Android+Opencv图形的形状颜色识别_android 三角形矩形识别

视频效果：https://www.bilibili.com/video/BV1654y1G7Py
啥也不说来看效果


项目介绍：Android+Opencv来识别从摄像头获取到的图片形状和颜色并统计个数

其实这东西做出来不难，难的是稳定，难点就几个问题
1、摄像头获取出来的图片不够清晰图片的分辨率640*360，图像对比度和饱和度太低 (调节摄像头参数)
2、识别时菱形和矩形的区别在哪里 (外界矩形和面积比不同)
3、不同底色的图片识别方法不同 (黑白底色的不能用HSV颜色分割)
记住这几个难点，但这是做到后期才会发现的难点。

先从简单的搞起吧

项目步骤：
1、提取屏幕矩形区域
2、HSV颜色空间分割不同颜色的形状
3、通过计算矩形有四个角、三角形有三个角… 进行判断形状

有点懒得写了。。。。。
直接上代码吧，看看反馈再写了，想要继续看的说一声，我再接着写。
有一些代码是别人的。。

```java

public class ShapeDetect {

    public static String TAG="ShapeDetect";
    //颜色数量统计变量
    public static int Cambridge_blue_Num=0,yellow_Num=0,blue_Num=0,qing_Num=0,red_Num=0,mag_Num=0,black_Num=0;
    //白底照片图形数量统计变量
    public static int triangle_Num=0,rectangle_Num=0,rhombus_Num=0,pentagon_Num=0,circle_Num=0;
    //图形数量统计变量
    public static int san_Num=0,rect_Num=0,lin_Num=0,star_Num=0,yuan_Num=0;
    //浅蓝0、//黄色1、//品红2、//浅红色3、//蓝色4、//青色5、// 深红色6、//黑色7
    //暗 S、V=214,211     亮 S、V=176,160
    //浅蓝0、//黄色1、//品红2、//浅红色3、//蓝色4、//青色5、// 深红色6、//黑色7      车牌蓝底9  车牌绿底10
    public static double[][] HSV_VALUE_LOW = {
            {13,176,160},//浅蓝0  12,214,211
            {67, 176,160},//黄色1
            {130, 176,160},//品红2  暗：100, 176,160   亮：130,176,160
            {126,176,160},//浅红色3
            {0, 176,160},//蓝色4
            {30, 176,160},//青色5   35
            {103,176,160},// 深红色6
            {0,0,0},//黑色7   暗：0,187,0   亮：0,0,0
            {0,0,192},//标准蓝8
            {0,150,190},//车牌蓝底9      暗的TFT：0,190,190   亮的：0,180,190
            {22,104,161}//车牌绿底10    暗的TFT H:21 S要调高一点:210  V:211  亮的TFT S值要调底一点：110    10,100,148
    };

    public static double[][] HSV_VALUE_HIGH = {
            {30,255,255},//浅蓝0
            {111, 255,255},//黄色1
            {241, 255, 255.0},//品红2
            {150,255, 255},//浅红色3
            {12, 255, 255},//蓝色4
            {70, 255.0, 255},//青色5   90
            {150,255,255},// 深红色6
            {255,255,150},//黑色7   暗：28,255,184    亮：255,255,150
            {45,238,255},//标准蓝8
            {126,255,255},//车牌蓝底9   亮暗一样
            {120,255,255}//车牌绿底10   暗H:66     亮H:83
    };

    // 转换工具
    public static Mat BitmapToMat(Bitmap bmp) {
        Mat mat = new Mat(bmp.getHeight(), bmp.getWidth(), CvType.CV_8UC4);
        Utils.bitmapToMat(bmp, mat);
        return mat;
    }

    public int[] shapeDetcte(Bitmap bmp){

        Cambridge_blue_Num=0;yellow_Num=0;blue_Num=0;qing_Num=0;red_Num=0;mag_Num=0;black_Num=0;
        triangle_Num=0;rectangle_Num=0;rhombus_Num=0;pentagon_Num=0;circle_Num=0;
        san_Num=0;rect_Num=0;lin_Num=0;star_Num=0;yuan_Num=0;
        Max_area=0;//最大面积查找之后要清零
        Max_area_Yanse="品红色";//参数复位
        Max_area_shape="triangle";//参数复位

        boolean black_white_Flag=false;//false为黑白底(默认黑白底)  true为白底

        Mat mRgba=BitmapToMat(bmp);
//        save_pic(mRgba,1);
//        Mat mRgba=read_pic(false,"plate1.jpg",1);
        Mat gray=new Mat();
        Imgproc.cvtColor(mRgba,gray,Imgproc.COLOR_BGR2GRAY);//灰度化

        Mat binary=new Mat();
        Imgproc.Canny(gray,binary,50,150);//二值化  边缘检测

        Mat kernel=Imgproc.getStructuringElement(Imgproc.MORPH_RECT,new Size(3,3));//  指定腐蚀膨胀核
        Imgproc.dilate(binary,binary,kernel);

        List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
        Mat hierarchy=new Mat();
        Imgproc.findContours(binary, contours, hierarchy,
                Imgproc.RETR_CCOMP, Imgproc.CHAIN_APPROX_SIMPLE);//查找轮廓

        double maxArea = 0;
        Iterator<MatOfPoint> each = contours.iterator();
        while (each.hasNext()) {
            MatOfPoint wrapper = each.next();
            double area = Imgproc.contourArea(wrapper);
            if (area > maxArea) {
                maxArea = area;
            }
        }

        Mat result=null;
        each = contours.iterator();
        while (each.hasNext()) {
            MatOfPoint contour = each.next();
            double area = Imgproc.contourArea(contour);
            if (area > 0.01 * maxArea) {
                // 多边形逼近 会使原图放大4倍
                Core.multiply(contour, new Scalar(4, 4), contour);
                MatOfPoint2f newcoutour = new MatOfPoint2f(contour.toArray());
                MatOfPoint2f resultcoutour = new MatOfPoint2f();
                double length = Imgproc.arcLength(newcoutour, true);
                Double epsilon = 0.01 * length;
                Imgproc.approxPolyDP(newcoutour, resultcoutour, epsilon, true);
                contour = new MatOfPoint(resultcoutour.toArray());
                // 进行修正，缩小4倍改变联通区域大小
                MatOfPoint new_contour=new MatOfPoint();
                new_contour=ChangeSize(contour);
                double new_area = Imgproc.contourArea(new_contour);//轮廓的面积
                // 求取中心点
                Moments mm = Imgproc.moments(contour);
                int center_x = (int) (mm.get_m10() / (mm.get_m00()));
                int center_y = (int) (mm.get_m01() / (mm.get_m00()));
                Point center = new Point(center_x, center_y);

                //最小外接矩形
                Rect rect = Imgproc.boundingRect(new_contour);
                double rectarea = rect.area();//最小外接矩形面积
                //轮廓的面积/最小外接矩形面积(一个圆和一个圆的外接矩形)  一定小于1 一般为0.1 0.2
                if (Math.abs((new_area/rectarea)-1)<0.2){
                    double wh = rect.size().width / rect.size().height;//宽高比值

                    if (Math.abs(wh - 1.7) < 0.7 && rect.width > 250) {

                        Mat imgSource=mRgba.clone();
                        // 绘制外接矩形
                        Imgproc.rectangle(imgSource, rect.tl(), rect.br(),
                                new Scalar(0, 0, 255), 2);
                        //*****图片裁剪***可以封装成函数*****************
                        rect.x+=5;
                        rect.width-=25;
                        rect.y+=2;// 10
                        rect.height-=3; //12
                        result=new Mat(imgSource,rect);

                        Mat black_while=result.clone();//剪切后的图片复制一份
                        Mat black_while_gray=new Mat();//存储剪切后的图片灰度化
                        Imgproc.cvtColor(black_while,black_while_gray,Imgproc.COLOR_BGR2GRAY);//灰度化图片

                        Mat hsv_gray_mask=new Mat();//存储二值化后的图片
                        Imgproc.threshold(black_while_gray,hsv_gray_mask,50,255,Imgproc.THRESH_BINARY);

                        Imgproc.resize(hsv_gray_mask,hsv_gray_mask,new Size(303,183));//放大规定的大小，

                        //统计黑白底和白底的像素差，用于判断是黑白底还是白底
                        int black_white_pixle_num=0;
                        for (int x = 0; x < hsv_gray_mask.width(); x++) {
                            for (int y = 0; y < hsv_gray_mask.height(); y++) {
                                double pixle[] = hsv_gray_mask.get(y, x);
                                if (pixle[0] == 255.0) {// 如果是白色
                                    black_white_pixle_num++;
                                }
                            }
                        }
                        //白底的白色像素比较多
                        if (black_white_pixle_num>41000){
                            black_white_Flag=true;//白底
                        }

                        //*****图片裁剪***可以封装成函数*****************
                        Imgproc.pyrUp(result,result);//向上采样,放大图片
                    }
                }
            }
        }



        if (result != null) {
            //******使用HSV阈值分割***************************
            Mat hsv_img=result.clone();
            Imgproc.cvtColor(hsv_img,hsv_img,Imgproc.COLOR_BGR2HSV);//Hsv颜色空间转换
            //show_bitmap(hsv_img);

            //浅蓝色0阈值分割
            Mat Cambridge_blue = new Mat();
            Core.inRange(hsv_img,new Scalar(HSV_VALUE_LOW[0]),new Scalar(HSV_VALUE_HIGH[0]),Cambridge_blue);
            Imgproc.erode(Cambridge_blue,Cambridge_blue,kernel);
            yanse(Cambridge_blue,0);//浅蓝色0颜色数量和该颜色对应的图形

            //黄色1阈值分割
            Mat yellow = new Mat();
            Core.inRange(hsv_img,new Scalar(HSV_VALUE_LOW[1]),new Scalar(HSV_VALUE_HIGH[1]),yellow);
            Imgproc.erode(yellow,yellow,kernel);
            yanse(yellow,1);//黄色1颜色数量和该颜色对应的图形

            //品红2阈值分割
            Mat purple = new Mat();
            Core.inRange(hsv_img,new Scalar(HSV_VALUE_LOW[2]),new Scalar(HSV_VALUE_HIGH[2]),purple);
            Imgproc.erode(purple,purple,kernel);
            Imgproc.erode(purple,purple,kernel);
            yanse(purple,2);//品红2颜色数量和该颜色对应的图形

            //浅红色3、深红色6阈值分割
            Mat red = new Mat();
            Mat dark_red=new Mat();
            Core.inRange(hsv_img,new Scalar(HSV_VALUE_LOW[3]),new Scalar(HSV_VALUE_HIGH[3]),red);//浅红色阈值
            Core.inRange(hsv_img,new Scalar(HSV_VALUE_LOW[6]),new Scalar(HSV_VALUE_HIGH[6]),dark_red);//深红色阈值
            Core.bitwise_or(red,dark_red,red);
            Imgproc.erode(red,red,kernel);
            yanse(red,6);//红色6颜色数量和该颜色对应的图形

            //蓝色4阈值分割
            Mat blue = new Mat();
            Core.inRange(hsv_img,new Scalar(HSV_VALUE_LOW[4]),new Scalar(HSV_VALUE_HIGH[4]),blue);
            Imgproc.erode(blue,blue,kernel);
            Imgproc.erode(blue,blue,kernel);
            yanse(blue,4);

            //青色5阈值分割
            Mat cyan = new Mat();
            Core.inRange(hsv_img,new Scalar(HSV_VALUE_LOW[5]),new Scalar(HSV_VALUE_HIGH[5]),cyan);
            Imgproc.erode(cyan,cyan,kernel);
            yanse(cyan,5);

            //黑色7阈值分割   白底 黑色进行阈值分割
            Mat black = new Mat();
            if (black_white_Flag==true){
                Core.inRange(hsv_img,new Scalar(HSV_VALUE_LOW[7]),new Scalar(HSV_VALUE_HIGH[7]),black);
                Imgproc.erode(black,black,kernel);
                yanse(black,7);
            }

            Log.e(TAG, "浅蓝色个数："+Cambridge_blue_Num+"黄色个数："+yellow_Num+"品红色个数:"+mag_Num
                    +"蓝色个数："+blue_Num+"青色个数:"+qing_Num+"红色个数:"+red_Num);


            Mat hsv_mask=Mat.zeros(cyan.size(),cyan.type());
            Core.bitwise_or(hsv_mask,Cambridge_blue,hsv_mask);
            Core.bitwise_or(hsv_mask,yellow,hsv_mask);
            Core.bitwise_or(hsv_mask,purple,hsv_mask);
            Core.bitwise_or(hsv_mask,red,hsv_mask);
            Core.bitwise_or(hsv_mask,blue,hsv_mask);
            Core.bitwise_or(hsv_mask,cyan,hsv_mask);

            //白底
            if (black_white_Flag==true){
                Core.bitwise_or(hsv_mask,black,hsv_mask);

            }

            Imgproc.erode(hsv_mask,hsv_mask,kernel);

            //*************HSV阈值分割******************************


            //#使用Canny阈值分割来弄****************************************
            Mat resutl_mask=null;
            Mat canny_all_hierarchy=new Mat();
            Mat canny_new_img=result.clone();//复制截取上采样之后的图片
            if (black_white_Flag==false){
                Mat canny_img=result.clone();//复制截取上采样之后的图片
                Mat canny_gray=new Mat();//灰度化
                Imgproc.cvtColor(canny_img,canny_gray,Imgproc.COLOR_BGR2GRAY);//灰度化处理

                Mat canny=new Mat();
                Imgproc.Canny(canny_gray,canny,50,150);//边缘化二值化检测

                Mat canny_kernel=Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE,new Size(1,1));//  指定腐蚀膨胀核
                Imgproc.morphologyEx(canny,canny,Imgproc.MORPH_ERODE,canny_kernel,new Point(-1,-1),3);//闭操作，先膨胀后腐蚀清楚小黑点，清楚连通区域
                Mat canny_kernel2=Imgproc.getStructuringElement(Imgproc.MORPH_RECT,new Size(3,3));//  指定腐蚀膨胀核
                Imgproc.morphologyEx(canny,canny,Imgproc.MORPH_CLOSE,canny_kernel2);//闭操作，先膨胀后腐蚀清楚小黑点，清楚连通区
                Imgproc.morphologyEx(canny,canny,Imgproc.MORPH_CLOSE,canny_kernel2);//闭操作，先膨胀后腐蚀清楚小黑点，清楚连通区
                Imgproc.morphologyEx(canny,canny,Imgproc.MORPH_DILATE,canny_kernel2);

                List<MatOfPoint> canny_all_contours = new ArrayList<MatOfPoint>();


                Imgproc.findContours(canny, canny_all_contours, canny_all_hierarchy,
                        Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_SIMPLE);//边缘化之后再查找所有的轮廓


                Mat canny_new_img2=new Mat(canny_new_img.size(),canny_new_img.type(),new Scalar(0,0,0));
                for (int i=0;i<canny_all_contours.size();i++){
                    double area=Imgproc.contourArea(canny_all_contours.get(i));
                    if (area>300){
                        Imgproc.drawContours(canny_new_img2,canny_all_contours,
                                i,new Scalar(0,0,255),-1,Imgproc.LINE_4,canny_all_hierarchy,1,new Point(0,0));
                        Imgproc.morphologyEx(canny_new_img2,canny_new_img2,Imgproc.MORPH_OPEN,kernel);
                    }
                }

                Mat mask_blue= new Mat();
                Core.inRange(canny_new_img2,new Scalar(HSV_VALUE_LOW[8]),new Scalar(HSV_VALUE_HIGH[8]),mask_blue);//常规蓝色
                Imgproc.erode(mask_blue,mask_blue,kernel);
                Imgproc.erode(mask_blue,mask_blue,kernel);

                Core.bitwise_or(mask_blue,hsv_mask,mask_blue);

                resutl_mask=mask_blue.clone();
            }
            //#使用Canny阈值分割来弄****************************************

            if (resutl_mask == null) {
                resutl_mask=hsv_mask;
            }

            //*********查找整张图片的轮廓，然后绘制出来********************************
            //1、轮廓查找
            List<MatOfPoint> resutl_contours = new ArrayList<MatOfPoint>();
            Imgproc.findContours(resutl_mask, resutl_contours, canny_all_hierarchy,
                    Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_SIMPLE);//边缘化之后再查找所有的轮廓

            Imgproc.drawContours(canny_new_img,resutl_contours,
                    -1,new Scalar(255,0,0),3);



            san_Num=0;rect_Num=0;lin_Num=0;star_Num=0;yuan_Num=0;
            for (MatOfPoint c : resutl_contours) {
                int area = 0;
                area = (int) Imgproc.contourArea(c);
                Log.i("ShapeDetect", "识别面积：" + area);
                //300、800、500  if (area > 500&&800<area)
                if (area > 500) {
                    DetectShape(canny_new_img,new MatOfPoint2f(c.toArray()));//统计形状个数
                }
            }

            save_pic(canny_new_img,2);



            if (black_white_Flag==false){
                Log.e("ShapeDetect黑底", "三角形个数："+san_Num+"矩形个数："+rect_Num+"菱形个数："+lin_Num
                        + "五角形个数："+star_Num+"圆形个数："+yuan_Num);
            }else{
                Log.e("ShapeDetect白底", "三角形个数："+triangle_Num+"矩形个数："+rectangle_Num+"菱形个数："+rhombus_Num
                        + "五角形个数："+pentagon_Num+"圆形个数："+circle_Num);
            }

            shape_yanse();//颜色数据

            Log.e(TAG, "最大面积为："+Max_area+" "+Max_area_Yanse+" "+Max_area_shape);

            result=null;

        }

        if (black_white_Flag!=false){
            return return_shape_num(triangle_Num,rectangle_Num,rhombus_Num,pentagon_Num,circle_Num);//白底
        }else{
            return return_shape_num(san_Num,rect_Num,lin_Num,star_Num,yuan_Num);//黑白底
        }

        // black_white_Flag==false为真，为黑白底   black_white_Flag==false?return_shape_num(san_Num,rect_Num,lin_Num,sta  //黑白底r_Num,yuan_Num):
        //                        return_shape_num(triangle_Num,rectangle_Num,rhombus_Num,pentagon_Num,circle_Num)
    }

    /**
     * 按键拍照存图片,每按一次就存一张图片
     * opencv中的图片格式是BGR,而手机中的图片是RGB
     *
     * Mat photo:需要保存的图片，int plate=1:为保存车牌图片,2为保存形状图片,3为交通灯图片
     */

    private static String ZIKU_PATH_plate = getSDPath() + java.io.File.separator +"plate";
    private static String ZIKU_PATH_shape = getSDPath() + java.io.File.separator +"shape";
    private static String ZIKU_PATH_light = getSDPath() + java.io.File.separator +"light";
    static int save_picture=0;
    public static void save_pic(Mat photo,int plate){
        save_picture++;
        Imgproc.cvtColor(photo,photo,Imgproc.COLOR_BGR2RGB);
        String filename=null;
        if(plate==1){
            filename=ZIKU_PATH_plate+"/plate"+String.valueOf(save_picture)+".jpg";
        }else if (plate==2){
            filename=ZIKU_PATH_shape+"/shape"+String.valueOf(save_picture)+".jpg";
        }else if (plate==3){
            filename=ZIKU_PATH_light+"/light"+String.valueOf(save_picture)+".jpg";
        }
        Imgcodecs.imwrite(filename,photo);
    }

    /**
     * 读取手机中的图片进行识别，
     * 但是要改变颜色空间，
     * 因为手机中的照片是RGB格式，
     * 而opencv显示的是BGR
     *
     * boolean auto=true自动读取，false固定名字读取，
     * String pic_name=plate1.jpg 固定图片名字
     * int plate=1读取车牌的，2读取形状的
     */
    static int read_picture=0;
    public static Mat read_pic(boolean auto,String pic_name,int plate){
        read_picture++;
        String filename=null;
        if (auto == false) {
            if(plate==1){
                filename=ZIKU_PATH_plate+"/"+pic_name;
            }else if(plate==2){
                filename=ZIKU_PATH_shape+"/"+pic_name;
            }else if(plate==3){
                filename=ZIKU_PATH_light+"/"+pic_name;
            }
        }else{
            if(plate==1){
                filename=ZIKU_PATH_plate+"/plate"+String.valueOf(read_picture)+".jpg";
            }else if (plate==2){
                filename=ZIKU_PATH_shape+"/shape"+String.valueOf(read_picture)+".jpg";
            }else if(plate==3){
                filename=ZIKU_PATH_light+"/light"+String.valueOf(read_picture)+".jpg";
            }
        }
        Log.i(TAG, "read_pic: "+filename);
        Mat photo=Imgcodecs.imread(filename);
        Imgproc.cvtColor(photo,photo,Imgproc.COLOR_RGB2BGR);
        return photo;
    }


    public static Mat DetectShape(Mat canny_new_img,MatOfPoint2f c1){

        double area=0;
        area = Imgproc.contourArea(c1);
        Moments moments = Imgproc.moments(c1);
        //计算轮廓中心
        int cx = (int) (moments.m10 / moments.m00);
        int cy = (int) (moments.m01 / moments.m00);

        Imgproc.circle(canny_new_img, new Point(cx, cy), 5, new Scalar(255, 0, 0), -1);

        //计算轮廓的周长
        double peri = Imgproc.arcLength(c1,true);
        //double side_lenght=peri/4;//计算出菱形或者正方形的边长，用于判断菱形与正方形和矩形的区别
        MatOfPoint2f approx = new MatOfPoint2f();
        //得到大概值
        Imgproc.approxPolyDP(c1,approx,0.028 * peri,true);

        //如果是三角形形状，则有三个顶点
        if (approx.toList().size()==3){
            Imgproc.putText(canny_new_img, "san", new Point(cx, cy), Core.FONT_HERSHEY_PLAIN, 2, new Scalar(255, 0, 0), 5);
            san_Num++;
        }


        //如果有四个顶点，则是正方形或者长方形
        else if (approx.toList().size()==4){

            double new_area = 0,minArea=0;

            new_area = Imgproc.contourArea(c1);

            RotatedRect rect1=Imgproc.minAreaRect(c1);

            minArea=rect1.size.area();

            double rec=area/minArea;
            if (rec>=0.83&&rec<1.15) {
                rect_Num++;
                Imgproc.putText(canny_new_img, "rect", new Point(cx, cy),
                        Core.FONT_HERSHEY_PLAIN, 2, new Scalar(255, 0, 0), 5);
            }
            else{
                lin_Num++;
                Imgproc.putText(canny_new_img, "lin", new Point(cx, cy),
                        Core.FONT_HERSHEY_PLAIN, 2, new Scalar(255, 0, 0), 5);
            }
        }

        //如果是五角形，则有五个顶点
        else if (approx.toList().size()>=10&&approx.toList().size()<=13){
            star_Num++;
            Imgproc.putText(canny_new_img, "star", new Point(cx, cy),
                    Core.FONT_HERSHEY_PLAIN, 2, new Scalar(255, 0, 0), 5);
        }

        else if(approx.toList().size()>4&&approx.toList().size()<10){
            yuan_Num++;
            Imgproc.putText(canny_new_img, "circle", new Point(cx, cy),
                    Core.FONT_HERSHEY_PLAIN, 2, new Scalar(255, 0, 0), 5);
        }

        //除了以上情况之外，我们假设为圆形
        else if(approx.toList().size()>13){
            yuan_Num++;
            Imgproc.putText(canny_new_img, "circle", new Point(cx, cy),
                    Core.FONT_HERSHEY_PLAIN, 2, new Scalar(255, 0, 0), 5);
        }
        return canny_new_img;
    }

    public static int[] shape_yanse(){
        Log.e(TAG, "浅蓝色个数："+Cambridge_blue_Num+"黄色个数："+yellow_Num+"品红色个数:"+mag_Num
                +"蓝色个数："+blue_Num+"青色个数:"+qing_Num+"红色个数:"+red_Num);
        int shapenum[]=new int[6];
        shapenum[0]=Cambridge_blue_Num;
        shapenum[1]=yellow_Num;
        shapenum[2]=mag_Num;
        shapenum[3]=blue_Num;
        shapenum[4]=qing_Num;
        shapenum[5]=red_Num;
        return shapenum;
    }

    public int[] return_shape_num(int san,int rect,int lin,int star,int yuan){
        int shapenum[]=new int[5];
        shapenum[0]=san;
        shapenum[1]=rect;
        shapenum[2]=lin;
        shapenum[3]=star;
        shapenum[4]=yuan;
        return shapenum;
    }


    /**
     *颜色识别
     *  mask->分割后的二值化图像
     *   i->0 浅蓝色、  i->1 黄色、  i->2品红、  i=>4 蓝色、i->5 青色(绿色)、i=>6 红色、 i->7黑色
     *   //浅蓝0、//黄色1、//品红2、//浅红色3、//蓝色4、//青色5、// 深红色6、//黑色7
     */
    static int Max_area=0;//一张图片中那个形状最大面积
    static String Max_area_Yanse="品红色";//最大面积对应的形状颜色
    static String Max_area_shape="triangle";//最大面积对应的形状
    static boolean Max_area_YanseFlag=false;
    public Mat yanse(Mat mask,int i){

        Mat mask1=mask.clone();
        List<MatOfPoint> contour = new ArrayList<MatOfPoint>();
        Mat hierarchy = new Mat();
        Imgproc.findContours(mask1, contour, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);

        for (MatOfPoint c : contour) {
            int area = 0;
            area = (int) Imgproc.contourArea(c);
            Log.i(TAG, "颜色形状识别各个轮廓的面积："+area);
            //300、800、
            if (area > 250&&area<2000) {

                //查找一张图片里面的最大轮廓
                if (area>Max_area){
                    Max_area=area;//查找最大面积
                    Max_area_YanseFlag=true;
                }

                Moments moments = Imgproc.moments(c);
                //计算轮廓中心
                int cx = (int) (moments.m10 / moments.m00);
                int cy = (int) (moments.m01 / moments.m00);
                //浅蓝0、//黄色1、//品红2、//浅红色3、//蓝色4、//青色5、// 深红色6、//黑色7
                //浅蓝色
                if (i==0){
                    Cambridge_blue_Num++;//计算浅蓝色图形个数
                    String shape =hsv_shape_detect(new MatOfPoint2f(c.toArray()));
                    Log.e(TAG, "0、颜色形状识别浅蓝色"+shape+"的面积："+area);
                    shape_Num(shape);
                    if (Max_area_YanseFlag==true){
                        Max_area_Yanse="浅蓝色";
                        Max_area_shape=shape;
                        Max_area_YanseFlag=false;
                    }
                }
                //黄色
                if (i==1){
                    yellow_Num++;//计算黄色图形个数
                    String shape =hsv_shape_detect(new MatOfPoint2f(c.toArray()));
                    Log.e(TAG, "1、颜色形状识别黄色"+shape+"的面积："+area);
                    shape_Num(shape);
                    if (Max_area_YanseFlag==true){
                        Max_area_Yanse="黄色";
                        Max_area_shape=shape;
                        Max_area_YanseFlag=false;
                    }
                }
                //品红色
                if (i==2){
                    mag_Num++;//计算品红色图形个数
                    String shape =hsv_shape_detect(new MatOfPoint2f(c.toArray()));
                    Log.e(TAG, "2、颜色形状识别品红色"+shape+"的面积："+area);
                    shape_Num(shape);
                    if (Max_area_YanseFlag==true){
                        Max_area_Yanse="品红色";
                        Max_area_shape=shape;
                        Max_area_YanseFlag=false;
                    }
                }
                //蓝色
                if (i==4){
                    blue_Num++;//计算蓝色图形个数
                    String shape =hsv_shape_detect(new MatOfPoint2f(c.toArray()));
                    Log.e(TAG, "4、颜色形状识别蓝色"+shape+"的面积："+area);
                    shape_Num(shape);
                    if (Max_area_YanseFlag==true){
                        Max_area_Yanse="蓝色";
                        Max_area_shape=shape;
                        Max_area_YanseFlag=false;
                    }
                }
                //青色
                if (i==5){
                    qing_Num++;//计算青色图形个数
                    String shape =hsv_shape_detect(new MatOfPoint2f(c.toArray()));
                    Log.e(TAG, "5、颜色形状识别青色"+shape+"的面积："+area);
                    shape_Num(shape);
                    if (Max_area_YanseFlag==true){
                        Max_area_Yanse="青色";
                        Max_area_shape=shape;
                        Max_area_YanseFlag=false;
                    }
                }
                //红色
                if (i ==6) {
                    red_Num++;//计算红色图形个数
                    String shape =hsv_shape_detect(new MatOfPoint2f(c.toArray()));
                    Log.e(TAG, "5、颜色形状识别红色"+shape+"的面积："+area);
                    shape_Num(shape);
                    if (Max_area_YanseFlag==true){
                        Max_area_Yanse="红色";
                        Max_area_shape=shape;
                        Max_area_YanseFlag=false;
                    }
                }
                //黑色
                if (i == 7) {
                    black_Num++;//计算黑色图形个数
                    String shape =hsv_shape_detect(new MatOfPoint2f(c.toArray()));
                    Log.e(TAG, "7、颜色形状识别黑色"+shape+"的面积："+area);
                    shape_Num(shape);
                    if (Max_area_YanseFlag==true){
                        Max_area_Yanse="黑色";
                        Max_area_shape=shape;
                        Max_area_YanseFlag=false;
                    }
                }
            }
        }

        hierarchy.release();
        return mask;
    }


    public static String hsv_shape_detect(MatOfPoint2f c){

        String shape = "unknown";
        //计算轮廓的周长
        double peri = Imgproc.arcLength(c,true);
        //double side_lenght=peri/4;//计算出菱形或者正方形的边长，用于判断菱形与正方形和矩形的区别
        MatOfPoint2f approx = new MatOfPoint2f();
        //得到大概值
        Imgproc.approxPolyDP(c,approx,0.028 * peri,true);

        //如果是三角形形状，则有三个顶点
        if (approx.toList().size()==3){
            shape = "triangle";
        }

        //如果有四个顶点，则是正方形或者长方形
        else if (approx.toList().size()==4){

            double area = 0,minArea=0;

            area = Imgproc.contourArea(c);

            RotatedRect rect1=Imgproc.minAreaRect(c);

            minArea=rect1.size.area();

            double rec=area/minArea;

            if (rec>=0.83&&rec<1.15)
                shape = "rectangle";
            else
                shape = "rhombus";
        }

        //如果是五角形，则有五个顶点
        else if (approx.toList().size()>=10&&approx.toList().size()<=13){
            shape = "pentagon";
        }

        //除了以上情况之外，我们假设为圆形
        else if(approx.toList().size()>13){
            shape = "circle";
        }

        return shape;
    }


    public static void shape_Num(String shape){
        //int[] shape_Num=new int[5];
        String triangle=new String("triangle");
        String rectangle=new String("rectangle");
        String pentagon=new String("pentagon");
        String circle=new String("circle");
        String rhombus=new String("rhombus");//菱形
        if(triangle.equals(shape)){
            //shape_Num[0]=triangle_Num++;
            triangle_Num++;
        }
        if(rectangle.equals(shape)){
//            shape_Num[1]=rectangle_Num++;
            rectangle_Num++;
        }
        if(pentagon.equals(shape)){
//            shape_Num[2]=pentagon_Num++;
            pentagon_Num++;
        }
        if(circle.equals(shape)){
            //shape_Num[3]=circle_Num++;
            circle_Num++;
        }
        if(rhombus.equals(shape)){
            //shape_Num[3]=circle_Num++;
            rhombus_Num++;
        }
        //return shape_Num;
    }

    //发送颜色值给LED的第二排
    public static void send_yanseToLED_two(int[] yanse_data){
        FirstActivity.Connect_Transport.digital(2,0XF0|yanse_data[0],0XF0|yanse_data[1],0XF0|yanse_data[2]);//发送颜色值给LED的第二排
        FirstActivity.Connect_Transport.yanchi(100);
        FirstActivity.Connect_Transport.digital(2,0XF0|yanse_data[0],0XF0|yanse_data[1],0XF0|yanse_data[2]);
        FirstActivity.Connect_Transport.yanchi(100);
        FirstActivity.Connect_Transport.digital(2,0XF0|yanse_data[0],0XF0|yanse_data[1],0XF0|yanse_data[2]);
        FirstActivity.Connect_Transport.yanchi(100);
    }


    private static short[] data = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00};
    //立体标志位显示最大面积的颜色
    public static int liti_yanse(){
//        data[0] = 0x13;
        int which=liti_yanse_index(Max_area_Yanse);
//        data[1] = (short) (which + 0x01);
//        FirstActivity.Connect_Transport.yanchi(500);
//        FirstActivity.Connect_Transport.infrared_stereo(data);
//        FirstActivity.Connect_Transport.yanchi(500);
//        FirstActivity.Connect_Transport.infrared_stereo(data);
        return which;
    }

    //发送颜色信息给立体
    public static void send_litiyanse(int which){
        data[0] = 0x13;
        data[1] = (short) (which + 0x01);
        FirstActivity.Connect_Transport.yanchi(500);
        FirstActivity.Connect_Transport.infrared_stereo(data);
        FirstActivity.Connect_Transport.yanchi(500);
        FirstActivity.Connect_Transport.infrared_stereo(data);
        FirstActivity.Connect_Transport.yanchi(500);
        FirstActivity.Connect_Transport.infrared_stereo(data);
    }

    //立体标志位显示最大面积的形状
    public static int liti_shape(){
//        data[0] = 0x12;
        int which=liti_shape_index(Max_area_shape);
//        data[1] = (short) (which + 0x01);
//        FirstActivity.Connect_Transport.yanchi(500);
//        FirstActivity.Connect_Transport.infrared_stereo(data);
//        FirstActivity.Connect_Transport.yanchi(500);
//        FirstActivity.Connect_Transport.infrared_stereo(data);
//        FirstActivity.Connect_Transport.yanchi(500);
        return which;
    }

    //发送形状信息给立体
    public static void send_litishape(int which){
        data[0] = 0x12;
        data[1] = (short) (which + 0x01);
        FirstActivity.Connect_Transport.yanchi(500);
        FirstActivity.Connect_Transport.infrared_stereo(data);
        FirstActivity.Connect_Transport.yanchi(500);
        FirstActivity.Connect_Transport.infrared_stereo(data);
        FirstActivity.Connect_Transport.yanchi(500);
        FirstActivity.Connect_Transport.infrared_stereo(data);
    }




    private static int liti_yanse_index(String yanse){
        int which=0;
        if (yanse.equals("红色")){
            which=0;
        }else if (yanse.equals("绿色")){
            which=1;
        }else if (yanse.equals("蓝色")){
            which=2;
        }else if (yanse.equals("黄色")){
            which=3;
        }else if (yanse.equals("品红色")){
            which=4;
        }else if (yanse.equals("青色")){
            which=5;
        }else if (yanse.equals("黑色")){
            which=6;
        }else if (yanse.equals("白色")){
            which=7;
        }
        return which;
    }

    private static int liti_shape_index(String shape){
        int which=0;
        if (shape.equals("rectangle")){
            which=0;
        }else if (shape.equals("circle")){
            which=1;
        }else if (shape.equals("triangle")){
            which=2;
        }else if (shape.equals("rhombus")){
            which=3;
        }else if (shape.equals("pentagon")){
            which=4;
        }
        return which;
    }


    public static void main(String[] argv){
//        System.out.print(shape_Index("A122B4"));
    }

    //车牌的 XY3YXY  有效图形为三角形=（3%2）+1=2
    public static int shape_Index(String plate_Flag){
        int num=2;
        if (plate_Flag!=null&&plate_Flag.length()==6){
            num= (plate_Flag.charAt(2)-'0');
            num=(num%2)+1;
        }
        return num;
    }

    public static int shape_Index2(int light_Flag){
        int num=2;
        num=light_Flag;
        num=(num%2)+1;
        return num;
    }

    // 把坐标降低到4分之一
    MatOfPoint ChangeSize(MatOfPoint contour) {
        for (int i = 0; i < contour.height(); i++) {
            double[] p = contour.get(i, 0);
            p[0] = p[0] / 4;
            p[1] = p[1] / 4;
            contour.put(i, 0, p);
        }
        return contour;
    }
}

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845

1