def test_imequalize(self, nb_rand_test=100):
def _imequalize(img):
# equalize the image using PIL.ImageOps.equalize
from PIL import ImageOps, Image
img = Image.fromarray(img)
equalized_img = np.asarray(ImageOps.equalize(img))
return equalized_img
img = np.array([[0, 128, 255], [1, 127, 254], [2, 129, 253]],
dtype=np.uint8)
img = np.stack([img, img, img], axis=-1)
equalized_img = mmcv.imequalize(img)
assert_array_equal(equalized_img, _imequalize(img))
# test equalize with case step=0
img = np.array([[0, 0, 0], [120, 120, 120], [255, 255, 255]],
dtype=np.uint8)
img = np.stack([img, img, img], axis=-1)
assert_array_equal(mmcv.imequalize(img), img)
# test equalize with randomly sampled image.
for _ in range(nb_rand_test):
img = np.clip(
np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0,
255).astype(np.uint8)
equalized_img = mmcv.imequalize(img)
assert_array_equal(equalized_img, _imequalize(img))
def __call__(self, results):
if np.random.rand() > self.prob:
return results
for key in results.get('img_fields', ['img']):
img = results[key]
img_equalized = mmcv.imequalize(img)
results[key] = img_equalized.astype(img.dtype)
return results
def _imequalize(self, results):
"""Equalizes the histogram of one image."""
for key in results.get('img_fields', ['img']):
img = results[key]
results[key] = mmcv.imequalize(img).astype(img.dtype)
