def test_adjust_gamma():
# test assertion if gamma <= 0
with pytest.raises(AssertionError):
transform = dict(type='AdjustGamma', gamma=0)
build_from_cfg(transform, PIPELINES)
# test assertion if gamma is list
with pytest.raises(AssertionError):
transform = dict(type='AdjustGamma', gamma=[1.2])
build_from_cfg(transform, PIPELINES)
# test with gamma = 1.2
transform = dict(type='AdjustGamma', gamma=1.2)
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)
inv_gamma = 1.0 / 1.2
table = np.array([((i / 255.0)**inv_gamma) * 255
for i in np.arange(0, 256)]).astype('uint8')
converted_img = mmcv.lut_transform(
np.array(original_img, dtype=np.uint8), table)
assert np.allclose(results['img'], converted_img)
assert str(transform) == f'AdjustGamma(gamma={1.2})'
def __call__(self, results):
"""Call function to process the image with gamma correction.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Processed results.
"""
results['img'] = mmcv.lut_transform(
np.array(results['img'], dtype=np.uint8), self.table)
return results
def test_lut_transform(self):
lut_table = np.array(list(range(256)))
# test assertion image values should between 0 and 255.
with pytest.raises(AssertionError):
mmcv.lut_transform(np.array([256]), lut_table)
with pytest.raises(AssertionError):
mmcv.lut_transform(np.array([-1]), lut_table)
# test assertion lut_table should be ndarray with shape (256, )
with pytest.raises(AssertionError):
mmcv.lut_transform(np.array([0]), list(range(256)))
with pytest.raises(AssertionError):
mmcv.lut_transform(np.array([1]), np.array(list(range(257))))
img = mmcv.lut_transform(self.img, lut_table)
baseline = cv2.LUT(self.img, lut_table)
assert np.allclose(img, baseline)
input_img = np.array(
[[[0, 128, 255], [255, 128, 0]], [[0, 128, 255], [255, 128, 0]]],
dtype=np.float)
img = mmcv.lut_transform(input_img, lut_table)
baseline = cv2.LUT(np.array(input_img, dtype=np.uint8), lut_table)
assert np.allclose(img, baseline)
input_img = np.random.randint(0, 256, size=(7, 8, 9, 10, 11))
img = mmcv.lut_transform(input_img, lut_table)
baseline = cv2.LUT(np.array(input_img, dtype=np.uint8), lut_table)
assert np.allclose(img, baseline)
