CropAndResize

public final class CropAndResize

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`.

Nested Classes

class CropAndResize.Options Optional attributes for CropAndResize

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

Output<TFloat32>	asOutput() Returns the symbolic handle of the tensor.
static CropAndResize	create(Scope scope, Operand<? extends TNumber> image, Operand<TFloat32> boxes, Operand<TInt32> boxInd, Operand<TInt32> cropSize, Options... options) Factory method to create a class wrapping a new CropAndResize operation.
Output<TFloat32>	crops() A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
static CropAndResize.Options	extrapolationValue(Float extrapolationValue)
static CropAndResize.Options	method(String method)

Inherited Methods

From class org.tensorflow.op.RawOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Return this unit of computation as a single `Operation`.
final String	toString()

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

From interface org.tensorflow.op.Op

abstract ExecutionEnvironment	env() Return the execution environment this op was created in.
abstract Operation	op() Return this unit of computation as a single `Operation`.

From interface org.tensorflow.Operand

abstract Output<TFloat32>	asOutput() Returns the symbolic handle of the tensor.
abstract TFloat32	asTensor() Returns the tensor at this operand.
abstract Shape	shape() Returns the (possibly partially known) shape of the tensor referred to by the `Output` of this operand.
abstract Class<TFloat32>	type() Returns the tensor type of this operand

From interface org.tensorflow.ndarray.Shaped

abstract int	rank()
abstract Shape	shape()
abstract long	size() Computes and returns the total size of this container, in number of values.

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "CropAndResize"

Public Methods

public Output<TFloat32> asOutput ()

Returns the symbolic handle of the tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static CropAndResize create (Scope scope, Operand<? extends TNumber> image, Operand<TFloat32> boxes, Operand<TInt32> boxInd, Operand<TInt32> cropSize, Options... options)

Factory method to create a class wrapping a new CropAndResize operation.

Parameters

scope	current scope
image	A 4-D tensor of shape `[batch, image_height, image_width, depth]`. Both `image_height` and `image_width` need to be positive.
boxes	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.
boxInd	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.
cropSize	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.
options	carries optional attributes values

Returns

a new instance of CropAndResize

public Output<TFloat32> crops ()

A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.

public static CropAndResize.Options extrapolationValue (Float extrapolationValue)

Parameters

extrapolationValue	Value used for extrapolation, when applicable.

public static CropAndResize.Options method (String method)

Parameters

method	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.