public final class NDImageUtils
extends java.lang.Object
NDImageUtils is an image processing utility to load, reshape, and convert images using
NDArray images.| Modifier and Type | Class and Description |
|---|---|
static class |
NDImageUtils.Flag
Flag indicates the color channel options for images.
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| Modifier and Type | Method and Description |
|---|---|
static NDArray |
centerCrop(NDArray image)
Crops an image to a square of size
min(width, height). |
static NDArray |
centerCrop(NDArray image,
int width,
int height)
Crops an image to a given width and height from the center of the image.
|
static NDArray |
crop(NDArray image,
int x,
int y,
int width,
int height)
Crops an image with a given location and size.
|
static NDArray |
normalize(NDArray input,
float[] mean,
float[] std)
Normalizes an image NDArray of shape CHW or NCHW with mean and standard deviation.
|
static NDArray |
normalize(NDArray input,
float mean,
float std)
Normalizes an image NDArray of shape CHW or NCHW with a single mean and standard deviation to
apply to all channels.
|
static NDArray |
resize(NDArray image,
int size)
Resizes an image to the given size.
|
static NDArray |
resize(NDArray image,
int width,
int height)
Resizes an image to the given width and height.
|
static NDArray |
toTensor(NDArray image)
Converts an image NDArray from preprocessing format to Neural Network format.
|
public static NDArray resize(NDArray image, int size)
image - the image to resizesize - the new size to use for both height and widthpublic static NDArray resize(NDArray image, int width, int height)
image - the image to resizewidth - the desired widthheight - the desired heightpublic static NDArray normalize(NDArray input, float mean, float std)
input - the image to normalizemean - the mean to normalize with (for all channels)std - the standard deviation to normalize with (for all channels)normalize(NDArray, float[], float[])public static NDArray normalize(NDArray input, float[] mean, float[] std)
Given mean (m1, ..., mn) and standard deviation (s1, ..., sn for n
channels, this transform normalizes each channel of the input tensor with: output[i] =
(input[i] - m1) / (s1).
input - the image to normalizemean - the mean to normalize with for each channelstd - the standard deviation to normalize with for each channelpublic static NDArray toTensor(NDArray image)
Converts an image NDArray of shape HWC in the range [0, 255] to a DataType.FLOAT32 tensor NDArray of shape CHW in the range [0,
1].
image - the image to convertpublic static NDArray centerCrop(NDArray image)
min(width, height).image - the image to cropcenterCrop(NDArray, int, int)public static NDArray centerCrop(NDArray image, int width, int height)
image - the image to cropwidth - the desired width of the cropped imageheight - the desired height of the cropped imagepublic static NDArray crop(NDArray image, int x, int y, int width, int height)
image - the image to cropx - the x coordinate of the top-left corner of the cropy - the y coordinate of the top-left corner of the cropwidth - the width of the cropped imageheight - the height of the cropped image