from models import ConverterBase from facelib import LandmarksProcessor from facelib import FaceType import cv2 import numpy as np from utils import image_utils class ConverterMasked(ConverterBase): #override def __init__(self, predictor, predictor_input_size=0, output_size=0, face_type=FaceType.FULL, erode_mask = True, blur_mask = True, clip_border_mask_per = 0, masked_hist_match = False, mode='seamless', erode_mask_modifier=0, blur_mask_modifier=0, output_face_scale_modifier=0.0, transfercolor=False, final_image_color_degrade_power=0, alpha=False, **in_options): super().__init__(predictor) self.predictor_input_size = predictor_input_size self.output_size = output_size self.face_type = face_type self.erode_mask = erode_mask self.blur_mask = blur_mask self.clip_border_mask_per = clip_border_mask_per self.masked_hist_match = masked_hist_match self.mode = mode self.erode_mask_modifier = erode_mask_modifier self.blur_mask_modifier = blur_mask_modifier self.output_face_scale = np.clip(1.0 + output_face_scale_modifier*0.01, 0.5, 1.0) self.transfercolor = transfercolor self.final_image_color_degrade_power = np.clip (final_image_color_degrade_power, 0, 100) self.alpha = alpha if self.erode_mask_modifier != 0 and not self.erode_mask: print ("Erode mask modifier not used in this model.") if self.blur_mask_modifier != 0 and not self.blur_mask: print ("Blur modifier not used in this model.") #override def get_mode(self): return ConverterBase.MODE_FACE #override def dummy_predict(self): self.predictor ( np.zeros ( (self.predictor_input_size,self.predictor_input_size,4), dtype=np.float32 ) ) #override 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] lenx = maxx - minx leny = maxy - miny 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 ("ero = %d, blur = %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: #making transfer color from original DST image to fake from skimage import io, color lab_clr = color.rgb2lab(img_bgr) #original DST, converting RGB to LAB color space lab_bw = color.rgb2lab(out_img) #fake, converting RGB to LAB color space tmp_channel, a_channel, b_channel = cv2.split(lab_clr) #taking color channel A and B from original dst image l_channel, tmp2_channel, tmp3_channel = cv2.split(lab_bw) #taking lightness channel L from merged fake img_LAB = cv2.merge((l_channel,a_channel, b_channel)) #merging light and color out_img = color.lab2rgb(img_LAB) #converting LAB to RGB 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