All Downloads are FREE. Search and download functionalities are using the official Maven repository.

org.bytedeco.opencv.opencv_dnn_superres.DnnSuperResImpl Maven / Gradle / Ivy

There is a newer version: 4.10.0-1.5.11
Show newest version
// Targeted by JavaCPP version 1.5.4: DO NOT EDIT THIS FILE

package org.bytedeco.opencv.opencv_dnn_superres;

import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;

import static org.bytedeco.javacpp.presets.javacpp.*;
import static org.bytedeco.openblas.global.openblas_nolapack.*;
import static org.bytedeco.openblas.global.openblas.*;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import org.bytedeco.opencv.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
import org.bytedeco.opencv.opencv_dnn.*;
import static org.bytedeco.opencv.global.opencv_dnn.*;
import org.bytedeco.opencv.opencv_ml.*;
import static org.bytedeco.opencv.global.opencv_ml.*;
import org.bytedeco.opencv.opencv_quality.*;
import static org.bytedeco.opencv.global.opencv_quality.*;

import static org.bytedeco.opencv.global.opencv_dnn_superres.*;


/** \addtogroup dnn_superres
 *  \{

/** \brief A class to upscale images via convolutional neural networks. The following four models are implemented:

- edsr - espcn - fsrcnn - lapsrn */ @Namespace("cv::dnn_superres") @NoOffset @Properties(inherit = org.bytedeco.opencv.presets.opencv_dnn_superres.class) public class DnnSuperResImpl extends Pointer { static { Loader.load(); } /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */ public DnnSuperResImpl(Pointer p) { super(p); } /** Native array allocator. Access with {@link Pointer#position(long)}. */ public DnnSuperResImpl(long size) { super((Pointer)null); allocateArray(size); } private native void allocateArray(long size); @Override public DnnSuperResImpl position(long position) { return (DnnSuperResImpl)super.position(position); } @Override public DnnSuperResImpl getPointer(long i) { return new DnnSuperResImpl(this).position(position + i); } /** \brief Empty constructor for python */ public static native @Ptr DnnSuperResImpl create(); // /** @brief Empty constructor // */ public DnnSuperResImpl() { super((Pointer)null); allocate(); } private native void allocate(); /** \brief Constructor which immediately sets the desired model @param algo String containing one of the desired models: - __edsr__ - __espcn__ - __fsrcnn__ - __lapsrn__ @param scale Integer specifying the upscale factor */ public DnnSuperResImpl(@Str BytePointer algo, int scale) { super((Pointer)null); allocate(algo, scale); } private native void allocate(@Str BytePointer algo, int scale); public DnnSuperResImpl(@Str String algo, int scale) { super((Pointer)null); allocate(algo, scale); } private native void allocate(@Str String algo, int scale); /** \brief Read the model from the given path @param path Path to the model file. */ public native void readModel(@Str BytePointer path); public native void readModel(@Str String path); /** \brief Read the model from the given path @param weights Path to the model weights file. @param definition Path to the model definition file. */ public native void readModel(@Str BytePointer weights, @Str BytePointer definition); public native void readModel(@Str String weights, @Str String definition); /** \brief Set desired model @param algo String containing one of the desired models: - __edsr__ - __espcn__ - __fsrcnn__ - __lapsrn__ @param scale Integer specifying the upscale factor */ public native void setModel(@Str BytePointer algo, int scale); public native void setModel(@Str String algo, int scale); /** \brief Set computation backend */ public native void setPreferableBackend(int backendId); /** \brief Set computation target */ public native void setPreferableTarget(int targetId); /** \brief Upsample via neural network @param img Image to upscale @param result Destination upscaled image */ public native void upsample(@ByVal Mat img, @ByVal Mat result); public native void upsample(@ByVal UMat img, @ByVal UMat result); public native void upsample(@ByVal GpuMat img, @ByVal GpuMat result); /** \brief Upsample via neural network of multiple outputs @param img Image to upscale @param imgs_new Destination upscaled images @param scale_factors Scaling factors of the output nodes @param node_names Names of the output nodes in the neural network */ public native void upsampleMultioutput(@ByVal Mat img, @ByRef MatVector imgs_new, @StdVector IntPointer scale_factors, @Const @ByRef StringVector node_names); public native void upsampleMultioutput(@ByVal Mat img, @ByRef MatVector imgs_new, @StdVector IntBuffer scale_factors, @Const @ByRef StringVector node_names); public native void upsampleMultioutput(@ByVal Mat img, @ByRef MatVector imgs_new, @StdVector int[] scale_factors, @Const @ByRef StringVector node_names); public native void upsampleMultioutput(@ByVal UMat img, @ByRef MatVector imgs_new, @StdVector IntPointer scale_factors, @Const @ByRef StringVector node_names); public native void upsampleMultioutput(@ByVal UMat img, @ByRef MatVector imgs_new, @StdVector IntBuffer scale_factors, @Const @ByRef StringVector node_names); public native void upsampleMultioutput(@ByVal UMat img, @ByRef MatVector imgs_new, @StdVector int[] scale_factors, @Const @ByRef StringVector node_names); public native void upsampleMultioutput(@ByVal GpuMat img, @ByRef MatVector imgs_new, @StdVector IntPointer scale_factors, @Const @ByRef StringVector node_names); public native void upsampleMultioutput(@ByVal GpuMat img, @ByRef MatVector imgs_new, @StdVector IntBuffer scale_factors, @Const @ByRef StringVector node_names); public native void upsampleMultioutput(@ByVal GpuMat img, @ByRef MatVector imgs_new, @StdVector int[] scale_factors, @Const @ByRef StringVector node_names); /** \brief Returns the scale factor of the model: @return Current scale factor. */ public native int getScale(); /** \brief Returns the scale factor of the model: @return Current algorithm. */ public native @Str BytePointer getAlgorithm(); }





© 2015 - 2024 Weber Informatics LLC | Privacy Policy