from models import ConverterBase from facelib import LandmarksProcessor from facelib import FaceType import cv2 import numpy as np from utils import image_utils from utils.console_utils import * class ConverterMasked(ConverterBase): #override def __init__(self, predictor, predictor_input_size=0, output_size=0, face_type=FaceType.FULL, base_erode_mask_modifier = 0, base_blur_mask_modifier = 0, clip_border_mask_per = 0, **in_options): super().__init__(predictor) self.predictor_input_size = predictor_input_size self.output_size = output_size self.face_type = face_type self.clip_border_mask_per = clip_border_mask_per self.TFLabConverter = None mode = input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless (default), (5) seamless hist match, (6) raw : ", 4) self.mode = {1:'overlay', 2:'hist-match', 3:'hist-match-bw', 4:'seamless', 5:'seamless-hist-match', 6:'raw'}.get (mode, 'seamless') if self.mode == 'raw': mode = input_int ("Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only, (4) predicted only : ", 2) self.raw_mode = {1:'rgb', 2:'rgb-mask', 3:'mask-only', 4:'predicted-only'}.get (mode, 'rgb-mask') if self.mode != 'raw': if self.mode == 'hist-match' or self.mode == 'hist-match-bw': self.masked_hist_match = input_bool("Masked hist match? (y/n skip:y) : ", True) if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match': self.hist_match_threshold = np.clip ( input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255) self.use_predicted_mask = input_bool("Use predicted mask? (y/n skip:y) : ", True) if self.mode != 'raw': self.erode_mask_modifier = base_erode_mask_modifier + np.clip ( input_int ("Choose erode mask modifier [-200..200] (skip:0) : ", 0), -200, 200) self.blur_mask_modifier = base_blur_mask_modifier + np.clip ( input_int ("Choose blur mask modifier [-200..200] (skip:0) : ", 0), -200, 200) self.seamless_erode_mask_modifier = 0 if self.mode == 'seamless' or self.mode == 'seamless-hist-match': self.seamless_erode_mask_modifier = np.clip ( input_int ("Choose seamless erode mask modifier [-100..100] (skip:0) : ", 0), -100, 100) self.output_face_scale = np.clip ( 1.0 + input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0)*0.01, 0.5, 1.5) if self.mode != 'raw': self.transfercolor = input_bool("Transfer color from dst face to converted final face? (y/n skip:n) : ", False) self.final_image_color_degrade_power = np.clip ( input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100) self.alpha = input_bool("Export png with alpha channel? (y/n skip:n) : ", False) print ("") #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.shape, 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) if not self.use_predicted_mask: prd_face_mask_a_0 = predictor_input_mask_a_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 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), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ) else: if maxregion.size != 0: miny,minx = maxregion.min(axis=0)[:2] maxy,maxx = maxregion.max(axis=0)[:2] if debug: print ("maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy ) ) lenx = maxx - minx leny = maxy - miny if lenx >= 4 and leny >= 4: masky = int(minx+(lenx//2)) maskx = int(miny+(leny//2)) lowest_len = min (lenx, leny) if debug: print ("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: print ("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: print ("seamless_erode_size = %d" % (ero) ) if ero > 0: img_face_mask_flatten_aaa = cv2.erode(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 ) elif ero < 0: img_face_mask_flatten_aaa = cv2.dilate(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 ) if self.blur_mask_modifier > 0: blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier ) if debug: print ("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 ) 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 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(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 == 'overlay': pass 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) 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, 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.transfercolor: if self.TFLabConverter is None: self.TFLabConverter = image_utils.TFLabConverter() img_lab_l, img_lab_a, img_lab_b = np.split ( self.TFLabConverter.bgr2lab (img_bgr), 3, axis=-1 ) out_img_lab_l, out_img_lab_a, out_img_lab_b = np.split ( self.TFLabConverter.bgr2lab (out_img), 3, axis=-1 ) out_img = self.TFLabConverter.lab2bgr ( np.concatenate([out_img_lab_l, img_lab_a, img_lab_b], axis=-1) ) 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 ) out_img = np.clip (out_img, 0.0, 1.0 ) if debug: debugs += [out_img.copy()] return debugs if debug else out_img