Wav2Lip Bypass missing face frames

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 platform

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('--face', type=str, 
					help='Filepath of video/image that contains faces to use', required=True)
parser.add_argument('--audio', type=str, 
					help='Filepath of video/audio file to use as raw audio source', 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)
parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)', 
					default=25., 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')

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

if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
	args.static = True

def get_smoothened_boxes(boxes, T):
	for i in range(len(boxes)):
		if i + T > len(boxes):
			window = boxes[len(boxes) - T:]
		else:
			window = boxes[i : i + T]
		boxes[i] = np.mean(window, axis=0)
	return boxes

def face_detect(images):
    detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, device=device)
    batch_size = args.face_det_batch_size
    
    while 1:
        predictions = []
        try:
            for i in tqdm(range(0, len(images), batch_size)):
                predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size])))
        except RuntimeError:
            if batch_size == 1: 
                raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument')
            batch_size //= 2
            print('Recovering from OOM error; New batch size: {}'.format(batch_size))
            continue
        break

    results = []
    pady1, pady2, padx1, padx2 = args.pads
    for rect, image in zip(predictions, images):
        if rect is None:
            # Add the original frame without face to results
            results.append([image, []])
        else:
            y1 = max(0, rect[1] - pady1)
            y2 = min(image.shape[0], rect[3] + pady2)
            x1 = max(0, rect[0] - padx1)
            x2 = min(image.shape[1], rect[2] + padx2)
        
            results.append([image[y1:y2, x1:x2], (y1, y2, x1, x2)])
    
    return results


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

    if args.box[0] == -1:
        if not args.static:
            face_det_results = face_detect(frames) # BGR2RGB for CNN face detection
        else:
            face_det_results = face_detect([frames[0]])
    else:
        print('Using the specified bounding box instead of face detection...')
        y1, y2, x1, x2 = args.box
        face_det_results = [[f[y1: y2, x1:x2], (y1, y2, x1, x2)] for f in frames]

    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()

        if len(coords) == 0: # Check if there are no face coordinates
            # Add a random matrix of shape (96x96x3) to img_batch and an empty list to coords_batch
            face = np.random.rand(args.img_size, args.img_size, 3) * 255
            coords_batch.append([])
        else:
            face = cv2.resize(face, (args.img_size, args.img_size))
            coords_batch.append(coords)
        
        img_batch.append(face)
        mel_batch.append(m)
        frame_batch.append(frame_to_save)

        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))
	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 main():
    if not os.path.isfile(args.face):
        raise ValueError('--face argument must be a valid path to video/image file')

    elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
        full_frames = [cv2.imread(args.face)]
        fps = args.fps

    else:
        video_stream = cv2.VideoCapture(args.face)
        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)

    print("Number of frames available for inference: " + str(len(full_frames)))

    if not args.audio.endswith('.wav'):
        print('Extracting raw audio...')
        command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, 'temp/temp.wav')

        subprocess.call(command, shell=True)
        args.audio = 'temp/temp.wav'

    wav = audio.load_wav(args.audio, 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)))

    full_frames = full_frames[:len(mel_chunks)]

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

    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('temp/result.avi',
                                  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  <- Move this line to lie 272
            
            if len(c) == 0:  # Check if there are no face coordinates (no face detected)
                out.write(f)  # Write the original frame without lip-syncing
            else:
                y1, y2, x1, x2 = c # <- here
                p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
                f[y1:y2, x1:x2] = p
                out.write(f)  # Write the lip-synced frame to the output video

    out.release()

    command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/result.avi', args.outfile)
    subprocess.call(command, shell=platform.system() != 'Windows')


if __name__ == '__main__':
    main()

(don't forget to make some minor corrections in sound.py file too, I did it, & it worked)

Can you please expand on what changes to make?

Hello
Thanks for your work

Could be very nive to have a diff

I would like to apply it on top of this project
https://github.com/anothermartz/Easy-Wav2Lip

But the inference.py are not very similar so quite not easy to merge / apply your modification