同时识别三种颜色—— opencv

//创建三个 Mat 来分别存储识别黄蓝红三色并二值化后的图片
Mat yellowImg, blueImg, redImg;

for (int i = 0; i < 3; i++){
    switch (i){
    case 0: //黄色
        minH = 16; 
        maxH = 35; 

        minS = 160; 
        maxS = 255;

        minV = 50; 
        maxV = 255;

        // inRange(原图像, 最小值的范围, 最大值的范围, 输出图像);
        //将图像二值化，即输出图像是黑白二值图像，其中 最小值<=像素点<=最大值 的像素点是白色
        inRange(hsvImg, Scalar(minH, minS, minV), Scalar(maxH, maxS, maxV), yellowImg); 
        break;

    case 1: //蓝色
        minH = 70; 
        maxH = 120;

        minS = 150; 
        maxS = 255;

        minV = 50; 
        maxV = 255;

        inRange(hsvImg, Scalar(minH, minS, minV), Scalar(maxH, maxS, maxV), blueImg);
        break;

    case 2: //红色
        minH = 0; 
        maxH = 15; 

        minS = 160;
        maxS = 255;

        minV = 50; 
        maxV = 255;

        inRange(hsvImg, Scalar(minH, minS, minV), Scalar(maxH, maxS, maxV), redImg);
        break;

    default:
        printf("颜色标志输入错误\n");
    }
}

Mat resultImg;
Mat yeBluImg;

//图像算数运算————合并（或）
bitwise_or(yellowImg, blueImg, yeBluImg); 
bitwise_or(yeBluImg, redImg, resultImg); 

//开操作 (去除一些噪点) 如果二值化后图片干扰部分依然很多，增大下面的size
Mat eleOpen = getStructuringElement(MORPH_RECT, Size(6, 6));//返回指定形状和尺寸的结构元素
morphologyEx(resultImg, resultImg, MORPH_OPEN, eleOpen);

#include<opencv.hpp>
using namespace cv;
using namespace std;
int main(){

    Mat srcImg = imread("C:/Users/齐明洋/Desktop/tem2.png");

    Mat hsvImg;

    //将RGB颜色空间转换为HSV颜色空间
    cvtColor(srcImg, hsvImg, COLOR_BGR2HSV); 

    int minH = 0; 
    int maxH = 0; 

    int minS = 0; 
    int maxS = 0;

    int minV = 0; 
    int maxV = 0;

    Mat yellowImg, blueImg, redImg;

    for (int i = 0; i < 3; i++){
        switch (i){
        case 0: //黄色
            minH = 16; 
            maxH = 35; 

            minS = 160; 
            maxS = 255;

            minV = 50; 
            maxV = 255;

            // inRange(原图像, 最小值的范围, 最大值的范围, 输出图像);
            //将图像二值化，即输出图像是黑白二值图像，其中 最小值<=像素点<=最大值 的像素点是白色
            inRange(hsvImg, Scalar(minH, minS, minV), Scalar(maxH, maxS, maxV), yellowImg); 
            break;

        case 1: //蓝色
            minH = 70; 
            maxH = 120;

            minS = 150; 
            maxS = 255;

            minV = 50; 
            maxV = 255;

            inRange(hsvImg, Scalar(minH, minS, minV), Scalar(maxH, maxS, maxV), blueImg);
            break;

        case 2: //红色
            minH = 0; 
            maxH = 15; 

            minS = 160;
            maxS = 255;

            minV = 50; 
            maxV = 255;

            inRange(hsvImg, Scalar(minH, minS, minV), Scalar(maxH, maxS, maxV), redImg);
            break;

        default:
            printf("颜色标志输入错误\n");
        }
    }

    Mat resultImg;
    Mat yeBluImg;

    //图像算数运算————合并（或）
    bitwise_or(yellowImg, blueImg, yeBluImg); 
    bitwise_or(yeBluImg, redImg, resultImg); 

    //开操作 (去除一些噪点) 如果二值化后图片干扰部分依然很多，增大下面的size
    Mat eleOpen = getStructuringElement(MORPH_RECT, Size(6, 6));//返回指定形状和尺寸的结构元素
    morphologyEx(resultImg, resultImg, MORPH_OPEN, eleOpen);

      imshow("原始图片", srcImg);
      imshow("识别结果", resultImg);

    waitKey(0);
    return 0;
}