//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.dnn;
import org.opencv.core.Mat;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.utils.Converters;
import java.util.List;
// C++: class Model
/**
* This class is presented high-level API for neural networks.
*
* Model allows to set params for preprocessing input image.
* Model creates net from file with trained weights and config,
* sets preprocessing input and runs forward pass.
*/
public class Model {
protected final long nativeObj;
protected Model(long addr) { nativeObj = addr; }
public long getNativeObjAddr() { return nativeObj; }
// internal usage only
public static Model __fromPtr__(long addr) { return new Model(addr); }
//
// C++: cv::dnn::Model::Model(String model, String config = "")
//
/**
* Create model from deep learning network represented in one of the supported formats.
* An order of {@code model} and {@code config} arguments does not matter.
* @param model Binary file contains trained weights.
* @param config Text file contains network configuration.
*/
public Model(String model, String config) {
nativeObj = Model_0(model, config);
}
/**
* Create model from deep learning network represented in one of the supported formats.
* An order of {@code model} and {@code config} arguments does not matter.
* @param model Binary file contains trained weights.
*/
public Model(String model) {
nativeObj = Model_1(model);
}
//
// C++: cv::dnn::Model::Model(Net network)
//
/**
* Create model from deep learning network.
* @param network Net object.
*/
public Model(Net network) {
nativeObj = Model_2(network.nativeObj);
}
//
// C++: Model cv::dnn::Model::setInputSize(Size size)
//
/**
* Set input size for frame.
* @param size New input size.
* <b>Note:</b> If shape of the new blob less than 0, then frame size not change.
* @return automatically generated
*/
public Model setInputSize(Size size) {
return new Model(setInputSize_0(nativeObj, size.width, size.height));
}
//
// C++: Model cv::dnn::Model::setInputSize(int width, int height)
//
/**
*
* @param width New input width.
* @param height New input height.
* @return automatically generated
*/
public Model setInputSize(int width, int height) {
return new Model(setInputSize_1(nativeObj, width, height));
}
//
// C++: Model cv::dnn::Model::setInputMean(Scalar mean)
//
/**
* Set mean value for frame.
* @param mean Scalar with mean values which are subtracted from channels.
* @return automatically generated
*/
public Model setInputMean(Scalar mean) {
return new Model(setInputMean_0(nativeObj, mean.val[0], mean.val[1], mean.val[2], mean.val[3]));
}
//
// C++: Model cv::dnn::Model::setInputScale(Scalar scale)
//
/**
* Set scalefactor value for frame.
* @param scale Multiplier for frame values.
* @return automatically generated
*/
public Model setInputScale(Scalar scale) {
return new Model(setInputScale_0(nativeObj, scale.val[0], scale.val[1], scale.val[2], scale.val[3]));
}
//
// C++: Model cv::dnn::Model::setInputCrop(bool crop)
//
/**
* Set flag crop for frame.
* @param crop Flag which indicates whether image will be cropped after resize or not.
* @return automatically generated
*/
public Model setInputCrop(boolean crop) {
return new Model(setInputCrop_0(nativeObj, crop));
}
//
// C++: Model cv::dnn::Model::setInputSwapRB(bool swapRB)
//
/**
* Set flag swapRB for frame.
* @param swapRB Flag which indicates that swap first and last channels.
* @return automatically generated
*/
public Model setInputSwapRB(boolean swapRB) {
return new Model(setInputSwapRB_0(nativeObj, swapRB));
}
//
// C++: void cv::dnn::Model::setInputParams(double scale = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false)
//
/**
* Set preprocessing parameters for frame.
* @param size New input size.
* @param mean Scalar with mean values which are subtracted from channels.
* @param scale Multiplier for frame values.
* @param swapRB Flag which indicates that swap first and last channels.
* @param crop Flag which indicates whether image will be cropped after resize or not.
* blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
*/
public void setInputParams(double scale, Size size, Scalar mean, boolean swapRB, boolean crop) {
setInputParams_0(nativeObj, scale, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop);
}
/**
* Set preprocessing parameters for frame.
* @param size New input size.
* @param mean Scalar with mean values which are subtracted from channels.
* @param scale Multiplier for frame values.
* @param swapRB Flag which indicates that swap first and last channels.
* blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
*/
public void setInputParams(double scale, Size size, Scalar mean, boolean swapRB) {
setInputParams_1(nativeObj, scale, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB);
}
/**
* Set preprocessing parameters for frame.
* @param size New input size.
* @param mean Scalar with mean values which are subtracted from channels.
* @param scale Multiplier for frame values.
* blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
*/
public void setInputParams(double scale, Size size, Scalar mean) {
setInputParams_2(nativeObj, scale, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3]);
}
/**
* Set preprocessing parameters for frame.
* @param size New input size.
* @param scale Multiplier for frame values.
* blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
*/
public void setInputParams(double scale, Size size) {
setInputParams_3(nativeObj, scale, size.width, size.height);
}
/**
* Set preprocessing parameters for frame.
* @param scale Multiplier for frame values.
* blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
*/
public void setInputParams(double scale) {
setInputParams_4(nativeObj, scale);
}
/**
* Set preprocessing parameters for frame.
* blob(n, c, y, x) = scale * resize( frame(y, x, c) ) - mean(c) )
*/
public void setInputParams() {
setInputParams_5(nativeObj);
}
//
// C++: void cv::dnn::Model::predict(Mat frame, vector_Mat& outs)
//
/**
* Given the {@code input} frame, create input blob, run net and return the output {@code blobs}.
* @param outs Allocated output blobs, which will store results of the computation.
* @param frame automatically generated
*/
public void predict(Mat frame, List<Mat> outs) {
Mat outs_mat = new Mat();
predict_0(nativeObj, frame.nativeObj, outs_mat.nativeObj);
Converters.Mat_to_vector_Mat(outs_mat, outs);
outs_mat.release();
}
//
// C++: Model cv::dnn::Model::setPreferableBackend(dnn_Backend backendId)
//
public Model setPreferableBackend(int backendId) {
return new Model(setPreferableBackend_0(nativeObj, backendId));
}
//
// C++: Model cv::dnn::Model::setPreferableTarget(dnn_Target targetId)
//
public Model setPreferableTarget(int targetId) {
return new Model(setPreferableTarget_0(nativeObj, targetId));
}
//
// C++: Model cv::dnn::Model::enableWinograd(bool useWinograd)
//
public Model enableWinograd(boolean useWinograd) {
return new Model(enableWinograd_0(nativeObj, useWinograd));
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: cv::dnn::Model::Model(String model, String config = "")
private static native long Model_0(String model, String config);
private static native long Model_1(String model);
// C++: cv::dnn::Model::Model(Net network)
private static native long Model_2(long network_nativeObj);
// C++: Model cv::dnn::Model::setInputSize(Size size)
private static native long setInputSize_0(long nativeObj, double size_width, double size_height);
// C++: Model cv::dnn::Model::setInputSize(int width, int height)
private static native long setInputSize_1(long nativeObj, int width, int height);
// C++: Model cv::dnn::Model::setInputMean(Scalar mean)
private static native long setInputMean_0(long nativeObj, double mean_val0, double mean_val1, double mean_val2, double mean_val3);
// C++: Model cv::dnn::Model::setInputScale(Scalar scale)
private static native long setInputScale_0(long nativeObj, double scale_val0, double scale_val1, double scale_val2, double scale_val3);
// C++: Model cv::dnn::Model::setInputCrop(bool crop)
private static native long setInputCrop_0(long nativeObj, boolean crop);
// C++: Model cv::dnn::Model::setInputSwapRB(bool swapRB)
private static native long setInputSwapRB_0(long nativeObj, boolean swapRB);
// C++: void cv::dnn::Model::setInputParams(double scale = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = false, bool crop = false)
private static native void setInputParams_0(long nativeObj, double scale, 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 void setInputParams_1(long nativeObj, double scale, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB);
private static native void setInputParams_2(long nativeObj, double scale, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3);
private static native void setInputParams_3(long nativeObj, double scale, double size_width, double size_height);
private static native void setInputParams_4(long nativeObj, double scale);
private static native void setInputParams_5(long nativeObj);
// C++: void cv::dnn::Model::predict(Mat frame, vector_Mat& outs)
private static native void predict_0(long nativeObj, long frame_nativeObj, long outs_mat_nativeObj);
// C++: Model cv::dnn::Model::setPreferableBackend(dnn_Backend backendId)
private static native long setPreferableBackend_0(long nativeObj, int backendId);
// C++: Model cv::dnn::Model::setPreferableTarget(dnn_Target targetId)
private static native long setPreferableTarget_0(long nativeObj, int targetId);
// C++: Model cv::dnn::Model::enableWinograd(bool useWinograd)
private static native long enableWinograd_0(long nativeObj, boolean useWinograd);
// native support for java finalize()
private static native void delete(long nativeObj);
}
