import time import traceback import cv2 import numpy as np import imagelib from facelib import FaceType, FANSegmentator, LandmarksProcessor from interact import interact as io from joblib import SubprocessFunctionCaller from utils.pickle_utils import AntiPickler from .Converter import Converter ''' default_mode = {1:'overlay', 2:'hist-match', 3:'hist-match-bw', 4:'seamless', 5:'seamless-hist-match', 6:'raw'} ''' class ConverterMasked(Converter): #override def __init__(self, predictor_func, predictor_input_size=0, predictor_masked=True, face_type=FaceType.FULL, default_mode = 4, base_erode_mask_modifier = 0, base_blur_mask_modifier = 0, default_erode_mask_modifier = 0, default_blur_mask_modifier = 0, clip_hborder_mask_per = 0, force_mask_mode=-1): super().__init__(predictor_func, Converter.TYPE_FACE) #dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower predictor_func ( np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ) ) time.sleep(2) predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(predictor_func) self.predictor_func_host = AntiPickler(predictor_func_host) self.predictor_func = predictor_func self.predictor_masked = predictor_masked self.predictor_input_size = predictor_input_size self.face_type = face_type self.clip_hborder_mask_per = clip_hborder_mask_per mode = io.input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless, (5) raw. Default - %d : " % (default_mode) , default_mode) mode_dict = {1:'overlay', 2:'hist-match', 3:'hist-match-bw', 4:'seamless', 5:'raw'} self.mode = mode_dict.get (mode, mode_dict[default_mode] ) if self.mode == 'raw': mode = io.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 == 'seamless': if io.input_bool("Seamless hist match? (y/n skip:n) : ", False): self.mode = 'seamless-hist-match' if self.mode == 'hist-match' or self.mode == 'hist-match-bw': self.masked_hist_match = io.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 ( io.input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255) if force_mask_mode != -1: self.mask_mode = force_mask_mode else: if face_type == FaceType.FULL: self.mask_mode = np.clip ( io.input_int ("Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd*FAN-dst (6) learned*FAN-prd*FAN-dst (?) help. Default - %d : " % (1) , 1, help_message="If you learned mask, then option 1 should be choosed. 'dst' mask is raw shaky mask from dst aligned images. 'FAN-prd' - using super smooth mask by pretrained FAN-model from predicted face. 'FAN-dst' - using super smooth mask by pretrained FAN-model from dst face. 'FAN-prd*FAN-dst' or 'learned*FAN-prd*FAN-dst' - using multiplied masks."), 1, 6 ) else: self.mask_mode = np.clip ( io.input_int ("Mask mode: (1) learned, (2) dst . Default - %d : " % (1) , 1), 1, 2 ) if self.mask_mode >= 3 and self.mask_mode <= 6: self.fan_seg = None if self.mode != 'raw': self.erode_mask_modifier = base_erode_mask_modifier + np.clip ( io.input_int ("Choose erode mask modifier [-200..200] (skip:%d) : " % (default_erode_mask_modifier), default_erode_mask_modifier), -200, 200) self.blur_mask_modifier = base_blur_mask_modifier + np.clip ( io.input_int ("Choose blur mask modifier [-200..200] (skip:%d) : " % (default_blur_mask_modifier), default_blur_mask_modifier), -200, 200) self.output_face_scale = np.clip ( 1.0 + io.input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0)*0.01, 0.5, 1.5) if self.mode != 'raw': self.color_transfer_mode = io.input_str ("Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct','lct']) self.super_resolution = io.input_bool("Apply super resolution? (y/n ?:help skip:n) : ", False, help_message="Enhance details by applying DCSCN network.") if self.mode != 'raw': self.final_image_color_degrade_power = np.clip ( io.input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100) self.alpha = io.input_bool("Export png with alpha channel? (y/n skip:n) : ", False) io.log_info ("") if self.super_resolution: host_proc, dc_upscale = SubprocessFunctionCaller.make_pair( imagelib.DCSCN().upscale ) self.dc_host = AntiPickler(host_proc) self.dc_upscale = dc_upscale else: self.dc_host = None #overridable def on_host_tick(self): self.predictor_func_host.obj.process_messages() if self.dc_host is not None: self.dc_host.obj.process_messages() #overridable def on_cli_initialize(self): if (self.mask_mode >= 3 and self.mask_mode <= 6) and self.fan_seg == None: self.fan_seg = FANSegmentator(256, FaceType.toString( self.face_type ) ) #override def cli_convert_face (self, img_bgr, img_face_landmarks, debug, **kwargs): 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) output_size = self.predictor_input_size if self.super_resolution: output_size *= 2 face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=self.face_type) face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=self.face_type, scale=self.output_face_scale) dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4 ) dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4 ) predictor_input_bgr = cv2.resize (dst_face_bgr, (self.predictor_input_size,self.predictor_input_size)) if self.predictor_masked: prd_face_bgr, prd_face_mask_a_0 = self.predictor_func (predictor_input_bgr) prd_face_bgr = np.clip (prd_face_bgr, 0, 1.0 ) prd_face_mask_a_0 = np.clip (prd_face_mask_a_0, 0.0, 1.0) else: predicted = self.predictor_func (predictor_input_bgr) prd_face_bgr = np.clip (predicted, 0, 1.0 ) prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (self.predictor_input_size,self.predictor_input_size)) if self.super_resolution: if debug: tmp = cv2.resize (prd_face_bgr, (output_size,output_size), cv2.INTER_CUBIC) debugs += [ np.clip( cv2.warpAffine( tmp, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] prd_face_bgr = self.dc_upscale(prd_face_bgr) if debug: debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ] if self.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 self.mask_mode == 2: #dst prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC) elif self.mask_mode >= 3 and self.mask_mode <= 6: if self.mask_mode == 3 or self.mask_mode == 5 or self.mask_mode == 6: prd_face_bgr_256 = cv2.resize (prd_face_bgr, (256,256) ) prd_face_bgr_256_mask = self.fan_seg.extract( prd_face_bgr_256 ) FAN_prd_face_mask_a_0 = cv2.resize (prd_face_bgr_256_mask, (output_size,output_size), cv2.INTER_CUBIC) if self.mask_mode == 4 or self.mask_mode == 5 or self.mask_mode == 6: face_256_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, 256, face_type=FaceType.FULL) dst_face_256_bgr = cv2.warpAffine(img_bgr, face_256_mat, (256, 256), flags=cv2.INTER_LANCZOS4 ) dst_face_256_mask = self.fan_seg.extract( dst_face_256_bgr ) FAN_dst_face_mask_a_0 = cv2.resize (dst_face_256_mask, (output_size,output_size), cv2.INTER_CUBIC) if self.mask_mode == 3: #FAN-prd prd_face_mask_a_0 = FAN_prd_face_mask_a_0 elif self.mask_mode == 4: #FAN-dst prd_face_mask_a_0 = FAN_dst_face_mask_a_0 elif self.mask_mode == 5: prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0 elif self.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 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_LANCZOS4 ) 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 debug: debugs += [img_face_mask_aaa.copy()] 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, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ) 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 debug: io.log_info ("lenx/leny:(%d/%d) " % (lenx, leny ) ) io.log_info ("lowest_len = %f" % (lowest_len) ) 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_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 ) img_mask_blurry_aaa = img_face_mask_aaa 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[-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) ) img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 ) face_mask_blurry_aaa = cv2.warpAffine( img_mask_blurry_aaa, face_mat, (output_size, output_size) ) if debug: debugs += [img_mask_blurry_aaa.copy()] if 'seamless' not in self.mode and 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 = 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) 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 = imagelib.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 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, self.hist_match_threshold ) #if self.masked_hist_match: # prd_face_bgr -= white 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: #mask used for cv2.seamlessClone img_face_seamless_mask_a = None img_face_mask_a = img_mask_blurry_aaa[...,0:1] 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_mask_blurry_aaa[...,0:1].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 try: #calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering 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*255).astype(np.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) 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 'seamless' in self.mode and self.color_transfer_mode is not None: out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) ) if self.color_transfer_mode == 'rct': if debug: debugs += [ np.clip( cv2.warpAffine( out_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) ] new_out_face_bgr = imagelib.reinhard_color_transfer ( np.clip( (out_face_bgr*255).astype(np.uint8), 0, 255), np.clip( (dst_face_bgr*255).astype(np.uint8), 0, 255), source_mask=face_mask_blurry_aaa, target_mask=face_mask_blurry_aaa) new_out_face_bgr = np.clip( new_out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( new_out_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( out_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) ] new_out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr) new_out_face_bgr = np.clip( new_out_face_bgr, 0.0, 1.0) if debug: debugs += [ np.clip( cv2.warpAffine( new_out_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) ] 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.mode == 'seamless-hist-match': out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) ) new_out_face_bgr = imagelib.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 = imagelib.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