def ConvertMaskedFace(cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks): #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) if cfg.mode == 'original': if cfg.export_mask_alpha: img_bgr = np.concatenate([img_bgr, img_face_mask_a], -1) return img_bgr, img_face_mask_a out_img = img_bgr.copy() out_merging_mask = None output_size = cfg.predictor_input_shape[0] if cfg.super_resolution_mode != 0: output_size *= 2 face_mat = LandmarksProcessor.get_transform_mat(img_face_landmarks, output_size, face_type=cfg.face_type) face_output_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, output_size, face_type=cfg.face_type, scale=1.0 + 0.01 * cfg.output_face_scale) dst_face_bgr = cv2.warpAffine(img_bgr, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC) dst_face_mask_a_0 = cv2.warpAffine(img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC) predictor_input_bgr = cv2.resize(dst_face_bgr, cfg.predictor_input_shape[0:2]) if cfg.predictor_masked: prd_face_bgr, prd_face_mask_a_0 = cfg.predictor_func( predictor_input_bgr) prd_face_bgr = np.clip(prd_face_bgr, 0, 1.0) prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0.0, 1.0) else: predicted = cfg.predictor_func(predictor_input_bgr) prd_face_bgr = np.clip(predicted, 0, 1.0) prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, cfg.predictor_input_shape[0:2]) if cfg.super_resolution_mode: #if debug: # tmp = cv2.resize (prd_face_bgr, (output_size,output_size), cv2.INTER_CUBIC) # debugs += [ np.clip( cv2.warpAffine( tmp, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] prd_face_bgr = cfg.superres_func(cfg.super_resolution_mode, prd_face_bgr) #if debug: # debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] if cfg.predictor_masked: prd_face_mask_a_0 = cv2.resize(prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) else: prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) if cfg.mask_mode == 2: #dst prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) elif cfg.mask_mode >= 3 and cfg.mask_mode <= 7: if cfg.mask_mode == 3 or cfg.mask_mode == 5 or cfg.mask_mode == 6: prd_face_fanseg_bgr = cv2.resize(prd_face_bgr, (cfg.fanseg_input_size, ) * 2) prd_face_fanseg_mask = cfg.fanseg_extract_func( FaceType.FULL, prd_face_fanseg_bgr) FAN_prd_face_mask_a_0 = cv2.resize(prd_face_fanseg_mask, (output_size, output_size), cv2.INTER_CUBIC) if cfg.mask_mode >= 4 or cfg.mask_mode <= 7: full_face_fanseg_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, cfg.fanseg_input_size, face_type=FaceType.FULL) dst_face_fanseg_bgr = cv2.warpAffine(img_bgr, full_face_fanseg_mat, (cfg.fanseg_input_size, ) * 2, flags=cv2.INTER_CUBIC) dst_face_fanseg_mask = cfg.fanseg_extract_func( FaceType.FULL, dst_face_fanseg_bgr) if cfg.face_type == FaceType.FULL: FAN_dst_face_mask_a_0 = cv2.resize(dst_face_fanseg_mask, (output_size, output_size), cv2.INTER_CUBIC) elif cfg.face_type == FaceType.HALF: half_face_fanseg_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, cfg.fanseg_input_size, face_type=FaceType.HALF) fanseg_rect_corner_pts = np.array( [[0, 0], [cfg.fanseg_input_size - 1, 0], [0, cfg.fanseg_input_size - 1]], dtype=np.float32) a = LandmarksProcessor.transform_points(fanseg_rect_corner_pts, half_face_fanseg_mat, invert=True) b = LandmarksProcessor.transform_points( a, full_face_fanseg_mat) m = cv2.getAffineTransform(b, fanseg_rect_corner_pts) FAN_dst_face_mask_a_0 = cv2.warpAffine( dst_face_fanseg_mask, m, (cfg.fanseg_input_size, ) * 2, flags=cv2.INTER_CUBIC) FAN_dst_face_mask_a_0 = cv2.resize(FAN_dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) else: raise ValueError("cfg.face_type unsupported") if cfg.mask_mode == 3: #FAN-prd prd_face_mask_a_0 = FAN_prd_face_mask_a_0 elif cfg.mask_mode == 4: #FAN-dst prd_face_mask_a_0 = FAN_dst_face_mask_a_0 elif cfg.mask_mode == 5: prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 elif cfg.mask_mode == 6: prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 elif cfg.mask_mode == 7: prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_dst_face_mask_a_0 prd_face_mask_a_0[prd_face_mask_a_0 < 0.001] = 0.0 prd_face_mask_a = prd_face_mask_a_0[..., np.newaxis] 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_CUBIC) 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 'raw' in cfg.mode: face_corner_pts = np.array( [[0, 0], [output_size - 1, 0], [output_size - 1, output_size - 1], [0, output_size - 1]], dtype=np.float32) square_mask = np.zeros(img_bgr.shape, dtype=np.float32) cv2.fillConvexPoly(square_mask, \ LandmarksProcessor.transform_points (face_corner_pts, face_output_mat, invert=True ).astype(np.int), \ (1,1,1) ) if cfg.mode == 'raw-rgb': out_merging_mask = square_mask if cfg.mode == 'raw-rgb' or cfg.mode == 'raw-rgb-mask': out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT) if cfg.mode == 'raw-rgb-mask': out_img = np.concatenate( [out_img, np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1) out_merging_mask = square_mask elif cfg.mode == 'raw-mask-only': out_img = img_face_mask_aaa out_merging_mask = img_face_mask_aaa elif cfg.mode == 'raw-predicted-only': out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT) out_merging_mask = square_mask out_img = np.clip(out_img, 0.0, 1.0) else: #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 if min(lenx, leny) >= 4: ar += [[lenx, leny]] if len(ar) > 0: lenx, leny = np.mean(ar, axis=0) lowest_len = min(lenx, leny) #if debug: # io.log_info ("lenx/leny:(%d/%d) " % (lenx, leny ) ) # io.log_info ("lowest_len = %f" % (lowest_len) ) if cfg.erode_mask_modifier != 0: ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * cfg.erode_mask_modifier) #if debug: # io.log_info ("erode_size = %d" % (ero) ) if ero > 0: img_face_mask_aaa = cv2.erode(img_face_mask_aaa, cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_face_mask_aaa = cv2.dilate(img_face_mask_aaa, cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) if cfg.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] * cfg.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[-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_CUBIC) img_prd_hborder_rect_mask_a = np.expand_dims( img_prd_hborder_rect_mask_a, -1) img_face_mask_aaa *= img_prd_hborder_rect_mask_a img_face_mask_aaa = np.clip(img_face_mask_aaa, 0, 1.0) #if debug: # debugs += [img_face_mask_aaa.copy()] if cfg.blur_mask_modifier > 0: blur = int(lowest_len * 0.10 * 0.01 * cfg.blur_mask_modifier) #if debug: # io.log_info ("blur_size = %d" % (blur) ) if blur > 0: img_face_mask_aaa = cv2.blur(img_face_mask_aaa, (blur, blur)) img_face_mask_aaa = np.clip(img_face_mask_aaa, 0, 1.0) #if debug: # debugs += [img_face_mask_aaa.copy()] if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0: if cfg.color_transfer_mode == 1: #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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] prd_face_bgr = imagelib.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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] elif cfg.color_transfer_mode == 2: #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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] prd_face_bgr = imagelib.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_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] if cfg.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 cfg.mode == 'hist-match' or cfg.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_CUBIC, cv2.BORDER_TRANSPARENT ) ] hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) if cfg.masked_hist_match: hist_mask_a *= prd_face_mask_a white = (1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_1 = prd_face_bgr * hist_mask_a + white hist_match_1[hist_match_1 > 1.0] = 1.0 hist_match_2 = dst_face_bgr * hist_mask_a + white hist_match_2[hist_match_1 > 1.0] = 1.0 prd_face_bgr = imagelib.color_hist_match( hist_match_1, hist_match_2, cfg.hist_match_threshold) #if cfg.masked_hist_match: # prd_face_bgr -= white if cfg.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_CUBIC, cv2.BORDER_TRANSPARENT) out_img = np.clip(out_img, 0.0, 1.0) #if debug: # debugs += [out_img.copy()] if cfg.mode == 'overlay': pass if 'seamless' in cfg.mode: #mask used for cv2.seamlessClone img_face_seamless_mask_a = None img_face_mask_a = img_face_mask_aaa[..., 0:1] for i in range(1, 10): a = img_face_mask_a > i / 10.0 if len(np.argwhere(a)) == 0: continue img_face_seamless_mask_a = img_face_mask_aaa[..., 0:1].copy() img_face_seamless_mask_a[a] = 1.0 img_face_seamless_mask_a[img_face_seamless_mask_a <= i / 10.0] = 0.0 break try: #calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering (not flickering) l, t, w, h = cv2.boundingRect( (img_face_seamless_mask_a * 255).astype(np.uint8)) s_maskx, s_masky = int(l + w / 2), int(t + h / 2) out_img = cv2.seamlessClone( (out_img * 255).astype(np.uint8), img_bgr_uint8, (img_face_seamless_mask_a * 255).astype(np.uint8), (s_maskx, s_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 = img_bgr * (1 - img_face_mask_aaa) + (out_img * img_face_mask_aaa) out_face_bgr = cv2.warpAffine(out_img, face_mat, (output_size, output_size)) if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0: if cfg.color_transfer_mode == 1: #if debug: # debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] face_mask_aaa = cv2.warpAffine(img_face_mask_aaa, face_mat, (output_size, output_size)) out_face_bgr = imagelib.reinhard_color_transfer( np.clip((out_face_bgr * 255).astype(np.uint8), 0, 255), np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255), source_mask=face_mask_aaa, target_mask=face_mask_aaa) out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) #if debug: # debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] elif cfg.color_transfer_mode == 2: #if debug: # debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] out_face_bgr = imagelib.linear_color_transfer( out_face_bgr, dst_face_bgr) out_face_bgr = np.clip(out_face_bgr, 0.0, 1.0) #if debug: # debugs += [ np.clip( cv2.warpAffine( out_face_bgr, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] if cfg.mode == 'seamless-hist-match': out_face_bgr = imagelib.color_hist_match( out_face_bgr, dst_face_bgr, cfg.hist_match_threshold) cfg_mp = cfg.motion_blur_power / 100.0 if cfg_mp != 0: k_size = int(frame_info.motion_power * cfg_mp) if k_size >= 1: k_size = np.clip(k_size + 1, 2, 50) if cfg.super_resolution_mode: k_size *= 2 out_face_bgr = imagelib.LinearMotionBlur( out_face_bgr, k_size, frame_info.motion_deg) if cfg.sharpen_mode != 0 and cfg.sharpen_amount != 0: out_face_bgr = cfg.sharpen_func(out_face_bgr, cfg.sharpen_mode, 3, cfg.sharpen_amount) new_out = cv2.warpAffine(out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT) out_img = np.clip( img_bgr * (1 - img_face_mask_aaa) + (new_out * img_face_mask_aaa), 0, 1.0) if cfg.color_degrade_power != 0: #if debug: # debugs += [out_img.copy()] out_img_reduced = imagelib.reduce_colors(out_img, 256) if cfg.color_degrade_power == 100: out_img = out_img_reduced else: alpha = cfg.color_degrade_power / 100.0 out_img = (out_img * (1.0 - alpha) + out_img_reduced * alpha) if cfg.export_mask_alpha: out_img = np.concatenate( [out_img, img_face_mask_aaa[:, :, 0:1]], -1) out_merging_mask = img_face_mask_aaa #if debug: # debugs += [out_img.copy()] return out_img, out_merging_mask
def cli_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) output_size = self.predictor_input_size if self.super_resolution: output_size *= 2 face_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, output_size, face_type=self.face_type) face_output_mat = LandmarksProcessor.get_transform_mat( img_face_landmarks, output_size, face_type=self.face_type, scale=self.output_face_scale) dst_face_bgr = cv2.warpAffine(img_bgr, face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4) dst_face_mask_a_0 = cv2.warpAffine(img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4) predictor_input_bgr = cv2.resize( dst_face_bgr, (self.predictor_input_size, self.predictor_input_size)) if self.predictor_masked: prd_face_bgr, prd_face_mask_a_0 = self.predictor_func( predictor_input_bgr) prd_face_bgr = np.clip(prd_face_bgr, 0, 1.0) prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0.0, 1.0) else: predicted = self.predictor_func(predictor_input_bgr) prd_face_bgr = np.clip(predicted, 0, 1.0) prd_face_mask_a_0 = cv2.resize( dst_face_mask_a_0, (self.predictor_input_size, self.predictor_input_size)) if self.super_resolution: if debug: tmp = cv2.resize(prd_face_bgr, (output_size, output_size), cv2.INTER_CUBIC) debugs += [ np.clip( cv2.warpAffine( tmp, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] prd_face_bgr = self.dc_upscale(prd_face_bgr) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0, 1.0) ] if self.predictor_masked: prd_face_mask_a_0 = cv2.resize(prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) else: prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) if self.mask_mode == 2: #dst prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC) elif self.mask_mode >= 3 and self.mask_mode <= 6: if self.mask_mode == 3 or self.mask_mode == 5 or self.mask_mode == 6: prd_face_bgr_256 = cv2.resize(prd_face_bgr, (256, 256)) prd_face_bgr_256_mask = self.fan_seg.extract_from_bgr( prd_face_bgr_256[np.newaxis, ...])[0] FAN_prd_face_mask_a_0 = cv2.resize(prd_face_bgr_256_mask, (output_size, output_size), cv2.INTER_CUBIC) if self.mask_mode == 4 or self.mask_mode == 5 or self.mask_mode == 6: 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( dst_face_256_bgr[np.newaxis, ...])[0] FAN_dst_face_mask_a_0 = cv2.resize(dst_face_256_mask, (output_size, output_size), cv2.INTER_CUBIC) if self.mask_mode == 3: #FAN-prd prd_face_mask_a_0 = FAN_prd_face_mask_a_0 elif self.mask_mode == 4: #FAN-dst prd_face_mask_a_0 = FAN_dst_face_mask_a_0 elif self.mask_mode == 5: prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 elif self.mask_mode == 6: prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 prd_face_mask_a_0[prd_face_mask_a_0 < 0.001] = 0.0 prd_face_mask_a = prd_face_mask_a_0[..., np.newaxis] 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()] 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: #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 if min(lenx, leny) >= 4: ar += [[lenx, leny]] if len(ar) > 0: lenx, leny = np.mean(ar, axis=0) lowest_len = min(lenx, leny) if debug: io.log_info("lenx/leny:(%d/%d) " % (lenx, leny)) io.log_info("lowest_len = %f" % (lowest_len)) 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_face_mask_aaa = cv2.erode( img_face_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ero, ero)), iterations=1) elif ero < 0: img_face_mask_aaa = cv2.dilate( img_face_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (-ero, -ero)), iterations=1) img_mask_blurry_aaa = img_face_mask_aaa 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) face_mask_blurry_aaa = cv2.warpAffine( img_mask_blurry_aaa, face_mat, (output_size, output_size)) if debug: debugs += [img_mask_blurry_aaa.copy()] if 'seamless' not in self.mode and 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 = imagelib.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 = imagelib.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 white = (1.0 - hist_mask_a) * np.ones( prd_face_bgr.shape[:2] + (1, ), dtype=np.float32) hist_match_1 = prd_face_bgr * hist_mask_a + white hist_match_1[hist_match_1 > 1.0] = 1.0 hist_match_2 = dst_face_bgr * hist_mask_a + white hist_match_2[hist_match_1 > 1.0] = 1.0 prd_face_bgr = imagelib.color_hist_match( hist_match_1, hist_match_2, self.hist_match_threshold) #if self.masked_hist_match: # prd_face_bgr -= white 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: #mask used for cv2.seamlessClone img_face_seamless_mask_a = None img_face_mask_a = img_mask_blurry_aaa[..., 0:1] for i in range(1, 10): a = img_face_mask_a > i / 10.0 if len(np.argwhere(a)) == 0: continue img_face_seamless_mask_a = img_mask_blurry_aaa[ ..., 0:1].copy() img_face_seamless_mask_a[a] = 1.0 img_face_seamless_mask_a[ img_face_seamless_mask_a <= i / 10.0] = 0.0 break try: #calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering l, t, w, h = cv2.boundingRect( (img_face_seamless_mask_a * 255).astype(np.uint8)) s_maskx, s_masky = int(l + w / 2), int(t + h / 2) out_img = cv2.seamlessClone( (out_img * 255).astype(np.uint8), (img_bgr * 255).astype(np.uint8), (img_face_seamless_mask_a * 255).astype(np.uint8), (s_maskx, s_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 'seamless' in self.mode and self.color_transfer_mode is not None: out_face_bgr = cv2.warpAffine(out_img, face_mat, (output_size, output_size)) if self.color_transfer_mode == 'rct': if debug: debugs += [ np.clip( cv2.warpAffine( out_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) ] new_out_face_bgr = imagelib.reinhard_color_transfer( np.clip((out_face_bgr * 255).astype(np.uint8), 0, 255), np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255), source_mask=face_mask_blurry_aaa, target_mask=face_mask_blurry_aaa) new_out_face_bgr = np.clip( new_out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( new_out_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( out_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) ] new_out_face_bgr = imagelib.linear_color_transfer( out_face_bgr, dst_face_bgr) new_out_face_bgr = np.clip(new_out_face_bgr, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( new_out_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) ] 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.mode == 'seamless-hist-match': out_face_bgr = cv2.warpAffine(out_img, face_mat, (output_size, output_size)) new_out_face_bgr = imagelib.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 = imagelib.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