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package org.nd4j.autodiff.samediff.ops;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ops.custom.AdjustContrast;
import org.nd4j.linalg.api.ops.custom.AdjustHue;
import org.nd4j.linalg.api.ops.custom.AdjustSaturation;
import org.nd4j.linalg.api.ops.custom.RandomCrop;
import org.nd4j.linalg.api.ops.impl.image.CropAndResize;
import org.nd4j.linalg.api.ops.impl.image.ExtractImagePatches;
import org.nd4j.linalg.api.ops.impl.image.NonMaxSuppression;
/**
* @author Alex Black
*/
public class SDImage extends SDOps {
public SDImage(SameDiff sameDiff) {
super(sameDiff);
}
/**
* Given an input image and some crop boxes, extract out the image subsets and resize them to the specified size.
*
* @param name May be null. Name for the output variable.
* @param image Input image, with shape [batch, height, width, channels]
* @param cropBoxes Float32 crop, shape [numBoxes, 4] with values in range 0 to 1
* @param boxIndices Indices: which image (index to dimension 0) the cropBoxes belong to. Rank 1, shape [numBoxes]
* @param cropOutSize Output size for the images - int32, rank 1 with values [outHeight, outWidth]
* @param method Image resize method
* @param extrapolationValue Used for extrapolation, when applicable. 0.0 should be used for the default
* @return Cropped and resized images
*/
public SDVariable cropAndResize(String name, SDVariable image, SDVariable cropBoxes, SDVariable boxIndices, SDVariable cropOutSize,
CropAndResize.Method method, double extrapolationValue) {
SDVariable out = new CropAndResize(sd, image, cropBoxes, boxIndices, cropOutSize, method, extrapolationValue).outputVariable();
return updateVariableNameAndReference(out, name);
}
/**
* Given an input image, extract out image patches (of size kSizes - h x w) and place them in the depth dimension.
*
* @param name Map be null. Name for the output variable
* @param image Input image to extract image patches from - shape [batch, height, width, channels]
* @param kSizes Kernel size - size of the image patches, [height, width]
* @param strides Stride in the input dimension for extracting image patches, [stride_height, stride_width]
* @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
* @param sameMode Padding algorithm. If true: use Same padding
* @return The extracted image patches
*/
public SDVariable extractImagePatches(String name, SDVariable image, @NonNull int[] kSizes,
@NonNull int[] strides, @NonNull int[] rates, boolean sameMode) {
SDVariable out = new ExtractImagePatches(sd, image, kSizes, strides, rates, sameMode).outputVariable();
return updateVariableNameAndReference(out, name);
}
/**
* Greedily selects a subset of bounding boxes in descending order of score
* @param name Might be null. Name for the output variable
* @param boxes 2D array of shape [num_boxes,4]
* @param scores vector of shape [num_boxes]
* @param maxOutSize scalar representing the maximum number of boxes to be selected
* @param iouThreshold float - threshold for deciding whether boxes overlap too much with respect to IOU
* @param scoreThreshold float - threshold for deciding when to remove boxes based on score
* @return vectort of shape [M] representing the selected indices from the boxes tensor, where M <= max_output_size
*/
public SDVariable nonMaxSuppression(String name, @NonNull SDVariable boxes, @NonNull SDVariable scores, @NonNull SDVariable maxOutSize,
@NonNull SDVariable iouThreshold, @NonNull SDVariable scoreThreshold){
SDVariable out = new NonMaxSuppression(sd, boxes, scores, maxOutSize, iouThreshold, scoreThreshold).outputVariable();
return updateVariableNameAndReference(out, name);
}
/**
* Adjusts contrast of RGB or grayscale images.
* @param name name for the output variable
* @param in images to adjust. 3D shape or higher.
* @param factor float multiplier for adjusting contrast.
* @return Contrast-adjusted image
*/
public SDVariable adjustContrast(String name, @NonNull SDVariable in, @NonNull SDVariable factor) {
SDVariable out = new AdjustContrast(sd, in, factor).outputVariable();
return updateVariableNameAndReference(out, name);
}
/**
* Adjust saturation of RGB images
* @param name name for the output variable
* @param in RGB image as 3D array
* @param factor factor for saturation
* @return adjusted image
*/
public SDVariable adjustSaturation(String name, @NonNull SDVariable in, @NonNull SDVariable factor) {
SDVariable out = new AdjustSaturation(sd, in, factor).outputVariable();
return updateVariableNameAndReference(out, name);
}
/**
* Adjust hue of RGB image
* @param name name for the output variable
* @param in RGB image as 3D array
* @param delta value to add to hue channel
* @return adjusted image
*/
public SDVariable adjustHue(String name, @NonNull SDVariable in, @NonNull SDVariable delta) {
SDVariable out = new AdjustHue(sd, in, delta).outputVariable();
return updateVariableNameAndReference(out, name);
}
/**
* Randomly crops image
* @param name name for the output variable
* @param input input array
* @param shape shape for crop
* @return cropped array
*/
public SDVariable randomCrop(String name, @NonNull SDVariable input, @NonNull SDVariable shape) {
SDVariable out = new RandomCrop(sd, input, shape).outputVariable();
return updateVariableNameAndReference(out, name);
}
}
