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- # Copyright (c) OpenMMLab. All rights reserved.
- 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])
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