java OpenCV找到k个最近邻c++到java的转换

java OpenCV find k nearest neighbor c++ to java conversion

本文关键字：java c++ 转换 近邻 最近 OpenCV 找到      更新时间：2023-10-16

我试图找到k最近的邻居与Knn分类器在OpenCV。我找到了这段c++代码:

class atsKNN{
public :
void knn(cv::Mat& trainingData, cv::Mat& trainingClasses, cv::Mat& testData, cv::Mat& testClasses, int K) 
{
    cv::KNearest knn(trainingData, trainingClasses, cv::Mat(), false, K);
    cv::Mat predicted(testClasses.rows, 1, CV_32F);
    for(int i = 0; i < testData.rows; i++) {
            const cv::Mat sample = testData.row(i);
            predicted.at<float>(i,0) = knn.find_nearest(sample, K);
    }
    float percentage = evaluate(predicted, testClasses) * 100;
    cout << "K Nearest Neighbor Evaluated Accuracy = " << percentage << "%" << endl;
    prediction = predicted;
}
void showplot(cv::Mat testData)
{
    plot_binary(testData, prediction, "Predictions Backpropagation");
}
private:
cv::Mat prediction;
};

评论提到它的工作真的很好，但我有问题将其转换为Java。没有Java的文档。我尝试使用c++到Java转换器，但产生的代码不工作。

下面是它生成的代码: 
public class atsKNN
{
public final void knn(cv.Mat trainingData, cv.Mat trainingClasses, cv.Mat testData, cv.Mat testClasses, int K)
{
    cv.KNearest knn = new cv.KNearest(trainingData, trainingClasses, cv.Mat(), false, K);
    cv.Mat predicted = new cv.Mat(testClasses.rows, 1, CV_32F);
    for (int i = 0; i < testData.rows; i++)
    {
            final cv.Mat sample = testData.row(i);
            predicted.<Float>at(i,0) = knn.find_nearest(sample, K);
    }
    float percentage = evaluate(predicted, testClasses) * 100;
    System.out.print("K Nearest Neighbor Evaluated Accuracy = ");
    System.out.print(percentage);
    System.out.print("%");
    System.out.print("n");
    prediction = predicted;
}
public final void showplot(cv.Mat testData)
{
    plot_binary(testData, prediction, "Predictions Backpropagation");
}
private cv.Mat prediction = new cv.Mat();
}

编辑:预测的直线。at(i,0) = knn。find_nearest(示例中,K);肯定是错的。现在在对象Mat中有函数at。也没有"求值函数"。另一件事是预测Mat属于哪里?在java中，你不能把它放在类的最后。谢谢=)

下面的代码用于查找数字

这里有一些代码可以尝试:

import org.opencv.core.*;
import org.opencv.imgproc.*;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.ml.*;
import org.opencv.utils.*;
import java.util.*;

class SimpleSample {
static{ System.loadLibrary(Core.NATIVE_LIBRARY_NAME); }
public static void main(String[] args) {
    // samples/data/digits.png, have a look at it.
    Mat digits = Imgcodecs.imread("digits.png", 0);
    // setup train/test data:
    Mat trainData = new Mat(),
        testData = new Mat();
    List<Integer> trainLabs = new ArrayList<Integer>(),
        testLabs = new ArrayList<Integer>();
    // 10 digits a 5 rows:
    for (int r=0; r<50; r++) { 
        // 100 digits per row:
        for (int c=0; c<100; c++) {
            // crop out 1 digit:
            Mat num = digits.submat(new Rect(c*20,r*20,20,20));
            // we need float data for knn:
            num.convertTo(num, CvType.CV_32F);
            // 50/50 train/test split:
            if (c % 2 == 0) {
                // for opencv ml, each feature has to be a single row:
                trainData.push_back(num.reshape(1,1));
                // add a label for that feature (the digit number):
                trainLabs.add(r/5);
            } else {
                testData.push_back(num.reshape(1,1));
                testLabs.add(r/5);
            }
        }                
    }
    // make a Mat of the train labels, and train knn:
    KNearest knn = KNearest.create();
    knn.train(trainData, Ml.ROW_SAMPLE, Converters.vector_int_to_Mat(trainLabs));
    // now test predictions:
    for (int i=0; i<testData.rows(); i++)
    {
        Mat one_feature = testData.row(i);
        int testLabel = testLabs.get(i);
        Mat res = new Mat();
        float p = knn.findNearest(one_feature, 1, res);
        System.out.println(testLabel + " " + p + " " + res.dump());
    }
    //// hmm, the 'real world' test case probably looks more like this:
    //// make sure, you follow the very same preprocessing steps used in the train phase:
    //  Mat one_feature = Imgcodecs.imread("one_digit.png", 0);
    //  Mat feature; one_feature.convertTo(feature, CvTypes.CV_32F);
    //  Imgproc.resize(feature, feature, Size(20,20));
    //  int predicted = knn.findNearest(feature.reshape(1,1), 1);
}
}
相关文章：