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op {
graph_op_name: "CropAndResize"
in_arg {
name: "image"
description: <<END
A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
Both `image_height` and `image_width` need to be positive.
END
}
in_arg {
name: "boxes"
description: <<END
A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
specifies the coordinates of a box in the `box_ind[i]` image and is specified
in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
`y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the
`[0, 1]` interval of normalized image height is mapped to
`[0, image_height - 1]` in image height coordinates. We do allow `y1` > `y2`, in
which case the sampled crop is an up-down flipped version of the original
image. The width dimension is treated similarly. Normalized coordinates
outside the `[0, 1]` range are allowed, in which case we use
`extrapolation_value` to extrapolate the input image values.
END
}
in_arg {
name: "box_ind"
description: <<END
A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
END
}
in_arg {
name: "crop_size"
description: <<END
A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All
cropped image patches are resized to this size. The aspect ratio of the image
content is not preserved. Both `crop_height` and `crop_width` need to be
positive.
END
}
out_arg {
name: "crops"
description: <<END
A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
END
}
attr {
name: "method"
description: <<END
A string specifying the sampling method for resizing. It can be either
`"bilinear"` or `"nearest"` and default to `"bilinear"`. Currently two sampling
methods are supported: Bilinear and Nearest Neighbor.
END
}
attr {
name: "extrapolation_value"
description: <<END
Value used for extrapolation, when applicable.
END
}
summary: "Extracts crops from the input image tensor and resizes them."
description: <<END
Extracts crops from the input image tensor and resizes them using bilinear
sampling or nearest neighbor sampling (possibly with aspect ratio change) to a
common output size specified by `crop_size`. This is more general than the
`crop_to_bounding_box` op which extracts a fixed size slice from the input image
and does not allow resizing or aspect ratio change.
Returns a tensor with `crops` from the input `image` at positions defined at the
bounding box locations in `boxes`. The cropped boxes are all resized (with
bilinear or nearest neighbor interpolation) to a fixed
`size = [crop_height, crop_width]`. The result is a 4-D tensor
`[num_boxes, crop_height, crop_width, depth]`. The resizing is corner aligned.
In particular, if `boxes = [[0, 0, 1, 1]]`, the method will give identical
results to using `tf.image.resize_bilinear()` or
`tf.image.resize_nearest_neighbor()`(depends on the `method` argument) with
`align_corners=True`.
END
}
|
