sandeep-krishnamurthy commented on a change in pull request #13679: add crop
gluon transform
URL: https://github.com/apache/incubator-mxnet/pull/13679#discussion_r251179545
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File path: python/mxnet/gluon/data/vision/transforms.py
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@@ -168,6 +168,57 @@ def hybrid_forward(self, F, x):
return F.image.normalize(x, self._mean, self._std)
+class Crop(HybridBlock):
+ """Crop the input image with and optionally resize it.
+
+ Makes a crop of the original image then optionally resize it to the
specified size.
+
+ Parameters
+ ----------
+ x0 : int
+ Left boundary of the cropping area
+ y0 : int
+ Top boundary of the cropping area
+ w : int
+ Width of the cropping area
+ h : int
+ Height of the cropping area
+ size : int or tuple of (w, h)
+ Optional, resize to new size after cropping
+ interp : int, optional
+ Optional, interpolation method. See opencv for details.
+
+ Inputs:
+ - **data**: input tensor with (H x W x C) or (N x H x W x C) shape.
+
+ Outputs:
+ - **out**: output tensor with (H x W x C) or (N x H x W x C) shape.
+
+ Examples
+ --------
+ >>> transformer = vision.transforms.Crop(0, 0, 100, 100)
+ >>> image = mx.nd.random.uniform(0, 255, (224, 224,
3)).astype(dtype=np.uint8)
+ >>> transformer(image)
+ <NDArray 500x1000x3 @cpu(0)>
+ >>> image = mx.nd.random.uniform(0, 255, (3, 224, 224,
3)).astype(dtype=np.uint8)
+ <NDArray 3x500x1000x3 @cpu(0)>
+ >>> transformer = vision.transforms.Crop(0, 0, 100, 100, (50, 50), 1)
+ >>> transformer(image)
+ <NDArray 3x50x50 @cpu(0)>
+ """
+ def __init__(self, x0, y0, width, height, size=None, interpolation=None):
+ super(Crop, self).__init__()
+ self._x0 = x0
+ self._y0 = y0
+ self._width = width
+ self._height = height
+ self._size = size
+ self._interpolation = interpolation
+
+ def hybrid_forward(self, F, x):
+ return F.image.crop(x, self._x0, self._y0, self._width, self._height,
self._size, self._interpolation)
Review comment:
@stu1130 - I think this is a good suggestion. What do you suggest?
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