[Feature] Add Rgb2Gray transform (#227)

* add transformer Rgb2Gray

* restore

* fix self.weights

* restore

* fix code

* restore

* fix syntax error

* restore

mmseg/datasets/pipelines/transforms.py

@@ -548,6 +548,61 @@ def __repr__(self):
         return repr_str
 
 
+@PIPELINES.register_module()
+class RGB2Gray(object):
+    """Convert RGB image to grayscale image.
+
+    This transform calculate the weighted mean of input image channels with
+    ``weights`` and then expand the channels to ``out_channels``. When
+    ``out_channels`` is None, the number of output channels is the same as
+    input channels.
+
+    Args:
+        out_channels (int): Expected number of output channels after
+            transforming. Default: None.
+        weights (tuple[float]): The weights to calculate the weighted mean.
+            Default: (0.299, 0.587, 0.114).
+    """
+
+    def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)):
+        assert out_channels is None or out_channels > 0
+        self.out_channels = out_channels
+        assert isinstance(weights, tuple)
+        for item in weights:
+            assert isinstance(item, (float, int))
+        self.weights = weights
+
+    def __call__(self, results):
+        """Call function to convert RGB image to grayscale image.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with grayscale image.
+        """
+        img = results['img']
+        assert len(img.shape) == 3
+        assert img.shape[2] == len(self.weights)
+        weights = np.array(self.weights).reshape((1, 1, -1))
+        img = (img * weights).sum(2, keepdims=True)
+        if self.out_channels is None:
+            img = img.repeat(weights.shape[2], axis=2)
+        else:
+            img = img.repeat(self.out_channels, axis=2)
+
+        results['img'] = img
+        results['img_shape'] = img.shape
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(out_channels={self.out_channels}, ' \
+                    f'weights={self.weights})'
+        return repr_str
+
+
 @PIPELINES.register_module()
 class SegRescale(object):
     """Rescale semantic segmentation maps.

tests/test_data/test_transform.py

@@ -263,6 +263,73 @@ def test_normalize():
     assert np.allclose(results['img'], converted_img)
 
 
+def test_rgb2gray():
+    # test assertion out_channels should be greater than 0
+    with pytest.raises(AssertionError):
+        transform = dict(type='RGB2Gray', out_channels=-1)
+        build_from_cfg(transform, PIPELINES)
+    # test assertion weights should be tuple[float]
+    with pytest.raises(AssertionError):
+        transform = dict(type='RGB2Gray', out_channels=1, weights=1.1)
+        build_from_cfg(transform, PIPELINES)
+
+    # test out_channels is None
+    transform = dict(type='RGB2Gray')
+    transform = build_from_cfg(transform, PIPELINES)
+
+    assert str(transform) == f'RGB2Gray(' \
+                             f'out_channels={None}, ' \
+                             f'weights={(0.299, 0.587, 0.114)})'
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+    h, w, c = img.shape
+    seg = np.array(
+        Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
+    results['img'] = img
+    results['gt_semantic_seg'] = seg
+    results['seg_fields'] = ['gt_semantic_seg']
+    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)
+    assert results['img'].shape == (h, w, c)
+    assert results['img_shape'] == (h, w, c)
+    assert results['ori_shape'] == (h, w, c)
+
+    # test out_channels = 2
+    transform = dict(type='RGB2Gray', out_channels=2)
+    transform = build_from_cfg(transform, PIPELINES)
+
+    assert str(transform) == f'RGB2Gray(' \
+                             f'out_channels={2}, ' \
+                             f'weights={(0.299, 0.587, 0.114)})'
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+    h, w, c = img.shape
+    seg = np.array(
+        Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
+    results['img'] = img
+    results['gt_semantic_seg'] = seg
+    results['seg_fields'] = ['gt_semantic_seg']
+    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)
+    assert results['img'].shape == (h, w, 2)
+    assert results['img_shape'] == (h, w, 2)
+    assert results['ori_shape'] == (h, w, c)
+
+
 def test_seg_rescale():
     results = dict()
     seg = np.array(