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.
Args
images
A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, half, float32, float64, bfloat16.
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]