def __call__(self, results):
if np.random.rand() > self.prob:
return results
for key in results.get('img_fields', ['img']):
img = results[key]
img_cutout = mmcv.cutout(img, self.shape, pad_val=self.pad_val)
results[key] = img_cutout.astype(img.dtype)
return results
def test_cutout(self):
img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).astype(np.uint8)
# shape must be int or tuple
with pytest.raises(AssertionError):
mmcv.cutout(img, 2.5)
# pad_val must be int or float or tuple with the same length
# of img channels
with pytest.raises(AssertionError):
mmcv.cutout(img, 1, (1, 2, 3))
with pytest.raises(TypeError):
mmcv.cutout(img, 1, None)
# test cutout the whole img
assert_array_equal(mmcv.cutout(img, 6), np.zeros_like(img))
# test not cutout
assert_array_equal(mmcv.cutout(img, 0), img)
# test cutout when shape is int
np.random.seed(0)
img_cutout = np.array([[1, 2, 3], [4, 0, 6], [7, 8,
9]]).astype(np.uint8)
assert_array_equal(mmcv.cutout(img, 1), img_cutout)
img_cutout = np.array([[1, 2, 3], [4, 10, 6], [7, 8,
9]]).astype(np.uint8)
assert_array_equal(mmcv.cutout(img, 1, pad_val=10), img_cutout)
# test cutout when shape is tuple
np.random.seed(0)
img_cutout = np.array([[1, 2, 3], [0, 0, 6], [7, 8,
9]]).astype(np.uint8)
assert_array_equal(mmcv.cutout(img, (1, 2)), img_cutout)
img_cutout = np.array([[1, 2, 3], [10, 10, 6], [7, 8,
9]]).astype(np.uint8)
assert_array_equal(mmcv.cutout(img, (1, 2), pad_val=10), img_cutout)