[Feature] add AdjustGamma transform

mmseg/__init__.py

@@ -3,7 +3,7 @@
 from .version import __version__, version_info
 
 MMCV_MIN = '1.1.4'
-MMCV_MAX = '1.2.0'
+MMCV_MAX = '1.3.0'
 
 
 def digit_version(version_str):

mmseg/datasets/pipelines/transforms.py

@@ -650,6 +650,42 @@ def __repr__(self):
  return repr_str
 
 
+@PIPELINES.register_module()
+class AdjustGamma(object):
+ """Using gamma correction to process the image.
+
+ Args:
+ gamma (float or int): Gamma value used in gamma correction.
+ Default: 1.0.
+ """
+
+ def __init__(self, gamma=1.0):
+ assert isinstance(gamma, float) or isinstance(gamma, int)
+ assert gamma > 0
+ self.gamma = gamma
+ inv_gamma = 1.0 / gamma
+ self.table = np.array([(i / 255.0)**inv_gamma * 255
+ for i in np.arange(256)]).astype('uint8')
+
+ 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 __repr__(self):
+ return self.__class__.__name__ + f'(gamma={self.gamma})'
+
+
 @PIPELINES.register_module()
 class SegRescale(object):
  """Rescale semantic segmentation maps.

tests/test_data/test_transform.py

@@ -330,6 +330,42 @@ def test_rgb2gray():
  assert results['ori_shape'] == (h, w, c)
 
 
+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 test_rerange():
  # test assertion if min_value or max_value is illegal
  with pytest.raises(AssertionError):