def test_clahe(self):
def _clahe(img, clip_limit=40.0, tile_grid_size=(8, 8)):
clahe = cv2.createCLAHE(clip_limit, tile_grid_size)
return clahe.apply(np.array(img, dtype=np.uint8))
# test assertion image should have the right shape
with pytest.raises(AssertionError):
mmcv.clahe(self.img)
# test assertion tile_grid_size should be a tuple with 2 integers
with pytest.raises(AssertionError):
mmcv.clahe(self.img[:, :, 0], tile_grid_size=(8.0, 8.0))
with pytest.raises(AssertionError):
mmcv.clahe(self.img[:, :, 0], tile_grid_size=(8, 8, 8))
with pytest.raises(AssertionError):
mmcv.clahe(self.img[:, :, 0], tile_grid_size=[8, 8])
# test with different channels
for i in range(self.img.shape[-1]):
img = mmcv.clahe(self.img[:, :, i])
img_std = _clahe(self.img[:, :, i])
assert np.allclose(img, img_std)
assert id(img) != id(self.img[:, :, i])
assert id(img_std) != id(self.img[:, :, i])
# test case with clip_limit=1.2
for i in range(self.img.shape[-1]):
img = mmcv.clahe(self.img[:, :, i], 1.2)
img_std = _clahe(self.img[:, :, i], 1.2)
assert np.allclose(img, img_std)
assert id(img) != id(self.img[:, :, i])
assert id(img_std) != id(self.img[:, :, i])
def __call__(self, results):
"""Call function to Use CLAHE method process images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Processed results.
"""
for i in range(results['img'].shape[2]):
results['img'][:, :, i] = mmcv.clahe(
np.array(results['img'][:, :, i], dtype=np.uint8),
self.clip_limit, self.tile_grid_size)
return results
def test_CLAHE():
# test assertion if clip_limit is None
with pytest.raises(AssertionError):
transform = dict(type='CLAHE', clip_limit=None)
build_from_cfg(transform, PIPELINES)
# test assertion if tile_grid_size is illegal
with pytest.raises(AssertionError):
transform = dict(type='CLAHE', tile_grid_size=(8.0, 8.0))
build_from_cfg(transform, PIPELINES)
# test assertion if tile_grid_size is illegal
with pytest.raises(AssertionError):
transform = dict(type='CLAHE', tile_grid_size=(9, 9, 9))
build_from_cfg(transform, PIPELINES)
transform = dict(type='CLAHE', clip_limit=2)
transform = build_from_cfg(transform, PIPELINES)
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
original_img = copy.deepcopy(img)
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
results = transform(results)
converted_img = np.empty(original_img.shape)
for i in range(original_img.shape[2]):
converted_img[:, :, i] = mmcv.clahe(
np.array(original_img[:, :, i], dtype=np.uint8), 2, (8, 8))
assert np.allclose(results['img'], converted_img)
assert str(transform) == f'CLAHE(clip_limit={2}, tile_grid_size={(8, 8)})'
