def convert_face(self, img_bgr, img_face_landmarks, debug): if debug: debugs = [img_bgr.copy()] img_size = img_bgr.shape[1], img_bgr.shape[0] img_face_mask_a = LandmarksProcessor.get_image_hull_mask( img_bgr.shape, img_face_landmarks) face_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, self.output_size, face_type=self.face_type) face_output_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, self.output_size, face_type=self.face_type, scale=self.output_face_scale) dst_face_bgr = cv2.warpAffine(img_bgr, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4) dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4) predictor_input_bgr = cv2.resize( dst_face_bgr, (self.predictor_input_size, self.predictor_input_size)) predictor_input_mask_a_0 = cv2.resize( dst_face_mask_a_0, (self.predictor_input_size, self.predictor_input_size)) predictor_input_mask_a = np.expand_dims(predictor_input_mask_a_0, -1) predicted_bgra = self.predictor( np.concatenate((predictor_input_bgr, predictor_input_mask_a), -1)) prd_face_bgr = np.clip(predicted_bgra[:, :, 0:3], 0, 1.0) prd_face_mask_a_0 = np.clip(predicted_bgra[:, :, 3], 0.0, 1.0) if not self.use_predicted_mask: prd_face_mask_a_0 = predictor_input_mask_a_0 prd_face_mask_a_0[prd_face_mask_a_0 < 0.001] = 0.0 prd_face_mask_a = np.expand_dims(prd_face_mask_a_0, axis=-1) prd_face_mask_aaa = np.repeat(prd_face_mask_a, (3, ), axis=-1) img_prd_face_mask_aaa = cv2.warpAffine(prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) img_prd_face_mask_aaa = np.clip(img_prd_face_mask_aaa, 0.0, 1.0) img_face_mask_aaa = img_prd_face_mask_aaa if debug: debugs += [img_face_mask_aaa.copy()] img_face_mask_aaa[img_face_mask_aaa <= 0.1] = 0.0 img_face_mask_flatten_aaa = img_face_mask_aaa.copy() img_face_mask_flatten_aaa[img_face_mask_flatten_aaa > 0.9] = 1.0 maxregion = np.argwhere(img_face_mask_flatten_aaa == 1.0) out_img = img_bgr.copy() if self.mode == 'raw': if self.raw_mode == 'rgb' or self.raw_mode == 'rgb-mask': out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) if self.raw_mode == 'rgb-mask': out_img = np.concatenate( [out_img, np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1) if self.raw_mode == 'mask-only': out_img = img_face_mask_aaa if self.raw_mode == 'predicted-only': out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(out_img.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) else: if maxregion.size != 0: miny, minx = maxregion.min(axis=0)[:2] maxy, maxx = maxregion.max(axis=0)[:2] if debug: print( "maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy)) lenx = maxx - minx leny = maxy - miny if lenx >= 4 and leny >= 4: masky = int(minx + (lenx // 2)) maskx = int(miny + (leny // 2)) lowest_len = min(lenx, leny) if debug: print("lowest_len = %f" % (lowest_len)) img_mask_blurry_aaa = img_face_mask_aaa if self.erode_mask_modifier != 0: ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * self.erode_mask_modifier) if debug: print("erode_size = %d" % (ero)) if ero > 0: img_mask_blurry_aaa = cv2.erode( img_mask_blurry_aaa, cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_mask_blurry_aaa = cv2.dilate( img_mask_blurry_aaa, cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) if self.seamless_erode_mask_modifier != 0: ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * self.seamless_erode_mask_modifier) if debug: print("seamless_erode_size = %d" % (ero)) if ero > 0: img_face_mask_flatten_aaa = cv2.erode( img_face_mask_flatten_aaa, cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_face_mask_flatten_aaa = cv2.dilate( img_face_mask_flatten_aaa, cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) if self.clip_hborder_mask_per > 0: #clip hborder before blur prd_hborder_rect_mask_a = np.ones( prd_face_mask_a.shape, dtype=np.float32) prd_border_size = int( prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per) prd_hborder_rect_mask_a[:, 0:prd_border_size, :] = 0 prd_hborder_rect_mask_a[:, -prd_border_size:, :] = 0 prd_hborder_rect_mask_a = np.expand_dims( cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size)), -1) img_prd_hborder_rect_mask_a = cv2.warpAffine( prd_hborder_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) img_prd_hborder_rect_mask_a = np.expand_dims( img_prd_hborder_rect_mask_a, -1) img_mask_blurry_aaa *= img_prd_hborder_rect_mask_a img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0) if debug: debugs += [img_mask_blurry_aaa.copy()] if self.blur_mask_modifier > 0: blur = int(lowest_len * 0.10 * 0.01 * self.blur_mask_modifier) if debug: print("blur_size = %d" % (blur)) if blur > 0: img_mask_blurry_aaa = cv2.blur( img_mask_blurry_aaa, (blur, blur)) img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0) if debug: debugs += [img_mask_blurry_aaa.copy()] if self.color_transfer_mode is not None: if self.color_transfer_mode == 'rct': if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] prd_face_bgr = image_utils.reinhard_color_transfer( np.clip((prd_face_bgr * 255).astype(np.uint8), 0, 255), np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255), source_mask=prd_face_mask_a, target_mask=prd_face_mask_a) prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] elif self.color_transfer_mode == 'lct': if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] prd_face_bgr = image_utils.linear_color_transfer( prd_face_bgr, dst_face_bgr) prd_face_bgr = np.clip(prd_face_bgr, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] if self.mode == 'hist-match-bw': prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY) prd_face_bgr = np.repeat( np.expand_dims(prd_face_bgr, -1), (3, ), -1) if self.mode == 'hist-match' or self.mode == 'hist-match-bw': if debug: debugs += [ cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) ] hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) if self.masked_hist_match: hist_mask_a *= prd_face_mask_a hist_match_1 = prd_face_bgr * hist_mask_a + ( 1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_1[hist_match_1 > 1.0] = 1.0 hist_match_2 = dst_face_bgr * hist_mask_a + ( 1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_2[hist_match_1 > 1.0] = 1.0 prd_face_bgr = image_utils.color_hist_match( hist_match_1, hist_match_2, self.hist_match_threshold) if self.mode == 'hist-match-bw': prd_face_bgr = prd_face_bgr.astype(dtype=np.float32) out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) out_img = np.clip(out_img, 0.0, 1.0) if debug: debugs += [out_img.copy()] if self.mode == 'overlay': pass if self.mode == 'seamless' or self.mode == 'seamless-hist-match': out_img = np.clip( img_bgr * (1 - img_face_mask_aaa) + (out_img * img_face_mask_aaa), 0, 1.0) if debug: debugs += [out_img.copy()] out_img = cv2.seamlessClone( (out_img * 255).astype(np.uint8), (img_bgr * 255).astype(np.uint8), (img_face_mask_flatten_aaa * 255).astype(np.uint8), (masky, maskx), cv2.NORMAL_CLONE) out_img = out_img.astype(dtype=np.float32) / 255.0 if debug: debugs += [out_img.copy()] out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (out_img * img_mask_blurry_aaa), 0, 1.0) if self.mode == 'seamless-hist-match': out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size)) new_out_face_bgr = image_utils.color_hist_match( out_face_bgr, dst_face_bgr, self.hist_match_threshold) new_out = cv2.warpAffine( new_out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (new_out * img_mask_blurry_aaa), 0, 1.0) if self.final_image_color_degrade_power != 0: if debug: debugs += [out_img.copy()] out_img_reduced = image_utils.reduce_colors( out_img, 256) if self.final_image_color_degrade_power == 100: out_img = out_img_reduced else: alpha = self.final_image_color_degrade_power / 100.0 out_img = (out_img * (1.0 - alpha) + out_img_reduced * alpha) if self.alpha: out_img = np.concatenate([ out_img, np.expand_dims(img_mask_blurry_aaa[:, :, 0], -1) ], -1) out_img = np.clip(out_img, 0.0, 1.0) if debug: debugs += [out_img.copy()] return debugs if debug else out_img
def convert_face(self, img_bgr, img_face_landmarks, debug): if debug: debugs = [img_bgr.copy()] img_size = img_bgr.shape[1], img_bgr.shape[0] img_face_mask_a = LandmarksProcessor.get_image_hull_mask( img_bgr, img_face_landmarks) face_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, self.output_size, face_type=self.face_type) face_output_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, self.output_size, face_type=self.face_type, scale=self.output_face_scale) dst_face_bgr = cv2.warpAffine(img_bgr, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4) dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4) predictor_input_bgr = cv2.resize( dst_face_bgr, (self.predictor_input_size, self.predictor_input_size)) predictor_input_mask_a_0 = cv2.resize( dst_face_mask_a_0, (self.predictor_input_size, self.predictor_input_size)) predictor_input_mask_a = np.expand_dims(predictor_input_mask_a_0, -1) predicted_bgra = self.predictor( np.concatenate((predictor_input_bgr, predictor_input_mask_a), -1)) prd_face_bgr = np.clip(predicted_bgra[:, :, 0:3], 0, 1.0) prd_face_mask_a_0 = np.clip(predicted_bgra[:, :, 3], 0.0, 1.0) prd_face_mask_a_0[prd_face_mask_a_0 < 0.001] = 0.0 prd_face_mask_a = np.expand_dims(prd_face_mask_a_0, axis=-1) prd_face_mask_aaa = np.repeat(prd_face_mask_a, (3, ), axis=-1) img_prd_face_mask_aaa = cv2.warpAffine(prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=float), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) img_prd_face_mask_aaa = np.clip(img_prd_face_mask_aaa, 0.0, 1.0) img_face_mask_aaa = img_prd_face_mask_aaa if debug: debugs += [img_face_mask_aaa.copy()] img_face_mask_aaa[img_face_mask_aaa <= 0.1] = 0.0 img_face_mask_flatten_aaa = img_face_mask_aaa.copy() img_face_mask_flatten_aaa[img_face_mask_flatten_aaa > 0.9] = 1.0 maxregion = np.argwhere(img_face_mask_flatten_aaa == 1.0) out_img = img_bgr.copy() if maxregion.size != 0: miny, minx = maxregion.min(axis=0)[:2] maxy, maxx = maxregion.max(axis=0)[:2] if debug: print("maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy)) lenx = maxx - minx leny = maxy - miny if lenx >= 4 and leny >= 4: masky = int(minx + (lenx // 2)) maskx = int(miny + (leny // 2)) lowest_len = min(lenx, leny) if debug: print("lowest_len = %f" % (lowest_len)) ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * self.erode_mask_modifier) blur = int(lowest_len * 0.10 * 0.01 * self.blur_mask_modifier) if debug: print("erode_size = %d, blur_size = %d" % (ero, blur)) img_mask_blurry_aaa = img_face_mask_aaa if self.erode_mask: if ero > 0: img_mask_blurry_aaa = cv2.erode( img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_mask_blurry_aaa = cv2.dilate( img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) if self.blur_mask and blur > 0: img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur)) img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0) if self.clip_border_mask_per > 0: prd_border_rect_mask_a = np.ones( prd_face_mask_a.shape, dtype=prd_face_mask_a.dtype) prd_border_size = int(prd_border_rect_mask_a.shape[1] * self.clip_border_mask_per) prd_border_rect_mask_a[0:prd_border_size, :, :] = 0 prd_border_rect_mask_a[-prd_border_size:, :, :] = 0 prd_border_rect_mask_a[:, 0:prd_border_size, :] = 0 prd_border_rect_mask_a[:, -prd_border_size:, :] = 0 prd_border_rect_mask_a = np.expand_dims( cv2.blur(prd_border_rect_mask_a, (prd_border_size, prd_border_size)), -1) if self.mode == 'hist-match-bw': prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY) prd_face_bgr = np.repeat(np.expand_dims(prd_face_bgr, -1), (3, ), -1) if self.mode == 'hist-match' or self.mode == 'hist-match-bw': if debug: debugs += [ cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) ] hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1, ), dtype=prd_face_bgr.dtype) if self.masked_hist_match: hist_mask_a *= prd_face_mask_a hist_match_1 = prd_face_bgr * hist_mask_a + ( 1.0 - hist_mask_a) * np.ones(prd_face_bgr.shape[:2] + (1, ), dtype=prd_face_bgr.dtype) hist_match_1[hist_match_1 > 1.0] = 1.0 hist_match_2 = dst_face_bgr * hist_mask_a + ( 1.0 - hist_mask_a) * np.ones(prd_face_bgr.shape[:2] + (1, ), dtype=prd_face_bgr.dtype) hist_match_2[hist_match_1 > 1.0] = 1.0 new_prd_face_bgr = image_utils.color_hist_match( hist_match_1, hist_match_2) prd_face_bgr = new_prd_face_bgr if self.mode == 'hist-match-bw': prd_face_bgr = prd_face_bgr.astype(np.float32) out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) if debug: debugs += [out_img.copy()] debugs += [img_mask_blurry_aaa.copy()] if self.mode == 'seamless' or self.mode == 'seamless-hist-match': out_img = np.clip( img_bgr * (1 - img_face_mask_aaa) + (out_img * img_face_mask_aaa), 0, 1.0) if debug: debugs += [out_img.copy()] out_img = cv2.seamlessClone( (out_img * 255).astype(np.uint8), (img_bgr * 255).astype(np.uint8), (img_face_mask_flatten_aaa * 255).astype(np.uint8), (masky, maskx), cv2.NORMAL_CLONE) out_img = out_img.astype(np.float32) / 255.0 if debug: debugs += [out_img.copy()] if self.clip_border_mask_per > 0: img_prd_border_rect_mask_a = cv2.warpAffine( prd_border_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) img_prd_border_rect_mask_a = np.expand_dims( img_prd_border_rect_mask_a, -1) out_img = out_img * img_prd_border_rect_mask_a + img_bgr * ( 1.0 - img_prd_border_rect_mask_a) img_mask_blurry_aaa *= img_prd_border_rect_mask_a out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (out_img * img_mask_blurry_aaa), 0, 1.0) if self.mode == 'seamless-hist-match': out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size)) new_out_face_bgr = image_utils.color_hist_match( out_face_bgr, dst_face_bgr) new_out = cv2.warpAffine( new_out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (new_out * img_mask_blurry_aaa), 0, 1.0) if self.transfercolor: if self.TFLabConverter is None: self.TFLabConverter = image_utils.TFLabConverter() img_lab_l, img_lab_a, img_lab_b = np.split( self.TFLabConverter.bgr2lab(img_bgr), 3, axis=-1) out_img_lab_l, out_img_lab_a, out_img_lab_b = np.split( self.TFLabConverter.bgr2lab(out_img), 3, axis=-1) out_img = self.TFLabConverter.lab2bgr( np.concatenate([out_img_lab_l, img_lab_a, img_lab_b], axis=-1)) if self.final_image_color_degrade_power != 0: if debug: debugs += [out_img.copy()] out_img_reduced = image_utils.reduce_colors(out_img, 256) if self.final_image_color_degrade_power == 100: out_img = out_img_reduced else: alpha = self.final_image_color_degrade_power / 100.0 out_img = (out_img * (1.0 - alpha) + out_img_reduced * alpha) if self.alpha: new_image = out_img.copy() new_image = (new_image * 255).astype( np.uint8) #convert image to int b_channel, g_channel, r_channel = cv2.split( new_image) #splitting RGB alpha_channel = img_mask_blurry_aaa.copy( ) #making copy of alpha channel alpha_channel = (alpha_channel * 255).astype(np.uint8) alpha_channel, tmp2, tmp3 = cv2.split( alpha_channel ) #splitting alpha to three channels, they all same in original alpha channel, we need just one out_img = cv2.merge( (b_channel, g_channel, r_channel, alpha_channel)) #mergin RGB with alpha out_img = out_img.astype(np.float32) / 255.0 if debug: debugs += [out_img.copy()] return debugs if debug else out_img
def convert_face(self, img_bgr, img_face_landmarks, debug): if (self.mask_mode == 3 or self.mask_mode == 4) and self.fan_seg == None: self.fan_seg = FANSegmentator(256, FaceType.toString(FaceType.FULL)) if self.over_res != 1: img_bgr = cv2.resize(img_bgr, (img_bgr.shape[1] * self.over_res, img_bgr.shape[0] * self.over_res)) img_face_landmarks = img_face_landmarks * self.over_res if debug: debugs = [img_bgr.copy()] img_size = img_bgr.shape[1], img_bgr.shape[0] img_face_mask_a = LandmarksProcessor.get_image_hull_mask( img_bgr.shape, img_face_landmarks) face_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, self.output_size, face_type=self.face_type) face_output_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, self.output_size, face_type=self.face_type, scale=self.output_face_scale) dst_face_bgr = cv2.warpAffine(img_bgr, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4) dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (self.output_size, self.output_size), flags=cv2.INTER_LANCZOS4) predictor_input_bgr = cv2.resize( dst_face_bgr, (self.predictor_input_size, self.predictor_input_size)) predictor_input_mask_a_0 = cv2.resize( dst_face_mask_a_0, (self.predictor_input_size, self.predictor_input_size)) predictor_input_mask_a = np.expand_dims(predictor_input_mask_a_0, -1) predicted_bgra = self.predictor_func( np.concatenate((predictor_input_bgr, predictor_input_mask_a), -1)) prd_face_bgr = np.clip(predicted_bgra[:, :, 0:3], 0, 1.0) prd_face_mask_a_0 = np.clip(predicted_bgra[:, :, 3], 0.0, 1.0) if self.mask_mode == 2: #dst prd_face_mask_a_0 = predictor_input_mask_a_0 elif self.mask_mode == 3: #FAN-prd prd_face_bgr_256 = cv2.resize(prd_face_bgr, (256, 256)) prd_face_bgr_256_mask = self.fan_seg.extract_from_bgr( np.expand_dims(prd_face_bgr_256, 0))[0] prd_face_mask_a_0 = cv2.resize( prd_face_bgr_256_mask, (self.predictor_input_size, self.predictor_input_size)) elif self.mask_mode == 4: #FAN-dst face_256_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, 256, face_type=FaceType.FULL) dst_face_256_bgr = cv2.warpAffine(img_bgr, face_256_mat, (256, 256), flags=cv2.INTER_LANCZOS4) dst_face_256_mask = self.fan_seg.extract_from_bgr( np.expand_dims(dst_face_256_bgr, 0))[0] prd_face_mask_a_0 = cv2.resize( dst_face_256_mask, (self.predictor_input_size, self.predictor_input_size)) prd_face_mask_a_0[prd_face_mask_a_0 < 0.001] = 0.0 prd_face_mask_a = np.expand_dims(prd_face_mask_a_0, axis=-1) prd_face_mask_aaa = np.repeat(prd_face_mask_a, (3, ), axis=-1) img_face_mask_aaa = cv2.warpAffine(prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) img_face_mask_aaa = np.clip(img_face_mask_aaa, 0.0, 1.0) img_face_mask_aaa[img_face_mask_aaa <= 0.1] = 0.0 #get rid of noise if debug: debugs += [img_face_mask_aaa.copy()] if 'seamless' in self.mode: #mask used for cv2.seamlessClone img_face_seamless_mask_aaa = None for i in range(9, 0, -1): a = img_face_mask_aaa > i / 10.0 if len(np.argwhere(a)) == 0: continue img_face_seamless_mask_aaa = img_face_mask_aaa.copy() img_face_seamless_mask_aaa[a] = 1.0 img_face_seamless_mask_aaa[img_face_seamless_mask_aaa <= i / 10.0] = 0.0 out_img = img_bgr.copy() if self.mode == 'raw': if self.raw_mode == 'rgb' or self.raw_mode == 'rgb-mask': out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) if self.raw_mode == 'rgb-mask': out_img = np.concatenate( [out_img, np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1) if self.raw_mode == 'mask-only': out_img = img_face_mask_aaa if self.raw_mode == 'predicted-only': out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(out_img.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) elif ('seamless' not in self.mode) or (img_face_seamless_mask_aaa is not None): #averaging [lenx, leny, maskx, masky] by grayscale gradients of upscaled mask ar = [] for i in range(1, 10): maxregion = np.argwhere(img_face_mask_aaa > i / 10.0) if maxregion.size != 0: miny, minx = maxregion.min(axis=0)[:2] maxy, maxx = maxregion.max(axis=0)[:2] lenx = maxx - minx leny = maxy - miny maskx = (minx + (lenx / 2)) masky = (miny + (leny / 2)) if lenx >= 4 and leny >= 4: ar += [[lenx, leny, maskx, masky]] if len(ar) > 0: lenx, leny, maskx, masky = np.mean(ar, axis=0) if debug: io.log_info("lenx/leny:(%d/%d) maskx/masky:(%f/%f)" % (lenx, leny, maskx, masky)) maskx = int(maskx) masky = int(masky) lowest_len = min(lenx, leny) if debug: io.log_info("lowest_len = %f" % (lowest_len)) img_mask_blurry_aaa = img_face_mask_aaa if self.erode_mask_modifier != 0: ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * self.erode_mask_modifier) if debug: io.log_info("erode_size = %d" % (ero)) if ero > 0: img_mask_blurry_aaa = cv2.erode( img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_mask_blurry_aaa = cv2.dilate( img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) if self.seamless_erode_mask_modifier != 0: ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * self.seamless_erode_mask_modifier) if debug: io.log_info("seamless_erode_size = %d" % (ero)) if ero > 0: img_face_seamless_mask_aaa = cv2.erode( img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_face_seamless_mask_aaa = cv2.dilate( img_face_seamless_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) img_face_seamless_mask_aaa = np.clip( img_face_seamless_mask_aaa, 0, 1) if self.clip_hborder_mask_per > 0: #clip hborder before blur prd_hborder_rect_mask_a = np.ones(prd_face_mask_a.shape, dtype=np.float32) prd_border_size = int(prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per) prd_hborder_rect_mask_a[:, 0:prd_border_size, :] = 0 prd_hborder_rect_mask_a[:, -prd_border_size:, :] = 0 prd_hborder_rect_mask_a = np.expand_dims( cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size)), -1) img_prd_hborder_rect_mask_a = cv2.warpAffine( prd_hborder_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) img_prd_hborder_rect_mask_a = np.expand_dims( img_prd_hborder_rect_mask_a, -1) img_mask_blurry_aaa *= img_prd_hborder_rect_mask_a img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0) if debug: debugs += [img_mask_blurry_aaa.copy()] if self.blur_mask_modifier > 0: blur = int(lowest_len * 0.10 * 0.01 * self.blur_mask_modifier) if debug: io.log_info("blur_size = %d" % (blur)) if blur > 0: img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur)) img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0) if debug: debugs += [img_mask_blurry_aaa.copy()] if self.color_transfer_mode is not None: if self.color_transfer_mode == 'rct': if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] prd_face_bgr = image_utils.reinhard_color_transfer( np.clip((prd_face_bgr * 255).astype(np.uint8), 0, 255), np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255), source_mask=prd_face_mask_a, target_mask=prd_face_mask_a) prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] elif self.color_transfer_mode == 'lct': if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] prd_face_bgr = image_utils.linear_color_transfer( prd_face_bgr, dst_face_bgr) prd_face_bgr = np.clip(prd_face_bgr, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] if self.mode == 'hist-match-bw': prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY) prd_face_bgr = np.repeat(np.expand_dims(prd_face_bgr, -1), (3, ), -1) if self.mode == 'hist-match' or self.mode == 'hist-match-bw': if debug: debugs += [ cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) ] hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) if self.masked_hist_match: hist_mask_a *= prd_face_mask_a hist_match_1 = prd_face_bgr * hist_mask_a + ( 1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_1[hist_match_1 > 1.0] = 1.0 hist_match_2 = dst_face_bgr * hist_mask_a + ( 1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_2[hist_match_1 > 1.0] = 1.0 prd_face_bgr = image_utils.color_hist_match( hist_match_1, hist_match_2, self.hist_match_threshold) if self.mode == 'hist-match-bw': prd_face_bgr = prd_face_bgr.astype(dtype=np.float32) out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) out_img = np.clip(out_img, 0.0, 1.0) if debug: debugs += [out_img.copy()] if self.mode == 'overlay': pass if 'seamless' in self.mode: try: out_img = cv2.seamlessClone( (out_img * 255).astype(np.uint8), (img_bgr * 255).astype(np.uint8), (img_face_seamless_mask_aaa * 255).astype( np.uint8), (maskx, masky), cv2.NORMAL_CLONE) out_img = out_img.astype(dtype=np.float32) / 255.0 except Exception as e: #seamlessClone may fail in some cases e_str = traceback.format_exc() if 'MemoryError' in e_str: raise Exception( "Seamless fail: " + e_str ) #reraise MemoryError in order to reprocess this data by other processes else: print("Seamless fail: " + e_str) if debug: debugs += [out_img.copy()] out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (out_img * img_mask_blurry_aaa), 0, 1.0) if self.mode == 'seamless-hist-match': out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size)) new_out_face_bgr = image_utils.color_hist_match( out_face_bgr, dst_face_bgr, self.hist_match_threshold) new_out = cv2.warpAffine( new_out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT) out_img = np.clip( img_bgr * (1 - img_mask_blurry_aaa) + (new_out * img_mask_blurry_aaa), 0, 1.0) if self.final_image_color_degrade_power != 0: if debug: debugs += [out_img.copy()] out_img_reduced = image_utils.reduce_colors(out_img, 256) if self.final_image_color_degrade_power == 100: out_img = out_img_reduced else: alpha = self.final_image_color_degrade_power / 100.0 out_img = (out_img * (1.0 - alpha) + out_img_reduced * alpha) if self.alpha: out_img = np.concatenate([ out_img, np.expand_dims(img_mask_blurry_aaa[:, :, 0], -1) ], -1) if self.over_res != 1: out_img = cv2.resize(out_img, (img_bgr.shape[1] // self.over_res, img_bgr.shape[0] // self.over_res)) out_img = np.clip(out_img, 0.0, 1.0) if debug: debugs += [out_img.copy()] return debugs if debug else out_img