mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
global refactoring and fixes,
removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG. fanseg model file in facelib/ is renamed
This commit is contained in:
Colombo
parent
921b464d5b
commit
61472cdaf7
82 changed files with 3838 additions and 3812 deletions
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@ -73,12 +73,19 @@ class SampleProcessor(object):
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if debug and is_face_sample:
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LandmarksProcessor.draw_landmarks (sample_bgr, sample_landmarks, (0, 1, 0))
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if sample_face_type == FaceType.MARK_ONLY:
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warp_resolution = np.max( [ opts.get('resolution', 0) for opts in output_sample_types ] )
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else:
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warp_resolution = w
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params = imagelib.gen_warp_params(warp_resolution, 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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params_per_resolution = {}
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warp_rnd_state = np.random.RandomState (sample_rnd_seed-1)
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for opts in output_sample_types:
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resolution = opts.get('resolution', None)
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if resolution is None:
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continue
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params_per_resolution[resolution] = imagelib.gen_warp_params(resolution,
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sample_process_options.random_flip,
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rotation_range=sample_process_options.rotation_range,
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scale_range=sample_process_options.scale_range,
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tx_range=sample_process_options.tx_range,
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ty_range=sample_process_options.ty_range,
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rnd_state=warp_rnd_state)
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outputs_sample = []
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for opts in output_sample_types:
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@ -89,6 +96,7 @@ class SampleProcessor(object):
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transform = opts.get('transform', False)
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motion_blur = opts.get('motion_blur', None)
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gaussian_blur = opts.get('gaussian_blur', None)
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random_bilinear_resize = opts.get('random_bilinear_resize', None)
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normalize_tanh = opts.get('normalize_tanh', False)
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ct_mode = opts.get('ct_mode', None)
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data_format = opts.get('data_format', 'NHWC')
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@ -137,31 +145,38 @@ class SampleProcessor(object):
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
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img = cv2.warpAffine( img, mat, (warp_resolution, warp_resolution), flags=cv2.INTER_LINEAR )
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_LINEAR )[...,None]
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img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_LINEAR )
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else:
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_LINEAR )[...,None]
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if face_type != sample_face_type:
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_LINEAR )
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else:
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if w != resolution:
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img = cv2.resize( img, (resolution, resolution), cv2.INTER_CUBIC )
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img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
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if len(img.shape) == 2:
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img = img[...,None]
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if channel_type == SPCT.G:
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out_sample = img.astype(np.float32)
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else:
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raise ValueError("only channel_type.G supported for the mask")
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elif sample_type == SPST.FACE_IMAGE:
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img = sample_bgr
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img = sample_bgr
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if sample_face_type == FaceType.MARK_ONLY:
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
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img = cv2.warpAffine( img, mat, (warp_resolution,warp_resolution), flags=cv2.INTER_CUBIC )
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
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else:
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate)
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if face_type != sample_face_type:
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC )
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC )
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else:
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if w != resolution:
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img = cv2.resize( img, (resolution, resolution), cv2.INTER_CUBIC )
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img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=border_replicate)
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img = np.clip(img.astype(np.float32), 0, 1)
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@ -195,6 +210,16 @@ class SampleProcessor(object):
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if gblur_rnd_chance < chance:
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img = cv2.GaussianBlur(img, (gblur_rnd_kernel,) *2 , 0)
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if random_bilinear_resize is not None:
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l_rnd_state = np.random.RandomState (sample_rnd_seed+2)
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chance, max_size_per = random_bilinear_resize
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chance = np.clip(chance, 0, 100)
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pick_chance = l_rnd_state.randint(100)
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resize_to = resolution - int( l_rnd_state.rand()* int(resolution*(max_size_per/100.0)) )
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img = cv2.resize (img, (resize_to,resize_to), cv2.INTER_LINEAR )
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img = cv2.resize (img, (resolution,resolution), cv2.INTER_LINEAR )
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# Transform from BGR to desired channel_type
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if channel_type == SPCT.BGR:
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out_sample = img
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@ -207,7 +232,7 @@ class SampleProcessor(object):
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h, s, v = cv2.split(hsv)
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h = (h + l_rnd_state.randint(360) ) % 360
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s = np.clip ( s + l_rnd_state.random()-0.5, 0, 1 )
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v = np.clip ( v + l_rnd_state.random()-0.5, 0, 1 )
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v = np.clip ( v + l_rnd_state.random()/2-0.25, 0, 1 )
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hsv = cv2.merge([h, s, v])
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out_sample = np.clip( cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) , 0, 1 )
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elif channel_type == SPCT.BGR_RANDOM_RGB_LEVELS:
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@ -234,7 +259,7 @@ class SampleProcessor(object):
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out_sample = np.transpose(out_sample, (2,0,1) )
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elif sample_type == SPST.IMAGE:
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img = sample_bgr
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=True)
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img = imagelib.warp_by_params (params_per_resolution[resolution], img, warp, transform, can_flip=True, border_replicate=True)
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img = cv2.resize( img, (resolution, resolution), cv2.INTER_CUBIC )
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out_sample = img
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