| |
| import numpy as np |
| from mmengine.testing import assert_allclose |
|
|
| from mmdet.structures.bbox import BaseBoxes, HorizontalBoxes |
| from mmdet.structures.mask import BitmapMasks, PolygonMasks |
|
|
|
|
| def create_random_bboxes(num_bboxes, img_w, img_h): |
| bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2)) |
| bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2)) |
| bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1) |
| bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype( |
| np.float32) |
| return bboxes |
|
|
|
|
| def create_full_masks(gt_bboxes, img_w, img_h): |
| xmin, ymin = gt_bboxes[:, 0:1], gt_bboxes[:, 1:2] |
| xmax, ymax = gt_bboxes[:, 2:3], gt_bboxes[:, 3:4] |
| gt_masks = np.zeros((len(gt_bboxes), img_h, img_w), dtype=np.uint8) |
| for i in range(len(gt_bboxes)): |
| gt_masks[i, int(ymin[i]):int(ymax[i]), int(xmin[i]):int(xmax[i])] = 1 |
| gt_masks = BitmapMasks(gt_masks, img_h, img_w) |
| return gt_masks |
|
|
|
|
| def construct_toy_data(poly2mask, use_box_type=False): |
| img = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], |
| dtype=np.uint8) |
| img = np.stack([img, img, img], axis=-1) |
| results = dict() |
| results['img'] = img |
| results['img_shape'] = img.shape[:2] |
| if use_box_type: |
| results['gt_bboxes'] = HorizontalBoxes( |
| np.array([[1, 0, 2, 2]], dtype=np.float32)) |
| else: |
| results['gt_bboxes'] = np.array([[1, 0, 2, 2]], dtype=np.float32) |
| results['gt_bboxes_labels'] = np.array([13], dtype=np.int64) |
| if poly2mask: |
| gt_masks = np.array([[0, 1, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0]], |
| dtype=np.uint8)[None, :, :] |
| results['gt_masks'] = BitmapMasks(gt_masks, 3, 4) |
| else: |
| raw_masks = [[np.array([1, 2, 1, 0, 2, 1], dtype=np.float32)]] |
| results['gt_masks'] = PolygonMasks(raw_masks, 3, 4) |
| results['gt_ignore_flags'] = np.array(np.array([1], dtype=bool)) |
| results['gt_seg_map'] = np.array( |
| [[255, 13, 255, 255], [255, 13, 13, 255], [255, 13, 255, 255]], |
| dtype=np.uint8) |
| return results |
|
|
|
|
| def check_result_same(results, pipeline_results, check_keys): |
| """Check whether the ``pipeline_results`` is the same with the predefined |
| ``results``. |
| |
| Args: |
| results (dict): Predefined results which should be the standard |
| output of the transform pipeline. |
| pipeline_results (dict): Results processed by the transform |
| pipeline. |
| check_keys (tuple): Keys that need to be checked between |
| results and pipeline_results. |
| """ |
| for key in check_keys: |
| if results.get(key, None) is None: |
| continue |
| if isinstance(results[key], (BitmapMasks, PolygonMasks)): |
| assert_allclose(pipeline_results[key].to_ndarray(), |
| results[key].to_ndarray()) |
| elif isinstance(results[key], BaseBoxes): |
| assert_allclose(pipeline_results[key].tensor, results[key].tensor) |
| else: |
| assert_allclose(pipeline_results[key], results[key]) |
|
|
