(self, idx)
| 29 | return len(self.img_paths) |
| 30 | |
| 31 | def __getitem__(self, idx): |
| 32 | img = load_img(self.img_paths[idx],'BGR') |
| 33 | img_whole_bbox = np.array([0, 0, img.shape[1],img.shape[0]]) |
| 34 | img, img2bb_trans, bb2img_trans, _, _ = \ |
| 35 | augmentation_keep_size(img, img_whole_bbox, 'test') |
| 36 | |
| 37 | cropped_img_shape=img.shape[:2] |
| 38 | img = (img.astype(np.float32)) |
| 39 | |
| 40 | |
| 41 | inputs = {'img': img} |
| 42 | targets = { |
| 43 | 'body_bbox_center': np.array(img_whole_bbox[None]), |
| 44 | 'body_bbox_size': np.array(img_whole_bbox[None])} |
| 45 | meta_info = { |
| 46 | 'ori_shape':np.array(self.resolution), |
| 47 | 'img_shape': np.array(img.shape[:2]), |
| 48 | 'img2bb_trans': img2bb_trans, |
| 49 | 'bb2img_trans': bb2img_trans, |
| 50 | 'ann_idx': idx} |
| 51 | result = {**inputs, **targets, **meta_info} |
| 52 | |
| 53 | result = self.normalize(result) |
| 54 | result = self.format(result) |
| 55 | |
| 56 | return result |
| 57 | |
| 58 | def inference(self, outs): |
| 59 | img_paths = self.img_paths |
nothing calls this directly
no test coverage detected