Android+Opencv图形的形状颜色识别 |
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Android+Opencv图形的形状颜色识别
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视频效果：https://www.bilibili.com/video/BV1654y1G7Py 啥也不说来看效果 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 contours = new ArrayList(); Mat hierarchy=new Mat(); Imgproc.findContours(binary, contours, hierarchy, Imgproc.RETR_CCOMP, Imgproc.CHAIN_APPROX_SIMPLE);//查找轮廓 double maxArea = 0; Iterator 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) 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 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 canny_all_contours = new ArrayList(); 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 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 resutl_contours = new ArrayList(); 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&&800 500) { 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 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() 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 contour = new ArrayList(); 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 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=10&&approx.toList().size() 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; 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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【本文地址】
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