[Feature] Add Rerange transform (#228)

* add rerange transform

* restore

* delete rerange config

* delete rerange config hint

* add min < max assert

* restore

mmseg/datasets/pipelines/transforms.py

@@ -392,6 +392,53 @@ class Normalize(object):
         return repr_str
 
 
+@PIPELINES.register_module()
+class Rerange(object):
+    """Rerange the image pixel value.
+
+    Args:
+        min_value (float or int): Minimum value of the reranged image.
+            Default: 0.
+        max_value (float or int): Maximum value of the reranged image.
+            Default: 255.
+    """
+
+    def __init__(self, min_value=0, max_value=255):
+        assert isinstance(min_value, float) or isinstance(min_value, int)
+        assert isinstance(max_value, float) or isinstance(max_value, int)
+        assert min_value < max_value
+        self.min_value = min_value
+        self.max_value = max_value
+
+    def __call__(self, results):
+        """Call function to rerange images.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Reranged results.
+        """
+
+        img = results['img']
+        img_min_value = np.min(img)
+        img_max_value = np.max(img)
+
+        assert img_min_value < img_max_value
+        # rerange to [0, 1]
+        img = (img - img_min_value) / (img_max_value - img_min_value)
+        # rerange to [min_value, max_value]
+        img = img * (self.max_value - self.min_value) + self.min_value
+        results['img'] = img
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(min_value={self.min_value}, max_value={self.max_value})'
+        return repr_str
+
+
 @PIPELINES.register_module()
 class RandomCrop(object):
     """Random crop the image & seg.

tests/test_data/test_transform.py

@@ -330,6 +330,49 @@ def test_rgb2gray():
     assert results['ori_shape'] == (h, w, c)
 
 
+def test_rerange():
+    # test assertion if min_value or max_value is illegal
+    with pytest.raises(AssertionError):
+        transform = dict(type='Rerange', min_value=[0], max_value=[255])
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion if min_value >= max_value
+    with pytest.raises(AssertionError):
+        transform = dict(type='Rerange', min_value=1, max_value=1)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion if img_min_value == img_max_value
+    with pytest.raises(AssertionError):
+        transform = dict(type='Rerange', min_value=0, max_value=1)
+        transform = build_from_cfg(transform, PIPELINES)
+        results = dict()
+        results['img'] = np.array([[1, 1], [1, 1]])
+        transform(results)
+
+    img_rerange_cfg = dict()
+    transform = dict(type='Rerange', **img_rerange_cfg)
+    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)
+
+    min_value = np.min(original_img)
+    max_value = np.max(original_img)
+    converted_img = (original_img - min_value) / (max_value - min_value) * 255
+
+    assert np.allclose(results['img'], converted_img)
+    assert str(transform) == f'Rerange(min_value={0}, max_value={255})'
+
+
 def test_seg_rescale():
     results = dict()
     seg = np.array(