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玉米（或大米）颗粒图像计数系统设计与实现

系统概述

本系统旨在通过图像处理技术快速、准确地计数玉米或其他谷物颗粒。该系统基于图像分割和特征检测技术，能够处理真实世界中的复杂场景，提供高精度的计数结果。

原理简述

该系统利用先进的图像处理算法，根据玉米颗粒的形态特征进行识别和计数。主要步骤包括：图像预处理、边缘检测、形态学操作、轮廓提取以及颗粒计数等。这些技术共同作用，实现对玉米颗粒的高效计数。

OpenCV3 代码实现

以下是基于OpenCV3的代码实现，用于玉米颗粒图像的预处理和计数。

#include 
   
    #include 
    
     #define PI 3.1415926using namespace std;using namespace cv;void fillHole(const Mat& srcBw, Mat& dstBw) {    Size mSize = srcBw.size();    Mat Temp(mSize.height + 2, mSize.width + 2, srcBw.type());    srcBw.copyTo(Temp(Range(1, mSize.height + 1), Range(1, mSize.width + 1)));    floodFill(Temp, Point(0, 0), Scalar(255));    Mat cutImg = Temp(Range(1, mSize.height + 1), Range(1, mSize.width + 1)).copyTo(cutImg);    dstBw = srcBw | (~cutImg);}int main() {    Mat srcImg;    srcImg = imread("1.jpg");    clock_t start = clock();    namedWindow("原图", 0);    imshow("原图", srcImg);    waitKey(1);        cvtColor(srcImg, srcImage, COLOR_BGR2GRAY);    srcImage = srcImage > 160;        Mat vec_rgb;    vec_rgb = Mat::zeros(srcImage.size(), CV_8UC1);    medianBlur(srcImage, vec_rgb, 3);        namedWindow("中值滤波", 0);    imshow("中值滤波", vec_rgb);    waitKey(1);        Canny(vec_rgb, vec_rgb, 3, 9, 3);        namedWindow("边缘检测", 0);    imshow("边缘检测", vec_rgb);    waitKey(1);        Mat element = getStructuringElement(MORPH_ELLIPSE, Size(3, 3), Point(1, 1));    erode(vec_rgb, vec_rgb, element);        fillHole(vec_rgb, vec_rgb);        namedWindow("填充处理", 0);    imshow("填充处理", vec_rgb);    waitKey(1);        vector
     
       contours;    vector
      
        hierarchy;    hierarchy.push_back(Vec4i(0, 0, 0, 0));    findContours(vec_rgb, contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE, Point(0, 0));        vector
       
        
         > contours_poly; vector
         
           boundRect; vector
          
            center; vector
           
             radius; for(int i = 0; i < contours.size(); i++) { vector
            
              approxContour; approxPolyDP(Mat(contours[i]), approxContour, 3, true); contours_poly.push_back(approxContour); boundRect.push_back(boundingRect(Mat(approxContour))); minEnclosingCircle(approxContour, center[i], radius[i]); Point zhongdian = center[i]; cout << "玉米（或大米）颗粒坐标" << zhongdian.x << "," << zhongdian.y << endl; } Mat drawing; drawing = Mat::zeros(vec_rgb.size(), CV_8UC3); for(int i = 0; i < contours.size(); i++) { Scalar color = (0, 0, 255); drawContours(drawing, contours_poly[i], i, color, 1, 8, hierarchy, 0, Point()); rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0); circle(drawing, center[i], (int)radius[i], color, 2, 8, 0); } namedWindow("包围矩形和圆形", 0); imshow("包围矩形和圆形", drawing); waitKey(1); cout << "玉米（或大米）颗粒个数" << contours.size() << endl; clock_t end = clock(); cout << "运行时间：" << ((double)(end - start)) / CLOCKS_PER_SEC << endl; waitKey(); return 0;}
            
           
          
         
        
       
      
     
    
   

OpenCV4 代码实现

以下是基于OpenCV4的相应更新版本。

#include 
   
    #include 
    
     #define PI 3.1415926using namespace std;using namespace cv;void fillHole(const Mat& srcBw, Mat& dstBw) {    Size mSize = srcBw.size();    Mat Temp(mSize.height + 2, mSize.width + 2, srcBw.type());    srcBw.copyTo(Temp(Range(1, mSize.height + 1), Range(1, mSize.width + 1)));    floodFill(Temp, Point(0, 0), Scalar(255));    Mat cutImg = Temp(Range(1, mSize.height + 1), Range(1, mSize.width + 1)).copyTo(cutImg);    dstBw = srcBw | (~cutImg);}int main() {    Mat srcImg;    srcImg = imread("1.jpg");    clock_t start = clock();        namedWindow("原图", 0);    imshow("原图", srcImg);    waitKey(1);        cvtColor(srcImg, srcImage, COLOR_BGR2GRAY);    srcImage = srcImage > 160;        Mat vec_rgb;    vec_rgb = Mat::zeros(srcImage.size(), CV_8UC1);    medianBlur(srcImage, vec_rgb, 3);        namedWindow("中值滤波", 0);    imshow("中值滤波", vec_rgb);    waitKey(1);        Canny(vec_rgb, vec_rgb, 3, 9, 3);        namedWindow("边缘检测", 0);    imshow("边缘检测", vec_rgb);    waitKey(1);        Mat element = getStructuringElement(MORPH_ELLIPSE, Size(3, 3), Point(1, 1));    erode(vec_rgb, vec_rgb, element);        fillHole(vec_rgb, vec_rgb);        namedWindow("填充处理", 0);    imshow("填充处理", vec_rgb);    waitKey(1);        vector
     
       contours;    vector
      
        hierarchy;    hierarchy.push_back(Vec4i(0, 0, 0, 0));    hierarchy.push_back(Vec4i(0, 0, 0, 0));    findContours(vec_rgb, contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE, Point(0, 0));        vector
       
        
         > contours_poly; vector
         
           boundRect; vector
          
            center; vector
           
             radius; for(int i = 0; i < contours.size(); i++) { vector
            
              approxContour; approxPolyDP(Mat(contours[i]), approxContour, 3, true); contours_poly.push_back(approxContour); boundRect.push_back(boundingRect(Mat(approxContour))); minEnclosingCircle(approxContour, center[i], radius[i]); Point zhongdian = center[i]; cout << "玉米（或大米）颗粒坐标" << zhongdian.x << "," << zhongdian.y << endl; } Mat drawing; drawing = Mat::zeros(vec_rgb.size(), CV_8UC3); for(int i = 0; i < contours.size(); i++) { Scalar color = (0, 0, 255); drawContours(drawing, contours_poly[i], i, color, 1, 8, hierarchy, 0, Point()); rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0); circle(drawing, center[i], (int)radius[i], color, 2, 8, 0); } namedWindow("包围矩形和圆形", 0); imshow("包围矩形和圆形", drawing); waitKey(1); cout << "玉米（或大米）颗粒个数" << contours.size() << endl; clock_t end = clock(); cout << "运行时间：" << ((double)(end - start)) / CLOCKS_PER_SEC << endl; waitKey(); return 0;}
            
           
          
         
        
       
      
     
    
   

效果展示

以下为系统处理后的效果图示，展示了玉米颗粒在不同预处理和处理阶段的变化：

总结

通过上述技术处理，系统实现了对玉米颗粒的高效、准确计数，具备较强的鲁棒性和适应性。代码基于OpenCV框架，兼容两大版本，适用于不同开发环境。该系统在禾场作业中的应用效果显著，能够快速高效地完成玉米或其他谷物的颗粒计数任务。