//
// 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.MatOfByte;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfRect;
import org.opencv.core.MatOfRect2d;
import org.opencv.core.MatOfRotatedRect;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.Net;
import org.opencv.utils.Converters;
// C++: class Dnn
//javadoc: Dnn
public class Dnn {
// C++: enum Backend
public static final int
DNN_BACKEND_DEFAULT = 0,
DNN_BACKEND_HALIDE = 1,
DNN_BACKEND_INFERENCE_ENGINE = 2,
DNN_BACKEND_OPENCV = 3;
// C++: enum Target
public static final int
DNN_TARGET_CPU = 0,
DNN_TARGET_OPENCL = 1,
DNN_TARGET_OPENCL_FP16 = 2,
DNN_TARGET_MYRIAD = 3,
DNN_TARGET_FPGA = 4;
//
// C++: Mat cv::dnn::blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
//
//javadoc: blobFromImage(image, scalefactor, size, mean, swapRB, crop, ddepth)
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth)
{
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, ddepth));
return retVal;
}
//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_1(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, scalefactor, size, mean, swapRB)
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB)
{
Mat retVal = new Mat(blobFromImage_2(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB));
return retVal;
}
//javadoc: blobFromImage(image, scalefactor, size, mean)
public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean)
{
Mat retVal = new Mat(blobFromImage_3(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3]));
return retVal;
}
//javadoc: blobFromImage(image, scalefactor, size)
public static Mat blobFromImage(Mat image, double scalefactor, Size size)
{
Mat retVal = new Mat(blobFromImage_4(image.nativeObj, scalefactor, size.width, size.height));
return retVal;
}
//javadoc: blobFromImage(image, scalefactor)
public static Mat blobFromImage(Mat image, double scalefactor)
{
Mat retVal = new Mat(blobFromImage_5(image.nativeObj, scalefactor));
return retVal;
}
//javadoc: blobFromImage(image)
public static Mat blobFromImage(Mat image)
{
Mat retVal = new Mat(blobFromImage_6(image.nativeObj));
return retVal;
}
//
// C++: Mat cv::dnn::blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
//
//javadoc: blobFromImages(images, scalefactor, size, mean, swapRB, crop, ddepth)
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth)
{
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, ddepth));
return retVal;
}
//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_1(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, scalefactor, size, mean, swapRB)
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB)
{
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat retVal = new Mat(blobFromImages_2(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB));
return retVal;
}
//javadoc: blobFromImages(images, scalefactor, size, mean)
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean)
{
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat retVal = new Mat(blobFromImages_3(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3]));
return retVal;
}
//javadoc: blobFromImages(images, scalefactor, size)
public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size)
{
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat retVal = new Mat(blobFromImages_4(images_mat.nativeObj, scalefactor, size.width, size.height));
return retVal;
}
//javadoc: blobFromImages(images, scalefactor)
public static Mat blobFromImages(List<Mat> images, double scalefactor)
{
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat retVal = new Mat(blobFromImages_5(images_mat.nativeObj, scalefactor));
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_6(images_mat.nativeObj));
return retVal;
}
//
// C++: Mat cv::dnn::readTensorFromONNX(String path)
//
//javadoc: readTensorFromONNX(path)
public static Mat readTensorFromONNX(String path)
{
Mat retVal = new Mat(readTensorFromONNX_0(path));
return retVal;
}
//
// C++: Mat cv::dnn::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 cv::dnn::readNet(String framework, vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
//
//javadoc: readNet(framework, bufferModel, bufferConfig)
public static Net readNet(String framework, MatOfByte bufferModel, MatOfByte bufferConfig)
{
Mat bufferModel_mat = bufferModel;
Mat bufferConfig_mat = bufferConfig;
Net retVal = new Net(readNet_0(framework, bufferModel_mat.nativeObj, bufferConfig_mat.nativeObj));
return retVal;
}
//javadoc: readNet(framework, bufferModel)
public static Net readNet(String framework, MatOfByte bufferModel)
{
Mat bufferModel_mat = bufferModel;
Net retVal = new Net(readNet_1(framework, bufferModel_mat.nativeObj));
return retVal;
}
//
// C++: Net cv::dnn::readNet(String model, String config = "", String framework = "")
//
//javadoc: readNet(model, config, framework)
public static Net readNet(String model, String config, String framework)
{
Net retVal = new Net(readNet_2(model, config, framework));
return retVal;
}
//javadoc: readNet(model, config)
public static Net readNet(String model, String config)
{
Net retVal = new Net(readNet_3(model, config));
return retVal;
}
//javadoc: readNet(model)
public static Net readNet(String model)
{
Net retVal = new Net(readNet_4(model));
return retVal;
}
//
// C++: Net cv::dnn::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 cv::dnn::readNetFromCaffe(vector_uchar bufferProto, vector_uchar bufferModel = std::vector<uchar>())
//
//javadoc: readNetFromCaffe(bufferProto, bufferModel)
public static Net readNetFromCaffe(MatOfByte bufferProto, MatOfByte bufferModel)
{
Mat bufferProto_mat = bufferProto;
Mat bufferModel_mat = bufferModel;
Net retVal = new Net(readNetFromCaffe_2(bufferProto_mat.nativeObj, bufferModel_mat.nativeObj));
return retVal;
}
//javadoc: readNetFromCaffe(bufferProto)
public static Net readNetFromCaffe(MatOfByte bufferProto)
{
Mat bufferProto_mat = bufferProto;
Net retVal = new Net(readNetFromCaffe_3(bufferProto_mat.nativeObj));
return retVal;
}
//
// C++: Net cv::dnn::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 cv::dnn::readNetFromDarknet(vector_uchar bufferCfg, vector_uchar bufferModel = std::vector<uchar>())
//
//javadoc: readNetFromDarknet(bufferCfg, bufferModel)
public static Net readNetFromDarknet(MatOfByte bufferCfg, MatOfByte bufferModel)
{
Mat bufferCfg_mat = bufferCfg;
Mat bufferModel_mat = bufferModel;
Net retVal = new Net(readNetFromDarknet_2(bufferCfg_mat.nativeObj, bufferModel_mat.nativeObj));
return retVal;
}
//javadoc: readNetFromDarknet(bufferCfg)
public static Net readNetFromDarknet(MatOfByte bufferCfg)
{
Mat bufferCfg_mat = bufferCfg;
Net retVal = new Net(readNetFromDarknet_3(bufferCfg_mat.nativeObj));
return retVal;
}
//
// C++: Net cv::dnn::readNetFromModelOptimizer(String xml, String bin)
//
//javadoc: readNetFromModelOptimizer(xml, bin)
public static Net readNetFromModelOptimizer(String xml, String bin)
{
Net retVal = new Net(readNetFromModelOptimizer_0(xml, bin));
return retVal;
}
//
// C++: Net cv::dnn::readNetFromONNX(String onnxFile)
//
//javadoc: readNetFromONNX(onnxFile)
public static Net readNetFromONNX(String onnxFile)
{
Net retVal = new Net(readNetFromONNX_0(onnxFile));
return retVal;
}
//
// C++: Net cv::dnn::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 cv::dnn::readNetFromTensorflow(vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
//
//javadoc: readNetFromTensorflow(bufferModel, bufferConfig)
public static Net readNetFromTensorflow(MatOfByte bufferModel, MatOfByte bufferConfig)
{
Mat bufferModel_mat = bufferModel;
Mat bufferConfig_mat = bufferConfig;
Net retVal = new Net(readNetFromTensorflow_2(bufferModel_mat.nativeObj, bufferConfig_mat.nativeObj));
return retVal;
}
//javadoc: readNetFromTensorflow(bufferModel)
public static Net readNetFromTensorflow(MatOfByte bufferModel)
{
Mat bufferModel_mat = bufferModel;
Net retVal = new Net(readNetFromTensorflow_3(bufferModel_mat.nativeObj));
return retVal;
}
//
// C++: Net cv::dnn::readNetFromTorch(String model, bool isBinary = true, bool evaluate = true)
//
//javadoc: readNetFromTorch(model, isBinary, evaluate)
public static Net readNetFromTorch(String model, boolean isBinary, boolean evaluate)
{
Net retVal = new Net(readNetFromTorch_0(model, isBinary, evaluate));
return retVal;
}
//javadoc: readNetFromTorch(model, isBinary)
public static Net readNetFromTorch(String model, boolean isBinary)
{
Net retVal = new Net(readNetFromTorch_1(model, isBinary));
return retVal;
}
//javadoc: readNetFromTorch(model)
public static Net readNetFromTorch(String model)
{
Net retVal = new Net(readNetFromTorch_2(model));
return retVal;
}
//
// C++: String cv::dnn::getInferenceEngineVPUType()
//
//javadoc: getInferenceEngineVPUType()
public static String getInferenceEngineVPUType()
{
String retVal = getInferenceEngineVPUType_0();
return retVal;
}
//
// C++: void cv::dnn::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, eta)
public static void NMSBoxes(MatOfRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
{
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, eta);
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_2(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
return;
}
//
// C++: void cv::dnn::NMSBoxes(vector_Rect2d 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(MatOfRect2d 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_3(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, eta)
public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
{
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxes_4(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta);
return;
}
//javadoc: NMSBoxes(bboxes, scores, score_threshold, nms_threshold, indices)
public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
{
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxes_5(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
return;
}
//
// C++: void cv::dnn::NMSBoxes(vector_RotatedRect bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
//
//javadoc: NMSBoxesRotated(bboxes, scores, score_threshold, nms_threshold, indices, eta, top_k)
public static void NMSBoxesRotated(MatOfRotatedRect 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;
NMSBoxesRotated_0(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta, top_k);
return;
}
//javadoc: NMSBoxesRotated(bboxes, scores, score_threshold, nms_threshold, indices, eta)
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
{
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxesRotated_1(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta);
return;
}
//javadoc: NMSBoxesRotated(bboxes, scores, score_threshold, nms_threshold, indices)
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
{
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
NMSBoxesRotated_2(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
return;
}
//
// C++: void cv::dnn::imagesFromBlob(Mat blob_, vector_Mat& images_)
//
//javadoc: imagesFromBlob(blob_, images_)
public static void imagesFromBlob(Mat blob_, List<Mat> images_)
{
Mat images__mat = new Mat();
imagesFromBlob_0(blob_.nativeObj, images__mat.nativeObj);
Converters.Mat_to_vector_Mat(images__mat, images_);
images__mat.release();
return;
}
//
// C++: void cv::dnn::resetMyriadDevice()
//
//javadoc: resetMyriadDevice()
public static void resetMyriadDevice()
{
resetMyriadDevice_0();
return;
}
//
// C++: void cv::dnn::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++: void cv::dnn::writeTextGraph(String model, String output)
//
//javadoc: writeTextGraph(model, output)
public static void writeTextGraph(String model, String output)
{
writeTextGraph_0(model, output);
return;
}
// C++: Mat cv::dnn::blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
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, int ddepth);
private static native long blobFromImage_1(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_2(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);
private static native long blobFromImage_3(long image_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3);
private static native long blobFromImage_4(long image_nativeObj, double scalefactor, double size_width, double size_height);
private static native long blobFromImage_5(long image_nativeObj, double scalefactor);
private static native long blobFromImage_6(long image_nativeObj);
// C++: Mat cv::dnn::blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false, int ddepth = CV_32F)
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, int ddepth);
private static native long blobFromImages_1(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_2(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);
private static native long blobFromImages_3(long images_mat_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3);
private static native long blobFromImages_4(long images_mat_nativeObj, double scalefactor, double size_width, double size_height);
private static native long blobFromImages_5(long images_mat_nativeObj, double scalefactor);
private static native long blobFromImages_6(long images_mat_nativeObj);
// C++: Mat cv::dnn::readTensorFromONNX(String path)
private static native long readTensorFromONNX_0(String path);
// C++: Mat cv::dnn::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 cv::dnn::readNet(String framework, vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
private static native long readNet_0(String framework, long bufferModel_mat_nativeObj, long bufferConfig_mat_nativeObj);
private static native long readNet_1(String framework, long bufferModel_mat_nativeObj);
// C++: Net cv::dnn::readNet(String model, String config = "", String framework = "")
private static native long readNet_2(String model, String config, String framework);
private static native long readNet_3(String model, String config);
private static native long readNet_4(String model);
// C++: Net cv::dnn::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 cv::dnn::readNetFromCaffe(vector_uchar bufferProto, vector_uchar bufferModel = std::vector<uchar>())
private static native long readNetFromCaffe_2(long bufferProto_mat_nativeObj, long bufferModel_mat_nativeObj);
private static native long readNetFromCaffe_3(long bufferProto_mat_nativeObj);
// C++: Net cv::dnn::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 cv::dnn::readNetFromDarknet(vector_uchar bufferCfg, vector_uchar bufferModel = std::vector<uchar>())
private static native long readNetFromDarknet_2(long bufferCfg_mat_nativeObj, long bufferModel_mat_nativeObj);
private static native long readNetFromDarknet_3(long bufferCfg_mat_nativeObj);
// C++: Net cv::dnn::readNetFromModelOptimizer(String xml, String bin)
private static native long readNetFromModelOptimizer_0(String xml, String bin);
// C++: Net cv::dnn::readNetFromONNX(String onnxFile)
private static native long readNetFromONNX_0(String onnxFile);
// C++: Net cv::dnn::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 cv::dnn::readNetFromTensorflow(vector_uchar bufferModel, vector_uchar bufferConfig = std::vector<uchar>())
private static native long readNetFromTensorflow_2(long bufferModel_mat_nativeObj, long bufferConfig_mat_nativeObj);
private static native long readNetFromTensorflow_3(long bufferModel_mat_nativeObj);
// C++: Net cv::dnn::readNetFromTorch(String model, bool isBinary = true, bool evaluate = true)
private static native long readNetFromTorch_0(String model, boolean isBinary, boolean evaluate);
private static native long readNetFromTorch_1(String model, boolean isBinary);
private static native long readNetFromTorch_2(String model);
// C++: String cv::dnn::getInferenceEngineVPUType()
private static native String getInferenceEngineVPUType_0();
// C++: void cv::dnn::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, float eta);
private static native void NMSBoxes_2(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj);
// C++: void cv::dnn::NMSBoxes(vector_Rect2d 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_3(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_4(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta);
private static native void NMSBoxes_5(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj);
// C++: void cv::dnn::NMSBoxes(vector_RotatedRect 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 NMSBoxesRotated_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 NMSBoxesRotated_1(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta);
private static native void NMSBoxesRotated_2(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj);
// C++: void cv::dnn::imagesFromBlob(Mat blob_, vector_Mat& images_)
private static native void imagesFromBlob_0(long blob__nativeObj, long images__mat_nativeObj);
// C++: void cv::dnn::resetMyriadDevice()
private static native void resetMyriadDevice_0();
// C++: void cv::dnn::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);
// C++: void cv::dnn::writeTextGraph(String model, String output)
private static native void writeTextGraph_0(String model, String output);
}
