def vis_bboxes(self, image_in, bboxes_list, name='default', sub_dir='bbox'): """ Show the diff bbox of individuals. """ base_dir = os.path.join(self.configer.get('project_dir'), DET_DIR, sub_dir) if isinstance(image_in, Image.Image): image = ImageHelper.rgb2bgr(ImageHelper.to_np(image_in)) else: image = image_in.copy() if not os.path.exists(base_dir): log.error('Dir:{} not exists!'.format(base_dir)) os.makedirs(base_dir) img_path = os.path.join( base_dir, name if ImageHelper.is_img(name) else '{}.jpg'.format(name)) for bbox in bboxes_list: image = cv2.rectangle(image, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imwrite(img_path, image)
def evaluate(self, pred_dir, gt_dir): img_cnt = 0 for filename in os.listdir(pred_dir): pred_path = os.path.join(pred_dir, filename) gt_path = os.path.join(gt_dir, filename) predmap = ImageHelper.to_np( ImageHelper.read_image(pred_path, tool='pil', mode='P')) gtmap = ImageHelper.to_np( ImageHelper.read_image(gt_path, tool='pil', mode='P')) predmap = self.relabel(predmap) gtmap = self.relabel(gtmap) self.seg_running_score.update(predmap[np.newaxis, :, :], gtmap[np.newaxis, :, :]) img_cnt += 1 Log.info('Evaluate {} images'.format(img_cnt)) Log.info('mIOU: {}'.format(self.seg_running_score.get_mean_iou())) Log.info('Pixel ACC: {}'.format( self.seg_running_score.get_pixel_acc()))
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.to_np(labelmap) 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), )