OpenCv的Java,C++开发环境配置

1.OpenCV 下载及安装配置

opencv的下载地址：http://opencv.org/downloads.html

最新版本：opencv3.0.0

注意：支持的visual studio2013

我们可以下载稳定版本：opencv2.4.11

安装：双击opencv-2.4.11解压到某一目录下即可

配置：在系统环境变量Path中，添加相应的路径。

      32位添加：C:\opencv\opencv2.4.11\build\x86\vc10\bin

      64位添加：C:\opencv\opencv2.4.11\build\x86\vc10\bin 和 C:\opencv\opencv2.4.11\build\x86\vc10\bin

2.VisualStudio 下 OpenCV调试

新建一个控制台程序

按照导航一步步完成项目创建

配置项目的include目录，指向opencv的安装目录，如下图所示

配置项目依赖的lib目录及依赖的库，如下图所示

 

环境配置好后，编写测试代码

#include "stdafx.h"

#include <opencv2/opencv.hpp>

#include <iostream>

#include <fstream>

#include <sstream>

#include <math.h>



void train_and_test_lda()

{

    string fn_csv = string("at.txt");

    //string fn_csv = string("feret.txt");

    vector<Mat> allImages,train_images,test_images;

    vector<int> allLabels,train_labels,test_labels;

    try {

        read_csv(fn_csv, allImages, allLabels);

    } catch (cv::Exception& e) {

        cerr <<"Error opening file "<< fn_csv <<". Reason: "<< e.msg << endl;

        // 文件有问题，我们啥也做不了了，退出了

        exit(1);

    }

    if(allImages.size()<=1) {

        string error_message ="This demo needs at least 2 images to work. Please add more images to your data set!";

        CV_Error(CV_StsError, error_message);

    }



    for(int i=0 ; i<allImages.size() ; i++)

        equalizeHist(allImages[i],allImages[i]);



    int photoNumber = allImages.size();

    for(int i=0 ; i<photoNumber ; i++)

    {

        if((i%g_photoNumberOfOnePerson)<g_howManyPhotoForTraining)

        {

            train_images.push_back(allImages[i]);

            train_labels.push_back(allLabels[i]);

        }

        else

        {

            test_images.push_back(allImages[i]);

            test_labels.push_back(allLabels[i]);

        }

    }



    /*Ptr<FaceRecognizer> model = createEigenFaceRecognizer();//定义pca模型  

    model->train(train_images, train_labels);//训练pca模型，这里的model包含了所有特征值和特征向量，没有损失  

    model->save("eigenface.yml");//保存训练结果，供检测时使用  */



    Ptr<FaceRecognizer> fishermodel = createFisherFaceRecognizer();  

    fishermodel->train(train_images,train_labels);//用保存的降维后的图片来训练fishermodel，后面的内容与原始代码就没什么变化了  

    fishermodel->save("fisherlda.yml");

    int iCorrectPrediction = 0;

    int predictedLabel;

    int testPhotoNumber = test_images.size();

    for(int i=0;i<testPhotoNumber;i++)

    {

        predictedLabel = fishermodel->predict(test_images[i]);

        if(predictedLabel == test_labels[i])

            iCorrectPrediction++;

    }

    string result_message = format("Test Number = %d / Actual Number = %d.", testPhotoNumber, iCorrectPrediction);

    cout << result_message << endl;

    cout<<"accuracy = "<<float(iCorrectPrediction)/testPhotoNumber<<endl;

}



int main() {



    cout<<"lda = "<<endl;

    train_and_test_lda();

    return 0 ;

}

编译通过后，正常运行说明opencv环境配置正确。

3.Eclipse下OpenCV开发环境配置

在eclipse中，新建java project，如下图所示

新建成功后，修改项目的build path,增加外部jar，如下图所示

选择C:\opencv\opencv2.4.11\build\java\opencv-2411.jar

点开opencv-2411.jar，选择Native library location， 点击edit

32位系统指向：C:\opencv\opencv2.4.11\build\java\x86

64位系统指向：C:\opencv\opencv2.4.11\build\java\x64

编写测试代码：

import org.opencv.core.Core;

import org.opencv.core.CvType;

import org.opencv.core.Mat;



public class TestOpencv {

    public static void main( String[] args )

       {

          System.loadLibrary( Core.NATIVE_LIBRARY_NAME );

          Mat mat = Mat.eye( 3, 3, CvType.CV_8UC1 );

          System.out.println( "mat = " + mat.dump() );

       }

}

运行输出如下，即环境配置正确：