259 lines
8.4 KiB
Python
259 lines
8.4 KiB
Python
from os import listdir, path
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import numpy as np
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import scipy, cv2, os, sys, argparse, audio
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import json, subprocess, random, string
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from tqdm import tqdm
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from glob import glob
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import torch, face_detection
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from models import Wav2Lip
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parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models')
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parser.add_argument('--checkpoint_path', type=str,
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help='Name of saved checkpoint to load weights from', required=True)
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parser.add_argument('--face', type=str,
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help='Filepath of video/image that contains faces to use', required=True)
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parser.add_argument('--audio', type=str,
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help='Filepath of video/audio file to use as raw audio source', required=True)
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parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.',
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default='results/result_voice.mp4')
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parser.add_argument('--static', type=bool,
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help='If True, then use only first video frame for inference', default=False)
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parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)',
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default=25., required=False)
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parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0],
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help='Padding (top, bottom, left, right). Please adjust to include chin at least')
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parser.add_argument('--face_det_batch_size', type=int,
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help='Batch size for face detection', default=16)
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parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=128)
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parser.add_argument('--resize_factor', default=1, type=int,
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help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p')
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parser.add_argument('--crop', nargs='+', type=int, default=[0, -1, 0, -1],
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help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor arg. '
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'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width')
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args = parser.parse_args()
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args.img_size = 96
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if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
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args.static = True
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def get_smoothened_boxes(boxes, T):
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for i in range(len(boxes)):
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if i + T > len(boxes):
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window = boxes[len(boxes) - T:]
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else:
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window = boxes[i : i + T]
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boxes[i] = np.mean(window, axis=0)
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return boxes
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def face_detect(images):
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detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D,
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flip_input=False, device=device)
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batch_size = args.face_det_batch_size
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while 1:
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predictions = []
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try:
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for i in tqdm(range(0, len(images), batch_size)):
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predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size])))
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except RuntimeError:
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if batch_size == 1:
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raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument')
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batch_size //= 2
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print('Recovering from OOM error; New batch size: {}'.format(batch_size))
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continue
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break
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results = []
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pady1, pady2, padx1, padx2 = args.pads
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for rect, image in zip(predictions, images):
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if rect is None:
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raise ValueError('Face not detected! Ensure the video contains a face in all the frames.')
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y1 = max(0, rect[1] - pady1)
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y2 = min(image.shape[0], rect[3] + pady2)
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x1 = max(0, rect[0] - padx1)
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x2 = min(image.shape[1], rect[2] + padx2)
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results.append([x1, y1, x2, y2])
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boxes = get_smoothened_boxes(np.array(results), T=5)
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results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)]
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del detector
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return results
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def datagen(frames, mels):
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img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
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if not args.static:
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face_det_results = face_detect(frames) # BGR2RGB for CNN face detection
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else:
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face_det_results = face_detect([frames[0]])
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for i, m in enumerate(mels):
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idx = 0 if args.static else i%len(frames)
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frame_to_save = frames[idx].copy()
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face, coords = face_det_results[idx].copy()
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face = cv2.resize(face, (args.img_size, args.img_size))
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img_batch.append(face)
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mel_batch.append(m)
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frame_batch.append(frame_to_save)
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coords_batch.append(coords)
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if len(img_batch) >= args.wav2lip_batch_size:
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img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
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img_masked = img_batch.copy()
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img_masked[:, args.img_size//2:] = 0
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img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
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mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
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yield img_batch, mel_batch, frame_batch, coords_batch
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img_batch, mel_batch, frame_batch, coords_batch = [], [], [], []
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if len(img_batch) > 0:
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img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch)
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img_masked = img_batch.copy()
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img_masked[:, args.img_size//2:] = 0
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img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
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mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
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yield img_batch, mel_batch, frame_batch, coords_batch
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mel_step_size = 16
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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print('Using {} for inference.'.format(device))
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def _load(checkpoint_path):
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if device == 'cuda':
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checkpoint = torch.load(checkpoint_path)
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else:
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checkpoint = torch.load(checkpoint_path,
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map_location=lambda storage, loc: storage)
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return checkpoint
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def load_model(path):
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model = Wav2Lip()
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print("Load checkpoint from: {}".format(path))
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checkpoint = _load(path)
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s = checkpoint["state_dict"]
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new_s = {}
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for k, v in s.items():
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new_s[k.replace('module.', '')] = v
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model.load_state_dict(new_s)
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model = model.to(device)
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return model.eval()
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def main():
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if not os.path.isfile(args.face):
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fnames = list(glob(os.path.join(args.face, '*.jpg')))
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sorted_fnames = sorted(fnames, key=lambda f: int(os.path.basename(f).split('.')[0]))
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full_frames = [cv2.imread(f) for f in sorted_fnames]
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elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
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full_frames = [cv2.imread(args.face)]
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fps = args.fps
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else:
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video_stream = cv2.VideoCapture(args.face)
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fps = video_stream.get(cv2.CAP_PROP_FPS)
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print('Reading video frames...')
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full_frames = []
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while 1:
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still_reading, frame = video_stream.read()
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if not still_reading:
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video_stream.release()
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break
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if args.resize_factor > 1:
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frame = cv2.resize(frame, (frame.shape[1]//args.resize_factor, frame.shape[0]//args.resize_factor))
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y1, y2, x1, x2 = args.crop
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if x2 == -1: x2 = frame.shape[1]
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if y2 == -1: y2 = frame.shape[0]
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frame = frame[y1:y2, x1:x2]
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full_frames.append(frame)
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print ("Number of frames available for inference: "+str(len(full_frames)))
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if not args.audio.endswith('.wav'):
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print('Extracting raw audio...')
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command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, 'temp/temp.wav')
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subprocess.call(command, shell=True)
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args.audio = 'temp/temp.wav'
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wav = audio.load_wav(args.audio, 16000)
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mel = audio.melspectrogram(wav)
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print(mel.shape)
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if np.isnan(mel.reshape(-1)).sum() > 0:
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raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')
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mel_chunks = []
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mel_idx_multiplier = 80./fps
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i = 0
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while 1:
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start_idx = int(i * mel_idx_multiplier)
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if start_idx + mel_step_size > len(mel[0]):
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break
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mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
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i += 1
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print("Length of mel chunks: {}".format(len(mel_chunks)))
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full_frames = full_frames[:len(mel_chunks)]
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batch_size = args.wav2lip_batch_size
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gen = datagen(full_frames.copy(), mel_chunks)
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for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen,
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total=int(np.ceil(float(len(mel_chunks))/batch_size)))):
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if i == 0:
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model = load_model(args.checkpoint_path)
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print ("Model loaded")
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frame_h, frame_w = full_frames[0].shape[:-1]
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out = cv2.VideoWriter('temp/result.avi',
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cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h))
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img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
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mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
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with torch.no_grad():
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pred = model(mel_batch, img_batch)
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pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
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for p, f, c in zip(pred, frames, coords):
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y1, y2, x1, x2 = c
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p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
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f[y1:y2, x1:x2] = p
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out.write(f)
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out.release()
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command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/result.avi', args.outfile)
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subprocess.call(command, shell=True)
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if __name__ == '__main__':
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main()
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