diff --git a/converters/ConverterMasked.py b/converters/ConverterMasked.py index 69113d5..e1cd15c 100644 --- a/converters/ConverterMasked.py +++ b/converters/ConverterMasked.py @@ -179,154 +179,153 @@ class ConverterMasked(Converter): maskx = int( maskx ) masky = int( masky ) - if lenx >= 4 and leny >= 4: - lowest_len = min (lenx, leny) - - if debug: - io.log_info ("lowest_len = %f" % (lowest_len) ) - - img_mask_blurry_aaa = img_face_mask_aaa + 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) ) - + 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.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.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) ) - if self.mode == 'hist-match' or self.mode == 'hist-match-bw': + 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 += [ 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 + 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) - 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) + 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) ] + - 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) + 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()] - 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 ) + 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.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.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 ) )