import sys import traceback import cv2 import numpy as np from core import imagelib from core.cv2ex import * from core.interact import interact as io from facelib import FaceType, LandmarksProcessor is_windows = sys.platform[0:3] == 'win' xseg_input_size = 256 def MergeMaskedFace (predictor_func, predictor_input_shape, face_enhancer_func, xseg_256_extract_func, 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) input_size = predictor_input_shape[0] mask_subres_size = input_size*4 output_size = input_size if cfg.super_resolution_power != 0: output_size *= 4 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) if mask_subres_size == output_size: face_mask_output_mat = face_output_mat else: face_mask_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, mask_subres_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_bgr = np.clip(dst_face_bgr, 0, 1) dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC ) dst_face_mask_a_0 = np.clip(dst_face_mask_a_0, 0, 1) predictor_input_bgr = cv2.resize (dst_face_bgr, (input_size,input_size) ) predicted = predictor_func (predictor_input_bgr) prd_face_bgr = np.clip (predicted[0], 0, 1.0) prd_face_mask_a_0 = np.clip (predicted[1], 0, 1.0) prd_face_dst_mask_a_0 = np.clip (predicted[2], 0, 1.0) if cfg.super_resolution_power != 0: prd_face_bgr_enhanced = face_enhancer_func(prd_face_bgr, is_tanh=True, preserve_size=False) mod = cfg.super_resolution_power / 100.0 prd_face_bgr = cv2.resize(prd_face_bgr, (output_size,output_size))*(1.0-mod) + prd_face_bgr_enhanced*mod prd_face_bgr = np.clip(prd_face_bgr, 0, 1) if cfg.super_resolution_power != 0: prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC) prd_face_dst_mask_a_0 = cv2.resize (prd_face_dst_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC) if cfg.mask_mode == 0: #full wrk_face_mask_a_0 = np.ones_like(dst_face_mask_a_0) elif cfg.mask_mode == 1: #dst wrk_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), interpolation=cv2.INTER_CUBIC) elif cfg.mask_mode == 2: #learned-prd wrk_face_mask_a_0 = prd_face_mask_a_0 elif cfg.mask_mode == 3: #learned-dst wrk_face_mask_a_0 = prd_face_dst_mask_a_0 elif cfg.mask_mode == 4: #learned-prd*learned-dst wrk_face_mask_a_0 = prd_face_mask_a_0*prd_face_dst_mask_a_0 elif cfg.mask_mode == 5: #learned-prd+learned-dst wrk_face_mask_a_0 = np.clip( prd_face_mask_a_0+prd_face_dst_mask_a_0, 0, 1) elif cfg.mask_mode >= 6 and cfg.mask_mode <= 9: #XSeg modes if cfg.mask_mode == 6 or cfg.mask_mode == 8 or cfg.mask_mode == 9: # obtain XSeg-prd prd_face_xseg_bgr = cv2.resize (prd_face_bgr, (xseg_input_size,)*2, interpolation=cv2.INTER_CUBIC) prd_face_xseg_mask = xseg_256_extract_func(prd_face_xseg_bgr) X_prd_face_mask_a_0 = cv2.resize ( prd_face_xseg_mask, (output_size, output_size), interpolation=cv2.INTER_CUBIC) if cfg.mask_mode >= 7 and cfg.mask_mode <= 9: # obtain XSeg-dst xseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, xseg_input_size, face_type=cfg.face_type) dst_face_xseg_bgr = cv2.warpAffine(img_bgr, xseg_mat, (xseg_input_size,)*2, flags=cv2.INTER_CUBIC ) dst_face_xseg_mask = xseg_256_extract_func(dst_face_xseg_bgr) X_dst_face_mask_a_0 = cv2.resize (dst_face_xseg_mask, (output_size,output_size), interpolation=cv2.INTER_CUBIC) if cfg.mask_mode == 6: #'XSeg-prd' wrk_face_mask_a_0 = X_prd_face_mask_a_0 elif cfg.mask_mode == 7: #'XSeg-dst' wrk_face_mask_a_0 = X_dst_face_mask_a_0 elif cfg.mask_mode == 8: #'XSeg-prd*XSeg-dst' wrk_face_mask_a_0 = X_prd_face_mask_a_0 * X_dst_face_mask_a_0 elif cfg.mask_mode == 9: #learned-prd*learned-dst*XSeg-prd*XSeg-dst wrk_face_mask_a_0 = prd_face_mask_a_0 * prd_face_dst_mask_a_0 * X_prd_face_mask_a_0 * X_dst_face_mask_a_0 wrk_face_mask_a_0[ wrk_face_mask_a_0 < (1.0/255.0) ] = 0.0 # get rid of noise # resize to mask_subres_size if wrk_face_mask_a_0.shape[0] != mask_subres_size: wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (mask_subres_size, mask_subres_size), interpolation=cv2.INTER_CUBIC) # process mask in local predicted space if 'raw' not in cfg.mode: # add zero pad wrk_face_mask_a_0 = np.pad (wrk_face_mask_a_0, input_size) ero = cfg.erode_mask_modifier blur = cfg.blur_mask_modifier if ero > 0: wrk_face_mask_a_0 = cv2.erode(wrk_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 ) elif ero < 0: wrk_face_mask_a_0 = cv2.dilate(wrk_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 ) # clip eroded/dilated mask in actual predict area # pad with half blur size in order to accuratelly fade to zero at the boundary clip_size = input_size + blur // 2 wrk_face_mask_a_0[:clip_size,:] = 0 wrk_face_mask_a_0[-clip_size:,:] = 0 wrk_face_mask_a_0[:,:clip_size] = 0 wrk_face_mask_a_0[:,-clip_size:] = 0 if blur > 0: blur = blur + (1-blur % 2) wrk_face_mask_a_0 = cv2.GaussianBlur(wrk_face_mask_a_0, (blur, blur) , 0) wrk_face_mask_a_0 = wrk_face_mask_a_0[input_size:-input_size,input_size:-input_size] wrk_face_mask_a_0 = np.clip(wrk_face_mask_a_0, 0, 1) img_face_mask_a = cv2.warpAffine( wrk_face_mask_a_0, face_mask_output_mat, img_size, np.zeros(img_bgr.shape[0:2], dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )[...,None] img_face_mask_a = np.clip (img_face_mask_a, 0.0, 1.0) img_face_mask_a [ img_face_mask_a < (1.0/255.0) ] = 0.0 # get rid of noise if wrk_face_mask_a_0.shape[0] != output_size: wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (output_size,output_size), interpolation=cv2.INTER_CUBIC) wrk_face_mask_a = wrk_face_mask_a_0[...,None] out_img = None out_merging_mask_a = None if cfg.mode == 'original': return img_bgr, img_face_mask_a elif 'raw' in cfg.mode: if cfg.mode == 'raw-rgb': out_img_face = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC) out_img_face_mask = cv2.warpAffine( np.ones_like(prd_face_bgr), face_output_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC) out_img = img_bgr*(1-out_img_face_mask) + out_img_face*out_img_face_mask out_merging_mask_a = img_face_mask_a elif cfg.mode == 'raw-predict': out_img = prd_face_bgr out_merging_mask_a = wrk_face_mask_a else: raise ValueError(f"undefined raw type {cfg.mode}") out_img = np.clip (out_img, 0.0, 1.0 ) else: # Process if the mask meets minimum size maxregion = np.argwhere( img_face_mask_a >= 0.1 ) 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: wrk_face_mask_area_a = wrk_face_mask_a.copy() wrk_face_mask_area_a[wrk_face_mask_area_a>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 (prd_face_bgr, dst_face_bgr, target_mask=wrk_face_mask_area_a, source_mask=wrk_face_mask_area_a) elif cfg.color_transfer_mode == 2: #lct prd_face_bgr = imagelib.linear_color_transfer (prd_face_bgr, dst_face_bgr) 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*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_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*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a) elif cfg.color_transfer_mode == 7: #sot-m prd_face_bgr = imagelib.color_transfer_sot (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a, steps=10, batch_size=30) prd_face_bgr = np.clip (prd_face_bgr, 0.0, 1.0) elif cfg.color_transfer_mode == 8: #mix-m prd_face_bgr = imagelib.color_transfer_mix (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a) if cfg.mode == 'hist-match': hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32) if cfg.masked_hist_match: hist_mask_a *= wrk_face_mask_area_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 'seamless' in cfg.mode: #mask used for cv2.seamlessClone 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 out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC ) out_img = np.clip(out_img, 0.0, 1.0) if 'seamless' in cfg.mode: 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) cfg_mp = cfg.motion_blur_power / 100.0 out_img = img_bgr*(1-img_face_mask_a) + (out_img*img_face_mask_a) if ('seamless' in cfg.mode and cfg.color_transfer_mode != 0) or \ cfg.mode == 'seamless-hist-match' or \ cfg_mp != 0 or \ cfg.blursharpen_amount != 0 or \ cfg.image_denoise_power != 0 or \ cfg.bicubic_degrade_power != 0: out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC ) if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0: if cfg.color_transfer_mode == 1: out_face_bgr = imagelib.reinhard_color_transfer (out_face_bgr, dst_face_bgr, target_mask=wrk_face_mask_area_a, source_mask=wrk_face_mask_area_a) elif cfg.color_transfer_mode == 2: #lct out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr) 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*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_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*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a) elif cfg.color_transfer_mode == 7: #sot-m out_face_bgr = imagelib.color_transfer_sot (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a, steps=10, batch_size=30) out_face_bgr = np.clip (out_face_bgr, 0.0, 1.0) elif cfg.color_transfer_mode == 8: #mix-m out_face_bgr = imagelib.color_transfer_mix (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a) if cfg.mode == 'seamless-hist-match': out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, cfg.hist_match_threshold) 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_power != 0: 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 = imagelib.blursharpen ( 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 ) ), interpolation=cv2.INTER_CUBIC) img_bgr = cv2.resize (img_bgr_downscaled, img_size, interpolation=cv2.INTER_CUBIC) new_out = cv2.warpAffine( out_face_bgr, face_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC ) out_img = np.clip( img_bgr*(1-img_face_mask_a) + (new_out*img_face_mask_a) , 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) out_merging_mask_a = img_face_mask_a if out_img is None: out_img = img_bgr.copy() return out_img, out_merging_mask_a def MergeMasked (predictor_func, predictor_input_shape, face_enhancer_func, xseg_256_extract_func, cfg, frame_info): img_bgr_uint8 = cv2_imread(frame_info.filepath) 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 = MergeMaskedFace (predictor_func, predictor_input_shape, face_enhancer_func, xseg_256_extract_func, cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks) outs += [ (out_img, out_img_merging_mask) ] #Combining multiple face outputs final_img = None final_mask = None for img, merging_mask in outs: h,w,c = img.shape if final_img is None: final_img = img final_mask = merging_mask else: final_img = final_img*(1-merging_mask) + img*merging_mask final_mask = np.clip (final_mask + merging_mask, 0, 1 ) final_img = np.concatenate ( [final_img, final_mask], -1) return (final_img*255).astype(np.uint8)