#include <image_ops.h>
Extracts crops from the input image tensor and resizes them.
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
.
Arguments:
[batch, image_height, image_width, depth]
. Both image_height
and image_width
need to be positive.[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.[num_boxes]
with int32 values in [0, batch)
. The value of box_ind[i]
specifies the image that the i
-th box refers to.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.Optional attributes (see Attrs
):
"bilinear"
or "nearest"
and default to "bilinear"
. Currently two sampling methods are supported: Bilinear and Nearest Neighbor.Returns:
Output
: A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth]
. Constructors and Destructors | |
---|---|
CropAndResize(const ::tensorflow::Scope & scope, ::tensorflow::Input image, ::tensorflow::Input boxes, ::tensorflow::Input box_ind, ::tensorflow::Input crop_size) | |
CropAndResize(const ::tensorflow::Scope & scope, ::tensorflow::Input image, ::tensorflow::Input boxes, ::tensorflow::Input box_ind, ::tensorflow::Input crop_size, const CropAndResize::Attrs & attrs) |
Public attributes | |
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crops | |
operation |
Public functions | |
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node() const | ::tensorflow::Node * |
operator::tensorflow::Input() const | |
operator::tensorflow::Output() const |
Public static functions | |
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ExtrapolationValue(float x) | |
Method(StringPiece x) |
Structs | |
---|---|
tensorflow::ops::CropAndResize::Attrs | Optional attribute setters for CropAndResize. |
::tensorflow::Output crops
Operation operation
CropAndResize( const ::tensorflow::Scope & scope, ::tensorflow::Input image, ::tensorflow::Input boxes, ::tensorflow::Input box_ind, ::tensorflow::Input crop_size )
CropAndResize( const ::tensorflow::Scope & scope, ::tensorflow::Input image, ::tensorflow::Input boxes, ::tensorflow::Input box_ind, ::tensorflow::Input crop_size, const CropAndResize::Attrs & attrs )
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output() const
Attrs ExtrapolationValue( float x )
Attrs Method( StringPiece x )
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/cc/class/tensorflow/ops/crop-and-resize