OpenCV4.8 开发实战系列专栏之 48 二值图像分析-轮廓发现

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专栏代码全部基于C++ 与Python双语演示。
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本文关键知识点：二值图像分析-轮廓发现

演示代码

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main(int argc, const char *argv[])
{
	Mat src = imread("D:/images/coins.jpg");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}
	namedWindow("input", WINDOW_AUTOSIZE);
	imshow("input", src);

	// 去噪声与二值化
	Mat dst, gray, binary;
	GaussianBlur(src, dst, Size(3, 3), 0, 0);
	cvtColor(dst, gray, COLOR_BGR2GRAY);
	threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU);
	imshow("binary", binary);
	imwrite("D:/binary.png", binary);

	// 轮廓发现与绘制
	vector<vector<Point>> contours;
	vector<Vec4i> hierarchy;
	findContours(binary, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point());
	for (size_t t = 0; t < contours.size(); t++) {
		drawContours(src, contours, t, Scalar(0, 0, 255), 2, 8);
	}
	imshow("contours", src);

	waitKey(0);
	return 0;
}

python 代码演示

import cv2 as cv
import numpy as np

def threshold_demo(image):
    # 去噪声+二值化
    dst = cv.GaussianBlur(image,(3, 3), 0)
    gray = cv.cvtColor(dst, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_OTSU | cv.THRESH_BINARY)
    cv.imshow("binary", binary)
    return binary


def canny_demo(image):
    t = 100
    canny_output = cv.Canny(image, t, t * 2)
    cv.imshow("canny_output", canny_output)
    return canny_output


src = cv.imread("D:/images/yuan_test.png")
cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
cv.imshow("input", src)
binary = threshold_demo(src)

# 轮廓发现
out, contours, hierarchy = cv.findContours(binary, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
for c in range(len(contours)):
    cv.drawContours(src, contours, c, (0, 0, 255), 2, 8)

# 显示
cv.imshow("contours-demo", src)

cv.waitKey(0)
cv.destroyAllWindows()

结束语

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