tf.image.resize_area( images, size, align_corners=False, name=None )
See the guide: Images > Resizing
size using area interpolation.
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.
Tensor. Must be one of the following types:
float64. 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.
align_corners: An optional
bool. Defaults to
False. 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.
name: A name for the operation (optional).
Tensor of type