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Colombo
parent
154820a954
commit
e8673e3fcc
10 changed files with 339 additions and 262 deletions
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@ -92,231 +92,218 @@ class SampleProcessor(object):
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}
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@staticmethod
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def process (sample, sample_process_options, output_sample_types, debug, ct_sample=None):
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def process (samples, sample_process_options, output_sample_types, debug, ct_sample=None):
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SPTF = SampleProcessor.Types
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sample_bgr = sample.load_bgr()
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ct_sample_bgr = None
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ct_sample_mask = None
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h,w,c = sample_bgr.shape
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is_face_sample = sample.landmarks is not None
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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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params = imagelib.gen_warp_params(sample_bgr, 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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cached_images = collections.defaultdict(dict)
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sample_rnd_seed = np.random.randint(0x80000000)
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outputs = []
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for opts in output_sample_types:
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for sample in samples:
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sample_bgr = sample.load_bgr()
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ct_sample_bgr = None
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ct_sample_mask = None
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h,w,c = sample_bgr.shape
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resolution = opts.get('resolution', 0)
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types = opts.get('types', [] )
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is_face_sample = sample.landmarks is not None
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border_replicate = opts.get('border_replicate', True)
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random_sub_res = opts.get('random_sub_res', 0)
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normalize_std_dev = opts.get('normalize_std_dev', False)
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normalize_vgg = opts.get('normalize_vgg', 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_hsv_shift = opts.get('random_hsv_shift', None)
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ct_mode = opts.get('ct_mode', 'None')
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normalize_tanh = opts.get('normalize_tanh', False)
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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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img_type = SPTF.NONE
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target_face_type = SPTF.NONE
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face_mask_type = SPTF.NONE
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mode_type = SPTF.NONE
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for t in types:
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if t >= SPTF.IMG_TYPE_BEGIN and t < SPTF.IMG_TYPE_END:
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img_type = t
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elif t >= SPTF.FACE_TYPE_BEGIN and t < SPTF.FACE_TYPE_END:
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target_face_type = t
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elif t >= SPTF.MODE_BEGIN and t < SPTF.MODE_END:
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mode_type = t
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params = imagelib.gen_warp_params(sample_bgr, 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, rnd_seed=sample_rnd_seed )
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if img_type == SPTF.NONE:
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raise ValueError ('expected IMG_ type')
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outputs_sample = []
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for opts in output_sample_types:
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if img_type == SPTF.IMG_LANDMARKS_ARRAY:
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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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elif img_type == SPTF.IMG_PITCH_YAW_ROLL or img_type == SPTF.IMG_PITCH_YAW_ROLL_SIGMOID:
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pitch_yaw_roll = sample.pitch_yaw_roll
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if pitch_yaw_roll is not None:
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pitch, yaw, roll = pitch_yaw_roll
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resolution = opts.get('resolution', 0)
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types = opts.get('types', [] )
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border_replicate = opts.get('border_replicate', True)
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random_sub_res = opts.get('random_sub_res', 0)
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normalize_std_dev = opts.get('normalize_std_dev', False)
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normalize_vgg = opts.get('normalize_vgg', 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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ct_mode = opts.get('ct_mode', 'None')
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normalize_tanh = opts.get('normalize_tanh', False)
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img_type = SPTF.NONE
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target_face_type = SPTF.NONE
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face_mask_type = SPTF.NONE
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mode_type = SPTF.NONE
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for t in types:
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if t >= SPTF.IMG_TYPE_BEGIN and t < SPTF.IMG_TYPE_END:
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img_type = t
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elif t >= SPTF.FACE_TYPE_BEGIN and t < SPTF.FACE_TYPE_END:
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target_face_type = t
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elif t >= SPTF.MODE_BEGIN and t < SPTF.MODE_END:
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mode_type = t
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if img_type == SPTF.NONE:
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raise ValueError ('expected IMG_ type')
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if img_type == SPTF.IMG_LANDMARKS_ARRAY:
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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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elif img_type == SPTF.IMG_PITCH_YAW_ROLL or img_type == SPTF.IMG_PITCH_YAW_ROLL_SIGMOID:
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pitch_yaw_roll = sample.pitch_yaw_roll
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if pitch_yaw_roll is not None:
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pitch, yaw, roll = pitch_yaw_roll
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else:
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pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll (sample.landmarks)
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if params['flip']:
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yaw = -yaw
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if img_type == SPTF.IMG_PITCH_YAW_ROLL_SIGMOID:
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pitch = (pitch+1.0) / 2.0
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yaw = (yaw+1.0) / 2.0
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roll = (roll+1.0) / 2.0
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img = (pitch, yaw, roll)
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else:
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pitch, yaw, roll = LandmarksProcessor.estimate_pitch_yaw_roll (sample.landmarks)
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if params['flip']:
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yaw = -yaw
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if mode_type == SPTF.NONE:
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raise ValueError ('expected MODE_ type')
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if img_type == SPTF.IMG_PITCH_YAW_ROLL_SIGMOID:
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pitch = (pitch+1.0) / 2.0
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yaw = (yaw+1.0) / 2.0
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roll = (roll+1.0) / 2.0
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def do_transform(img, mask):
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warp = (img_type==SPTF.IMG_WARPED or img_type==SPTF.IMG_WARPED_TRANSFORMED)
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transform = (img_type==SPTF.IMG_WARPED_TRANSFORMED or img_type==SPTF.IMG_TRANSFORMED)
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flip = img_type != SPTF.IMG_WARPED
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img = (pitch, yaw, roll)
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else:
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if mode_type == SPTF.NONE:
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raise ValueError ('expected MODE_ type')
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img = imagelib.warp_by_params (params, img, warp, transform, flip, border_replicate)
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if mask is not None:
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mask = imagelib.warp_by_params (params, mask, warp, transform, flip, False)
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if len(mask.shape) == 2:
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mask = mask[...,np.newaxis]
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def do_transform(img, mask):
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warp = (img_type==SPTF.IMG_WARPED or img_type==SPTF.IMG_WARPED_TRANSFORMED)
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transform = (img_type==SPTF.IMG_WARPED_TRANSFORMED or img_type==SPTF.IMG_TRANSFORMED)
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flip = img_type != SPTF.IMG_WARPED
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img = np.concatenate( (img, mask ), -1 )
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return img
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img = imagelib.warp_by_params (params, img, warp, transform, flip, border_replicate)
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if mask is not None:
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mask = imagelib.warp_by_params (params, mask, warp, transform, flip, False)
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if len(mask.shape) == 2:
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mask = mask[...,np.newaxis]
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img = sample_bgr
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img = np.concatenate( (img, mask ), -1 )
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return img
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### Prepare a mask
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mask = None
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if is_face_sample:
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mask = sample.load_fanseg_mask() #using fanseg_mask if exist
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img = sample_bgr
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if mask is None:
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if sample.eyebrows_expand_mod is not None:
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mask = LandmarksProcessor.get_image_hull_mask (img.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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else:
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mask = LandmarksProcessor.get_image_hull_mask (img.shape, sample.landmarks)
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### Prepare a mask
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mask = None
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if is_face_sample:
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mask = sample.load_fanseg_mask() #using fanseg_mask if exist
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if sample.ie_polys is not None:
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sample.ie_polys.overlay_mask(mask)
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##################
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if mask is None:
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if sample.eyebrows_expand_mod is not None:
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mask = LandmarksProcessor.get_image_hull_mask (img.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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if motion_blur is not None:
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chance, mb_max_size = motion_blur
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chance = np.clip(chance, 0, 100)
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if np.random.randint(100) < chance:
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img = imagelib.LinearMotionBlur (img, np.random.randint( mb_max_size )+1, np.random.randint(360) )
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if gaussian_blur is not None:
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chance, kernel_max_size = gaussian_blur
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chance = np.clip(chance, 0, 100)
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if np.random.randint(100) < chance:
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img = cv2.GaussianBlur(img, ( np.random.randint( kernel_max_size )*2+1 ,) *2 , 0)
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if is_face_sample and target_face_type != SPTF.NONE:
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target_ft = SampleProcessor.SPTF_FACETYPE_TO_FACETYPE[target_face_type]
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if target_ft > 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_ft) )
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if sample.face_type == FaceType.MARK_ONLY:
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#first warp to target facetype
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img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
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mask = cv2.warpAffine( mask, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
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#then apply transforms
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img = do_transform (img, mask)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
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else:
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mask = LandmarksProcessor.get_image_hull_mask (img.shape, sample.landmarks)
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img = do_transform (img, mask)
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img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, target_ft), (resolution,resolution), borderMode=(cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC )
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if sample.ie_polys is not None:
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sample.ie_polys.overlay_mask(mask)
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##################
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if motion_blur is not None:
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chance, mb_max_size = motion_blur
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chance = np.clip(chance, 0, 100)
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if np.random.randint(100) < chance:
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img = imagelib.LinearMotionBlur (img, np.random.randint( mb_max_size )+1, np.random.randint(360) )
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if gaussian_blur is not None:
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chance, kernel_max_size = gaussian_blur
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chance = np.clip(chance, 0, 100)
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if np.random.randint(100) < chance:
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img = cv2.GaussianBlur(img, ( np.random.randint( kernel_max_size )*2+1 ,) *2 , 0)
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if is_face_sample and target_face_type != SPTF.NONE:
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target_ft = SampleProcessor.SPTF_FACETYPE_TO_FACETYPE[target_face_type]
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if target_ft > 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_ft) )
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if sample.face_type == FaceType.MARK_ONLY:
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#first warp to target facetype
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img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
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mask = cv2.warpAffine( mask, LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
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#then apply transforms
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else:
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img = do_transform (img, mask)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
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else:
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img = do_transform (img, mask)
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img = cv2.warpAffine( img, LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, target_ft), (resolution,resolution), borderMode=(cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC )
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else:
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img = do_transform (img, mask)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
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if random_sub_res != 0:
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sub_size = resolution - random_sub_res
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rnd_state = np.random.RandomState (sample_rnd_seed+random_sub_res)
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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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if random_sub_res != 0:
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sub_size = resolution - random_sub_res
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rnd_state = np.random.RandomState (sample_rnd_seed+random_sub_res)
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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 = np.clip(img, 0, 1).astype(np.float32)
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img_bgr = img[...,0:3]
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img_mask = img[...,3:4]
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img = np.clip(img, 0, 1).astype(np.float32)
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img_bgr = img[...,0:3]
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img_mask = img[...,3:4]
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if ct_mode is not None and ct_sample is not None:
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if ct_sample_bgr is None:
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ct_sample_bgr = ct_sample.load_bgr()
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if ct_mode is not None and ct_sample is not None:
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if ct_sample_bgr is None:
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ct_sample_bgr = ct_sample.load_bgr()
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ct_sample_bgr_resized = cv2.resize( ct_sample_bgr, (resolution,resolution), cv2.INTER_LINEAR )
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ct_sample_bgr_resized = cv2.resize( ct_sample_bgr, (resolution,resolution), cv2.INTER_LINEAR )
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if ct_mode == 'lct':
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img_bgr = imagelib.linear_color_transfer (img_bgr, ct_sample_bgr_resized)
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img_bgr = np.clip( img_bgr, 0.0, 1.0)
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elif ct_mode == 'rct':
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img_bgr = imagelib.reinhard_color_transfer ( np.clip( (img_bgr*255).astype(np.uint8), 0, 255),
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np.clip( (ct_sample_bgr_resized*255).astype(np.uint8), 0, 255) )
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img_bgr = np.clip( img_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
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elif ct_mode == 'mkl':
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img_bgr = imagelib.color_transfer_mkl (img_bgr, ct_sample_bgr_resized)
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elif ct_mode == 'idt':
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img_bgr = imagelib.color_transfer_idt (img_bgr, ct_sample_bgr_resized)
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elif ct_mode == 'sot':
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img_bgr = imagelib.color_transfer_sot (img_bgr, ct_sample_bgr_resized)
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img_bgr = np.clip( img_bgr, 0.0, 1.0)
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if ct_mode == 'lct':
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img_bgr = imagelib.linear_color_transfer (img_bgr, ct_sample_bgr_resized)
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img_bgr = np.clip( img_bgr, 0.0, 1.0)
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elif ct_mode == 'rct':
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img_bgr = imagelib.reinhard_color_transfer ( np.clip( (img_bgr*255).astype(np.uint8), 0, 255),
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np.clip( (ct_sample_bgr_resized*255).astype(np.uint8), 0, 255) )
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img_bgr = np.clip( img_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
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elif ct_mode == 'mkl':
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img_bgr = imagelib.color_transfer_mkl (img_bgr, ct_sample_bgr_resized)
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elif ct_mode == 'idt':
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img_bgr = imagelib.color_transfer_idt (img_bgr, ct_sample_bgr_resized)
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elif ct_mode == 'sot':
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img_bgr = imagelib.color_transfer_sot (img_bgr, ct_sample_bgr_resized)
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img_bgr = np.clip( img_bgr, 0.0, 1.0)
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if random_hsv_shift:
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rnd_state = np.random.RandomState (sample_rnd_seed)
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hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
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h, s, v = cv2.split(hsv)
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h = (h + rnd_state.randint(360) ) % 360
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s = np.clip ( s + rnd_state.random()-0.5, 0, 1 )
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v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )
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hsv = cv2.merge([h, s, v])
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img_bgr = np.clip( cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) , 0, 1 )
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if normalize_std_dev:
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img_bgr = (img_bgr - img_bgr.mean( (0,1)) ) / img_bgr.std( (0,1) )
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elif normalize_vgg:
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img_bgr = np.clip(img_bgr*255, 0, 255)
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img_bgr[:,:,0] -= 103.939
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img_bgr[:,:,1] -= 116.779
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img_bgr[:,:,2] -= 123.68
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if mode_type == SPTF.MODE_BGR:
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img = img_bgr
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elif mode_type == SPTF.MODE_BGR_SHUFFLE:
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rnd_state = np.random.RandomState (sample_rnd_seed)
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img = np.take (img_bgr, rnd_state.permutation(img_bgr.shape[-1]), axis=-1)
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elif mode_type == SPTF.MODE_BGR_RANDOM_HSV_SHIFT:
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rnd_state = np.random.RandomState (sample_rnd_seed)
|
||||
hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
|
||||
h, s, v = cv2.split(hsv)
|
||||
h = (h + rnd_state.randint(360) ) % 360
|
||||
s = np.clip ( s + rnd_state.random()-0.5, 0, 1 )
|
||||
v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )
|
||||
hsv = cv2.merge([h, s, v])
|
||||
img = np.clip( cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) , 0, 1 )
|
||||
elif mode_type == SPTF.MODE_G:
|
||||
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)[...,None]
|
||||
elif mode_type == SPTF.MODE_GGG:
|
||||
img = np.repeat ( np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1), (3,), -1)
|
||||
elif mode_type == SPTF.MODE_M and is_face_sample:
|
||||
img = img_mask
|
||||
|
||||
if normalize_std_dev:
|
||||
img_bgr = (img_bgr - img_bgr.mean( (0,1)) ) / img_bgr.std( (0,1) )
|
||||
elif normalize_vgg:
|
||||
img_bgr = np.clip(img_bgr*255, 0, 255)
|
||||
img_bgr[:,:,0] -= 103.939
|
||||
img_bgr[:,:,1] -= 116.779
|
||||
img_bgr[:,:,2] -= 123.68
|
||||
|
||||
if mode_type == SPTF.MODE_BGR:
|
||||
img = img_bgr
|
||||
elif mode_type == SPTF.MODE_BGR_SHUFFLE:
|
||||
rnd_state = np.random.RandomState (sample_rnd_seed)
|
||||
img = np.take (img_bgr, rnd_state.permutation(img_bgr.shape[-1]), axis=-1)
|
||||
elif mode_type == SPTF.MODE_G:
|
||||
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)[...,None]
|
||||
elif mode_type == SPTF.MODE_GGG:
|
||||
img = np.repeat ( np.expand_dims(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY),-1), (3,), -1)
|
||||
elif mode_type == SPTF.MODE_M and is_face_sample:
|
||||
img = img_mask
|
||||
if not debug:
|
||||
if normalize_tanh:
|
||||
img = np.clip (img * 2.0 - 1.0, -1.0, 1.0)
|
||||
else:
|
||||
img = np.clip (img, 0.0, 1.0)
|
||||
|
||||
if not debug:
|
||||
if normalize_tanh:
|
||||
img = np.clip (img * 2.0 - 1.0, -1.0, 1.0)
|
||||
else:
|
||||
img = np.clip (img, 0.0, 1.0)
|
||||
|
||||
outputs.append ( img )
|
||||
|
||||
if debug:
|
||||
result = []
|
||||
|
||||
for output in outputs:
|
||||
if output.shape[2] < 4:
|
||||
result += [output,]
|
||||
elif output.shape[2] == 4:
|
||||
result += [output[...,0:3]*output[...,3:4],]
|
||||
|
||||
return result
|
||||
else:
|
||||
return outputs
|
||||
outputs_sample.append ( img )
|
||||
outputs += [outputs_sample]
|
||||
|
||||
return outputs
|
||||
|
||||
"""
|
||||
close_sample = sample.close_target_list[ np.random.randint(0, len(sample.close_target_list)) ] if sample.close_target_list is not None else None
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue