class FCNSegmentorTest(object): def __init__(self, configer): self.configer = configer self.blob_helper = BlobHelper(configer) self.seg_visualizer = SegVisualizer(configer) self.seg_parser = SegParser(configer) self.seg_model_manager = SegModelManager(configer) self.seg_data_loader = DataLoader(configer) self.device = torch.device('cpu' if self.configer.get('gpu') is None else 'cuda') self.seg_net = None self._init_model() def _init_model(self): self.seg_net = self.seg_model_manager.semantic_segmentor() self.seg_net = RunnerHelper.load_net(self, self.seg_net) self.seg_net.eval() def _get_blob(self, ori_image, scale=None): assert scale is not None image = None if self.configer.exists('test', 'input_size'): image = self.blob_helper.make_input(image=ori_image, input_size=self.configer.get('test', 'input_size'), scale=scale) elif self.configer.exists('test', 'min_side_length') and not self.configer.exists('test', 'max_side_length'): image = self.blob_helper.make_input(image=ori_image, min_side_length=self.configer.get('test', 'min_side_length'), scale=scale) elif not self.configer.exists('test', 'min_side_length') and self.configer.exists('test', 'max_side_length'): image = self.blob_helper.make_input(image=ori_image, max_side_length=self.configer.get('test', 'max_side_length'), scale=scale) elif self.configer.exists('test', 'min_side_length') and self.configer.exists('test', 'max_side_length'): image = self.blob_helper.make_input(image=ori_image, min_side_length=self.configer.get('test', 'min_side_length'), max_side_length=self.configer.get('test', 'max_side_length'), scale=scale) else: Log.error('Test setting error') exit(1) b, c, h, w = image.size() border_hw = [h, w] if self.configer.exists('test', 'fit_stride'): stride = self.configer.get('test', 'fit_stride') pad_w = 0 if (w % stride == 0) else stride - (w % stride) # right pad_h = 0 if (h % stride == 0) else stride - (h % stride) # down expand_image = torch.zeros((b, c, h + pad_h, w + pad_w)).to(image.device) expand_image[:, :, 0:h, 0:w] = image image = expand_image return image, border_hw def test_img(self, image_path, label_path, vis_path, raw_path): Log.info('Image Path: {}'.format(image_path)) ori_image = ImageHelper.read_image(image_path, tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) total_logits = None if self.configer.get('test', 'mode') == 'ss_test': total_logits = self.ss_test(ori_image) elif self.configer.get('test', 'mode') == 'sscrop_test': total_logits = self.sscrop_test(ori_image) elif self.configer.get('test', 'mode') == 'ms_test': total_logits = self.ms_test(ori_image) elif self.configer.get('test', 'mode') == 'mscrop_test': total_logits = self.mscrop_test(ori_image) else: Log.error('Invalid test mode:{}'.format(self.configer.get('test', 'mode'))) exit(1) label_map = np.argmax(total_logits, axis=-1) label_img = np.array(label_map, dtype=np.uint8) ori_img_bgr = ImageHelper.get_cv2_bgr(ori_image, mode=self.configer.get('data', 'input_mode')) image_canvas = self.seg_parser.colorize(label_img, image_canvas=ori_img_bgr) ImageHelper.save(image_canvas, save_path=vis_path) ImageHelper.save(ori_image, save_path=raw_path) if self.configer.exists('data', 'label_list'): label_img = self.__relabel(label_img) if self.configer.exists('data', 'reduce_zero_label') and self.configer.get('data', 'reduce_zero_label'): label_img = label_img + 1 label_img = label_img.astype(np.uint8) label_img = Image.fromarray(label_img, 'P') Log.info('Label Path: {}'.format(label_path)) ImageHelper.save(label_img, label_path) def ss_test(self, ori_image): ori_width, ori_height = ImageHelper.get_size(ori_image) total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32) image, border_hw = self._get_blob(ori_image, scale=1.0) results = self._predict(image) results = cv2.resize(results[:border_hw[0], :border_hw[1]], (ori_width, ori_height), interpolation=cv2.INTER_CUBIC) total_logits += results return total_logits def sscrop_test(self, ori_image): ori_width, ori_height = ImageHelper.get_size(ori_image) total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32) image, _ = self._get_blob(ori_image, scale=1.0) crop_size = self.configer.get('test', 'crop_size') if image.size()[3] > crop_size[0] and image.size()[2] > crop_size[1]: results = self._crop_predict(image, crop_size) else: results = self._predict(image) results = cv2.resize(results, (ori_width, ori_height), interpolation=cv2.INTER_CUBIC) total_logits += results return total_logits def mscrop_test(self, ori_image): ori_width, ori_height = ImageHelper.get_size(ori_image) total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32) for scale in self.configer.get('test', 'scale_search'): image, _ = self._get_blob(ori_image, scale=scale) crop_size = self.configer.get('test', 'crop_size') if image.size()[3] > crop_size[0] and image.size()[2] > crop_size[1]: results = self._crop_predict(image, crop_size) else: results = self._predict(image) results = cv2.resize(results, (ori_width, ori_height), interpolation=cv2.INTER_CUBIC) total_logits += results return total_logits def ms_test(self, ori_image): ori_width, ori_height = ImageHelper.get_size(ori_image) total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32) for scale in self.configer.get('test', 'scale_search'): image, border_hw = self._get_blob(ori_image, scale=scale) results = self._predict(image) results = cv2.resize(results[:border_hw[0], :border_hw[1]], (ori_width, ori_height), interpolation=cv2.INTER_CUBIC) total_logits += results if self.configer.get('data', 'image_tool') == 'cv2': mirror_image = cv2.flip(ori_image, 1) else: mirror_image = ori_image.transpose(Image.FLIP_LEFT_RIGHT) image, border_hw = self._get_blob(mirror_image, scale=1.0) results = self._predict(image) results = results[:border_hw[0], :border_hw[1]] results = cv2.resize(results[:, ::-1], (ori_width, ori_height), interpolation=cv2.INTER_CUBIC) total_logits += results return total_logits def _crop_predict(self, image, crop_size): height, width = image.size()[2:] np_image = image.squeeze(0).permute(1, 2, 0).cpu().numpy() height_starts = self._decide_intersection(height, crop_size[1]) width_starts = self._decide_intersection(width, crop_size[0]) split_crops = [] for height in height_starts: for width in width_starts: image_crop = np_image[height:height + crop_size[1], width:width + crop_size[0]] split_crops.append(image_crop[np.newaxis, :]) split_crops = np.concatenate(split_crops, axis=0) # (n, crop_image_size, crop_image_size, 3) inputs = torch.from_numpy(split_crops).permute(0, 3, 1, 2).to(self.device) with torch.no_grad(): results = self.seg_net.forward(inputs) results = results[-1].permute(0, 2, 3, 1).cpu().numpy() reassemble = np.zeros((np_image.shape[0], np_image.shape[1], results.shape[-1]), np.float32) index = 0 for height in height_starts: for width in width_starts: reassemble[height:height+crop_size[1], width:width+crop_size[0]] += results[index] index += 1 return reassemble def _decide_intersection(self, total_length, crop_length): stride = int(crop_length * self.configer.get('test', 'crop_stride_ratio')) # set the stride as the paper do times = (total_length - crop_length) // stride + 1 cropped_starting = [] for i in range(times): cropped_starting.append(stride*i) if total_length - cropped_starting[-1] > crop_length: cropped_starting.append(total_length - crop_length) # must cover the total image return cropped_starting def _predict(self, inputs): with torch.no_grad(): results = self.seg_net.forward(inputs) results = results[-1].squeeze(0).permute(1, 2, 0).cpu().numpy() return results def __relabel(self, label_map): height, width = label_map.shape label_dst = np.zeros((height, width), dtype=np.uint8) for i in range(self.configer.get('data', 'num_classes')): label_dst[label_map == i] = self.configer.get('data', 'label_list')[i] label_dst = np.array(label_dst, dtype=np.uint8) return label_dst def debug(self, vis_dir): count = 0 for i, data_dict in enumerate(self.seg_data_loader.get_trainloader()): inputs = data_dict['img'] targets = data_dict['labelmap'] for j in range(inputs.size(0)): count = count + 1 if count > 20: exit(1) image_bgr = self.blob_helper.tensor2bgr(inputs[j]) label_map = targets[j].numpy() image_canvas = self.seg_parser.colorize(label_map, image_canvas=image_bgr) cv2.imwrite(os.path.join(vis_dir, '{}_{}_vis.png'.format(i, j)), image_canvas) cv2.imshow('main', image_canvas) cv2.waitKey()
class FCNSegmentorTest(object): def __init__(self, configer): self.configer = configer self.seg_visualizer = SegVisualizer(configer) self.seg_parser = SegParser(configer) self.seg_model_manager = SegModelManager(configer) self.seg_data_loader = SegDataLoader(configer) self.module_utilizer = ModuleUtilizer(configer) self.device = torch.device( 'cpu' if self.configer.get('gpu') is None else 'cuda') self.seg_net = None def init_model(self): self.seg_net = self.seg_model_manager.semantic_segmentor() self.seg_net = self.module_utilizer.load_net(self.seg_net) self.seg_net.eval() def __test_img(self, image_path, save_path): image = ImageHelper.pil_open_rgb(image_path) ori_width, ori_height = image.size image = Scale(size=self.configer.get('data', 'input_size'))(image) image = ToTensor()(image) image = Normalize(mean=self.configer.get('trans_params', 'mean'), std=self.configer.get('trans_params', 'std'))(image) with torch.no_grad(): inputs = image.unsqueeze(0).to(self.device) results = self.seg_net.forward(inputs) label_map = results.data.cpu().numpy().argmax(axis=1)[0].squeeze() label_img = np.array(label_map, dtype=np.uint8) if not self.configer.is_empty('details', 'label_list'): label_img = self.__relabel(label_img) label_img = Image.fromarray(label_img, 'P') label_img = label_img.resize((ori_width, ori_height), Image.NEAREST) label_img.save(save_path) def __relabel(self, label_map): height, width = label_map.shape label_dst = np.zeros((height, width), dtype=np.uint8) for i in range(self.configer.get('data', 'num_classes')): label_dst[label_map == i] = self.configer.get( 'details', 'label_list')[i] label_dst = np.array(label_dst, dtype=np.uint8) return label_dst def test(self): base_dir = os.path.join(self.configer.get('output_dir'), 'val/results/seg', self.configer.get('dataset')) test_img = self.configer.get('test_img') test_dir = self.configer.get('test_dir') if test_img is None and test_dir is None: Log.error('test_img & test_dir not exists.') exit(1) if test_img is not None and test_dir is not None: Log.error('Either test_img or test_dir.') exit(1) if test_img is not None: base_dir = os.path.join(base_dir, 'test_img') if not os.path.exists(base_dir): os.makedirs(base_dir) filename = test_img.rstrip().split('/')[-1] save_path = os.path.join(base_dir, filename) self.__test_img(test_img, save_path) else: base_dir = os.path.join(base_dir, 'test_dir', test_dir.rstrip('/').split('/')[-1]) if not os.path.exists(base_dir): os.makedirs(base_dir) for filename in FileHelper.list_dir(test_dir): image_path = os.path.join(test_dir, filename) save_path = os.path.join(base_dir, filename) if not os.path.exists(os.path.dirname(save_path)): os.makedirs(os.path.dirname(save_path)) self.__test_img(image_path, save_path) def debug(self): base_dir = os.path.join(self.configer.get('project_dir'), 'vis/results/seg', self.configer.get('dataset'), 'debug') if not os.path.exists(base_dir): os.makedirs(base_dir) val_data_loader = self.seg_data_loader.get_valloader() count = 0 for i, (inputs, targets) in enumerate(val_data_loader): for j in range(inputs.size(0)): count = count + 1 if count > 20: exit(1) ori_img = DeNormalize( mean=self.configer.get('trans_params', 'mean'), std=self.configer.get('trans_params', 'std'))(inputs[j]) ori_img = ori_img.numpy().transpose(1, 2, 0).astype(np.uint8) image_bgr = cv2.cvtColor(ori_img, cv2.COLOR_RGB2BGR) label_map = targets[j].numpy() image_canvas = self.seg_parser.colorize(label_map, image_canvas=image_bgr) cv2.imwrite( os.path.join(base_dir, '{}_{}_vis.png'.format(i, j)), image_canvas) cv2.imshow('main', image_canvas) cv2.waitKey()
class FCNSegmentorTest(object): def __init__(self, configer): self.configer = configer self.blob_helper = BlobHelper(configer) self.seg_visualizer = SegVisualizer(configer) self.seg_parser = SegParser(configer) self.seg_model_manager = SegModelManager(configer) self.seg_data_loader = SegDataLoader(configer) self.module_utilizer = ModuleUtilizer(configer) self.data_transformer = DataTransformer(configer) self.device = torch.device('cpu' if self.configer.get('gpu') is None else 'cuda') self.seg_net = None self._init_model() def _init_model(self): self.seg_net = self.seg_model_manager.semantic_segmentor() self.seg_net = self.module_utilizer.load_net(self.seg_net) self.seg_net.eval() def __test_img(self, image_path, label_path, vis_path, raw_path): Log.info('Image Path: {}'.format(image_path)) ori_image = ImageHelper.read_image(image_path, tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) ori_width, ori_height = ImageHelper.get_size(ori_image) total_logits = np.zeros((ori_height, ori_width, self.configer.get('data', 'num_classes')), np.float32) for scale in self.configer.get('test', 'scale_search'): image = self.blob_helper.make_input(image=ori_image, input_size=self.configer.get('test', 'input_size'), scale=scale) if self.configer.get('test', 'crop_test'): crop_size = self.configer.get('test', 'crop_size') if image.size()[3] > crop_size[0] and image.size()[2] > crop_size[1]: results = self._crop_predict(image, crop_size) else: results = self._predict(image) else: results = self._predict(image) results = cv2.resize(results, (ori_width, ori_height), interpolation=cv2.INTER_LINEAR) total_logits += results if self.configer.get('test', 'mirror'): if self.configer.get('data', 'image_tool') == 'cv2': image = cv2.flip(ori_image, 1) else: image = ori_image.transpose(Image.FLIP_LEFT_RIGHT) image = self.blob_helper.make_input(image, input_size=self.configer.get('test', 'input_size'), scale=1.0) if self.configer.get('test', 'crop_test'): crop_size = self.configer.get('test', 'crop_size') if image.size()[3] > crop_size[0] and image.size()[2] > crop_size[1]: results = self._crop_predict(image, crop_size) else: results = self._predict(image) else: results = self._predict(image) results = cv2.resize(results[:, ::-1], (ori_width, ori_height), interpolation=cv2.INTER_LINEAR) total_logits += results label_map = np.argmax(total_logits, axis=-1) label_img = np.array(label_map, dtype=np.uint8) image_bgr = cv2.cvtColor(np.array(ori_image), cv2.COLOR_RGB2BGR) image_canvas = self.seg_parser.colorize(label_img, image_canvas=image_bgr) ImageHelper.save(image_canvas, save_path=vis_path) ImageHelper.save(ori_image, save_path=raw_path) if not self.configer.is_empty('data', 'label_list'): label_img = self.__relabel(label_img) label_img = Image.fromarray(label_img, 'P') Log.info('Label Path: {}'.format(label_path)) ImageHelper.save(label_img, label_path) def _crop_predict(self, image, crop_size): height, width = image.size()[2:] np_image = image.squeeze(0).permute(1, 2, 0).cpu().numpy() height_starts = self._decide_intersection(height, crop_size[1]) width_starts = self._decide_intersection(width, crop_size[0]) split_crops = [] for height in height_starts: for width in width_starts: image_crop = np_image[height:height + crop_size[1], width:width + crop_size[0]] split_crops.append(image_crop[np.newaxis, :]) split_crops = np.concatenate(split_crops, axis=0) # (n, crop_image_size, crop_image_size, 3) inputs = torch.from_numpy(split_crops).permute(0, 3, 1, 2).to(self.device) with torch.no_grad(): results = self.seg_net.forward(inputs) results = results[0].permute(0, 2, 3, 1).cpu().numpy() reassemble = np.zeros((np_image.shape[0], np_image.shape[1], results.shape[-1]), np.float32) index = 0 for height in height_starts: for width in width_starts: reassemble[height:height+crop_size[1], width:width+crop_size[0]] += results[index] index += 1 return reassemble def _decide_intersection(self, total_length, crop_length): stride = int(crop_length * self.configer.get('test', 'crop_stride_ratio')) # set the stride as the paper do times = (total_length - crop_length) // stride + 1 cropped_starting = [] for i in range(times): cropped_starting.append(stride*i) if total_length - cropped_starting[-1] > crop_length: cropped_starting.append(total_length - crop_length) # must cover the total image return cropped_starting def _predict(self, inputs): with torch.no_grad(): results = self.seg_net.forward(inputs) results = results[0].squeeze().permute(1, 2, 0).cpu().numpy() return results def __relabel(self, label_map): height, width = label_map.shape label_dst = np.zeros((height, width), dtype=np.uint8) for i in range(self.configer.get('data', 'num_classes')): label_dst[label_map == i] = self.configer.get('data', 'label_list')[i] label_dst = np.array(label_dst, dtype=np.uint8) return label_dst def test(self): base_dir = os.path.join(self.configer.get('project_dir'), 'val/results/seg', self.configer.get('dataset')) test_img = self.configer.get('test_img') test_dir = self.configer.get('test_dir') if test_img is None and test_dir is None: Log.error('test_img & test_dir not exists.') exit(1) if test_img is not None and test_dir is not None: Log.error('Either test_img or test_dir.') exit(1) if test_img is not None: base_dir = os.path.join(base_dir, 'test_img') filename = test_img.rstrip().split('/')[-1] label_path = os.path.join(base_dir, 'label', '{}.png'.format('.'.join(filename.split('.')[:-1]))) raw_path = os.path.join(base_dir, 'raw', filename) vis_path = os.path.join(base_dir, 'vis', '{}_vis.png'.format('.'.join(filename.split('.')[:-1]))) FileHelper.make_dirs(label_path, is_file=True) FileHelper.make_dirs(raw_path, is_file=True) FileHelper.make_dirs(vis_path, is_file=True) self.__test_img(test_img, label_path, vis_path, raw_path) else: base_dir = os.path.join(base_dir, 'test_dir', test_dir.rstrip('/').split('/')[-1]) FileHelper.make_dirs(base_dir) for filename in FileHelper.list_dir(test_dir): image_path = os.path.join(test_dir, filename) label_path = os.path.join(base_dir, 'label', '{}.png'.format('.'.join(filename.split('.')[:-1]))) raw_path = os.path.join(base_dir, 'raw', filename) vis_path = os.path.join(base_dir, 'vis', '{}_vis.png'.format('.'.join(filename.split('.')[:-1]))) FileHelper.make_dirs(label_path, is_file=True) FileHelper.make_dirs(raw_path, is_file=True) FileHelper.make_dirs(vis_path, is_file=True) self.__test_img(image_path, label_path, vis_path, raw_path) def debug(self): base_dir = os.path.join(self.configer.get('project_dir'), 'vis/results/seg', self.configer.get('dataset'), 'debug') if not os.path.exists(base_dir): os.makedirs(base_dir) count = 0 for i, data_dict in enumerate(self.seg_data_loader.get_trainloader()): inputs = data_dict['img'] targets = data_dict['labelmap'] for j in range(inputs.size(0)): count = count + 1 if count > 20: exit(1) image_bgr = self.blob_helper.tensor2bgr(inputs[j]) label_map = targets[j].numpy() image_canvas = self.seg_parser.colorize(label_map, image_canvas=image_bgr) cv2.imwrite(os.path.join(base_dir, '{}_{}_vis.png'.format(i, j)), image_canvas) cv2.imshow('main', image_canvas) cv2.waitKey()