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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 13:02:15 -07:00
increased speed of sort by hist sim for ten thousands of faces
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iperov
parent
08550ac856
commit
b5ba7d52cb
6 changed files with 226 additions and 140 deletions
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@ -12,13 +12,14 @@ class SampleProcessor(object):
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WARPED = 0x00000002,
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WARPED_TRANSFORMED = 0x00000004,
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TRANSFORMED = 0x00000008,
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FACE_ALIGN_HALF = 0x00000010,
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FACE_ALIGN_FULL = 0x00000020,
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FACE_ALIGN_HEAD = 0x00000040,
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FACE_ALIGN_AVATAR = 0x00000080,
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FACE_MASK_FULL = 0x00000100,
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FACE_MASK_EYES = 0x00000200,
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LANDMARKS_ARRAY = 0x00000010, #currently unused
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FACE_ALIGN_HALF = 0x00000100,
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FACE_ALIGN_FULL = 0x00000200,
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FACE_ALIGN_HEAD = 0x00000400,
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FACE_ALIGN_AVATAR = 0x00000800,
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FACE_MASK_FULL = 0x00001000,
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FACE_MASK_EYES = 0x00002000,
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MODE_BGR = 0x01000000, #BGR
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MODE_G = 0x02000000, #Grayscale
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@ -47,7 +48,7 @@ class SampleProcessor(object):
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params = image_utils.gen_warp_params(source, sample_process_options.random_flip, rotation_range=sample_process_options.rotation_range, scale_range=sample_process_options.scale_range, tx_range=sample_process_options.tx_range, ty_range=sample_process_options.ty_range )
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images = [[None]*3 for _ in range(4)]
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images = [[None]*3 for _ in range(5)]
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sample_rnd_seed = np.random.randint(0x80000000)
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@ -65,6 +66,8 @@ class SampleProcessor(object):
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img_type = 2
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elif f & SampleProcessor.TypeFlags.TRANSFORMED != 0:
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img_type = 3
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elif f & SampleProcessor.TypeFlags.LANDMARKS_ARRAY != 0:
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img_type = 4
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else:
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raise ValueError ('expected SampleTypeFlags type')
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@ -83,58 +86,64 @@ class SampleProcessor(object):
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target_face_type = FaceType.HEAD
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elif f & SampleProcessor.TypeFlags.FACE_ALIGN_AVATAR != 0:
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target_face_type = FaceType.AVATAR
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if images[img_type][face_mask_type] is None:
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img = source
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if is_face_sample:
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if face_mask_type == 1:
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img = np.concatenate( (img, LandmarksProcessor.get_image_hull_mask (source, sample.landmarks) ), -1 )
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elif face_mask_type == 2:
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mask = LandmarksProcessor.get_image_eye_mask (source, sample.landmarks)
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mask = np.expand_dims (cv2.blur (mask, ( w // 32, w // 32 ) ), -1)
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mask[mask > 0.0] = 1.0
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img = np.concatenate( (img, mask ), -1 )
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if img_type == 4:
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l = sample.landmarks
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l = np.concatenate ( [ np.expand_dims(l[:,0] / w,-1), np.expand_dims(l[:,1] / h,-1) ], -1 )
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l = np.clip(l, 0.0, 1.0)
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img = l
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else:
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if images[img_type][face_mask_type] is None:
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img = source
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if is_face_sample:
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if face_mask_type == 1:
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img = np.concatenate( (img, LandmarksProcessor.get_image_hull_mask (source, sample.landmarks) ), -1 )
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elif face_mask_type == 2:
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mask = LandmarksProcessor.get_image_eye_mask (source, sample.landmarks)
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mask = np.expand_dims (cv2.blur (mask, ( w // 32, w // 32 ) ), -1)
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mask[mask > 0.0] = 1.0
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img = np.concatenate( (img, mask ), -1 )
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images[img_type][face_mask_type] = image_utils.warp_by_params (params, img, (img_type==1 or img_type==2), (img_type==2 or img_type==3), img_type != 0, face_mask_type == 0)
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img = images[img_type][face_mask_type]
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images[img_type][face_mask_type] = image_utils.warp_by_params (params, img, (img_type==1 or img_type==2), (img_type==2 or img_type==3), img_type != 0, face_mask_type == 0)
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if is_face_sample and target_face_type != -1:
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if target_face_type > sample.face_type:
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raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, target_face_type) )
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img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, size, target_face_type), (size,size), flags=cv2.INTER_LANCZOS4 )
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else:
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img = cv2.resize( img, (size,size), cv2.INTER_LANCZOS4 )
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if random_sub_size != 0:
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sub_size = size - random_sub_size
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rnd_state = np.random.RandomState (sample_rnd_seed+random_sub_size)
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start_x = rnd_state.randint(sub_size+1)
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start_y = rnd_state.randint(sub_size+1)
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img = img[start_y:start_y+sub_size,start_x:start_x+sub_size,:]
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img = images[img_type][face_mask_type]
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if is_face_sample and target_face_type != -1:
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if target_face_type > sample.face_type:
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raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, target_face_type) )
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img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, size, target_face_type), (size,size), flags=cv2.INTER_LANCZOS4 )
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else:
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img = cv2.resize( img, (size,size), cv2.INTER_LANCZOS4 )
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if random_sub_size != 0:
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sub_size = size - random_sub_size
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rnd_state = np.random.RandomState (sample_rnd_seed+random_sub_size)
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start_x = rnd_state.randint(sub_size+1)
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start_y = rnd_state.randint(sub_size+1)
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img = img[start_y:start_y+sub_size,start_x:start_x+sub_size,:]
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img_bgr = img[...,0:3]
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img_mask = img[...,3:4]
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img_bgr = img[...,0:3]
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img_mask = img[...,3:4]
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if f & SampleProcessor.TypeFlags.MODE_BGR != 0:
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img = img
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elif f & SampleProcessor.TypeFlags.MODE_BGR_SHUFFLE != 0:
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img_bgr = np.take (img_bgr, np.random.permutation(img_bgr.shape[-1]), axis=-1)
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img = np.concatenate ( (img_bgr,img_mask) , -1 )
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elif f & SampleProcessor.TypeFlags.MODE_G != 0:
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img = np.concatenate ( (np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1),img_mask) , -1 )
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elif f & SampleProcessor.TypeFlags.MODE_GGG != 0:
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img = np.concatenate ( ( np.repeat ( np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1), (3,), -1), img_mask), -1)
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elif is_face_sample and f & SampleProcessor.TypeFlags.MODE_M != 0:
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if face_mask_type== 0:
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raise ValueError ('no face_mask_type defined')
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img = img_mask
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else:
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raise ValueError ('expected SampleTypeFlags mode')
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if not debug and sample_process_options.normalize_tanh:
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img = img * 2.0 - 1.0
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if f & SampleProcessor.TypeFlags.MODE_BGR != 0:
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img = img
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elif f & SampleProcessor.TypeFlags.MODE_BGR_SHUFFLE != 0:
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img_bgr = np.take (img_bgr, np.random.permutation(img_bgr.shape[-1]), axis=-1)
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img = np.concatenate ( (img_bgr,img_mask) , -1 )
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elif f & SampleProcessor.TypeFlags.MODE_G != 0:
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img = np.concatenate ( (np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1),img_mask) , -1 )
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elif f & SampleProcessor.TypeFlags.MODE_GGG != 0:
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img = np.concatenate ( ( np.repeat ( np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1), (3,), -1), img_mask), -1)
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elif is_face_sample and f & SampleProcessor.TypeFlags.MODE_M != 0:
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if face_mask_type== 0:
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raise ValueError ('no face_mask_type defined')
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img = img_mask
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else:
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raise ValueError ('expected SampleTypeFlags mode')
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if not debug and sample_process_options.normalize_tanh:
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img = img * 2.0 - 1.0
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outputs.append ( img )
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