tensorflow::ops::ResizeArea

Resize images to size using area interpolation.

Summary

Input images can be of different types but output images are always float.

The range of pixel values for the output image might be slightly different from the range for the input image because of limited numerical precision. To guarantee an output range, for example [0.0, 1.0], apply tf.clip_by_value to the output.

Each output pixel is computed by first transforming the pixel's footprint into the input tensor and then averaging the pixels that intersect the footprint. An input pixel's contribution to the average is weighted by the fraction of its area that intersects the footprint. This is the same as OpenCV's INTER_AREA.

Arguments:

• scope: A Scope object
• images: 4-D with shape [batch, height, width, channels].
• size: = A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.

Optional attributes (see Attrs):

• align_corners: If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false.

Returns:

• Output: 4-D with shape [batch, new_height, new_width, channels].

Public attributes

Operation operation

::tensorflow::Output resized_images

 ResizeArea(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input images,
  ::tensorflow::Input size
)

 ResizeArea(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input images,
  ::tensorflow::Input size,
  const ResizeArea::Attrs & attrs
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Attrs AlignCorners(
  bool x
)