def test_load_seg(self):
results = dict(
seg_prefix=self.data_prefix,
ann_info=dict(seg_map='seg.png'),
seg_fields=[])
transform = LoadAnnotations()
results = transform(copy.deepcopy(results))
assert results['seg_fields'] == ['gt_semantic_seg']
assert results['gt_semantic_seg'].shape == (288, 512)
assert results['gt_semantic_seg'].dtype == np.uint8
assert repr(transform) == transform.__class__.__name__ + \
"(reduce_zero_label=False,imdecode_backend='pillow')"
# no img_prefix
results = dict(
seg_prefix=None,
ann_info=dict(seg_map='tests/data/seg.png'),
seg_fields=[])
transform = LoadAnnotations()
results = transform(copy.deepcopy(results))
assert results['gt_semantic_seg'].shape == (288, 512)
assert results['gt_semantic_seg'].dtype == np.uint8
# reduce_zero_label
transform = LoadAnnotations(reduce_zero_label=True)
results = transform(copy.deepcopy(results))
assert results['gt_semantic_seg'].shape == (288, 512)
assert results['gt_semantic_seg'].dtype == np.uint8
# mmcv backend
results = dict(
seg_prefix=self.data_prefix,
ann_info=dict(seg_map='seg.png'),
seg_fields=[])
transform = LoadAnnotations(imdecode_backend='pillow')
results = transform(copy.deepcopy(results))
# this image is saved by PIL
assert results['gt_semantic_seg'].shape == (288, 512)
assert results['gt_semantic_seg'].dtype == np.uint8
def test_load_seg_custom_classes(self):
test_img = np.random.rand(10, 10)
test_gt = np.zeros_like(test_img)
test_gt[2:4, 2:4] = 1
test_gt[2:4, 6:8] = 2
test_gt[6:8, 2:4] = 3
test_gt[6:8, 6:8] = 4
tmp_dir = tempfile.TemporaryDirectory()
img_path = osp.join(tmp_dir.name, 'img.jpg')
gt_path = osp.join(tmp_dir.name, 'gt.png')
mmcv.imwrite(test_img, img_path)
mmcv.imwrite(test_gt, gt_path)
# test only train with label with id 3
results = dict(img_info=dict(filename=img_path),
ann_info=dict(seg_map=gt_path),
label_map={
0: 0,
1: 0,
2: 0,
3: 1,
4: 0
},
seg_fields=[])
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
gt_array = results['gt_semantic_seg']
true_mask = np.zeros_like(gt_array)
true_mask[6:8, 2:4] = 1
assert results['seg_fields'] == ['gt_semantic_seg']
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, true_mask)
# test only train with label with id 4 and 3
results = dict(img_info=dict(filename=img_path),
ann_info=dict(seg_map=gt_path),
label_map={
0: 0,
1: 0,
2: 0,
3: 2,
4: 1
},
seg_fields=[])
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
gt_array = results['gt_semantic_seg']
true_mask = np.zeros_like(gt_array)
true_mask[6:8, 2:4] = 2
true_mask[6:8, 6:8] = 1
assert results['seg_fields'] == ['gt_semantic_seg']
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, true_mask)
# test no custom classes
results = dict(img_info=dict(filename=img_path),
ann_info=dict(seg_map=gt_path),
seg_fields=[])
load_imgs = LoadImageFromFile()
results = load_imgs(copy.deepcopy(results))
load_anns = LoadAnnotations()
results = load_anns(copy.deepcopy(results))
gt_array = results['gt_semantic_seg']
assert results['seg_fields'] == ['gt_semantic_seg']
assert gt_array.shape == (10, 10)
assert gt_array.dtype == np.uint8
np.testing.assert_array_equal(gt_array, test_gt)
tmp_dir.cleanup()