mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-15 00:03:06 +00:00
Add MocapNET
This commit is contained in:
Justin John
@@ -0,0 +1,316 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
import numpy as np
|
||||
import gc
|
||||
import os
|
||||
import sys
|
||||
import csv
|
||||
import time
|
||||
import array
|
||||
|
||||
import matplotlib
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.animation as animation
|
||||
from mpl_toolkits.mplot3d import axes3d, Axes3D
|
||||
|
||||
class bcolors:
|
||||
HEADER = '\033[95m'
|
||||
OKBLUE = '\033[94m'
|
||||
OKGREEN = '\033[92m'
|
||||
WARNING = '\033[93m'
|
||||
FAIL = '\033[91m'
|
||||
ENDC = '\033[0m'
|
||||
BOLD = '\033[1m'
|
||||
UNDERLINE = '\033[4m'
|
||||
|
||||
def pointListReturnXYZListForScatterPlot(A):
|
||||
numberOfPoints=A.shape[0]
|
||||
xs=list()
|
||||
ys=list()
|
||||
zs=list()
|
||||
for i in range(0,int(numberOfPoints/3)):
|
||||
xs.append(float(-1000*10*A[i*3+0]))
|
||||
ys.append(float(1000*10*A[i*3+1]))
|
||||
zs.append(float(1000*10*A[i*3+2]))
|
||||
return xs,ys,zs
|
||||
|
||||
def checkIfFileExists(filename):
|
||||
return os.path.isfile(filename)
|
||||
|
||||
def convert_bytes(num):
|
||||
"""
|
||||
this function will convert bytes to MB.... GB... etc
|
||||
"""
|
||||
step_unit = 1000.0 #1024 bad the size
|
||||
|
||||
for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
|
||||
if num < step_unit:
|
||||
return "%3.1f %s" % (num, x)
|
||||
num /= step_unit
|
||||
|
||||
def getNumberOfLines(filename):
|
||||
print("Counting number of lines in file ",filename)
|
||||
with open(filename) as f:
|
||||
return sum(1 for line in f)
|
||||
|
||||
#---------------------------------------------------------------------------------------------------------------------------------------------
|
||||
#---------------------------------------------------------------------------------------------------------------------------------------------
|
||||
#---------------------------------------------------------------------------------------------------------------------------------------------
|
||||
def readCSVFile(filename,memPercentage,csvDelimiter,useHalfFloats,groupOutput):
|
||||
print("CSV file :",filename,"..\n")
|
||||
|
||||
if (not checkIfFileExists(filename)):
|
||||
print( bcolors.WARNING + "Input file "+filename+" does not exist, cannot read ground truth.." + bcolors.ENDC)
|
||||
print("Current Directory was "+os.getcwd())
|
||||
sys.exit(0)
|
||||
start = time.time()
|
||||
|
||||
dtypeSelected=np.dtype(np.float32)
|
||||
dtypeSelectedByteSize=int(dtypeSelected.itemsize)
|
||||
if (useHalfFloats):
|
||||
dtypeSelected=np.dtype(np.float16)
|
||||
dtypeSelectedByteSize=int(dtypeSelected.itemsize)
|
||||
|
||||
progress=0.0
|
||||
sampleNumber=0
|
||||
receivedHeader=0
|
||||
inputNumberOfColumns=0
|
||||
|
||||
inputLabels=list()
|
||||
|
||||
#-------------------------------------------------------------------------------------------------
|
||||
numberOfSamplesInput=getNumberOfLines(filename)-2
|
||||
print(" Input file has ",numberOfSamplesInput," training samples\n")
|
||||
#-------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
numberOfSamples = numberOfSamplesInput
|
||||
numberOfSamplesLimit=int(numberOfSamples*memPercentage)
|
||||
#-------------------------------------------------------------------------------------------------
|
||||
if (memPercentage==0.0):
|
||||
print("readGroundTruthFile was asked to occupy 0 memory so this probably means we just want one record")
|
||||
numberOfSamplesLimit=2
|
||||
if (memPercentage>1.0):
|
||||
print("Memory Limit will be interpreted as a raw value..")
|
||||
numberOfSamplesLimit=int(memPercentage)
|
||||
#-------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
thisInput = array.array('f')
|
||||
#---------------------------------
|
||||
|
||||
fi = open(filename, "r")
|
||||
readerIn = csv.reader( fi , delimiter=csvDelimiter, skipinitialspace=True)
|
||||
for rowIn in readerIn:
|
||||
#------------------------------------------------------
|
||||
if (not receivedHeader): #use header to get labels
|
||||
#------------------------------------------------------
|
||||
inputNumberOfColumns=len(rowIn)
|
||||
|
||||
#Make sure CSV files that end with delimiter are correctly handled..
|
||||
if (inputNumberOfColumns>0):
|
||||
if (rowIn[inputNumberOfColumns-1]==''):
|
||||
inputNumberOfColumns=inputNumberOfColumns-1
|
||||
#-----------------------------------------------------
|
||||
inputLabels = list(rowIn[i] for i in range(0,inputNumberOfColumns) )
|
||||
print("Number of Input elements : ",len(inputLabels))
|
||||
#------------------------------------------------------
|
||||
|
||||
if (memPercentage==0):
|
||||
print("Will only return labels\n")
|
||||
return {'labels':inputLabels};
|
||||
|
||||
|
||||
#i=0
|
||||
#print("class Input(Enum):")
|
||||
#for label in inputLabels:
|
||||
# print(" ",label," = ",i," #",int(i/3))
|
||||
# print(" ",label,"=",int(i/3))
|
||||
# i=i+1
|
||||
|
||||
#---------------------------------
|
||||
# Allocate Lists
|
||||
#---------------------------------
|
||||
for i in range(inputNumberOfColumns):
|
||||
thisInput.append(0.0)
|
||||
#---------------------------------
|
||||
|
||||
|
||||
#---------------------------------
|
||||
# Allocate Numpy Arrays
|
||||
#---------------------------------
|
||||
inputSize=0
|
||||
startCompressed=0
|
||||
|
||||
inputSize=inputSize+inputNumberOfColumns
|
||||
startCompressed=inputNumberOfColumns
|
||||
|
||||
npInputBytesize=0+numberOfSamplesLimit * inputSize * dtypeSelectedByteSize
|
||||
print(" Input file on disk has a shape of [",numberOfSamples,",",inputSize,"]")
|
||||
print(" Input we will read has a shape of [",numberOfSamplesLimit,",",inputSize,"]")
|
||||
print(" Input will occupy ",convert_bytes(npInputBytesize)," of RAM\n")
|
||||
npInput = np.full([numberOfSamplesLimit,inputSize],fill_value=0,dtype=dtypeSelected,order='C')
|
||||
#----------------------------------------------------------------------------------------------------------
|
||||
receivedHeader=1
|
||||
#sys.exit(0)
|
||||
else:
|
||||
#-------------------------------------------
|
||||
# First convert our string INPUT to floats
|
||||
#-------------------------------------------
|
||||
for i in range(inputNumberOfColumns):
|
||||
thisInput[i]=float(rowIn[i])
|
||||
#-------------------------------------------
|
||||
for num in range(0,inputNumberOfColumns):
|
||||
npInput[sampleNumber,num]=float(thisInput[num]);
|
||||
#-------------------------------------------
|
||||
sampleNumber=sampleNumber+1
|
||||
|
||||
if (numberOfSamples>0):
|
||||
progress=sampleNumber/numberOfSamplesLimit
|
||||
|
||||
if (sampleNumber%1000==0) :
|
||||
progressString = "%0.2f"%float(100*progress)
|
||||
print("\rReading from disk (",sampleNumber,") - ",progressString," % \r", end="", flush=True)
|
||||
|
||||
if (numberOfSamplesLimit<=sampleNumber):
|
||||
print("\rStopping reading file to obey memory limit given by parameter --mem ",memPercentage,"\n")
|
||||
break
|
||||
#-------------------------------------------
|
||||
fi.close()
|
||||
del readerIn
|
||||
gc.collect()
|
||||
|
||||
|
||||
print("\n read, Samples: ",sampleNumber,", was expecting ",numberOfSamples," samples\n")
|
||||
print(npInput.shape)
|
||||
|
||||
totalNumberOfBytes=npInput.nbytes;
|
||||
totalNumberOfGigaBytes=totalNumberOfBytes/1073741824;
|
||||
print("Size Occupied by data = ",totalNumberOfGigaBytes," GB \n")
|
||||
|
||||
end = time.time()
|
||||
print("Time elapsed : ",(end-start)/60," mins")
|
||||
#---------------------------------------------------------------------
|
||||
|
||||
if (groupOutput==0):
|
||||
#New better dictionary
|
||||
output = dict()
|
||||
for i in range(0,len(inputLabels)):
|
||||
lowerCaseName = inputLabels[i].lower()
|
||||
#print("Joint ",lowerCaseName)
|
||||
output[lowerCaseName]=list()
|
||||
for frameID in range(0,len(npInput)):
|
||||
output[lowerCaseName].append(npInput[frameID][i])
|
||||
return output
|
||||
else:
|
||||
#This is the old dictionary way (better for tensorflow training)
|
||||
return {'label':inputLabels, 'body':npInput };
|
||||
|
||||
|
||||
|
||||
#---------------------------------------------------------------------------------------------------------------------------------------------
|
||||
#---------------------------------------------------------------------------------------------------------------------------------------------
|
||||
#---------------------------------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
|
||||
drawPlot=1
|
||||
|
||||
ground=readCSVFile("ground3D.csv",1.0,',',0,1)
|
||||
|
||||
|
||||
if (drawPlot):
|
||||
print("Using matplotlib:",matplotlib.__version__)
|
||||
# === Plot and animate ===
|
||||
fig = plt.figure()
|
||||
fig.set_size_inches(19.2, 10.8, forward=True)
|
||||
|
||||
ax = fig.add_subplot(1, 2, 1, projection='3d')
|
||||
ax2 = fig.add_subplot(1, 2, 2)
|
||||
#ax3 = fig.add_subplot(2, 2, 3)
|
||||
#ax4 = fig.add_subplot(2, 2, 4)
|
||||
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
|
||||
|
||||
ax.set_xlabel('X Axis')
|
||||
ax.set_ylabel('Y Axis')
|
||||
ax.set_zlabel('Z Axis')
|
||||
ax.view_init(90, 90)
|
||||
|
||||
|
||||
print("Ground truth file has %u elements ",len(ground['body']))
|
||||
|
||||
|
||||
for i in range(0,len(ground['body'])):
|
||||
if (drawPlot):
|
||||
plt.cla()
|
||||
ax.cla()
|
||||
ax2.cla()
|
||||
#ax3.cla()
|
||||
#ax4.cla()
|
||||
|
||||
|
||||
categories = np.array([0,
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
4,
|
||||
5,
|
||||
6,
|
||||
7,
|
||||
8,
|
||||
9,
|
||||
10,
|
||||
11,
|
||||
12,
|
||||
13,
|
||||
14,
|
||||
15,
|
||||
16,
|
||||
17,
|
||||
18,
|
||||
19,
|
||||
20
|
||||
])
|
||||
|
||||
color0=(0.0, 1.0, 1.0, 1.0) #Palm
|
||||
color1=(0.6, 0.0, 0.0, 1.0) #Thumb
|
||||
color2=(0.8, 0.0, 0.0, 1.0) #Finger 1.2
|
||||
color3=(1.0, 0.0, 0.0, 1.0) #Finger 1.3
|
||||
color4=(0.0, 1.0, 1.0, 1.0) #BaseOfFourFingers
|
||||
color5=(0.0, 1.0, 0.0, 1.0) #Finger 2.3
|
||||
color6=(0.0, 0.8, 0.0, 1.0) #Finger 2.2
|
||||
color7=(0.0, 0.6, 0.0, 1.0) #Finger 2.1
|
||||
color8=(0.0, 0.4, 0.0, 1.0) #Metacarpal 1
|
||||
color9=(0.0, 0.0, 1.0, 1.0) #Finger 3.3
|
||||
color10=(0.0, 0.0, 0.8, 1.0) #Finger 3.2
|
||||
color11=(0.0, 0.0, 0.6, 1.0) #Finger 3.1
|
||||
color12=(0.0, 0.0, 0.4, 1.0) #Metacarpal 2
|
||||
color13=(0.0, 1.0, 1.0, 1.0) #Finger 4.3
|
||||
color14=(0.0, 0.8, 0.8, 1.0) #Finger 4.2
|
||||
color15=(0.0, 0.6, 0.6, 1.0) #Finger 4.1
|
||||
color16=(0.0, 0.4, 0.4, 1.0) #Metacarpal 3
|
||||
color17=(1.0, 1.0, 0.0, 1.0) #Finger 5.3
|
||||
color18=(0.8, 0.8, 0.0, 1.0) #Finger 5.2
|
||||
color19=(0.6, 0.6, 0.0, 1.0) #Finger 5.1
|
||||
color20=(0.4, 0.4, 0.0, 1.0) #Metacarpal 4
|
||||
|
||||
colormap = np.array([color0,color1,color2,color3,color4,color5,color6,color7,color8,color9,color10,color11,color12,color13,color14,color15,color16,color17,color18,color19,color20])
|
||||
|
||||
xs, ys, zs = pointListReturnXYZListForScatterPlot(ground['body'][i])
|
||||
|
||||
ax.scatter(xs, ys, zs, c=colormap[categories])
|
||||
img = plt.imread("images/im%u.png" % i)
|
||||
ax2.imshow(img)
|
||||
|
||||
#-------------------------
|
||||
ax.set_xlim(auto=False,left=-600,right=300)
|
||||
ax.set_ylim(auto=False,bottom=-1600,top=200)
|
||||
ax.set_zlim(auto=False,bottom=2000,top=6000)
|
||||
ax.set_xlabel('X Axis')
|
||||
ax.set_ylabel('Y Axis')
|
||||
ax.set_zlabel('Z Axis')
|
||||
#-------------------------
|
||||
plt.show(block=False)
|
||||
#plt.savefig('p%05u.png'%i)
|
||||
#fig.canvas.draw()
|
||||
plt.pause(0.001)
|
||||
Reference in New Issue
Block a user