Dnn.java
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//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.dnn;
import java.lang.String;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Mat;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfRect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.utils.Converters;
// C++: class Dnn
//javadoc: Dnn
public class Dnn {
public static final int
DNN_BACKEND_DEFAULT = 0,
DNN_BACKEND_HALIDE = 1,
DNN_TARGET_CPU = 0,
DNN_TARGET_OPENCL = 1;
//
// C++: Mat blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = true, bool crop = true)
//
//javadoc: blobFromImage(image, scalefactor, size, mean, swapRB, crop)
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)
{
Mat retVal = new Mat(blobFromImage_0(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop));
return retVal;
}
//javadoc: blobFromImage(image)
public static Mat blobFromImage(Mat image)
{
Mat retVal = new Mat(blobFromImage_1(image.nativeObj));
return retVal;
}
//
// C++: Mat blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = true, bool crop = true)
//
//javadoc: blobFromImages(images, scalefactor, size, mean, swapRB, crop)
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)
{
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat retVal = new Mat(blobFromImages_0(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop));
return retVal;
}
//javadoc: blobFromImages(images)
public static Mat blobFromImages(List<Mat> images)
{
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat retVal = new Mat(blobFromImages_1(images_mat.nativeObj));
return retVal;
}
//
// C++: Mat readTorchBlob(String filename, bool isBinary = true)
//
//javadoc: readTorchBlob(filename, isBinary)
public static Mat readTorchBlob(String filename, boolean isBinary)
{
Mat retVal = new Mat(readTorchBlob_0(filename, isBinary));
return retVal;
}
//javadoc: readTorchBlob(filename)
public static Mat readTorchBlob(String filename)
{
Mat retVal = new Mat(readTorchBlob_1(filename));
return retVal;
}
//
// C++: Net readNetFromCaffe(String prototxt, String caffeModel = String())
//
//javadoc: readNetFromCaffe(prototxt, caffeModel)
public static Net readNetFromCaffe(String prototxt, String caffeModel)
{
Net retVal = new Net(readNetFromCaffe_0(prototxt, caffeModel));
return retVal;
}
//javadoc: readNetFromCaffe(prototxt)
public static Net readNetFromCaffe(String prototxt)
{
Net retVal = new Net(readNetFromCaffe_1(prototxt));
return retVal;
}
//
// C++: Net readNetFromDarknet(String cfgFile, String darknetModel = String())
//
//javadoc: readNetFromDarknet(cfgFile, darknetModel)
public static Net readNetFromDarknet(String cfgFile, String darknetModel)
{
Net retVal = new Net(readNetFromDarknet_0(cfgFile, darknetModel));
return retVal;
}
//javadoc: readNetFromDarknet(cfgFile)
public static Net readNetFromDarknet(String cfgFile)
{
Net retVal = new Net(readNetFromDarknet_1(cfgFile));
return retVal;
}
//
// C++: Net readNetFromTensorflow(String model, String config = String())
//
//javadoc: readNetFromTensorflow(model, config)
public static Net readNetFromTensorflow(String model, String config)
{
Net retVal = new Net(readNetFromTensorflow_0(model, config));
return retVal;
}
//javadoc: readNetFromTensorflow(model)
public static Net readNetFromTensorflow(String model)
{
Net retVal = new Net(readNetFromTensorflow_1(model));
return retVal;
}
//
// C++: Net readNetFromTorch(String model, bool isBinary = true)
//
//javadoc: readNetFromTorch(model, isBinary)
public static Net readNetFromTorch(String model, boolean isBinary)
{
Net retVal = new Net(readNetFromTorch_0(model, isBinary));
return retVal;
}
//javadoc: readNetFromTorch(model)
public static Net readNetFromTorch(String model)
{
Net retVal = new Net(readNetFromTorch_1(model));
return retVal;
}
//
// C++: void NMSBoxes(vector_Rect bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
//
//javadoc: NMSBoxes(bboxes, scores, score_threshold, nms_threshold, indices, eta, top_k)
public static void NMSBoxes(MatOfRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
{
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxes_0(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta, top_k);
return;
}
//javadoc: NMSBoxes(bboxes, scores, score_threshold, nms_threshold, indices)
public static void NMSBoxes(MatOfRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
{
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxes_1(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
return;
}
//
// C++: void shrinkCaffeModel(String src, String dst, vector_String layersTypes = std::vector<String>())
//
//javadoc: shrinkCaffeModel(src, dst, layersTypes)
public static void shrinkCaffeModel(String src, String dst, List<String> layersTypes)
{
shrinkCaffeModel_0(src, dst, layersTypes);
return;
}
//javadoc: shrinkCaffeModel(src, dst)
public static void shrinkCaffeModel(String src, String dst)
{
shrinkCaffeModel_1(src, dst);
return;
}
// C++: Mat blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = true, bool crop = true)
private static native long blobFromImage_0(long image_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB, boolean crop);
private static native long blobFromImage_1(long image_nativeObj);
// C++: Mat blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = true, bool crop = true)
private static native long blobFromImages_0(long images_mat_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB, boolean crop);
private static native long blobFromImages_1(long images_mat_nativeObj);
// C++: Mat readTorchBlob(String filename, bool isBinary = true)
private static native long readTorchBlob_0(String filename, boolean isBinary);
private static native long readTorchBlob_1(String filename);
// C++: Net readNetFromCaffe(String prototxt, String caffeModel = String())
private static native long readNetFromCaffe_0(String prototxt, String caffeModel);
private static native long readNetFromCaffe_1(String prototxt);
// C++: Net readNetFromDarknet(String cfgFile, String darknetModel = String())
private static native long readNetFromDarknet_0(String cfgFile, String darknetModel);
private static native long readNetFromDarknet_1(String cfgFile);
// C++: Net readNetFromTensorflow(String model, String config = String())
private static native long readNetFromTensorflow_0(String model, String config);
private static native long readNetFromTensorflow_1(String model);
// C++: Net readNetFromTorch(String model, bool isBinary = true)
private static native long readNetFromTorch_0(String model, boolean isBinary);
private static native long readNetFromTorch_1(String model);
// C++: void NMSBoxes(vector_Rect bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
private static native void NMSBoxes_0(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta, int top_k);
private static native void NMSBoxes_1(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj);
// C++: void shrinkCaffeModel(String src, String dst, vector_String layersTypes = std::vector<String>())
private static native void shrinkCaffeModel_0(String src, String dst, List<String> layersTypes);
private static native void shrinkCaffeModel_1(String src, String dst);
}