__A) L#__A
#define CVAUX_STRW(__A) CVAUX_STRW_EXP(__A)
#if CV_VERSION_REVISION
# define CV_VERSION CVAUX_STR(CV_VERSION_EPOCH) "." CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR) "." CVAUX_STR(CV_VERSION_REVISION)
#else
# define CV_VERSION CVAUX_STR(CV_VERSION_EPOCH) "." CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR)
#endif
/* old style version constants*/
#define CV_MAJOR_VERSION CV_VERSION_EPOCH
#define CV_MINOR_VERSION CV_VERSION_MAJOR
#define CV_SUBMINOR_VERSION CV_VERSION_MINOR
#endif
include/opencv2/core/wimage.hpp
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///////////////////////////////////////////////////////////////////////////////
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to
// this license. If you do not agree to this license, do not download,
// install, copy or use the software.
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2008, Google, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation or contributors may not be used to endorse
// or promote products derived from this software without specific
// prior written permission.
//
// This software is provided by the copyright holders and contributors "as is"
// and any express or implied warranties, including, but not limited to, the
// implied warranties of merchantability and fitness for a particular purpose
// are disclaimed. In no event shall the Intel Corporation or contributors be
// liable for any direct, indirect, incidental, special, exemplary, or
// consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
/////////////////////////////////////////////////////////////////////////////////
//
// Image class which provides a thin layer around an IplImage. The goals
// of the class design are:
// 1. All the data has explicit ownership to avoid memory leaks
// 2. No hidden allocations or copies for performance.
// 3. Easy access to OpenCV methods (which will access IPP if available)
// 4. Can easily treat external data as an image
// 5. Easy to create images which are subsets of other images
// 6. Fast pixel access which can take advantage of number of channels
// if known at compile time.
//
// The WImage class is the image class which provides the data accessors.
// The 'W' comes from the fact that it is also a wrapper around the popular
// but inconvenient IplImage class. A WImage can be constructed either using a
// WImageBuffer class which allocates and frees the data,
// or using a WImageView class which constructs a subimage or a view into
// external data. The view class does no memory management. Each class
// actually has two versions, one when the number of channels is known at
// compile time and one when it isn't. Using the one with the number of
// channels specified can provide some compile time optimizations by using the
// fact that the number of channels is a constant.
//
// We use the convention (c,r) to refer to column c and row r with (0,0) being
// the upper left corner. This is similar to standard Euclidean coordinates
// with the first coordinate varying in the horizontal direction and the second
// coordinate varying in the vertical direction.
// Thus (c,r) is usually in the domain [0, width) X [0, height)
//
// Example usage:
// WImageBuffer3_b im(5,7); // Make a 5X7 3 channel image of type uchar
// WImageView3_b sub_im(im, 2,2, 3,3); // 3X3 submatrix
// vector<float> vec(10, 3.0f);
// WImageView1_f user_im(&vec[0], 2, 5); // 2X5 image w/ supplied data
//
// im.SetZero(); // same as cvSetZero(im.Ipl())
// *im(2, 3) = 15; // Modify the element at column 2, row 3
// MySetRand(&sub_im);
//
// // Copy the second row into the first. This can be done with no memory
// // allocation and will use SSE if IPP is available.
// int w = im.Width();
// im.View(0,0, w,1).CopyFrom(im.View(0,1, w,1));
//
// // Doesn't care about source of data since using WImage
// void MySetRand(WImage_b* im) { // Works with any number of channels
// for (int r = 0; r < im->Height(); ++r) {
// float* row = im->Row(r);
// for (int c = 0; c < im->Width(); ++c) {
// for (int ch = 0; ch < im->Channels(); ++ch, ++row) {
// *row = uchar(rand() & 255);
// }
// }
// }
// }
//
// Functions that are not part of the basic image allocation, viewing, and
// access should come from OpenCV, except some useful functions that are not
// part of OpenCV can be found in wimage_util.h
#ifndef __OPENCV_CORE_WIMAGE_HPP__
#define __OPENCV_CORE_WIMAGE_HPP__
#include "opencv2/core/core_c.h"
#ifdef __cplusplus
namespace cv {
template <typename T> class WImage;
template <typename T> class WImageBuffer;
template <typename T> class WImageView;
template<typename T, int C> class WImageC;
template<typename T, int C> class WImageBufferC;
template<typename T, int C> class WImageViewC;
// Commonly used typedefs.
typedef WImage<uchar> WImage_b;
typedef WImageView<uchar> WImageView_b;
typedef WImageBuffer<uchar> WImageBuffer_b;
typedef WImageC<uchar, 1> WImage1_b;
typedef WImageViewC<uchar, 1> WImageView1_b;
typedef WImageBufferC<uchar, 1> WImageBuffer1_b;
typedef WImageC<uchar, 3> WImage3_b;
typedef WImageViewC<uchar, 3> WImageView3_b;
typedef WImageBufferC<uchar, 3> WImageBuffer3_b;
typedef WImage<float> WImage_f;
typedef WImageView<float> WImageView_f;
typedef WImageBuffer<float> WImageBuffer_f;
typedef WImageC<float, 1> WImage1_f;
typedef WImageViewC<float, 1> WImageView1_f;
typedef WImageBufferC<float, 1> WImageBuffer1_f;
typedef WImageC<float, 3> WImage3_f;
typedef WImageViewC<float, 3> WImageView3_f;
typedef WImageBufferC<float, 3> WImageBuffer3_f;
// There isn't a standard for signed and unsigned short so be more
// explicit in the typename for these cases.
typedef WImage<short> WImage_16s;
typedef WImageView<short> WImageView_16s;
typedef WImageBuffer<short> WImageBuffer_16s;
typedef WImageC<short, 1> WImage1_16s;
typedef WImageViewC<short, 1> WImageView1_16s;
typedef WImageBufferC<short, 1> WImageBuffer1_16s;
typedef WImageC<short, 3> WImage3_16s;
typedef WImageViewC<short, 3> WImageView3_16s;
typedef WImageBufferC<short, 3> WImageBuffer3_16s;
typedef WImage<ushort> WImage_16u;
typedef WImageView<ushort> WImageView_16u;
typedef WImageBuffer<ushort> WImageBuffer_16u;
typedef WImageC<ushort, 1> WImage1_16u;
typedef WImageViewC<ushort, 1> WImageView1_16u;
typedef WImageBufferC<ushort, 1> WImageBuffer1_16u;
typedef WImageC<ushort, 3> WImage3_16u;
typedef WImageViewC<ushort, 3> WImageView3_16u;
typedef WImageBufferC<ushort, 3> WImageBuffer3_16u;
//
// WImage definitions
//
// This WImage class gives access to the data it refers to. It can be
// constructed either by allocating the data with a WImageBuffer class or
// using the WImageView class to refer to a subimage or outside data.
template<typename T>
class WImage
{
public:
typedef T BaseType;
// WImage is an abstract class with no other virtual methods so make the
// destructor virtual.
virtual ~WImage() = 0;
// Accessors
IplImage* Ipl() {return image_; }
const IplImage* Ipl() const {return image_; }
T* ImageData() { return reinterpret_cast<T*>(image_->imageData); }
const T* ImageData() const {
return reinterpret_cast<const T*>(image_->imageData);
}
int Width() const {return image_->width; }
int Height() const {return image_->height; }
// WidthStep is the number of bytes to go to the pixel with the next y coord
int WidthStep() const {return image_->widthStep; }
int Channels() const {return image_->nChannels; }
int ChannelSize() const {return sizeof(T); } // number of bytes per channel
// Number of bytes per pixel
int PixelSize() const {return Channels() * ChannelSize(); }
// Return depth type (e.g. IPL_DEPTH_8U, IPL_DEPTH_32F) which is the number
// of bits per channel and with the signed bit set.
// This is known at compile time using specializations.
int Depth() const;
inline const T* Row(int r) const {
return reinterpret_cast<T*>(image_->imageData + r*image_->widthStep);
}
inline T* Row(int r) {
return reinterpret_cast<T*>(image_->imageData + r*image_->widthStep);
}
// Pixel accessors which returns a pointer to the start of the channel
inline T* operator() (int c, int r) {
return reinterpret_cast<T*>(image_->imageData + r*image_->widthStep) +
c*Channels();
}
inline const T* operator() (int c, int r) const {
return reinterpret_cast<T*>(image_->imageData + r*image_->widthStep) +
c*Channels();
}
// Copy the contents from another image which is just a convenience to cvCopy
void CopyFrom(const WImage<T>& src) { cvCopy(src.Ipl(), image_); }
// Set contents to zero which is just a convenient to cvSetZero
void SetZero() { cvSetZero(image_); }
// Construct a view into a region of this image
WImageView<T> View(int c, int r, int width, int height);
protected:
// Disallow copy and assignment
WImage(const WImage&);
void operator=(const WImage&);
explicit WImage(IplImage* img) : image_(img) {
assert(!img || img->depth == Depth());
}
void SetIpl(IplImage* image) {
assert(!image || image->depth == Depth());
image_ = image;
}
IplImage* image_;
};
// Image class when both the pixel type and number of channels
// are known at compile time. This wrapper will speed up some of the operations
// like accessing individual pixels using the () operator.
template<typename T, int C>
class WImageC : public WImage<T>
{
public:
typedef typename WImage<T>::BaseType BaseType;
enum { kChannels = C };
explicit WImageC(IplImage* img) : WImage<T>(img) {
assert(!img || img->nChannels == Channels());
}
// Construct a view into a region of this image
WImageViewC<T, C> View(int c, int r, int width, int height);
// Copy the contents from another image which is just a convenience to cvCopy
void CopyFrom(const WImageC<T, C>& src) {
cvCopy(src.Ipl(), WImage<T>::image_);
}
// WImageC is an abstract class with no other virtual methods so make the
// destructor virtual.
virtual ~WImageC() = 0;
int Channels() const {return C; }
protected:
// Disallow copy and assignment
WImageC(const WImageC&);
void operator=(const WImageC&);
void SetIpl(IplImage* image) {
assert(!image || image->depth == WImage<T>::Depth());
WImage<T>::SetIpl(image);
}
};
//
// WImageBuffer definitions
//
// Image class which owns the data, so it can be allocated and is always
// freed. It cannot be copied but can be explicity cloned.
//
template<typename T>
class WImageBuffer : public WImage<T>
{
public:
typedef typename WImage<T>::BaseType BaseType;
// Default constructor which creates an object that can be
WImageBuffer() : WImage<T>(0) {}
WImageBuffer(int width, int height, int nchannels) : WImage<T>(0) {
Allocate(width, height, nchannels);
}
// Constructor which takes ownership of a given IplImage so releases
// the image on destruction.
explicit WImageBuffer(IplImage* img) : WImage<T>(img) {}
// Allocate an image. Does nothing if current size is the same as
// the new size.
void Allocate(int width, int height, int nchannels);
// Set the data to point to an image, releasing the old data
void SetIpl(IplImage* img) {
ReleaseImage();
WImage<T>::SetIpl(img);
}
// Clone an image which reallocates the image if of a different dimension.
void CloneFrom(const WImage<T>& src) {
Allocate(src.Width(), src.Height(), src.Channels());
CopyFrom(src);
}
~WImageBuffer() {
ReleaseImage();
}
// Release the image if it isn't null.
void ReleaseImage() {
if (WImage<T>::image_) {
IplImage* image = WImage<T>::image_;
cvReleaseImage(&image);
WImage<T>::SetIpl(0);
}
}
bool IsNull() const {return WImage<T>::image_ == NULL; }
private:
// Disallow copy and assignment
WImageBuffer(const WImageBuffer&);
void operator=(const WImageBuffer&);
};
// Like a WImageBuffer class but when the number of channels is known
// at compile time.
template<typename T, int C>
class WImageBufferC : public WImageC<T, C>
{
public:
typedef typename WImage<T>::BaseType BaseType;
enum { kChannels = C };
// Default constructor which creates an object that can be
WImageBufferC() : WImageC<T, C>(0) {}
WImageBufferC(int width, int height) : WImageC<T, C>(0) {
Allocate(width, height);
}
// Constructor which takes ownership of a given IplImage so releases
// the image on destruction.
explicit WImageBufferC(IplImage* img) : WImageC<T, C>(img) {}
// Allocate an image. Does nothing if current size is the same as
// the new size.
void Allocate(int width, int height);
// Set the data to point to an image, releasing the old data
void SetIpl(IplImage* img) {
ReleaseImage();
WImageC<T, C>::SetIpl(img);
}
// Clone an image which reallocates the image if of a different dimension.
void CloneFrom(const WImageC<T, C>& src) {
Allocate(src.Width(), src.Height());
CopyFrom(src);
}
~WImageBufferC() {
ReleaseImage();
}
// Release the image if it isn't null.
void ReleaseImage() {
if (WImage<T>::image_) {
IplImage* image = WImage<T>::image_;
cvReleaseImage(&image);
WImageC<T, C>::SetIpl(0);
}
}
bool IsNull() const {return WImage<T>::image_ == NULL; }
private:
// Disallow copy and assignment
WImageBufferC(const WImageBufferC&);
void operator=(const WImageBufferC&);
};
//
// WImageView definitions
//
// View into an image class which allows treating a subimage as an image
// or treating external data as an image
//
template<typename T>
class WImageView : public WImage<T>
{
public:
typedef typename WImage<T>::BaseType BaseType;
// Construct a subimage. No checks are done that the subimage lies
// completely inside the original image.
WImageView(WImage<T>* img, int c, int r, int width, int height);
// Refer to external data.
// If not given width_step assumed to be same as width.
WImageView(T* data, int width, int height, int channels, int width_step = -1);
// Refer to external data. This does NOT take ownership
// of the supplied IplImage.
WImageView(IplImage* img) : WImage<T>(img) {}
// Copy constructor
WImageView(const WImage<T>& img) : WImage<T>(0) {
header_ = *(img.Ipl());
WImage<T>::SetIpl(&header_);
}
WImageView& operator=(const WImage<T>& img) {
header_ = *(img.Ipl());
WImage<T>::SetIpl(&header_);
return *this;
}
protected:
IplImage header_;
};
template<typename T, int C>
class WImageViewC : public WImageC<T, C>
{
public:
typedef typename WImage<T>::BaseType BaseType;
enum { kChannels = C };
// Default constructor needed for vectors of views.
WImageViewC();
virtual ~WImageViewC() {}
// Construct a subimage. No checks are done that the subimage lies
// completely inside the original image.
WImageViewC(WImageC<T, C>* img,
int c, int r, int width, int height);
// Refer to external data
WImageViewC(T* data, int width, int height, int width_step = -1);
// Refer to external data. This does NOT take ownership
// of the supplied IplImage.
WImageViewC(IplImage* img) : WImageC<T, C>(img) {}
// Copy constructor which does a shallow copy to allow multiple views
// of same data. gcc-4.1.1 gets confused if both versions of
// the constructor and assignment operator are not provided.
WImageViewC(const WImageC<T, C>& img) : WImageC<T, C>(0) {
header_ = *(img.Ipl());
WImageC<T, C>::SetIpl(&header_);
}
WImageViewC(const WImageViewC<T, C>& img) : WImageC<T, C>(0) {
header_ = *(img.Ipl());
WImageC<T, C>::SetIpl(&header_);
}
WImageViewC& operator=(const WImageC<T, C>& img) {
header_ = *(img.Ipl());
WImageC<T, C>::SetIpl(&header_);
return *this;
}
WImageViewC& operator=(const WImageViewC<T, C>& img) {
header_ = *(img.Ipl());
WImageC<T, C>::SetIpl(&header_);
return *this;
}
protected:
IplImage header_;
};
// Specializations for depth
template<>
inline int WImage<uchar>::Depth() const {return IPL_DEPTH_8U; }
template<>
inline int WImage<signed char>::Depth() const {return IPL_DEPTH_8S; }
template<>
inline int WImage<short>::Depth() const {return IPL_DEPTH_16S; }
template<>
inline int WImage<ushort>::Depth() const {return IPL_DEPTH_16U; }
template<>
inline int WImage<int>::Depth() const {return IPL_DEPTH_32S; }
template<>
inline int WImage<float>::Depth() const {return IPL_DEPTH_32F; }
template<>
inline int WImage<double>::Depth() const {return IPL_DEPTH_64F; }
//
// Pure virtual destructors still need to be defined.
//
template<typename T> inline WImage<T>::~WImage() {}
template<typename T, int C> inline WImageC<T, C>::~WImageC() {}
//
// Allocate ImageData
//
template<typename T>
inline void WImageBuffer<T>::Allocate(int width, int height, int nchannels)
{
if (IsNull() || WImage<T>::Width() != width ||
WImage<T>::Height() != height || WImage<T>::Channels() != nchannels) {
ReleaseImage();
WImage<T>::image_ = cvCreateImage(cvSize(width, height),
WImage<T>::Depth(), nchannels);
}
}
template<typename T, int C>
inline void WImageBufferC<T, C>::Allocate(int width, int height)
{
if (IsNull() || WImage<T>::Width() != width || WImage<T>::Height() != height) {
ReleaseImage();
WImageC<T, C>::SetIpl(cvCreateImage(cvSize(width, height),WImage<T>::Depth(), C));
}
}
//
// ImageView methods
//
template<typename T>
WImageView<T>::WImageView(WImage<T>* img, int c, int r, int width, int height)
: WImage<T>(0)
{
header_ = *(img->Ipl());
header_.imageData = reinterpret_cast<char*>((*img)(c, r));
header_.width = width;
header_.height = height;
WImage<T>::SetIpl(&header_);
}
template<typename T>
WImageView<T>::WImageView(T* data, int width, int height, int nchannels, int width_step)
: WImage<T>(0)
{
cvInitImageHeader(&header_, cvSize(width, height), WImage<T>::Depth(), nchannels);
header_.imageData = reinterpret_cast<char*>(data);
if (width_step > 0) {
header_.widthStep = width_step;
}
WImage<T>::SetIpl(&header_);
}
template<typename T, int C>
WImageViewC<T, C>::WImageViewC(WImageC<T, C>* img, int c, int r, int width, int height)
: WImageC<T, C>(0)
{
header_ = *(img->Ipl());
header_.imageData = reinterpret_cast<char*>((*img)(c, r));
header_.width = width;
header_.height = height;
WImageC<T, C>::SetIpl(&header_);
}
template<typename T, int C>
WImageViewC<T, C>::WImageViewC() : WImageC<T, C>(0) {
cvInitImageHeader(&header_, cvSize(0, 0), WImage<T>::Depth(), C);
header_.imageData = reinterpret_cast<char*>(0);
WImageC<T, C>::SetIpl(&header_);
}
template<typename T, int C>
WImageViewC<T, C>::WImageViewC(T* data, int width, int height, int width_step)
: WImageC<T, C>(0)
{
cvInitImageHeader(&header_, cvSize(width, height), WImage<T>::Depth(), C);
header_.imageData = reinterpret_cast<char*>(data);
if (width_step > 0) {
header_.widthStep = width_step;
}
WImageC<T, C>::SetIpl(&header_);
}
// Construct a view into a region of an image
template<typename T>
WImageView<T> WImage<T>::View(int c, int r, int width, int height) {
return WImageView<T>(this, c, r, width, height);
}
template<typename T, int C>
WImageViewC<T, C> WImageC<T, C>::View(int c, int r, int width, int height) {
return WImageViewC<T, C>(this, c, r, width, height);
}
} // end of namespace
#endif // __cplusplus
#endif
include/opencv2/features2d/features2d.hpp
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include/opencv2/flann/all_indices.h
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_ALL_INDICES_H_
#define OPENCV_FLANN_ALL_INDICES_H_
#include "general.h"
#include "nn_index.h"
#include "kdtree_index.h"
#include "kdtree_single_index.h"
#include "kmeans_index.h"
#include "composite_index.h"
#include "linear_index.h"
#include "hierarchical_clustering_index.h"
#include "lsh_index.h"
#include "autotuned_index.h"
namespace cvflann
{
template<typename KDTreeCapability, typename VectorSpace, typename Distance>
struct index_creator
{
static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
{
flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
NNIndex<Distance>* nnIndex;
switch (index_type) {
case FLANN_INDEX_LINEAR:
nnIndex = new LinearIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_KDTREE_SINGLE:
nnIndex = new KDTreeSingleIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_KDTREE:
nnIndex = new KDTreeIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_KMEANS:
nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_COMPOSITE:
nnIndex = new CompositeIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_AUTOTUNED:
nnIndex = new AutotunedIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_HIERARCHICAL:
nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_LSH:
nnIndex = new LshIndex<Distance>(dataset, params, distance);
break;
default:
throw FLANNException("Unknown index type");
}
return nnIndex;
}
};
template<typename VectorSpace, typename Distance>
struct index_creator<False,VectorSpace,Distance>
{
static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
{
flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
NNIndex<Distance>* nnIndex;
switch (index_type) {
case FLANN_INDEX_LINEAR:
nnIndex = new LinearIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_KMEANS:
nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_HIERARCHICAL:
nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_LSH:
nnIndex = new LshIndex<Distance>(dataset, params, distance);
break;
default:
throw FLANNException("Unknown index type");
}
return nnIndex;
}
};
template<typename Distance>
struct index_creator<False,False,Distance>
{
static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
{
flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
NNIndex<Distance>* nnIndex;
switch (index_type) {
case FLANN_INDEX_LINEAR:
nnIndex = new LinearIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_HIERARCHICAL:
nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
break;
case FLANN_INDEX_LSH:
nnIndex = new LshIndex<Distance>(dataset, params, distance);
break;
default:
throw FLANNException("Unknown index type");
}
return nnIndex;
}
};
template<typename Distance>
NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
{
return index_creator<typename Distance::is_kdtree_distance,
typename Distance::is_vector_space_distance,
Distance>::create(dataset, params,distance);
}
}
#endif /* OPENCV_FLANN_ALL_INDICES_H_ */
include/opencv2/flann/allocator.h
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_ALLOCATOR_H_
#define OPENCV_FLANN_ALLOCATOR_H_
#include <stdlib.h>
#include <stdio.h>
namespace cvflann
{
/**
* Allocates (using C's malloc) a generic type T.
*
* Params:
* count = number of instances to allocate.
* Returns: pointer (of type T*) to memory buffer
*/
template <typename T>
T* allocate(size_t count = 1)
{
T* mem = (T*) ::malloc(sizeof(T)*count);
return mem;
}
/**
* Pooled storage allocator
*
* The following routines allow for the efficient allocation of storage in
* small chunks from a specified pool. Rather than allowing each structure
* to be freed individually, an entire pool of storage is freed at once.
* This method has two advantages over just using malloc() and free(). First,
* it is far more efficient for allocating small objects, as there is
* no overhead for remembering all the information needed to free each
* object or consolidating fragmented memory. Second, the decision about
* how long to keep an object is made at the time of allocation, and there
* is no need to track down all the objects to free them.
*
*/
const size_t WORDSIZE=16;
const size_t BLOCKSIZE=8192;
class PooledAllocator
{
/* We maintain memory alignment to word boundaries by requiring that all
allocations be in multiples of the machine wordsize. */
/* Size of machine word in bytes. Must be power of 2. */
/* Minimum number of bytes requested at a time from the system. Must be multiple of WORDSIZE. */
int remaining; /* Number of bytes left in current block of storage. */
void* base; /* Pointer to base of current block of storage. */
void* loc; /* Current location in block to next allocate memory. */
int blocksize;
public:
int usedMemory;
int wastedMemory;
/**
Default constructor. Initializes a new pool.
*/
PooledAllocator(int blockSize = BLOCKSIZE)
{
blocksize = blockSize;
remaining = 0;
base = NULL;
usedMemory = 0;
wastedMemory = 0;
}
/**
* Destructor. Frees all the memory allocated in this pool.
*/
~PooledAllocator()
{
void* prev;
while (base != NULL) {
prev = *((void**) base); /* Get pointer to prev block. */
::free(base);
base = prev;
}
}
/**
* Returns a pointer to a piece of new memory of the given size in bytes
* allocated from the pool.
*/
void* allocateMemory(int size)
{
int blockSize;
/* Round size up to a multiple of wordsize. The following expression
only works for WORDSIZE that is a power of 2, by masking last bits of
incremented size to zero.
*/
size = (size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
/* Check whether a new block must be allocated. Note that the first word
of a block is reserved for a pointer to the previous block.
*/
if (size > remaining) {
wastedMemory += remaining;
/* Allocate new storage. */
blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE;
// use the standard C malloc to allocate memory
void* m = ::malloc(blockSize);
if (!m) {
fprintf(stderr,"Failed to allocate memory.\n");
return NULL;
}
/* Fill first word of new block with pointer to previous block. */
((void**) m)[0] = base;
base = m;
int shift = 0;
//int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);
remaining = blockSize - sizeof(void*) - shift;
loc = ((char*)m + sizeof(void*) + shift);
}
void* rloc = loc;
loc = (char*)loc + size;
remaining -= size;
usedMemory += size;
return rloc;
}
/**
* Allocates (using this pool) a generic type T.
*
* Params:
* count = number of instances to allocate.
* Returns: pointer (of type T*) to memory buffer
*/
template <typename T>
T* allocate(size_t count = 1)
{
T* mem = (T*) this->allocateMemory((int)(sizeof(T)*count));
return mem;
}
};
}
#endif //OPENCV_FLANN_ALLOCATOR_H_
include/opencv2/flann/any.h
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#ifndef OPENCV_FLANN_ANY_H_
#define OPENCV_FLANN_ANY_H_
/*
* (C) Copyright Christopher Diggins 2005-2011
* (C) Copyright Pablo Aguilar 2005
* (C) Copyright Kevlin Henney 2001
*
* Distributed under the Boost Software License, Version 1.0. (See
* accompanying file LICENSE_1_0.txt or copy at
* http://www.boost.org/LICENSE_1_0.txt
*
* Adapted for FLANN by Marius Muja
*/
#include "defines.h"
#include <stdexcept>
#include <ostream>
#include <typeinfo>
namespace cvflann
{
namespace anyimpl
{
struct bad_any_cast
{
};
struct empty_any
{
};
inline std::ostream& operator <<(std::ostream& out, const empty_any&)
{
out << "[empty_any]";
return out;
}
struct base_any_policy
{
virtual void static_delete(void** x) = 0;
virtual void copy_from_value(void const* src, void** dest) = 0;
virtual void clone(void* const* src, void** dest) = 0;
virtual void move(void* const* src, void** dest) = 0;
virtual void* get_value(void** src) = 0;
virtual ::size_t get_size() = 0;
virtual const std::type_info& type() = 0;
virtual void print(std::ostream& out, void* const* src) = 0;
#ifdef OPENCV_CAN_BREAK_BINARY_COMPATIBILITY
virtual ~base_any_policy() {}
#endif
};
template<typename T>
struct typed_base_any_policy : base_any_policy
{
virtual ::size_t get_size() { return sizeof(T); }
virtual const std::type_info& type() { return typeid(T); }
};
template<typename T>
struct small_any_policy : typed_base_any_policy<T>
{
virtual void static_delete(void**) { }
virtual void copy_from_value(void const* src, void** dest)
{
new (dest) T(* reinterpret_cast<T const*>(src));
}
virtual void clone(void* const* src, void** dest) { *dest = *src; }
virtual void move(void* const* src, void** dest) { *dest = *src; }
virtual void* get_value(void** src) { return reinterpret_cast<void*>(src); }
virtual void print(std::ostream& out, void* const* src) { out << *reinterpret_cast<T const*>(src); }
};
template<typename T>
struct big_any_policy : typed_base_any_policy<T>
{
virtual void static_delete(void** x)
{
if (* x) delete (* reinterpret_cast<T**>(x)); *x = NULL;
}
virtual void copy_from_value(void const* src, void** dest)
{
*dest = new T(*reinterpret_cast<T const*>(src));
}
virtual void clone(void* const* src, void** dest)
{
*dest = new T(**reinterpret_cast<T* const*>(src));
}
virtual void move(void* const* src, void** dest)
{
(*reinterpret_cast<T**>(dest))->~T();
**reinterpret_cast<T**>(dest) = **reinterpret_cast<T* const*>(src);
}
virtual void* get_value(void** src) { return *src; }
virtual void print(std::ostream& out, void* const* src) { out << *reinterpret_cast<T const*>(*src); }
};
template<> inline void big_any_policy<flann_centers_init_t>::print(std::ostream& out, void* const* src)
{
out << int(*reinterpret_cast<flann_centers_init_t const*>(*src));
}
template<> inline void big_any_policy<flann_algorithm_t>::print(std::ostream& out, void* const* src)
{
out << int(*reinterpret_cast<flann_algorithm_t const*>(*src));
}
template<typename T>
struct choose_policy
{
typedef big_any_policy<T> type;
};
template<typename T>
struct choose_policy<T*>
{
typedef small_any_policy<T*> type;
};
struct any;
/// Choosing the policy for an any type is illegal, but should never happen.
/// This is designed to throw a compiler error.
template<>
struct choose_policy<any>
{
typedef void type;
};
/// Specializations for small types.
#define SMALL_POLICY(TYPE) \
template<> \
struct choose_policy<TYPE> { typedef small_any_policy<TYPE> type; \
}
SMALL_POLICY(signed char);
SMALL_POLICY(unsigned char);
SMALL_POLICY(signed short);
SMALL_POLICY(unsigned short);
SMALL_POLICY(signed int);
SMALL_POLICY(unsigned int);
SMALL_POLICY(signed long);
SMALL_POLICY(unsigned long);
SMALL_POLICY(float);
SMALL_POLICY(bool);
#undef SMALL_POLICY
/// This function will return a different policy for each type.
template<typename T>
base_any_policy* get_policy()
{
static typename choose_policy<T>::type policy;
return &policy;
}
} // namespace anyimpl
struct any
{
private:
// fields
anyimpl::base_any_policy* policy;
void* object;
public:
/// Initializing constructor.
template <typename T>
any(const T& x)
: policy(anyimpl::get_policy<anyimpl::empty_any>()), object(NULL)
{
assign(x);
}
/// Empty constructor.
any()
: policy(anyimpl::get_policy<anyimpl::empty_any>()), object(NULL)
{ }
/// Special initializing constructor for string literals.
any(const char* x)
: policy(anyimpl::get_policy<anyimpl::empty_any>()), object(NULL)
{
assign(x);
}
/// Copy constructor.
any(const any& x)
: policy(anyimpl::get_policy<anyimpl::empty_any>()), object(NULL)
{
assign(x);
}
/// Destructor.
~any()
{
policy->static_delete(&object);
}
/// Assignment function from another any.
any& assign(const any& x)
{
reset();
policy = x.policy;
policy->clone(&x.object, &object);
return *this;
}
/// Assignment function.
template <typename T>
any& assign(const T& x)
{
reset();
policy = anyimpl::get_policy<T>();
policy->copy_from_value(&x, &object);
return *this;
}
/// Assignment operator.
template<typename T>
any& operator=(const T& x)
{
return assign(x);
}
/// Assignment operator, specialed for literal strings.
/// They have types like const char [6] which don't work as expected.
any& operator=(const char* x)
{
return assign(x);
}
/// Utility functions
any& swap(any& x)
{
std::swap(policy, x.policy);
std::swap(object, x.object);
return *this;
}
/// Cast operator. You can only cast to the original type.
template<typename T>
T& cast()
{
if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast();
T* r = reinterpret_cast<T*>(policy->get_value(&object));
return *r;
}
/// Cast operator. You can only cast to the original type.
template<typename T>
const T& cast() const
{
if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast();
T* r = reinterpret_cast<T*>(policy->get_value(const_cast<void **>(&object)));
return *r;
}
/// Returns true if the any contains no value.
bool empty() const
{
return policy->type() == typeid(anyimpl::empty_any);
}
/// Frees any allocated memory, and sets the value to NULL.
void reset()
{
policy->static_delete(&object);
policy = anyimpl::get_policy<anyimpl::empty_any>();
}
/// Returns true if the two types are the same.
bool compatible(const any& x) const
{
return policy->type() == x.policy->type();
}
/// Returns if the type is compatible with the policy
template<typename T>
bool has_type()
{
return policy->type() == typeid(T);
}
const std::type_info& type() const
{
return policy->type();
}
friend std::ostream& operator <<(std::ostream& out, const any& any_val);
};
inline std::ostream& operator <<(std::ostream& out, const any& any_val)
{
any_val.policy->print(out,&any_val.object);
return out;
}
}
#endif // OPENCV_FLANN_ANY_H_
include/opencv2/flann/autotuned_index.h
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_AUTOTUNED_INDEX_H_
#define OPENCV_FLANN_AUTOTUNED_INDEX_H_
#include "general.h"
#include "nn_index.h"
#include "ground_truth.h"
#include "index_testing.h"
#include "sampling.h"
#include "kdtree_index.h"
#include "kdtree_single_index.h"
#include "kmeans_index.h"
#include "composite_index.h"
#include "linear_index.h"
#include "logger.h"
namespace cvflann
{
template<typename Distance>
NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance);
struct AutotunedIndexParams : public IndexParams
{
AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1)
{
(*this)["algorithm"] = FLANN_INDEX_AUTOTUNED;
// precision desired (used for autotuning, -1 otherwise)
(*this)["target_precision"] = target_precision;
// build tree time weighting factor
(*this)["build_weight"] = build_weight;
// index memory weighting factor
(*this)["memory_weight"] = memory_weight;
// what fraction of the dataset to use for autotuning
(*this)["sample_fraction"] = sample_fraction;
}
};
template <typename Distance>
class AutotunedIndex : public NNIndex<Distance>
{
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
AutotunedIndex(const Matrix<ElementType>& inputData, const IndexParams& params = AutotunedIndexParams(), Distance d = Distance()) :
dataset_(inputData), distance_(d)
{
target_precision_ = get_param(params, "target_precision",0.8f);
build_weight_ = get_param(params,"build_weight", 0.01f);
memory_weight_ = get_param(params, "memory_weight", 0.0f);
sample_fraction_ = get_param(params,"sample_fraction", 0.1f);
bestIndex_ = NULL;
}
AutotunedIndex(const AutotunedIndex&);
AutotunedIndex& operator=(const AutotunedIndex&);
virtual ~AutotunedIndex()
{
if (bestIndex_ != NULL) {
delete bestIndex_;
bestIndex_ = NULL;
}
}
/**
* Method responsible with building the index.
*/
virtual void buildIndex()
{
bestParams_ = estimateBuildParams();
Logger::info("----------------------------------------------------\n");
Logger::info("Autotuned parameters:\n");
print_params(bestParams_);
Logger::info("----------------------------------------------------\n");
bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_);
bestIndex_->buildIndex();
speedup_ = estimateSearchParams(bestSearchParams_);
Logger::info("----------------------------------------------------\n");
Logger::info("Search parameters:\n");
print_params(bestSearchParams_);
Logger::info("----------------------------------------------------\n");
}
/**
* Saves the index to a stream
*/
virtual void saveIndex(FILE* stream)
{
save_value(stream, (int)bestIndex_->getType());
bestIndex_->saveIndex(stream);
save_value(stream, get_param<int>(bestSearchParams_, "checks"));
}
/**
* Loads the index from a stream
*/
virtual void loadIndex(FILE* stream)
{
int index_type;
load_value(stream, index_type);
IndexParams params;
params["algorithm"] = (flann_algorithm_t)index_type;
bestIndex_ = create_index_by_type<Distance>(dataset_, params, distance_);
bestIndex_->loadIndex(stream);
int checks;
load_value(stream, checks);
bestSearchParams_["checks"] = checks;
}
/**
* Method that searches for nearest-neighbors
*/
virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
{
int checks = get_param<int>(searchParams,"checks",FLANN_CHECKS_AUTOTUNED);
if (checks == FLANN_CHECKS_AUTOTUNED) {
bestIndex_->findNeighbors(result, vec, bestSearchParams_);
}
else {
bestIndex_->findNeighbors(result, vec, searchParams);
}
}
IndexParams getParameters() const
{
return bestIndex_->getParameters();
}
SearchParams getSearchParameters() const
{
return bestSearchParams_;
}
float getSpeedup() const
{
return speedup_;
}
/**
* Number of features in this index.
*/
virtual size_t size() const
{
return bestIndex_->size();
}
/**
* The length of each vector in this index.
*/
virtual size_t veclen() const
{
return bestIndex_->veclen();
}
/**
* The amount of memory (in bytes) this index uses.
*/
virtual int usedMemory() const
{
return bestIndex_->usedMemory();
}
/**
* Algorithm name
*/
virtual flann_algorithm_t getType() const
{
return FLANN_INDEX_AUTOTUNED;
}
private:
struct CostData
{
float searchTimeCost;
float buildTimeCost;
float memoryCost;
float totalCost;
IndexParams params;
};
void evaluate_kmeans(CostData& cost)
{
StartStopTimer t;
int checks;
const int nn = 1;
Logger::info("KMeansTree using params: max_iterations=%d, branching=%d\n",
get_param<int>(cost.params,"iterations"),
get_param<int>(cost.params,"branching"));
KMeansIndex<Distance> kmeans(sampledDataset_, cost.params, distance_);
// measure index build time
t.start();
kmeans.buildIndex();
t.stop();
float buildTime = (float)t.value;
// measure search time
float searchTime = test_index_precision(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
cost.memoryCost = (kmeans.usedMemory() + datasetMemory) / datasetMemory;
cost.searchTimeCost = searchTime;
cost.buildTimeCost = buildTime;
Logger::info("KMeansTree buildTime=%g, searchTime=%g, build_weight=%g\n", buildTime, searchTime, build_weight_);
}
void evaluate_kdtree(CostData& cost)
{
StartStopTimer t;
int checks;
const int nn = 1;
Logger::info("KDTree using params: trees=%d\n", get_param<int>(cost.params,"trees"));
KDTreeIndex<Distance> kdtree(sampledDataset_, cost.params, distance_);
t.start();
kdtree.buildIndex();
t.stop();
float buildTime = (float)t.value;
//measure search time
float searchTime = test_index_precision(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
cost.memoryCost = (kdtree.usedMemory() + datasetMemory) / datasetMemory;
cost.searchTimeCost = searchTime;
cost.buildTimeCost = buildTime;
Logger::info("KDTree buildTime=%g, searchTime=%g\n", buildTime, searchTime);
}
// struct KMeansSimpleDownhillFunctor {
//
// Autotune& autotuner;
// KMeansSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {};
//
// float operator()(int* params) {
//
// float maxFloat = numeric_limits<float>::max();
//
// if (params[0]<2) return maxFloat;
// if (params[1]<0) return maxFloat;
//
// CostData c;
// c.params["algorithm"] = KMEANS;
// c.params["centers-init"] = CENTERS_RANDOM;
// c.params["branching"] = params[0];
// c.params["max-iterations"] = params[1];
//
// autotuner.evaluate_kmeans(c);
//
// return c.timeCost;
//
// }
// };
//
// struct KDTreeSimpleDownhillFunctor {
//
// Autotune& autotuner;
// KDTreeSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {};
//
// float operator()(int* params) {
// float maxFloat = numeric_limits<float>::max();
//
// if (params[0]<1) return maxFloat;
//
// CostData c;
// c.params["algorithm"] = KDTREE;
// c.params["trees"] = params[0];
//
// autotuner.evaluate_kdtree(c);
//
// return c.timeCost;
//
// }
// };
void optimizeKMeans(std::vector<CostData>& costs)
{
Logger::info("KMEANS, Step 1: Exploring parameter space\n");
// explore kmeans parameters space using combinations of the parameters below
int maxIterations[] = { 1, 5, 10, 15 };
int branchingFactors[] = { 16, 32, 64, 128, 256 };
int kmeansParamSpaceSize = FLANN_ARRAY_LEN(maxIterations) * FLANN_ARRAY_LEN(branchingFactors);
costs.reserve(costs.size() + kmeansParamSpaceSize);
// evaluate kmeans for all parameter combinations
for (size_t i = 0; i < FLANN_ARRAY_LEN(maxIterations); ++i) {
for (size_t j = 0; j < FLANN_ARRAY_LEN(branchingFactors); ++j) {
CostData cost;
cost.params["algorithm"] = FLANN_INDEX_KMEANS;
cost.params["centers_init"] = FLANN_CENTERS_RANDOM;
cost.params["iterations"] = maxIterations[i];
cost.params["branching"] = branchingFactors[j];
evaluate_kmeans(cost);
costs.push_back(cost);
}
}
// Logger::info("KMEANS, Step 2: simplex-downhill optimization\n");
//
// const int n = 2;
// // choose initial simplex points as the best parameters so far
// int kmeansNMPoints[n*(n+1)];
// float kmeansVals[n+1];
// for (int i=0;i<n+1;++i) {
// kmeansNMPoints[i*n] = (int)kmeansCosts[i].params["branching"];
// kmeansNMPoints[i*n+1] = (int)kmeansCosts[i].params["max-iterations"];
// kmeansVals[i] = kmeansCosts[i].timeCost;
// }
// KMeansSimpleDownhillFunctor kmeans_cost_func(*this);
// // run optimization
// optimizeSimplexDownhill(kmeansNMPoints,n,kmeans_cost_func,kmeansVals);
// // store results
// for (int i=0;i<n+1;++i) {
// kmeansCosts[i].params["branching"] = kmeansNMPoints[i*2];
// kmeansCosts[i].params["max-iterations"] = kmeansNMPoints[i*2+1];
// kmeansCosts[i].timeCost = kmeansVals[i];
// }
}
void optimizeKDTree(std::vector<CostData>& costs)
{
Logger::info("KD-TREE, Step 1: Exploring parameter space\n");
// explore kd-tree parameters space using the parameters below
int testTrees[] = { 1, 4, 8, 16, 32 };
// evaluate kdtree for all parameter combinations
for (size_t i = 0; i < FLANN_ARRAY_LEN(testTrees); ++i) {
CostData cost;
cost.params["trees"] = testTrees[i];
evaluate_kdtree(cost);
costs.push_back(cost);
}
// Logger::info("KD-TREE, Step 2: simplex-downhill optimization\n");
//
// const int n = 1;
// // choose initial simplex points as the best parameters so far
// int kdtreeNMPoints[n*(n+1)];
// float kdtreeVals[n+1];
// for (int i=0;i<n+1;++i) {
// kdtreeNMPoints[i] = (int)kdtreeCosts[i].params["trees"];
// kdtreeVals[i] = kdtreeCosts[i].timeCost;
// }
// KDTreeSimpleDownhillFunctor kdtree_cost_func(*this);
// // run optimization
// optimizeSimplexDownhill(kdtreeNMPoints,n,kdtree_cost_func,kdtreeVals);
// // store results
// for (int i=0;i<n+1;++i) {
// kdtreeCosts[i].params["trees"] = kdtreeNMPoints[i];
// kdtreeCosts[i].timeCost = kdtreeVals[i];
// }
}
/**
* Chooses the best nearest-neighbor algorithm and estimates the optimal
* parameters to use when building the index (for a given precision).
* Returns a dictionary with the optimal parameters.
*/
IndexParams estimateBuildParams()
{
std::vector<CostData> costs;
int sampleSize = int(sample_fraction_ * dataset_.rows);
int testSampleSize = std::min(sampleSize / 10, 1000);
Logger::info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g\n", dataset_.rows, sampleSize, testSampleSize, target_precision_);
// For a very small dataset, it makes no sense to build any fancy index, just
// use linear search
if (testSampleSize < 10) {
Logger::info("Choosing linear, dataset too small\n");
return LinearIndexParams();
}
// We use a fraction of the original dataset to speedup the autotune algorithm
sampledDataset_ = random_sample(dataset_, sampleSize);
// We use a cross-validation approach, first we sample a testset from the dataset
testDataset_ = random_sample(sampledDataset_, testSampleSize, true);
// We compute the ground truth using linear search
Logger::info("Computing ground truth... \n");
gt_matches_ = Matrix<int>(new int[testDataset_.rows], testDataset_.rows, 1);
StartStopTimer t;
t.start();
compute_ground_truth<Distance>(sampledDataset_, testDataset_, gt_matches_, 0, distance_);
t.stop();
CostData linear_cost;
linear_cost.searchTimeCost = (float)t.value;
linear_cost.buildTimeCost = 0;
linear_cost.memoryCost = 0;
linear_cost.params["algorithm"] = FLANN_INDEX_LINEAR;
costs.push_back(linear_cost);
// Start parameter autotune process
Logger::info("Autotuning parameters...\n");
optimizeKMeans(costs);
optimizeKDTree(costs);
float bestTimeCost = costs[0].searchTimeCost;
for (size_t i = 0; i < costs.size(); ++i) {
float timeCost = costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost;
if (timeCost < bestTimeCost) {
bestTimeCost = timeCost;
}
}
float bestCost = costs[0].searchTimeCost / bestTimeCost;
IndexParams bestParams = costs[0].params;
if (bestTimeCost > 0) {
for (size_t i = 0; i < costs.size(); ++i) {
float crtCost = (costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost) / bestTimeCost +
memory_weight_ * costs[i].memoryCost;
if (crtCost < bestCost) {
bestCost = crtCost;
bestParams = costs[i].params;
}
}
}
delete[] gt_matches_.data;
delete[] testDataset_.data;
delete[] sampledDataset_.data;
return bestParams;
}
/**
* Estimates the search time parameters needed to get the desired precision.
* Precondition: the index is built
* Postcondition: the searchParams will have the optimum params set, also the speedup obtained over linear search.
*/
float estimateSearchParams(SearchParams& searchParams)
{
const int nn = 1;
const size_t SAMPLE_COUNT = 1000;
assert(bestIndex_ != NULL); // must have a valid index
float speedup = 0;
int samples = (int)std::min(dataset_.rows / 10, SAMPLE_COUNT);
if (samples > 0) {
Matrix<ElementType> testDataset = random_sample(dataset_, samples);
Logger::info("Computing ground truth\n");
// we need to compute the ground truth first
Matrix<int> gt_matches(new int[testDataset.rows], testDataset.rows, 1);
StartStopTimer t;
t.start();
compute_ground_truth<Distance>(dataset_, testDataset, gt_matches, 1, distance_);
t.stop();
float linear = (float)t.value;
int checks;
Logger::info("Estimating number of checks\n");
float searchTime;
float cb_index;
if (bestIndex_->getType() == FLANN_INDEX_KMEANS) {
Logger::info("KMeans algorithm, estimating cluster border factor\n");
KMeansIndex<Distance>* kmeans = (KMeansIndex<Distance>*)bestIndex_;
float bestSearchTime = -1;
float best_cb_index = -1;
int best_checks = -1;
for (cb_index = 0; cb_index < 1.1f; cb_index += 0.2f) {
kmeans->set_cb_index(cb_index);
searchTime = test_index_precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
if ((searchTime < bestSearchTime) || (bestSearchTime == -1)) {
bestSearchTime = searchTime;
best_cb_index = cb_index;
best_checks = checks;
}
}
searchTime = bestSearchTime;
cb_index = best_cb_index;
checks = best_checks;
kmeans->set_cb_index(best_cb_index);
Logger::info("Optimum cb_index: %g\n", cb_index);
bestParams_["cb_index"] = cb_index;
}
else {
searchTime = test_index_precision(*bestIndex_, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
}
Logger::info("Required number of checks: %d \n", checks);
searchParams["checks"] = checks;
speedup = linear / searchTime;
delete[] gt_matches.data;
delete[] testDataset.data;
}
return speedup;
}
private:
NNIndex<Distance>* bestIndex_;
IndexParams bestParams_;
SearchParams bestSearchParams_;
Matrix<ElementType> sampledDataset_;
Matrix<ElementType> testDataset_;
Matrix<int> gt_matches_;
float speedup_;
/**
* The dataset used by this index
*/
const Matrix<ElementType> dataset_;
/**
* Index parameters
*/
float target_precision_;
float build_weight_;
float memory_weight_;
float sample_fraction_;
Distance distance_;
};
}
#endif /* OPENCV_FLANN_AUTOTUNED_INDEX_H_ */
include/opencv2/flann/composite_index.h
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_COMPOSITE_INDEX_H_
#define OPENCV_FLANN_COMPOSITE_INDEX_H_
#include "general.h"
#include "nn_index.h"
#include "kdtree_index.h"
#include "kmeans_index.h"
namespace cvflann
{
/**
* Index parameters for the CompositeIndex.
*/
struct CompositeIndexParams : public IndexParams
{
CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
{
(*this)["algorithm"] = FLANN_INDEX_KMEANS;
// number of randomized trees to use (for kdtree)
(*this)["trees"] = trees;
// branching factor
(*this)["branching"] = branching;
// max iterations to perform in one kmeans clustering (kmeans tree)
(*this)["iterations"] = iterations;
// algorithm used for picking the initial cluster centers for kmeans tree
(*this)["centers_init"] = centers_init;
// cluster boundary index. Used when searching the kmeans tree
(*this)["cb_index"] = cb_index;
}
};
/**
* This index builds a kd-tree index and a k-means index and performs nearest
* neighbour search both indexes. This gives a slight boost in search performance
* as some of the neighbours that are missed by one index are found by the other.
*/
template <typename Distance>
class CompositeIndex : public NNIndex<Distance>
{
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
/**
* Index constructor
* @param inputData dataset containing the points to index
* @param params Index parameters
* @param d Distance functor
* @return
*/
CompositeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = CompositeIndexParams(),
Distance d = Distance()) : index_params_(params)
{
kdtree_index_ = new KDTreeIndex<Distance>(inputData, params, d);
kmeans_index_ = new KMeansIndex<Distance>(inputData, params, d);
}
CompositeIndex(const CompositeIndex&);
CompositeIndex& operator=(const CompositeIndex&);
virtual ~CompositeIndex()
{
delete kdtree_index_;
delete kmeans_index_;
}
/**
* @return The index type
*/
flann_algorithm_t getType() const
{
return FLANN_INDEX_COMPOSITE;
}
/**
* @return Size of the index
*/
size_t size() const
{
return kdtree_index_->size();
}
/**
* \returns The dimensionality of the features in this index.
*/
size_t veclen() const
{
return kdtree_index_->veclen();
}
/**
* \returns The amount of memory (in bytes) used by the index.
*/
int usedMemory() const
{
return kmeans_index_->usedMemory() + kdtree_index_->usedMemory();
}
/**
* \brief Builds the index
*/
void buildIndex()
{
Logger::info("Building kmeans tree...\n");
kmeans_index_->buildIndex();
Logger::info("Building kdtree tree...\n");
kdtree_index_->buildIndex();
}
/**
* \brief Saves the index to a stream
* \param stream The stream to save the index to
*/
void saveIndex(FILE* stream)
{
kmeans_index_->saveIndex(stream);
kdtree_index_->saveIndex(stream);
}
/**
* \brief Loads the index from a stream
* \param stream The stream from which the index is loaded
*/
void loadIndex(FILE* stream)
{
kmeans_index_->loadIndex(stream);
kdtree_index_->loadIndex(stream);
}
/**
* \returns The index parameters
*/
IndexParams getParameters() const
{
return index_params_;
}
/**
* \brief Method that searches for nearest-neighbours
*/
void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
{
kmeans_index_->findNeighbors(result, vec, searchParams);
kdtree_index_->findNeighbors(result, vec, searchParams);
}
private:
/** The k-means index */
KMeansIndex<Distance>* kmeans_index_;
/** The kd-tree index */
KDTreeIndex<Distance>* kdtree_index_;
/** The index parameters */
const IndexParams index_params_;
};
}
#endif //OPENCV_FLANN_COMPOSITE_INDEX_H_
include/opencv2/flann/config.h
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_CONFIG_H_
#define OPENCV_FLANN_CONFIG_H_
#ifdef FLANN_VERSION_
#undef FLANN_VERSION_
#endif
#define FLANN_VERSION_ "1.6.10"
#endif /* OPENCV_FLANN_CONFIG_H_ */
include/opencv2/flann/defines.h
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2011 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2011 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_DEFINES_H_
#define OPENCV_FLANN_DEFINES_H_
#include "config.h"
#ifdef FLANN_EXPORT
#undef FLANN_EXPORT
#endif
#ifdef WIN32
/* win32 dll export/import directives */
#ifdef FLANN_EXPORTS
#define FLANN_EXPORT __declspec(dllexport)
#elif defined(FLANN_STATIC)
#define FLANN_EXPORT
#else
#define FLANN_EXPORT __declspec(dllimport)
#endif
#else
/* unix needs nothing */
#define FLANN_EXPORT
#endif
#ifdef FLANN_DEPRECATED
#undef FLANN_DEPRECATED
#endif
#ifdef __GNUC__
#define FLANN_DEPRECATED __attribute__ ((deprecated))
#elif defined(_MSC_VER)
#define FLANN_DEPRECATED __declspec(deprecated)
#else
#pragma message("WARNING: You need to implement FLANN_DEPRECATED for this compiler")
#define FLANN_DEPRECATED
#endif
#undef FLANN_PLATFORM_32_BIT
#undef FLANN_PLATFORM_64_BIT
#if defined __amd64__ || defined __x86_64__ || defined _WIN64 || defined _M_X64
#define FLANN_PLATFORM_64_BIT
#else
#define FLANN_PLATFORM_32_BIT
#endif
#undef FLANN_ARRAY_LEN
#define FLANN_ARRAY_LEN(a) (sizeof(a)/sizeof(a[0]))
namespace cvflann {
/* Nearest neighbour index algorithms */
enum flann_algorithm_t
{
FLANN_INDEX_LINEAR = 0,
FLANN_INDEX_KDTREE = 1,
FLANN_INDEX_KMEANS = 2,
FLANN_INDEX_COMPOSITE = 3,
FLANN_INDEX_KDTREE_SINGLE = 4,
FLANN_INDEX_HIERARCHICAL = 5,
FLANN_INDEX_LSH = 6,
FLANN_INDEX_SAVED = 254,
FLANN_INDEX_AUTOTUNED = 255,
// deprecated constants, should use the FLANN_INDEX_* ones instead
LINEAR = 0,
KDTREE = 1,
KMEANS = 2,
COMPOSITE = 3,
KDTREE_SINGLE = 4,
SAVED = 254,
AUTOTUNED = 255
};
enum flann_centers_init_t
{
FLANN_CENTERS_RANDOM = 0,
FLANN_CENTERS_GONZALES = 1,
FLANN_CENTERS_KMEANSPP = 2,
// deprecated constants, should use the FLANN_CENTERS_* ones instead
CENTERS_RANDOM = 0,
CENTERS_GONZALES = 1,
CENTERS_KMEANSPP = 2
};
enum flann_log_level_t
{
FLANN_LOG_NONE = 0,
FLANN_LOG_FATAL = 1,
FLANN_LOG_ERROR = 2,
FLANN_LOG_WARN = 3,
FLANN_LOG_INFO = 4
};
enum flann_distance_t
{
FLANN_DIST_EUCLIDEAN = 1,
FLANN_DIST_L2 = 1,
FLANN_DIST_MANHATTAN = 2,
FLANN_DIST_L1 = 2,
FLANN_DIST_MINKOWSKI = 3,
FLANN_DIST_MAX = 4,
FLANN_DIST_HIST_INTERSECT = 5,
FLANN_DIST_HELLINGER = 6,
FLANN_DIST_CHI_SQUARE = 7,
FLANN_DIST_CS = 7,
FLANN_DIST_KULLBACK_LEIBLER = 8,
FLANN_DIST_KL = 8,
FLANN_DIST_HAMMING = 9,
// deprecated constants, should use the FLANN_DIST_* ones instead
EUCLIDEAN = 1,
MANHATTAN = 2,
MINKOWSKI = 3,
MAX_DIST = 4,
HIST_INTERSECT = 5,
HELLINGER = 6,
CS = 7,
KL = 8,
KULLBACK_LEIBLER = 8
};
enum flann_datatype_t
{
FLANN_INT8 = 0,
FLANN_INT16 = 1,
FLANN_INT32 = 2,
FLANN_INT64 = 3,
FLANN_UINT8 = 4,
FLANN_UINT16 = 5,
FLANN_UINT32 = 6,
FLANN_UINT64 = 7,
FLANN_FLOAT32 = 8,
FLANN_FLOAT64 = 9
};
enum
{
FLANN_CHECKS_UNLIMITED = -1,
FLANN_CHECKS_AUTOTUNED = -2
};
}
#endif /* OPENCV_FLANN_DEFINES_H_ */
include/opencv2/flann/dist.h
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_DIST_H_
#define OPENCV_FLANN_DIST_H_
#include <cmath>
#include <cstdlib>
#include <string.h>
#ifdef _MSC_VER
typedef unsigned __int32 uint32_t;
typedef unsigned __int64 uint64_t;
#else
#include <stdint.h>
#endif
#include "defines.h"
#if (defined WIN32 || defined _WIN32) && defined(_M_ARM)
# include <Intrin.h>
#endif
#ifdef __ARM_NEON__
# include "arm_neon.h"
#endif
namespace cvflann
{
template<typename T>
inline T abs(T x) { return (x<0) ? -x : x; }
template<>
inline int abs<int>(int x) { return ::abs(x); }
template<>
inline float abs<float>(float x) { return fabsf(x); }
template<>
inline double abs<double>(double x) { return fabs(x); }
template<typename T>
struct Accumulator { typedef T Type; };
template<>
struct Accumulator<unsigned char> { typedef float Type; };
template<>
struct Accumulator<unsigned short> { typedef float Type; };
template<>
struct Accumulator<unsigned int> { typedef float Type; };
template<>
struct Accumulator<char> { typedef float Type; };
template<>
struct Accumulator<short> { typedef float Type; };
template<>
struct Accumulator<int> { typedef float Type; };
#undef True
#undef False
class True
{
};
class False
{
};
/**
* Squared Euclidean distance functor.
*
* This is the simpler, unrolled version. This is preferable for
* very low dimensionality data (eg 3D points)
*/
template<class T>
struct L2_Simple
{
typedef True is_kdtree_distance;
typedef True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
{
ResultType result = ResultType();
ResultType diff;
for(size_t i = 0; i < size; ++i ) {
diff = *a++ - *b++;
result += diff*diff;
}
return result;
}
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
return (a-b)*(a-b);
}
};
/**
* Squared Euclidean distance functor, optimized version
*/
template<class T>
struct L2
{
typedef True is_kdtree_distance;
typedef True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
/**
* Compute the squared Euclidean distance between two vectors.
*
* This is highly optimised, with loop unrolling, as it is one
* of the most expensive inner loops.
*
* The computation of squared root at the end is omitted for
* efficiency.
*/
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
{
ResultType result = ResultType();
ResultType diff0, diff1, diff2, diff3;
Iterator1 last = a + size;
Iterator1 lastgroup = last - 3;
/* Process 4 items with each loop for efficiency. */
while (a < lastgroup) {
diff0 = (ResultType)(a[0] - b[0]);
diff1 = (ResultType)(a[1] - b[1]);
diff2 = (ResultType)(a[2] - b[2]);
diff3 = (ResultType)(a[3] - b[3]);
result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
a += 4;
b += 4;
if ((worst_dist>0)&&(result>worst_dist)) {
return result;
}
}
/* Process last 0-3 pixels. Not needed for standard vector lengths. */
while (a < last) {
diff0 = (ResultType)(*a++ - *b++);
result += diff0 * diff0;
}
return result;
}
/**
* Partial euclidean distance, using just one dimension. This is used by the
* kd-tree when computing partial distances while traversing the tree.
*
* Squared root is omitted for efficiency.
*/
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
return (a-b)*(a-b);
}
};
/*
* Manhattan distance functor, optimized version
*/
template<class T>
struct L1
{
typedef True is_kdtree_distance;
typedef True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
/**
* Compute the Manhattan (L_1) distance between two vectors.
*
* This is highly optimised, with loop unrolling, as it is one
* of the most expensive inner loops.
*/
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
{
ResultType result = ResultType();
ResultType diff0, diff1, diff2, diff3;
Iterator1 last = a + size;
Iterator1 lastgroup = last - 3;
/* Process 4 items with each loop for efficiency. */
while (a < lastgroup) {
diff0 = (ResultType)abs(a[0] - b[0]);
diff1 = (ResultType)abs(a[1] - b[1]);
diff2 = (ResultType)abs(a[2] - b[2]);
diff3 = (ResultType)abs(a[3] - b[3]);
result += diff0 + diff1 + diff2 + diff3;
a += 4;
b += 4;
if ((worst_dist>0)&&(result>worst_dist)) {
return result;
}
}
/* Process last 0-3 pixels. Not needed for standard vector lengths. */
while (a < last) {
diff0 = (ResultType)abs(*a++ - *b++);
result += diff0;
}
return result;
}
/**
* Partial distance, used by the kd-tree.
*/
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
return abs(a-b);
}
};
template<class T>
struct MinkowskiDistance
{
typedef True is_kdtree_distance;
typedef True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
int order;
MinkowskiDistance(int order_) : order(order_) {}
/**
* Compute the Minkowsky (L_p) distance between two vectors.
*
* This is highly optimised, with loop unrolling, as it is one
* of the most expensive inner loops.
*
* The computation of squared root at the end is omitted for
* efficiency.
*/
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
{
ResultType result = ResultType();
ResultType diff0, diff1, diff2, diff3;
Iterator1 last = a + size;
Iterator1 lastgroup = last - 3;
/* Process 4 items with each loop for efficiency. */
while (a < lastgroup) {
diff0 = (ResultType)abs(a[0] - b[0]);
diff1 = (ResultType)abs(a[1] - b[1]);
diff2 = (ResultType)abs(a[2] - b[2]);
diff3 = (ResultType)abs(a[3] - b[3]);
result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order);
a += 4;
b += 4;
if ((worst_dist>0)&&(result>worst_dist)) {
return result;
}
}
/* Process last 0-3 pixels. Not needed for standard vector lengths. */
while (a < last) {
diff0 = (ResultType)abs(*a++ - *b++);
result += pow(diff0,order);
}
return result;
}
/**
* Partial distance, used by the kd-tree.
*/
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
return pow(static_cast<ResultType>(abs(a-b)),order);
}
};
template<class T>
struct MaxDistance
{
typedef False is_kdtree_distance;
typedef True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
/**
* Compute the max distance (L_infinity) between two vectors.
*
* This distance is not a valid kdtree distance, it's not dimensionwise additive.
*/
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
{
ResultType result = ResultType();
ResultType diff0, diff1, diff2, diff3;
Iterator1 last = a + size;
Iterator1 lastgroup = last - 3;
/* Process 4 items with each loop for efficiency. */
while (a < lastgroup) {
diff0 = abs(a[0] - b[0]);
diff1 = abs(a[1] - b[1]);
diff2 = abs(a[2] - b[2]);
diff3 = abs(a[3] - b[3]);
if (diff0>result) {result = diff0; }
if (diff1>result) {result = diff1; }
if (diff2>result) {result = diff2; }
if (diff3>result) {result = diff3; }
a += 4;
b += 4;
if ((worst_dist>0)&&(result>worst_dist)) {
return result;
}
}
/* Process last 0-3 pixels. Not needed for standard vector lengths. */
while (a < last) {
diff0 = abs(*a++ - *b++);
result = (diff0>result) ? diff0 : result;
}
return result;
}
/* This distance functor is not dimension-wise additive, which
* makes it an invalid kd-tree distance, not implementing the accum_dist method */
};
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/**
* Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
* bit count of A exclusive XOR'ed with B
*/
struct HammingLUT
{
typedef False is_kdtree_distance;
typedef False is_vector_space_distance;
typedef unsigned char ElementType;
typedef int ResultType;
/** this will count the bits in a ^ b
*/
ResultType operator()(const unsigned char* a, const unsigned char* b, int size) const
{
static const uchar popCountTable[] =
{
0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8
};
ResultType result = 0;
for (int i = 0; i < size; i++) {
result += popCountTable[a[i] ^ b[i]];
}
return result;
}
};
/**
* Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
* bit count of A exclusive XOR'ed with B
*/
struct HammingLUT2
{
typedef False is_kdtree_distance;
typedef False is_vector_space_distance;
typedef unsigned char ElementType;
typedef int ResultType;
/** this will count the bits in a ^ b
*/
ResultType operator()(const unsigned char* a, const unsigned char* b, size_t size) const
{
static const uchar popCountTable[] =
{
0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8
};
ResultType result = 0;
for (size_t i = 0; i < size; i++) {
result += popCountTable[a[i] ^ b[i]];
}
return result;
}
};
/**
* Hamming distance functor (pop count between two binary vectors, i.e. xor them and count the number of bits set)
* That code was taken from brief.cpp in OpenCV
*/
template<class T>
struct Hamming
{
typedef False is_kdtree_distance;
typedef False is_vector_space_distance;
typedef T ElementType;
typedef int ResultType;
template<typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
{
ResultType result = 0;
#ifdef __ARM_NEON__
{
uint32x4_t bits = vmovq_n_u32(0);
for (size_t i = 0; i < size; i += 16) {
uint8x16_t A_vec = vld1q_u8 (a + i);
uint8x16_t B_vec = vld1q_u8 (b + i);
uint8x16_t AxorB = veorq_u8 (A_vec, B_vec);
uint8x16_t bitsSet = vcntq_u8 (AxorB);
uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);
bits = vaddq_u32(bits, bitSet4);
}
uint64x2_t bitSet2 = vpaddlq_u32 (bits);
result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0);
result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2);
}
#elif __GNUC__
{
//for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll)
typedef unsigned long long pop_t;
const size_t modulo = size % sizeof(pop_t);
const pop_t* a2 = reinterpret_cast<const pop_t*> (a);
const pop_t* b2 = reinterpret_cast<const pop_t*> (b);
const pop_t* a2_end = a2 + (size / sizeof(pop_t));
for (; a2 != a2_end; ++a2, ++b2) result += __builtin_popcountll((*a2) ^ (*b2));
if (modulo) {
//in the case where size is not dividable by sizeof(size_t)
//need to mask off the bits at the end
pop_t a_final = 0, b_final = 0;
memcpy(&a_final, a2, modulo);
memcpy(&b_final, b2, modulo);
result += __builtin_popcountll(a_final ^ b_final);
}
}
#else // NO NEON and NOT GNUC
typedef unsigned long long pop_t;
HammingLUT lut;
result = lut(reinterpret_cast<const unsigned char*> (a),
reinterpret_cast<const unsigned char*> (b), size * sizeof(pop_t));
#endif
return result;
}
};
template<typename T>
struct Hamming2
{
typedef False is_kdtree_distance;
typedef False is_vector_space_distance;
typedef T ElementType;
typedef int ResultType;
/** This is popcount_3() from:
* http://en.wikipedia.org/wiki/Hamming_weight */
unsigned int popcnt32(uint32_t n) const
{
n -= ((n >> 1) & 0x55555555);
n = (n & 0x33333333) + ((n >> 2) & 0x33333333);
return (((n + (n >> 4))& 0xF0F0F0F)* 0x1010101) >> 24;
}
#ifdef FLANN_PLATFORM_64_BIT
unsigned int popcnt64(uint64_t n) const
{
n -= ((n >> 1) & 0x5555555555555555);
n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333);
return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56;
}
#endif
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
{
#ifdef FLANN_PLATFORM_64_BIT
const uint64_t* pa = reinterpret_cast<const uint64_t*>(a);
const uint64_t* pb = reinterpret_cast<const uint64_t*>(b);
ResultType result = 0;
size /= (sizeof(uint64_t)/sizeof(unsigned char));
for(size_t i = 0; i < size; ++i ) {
result += popcnt64(*pa ^ *pb);
++pa;
++pb;
}
#else
const uint32_t* pa = reinterpret_cast<const uint32_t*>(a);
const uint32_t* pb = reinterpret_cast<const uint32_t*>(b);
ResultType result = 0;
size /= (sizeof(uint32_t)/sizeof(unsigned char));
for(size_t i = 0; i < size; ++i ) {
result += popcnt32(*pa ^ *pb);
++pa;
++pb;
}
#endif
return result;
}
};
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template<class T>
struct HistIntersectionDistance
{
typedef True is_kdtree_distance;
typedef True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
/**
* Compute the histogram intersection distance
*/
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
{
ResultType result = ResultType();
ResultType min0, min1, min2, min3;
Iterator1 last = a + size;
Iterator1 lastgroup = last - 3;
/* Process 4 items with each loop for efficiency. */
while (a < lastgroup) {
min0 = (ResultType)(a[0] < b[0] ? a[0] : b[0]);
min1 = (ResultType)(a[1] < b[1] ? a[1] : b[1]);
min2 = (ResultType)(a[2] < b[2] ? a[2] : b[2]);
min3 = (ResultType)(a[3] < b[3] ? a[3] : b[3]);
result += min0 + min1 + min2 + min3;
a += 4;
b += 4;
if ((worst_dist>0)&&(result>worst_dist)) {
return result;
}
}
/* Process last 0-3 pixels. Not needed for standard vector lengths. */
while (a < last) {
min0 = (ResultType)(*a < *b ? *a : *b);
result += min0;
++a;
++b;
}
return result;
}
/**
* Partial distance, used by the kd-tree.
*/
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
return a<b ? a : b;
}
};
template<class T>
struct HellingerDistance
{
typedef True is_kdtree_distance;
typedef True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
/**
* Compute the histogram intersection distance
*/
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
{
ResultType result = ResultType();
ResultType diff0, diff1, diff2, diff3;
Iterator1 last = a + size;
Iterator1 lastgroup = last - 3;
/* Process 4 items with each loop for efficiency. */
while (a < lastgroup) {
diff0 = sqrt(static_cast<ResultType>(a[0])) - sqrt(static_cast<ResultType>(b[0]));
diff1 = sqrt(static_cast<ResultType>(a[1])) - sqrt(static_cast<ResultType>(b[1]));
diff2 = sqrt(static_cast<ResultType>(a[2])) - sqrt(static_cast<ResultType>(b[2]));
diff3 = sqrt(static_cast<ResultType>(a[3])) - sqrt(static_cast<ResultType>(b[3]));
result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
a += 4;
b += 4;
}
while (a < last) {
diff0 = sqrt(static_cast<ResultType>(*a++)) - sqrt(static_cast<ResultType>(*b++));
result += diff0 * diff0;
}
return result;
}
/**
* Partial distance, used by the kd-tree.
*/
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
return sqrt(static_cast<ResultType>(a)) - sqrt(static_cast<ResultType>(b));
}
};
template<class T>
struct ChiSquareDistance
{
typedef True is_kdtree_distance;
typedef True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
/**
* Compute the chi-square distance
*/
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
{
ResultType result = ResultType();
ResultType sum, diff;
Iterator1 last = a + size;
while (a < last) {
sum = (ResultType)(*a + *b);
if (sum>0) {
diff = (ResultType)(*a - *b);
result += diff*diff/sum;
}
++a;
++b;
if ((worst_dist>0)&&(result>worst_dist)) {
return result;
}
}
return result;
}
/**
* Partial distance, used by the kd-tree.
*/
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
ResultType result = ResultType();
ResultType sum, diff;
sum = (ResultType)(a+b);
if (sum>0) {
diff = (ResultType)(a-b);
result = diff*diff/sum;
}
return result;
}
};
template<class T>
struct KL_Divergence
{
typedef True is_kdtree_distance;
typedef True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
/**
* Compute the Kullback–Leibler divergence
*/
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
{
ResultType result = ResultType();
Iterator1 last = a + size;
while (a < last) {
if (* a != 0) {
ResultType ratio = (ResultType)(*a / *b);
if (ratio>0) {
result += *a * log(ratio);
}
}
++a;
++b;
if ((worst_dist>0)&&(result>worst_dist)) {
return result;
}
}
return result;
}
/**
* Partial distance, used by the kd-tree.
*/
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
ResultType result = ResultType();
ResultType ratio = (ResultType)(a / b);
if (ratio>0) {
result = a * log(ratio);
}
return result;
}
};
/*
* This is a "zero iterator". It basically behaves like a zero filled
* array to all algorithms that use arrays as iterators (STL style).
* It's useful when there's a need to compute the distance between feature
* and origin it and allows for better compiler optimisation than using a
* zero-filled array.
*/
template <typename T>
struct ZeroIterator
{
T operator*()
{
return 0;
}
T operator[](int)
{
return 0;
}
const ZeroIterator<T>& operator ++()
{
return *this;
}
ZeroIterator<T> operator ++(int)
{
return *this;
}
ZeroIterator<T>& operator+=(int)
{
return *this;
}
};
}
#endif //OPENCV_FLANN_DIST_H_
include/opencv2/flann/dummy.h
+16
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@@ -0,0 +1,16 @@
#ifndef OPENCV_FLANN_DUMMY_H_
#define OPENCV_FLANN_DUMMY_H_
namespace cvflann
{
#if (defined WIN32 || defined _WIN32 || defined WINCE) && defined CVAPI_EXPORTS
__declspec(dllexport)
#endif
void dummyfunc();
}
#endif /* OPENCV_FLANN_DUMMY_H_ */
include/opencv2/flann/dynamic_bitset.h
+159
View File
@@ -0,0 +1,159 @@
/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
/***********************************************************************
* Author: Vincent Rabaud
*************************************************************************/
#ifndef OPENCV_FLANN_DYNAMIC_BITSET_H_
#define OPENCV_FLANN_DYNAMIC_BITSET_H_
#ifndef FLANN_USE_BOOST
# define FLANN_USE_BOOST 0
#endif
//#define FLANN_USE_BOOST 1
#if FLANN_USE_BOOST
#include <boost/dynamic_bitset.hpp>
typedef boost::dynamic_bitset<> DynamicBitset;
#else
#include <limits.h>
#include "dist.h"
namespace cvflann {
/** Class re-implementing the boost version of it
* This helps not depending on boost, it also does not do the bound checks
* and has a way to reset a block for speed
*/
class DynamicBitset
{
public:
/** @param default constructor
*/
DynamicBitset()
{
}
/** @param only constructor we use in our code
* @param the size of the bitset (in bits)
*/
DynamicBitset(size_t sz)
{
resize(sz);
reset();
}
/** Sets all the bits to 0
*/
void clear()
{
std::fill(bitset_.begin(), bitset_.end(), 0);
}
/** @brief checks if the bitset is empty
* @return true if the bitset is empty
*/
bool empty() const
{
return bitset_.empty();
}
/** @param set all the bits to 0
*/
void reset()
{
std::fill(bitset_.begin(), bitset_.end(), 0);
}
/** @brief set one bit to 0
* @param
*/
void reset(size_t index)
{
bitset_[index / cell_bit_size_] &= ~(size_t(1) << (index % cell_bit_size_));
}
/** @brief sets a specific bit to 0, and more bits too
* This function is useful when resetting a given set of bits so that the
* whole bitset ends up being 0: if that's the case, we don't care about setting
* other bits to 0
* @param
*/
void reset_block(size_t index)
{
bitset_[index / cell_bit_size_] = 0;
}
/** @param resize the bitset so that it contains at least size bits
* @param size
*/
void resize(size_t sz)
{
size_ = sz;
bitset_.resize(sz / cell_bit_size_ + 1);
}
/** @param set a bit to true
* @param index the index of the bit to set to 1
*/
void set(size_t index)
{
bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_);
}
/** @param gives the number of contained bits
*/
size_t size() const
{
return size_;
}
/** @param check if a bit is set
* @param index the index of the bit to check
* @return true if the bit is set
*/
bool test(size_t index) const
{
return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0;
}
private:
std::vector<size_t> bitset_;
size_t size_;
static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t);
};
} // namespace cvflann
#endif
#endif // OPENCV_FLANN_DYNAMIC_BITSET_H_
include/opencv2/flann/flann.hpp
+427
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@@ -0,0 +1,427 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef _OPENCV_FLANN_HPP_
#define _OPENCV_FLANN_HPP_
#ifdef __cplusplus
#include "opencv2/core/types_c.h"
#include "opencv2/core/core.hpp"
#include "opencv2/flann/flann_base.hpp"
#include "opencv2/flann/miniflann.hpp"
namespace cvflann
{
CV_EXPORTS flann_distance_t flann_distance_type();
FLANN_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order);
}
namespace cv
{
namespace flann
{
template <typename T> struct CvType {};
template <> struct CvType<unsigned char> { static int type() { return CV_8U; } };
template <> struct CvType<char> { static int type() { return CV_8S; } };
template <> struct CvType<unsigned short> { static int type() { return CV_16U; } };
template <> struct CvType<short> { static int type() { return CV_16S; } };
template <> struct CvType<int> { static int type() { return CV_32S; } };
template <> struct CvType<float> { static int type() { return CV_32F; } };
template <> struct CvType<double> { static int type() { return CV_64F; } };
// bring the flann parameters into this namespace
using ::cvflann::get_param;
using ::cvflann::print_params;
// bring the flann distances into this namespace
using ::cvflann::L2_Simple;
using ::cvflann::L2;
using ::cvflann::L1;
using ::cvflann::MinkowskiDistance;
using ::cvflann::MaxDistance;
using ::cvflann::HammingLUT;
using ::cvflann::Hamming;
using ::cvflann::Hamming2;
using ::cvflann::HistIntersectionDistance;
using ::cvflann::HellingerDistance;
using ::cvflann::ChiSquareDistance;
using ::cvflann::KL_Divergence;
template <typename Distance>
class GenericIndex
{
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
GenericIndex(const Mat& features, const ::cvflann::IndexParams& params, Distance distance = Distance());
~GenericIndex();
void knnSearch(const vector<ElementType>& query, vector<int>& indices,
vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& params);
void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params);
int radiusSearch(const vector<ElementType>& query, vector<int>& indices,
vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& params);
int radiusSearch(const Mat& query, Mat& indices, Mat& dists,
DistanceType radius, const ::cvflann::SearchParams& params);
void save(std::string filename) { nnIndex->save(filename); }
int veclen() const { return nnIndex->veclen(); }
int size() const { return nnIndex->size(); }
::cvflann::IndexParams getParameters() { return nnIndex->getParameters(); }
FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); }
private:
::cvflann::Index<Distance>* nnIndex;
};
#define FLANN_DISTANCE_CHECK \
if ( ::cvflann::flann_distance_type() != cvflann::FLANN_DIST_L2) { \
printf("[WARNING] You are using cv::flann::Index (or cv::flann::GenericIndex) and have also changed "\
"the distance using cvflann::set_distance_type. This is no longer working as expected "\
"(cv::flann::Index always uses L2). You should create the index templated on the distance, "\
"for example for L1 distance use: GenericIndex< L1<float> > \n"); \
}
template <typename Distance>
GenericIndex<Distance>::GenericIndex(const Mat& dataset, const ::cvflann::IndexParams& params, Distance distance)
{
CV_Assert(dataset.type() == CvType<ElementType>::type());
CV_Assert(dataset.isContinuous());
::cvflann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, dataset.cols);
nnIndex = new ::cvflann::Index<Distance>(m_dataset, params, distance);
FLANN_DISTANCE_CHECK
nnIndex->buildIndex();
}
template <typename Distance>
GenericIndex<Distance>::~GenericIndex()
{
delete nnIndex;
}
template <typename Distance>
void GenericIndex<Distance>::knnSearch(const vector<ElementType>& query, vector<int>& indices, vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
FLANN_DISTANCE_CHECK
nnIndex->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
}
template <typename Distance>
void GenericIndex<Distance>::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
CV_Assert(queries.type() == CvType<ElementType>::type());
CV_Assert(queries.isContinuous());
::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);
CV_Assert(indices.type() == CV_32S);
CV_Assert(indices.isContinuous());
::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
CV_Assert(dists.type() == CvType<DistanceType>::type());
CV_Assert(dists.isContinuous());
::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
FLANN_DISTANCE_CHECK
nnIndex->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
}
template <typename Distance>
int GenericIndex<Distance>::radiusSearch(const vector<ElementType>& query, vector<int>& indices, vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
FLANN_DISTANCE_CHECK
return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
template <typename Distance>
int GenericIndex<Distance>::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
CV_Assert(query.type() == CvType<ElementType>::type());
CV_Assert(query.isContinuous());
::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);
CV_Assert(indices.type() == CV_32S);
CV_Assert(indices.isContinuous());
::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
CV_Assert(dists.type() == CvType<DistanceType>::type());
CV_Assert(dists.isContinuous());
::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
FLANN_DISTANCE_CHECK
return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
/**
* @deprecated Use GenericIndex class instead
*/
template <typename T>
class
#ifndef _MSC_VER
FLANN_DEPRECATED
#endif
Index_ {
public:
typedef typename L2<T>::ElementType ElementType;
typedef typename L2<T>::ResultType DistanceType;
Index_(const Mat& features, const ::cvflann::IndexParams& params);
~Index_();
void knnSearch(const vector<ElementType>& query, vector<int>& indices, vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& params);
void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params);
int radiusSearch(const vector<ElementType>& query, vector<int>& indices, vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& params);
int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& params);
void save(std::string filename)
{
if (nnIndex_L1) nnIndex_L1->save(filename);
if (nnIndex_L2) nnIndex_L2->save(filename);
}
int veclen() const
{
if (nnIndex_L1) return nnIndex_L1->veclen();
if (nnIndex_L2) return nnIndex_L2->veclen();
}
int size() const
{
if (nnIndex_L1) return nnIndex_L1->size();
if (nnIndex_L2) return nnIndex_L2->size();
}
::cvflann::IndexParams getParameters()
{
if (nnIndex_L1) return nnIndex_L1->getParameters();
if (nnIndex_L2) return nnIndex_L2->getParameters();
}
FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters()
{
if (nnIndex_L1) return nnIndex_L1->getIndexParameters();
if (nnIndex_L2) return nnIndex_L2->getIndexParameters();
}
private:
// providing backwards compatibility for L2 and L1 distances (most common)
::cvflann::Index< L2<ElementType> >* nnIndex_L2;
::cvflann::Index< L1<ElementType> >* nnIndex_L1;
};
#ifdef _MSC_VER
template <typename T>
class FLANN_DEPRECATED Index_;
#endif
template <typename T>
Index_<T>::Index_(const Mat& dataset, const ::cvflann::IndexParams& params)
{
printf("[WARNING] The cv::flann::Index_<T> class is deperecated, use cv::flann::GenericIndex<Distance> instead\n");
CV_Assert(dataset.type() == CvType<ElementType>::type());
CV_Assert(dataset.isContinuous());
::cvflann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, dataset.cols);
if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
nnIndex_L1 = NULL;
nnIndex_L2 = new ::cvflann::Index< L2<ElementType> >(m_dataset, params);
}
else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
nnIndex_L1 = new ::cvflann::Index< L1<ElementType> >(m_dataset, params);
nnIndex_L2 = NULL;
}
else {
printf("[ERROR] cv::flann::Index_<T> only provides backwards compatibility for the L1 and L2 distances. "
"For other distance types you must use cv::flann::GenericIndex<Distance>\n");
CV_Assert(0);
}
if (nnIndex_L1) nnIndex_L1->buildIndex();
if (nnIndex_L2) nnIndex_L2->buildIndex();
}
template <typename T>
Index_<T>::~Index_()
{
if (nnIndex_L1) delete nnIndex_L1;
if (nnIndex_L2) delete nnIndex_L2;
}
template <typename T>
void Index_<T>::knnSearch(const vector<ElementType>& query, vector<int>& indices, vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
}
template <typename T>
void Index_<T>::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
CV_Assert(queries.type() == CvType<ElementType>::type());
CV_Assert(queries.isContinuous());
::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);
CV_Assert(indices.type() == CV_32S);
CV_Assert(indices.isContinuous());
::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
CV_Assert(dists.type() == CvType<DistanceType>::type());
CV_Assert(dists.isContinuous());
::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
}
template <typename T>
int Index_<T>::radiusSearch(const vector<ElementType>& query, vector<int>& indices, vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
template <typename T>
int Index_<T>::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
CV_Assert(query.type() == CvType<ElementType>::type());
CV_Assert(query.isContinuous());
::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);
CV_Assert(indices.type() == CV_32S);
CV_Assert(indices.isContinuous());
::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
CV_Assert(dists.type() == CvType<DistanceType>::type());
CV_Assert(dists.isContinuous());
::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
template <typename Distance>
int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params,
Distance d = Distance())
{
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
CV_Assert(features.type() == CvType<ElementType>::type());
CV_Assert(features.isContinuous());
::cvflann::Matrix<ElementType> m_features((ElementType*)features.ptr<ElementType>(0), features.rows, features.cols);
CV_Assert(centers.type() == CvType<DistanceType>::type());
CV_Assert(centers.isContinuous());
::cvflann::Matrix<DistanceType> m_centers((DistanceType*)centers.ptr<DistanceType>(0), centers.rows, centers.cols);
return ::cvflann::hierarchicalClustering<Distance>(m_features, m_centers, params, d);
}
template <typename ELEM_TYPE, typename DIST_TYPE>
FLANN_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params)
{
printf("[WARNING] cv::flann::hierarchicalClustering<ELEM_TYPE,DIST_TYPE> is deprecated, use "
"cv::flann::hierarchicalClustering<Distance> instead\n");
if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
return hierarchicalClustering< L2<ELEM_TYPE> >(features, centers, params);
}
else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
return hierarchicalClustering< L1<ELEM_TYPE> >(features, centers, params);
}
else {
printf("[ERROR] cv::flann::hierarchicalClustering<ELEM_TYPE,DIST_TYPE> only provides backwards "
"compatibility for the L1 and L2 distances. "
"For other distance types you must use cv::flann::hierarchicalClustering<Distance>\n");
CV_Assert(0);
}
}
} } // namespace cv::flann
#endif // __cplusplus
#endif
include/opencv2/flann/flann_base.hpp
+291
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_BASE_HPP_
#define OPENCV_FLANN_BASE_HPP_
#include <vector>
#include <string>
#include <cassert>
#include <cstdio>
#include "general.h"
#include "matrix.h"
#include "params.h"
#include "saving.h"
#include "all_indices.h"
namespace cvflann
{
/**
* Sets the log level used for all flann functions
* @param level Verbosity level
*/
inline void log_verbosity(int level)
{
if (level >= 0) {
Logger::setLevel(level);
}
}
/**
* (Deprecated) Index parameters for creating a saved index.
*/
struct SavedIndexParams : public IndexParams
{
SavedIndexParams(std::string filename)
{
(* this)["algorithm"] = FLANN_INDEX_SAVED;
(*this)["filename"] = filename;
}
};
template<typename Distance>
NNIndex<Distance>* load_saved_index(const Matrix<typename Distance::ElementType>& dataset, const std::string& filename, Distance distance)
{
typedef typename Distance::ElementType ElementType;
FILE* fin = fopen(filename.c_str(), "rb");
if (fin == NULL) {
return NULL;
}
IndexHeader header = load_header(fin);
if (header.data_type != Datatype<ElementType>::type()) {
throw FLANNException("Datatype of saved index is different than of the one to be created.");
}
if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) {
throw FLANNException("The index saved belongs to a different dataset");
}
IndexParams params;
params["algorithm"] = header.index_type;
NNIndex<Distance>* nnIndex = create_index_by_type<Distance>(dataset, params, distance);
nnIndex->loadIndex(fin);
fclose(fin);
return nnIndex;
}
template<typename Distance>
class Index : public NNIndex<Distance>
{
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::ResultType DistanceType;
Index(const Matrix<ElementType>& features, const IndexParams& params, Distance distance = Distance() )
: index_params_(params)
{
flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
loaded_ = false;
if (index_type == FLANN_INDEX_SAVED) {
nnIndex_ = load_saved_index<Distance>(features, get_param<std::string>(params,"filename"), distance);
loaded_ = true;
}
else {
nnIndex_ = create_index_by_type<Distance>(features, params, distance);
}
}
~Index()
{
delete nnIndex_;
}
/**
* Builds the index.
*/
void buildIndex()
{
if (!loaded_) {
nnIndex_->buildIndex();
}
}
void save(std::string filename)
{
FILE* fout = fopen(filename.c_str(), "wb");
if (fout == NULL) {
throw FLANNException("Cannot open file");
}
save_header(fout, *nnIndex_);
saveIndex(fout);
fclose(fout);
}
/**
* \brief Saves the index to a stream
* \param stream The stream to save the index to
*/
virtual void saveIndex(FILE* stream)
{
nnIndex_->saveIndex(stream);
}
/**
* \brief Loads the index from a stream
* \param stream The stream from which the index is loaded
*/
virtual void loadIndex(FILE* stream)
{
nnIndex_->loadIndex(stream);
}
/**
* \returns number of features in this index.
*/
size_t veclen() const
{
return nnIndex_->veclen();
}
/**
* \returns The dimensionality of the features in this index.
*/
size_t size() const
{
return nnIndex_->size();
}
/**
* \returns The index type (kdtree, kmeans,...)
*/
flann_algorithm_t getType() const
{
return nnIndex_->getType();
}
/**
* \returns The amount of memory (in bytes) used by the index.
*/
virtual int usedMemory() const
{
return nnIndex_->usedMemory();
}
/**
* \returns The index parameters
*/
IndexParams getParameters() const
{
return nnIndex_->getParameters();
}
/**
* \brief Perform k-nearest neighbor search
* \param[in] queries The query points for which to find the nearest neighbors
* \param[out] indices The indices of the nearest neighbors found
* \param[out] dists Distances to the nearest neighbors found
* \param[in] knn Number of nearest neighbors to return
* \param[in] params Search parameters
*/
void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
{
nnIndex_->knnSearch(queries, indices, dists, knn, params);
}
/**
* \brief Perform radius search
* \param[in] query The query point
* \param[out] indices The indinces of the neighbors found within the given radius
* \param[out] dists The distances to the nearest neighbors found
* \param[in] radius The radius used for search
* \param[in] params Search parameters
* \returns Number of neighbors found
*/
int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params)
{
return nnIndex_->radiusSearch(query, indices, dists, radius, params);
}
/**
* \brief Method that searches for nearest-neighbours
*/
void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
{
nnIndex_->findNeighbors(result, vec, searchParams);
}
/**
* \brief Returns actual index
*/
FLANN_DEPRECATED NNIndex<Distance>* getIndex()
{
return nnIndex_;
}
/**
* \brief Returns index parameters.
* \deprecated use getParameters() instead.
*/
FLANN_DEPRECATED const IndexParams* getIndexParameters()
{
return &index_params_;
}
private:
/** Pointer to actual index class */
NNIndex<Distance>* nnIndex_;
/** Indices if the index was loaded from a file */
bool loaded_;
/** Parameters passed to the index */
IndexParams index_params_;
};
/**
* Performs a hierarchical clustering of the points passed as argument and then takes a cut in the
* the clustering tree to return a flat clustering.
* @param[in] points Points to be clustered
* @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the
* number of clusters requested.
* @param params Clustering parameters (The same as for cvflann::KMeansIndex)
* @param d Distance to be used for clustering (eg: cvflann::L2)
* @return number of clusters computed (can be different than clusters.rows and is the highest number
* of the form (branching-1)*K+1 smaller than clusters.rows).
*/
template <typename Distance>
int hierarchicalClustering(const Matrix<typename Distance::ElementType>& points, Matrix<typename Distance::ResultType>& centers,
const KMeansIndexParams& params, Distance d = Distance())
{
KMeansIndex<Distance> kmeans(points, params, d);
kmeans.buildIndex();
int clusterNum = kmeans.getClusterCenters(centers);
return clusterNum;
}
}
#endif /* OPENCV_FLANN_BASE_HPP_ */
include/opencv2/flann/general.h
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_GENERAL_H_
#define OPENCV_FLANN_GENERAL_H_
#include "defines.h"
#include <stdexcept>
#include <cassert>
namespace cvflann
{
class FLANNException : public std::runtime_error
{
public:
FLANNException(const char* message) : std::runtime_error(message) { }
FLANNException(const std::string& message) : std::runtime_error(message) { }
};
}
#endif /* OPENCV_FLANN_GENERAL_H_ */
include/opencv2/flann/ground_truth.h
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#ifndef OPENCV_FLANN_GROUND_TRUTH_H_
#define OPENCV_FLANN_GROUND_TRUTH_H_
#include "dist.h"
#include "matrix.h"
namespace cvflann
{
template <typename Distance>
void find_nearest(const Matrix<typename Distance::ElementType>& dataset, typename Distance::ElementType* query, int* matches, int nn,
int skip = 0, Distance distance = Distance())
{
typedef typename Distance::ResultType DistanceType;
int n = nn + skip;
std::vector<int> match(n);
std::vector<DistanceType> dists(n);
dists[0] = distance(dataset[0], query, dataset.cols);
match[0] = 0;
int dcnt = 1;
for (size_t i=1; i<dataset.rows; ++i) {
DistanceType tmp = distance(dataset[i], query, dataset.cols);
if (dcnt<n) {
match[dcnt] = (int)i;
dists[dcnt++] = tmp;
}
else if (tmp < dists[dcnt-1]) {
dists[dcnt-1] = tmp;
match[dcnt-1] = (int)i;
}
int j = dcnt-1;
// bubble up
while (j>=1 && dists[j]<dists[j-1]) {
std::swap(dists[j],dists[j-1]);
std::swap(match[j],match[j-1]);
j--;
}
}
for (int i=0; i<nn; ++i) {
matches[i] = match[i+skip];
}
}
template <typename Distance>
void compute_ground_truth(const Matrix<typename Distance::ElementType>& dataset, const Matrix<typename Distance::ElementType>& testset, Matrix<int>& matches,
int skip=0, Distance d = Distance())
{
for (size_t i=0; i<testset.rows; ++i) {
find_nearest<Distance>(dataset, testset[i], matches[i], (int)matches.cols, skip, d);
}
}
}
#endif //OPENCV_FLANN_GROUND_TRUTH_H_

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