#include <imgproc/imgproc.hpp> #include <opencv2/core/core.hpp> #include<opencv2/highgui/highgui.hpp> #include <iostream> #include <opencv2/opencv.hpp> #include <opencv2/highgui/highgui.hpp> #include<string> #include<vector> #define BIGGEST(a,b,c) (a>b?(a>c?a:c):(b>c?b:c)) #define RED 1 #define GREEN 2 #define BLUE 3 //HSV范围 //红色HSV范围 #define iLowH_R 125 #define iHighH_R 155 #define iLowS_R 43 #define iHighS_R 255 #define iLowV_R 46 #define iHighV_R 255 //绿色HSV范围 #define iLowH_G 35 #define iHighH_G 77 #define iLowS_G 43 #define iHighS_G 255 #define iLowV_G 46 #define iHighV_G 255 //蓝色HSV范围 #define iLowH_B 100 #define iHighH_B 124 #define iLowS_B 128 #define iHighS_B 255 #define iLowV_B 102 #define iHighV_B 255 using namespace cv; using namespace std; Mat WhiteBalance(Mat image); double PixelTotal(Mat mask); int HSV(Mat img); int main() { IplImage* example = cvLoadImage("C:\\Users\\Lenovo\\Desktop\\Picprocess\\DDD.jpg", 1); Mat Img, exampleWB; int color; Img = cvarrToMat(example); exampleWB = WhiteBalance(Img); color = HSV(exampleWB); printf("return value:%d\n",color); } Mat WhiteBalance(Mat image) { vector<Mat> imageRGB; //RGB三通道分离 split(image, imageRGB); //求原始图像的RGB分量的均值 double R, G, B; B = mean(imageRGB[0])[0]; G = mean(imageRGB[1])[0]; R = mean(imageRGB[2])[0]; //需要调整的RGB分量的增益 double KR, KG, KB; KB = (R + G + B) / (3 * B); KG = (R + G + B) / (3 * G); KR = (R + G + B) / (3 * R); //调整RGB三个通道各自的值 imageRGB[0] = imageRGB[0] * KB;//蓝色 imageRGB[1] = imageRGB[1] * KG;//绿色 imageRGB[2] = imageRGB[2] * KR;//红色 //RGB三通道图像合并 merge(imageRGB, image); waitKey(0); return image; }//白平衡子函数 double PixelTotal(Mat mask) { IplImage *image; image = &IplImage(mask); double i, j, pixel = 0; CvScalar s; for (i = 0; i < image->height; i++) { for (j = 0; j < image->width; j++) { s = cvGet2D(image, i, j); pixel += s.val[0]; } } return pixel; } int HSV(Mat img) {//Mat imgOutR, Mat imgOutB, Mat imgOutG) { Mat imgHSV; Mat imgGray; cvtColor(img, imgHSV, COLOR_BGR2HSV);//转为HSV cvtColor(img, imgGray, COLOR_RGB2GRAY); imwrite("C:\\Users\\Lenovo\\Desktop\\Picprocess\\hsv.jpg", imgHSV); Mat maskR, maskG, maskB; inRange(imgHSV, Scalar(iLowH_G, iLowS_G, iLowV_G), Scalar(iHighH_G, iHighS_G, iHighV_G), maskG); //Threshold the image inRange(imgHSV, Scalar(iLowH_R, iLowS_R, iLowV_R), Scalar(iHighH_R, iHighS_R, iHighV_R), maskR); inRange(imgHSV, Scalar(iLowH_B, iLowS_B, iLowV_B), Scalar(iHighH_B, iHighS_B, iHighV_B), maskB); //开操作 (去除一些噪点) Mat element = getStructuringElement(MORPH_RECT, Size(10, 10)); morphologyEx(maskG, maskG, MORPH_OPEN, element);//如果二值化后图片干扰部分依然很多，增大下面的size morphologyEx(maskR, maskR, MORPH_OPEN, element); morphologyEx(maskB, maskB, MORPH_OPEN, element); imwrite("C:\\Users\\Lenovo\\Desktop\\Picprocess\\G.jpg", maskG); imwrite("C:\\Users\\Lenovo\\Desktop\\Picprocess\\B.jpg", maskB); imwrite("C:\\Users\\Lenovo\\Desktop\\Picprocess\\R.jpg", maskR); double x_r = 0, x_b = 0, x_g = 0, x_total = 0; x_r = PixelTotal(maskR); x_g = PixelTotal(maskG); x_b = PixelTotal(maskB); x_total = BIGGEST(x_b, x_g, x_r); if (x_total == x_b) { printf("This is blue, pixel %lf\n", x_total); return BLUE; } else if (x_total == x_g) { printf("This is green, pixel %lf\n", x_total); return GREEN; } else { printf("This is red, pixel %lf\n", x_total); return RED; } waitKey(0); }//RGB转化为HSV，并将图像二值化，根据需求得到需要的色块处理图像文章来源: opencv颜色识别