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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmdet.core.mask import BitmapMasks, PolygonMasks
def _check_fields(results, pipeline_results, keys):
"""Check data in fields from two results are same."""
for key in keys:
if isinstance(results[key], (BitmapMasks, PolygonMasks)):
assert np.equal(results[key].to_ndarray(),
pipeline_results[key].to_ndarray()).all()
else:
assert np.equal(results[key], pipeline_results[key]).all()
assert results[key].dtype == pipeline_results[key].dtype
def check_result_same(results, pipeline_results):
"""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 image
_check_fields(results, pipeline_results,
results.get('img_fields', ['img']))
# check bboxes
_check_fields(results, pipeline_results, results.get('bbox_fields', []))
# check masks
_check_fields(results, pipeline_results, results.get('mask_fields', []))
# check segmentations
_check_fields(results, pipeline_results, results.get('seg_fields', []))
# check gt_labels
if 'gt_labels' in results:
assert np.equal(results['gt_labels'],
pipeline_results['gt_labels']).all()
def construct_toy_data(poly2mask=True):
img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8)
img = np.stack([img, img, img], axis=-1)
results = dict()
# image
results['img'] = img
results['img_shape'] = img.shape
results['img_fields'] = ['img']
# bboxes
results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32)
results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]],
dtype=np.float32)
# labels
results['gt_labels'] = np.array([1], dtype=np.int64)
# masks
results['mask_fields'] = ['gt_masks']
if poly2mask:
gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]],
dtype=np.uint8)[None, :, :]
results['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
else:
raw_masks = [[np.array([0, 0, 2, 0, 2, 1, 0, 1], dtype=np.float)]]
results['gt_masks'] = PolygonMasks(raw_masks, 2, 4)
# segmentations
results['seg_fields'] = ['gt_semantic_seg']
results['gt_semantic_seg'] = img[..., 0]
return results
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
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