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/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.linalg.factory.ops;
import static org.nd4j.linalg.factory.NDValidation.isSameType;
import org.nd4j.common.base.Preconditions;
import org.nd4j.enums.ImageResizeMethod;
import org.nd4j.enums.Mode;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.NDValidation;
import org.nd4j.linalg.factory.Nd4j;
public class NDImage {
public NDImage() {
}
/**
* Given an input image and some crop boxes, extract out the image subsets and resize them to the specified size.
*
* @param image Input image, with shape [batch, height, width, channels] (NUMERIC type)
* @param cropBoxes Float32 crop, shape [numBoxes, 4] with values in range 0 to 1 (NUMERIC type)
* @param boxIndices Indices: which image (index to dimension 0) the cropBoxes belong to. Rank 1, shape [numBoxes] (NUMERIC type)
* @param cropOutSize Output size for the images - int32, rank 1 with values [outHeight, outWidth] (INT type)
* @param extrapolationValue Used for extrapolation, when applicable. 0.0 should be used for the default
* @return output Cropped and resized images (NUMERIC type)
*/
public INDArray cropAndResize(INDArray image, INDArray cropBoxes, INDArray boxIndices,
INDArray cropOutSize, double extrapolationValue) {
NDValidation.validateNumerical("CropAndResize", "image", image);
NDValidation.validateNumerical("CropAndResize", "cropBoxes", cropBoxes);
NDValidation.validateNumerical("CropAndResize", "boxIndices", boxIndices);
NDValidation.validateInteger("CropAndResize", "cropOutSize", cropOutSize);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.CropAndResize(image, cropBoxes, boxIndices, cropOutSize, extrapolationValue))[0];
}
/**
* Given an input image and some crop boxes, extract out the image subsets and resize them to the specified size.
*
* @param image Input image, with shape [batch, height, width, channels] (NUMERIC type)
* @param cropBoxes Float32 crop, shape [numBoxes, 4] with values in range 0 to 1 (NUMERIC type)
* @param boxIndices Indices: which image (index to dimension 0) the cropBoxes belong to. Rank 1, shape [numBoxes] (NUMERIC type)
* @param cropOutSize Output size for the images - int32, rank 1 with values [outHeight, outWidth] (INT type)
* @return output Cropped and resized images (NUMERIC type)
*/
public INDArray cropAndResize(INDArray image, INDArray cropBoxes, INDArray boxIndices,
INDArray cropOutSize) {
NDValidation.validateNumerical("CropAndResize", "image", image);
NDValidation.validateNumerical("CropAndResize", "cropBoxes", cropBoxes);
NDValidation.validateNumerical("CropAndResize", "boxIndices", boxIndices);
NDValidation.validateInteger("CropAndResize", "cropOutSize", cropOutSize);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.CropAndResize(image, cropBoxes, boxIndices, cropOutSize, 0.0))[0];
}
/**
* Adjusts contrast of RGB or grayscale images.
*
* @param in images to adjust. 3D shape or higher (NUMERIC type)
* @param factor multiplier for adjusting contrast
* @return output Contrast-adjusted image (NUMERIC type)
*/
public INDArray adjustContrast(INDArray in, double factor) {
NDValidation.validateNumerical("adjustContrast", "in", in);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.AdjustContrast(in, factor))[0];
}
/**
* Adjust hue of RGB image
*
* @param in image as 3D array (NUMERIC type)
* @param delta value to add to hue channel
* @return output adjusted image (NUMERIC type)
*/
public INDArray adjustHue(INDArray in, double delta) {
NDValidation.validateNumerical("adjustHue", "in", in);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.AdjustHue(in, delta))[0];
}
/**
* Adjust saturation of RGB images
*
* @param in RGB image as 3D array (NUMERIC type)
* @param factor factor for saturation
* @return output adjusted image (NUMERIC type)
*/
public INDArray adjustSaturation(INDArray in, double factor) {
NDValidation.validateNumerical("adjustSaturation", "in", in);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.AdjustSaturation(in, factor))[0];
}
/**
* Given an input image, extract out image patches (of size kSizes - h x w) and place them in the depth dimension.
*
* @param image Input image to extract image patches from - shape [batch, height, width, channels] (NUMERIC type)
* @param kSizes Kernel size - size of the image patches, [height, width] (Size: Exactly(count=2))
* @param strides Stride in the input dimension for extracting image patches, [stride_height, stride_width] (Size: Exactly(count=2))
* @param rates Usually [1,1]. Equivalent to dilation rate in dilated convolutions - how far apart the output pixels
* in the patches should be, in the input. A dilation of [a,b] means every {@code a}th pixel is taken
* along the height/rows dimension, and every {@code b}th pixel is take along the width/columns dimension (Size: AtLeast(min=0))
* @param sameMode Padding algorithm. If true: use Same padding
* @return output The extracted image patches (NUMERIC type)
*/
public INDArray extractImagePatches(INDArray image, int[] kSizes, int[] strides, int[] rates,
boolean sameMode) {
NDValidation.validateNumerical("extractImagePatches", "image", image);
Preconditions.checkArgument(kSizes.length == 2, "kSizes has incorrect size/length. Expected: kSizes.length == 2, got %s", kSizes.length);
Preconditions.checkArgument(strides.length == 2, "strides has incorrect size/length. Expected: strides.length == 2, got %s", strides.length);
Preconditions.checkArgument(rates.length >= 0, "rates has incorrect size/length. Expected: rates.length >= 0, got %s", rates.length);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.ExtractImagePatches(image, kSizes, strides, rates, sameMode))[0];
}
/**
* Converting image from HSV to RGB format
*
* @param input 3D image (NUMERIC type)
* @return output 3D image (NUMERIC type)
*/
public INDArray hsvToRgb(INDArray input) {
NDValidation.validateNumerical("hsvToRgb", "input", input);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.HsvToRgb(input))[0];
}
/**
* Resize images to size using the specified method.
*
* @param input 4D image [NHWC] (NUMERIC type)
* @param size new height and width (INT type)
* @param preserveAspectRatio Whether to preserve the aspect ratio. If this is set, then images will be resized to a size that fits in size while preserving the aspect ratio of the original image. Scales up the image if size is bigger than the current size of the image. Defaults to False.
* @param antialias Whether to use an anti-aliasing filter when downsampling an image
* @param ImageResizeMethod ResizeBilinear: Bilinear interpolation. If 'antialias' is true, becomes a hat/tent filter function with radius 1 when downsampling.
* ResizeLanczos5: Lanczos kernel with radius 5. Very-high-quality filter but may have stronger ringing.
* ResizeBicubic: Cubic interpolant of Keys. Equivalent to Catmull-Rom kernel. Reasonably good quality and faster than Lanczos3Kernel, particularly when upsampling.
* ResizeGaussian: Gaussian kernel with radius 3, sigma = 1.5 / 3.0.
* ResizeNearest: Nearest neighbor interpolation. 'antialias' has no effect when used with nearest neighbor interpolation.
* ResizeArea: Anti-aliased resampling with area interpolation. 'antialias' has no effect when used with area interpolation; it always anti-aliases.
* ResizeMitchellcubic: Mitchell-Netravali Cubic non-interpolating filter. For synthetic images (especially those lacking proper prefiltering), less ringing than Keys cubic kernel but less sharp.
* @return output Output image (NUMERIC type)
*/
public INDArray imageResize(INDArray input, INDArray size, boolean preserveAspectRatio,
boolean antialias, ImageResizeMethod ImageResizeMethod) {
NDValidation.validateNumerical("imageResize", "input", input);
NDValidation.validateInteger("imageResize", "size", size);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.ImageResize(input, size, preserveAspectRatio, antialias, ImageResizeMethod))[0];
}
/**
* Resize images to size using the specified method.
*
* @param input 4D image [NHWC] (NUMERIC type)
* @param size new height and width (INT type)
* @param ImageResizeMethod ResizeBilinear: Bilinear interpolation. If 'antialias' is true, becomes a hat/tent filter function with radius 1 when downsampling.
* ResizeLanczos5: Lanczos kernel with radius 5. Very-high-quality filter but may have stronger ringing.
* ResizeBicubic: Cubic interpolant of Keys. Equivalent to Catmull-Rom kernel. Reasonably good quality and faster than Lanczos3Kernel, particularly when upsampling.
* ResizeGaussian: Gaussian kernel with radius 3, sigma = 1.5 / 3.0.
* ResizeNearest: Nearest neighbor interpolation. 'antialias' has no effect when used with nearest neighbor interpolation.
* ResizeArea: Anti-aliased resampling with area interpolation. 'antialias' has no effect when used with area interpolation; it always anti-aliases.
* ResizeMitchellcubic: Mitchell-Netravali Cubic non-interpolating filter. For synthetic images (especially those lacking proper prefiltering), less ringing than Keys cubic kernel but less sharp.
* @return output Output image (NUMERIC type)
*/
public INDArray imageResize(INDArray input, INDArray size, ImageResizeMethod ImageResizeMethod) {
NDValidation.validateNumerical("imageResize", "input", input);
NDValidation.validateInteger("imageResize", "size", size);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.ImageResize(input, size, false, false, ImageResizeMethod))[0];
}
/**
* Greedily selects a subset of bounding boxes in descending order of score
*
* @param boxes Might be null. Name for the output variable (NUMERIC type)
* @param scores vector of shape [num_boxes] (NUMERIC type)
* @param maxOutSize scalar representing the maximum number of boxes to be selected
* @param iouThreshold threshold for deciding whether boxes overlap too much with respect to IOU
* @param scoreThreshold threshold for deciding when to remove boxes based on score
* @return output vectort of shape [M] representing the selected indices from the boxes tensor, where M <= max_output_size (NUMERIC type)
*/
public INDArray nonMaxSuppression(INDArray boxes, INDArray scores, int maxOutSize,
double iouThreshold, double scoreThreshold) {
NDValidation.validateNumerical("nonMaxSuppression", "boxes", boxes);
NDValidation.validateNumerical("nonMaxSuppression", "scores", scores);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.NonMaxSuppression(boxes, scores, maxOutSize, iouThreshold, scoreThreshold))[0];
}
/**
* Pads an image according to the given padding type
*
* @param input input array (NUMERIC type)
* @param padding padding input (NUMERIC type)
* @param Mode padding mode: CONSTANT, REFLECT, SYMMETRIC
* @param padValue The value to pad with
* @return output the padded array (NUMERIC type)
*/
public INDArray pad(INDArray input, INDArray padding, Mode Mode, double padValue) {
NDValidation.validateNumerical("pad", "input", input);
NDValidation.validateNumerical("pad", "padding", padding);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.transforms.Pad(input, padding, Mode, padValue))[0];
}
/**
* Randomly crops image
*
* @param input input array (NUMERIC type)
* @param shape shape for crop (INT type)
* @return output cropped array (NUMERIC type)
*/
public INDArray randomCrop(INDArray input, INDArray shape) {
NDValidation.validateNumerical("randomCrop", "input", input);
NDValidation.validateInteger("randomCrop", "shape", shape);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.RandomCrop(input, shape))[0];
}
/**
* Resize images to size using the specified method.
*
* @param input 4D image (NUMERIC type)
* @param size the target size to resize to (INT type)
* @param alignCorners whether to align corners during resizing. Images are aligned to preserve corners.
* @param alignPixelCenters When resizing, assumes pixels are centered at 0.5.
* @return output Output image (NUMERIC type)
*/
public INDArray resizeBiCubic(INDArray input, INDArray size, boolean alignCorners,
boolean alignPixelCenters) {
NDValidation.validateNumerical("resizeBiCubic", "input", input);
NDValidation.validateInteger("resizeBiCubic", "size", size);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.ResizeBicubic(input, size, alignCorners, alignPixelCenters))[0];
}
/**
* Resize images to size using the specified method.
*
* @param input 4D image (NUMERIC type)
* @param height target height for resizing to
* @param width target width for resizing to
* @param alignCorners whether to align corners during resizing. Images are aligned to preserve corners.
* @param halfPixelCenters When resizing, assumes pixels are centered at 0.5.
* @return output Output image (NUMERIC type)
*/
public INDArray resizeBiLinear(INDArray input, int height, int width, boolean alignCorners,
boolean halfPixelCenters) {
NDValidation.validateNumerical("resizeBiLinear", "input", input);
return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.image.ResizeBilinear(input, height, width, alignCorners, halfPixelCenters))[0];
}
/**
* Converting array from HSV to RGB format
*
* @param input 3D image (NUMERIC type)
* @return output 3D image (NUMERIC type)
*/
public INDArray rgbToHsv(INDArray input) {
NDValidation.validateNumerical("rgbToHsv", "input", input);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.RgbToHsv(input))[0];
}
/**
* Converting array from RGB to YIQ format
*
* @param input 3D image (NUMERIC type)
* @return output 3D image (NUMERIC type)
*/
public INDArray rgbToYiq(INDArray input) {
NDValidation.validateNumerical("rgbToYiq", "input", input);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.RgbToYiq(input))[0];
}
/**
* Converting array from RGB to YUV format
*
* @param input 3D image (NUMERIC type)
* @return output 3D image (NUMERIC type)
*/
public INDArray rgbToYuv(INDArray input) {
NDValidation.validateNumerical("rgbToYuv", "input", input);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.RgbToYuv(input))[0];
}
/**
* Converting image from YIQ to RGB format
*
* @param input 3D image (NUMERIC type)
* @return output 3D image (NUMERIC type)
*/
public INDArray yiqToRgb(INDArray input) {
NDValidation.validateNumerical("yiqToRgb", "input", input);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.YiqToRgb(input))[0];
}
/**
* Converting image from YUV to RGB format
*
* @param input 3D image (NUMERIC type)
* @return output 3D image (NUMERIC type)
*/
public INDArray yuvToRgb(INDArray input) {
NDValidation.validateNumerical("yuvToRgb", "input", input);
return Nd4j.exec(new org.nd4j.linalg.api.ops.custom.YuvToRgb(input))[0];
}
}
