def __call__(self, results):
if np.random.rand() > self.prob:
return results
for key in results.get('img_fields', ['img']):
img = results[key]
img_contrasted = mmcv.auto_contrast(img)
results[key] = img_contrasted.astype(img.dtype)
return results
def test_auto_contrast(self, nb_rand_test=100):
def _auto_contrast(img, cutoff=0):
from PIL import Image
from PIL.ImageOps import autocontrast
# Image.fromarray defaultly supports RGB, not BGR.
# convert from BGR to RGB
img = Image.fromarray(img[..., ::-1], mode='RGB')
contrasted_img = autocontrast(img, cutoff)
# convert from RGB to BGR
return np.asarray(contrasted_img)[..., ::-1]
img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
dtype=np.uint8)
img = np.stack([img, img, img], axis=-1)
# test case without cut-off
assert_array_equal(mmcv.auto_contrast(img), _auto_contrast(img))
# test case with cut-off as int
assert_array_equal(mmcv.auto_contrast(img, 10),
_auto_contrast(img, 10))
# test case with cut-off as float
assert_array_equal(mmcv.auto_contrast(img, 12.5),
_auto_contrast(img, 12.5))
# test case with cut-off as tuple
assert_array_equal(mmcv.auto_contrast(img, (10, 10)),
_auto_contrast(img, 10))
# test case with cut-off with sum over 100
assert_array_equal(mmcv.auto_contrast(img, 60),
_auto_contrast(img, 60))
# test auto_contrast with randomly sampled images and factors.
for _ in range(nb_rand_test):
img = np.clip(
np.random.uniform(0, 1, (1200, 1000, 3)) * 260, 0,
255).astype(np.uint8)
# cut-offs are not set as tuple since in `build.yml`, pillow 6.2.2
# is installed, which does not support setting low cut-off and high
# cut-off differently.
# With pillow above 8.0.0, cutoff can be set as tuple
cutoff = np.random.rand() * 100
assert_array_equal(mmcv.auto_contrast(img, cutoff),
_auto_contrast(img, cutoff))
