javaandroid中opencv的使用

墨蓝 2022-08-20 03:28 233阅读 0赞

opencv使用帮助  
[http://docs.opencv.org/][http_docs.opencv.org]

opencv图片灰度化:

package com.testopencv.haveimgfun; 
     public class LibImgFun {   
     static {   
             System.loadLibrary("ImgFun");   
            }   
           /** * @param width the current view width * @param height the current view height */ 
         public static native int[] ImgFun(int[] buf, int w, int h);  
     }
    
    
     class ClickEvent implements View.OnClickListener {  
            public void onClick(View v) {  
                if (v == btnNDK) {  
                    long current = System.currentTimeMillis();  
                    Bitmap img1 = ((BitmapDrawable) getResources().getDrawable(  
                            R.drawable.lena)).getBitmap();  
                    int w = img1.getWidth(), h = img1.getHeight();  
                    int[] pix = new int[w * h];  
                    img1.getPixels(pix, 0, w, 0, 0, w, h);  
                    int[] resultInt = LibImgFun.ImgFun(pix, w, h);  
                    Bitmap resultImg = Bitmap.createBitmap(w, h, Config.RGB_565);  
                    resultImg.setPixels(resultInt, 0, w, 0, 0, w, h);  
                    long performance = System.currentTimeMillis() - current;  
                    imgView.setImageBitmap(resultImg);  
                    HaveImgFun.this.setTitle("w:" + String.valueOf(img1.getWidth())  
                            + ",h:" + String.valueOf(img1.getHeight()) + "NDK耗时"  
                            + String.valueOf(performance) + " 毫秒");  
                } else if (v == btnRestore) {  
                    Bitmap img2 = ((BitmapDrawable) getResources().getDrawable(  
                            R.drawable.lena)).getBitmap();  
                    imgView.setImageBitmap(img2);  
                    HaveImgFun.this.setTitle("使用OpenCV进行图像处理");  
                }  
            }

LibImgFun代码:

#include <jni.h>
    #include <stdio.h>
    #include <stdlib.h>
    #include <opencv2/opencv.hpp>
    using namespace cv;
    IplImage * change4channelTo3InIplImage(IplImage * src);
    
    extern "C" {
    JNIEXPORT jintArray JNICALL Java_com_testopencv_haveimgfun_LibImgFun_ImgFun(
            JNIEnv* env, jobject obj, jintArray buf, int w, int h);
    JNIEXPORT jintArray JNICALL Java_com_testopencv_haveimgfun_LibImgFun_ImgFun(
            JNIEnv* env, jobject obj, jintArray buf, int w, int h) {
    
        jint *cbuf;
        cbuf = env->GetIntArrayElements(buf, false);
        if (cbuf == NULL) {
            return 0;
        }
    
        Mat myimg(h, w, CV_8UC4, (unsigned char*) cbuf);
        IplImage image=IplImage(myimg);
        IplImage* image3channel = change4channelTo3InIplImage(&image);
    
        IplImage* pCannyImage=cvCreateImage(cvGetSize(image3channel),IPL_DEPTH_8U,1);
    
        cvCanny(image3channel,pCannyImage,50,150,3);
    
        int* outImage=new int[w*h];
        for(int i=0;i<w*h;i++)
        {
            outImage[i]=(int)pCannyImage->imageData[i];
        }
    
        int size = w * h;
        jintArray result = env->NewIntArray(size);
        env->SetIntArrayRegion(result, 0, size, outImage);
        env->ReleaseIntArrayElements(buf, cbuf, 0);
        return result;
    }
    }
    
    IplImage * change4channelTo3InIplImage(IplImage * src) {
        if (src->nChannels != 4) {
            return NULL;
        }
    
        IplImage * destImg = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 3);
        for (int row = 0; row < src->height; row++) {
            for (int col = 0; col < src->width; col++) {
                CvScalar s = cvGet2D(src, row, col);
                cvSet2D(destImg, row, col, s);
            }
        }
    
        return destImg;
    }

来自:[http://blog.csdn.net/watkinsong/article/details/9849973][http_blog.csdn.net_watkinsong_article_details_9849973]

二值化:

#include "opencv2/opencv.hpp" 
    using namespace cv;  
    
    void Java_com_test_MainActivity(JNIEnv* env jclass jthis){  
        Mat mat = imread("/test.jpg");  
        mat = mat >50;    //二值化 
       imwrite("out.jpg",mat);  
    }

人脸检测:

package com.tcl.uviewer.features.featuresImpl;
    
    import org.opencv.core.Core;
    import org.opencv.core.Mat;
    import org.opencv.core.MatOfRect;
    import org.opencv.core.Point;
    import org.opencv.core.Scalar;
    import org.opencv.highgui.Highgui;
    import org.opencv.objdetect.CascadeClassifier;
    
    public class Test
    {
    
    public static void detectFace(String imagePath)
    {
    System.out.println("\nRunning DetectFaceDemo");
    
    // 导入opencv的库
    System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
    
    // 从配置文件lbpcascade_frontalface.xml中创建一个人脸识别器，该文件位于opencv安装目录中
    CascadeClassifier faceDetector = new CascadeClassifier(
    "C:/lbpcascade_frontalface.xml");
    Mat image = Highgui.imread(imagePath);
    
    // 在图片中检测人脸
    MatOfRect faceDetections = new MatOfRect();
    faceDetector.detectMultiScale(image, faceDetections);
    
    System.out.println(String.format("Detected %s faces",
    faceDetections.toArray().length));
    
    // 在每一个识别出来的人脸周围画出一个方框
    for (org.opencv.core.Rect rect : faceDetections.toArray())
    {
    Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x
    + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
    }
    
    // 将结果保存到文件
    String filename = "faceDetection.png";
    System.out.println(String.format("Writing %s", filename));
    Highgui.imwrite(filename, image);
    }
    
    public static void main(String[] args)
    {
    String imagePath = "C:/005.jpg";
    Test.detectFace(imagePath);
    }
    }

System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
            System.out.println("\nRunning FaceDetector");
    
            CascadeClassifier faceDetector = new CascadeClassifier(FaceDetector.class.getResource("haarcascade_frontalface_alt.xml").getPath());
            Mat image = Highgui
                    .imread(FaceDetector.class.getResource("shekhar.JPG").getPath());
    
            MatOfRect faceDetections = new MatOfRect();
            faceDetector.detectMultiScale(image, faceDetections);
    
            System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));
    
            for (Rect rect : faceDetections.toArray()) {
                Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
                        new Scalar(0, 255, 0));
            }
    
            String filename = "ouput.png";
            System.out.println(String.format("Writing %s", filename));
            Highgui.imwrite(filename, image);

识别结果:  
![这里写图片描述][085116283.jpg]  
来源：  
[http://yuanhuan.blog.51cto.com/3367116/1301368][http_yuanhuan.blog.51cto.com_3367116_1301368]  
[http://yjdingkai.iteye.com/blog/1532450][http_yjdingkai.iteye.com_blog_1532450]  
[http://www.cnblogs.com/endless-on/p/3491154.html][http_www.cnblogs.com_endless-on_p_3491154.html]

人脸识别匹配代码:

public double CmpPic(String path) {  
            int l_bins = 20;  
            int hist_size[] = { l_bins };  
    
            float v_ranges[] = { 0, 100 };  
            float ranges[][] = { v_ranges };  
    
            IplImage Image1 = cvLoadImage(Environment.getExternalStorageDirectory()  
                    + "/FaceDetect/faceDone.jpg", CV_LOAD_IMAGE_GRAYSCALE);  
            IplImage Image2 = cvLoadImage(path, CV_LOAD_IMAGE_GRAYSCALE);  
    
            IplImage imageArr1[] = { Image1 };  
            IplImage imageArr2[] = { Image2 };  
    
            CvHistogram Histogram1 = CvHistogram.create(1, hist_size,  
                    CV_HIST_ARRAY, ranges, 1);  
            CvHistogram Histogram2 = CvHistogram.create(1, hist_size,  
                    CV_HIST_ARRAY, ranges, 1);  
    
            cvCalcHist(imageArr1, Histogram1, 0, null);  
            cvCalcHist(imageArr2, Histogram2, 0, null);  
    
            cvNormalizeHist(Histogram1, 100.0);  
            cvNormalizeHist(Histogram2, 100.0);  
    
            return cvCompareHist(Histogram1, Histogram2, CV_COMP_CORREL);  
        }

参考：[http://blog.csdn.net/sky286753213/article/details/11887913][http_blog.csdn.net_sky286753213_article_details_11887913]  
demo下载地址:[http://download.csdn.net/detail/sky286753213/6617075][http_download.csdn.net_detail_sky286753213_6617075]

androidstudio opencv demo:  
[https://github.com/quanhua92/OpenCV\_Java\_AndroidStudio][https_github.com_quanhua92_OpenCV_Java_AndroidStudio]

how to use opencv-java in androidstudio?:  
[http://www.quan404.com/2015/07/how-to-use-opencv-android-in-android.html][http_www.quan404.com_2015_07_how-to-use-opencv-android-in-android.html]

opencv的匹配图片:

mMatches为识别结果
        /** * * 对场景图片帧进行相关处理 * 其中在这里获取匹配对 * 然后绘制获取的线框 */
        @Override
        public void apply(final Mat src, final Mat dst) {
            Imgproc.cvtColor(src, mGraySrc, Imgproc.COLOR_RGBA2GRAY);
    
            mFeatureDetector.detect(mGraySrc, mSceneKeypoints);
            mDescriptorExtractor.compute(mGraySrc, mSceneKeypoints,
                    mSceneDescriptors);
            mDescriptorMatcher.match(mSceneDescriptors,
                    mReferenceDescriptors, mMatches);
    
            findSceneCorners();
            draw(src, dst);
        }
    
        private void findSceneCorners() {
    
            List<DMatch> matchesList = mMatches.toList();
            // 匹配对太少
            if (matchesList.size() < 4) {
                // There are too few matches to find the homography.
                return;
            }       
            // 将MatOfKeyPoint数据结构存储的特征点数据转换成List，便于后面获取
            List<KeyPoint> referenceKeypointsList =
                    mReferenceKeypoints.toList();
            List<KeyPoint> sceneKeypointsList =
                    mSceneKeypoints.toList();
    
            // Calculate the max and min distances between keypoints.
            // 计算特征点之间的最大和最小距离
            double maxDist = 0.0;
            double minDist = Double.MAX_VALUE;
            for(DMatch match : matchesList) {
                double dist = match.distance;
                if (dist < minDist) {
                    minDist = dist;
                }
                if (dist > maxDist) {
                    maxDist = dist;
                }
            }
    
            // The thresholds for minDist are chosen subjectively
            // based on testing. The unit is not related to pixel
            // distances; it is related to the number of failed tests
            // for similarity between the matched descriptors.
            // 根据距离对角点进行取舍
            if (minDist > 50.0) {
                // The target is completely lost.
                // Discard any previously found corners.
                mSceneCorners.create(0, 0, mSceneCorners.type());
                return;
            } else if (minDist > 25.0) {
                // The target is lost but maybe it is still close.
                // Keep any previously found corners.
                return;
            }
    
            // Identify "good" keypoints based on match distance.
            ArrayList<Point> goodReferencePointsList =
                    new ArrayList<Point>();
            ArrayList<Point> goodScenePointsList =
                    new ArrayList<Point>();
            // 最佳距离极限为minDist的1.75倍，然后拾取在此范围的点到ArrayList中
            double maxGoodMatchDist = 1.75 * minDist;
            for(DMatch match : matchesList) {
                if (match.distance < maxGoodMatchDist) {
                   goodReferencePointsList.add(
                           referenceKeypointsList.get(match.trainIdx).pt);
                   goodScenePointsList.add(
                           sceneKeypointsList.get(match.queryIdx).pt);
                }
            }
    
            // 如果在范围内的（拾取到的）点数小于4,则表示没有发现标志
            if (goodReferencePointsList.size() < 4 ||
                    goodScenePointsList.size() < 4) {
                // There are too few good points to find the homography.
                return;
            }
    
            // 再从ArrayList转换成MatOfPoint2f数据结构（OpenCV中），同样是为了后面的处理
            // 一个是参考图片的点数据，一个是图像帧的点数据
            MatOfPoint2f goodReferencePoints = new MatOfPoint2f();
            goodReferencePoints.fromList(goodReferencePointsList);
    
            MatOfPoint2f goodScenePoints = new MatOfPoint2f();
            goodScenePoints.fromList(goodScenePointsList);
    
            // 计算单应性矩阵，根据最佳参考图像和场景图片的特征点（需要描述）
            Mat homography = Calib3d.findHomography(
                    goodReferencePoints, goodScenePoints);
            /** * 根据单应性矩阵对参考图像帧进行透视变换，将2D场景转换成3D * 保存在mCandidateSceneCorners */
            Core.perspectiveTransform(mReferenceCorners,
                    mCandidateSceneCorners, homography);
            // 对mCandidateSceneCorners进行类型转换
            mCandidateSceneCorners.convertTo(mIntSceneCorners,
                    CvType.CV_32S);
            // 输入数据（四边形）必须是凸面
            if (Imgproc.isContourConvex(mIntSceneCorners)) {
                mCandidateSceneCorners.copyTo(mSceneCorners);
            }
        }

显示结果:  
![这里写图片描述][Center]  
参考：[http://arvrschool.com/read.php?tid=163&fid=52][http_arvrschool.com_read.php_tid_163_fid_52]

[http_docs.opencv.org]: http://docs.opencv.org/
[http_blog.csdn.net_watkinsong_article_details_9849973]: http://blog.csdn.net/watkinsong/article/details/9849973
[085116283.jpg]: /images/20220731/a39e4436835c4c1f84c20791f03acda0.png
[http_yuanhuan.blog.51cto.com_3367116_1301368]: http://yuanhuan.blog.51cto.com/3367116/1301368
[http_yjdingkai.iteye.com_blog_1532450]: http://yjdingkai.iteye.com/blog/1532450
[http_www.cnblogs.com_endless-on_p_3491154.html]: http://www.cnblogs.com/endless-on/p/3491154.html
[http_blog.csdn.net_sky286753213_article_details_11887913]: http://blog.csdn.net/sky286753213/article/details/11887913
[http_download.csdn.net_detail_sky286753213_6617075]: http://download.csdn.net/detail/sky286753213/6617075
[https_github.com_quanhua92_OpenCV_Java_AndroidStudio]: https://github.com/quanhua92/OpenCV_Java_AndroidStudio
[http_www.quan404.com_2015_07_how-to-use-opencv-android-in-android.html]: http://www.quan404.com/2015/07/how-to-use-opencv-android-in-android.html
[Center]: /images/20220731/048a8ae982104a85809c94a7e51b1728.png
[http_arvrschool.com_read.php_tid_163_fid_52]: http://arvrschool.com/read.php?tid=163&fid=52