-
Notifications
You must be signed in to change notification settings - Fork 2.2k
/
inference.py
277 lines (211 loc) · 9.42 KB
/
inference.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
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
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')
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:
cv2.imwrite('temp/faulty_frame.jpg', image) # check this frame where the face was not detected.
raise ValueError('Face not detected! Ensure the video contains a face in all the frames.')
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([x1, y1, x2, y2])
boxes = get_smoothened_boxes(np.array(results), T=5)
results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)]
del detector
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()
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))
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):
fnames = list(glob(os.path.join(args.face, '*.jpg')))
sorted_fnames = sorted(fnames, key=lambda f: int(os.path.basename(f).split('.')[0]))
full_frames = [cv2.imread(f) for f in sorted_fnames]
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
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, 'temp/result.avi', args.outfile)
subprocess.call(command, shell=True)
if __name__ == '__main__':
main()