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6680833
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1 Parent(s): 54aa931

Update refacer.py

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  1. refacer.py +101 -49
refacer.py CHANGED
@@ -20,15 +20,12 @@ from insightface.app.common import Face
20
  from insightface.utils.storage import ensure_available
21
  import re
22
  import subprocess
23
- import tempfile
24
-
25
 
26
  class RefacerMode(Enum):
27
- CPU, CUDA, COREML, TENSORRT = range(1, 5)
28
-
29
 
30
  class Refacer:
31
- def __init__(self, force_cpu=False, colab_performance=False):
32
  self.first_face = False
33
  self.force_cpu = force_cpu
34
  self.colab_performance = colab_performance
@@ -38,7 +35,7 @@ class Refacer:
38
  self.__init_apps()
39
 
40
  def __check_providers(self):
41
- if self.force_cpu:
42
  self.providers = ['CPUExecutionProvider']
43
  else:
44
  self.providers = rt.get_available_providers()
@@ -49,18 +46,18 @@ class Refacer:
49
 
50
  if len(self.providers) == 1 and 'CPUExecutionProvider' in self.providers:
51
  self.mode = RefacerMode.CPU
52
- self.use_num_cpus = mp.cpu_count() - 1
53
- self.sess_options.intra_op_num_threads = int(self.use_num_cpus / 3)
54
  print(f"CPU mode with providers {self.providers}")
55
  elif self.colab_performance:
56
  self.mode = RefacerMode.TENSORRT
57
- self.use_num_cpus = mp.cpu_count() - 1
58
- self.sess_options.intra_op_num_threads = int(self.use_num_cpus / 3)
59
  print(f"TENSORRT mode with providers {self.providers}")
60
  elif 'CoreMLExecutionProvider' in self.providers:
61
  self.mode = RefacerMode.COREML
62
- self.use_num_cpus = mp.cpu_count() - 1
63
- self.sess_options.intra_op_num_threads = int(self.use_num_cpus / 3)
64
  print(f"CoreML mode with providers {self.providers}")
65
  elif 'CUDAExecutionProvider' in self.providers:
66
  self.mode = RefacerMode.CUDA
@@ -69,31 +66,42 @@ class Refacer:
69
  if 'TensorrtExecutionProvider' in self.providers:
70
  self.providers.remove('TensorrtExecutionProvider')
71
  print(f"CUDA mode with providers {self.providers}")
 
 
 
 
 
 
 
 
 
 
72
 
73
  def __init_apps(self):
74
  assets_dir = ensure_available('models', 'buffalo_l', root='~/.insightface')
75
 
76
  model_path = os.path.join(assets_dir, 'det_10g.onnx')
77
  sess_face = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
78
- self.face_detector = SCRFD(model_path, sess_face)
79
- self.face_detector.prepare(0, input_size=(640, 640))
80
 
81
- model_path = os.path.join(assets_dir, 'w600k_r50.onnx')
82
  sess_rec = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
83
- self.rec_app = ArcFaceONNX(model_path, sess_rec)
84
  self.rec_app.prepare(0)
85
 
86
  model_path = 'inswapper_128.onnx'
87
  sess_swap = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
88
- self.face_swapper = INSwapper(model_path, sess_swap)
89
 
90
  def prepare_faces(self, faces):
91
- self.replacement_faces = []
92
  for face in faces:
 
93
  if "origin" in face:
94
  face_threshold = face['threshold']
95
- bboxes1, kpss1 = self.face_detector.autodetect(face['origin'], max_num=1)
96
- if len(kpss1) < 1:
97
  raise Exception('No face detected on "Face to replace" image')
98
  feat_original = self.rec_app.get(face['origin'], kpss1[0])
99
  else:
@@ -101,19 +109,21 @@ class Refacer:
101
  self.first_face = True
102
  feat_original = None
103
  print('No origin image: First face change')
104
- _faces = self.__get_faces(face['destination'], max_num=1)
105
- if len(_faces) < 1:
 
106
  raise Exception('No face detected on "Destination face" image')
107
- self.replacement_faces.append((feat_original, _faces[0], face_threshold))
108
 
109
- def __convert_video(self, video_path, output_video_path):
110
  if self.video_has_audio:
111
  print("Merging audio with the refaced video...")
112
- new_path = output_video_path + str(random.randint(0, 999)) + "_c.mp4"
 
113
  in1 = ffmpeg.input(output_video_path)
114
  in2 = ffmpeg.input(video_path)
115
- out = ffmpeg.output(in1.video, in2.audio, new_path, video_bitrate=self.ffmpeg_video_bitrate, vcodec=self.ffmpeg_video_encoder)
116
- out.run(overwrite_output=True, quiet=True)
117
  else:
118
  new_path = output_video_path
119
  print("The video doesn't have audio, so post-processing is not necessary")
@@ -121,8 +131,9 @@ class Refacer:
121
  print(f"The process has finished.\nThe refaced video can be found at {os.path.abspath(new_path)}")
122
  return new_path
123
 
124
- def __get_faces(self, frame, max_num=0):
125
- bboxes, kpss = self.face_detector.detect(frame, max_num=max_num, metric='default')
 
126
 
127
  if bboxes.shape[0] == 0:
128
  return []
@@ -138,42 +149,42 @@ class Refacer:
138
  ret.append(face)
139
  return ret
140
 
141
- def process_first_face(self, frame):
142
- faces = self.__get_faces(frame, max_num=1)
143
  if len(faces) != 0:
144
  frame = self.face_swapper.get(frame, faces[0], self.replacement_faces[0][1], paste_back=True)
145
  return frame
146
 
147
- def process_faces(self, frame):
148
- faces = self.__get_faces(frame, max_num=0)
149
  for rep_face in self.replacement_faces:
150
  for i in range(len(faces) - 1, -1, -1):
151
  sim = self.rec_app.compute_sim(rep_face[0], faces[i].embedding)
152
- if sim >= rep_face[2]:
153
  frame = self.face_swapper.get(frame, faces[i], rep_face[1], paste_back=True)
154
  del faces[i]
155
  break
156
  return frame
157
 
158
- def __check_video_has_audio(self, video_path):
159
  self.video_has_audio = False
160
  probe = ffmpeg.probe(video_path)
161
  audio_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'audio'), None)
162
  if audio_stream is not None:
163
  self.video_has_audio = True
164
-
165
  def reface_group(self, faces, frames, output):
166
- with ThreadPoolExecutor(max_workers=self.use_num_cpus) as executor:
167
  if self.first_face:
168
- results = list(tqdm(executor.map(self.process_first_face, frames), total=len(frames), desc="Processing frames"))
169
  else:
170
- results = list(tqdm(executor.map(self.process_faces, frames), total=len(frames), desc="Processing frames"))
171
  for result in results:
172
  output.write(result)
173
 
174
  def reface(self, video_path, faces):
175
  self.__check_video_has_audio(video_path)
176
- output_video_path = os.path.join('out', Path(video_path).name)
177
  self.prepare_faces(faces)
178
 
179
  cap = cv2.VideoCapture(video_path)
@@ -187,24 +198,65 @@ class Refacer:
187
  fourcc = cv2.VideoWriter_fourcc(*'mp4v')
188
  output = cv2.VideoWriter(output_video_path, fourcc, fps, (frame_width, frame_height))
189
 
190
- frames = []
191
  self.k = 1
192
- with tqdm(total=total_frames, desc="Extracting frames") as pbar:
193
  while cap.isOpened():
194
  flag, frame = cap.read()
195
- if flag and len(frame) > 0:
196
  frames.append(frame.copy())
197
  pbar.update()
198
  else:
199
  break
200
- if len(frames) > 1000:
201
- self.reface_group(faces, frames, output)
202
- frames = []
203
 
204
  cap.release()
205
  pbar.close()
206
 
207
- self.reface_group(faces, frames, output)
208
- self.__convert_video(video_path, output_video_path)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
209
 
210
- return output_video_path
 
 
 
 
 
 
 
 
20
  from insightface.utils.storage import ensure_available
21
  import re
22
  import subprocess
 
 
23
 
24
  class RefacerMode(Enum):
25
+ CPU, CUDA, COREML, TENSORRT = range(1, 5)
 
26
 
27
  class Refacer:
28
+ def __init__(self,force_cpu=False,colab_performance=False):
29
  self.first_face = False
30
  self.force_cpu = force_cpu
31
  self.colab_performance = colab_performance
 
35
  self.__init_apps()
36
 
37
  def __check_providers(self):
38
+ if self.force_cpu :
39
  self.providers = ['CPUExecutionProvider']
40
  else:
41
  self.providers = rt.get_available_providers()
 
46
 
47
  if len(self.providers) == 1 and 'CPUExecutionProvider' in self.providers:
48
  self.mode = RefacerMode.CPU
49
+ self.use_num_cpus = mp.cpu_count()-1
50
+ self.sess_options.intra_op_num_threads = int(self.use_num_cpus/3)
51
  print(f"CPU mode with providers {self.providers}")
52
  elif self.colab_performance:
53
  self.mode = RefacerMode.TENSORRT
54
+ self.use_num_cpus = mp.cpu_count()-1
55
+ self.sess_options.intra_op_num_threads = int(self.use_num_cpus/3)
56
  print(f"TENSORRT mode with providers {self.providers}")
57
  elif 'CoreMLExecutionProvider' in self.providers:
58
  self.mode = RefacerMode.COREML
59
+ self.use_num_cpus = mp.cpu_count()-1
60
+ self.sess_options.intra_op_num_threads = int(self.use_num_cpus/3)
61
  print(f"CoreML mode with providers {self.providers}")
62
  elif 'CUDAExecutionProvider' in self.providers:
63
  self.mode = RefacerMode.CUDA
 
66
  if 'TensorrtExecutionProvider' in self.providers:
67
  self.providers.remove('TensorrtExecutionProvider')
68
  print(f"CUDA mode with providers {self.providers}")
69
+ """
70
+ elif 'TensorrtExecutionProvider' in self.providers:
71
+ self.mode = RefacerMode.TENSORRT
72
+ #self.use_num_cpus = 1
73
+ #self.sess_options.intra_op_num_threads = 1
74
+ self.use_num_cpus = mp.cpu_count()-1
75
+ self.sess_options.intra_op_num_threads = int(self.use_num_cpus/3)
76
+ print(f"TENSORRT mode with providers {self.providers}")
77
+ """
78
+
79
 
80
  def __init_apps(self):
81
  assets_dir = ensure_available('models', 'buffalo_l', root='~/.insightface')
82
 
83
  model_path = os.path.join(assets_dir, 'det_10g.onnx')
84
  sess_face = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
85
+ self.face_detector = SCRFD(model_path,sess_face)
86
+ self.face_detector.prepare(0,input_size=(640, 640))
87
 
88
+ model_path = os.path.join(assets_dir , 'w600k_r50.onnx')
89
  sess_rec = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
90
+ self.rec_app = ArcFaceONNX(model_path,sess_rec)
91
  self.rec_app.prepare(0)
92
 
93
  model_path = 'inswapper_128.onnx'
94
  sess_swap = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
95
+ self.face_swapper = INSwapper(model_path,sess_swap)
96
 
97
  def prepare_faces(self, faces):
98
+ self.replacement_faces=[]
99
  for face in faces:
100
+ #image1 = cv2.imread(face.origin)
101
  if "origin" in face:
102
  face_threshold = face['threshold']
103
+ bboxes1, kpss1 = self.face_detector.autodetect(face['origin'], max_num=1)
104
+ if len(kpss1)<1:
105
  raise Exception('No face detected on "Face to replace" image')
106
  feat_original = self.rec_app.get(face['origin'], kpss1[0])
107
  else:
 
109
  self.first_face = True
110
  feat_original = None
111
  print('No origin image: First face change')
112
+ #image2 = cv2.imread(face.destination)
113
+ _faces = self.__get_faces(face['destination'],max_num=1)
114
+ if len(_faces)<1:
115
  raise Exception('No face detected on "Destination face" image')
116
+ self.replacement_faces.append((feat_original,_faces[0],face_threshold))
117
 
118
+ def __convert_video(self,video_path,output_video_path):
119
  if self.video_has_audio:
120
  print("Merging audio with the refaced video...")
121
+ new_path = output_video_path + str(random.randint(0,999)) + "_c.mp4"
122
+ #stream = ffmpeg.input(output_video_path)
123
  in1 = ffmpeg.input(output_video_path)
124
  in2 = ffmpeg.input(video_path)
125
+ out = ffmpeg.output(in1.video, in2.audio, new_path,video_bitrate=self.ffmpeg_video_bitrate,vcodec=self.ffmpeg_video_encoder)
126
+ out.run(overwrite_output=True,quiet=True)
127
  else:
128
  new_path = output_video_path
129
  print("The video doesn't have audio, so post-processing is not necessary")
 
131
  print(f"The process has finished.\nThe refaced video can be found at {os.path.abspath(new_path)}")
132
  return new_path
133
 
134
+ def __get_faces(self,frame,max_num=0):
135
+
136
+ bboxes, kpss = self.face_detector.detect(frame,max_num=max_num,metric='default')
137
 
138
  if bboxes.shape[0] == 0:
139
  return []
 
149
  ret.append(face)
150
  return ret
151
 
152
+ def process_first_face(self,frame):
153
+ faces = self.__get_faces(frame,max_num=1)
154
  if len(faces) != 0:
155
  frame = self.face_swapper.get(frame, faces[0], self.replacement_faces[0][1], paste_back=True)
156
  return frame
157
 
158
+ def process_faces(self,frame):
159
+ faces = self.__get_faces(frame,max_num=0)
160
  for rep_face in self.replacement_faces:
161
  for i in range(len(faces) - 1, -1, -1):
162
  sim = self.rec_app.compute_sim(rep_face[0], faces[i].embedding)
163
+ if sim>=rep_face[2]:
164
  frame = self.face_swapper.get(frame, faces[i], rep_face[1], paste_back=True)
165
  del faces[i]
166
  break
167
  return frame
168
 
169
+ def __check_video_has_audio(self,video_path):
170
  self.video_has_audio = False
171
  probe = ffmpeg.probe(video_path)
172
  audio_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'audio'), None)
173
  if audio_stream is not None:
174
  self.video_has_audio = True
175
+
176
  def reface_group(self, faces, frames, output):
177
+ with ThreadPoolExecutor(max_workers = self.use_num_cpus) as executor:
178
  if self.first_face:
179
+ results = list(tqdm(executor.map(self.process_first_face, frames), total=len(frames),desc="Processing frames"))
180
  else:
181
+ results = list(tqdm(executor.map(self.process_faces, frames), total=len(frames),desc="Processing frames"))
182
  for result in results:
183
  output.write(result)
184
 
185
  def reface(self, video_path, faces):
186
  self.__check_video_has_audio(video_path)
187
+ output_video_path = os.path.join('out',Path(video_path).name)
188
  self.prepare_faces(faces)
189
 
190
  cap = cv2.VideoCapture(video_path)
 
198
  fourcc = cv2.VideoWriter_fourcc(*'mp4v')
199
  output = cv2.VideoWriter(output_video_path, fourcc, fps, (frame_width, frame_height))
200
 
201
+ frames=[]
202
  self.k = 1
203
+ with tqdm(total=total_frames,desc="Extracting frames") as pbar:
204
  while cap.isOpened():
205
  flag, frame = cap.read()
206
+ if flag and len(frame)>0:
207
  frames.append(frame.copy())
208
  pbar.update()
209
  else:
210
  break
211
+ if (len(frames) > 1000):
212
+ self.reface_group(faces,frames,output)
213
+ frames=[]
214
 
215
  cap.release()
216
  pbar.close()
217
 
218
+ self.reface_group(faces,frames,output)
219
+ frames=[]
220
+ output.release()
221
+
222
+ return self.__convert_video(video_path,output_video_path)
223
+
224
+ def __try_ffmpeg_encoder(self, vcodec):
225
+ print(f"Trying FFMPEG {vcodec} encoder")
226
+ command = ['ffmpeg', '-y', '-f','lavfi','-i','testsrc=duration=1:size=1280x720:rate=30','-vcodec',vcodec,'testsrc.mp4']
227
+ try:
228
+ subprocess.run(command, check=True, capture_output=True).stderr
229
+ except subprocess.CalledProcessError as e:
230
+ print(f"FFMPEG {vcodec} encoder doesn't work -> Disabled.")
231
+ return False
232
+ print(f"FFMPEG {vcodec} encoder works")
233
+ return True
234
+
235
+ def __check_encoders(self):
236
+ self.ffmpeg_video_encoder='libx264'
237
+ self.ffmpeg_video_bitrate='0'
238
+
239
+ pattern = r"encoders: ([a-zA-Z0-9_]+(?: [a-zA-Z0-9_]+)*)"
240
+ command = ['ffmpeg', '-codecs', '--list-encoders']
241
+ commandout = subprocess.run(command, check=True, capture_output=True).stdout
242
+ result = commandout.decode('utf-8').split('\n')
243
+ for r in result:
244
+ if "264" in r:
245
+ encoders = re.search(pattern, r).group(1).split(' ')
246
+ for v_c in Refacer.VIDEO_CODECS:
247
+ for v_k in encoders:
248
+ if v_c == v_k:
249
+ if self.__try_ffmpeg_encoder(v_k):
250
+ self.ffmpeg_video_encoder=v_k
251
+ self.ffmpeg_video_bitrate=Refacer.VIDEO_CODECS[v_k]
252
+ print(f"Video codec for FFMPEG: {self.ffmpeg_video_encoder}")
253
+ return
254
 
255
+ VIDEO_CODECS = {
256
+ 'h264_videotoolbox':'0', #osx HW acceleration
257
+ 'h264_nvenc':'0', #NVIDIA HW acceleration
258
+ #'h264_qsv', #Intel HW acceleration
259
+ #'h264_vaapi', #Intel HW acceleration
260
+ #'h264_omx', #HW acceleration
261
+ 'libx264':'0' #No HW acceleration
262
+ }