def __getitem__(self, index): img = ImageHelper.read_image(self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) img_size = ImageHelper.get_size(img) bboxes, labels = self.__read_json_file(self.json_list[index]) if self.aug_transform is not None: img, bboxes, labels = self.aug_transform(img, bboxes=bboxes, labels=labels) img_scale = ImageHelper.get_size(img)[0] / img_size[0] labels = torch.from_numpy(labels).long() bboxes = torch.from_numpy(bboxes).float() meta = dict( ori_img_size=img_size, border_size=ImageHelper.get_size(img), img_scale=img_scale, ) if self.img_transform is not None: img = self.img_transform(img) return dict( img=DataContainer(img, stack=True), bboxes=DataContainer(bboxes, stack=False), labels=DataContainer(labels, stack=False), meta=DataContainer(meta, stack=False, cpu_only=True) )
def __getitem__(self, index): img = ImageHelper.read_image( self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) ori_img_size = ImageHelper.get_size(img) if self.aug_transform is not None: img = self.aug_transform(img) border_hw = ImageHelper.get_size(img)[::-1] if self.img_transform is not None: img = self.img_transform(img) meta = dict(ori_img_size=ori_img_size, border_hw=border_hw, img_path=self.img_list[index]) return dict(img=DataContainer(img, stack=True, return_dc=True, samples_per_gpu=True), meta=DataContainer(meta, stack=False, cpu_only=True, return_dc=True, samples_per_gpu=True))
def make_input(self, image=None, input_size=None, min_side_length=None, max_side_length=None, scale=None): in_width, in_height = None, None if input_size is None and min_side_length is None and max_side_length is None: in_width, in_height = ImageHelper.get_size(image) elif input_size is not None and min_side_length is None and max_side_length is None: in_width, in_height = input_size elif input_size is None and min_side_length is not None and max_side_length is None: width, height = ImageHelper.get_size(image) scale_ratio = min_side_length / min(width, height) w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int(round(height * h_scale_ratio)) elif input_size is None and min_side_length is None and max_side_length is not None: width, height = ImageHelper.get_size(image) scale_ratio = max_side_length / max(width, height) w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int(round(height * h_scale_ratio)) else: Log.error('Incorrect target size setting.') exit(1) if not isinstance(scale, (list, tuple)): image = ImageHelper.resize(image, (int(in_width * scale), int(in_height * scale)), interpolation='linear') img_tensor = ToTensor()(image) img_tensor = Normalize(div_value=self.configer.get('normalize', 'div_value'), mean=self.configer.get('normalize', 'mean'), std=self.configer.get('normalize', 'std'))(img_tensor) img_tensor = img_tensor.unsqueeze(0).to(torch.device('cpu' if self.configer.get('gpu') is None else 'cuda')) return img_tensor else: img_tensor_list = [] for s in scale: image = ImageHelper.resize(image, (int(in_width * s), int(in_height * s)), interpolation='linear') img_tensor = ToTensor()(image) img_tensor = Normalize(div_value=self.configer.get('normalize', 'div_value'), mean=self.configer.get('normalize', 'mean'), std=self.configer.get('normalize', 'std'))(img_tensor) img_tensor = img_tensor.unsqueeze(0).to( torch.device('cpu' if self.configer.get('gpu') is None else 'cuda')) img_tensor_list.append(img_tensor) return img_tensor_list
def __test_img(self, image_path, json_path, raw_path, vis_path): Log.info('Image Path: {}'.format(image_path)) img = ImageHelper.read_image( image_path, tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) ori_img_bgr = ImageHelper.get_cv2_bgr(img, mode=self.configer.get( 'data', 'input_mode')) img, scale = BoundResize()(img) inputs = self.blob_helper.make_input(img, scale=1.0) with torch.no_grad(): # Forward pass. test_group = self.det_net(inputs, scale) test_indices_and_rois, test_roi_locs, test_roi_scores, test_rois_num = test_group batch_detections = self.decode(test_roi_locs, test_roi_scores, test_indices_and_rois, test_rois_num, self.configer, ImageHelper.get_size(img)) json_dict = self.__get_info_tree(batch_detections[0], ori_img_bgr, scale=scale) image_canvas = self.det_parser.draw_bboxes( ori_img_bgr.copy(), json_dict, conf_threshold=self.configer.get('vis', 'conf_threshold')) cv2.imwrite(vis_path, image_canvas) cv2.imwrite(raw_path, ori_img_bgr) Log.info('Json Path: {}'.format(json_path)) JsonHelper.save_file(json_dict, json_path) return json_dict
def __getitem__(self, index): img = ImageHelper.read_image( self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) img_size = ImageHelper.get_size(img) bboxes, labels = self.__read_json_file(self.json_list[index]) if self.aug_transform is not None: img, bboxes, labels = self.aug_transform(img, bboxes=bboxes, labels=labels) labels = torch.from_numpy(labels).long() bboxes = torch.from_numpy(bboxes).float() scale1 = 600 / min(img_size) scale2 = 1000 / max(img_size) scale = min(scale1, scale2) if self.img_transform is not None: img = self.img_transform(img) return img, scale, bboxes, labels
def __test_img(self, image_path, save_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) ori_img_bgr = ImageHelper.get_cv2_bgr(ori_image, mode=self.configer.get( 'data', 'input_mode')) heatmap_avg = np.zeros( (ori_height, ori_width, self.configer.get('network', 'heatmap_out'))) for i, scale in enumerate(self.configer.get('test', 'scale_search')): image = self.blob_helper.make_input(ori_image, input_size=self.configer.get( 'test', 'input_size'), scale=scale) with torch.no_grad(): heatmap_out_list = self.pose_net(image) heatmap_out = heatmap_out_list[-1] # extract outputs, resize, and remove padding heatmap = heatmap_out.squeeze(0).cpu().numpy().transpose( 1, 2, 0) heatmap = cv2.resize(heatmap, (ori_width, ori_height), interpolation=cv2.INTER_CUBIC) heatmap_avg = heatmap_avg + heatmap / len( self.configer.get('test', 'scale_search')) all_peaks = self.__extract_heatmap_info(heatmap_avg) image_canvas = self.__draw_key_point(all_peaks, ori_img_bgr) ImageHelper.save(image_canvas, save_path)
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 __list_dirs(self, root_dir, dataset): img_list = list() label_list = list() size_list = list() image_dir = os.path.join(root_dir, dataset, 'image') label_dir = os.path.join(root_dir, dataset, 'label') img_extension = os.listdir(image_dir)[0].split('.')[-1] for file_name in os.listdir(label_dir): image_name = '.'.join(file_name.split('.')[:-1]) img_path = os.path.join(image_dir, '{}.{}'.format(image_name, img_extension)) label_path = os.path.join(label_dir, file_name) if not os.path.exists(label_path) or not os.path.exists(img_path): Log.error('Label Path: {} not exists.'.format(label_path)) continue img_list.append(img_path) label_list.append(label_path) img = ImageHelper.read_image( img_path, tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) size_list.append(ImageHelper.get_size(img)) if dataset == 'train' and self.configer.get('data', 'include_val'): image_dir = os.path.join(root_dir, 'val/image') label_dir = os.path.join(root_dir, 'val/label') for file_name in os.listdir(label_dir): image_name = '.'.join(file_name.split('.')[:-1]) img_path = os.path.join( image_dir, '{}.{}'.format(image_name, img_extension)) label_path = os.path.join(label_dir, file_name) if not os.path.exists(label_path) or not os.path.exists( img_path): Log.error('Label Path: {} not exists.'.format(label_path)) continue img_list.append(img_path) label_list.append(label_path) img = ImageHelper.read_image( img_path, tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) size_list.append(ImageHelper.get_size(img)) return img_list, label_list, size_list
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 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 __call__(self, img): img_size = ImageHelper.get_size(img) scale1 = self.resize_bound[0] / min(img_size) scale2 = self.resize_bound[1] / max(img_size) scale = min(scale1, scale2) target_size = [int(round(i * scale)) for i in img_size] img = ImageHelper.resize(img, target_size=target_size, interpolation='cubic') return img, scale
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 __getitem__(self, index): img = ImageHelper.read_image( self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) img_size = ImageHelper.get_size(img) labelmap = ImageHelper.read_image(self.label_list[index], tool=self.configer.get( 'data', 'image_tool'), mode='P') if self.configer.exists('data', 'label_list'): labelmap = self._encode_label(labelmap) if self.configer.exists('data', 'reduce_zero_label'): labelmap = self._reduce_zero_label(labelmap) ori_target = ImageHelper.tonp(labelmap) ori_target[ori_target == 255] = -1 if self.aug_transform is not None: img, labelmap = self.aug_transform(img, labelmap=labelmap) border_size = ImageHelper.get_size(img) if self.img_transform is not None: img = self.img_transform(img) if self.label_transform is not None: labelmap = self.label_transform(labelmap) meta = dict(ori_img_size=img_size, border_size=border_size, ori_target=ori_target) return dict( img=DataContainer(img, stack=True), labelmap=DataContainer(labelmap, stack=True), meta=DataContainer(meta, stack=False, cpu_only=True), )
def make_input(self, image=None, input_size=None, min_side_length=None, max_side_length=None, scale=None): if input_size is not None and min_side_length is None and max_side_length is None: if input_size[0] == -1 and input_size[1] == -1: in_width, in_height = ImageHelper.get_size(image) elif input_size[0] != -1 and input_size[1] != -1: in_width, in_height = input_size elif input_size[0] == -1 and input_size[1] != -1: width, height = ImageHelper.get_size(image) scale_ratio = input_size[1] / height w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int(round(height * h_scale_ratio)) else: assert input_size[0] != -1 and input_size[1] == -1 width, height = ImageHelper.get_size(image) scale_ratio = input_size[0] / width w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int(round(height * h_scale_ratio)) elif input_size is None and min_side_length is not None and max_side_length is None: width, height = ImageHelper.get_size(image) scale_ratio = min_side_length / min(width, height) w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int(round(height * h_scale_ratio)) elif input_size is None and min_side_length is None and max_side_length is not None: width, height = ImageHelper.get_size(image) scale_ratio = max_side_length / max(width, height) w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int(round(height * h_scale_ratio)) elif input_size is None and min_side_length is not None and max_side_length is not None: width, height = ImageHelper.get_size(image) scale_ratio = min_side_length / min(width, height) bound_scale_ratio = max_side_length / max(width, height) scale_ratio = min(scale_ratio, bound_scale_ratio) w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int(round(height * h_scale_ratio)) else: in_width, in_height = ImageHelper.get_size(image) image = ImageHelper.resize(image, (int(in_width * scale), int(in_height * scale)), interpolation='cubic') img_tensor = ToTensor()(image) img_tensor = Normalize(div_value=self.configer.get('normalize', 'div_value'), mean=self.configer.get('normalize', 'mean'), std=self.configer.get('normalize', 'std'))(img_tensor) img_tensor = img_tensor.unsqueeze(0).to(torch.device('cpu' if self.configer.get('gpu') is None else 'cuda')) return img_tensor
def __test_img(self, image_path, json_path, raw_path, vis_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) ori_img_bgr = ImageHelper.get_cv2_bgr(ori_image, mode=self.configer.get('data', 'input_mode')) heatmap_avg = np.zeros((ori_height, ori_width, self.configer.get('network', 'heatmap_out'))) paf_avg = np.zeros((ori_height, ori_width, self.configer.get('network', 'paf_out'))) multiplier = [scale * self.configer.get('test', 'input_size')[1] / ori_height for scale in self.configer.get('test', 'scale_search')] stride = self.configer.get('network', 'stride') for i, scale in enumerate(multiplier): image, border_hw = self._get_blob(ori_image, scale=scale) with torch.no_grad(): paf_out_list, heatmap_out_list = self.pose_net(image) paf_out = paf_out_list[-1] heatmap_out = heatmap_out_list[-1] # extract outputs, resize, and remove padding heatmap = heatmap_out.squeeze(0).cpu().numpy().transpose(1, 2, 0) heatmap = cv2.resize(heatmap, None, fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) heatmap = cv2.resize(heatmap[:border_hw[0], :border_hw[1]], (ori_width, ori_height), interpolation=cv2.INTER_CUBIC) paf = paf_out.squeeze(0).cpu().numpy().transpose(1, 2, 0) paf = cv2.resize(paf, None, fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) paf = cv2.resize(paf[:border_hw[0], :border_hw[1]], (ori_width, ori_height), interpolation=cv2.INTER_CUBIC) heatmap_avg = heatmap_avg + heatmap / len(multiplier) paf_avg = paf_avg + paf / len(multiplier) all_peaks = self.__extract_heatmap_info(heatmap_avg) special_k, connection_all = self.__extract_paf_info(ori_img_bgr, paf_avg, all_peaks) subset, candidate = self.__get_subsets(connection_all, special_k, all_peaks) json_dict = self.__get_info_tree(ori_img_bgr, subset, candidate) image_canvas = self.pose_parser.draw_points(ori_img_bgr.copy(), json_dict) image_canvas = self.pose_parser.link_points(image_canvas, json_dict) ImageHelper.save(image_canvas, vis_path) ImageHelper.save(ori_img_bgr, raw_path) Log.info('Json Save Path: {}'.format(json_path)) JsonHelper.save_file(json_dict, json_path)
def __getitem__(self, index): img = ImageHelper.read_image( self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) if os.path.exists(self.mask_list[index]): maskmap = ImageHelper.read_image(self.mask_list[index], tool=self.configer.get( 'data', 'image_tool'), mode='P') else: maskmap = np.ones((img.size[1], img.size[0]), dtype=np.uint8) if self.configer.get('data', 'image_tool') == 'pil': maskmap = ImageHelper.np2img(maskmap) kpts, bboxes = self.__read_json_file(self.json_list[index]) if self.aug_transform is not None and len(bboxes) > 0: img, maskmap, kpts, bboxes = self.aug_transform(img, maskmap=maskmap, kpts=kpts, bboxes=bboxes) elif self.aug_transform is not None: img, maskmap, kpts = self.aug_transform(img, maskmap=maskmap, kpts=kpts) width, height = ImageHelper.get_size(maskmap) maskmap = ImageHelper.resize( maskmap, (width // self.configer.get('network', 'stride'), height // self.configer.get('network', 'stride')), interpolation='nearest') maskmap = torch.from_numpy(np.array(maskmap, dtype=np.float32)) maskmap = maskmap.unsqueeze(0) kpts = torch.from_numpy(kpts).float() heatmap = self.heatmap_generator(kpts, [width, height], maskmap) vecmap = self.paf_generator(kpts, [width, height], maskmap) if self.img_transform is not None: img = self.img_transform(img) return dict(img=DataContainer(img, stack=True), heatmap=DataContainer(heatmap, stack=True), maskmap=DataContainer(maskmap, stack=True), vecmap=DataContainer(vecmap, stack=True))
def __getitem__(self, index): img = ImageHelper.read_image( self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) kpts, bboxes = self.__read_json_file(self.json_list[index]) if self.aug_transform is not None: img, kpts, bboxes = self.aug_transform(img, kpts=kpts, bboxes=bboxes) kpts = torch.from_numpy(kpts).float() heatmap = self.heatmap_generator(kpts, ImageHelper.get_size(img)) if self.img_transform is not None: img = self.img_transform(img) return img, heatmap
def __test_img(self, image_path, json_path, raw_path, vis_path): Log.info('Image Path: {}'.format(image_path)) image = ImageHelper.read_image( image_path, tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) ori_img_bgr = ImageHelper.get_cv2_bgr(image, mode=self.configer.get( 'data', 'input_mode')) width, height = ImageHelper.get_size(image) scale1 = self.configer.get('test', 'resize_bound')[0] / min( width, height) scale2 = self.configer.get('test', 'resize_bound')[1] / max( width, height) scale = min(scale1, scale2) inputs = self.blob_helper.make_input(image, scale=scale) b, c, h, w = inputs.size() border_wh = [w, h] 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(inputs.device) expand_image[:, :, 0:h, 0:w] = inputs inputs = expand_image data_dict = dict( img=inputs, meta=DataContainer([[ dict(ori_img_size=ImageHelper.get_size(ori_img_bgr), aug_img_size=border_wh, img_scale=scale, input_size=[inputs.size(3), inputs.size(2)]) ]], cpu_only=True)) with torch.no_grad(): # Forward pass. test_group = self.det_net(data_dict) test_indices_and_rois, test_roi_locs, test_roi_scores, test_rois_num = test_group batch_detections = self.decode(test_roi_locs, test_roi_scores, test_indices_and_rois, test_rois_num, self.configer, DCHelper.tolist(data_dict['meta'])) json_dict = self.__get_info_tree(batch_detections[0], ori_img_bgr, scale=scale) image_canvas = self.det_parser.draw_bboxes( ori_img_bgr.copy(), json_dict, conf_threshold=self.configer.get('res', 'vis_conf_thre')) cv2.imwrite(vis_path, image_canvas) cv2.imwrite(raw_path, ori_img_bgr) Log.info('Json Path: {}'.format(json_path)) JsonHelper.save_file(json_dict, json_path) return json_dict
def __getitem__(self, index): image = ImageHelper.read_image( self.img_list[index], tool=self.configer.get('data', 'image_tool'), mode=self.configer.get('data', 'input_mode')) img_size = ImageHelper.get_size(image) if self.configer.exists('test', 'input_size'): input_size = self.configer.get('test', 'input_size') if input_size[0] == -1 and input_size[1] == -1: in_width, in_height = ImageHelper.get_size(image) elif input_size[0] != -1 and input_size[1] != -1: in_width, in_height = input_size elif input_size[0] == -1 and input_size[1] != -1: width, height = ImageHelper.get_size(image) scale_ratio = input_size[1] / height w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int( round(height * h_scale_ratio)) else: assert input_size[0] != -1 and input_size[1] == -1 width, height = ImageHelper.get_size(image) scale_ratio = input_size[0] / width w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int( round(height * h_scale_ratio)) elif self.configer.exists( 'test', 'min_side_length') and not self.configer.exists( 'test', 'max_side_length'): width, height = ImageHelper.get_size(image) scale_ratio = self.configer.get('test', 'min_side_length') / min( width, height) w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int( round(height * h_scale_ratio)) elif not self.configer.exists( 'test', 'min_side_length') and self.configer.exists( 'test', 'max_side_length'): width, height = ImageHelper.get_size(image) scale_ratio = self.configer.get('test', 'max_side_length') / max( width, height) w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int( round(height * h_scale_ratio)) elif self.configer.exists('test', 'min_side_length') and self.configer.exists( 'test', 'max_side_length'): width, height = ImageHelper.get_size(image) scale_ratio = self.configer.get('test', 'min_side_length') / min( width, height) bound_scale_ratio = self.configer.get( 'test', 'max_side_length') / max(width, height) scale_ratio = min(scale_ratio, bound_scale_ratio) w_scale_ratio, h_scale_ratio = scale_ratio, scale_ratio in_width, in_height = int(round(width * w_scale_ratio)), int( round(height * h_scale_ratio)) else: in_width, in_height = ImageHelper.get_size(image) img = ImageHelper.resize(image, (int(in_width), int(in_height)), interpolation='linear') if self.img_transform is not None: img = self.img_transform(img) meta = dict(ori_img_size=img_size, border_hw=[in_height, in_width], img_path=self.img_list[index]) return dict(img=DataContainer(img, stack=True, return_dc=True, samples_per_gpu=True), meta=DataContainer(meta, stack=False, cpu_only=True, return_dc=True, samples_per_gpu=True))