storyteller-ml/junk/models-from-rust-monorepo/Wav2Lip/vocodes_process_inference.py

from os import listdir, path
import numpy as np
import scipy, cv2, os, sys, argparse, audio
import json, subprocess, random, string
from tqdm import tqdm
from glob import glob
import torch, face_detection
from models import Wav2Lip
import tempfile
import shutil
import datetime
import pickle
import magic
from PIL import Image

if 'PRINT_ENV' in os.environ and os.environ['PRINT_ENV']:
  for k, v in os.environ.items():
    print('{}={}'.format(k, v))

print('========================================')
print('Python interpreter', sys.executable)
print('PyTorch version', torch.__version__)
print('CUDA Available?', torch.cuda.is_available())
print('CUDA Device count', torch.cuda.device_count())
print('========================================', flush=True)

parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models')

parser.add_argument('--checkpoint_path', type=str,
                    help='Name of saved checkpoint to load weights from', required=True)

parser.add_argument('--image_or_video_filename', type=str,
                    help='Filepath of video/image that contains faces to use', required=True)

#parser.add_argument('--image_or_video_type', type=str,
#                    help='either "image" or "video', required=True)

# This is a little more deliberate than "static" and causes us to pick an FPS, etc.
# This is useful when the filename doesn't have an extension.
parser.add_argument('--is_image', default=False, action='store_true',
                    help='Denote that the input is a single-frame image, not a video')

parser.add_argument('--cached_faces_filename', type=str,
                    help='Filename for the cached faces file to use', required=True)

#parser.add_argument('--face', type=str,
#                    help='Filepath of video/image that contains faces to use', required=True)
parser.add_argument('--audio_filename', type=str,
                    help='Filepath of video/audio file to use as raw audio source', required=True)

parser.add_argument('--end_bump_file', type=str,
                    help='Video file to concatenate at the end')

parser.add_argument('--output_video_filename', type=str,
                    help='Output filename for the final video', required=True)

parser.add_argument('--output_metadata_filename', type=str,
                    help='Output filename for the JSON containing width, height, etc.', required=True)

#parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.',
#                                default='results/result_voice.mp4')

parser.add_argument('--static', type=bool,
                    help='If True, then use only first video frame for inference', default=False)

# NB: I'm adjusting the FPS to match that of the end bump I made. The old default FPS was "25." (float 25)
parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 29.97)',
                    default=29.97, required=False)

parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0],
                    help='Padding (top, bottom, left, right). Please adjust to include chin at least')

parser.add_argument('--face_det_batch_size', type=int,
                    help='Batch size for face detection', default=16)
parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=128)

parser.add_argument('--resize_factor', default=1, type=int,
            help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p')

parser.add_argument('--crop', nargs='+', type=int, default=[0, -1, 0, -1],
                    help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. '
                    'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width')

parser.add_argument('--box', nargs='+', type=int, default=[-1, -1, -1, -1],
                    help='Specify a constant bounding box for the face. Use only as a last resort if the face is not detected.'
                    'Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).')

parser.add_argument('--rotate', default=False, action='store_true',
                    help='Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg.'
                    'Use if you get a flipped result, despite feeding a normal looking video')

parser.add_argument('--nosmooth', default=False, action='store_true',
                    help='Prevent smoothing face detections over a short temporal window')

parser.add_argument('--audio_start_pad_millis', default=0, type=int,
                    help='Seconds to pad the start of the audio')

parser.add_argument('--audio_end_pad_millis', default=0, type=int,
                    help='Seconds to pad the end of the audio')

# Purely for debugging on the host machine:
parser.add_argument('--preserve_tempdir', default=False, action='store_true',
                    help='Keep the tempdir arround for debugging')

args = parser.parse_args()
args.img_size = 96

def datagen(frames, face_det_results, mels):
    img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []

    for i, m in enumerate(mels):
        idx = 0 if args.static else i%len(frames)
        frame_to_save = frames[idx].copy()
        face, coords = face_det_results[idx].copy()

        face = cv2.resize(face, (args.img_size, args.img_size))

        img_batch.append(face)
        mel_batch.append(m)
        frame_batch.append(frame_to_save)
        coords_batch.append(coords)

        if len(img_batch) >= args.wav2lip_batch_size:
            img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)

            img_masked = img_batch.copy()
            img_masked[:, args.img_size//2:] = 0

            img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
            mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])

            yield img_batch, mel_batch, frame_batch, coords_batch
            img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []

    if len(img_batch) > 0:
        img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)

        img_masked = img_batch.copy()
        img_masked[:, args.img_size//2:] = 0

        img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
        mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])

        yield img_batch, mel_batch, frame_batch, coords_batch

mel_step_size = 16
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('Using {} for inference.'.format(device))

def _load(checkpoint_path):
    if device == 'cuda':
        checkpoint = torch.load(checkpoint_path)
    else:
        checkpoint = torch.load(checkpoint_path,
                                map_location=lambda storage, loc: storage)
    return checkpoint

def load_model(path):
    model = Wav2Lip()
    print("Load checkpoint from: {}".format(path), flush=True)
    checkpoint = _load(path)
    s = checkpoint["state_dict"]
    new_s = {}
    for k, v in s.items():
        new_s[k.replace('module.', '')] = v
    model.load_state_dict(new_s)

    model = model.to(device)
    return model.eval()

def maybe_pad_audio_file(tempdir, args):
    if not args.audio_start_pad_millis and not args.audio_end_pad_millis:
        return

    print('Padding audio with {} millis at start, {} millis at end.'.format(
        args.audio_start_pad_millis, args.audio_end_pad_millis), flush=True)

    padded_wav_filename = os.path.join(tempdir, 'padded_audio.wav')
    # ffmpeg padding: https://superuser.com/a/579110
    # Structure of filters: https://stackoverflow.com/a/55463101
    command = ' '.join([
        'ffmpeg',
        '-i {}'.format(args.audio_filename),
        '-filter_complex "[0] adelay={}ms:all=true [a] ; [a] apad=pad_dur={}ms"'.format(args.audio_start_pad_millis, args.audio_end_pad_millis),
        '{}'.format(padded_wav_filename)
    ])
    print('command:', command, flush=True)
    subprocess.call(command, shell=True)
    args.audio_filename = padded_wav_filename


def maybe_concatenate_end_bump(tempdir, args, frame_w, frame_h):
    if not args.end_bump_file:
        print('No end bump file.', flush=True)
        return

    print('Adding end bump file: {}'.format(args.end_bump_file), flush=True)

    bumpfile = args.end_bump_file

    # May need to resize our bump file...
    # After many attempts to get this into the same ffmpeg pipeline,
    # I'm giving up and just adding another step.
    #
    # NOTE: The FPS of the bump needs to match the video. Especially
    # important for videos generated from still photos, because that defaults
    # to the odd choice of "25 fps".
    if frame_w is not 1920 or frame_h is not 1080:
        print('Need to resize bump.', flush=True)
        bumpfile = os.path.join(tempdir, 'end_bump_resized.mp4')

        # Scale / aspect ratio / sar / resizing / padding:
        # https://stackoverflow.com/a/51946719
        command = ' '.join([
            'ffmpeg',
            '-i {}'.format(args.end_bump_file),
            '-vf "scale={}:{}:force_original_aspect_ratio=decrease,pad={}:{}:(ow-iw)/2:(oh-ih)/2,setsar=1"'.format(frame_w, frame_h, frame_w, frame_h),
            '{}'.format(bumpfile)
        ])
        print('command:', command, flush=True)
        failure_code = subprocess.call(command, shell=True)

        if failure_code:
            print('Failed to resize end bump')
            return

    padded_wav_filename = os.path.join(tempdir, 'output_with_end_bump.mp4')
    # ffmpeg video concatenation: https://stackoverflow.com/a/11175851
    # Structure of filters: https://stackoverflow.com/a/55463101
    # More info on filters: https://stackoverflow.com/a/22958746
    command = ' '.join([
        'ffmpeg',
        '-i {}'.format(args.output_video_filename),
        '-i {}'.format(bumpfile),
        '-filter_complex "concat=n=2:v=1:a=1"',
        '{}'.format(padded_wav_filename)
    ])
    print('command:', command, flush=True)
    failure_code = subprocess.call(command, shell=True)

    if failure_code:
        print('Failed to concatenate end bump')
    else:
        print('Done concatenating end bump; renaming file...', flush=True)
        os.rename(padded_wav_filename, args.output_video_filename)


def main(tempdir):
    frame_w = 0
    frame_h = 0
    fps = 0

    if not os.path.isfile(args.image_or_video_filename):
        raise ValueError('image_or_video_filename is not a file')

    if not os.path.isfile(args.cached_faces_filename):
        raise ValueError('cached_faces_filename is not a file')

    if not os.path.isfile(args.audio_filename):
        raise ValueError('audio_filename is not a file')

    if os.path.splitext(args.image_or_video_filename)[1] in ['.jpg', '.png', '.jpeg'] \
            or args.is_image:
        full_frames = [cv2.imread(args.image_or_video_filename)]
        fps = args.fps
        frame_h, frame_w = full_frames[0].shape[:-1]

    else:
        video_stream = cv2.VideoCapture(args.image_or_video_filename)
        fps = video_stream.get(cv2.CAP_PROP_FPS)

        print('Reading video frames...')

        full_frames = []
        while 1:
            still_reading, frame = video_stream.read()
            if not still_reading:
                video_stream.release()
                break
            if args.resize_factor > 1:
                frame = cv2.resize(frame, (frame.shape[1]//args.resize_factor, frame.shape[0]//args.resize_factor))

            if args.rotate:
                frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE)

            y1, y2, x1, x2 = args.crop
            if x2 == -1: x2 = frame.shape[1]
            if y2 == -1: y2 = frame.shape[0]

            frame = frame[y1:y2, x1:x2]

            full_frames.append(frame)

        frame_h, frame_w = full_frames[0].shape[:-1]

    print("Number of frames available for inference: "+str(len(full_frames)), flush=True)
    print("Frame dimensions: {}x{}".format(frame_w, frame_h), flush=True)

    # TODO(bt): This could be more efficient
    if not args.audio_filename.endswith('.wav'):
        print('Extracting raw audio...')
        temp_wav_filename = os.path.join(tempdir, 'temp.wav') # previously 'temp/temp.wav'
        command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio_filename, temp_wav_filename)

        print('command:', command, flush=True)
        subprocess.call(command, shell=True)
        args.audio_filename = temp_wav_filename

    maybe_pad_audio_file(tempdir, args)

    # TODO(bt): Save the spectrogram?
    wav = audio.load_wav(args.audio_filename, 16000)
    mel = audio.melspectrogram(wav)
    print(mel.shape)

    if np.isnan(mel.reshape(-1)).sum() > 0:
        raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')

    mel_chunks = []
    mel_idx_multiplier = 80./fps
    i = 0
    while 1:
        start_idx = int(i * mel_idx_multiplier)
        if start_idx + mel_step_size > len(mel[0]):
            mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
            break
        mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
        i += 1

    print("Length of mel chunks: {}".format(len(mel_chunks)), flush=True)

    if len(mel_chunks) > 2000:
        # TODO(bt): Catch that the file is too long earlier on.
        raise Exception("Too many mel chunks: {}".format(len(mel_chunks)))

    full_frames = full_frames[:len(mel_chunks)]

    # Load face detection results from pickle file
    video_faces_pickle_file = args.cached_faces_filename
    with open(video_faces_pickle_file, 'rb') as f:
        face_det_results = pickle.load(f)

    face_det_results = face_det_results[:len(mel_chunks)]

    batch_size = args.wav2lip_batch_size
    gen = datagen(full_frames.copy(), face_det_results.copy(), mel_chunks)

    output_video_filename = os.path.join(tempdir, 'result.avi') # previously 'temp/result.avi'

    for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen,
                                            total=int(np.ceil(float(len(mel_chunks))/batch_size)))):
        if i == 0:
            model = load_model(args.checkpoint_path)
            print ("Model loaded")

            #frame_h, frame_w = full_frames[0].shape[:-1]
            out = cv2.VideoWriter(output_video_filename,
                                    cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))

        img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
        mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)

        with torch.no_grad():
            pred = model(mel_batch, img_batch)

        pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.

        for p, f, c in zip(pred, frames, coords):
            y1, y2, x1, x2 = c
            p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))

            f[y1:y2, x1:x2] = p
            out.write(f)

    out.release()

    command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio_filename, output_video_filename, args.output_video_filename)
    print('command:', command, flush=True)
    subprocess.call(command, shell=True)

    maybe_concatenate_end_bump(tempdir, args, frame_w, frame_h)

    # ==== METADATA (1) ====

    command = [
        "ffprobe",
        "-loglevel",  "quiet",
        "-print_format", "json",
        "-show_format",
        "-show_streams",
        args.output_video_filename
    ]

    pipe = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
    out, err = pipe.communicate()
    ffmpeg_metadata = json.loads(out)

    # ==== METADATA (2) ====
    mime_type = magic.from_file(args.output_video_filename, mime=True)
    file_size_bytes = os.path.getsize(args.output_video_filename)

    print(ffmpeg_metadata)

    # We can't really get frame count anymore. The original source is invalid.
    # ffmpeg reports fps, but it's a ratio, eg. 25/1
    #frame_count = len(full_frames)
    #duration_seconds = frame_count / fps

    duration_millis = int(float(ffmpeg_metadata['format']['duration']) * 1000)

    metadata = {
        'is_video': True,
        'width': frame_w,
        'height': frame_h,
        #'num_frames': frame_count,
        #'fps': fps,
        'duration_millis': duration_millis,
        'mimetype': mime_type,
        'file_size_bytes': file_size_bytes,
    }

    with open(args.output_metadata_filename, 'w') as json_file:
        json.dump(metadata, json_file)

if __name__ == '__main__':
    tempdir = tempfile.mkdtemp()
    print('Using Python tempdir: {}'.format(tempdir), flush=True)
    try:
        main(tempdir)
    finally:
        if not args.preserve_tempdir:
            shutil.rmtree(tempdir)