[Feature] add AdjustGamma transform (#232)

* add AdjustGamma transform

* restore

* change cv2 to mmcv

* simplify AdjustGamma

* fix syntax error

* modify

* fix syntax error

* change mmcv version to 1.3.0

* fix lut function name error

* fix syntax error

* fix range

mmseg/__init__.py

@@ -3,7 +3,7 @@ import mmcv
 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 @@ class RGB2Gray(object):
         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):