def apply_xseg(input_path, model_path): if not input_path.exists(): raise ValueError(f'{input_path} not found. Please ensure it exists.') if not model_path.exists(): raise ValueError(f'{model_path} not found. Please ensure it exists.') io.log_info(f'Applying trained XSeg model to {input_path.name}/ folder.') device_config = nn.DeviceConfig.ask_choose_device(choose_only_one=True) nn.initialize(device_config) xseg = XSegNet(name='XSeg', load_weights=True, weights_file_root=model_path, data_format=nn.data_format, raise_on_no_model_files=True) res = xseg.get_resolution() images_paths = pathex.get_image_paths(input_path, return_Path_class=True) for filepath in io.progress_bar_generator(images_paths, "Processing"): dflimg = DFLIMG.load(filepath) if dflimg is None or not dflimg.has_data(): io.log_info(f'{filepath} is not a DFLIMG') continue img = cv2_imread(filepath).astype(np.float32) / 255.0 h, w, c = img.shape if w != res: img = cv2.resize(img, (res, res), interpolation=cv2.INTER_CUBIC) if len(img.shape) == 2: img = img[..., None] mask = xseg.extract(img) mask[mask < 0.5] = 0 mask[mask >= 0.5] = 1 dflimg.set_xseg_mask(mask) dflimg.save()
def apply_xseg(input_path, model_path): if not input_path.exists(): raise ValueError(f'{input_path} not found. Please ensure it exists.') if not model_path.exists(): raise ValueError(f'{model_path} not found. Please ensure it exists.') face_type = None model_dat = model_path / 'XSeg_data.dat' if model_dat.exists(): dat = pickle.loads( model_dat.read_bytes() ) dat_options = dat.get('options', None) if dat_options is not None: face_type = dat_options.get('face_type', None) if face_type is None: face_type = io.input_str ("XSeg model face type", 'same', ['h','mf','f','wf','head','same'], help_message="Specify face type of trained XSeg model. For example if XSeg model trained as WF, but faceset is HEAD, specify WF to apply xseg only on WF part of HEAD. Default is 'same'").lower() if face_type == 'same': face_type = None if face_type is not None: face_type = {'h' : FaceType.HALF, 'mf' : FaceType.MID_FULL, 'f' : FaceType.FULL, 'wf' : FaceType.WHOLE_FACE, 'head' : FaceType.HEAD}[face_type] io.log_info(f'Applying trained XSeg model to {input_path.name}/ folder.') device_config = nn.DeviceConfig.ask_choose_device(choose_only_one=True) nn.initialize(device_config) xseg = XSegNet(name='XSeg', load_weights=True, weights_file_root=model_path, data_format=nn.data_format, raise_on_no_model_files=True) xseg_res = xseg.get_resolution() images_paths = pathex.get_image_paths(input_path, return_Path_class=True) for filepath in io.progress_bar_generator(images_paths, "Processing"): dflimg = DFLIMG.load(filepath) if dflimg is None or not dflimg.has_data(): io.log_info(f'{filepath} is not a DFLIMG') continue img = cv2_imread(filepath).astype(np.float32) / 255.0 h,w,c = img.shape img_face_type = FaceType.fromString( dflimg.get_face_type() ) if face_type is not None and img_face_type != face_type: lmrks = dflimg.get_source_landmarks() fmat = LandmarksProcessor.get_transform_mat(lmrks, w, face_type) imat = LandmarksProcessor.get_transform_mat(lmrks, w, img_face_type) g_p = LandmarksProcessor.transform_points (np.float32([(0,0),(w,0),(0,w) ]), fmat, True) g_p2 = LandmarksProcessor.transform_points (g_p, imat) mat = cv2.getAffineTransform( g_p2, np.float32([(0,0),(w,0),(0,w) ]) ) img = cv2.warpAffine(img, mat, (w, w), cv2.INTER_LANCZOS4) img = cv2.resize(img, (xseg_res, xseg_res), interpolation=cv2.INTER_LANCZOS4) else: if w != xseg_res: img = cv2.resize( img, (xseg_res,xseg_res), interpolation=cv2.INTER_LANCZOS4 ) if len(img.shape) == 2: img = img[...,None] mask = xseg.extract(img) if face_type is not None and img_face_type != face_type: mask = cv2.resize(mask, (w, w), interpolation=cv2.INTER_LANCZOS4) mask = cv2.warpAffine( mask, mat, (w,w), np.zeros( (h,w,c), dtype=np.float), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4) mask = cv2.resize(mask, (xseg_res, xseg_res), interpolation=cv2.INTER_LANCZOS4) mask[mask < 0.5]=0 mask[mask >= 0.5]=1 dflimg.set_xseg_mask(mask) dflimg.save()