Note: This tutorial is a record of teacher Jia Zhigang’s opencv course study. I would like to express my gratitude to teacher Jia.
Requirements: Calculate the perimeter and area of the cells in the picture
Idea: Through binary segmentation + image morphology + contour extraction
Code:
#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>
using namespace cv;
using namespace std;
int main(int argc, char** argv) {
Mat src = imread("/home/fuhong/code/cpp/opencv_learning/src/small_case/imgs/case6.png");
if (src.empty()) {
printf("could not load image...\n");
return -1;
}
namedWindow("input image", CV_WINDOW_AUTOSIZE);
imshow("input image", src);
Mat blurImage;
GaussianBlur(src, blurImage, Size(15, 15), 0, 0);
imshow("blur", blurImage);
Mat gray_src, binary;
cvtColor(blurImage, gray_src, COLOR_BGR2GRAY);
threshold(gray_src, binary, 0, 255, THRESH_BINARY | THRESH_TRIANGLE);
imshow("binary", binary);
Mat morphImage;
Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
morphologyEx(binary, morphImage, MORPH_CLOSE, kernel, Point(-1, -1), 2);
imshow("morphology", morphImage);
vector<vector<Point>> contours;
vector<Vec4i> hireachy;
findContours(morphImage, contours, hireachy, CV_RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point());
Mat connImage = Mat::zeros(src.size(), CV_8UC3);
for (size_t t = 0; t < contours.size(); t++) {
Rect rect = boundingRect(contours[t]);
if (rect.width < src.cols / 2) continue;
if (rect.width > (src.cols - 20)) continue;
double area = contourArea(contours[t]);
double len = arcLength(contours[t], true);
drawContours(connImage, contours, static_cast<int>(t), Scalar(0, 0, 255), 1, 8, hireachy);
printf("area of star could : %f\n", area);
printf("length of star could : %f\n", len);
}
imshow("result", connImage);
waitKey(0);
return 0;
}
The results are as follows:
