import traceback import cv2 import numpy as np import imagelib from facelib import FaceType, LandmarksProcessor from interact import interact as io from utils.cv2_utils import * def ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks): 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 = 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, predictor_input_shape[0:2] ) predicted = predictor_func (predictor_input_bgr) if isinstance(predicted, tuple): #converter return bgr,mask prd_face_bgr = np.clip (predicted[0], 0, 1.0) prd_face_mask_a_0 = np.clip (predicted[1], 0, 1.0) predictor_masked = True else: #converter return bgr only, using dst mask prd_face_bgr = np.clip (predicted, 0, 1.0 ) prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, predictor_input_shape[0:2] ) predictor_masked = False if cfg.super_resolution_mode: prd_face_bgr = cfg.superres_func(cfg.super_resolution_mode, prd_face_bgr) if 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 <= 8: 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 and 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) else: face_fanseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, cfg.fanseg_input_size, face_type=cfg.face_type) 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, 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) """ if cfg.mask_mode == 8: #FANCHQ-dst full_face_fanchq_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, cfg.fanchq_input_size, face_type=FaceType.FULL) dst_face_fanchq_bgr = cv2.warpAffine(img_bgr, full_face_fanchq_mat, (cfg.fanchq_input_size,)*2, flags=cv2.INTER_CUBIC ) dst_face_fanchq_mask = cfg.fanchq_extract_func( FaceType.FULL, dst_face_fanchq_bgr ) if cfg.face_type == FaceType.FULL: FANCHQ_dst_face_mask_a_0 = cv2.resize (dst_face_fanchq_mask, (output_size,output_size), cv2.INTER_CUBIC) else: face_fanchq_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, cfg.fanchq_input_size, face_type=cfg.face_type) fanchq_rect_corner_pts = np.array ( [ [0,0], [cfg.fanchq_input_size-1,0], [0,cfg.fanchq_input_size-1] ], dtype=np.float32 ) a = LandmarksProcessor.transform_points (fanchq_rect_corner_pts, face_fanchq_mat, invert=True ) b = LandmarksProcessor.transform_points (a, full_face_fanchq_mat ) m = cv2.getAffineTransform(b, fanchq_rect_corner_pts) FAN_dst_face_mask_a_0 = cv2.warpAffine(dst_face_fanchq_mask, m, (cfg.fanchq_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) """ 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 #elif cfg.mask_mode == 8: #FANCHQ-dst # prd_face_mask_a_0 = FANCHQ_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 '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 cfg.erode_mask_modifier != 0: ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*cfg.erode_mask_modifier ) 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 cfg.blur_mask_modifier > 0: blur = int( lowest_len * 0.10 * 0.01*cfg.blur_mask_modifier ) 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 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0: if cfg.color_transfer_mode == 1: #rct 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) elif cfg.color_transfer_mode == 2: #lct 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) elif cfg.color_transfer_mode == 3: #mkl prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 4: #mkl-m prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) elif cfg.color_transfer_mode == 5: #idt prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 6: #idt-m prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) elif cfg.color_transfer_mode == 7: #ebs, currently unused prd_face_bgr = cfg.ebs_ct_func ( np.clip( (dst_face_bgr*255), 0, 255).astype(np.uint8), np.clip( (prd_face_bgr*255), 0, 255).astype(np.uint8), )#prd_face_mask_a prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 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': 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 ).astype(dtype=np.float32) if cfg.mode == 'hist-match-bw': prd_face_bgr = prd_face_bgr.astype(dtype=np.float32) if 'seamless' in cfg.mode: #mask used for cv2.seamlessClone img_face_mask_a = img_face_mask_aaa[...,0:1] if cfg.mode == 'seamless2': img_face_mask_a = cv2.warpAffine( img_face_mask_a, face_output_mat, (output_size, output_size), flags=cv2.INTER_CUBIC ) img_face_seamless_mask_a = None 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_a.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 if cfg.mode == 'seamless2': face_seamless = imagelib.seamless_clone ( prd_face_bgr, dst_face_bgr, img_face_seamless_mask_a ) out_img = cv2.warpAffine( face_seamless, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT ) else: 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 'seamless' in cfg.mode and cfg.mode != 'seamless2': 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) 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: 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), 0, 255).astype(np.uint8), np.clip( (dst_face_bgr*255), 0, 255).astype(np.uint8), 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) elif cfg.color_transfer_mode == 2: #lct 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) elif cfg.color_transfer_mode == 3: #mkl out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 4: #mkl-m out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) elif cfg.color_transfer_mode == 5: #idt out_face_bgr = imagelib.color_transfer_idt (out_face_bgr, dst_face_bgr) elif cfg.color_transfer_mode == 6: #idt-m out_face_bgr = imagelib.color_transfer_idt (out_face_bgr*prd_face_mask_a, dst_face_bgr*prd_face_mask_a) elif cfg.color_transfer_mode == 7: #ebs out_face_bgr = cfg.ebs_ct_func ( np.clip( (dst_face_bgr*255), 0, 255).astype(np.uint8), np.clip( (out_face_bgr*255), 0, 255).astype(np.uint8), ) out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 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.blursharpen_amount != 0: out_face_bgr = cfg.blursharpen_func ( out_face_bgr, cfg.sharpen_mode, 3, cfg.blursharpen_amount) if cfg.image_denoise_power != 0: n = cfg.image_denoise_power while n > 0: img_bgr_denoised = cv2.medianBlur(img_bgr, 5) if int(n / 100) != 0: img_bgr = img_bgr_denoised else: pass_power = (n % 100) / 100.0 img_bgr = img_bgr*(1.0-pass_power)+img_bgr_denoised*pass_power n = max(n-10,0) if cfg.bicubic_degrade_power != 0: p = 1.0 - cfg.bicubic_degrade_power / 101.0 img_bgr_downscaled = cv2.resize (img_bgr, ( int(img_size[0]*p), int(img_size[1]*p ) ), cv2.INTER_CUBIC) img_bgr = cv2.resize (img_bgr_downscaled, img_size, cv2.INTER_CUBIC) 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: 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 return out_img, out_merging_mask def ConvertMasked (predictor_func, predictor_input_shape, cfg, frame_info): img_bgr_uint8 = cv2_imread(frame_info.filename) img_bgr_uint8 = imagelib.normalize_channels (img_bgr_uint8, 3) img_bgr = img_bgr_uint8.astype(np.float32) / 255.0 outs = [] for face_num, img_landmarks in enumerate( frame_info.landmarks_list ): out_img, out_img_merging_mask = ConvertMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks) outs += [ (out_img, out_img_merging_mask) ] #Combining multiple face outputs final_img = None for img, merging_mask in outs: h,w,c = img.shape if final_img is None: final_img = img else: merging_mask = merging_mask[...,0:1] if c == 3: final_img = final_img*(1-merging_mask) + img*merging_mask elif c == 4: final_img_bgr = final_img[...,0:3]*(1-merging_mask) + img[...,0:3]*merging_mask final_img_mask = np.clip ( final_img[...,3:4] + img[...,3:4], 0, 1 ) final_img = np.concatenate ( [final_img_bgr, final_img_mask], -1 ) return (final_img*255).astype(np.uint8)