opencv 模板匹配_OpenCV之图像模板匹配

python代码：import cv2 as cvimport numpy as npdef template_demo():src = cv.imread("./test.png")tpl = cv.imread("./test01.png")cv.imshow("input", src)cv.imshow("tpl", tpl)th, ...

weixin_39618806

313人浏览 · 2020-11-23 18:01:48

weixin_39618806 · 2020-11-23 18:01:48 发布

python代码：

import cv2 as cv
import numpy as np


def template_demo():
    src = cv.imread("./test.png")
    tpl = cv.imread("./test01.png")
    cv.imshow("input", src)
    cv.imshow("tpl", tpl)
    th, tw = tpl.shape[:2]
    result = cv.matchTemplate(src, tpl, cv.TM_CCORR_NORMED)
    cv.imshow("result", result)
    cv.imwrite("D:/039_003.png", np.uint8(result*255))
    t = 0.98
    loc = np.where(result > t)

    for pt in zip(*loc[::-1]):
        cv.rectangle(src, pt, (pt[0] + tw, pt[1] + th), (255, 0, 0), 1, 8, 0)
    cv.imshow("llk-demo", src)
    cv.imwrite("D:/039_004.png", src)


template_demo()
cv.waitKey(0)
cv.destroyAllWindows()

C++代码：

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

using namespace cv;
using namespace std;
const float t = 0.95;
int main(int artc, char** argv) {
	Mat src = imread("./test01.png");
	Mat tpl = imread("./test.png");
	if (src.empty() || tpl.empty()) {
		printf("could not load image...n");
		return -1;
	}
	imshow("input", src);
	imshow("tpl", tpl);

	int result_h = src.rows - tpl.rows + 1;
	int result_w = src.cols - tpl.cols + 1;
	Mat result = Mat::zeros(Size(result_w, result_h), CV_32FC1);
	matchTemplate(src, tpl, result, TM_CCOEFF_NORMED);
	imshow("result image", result);
	int h = result.rows;
	int w = result.cols;
	for (int row = 0; row < h; row++) {
		for (int col = 0; col < w; col++) {
			float v = result.at<float>(row, col);
			// printf("v = %.2fn", v);
			if (v > t) {
				rectangle(src, Point(col, row), Point(col + tpl.cols, row + tpl.rows), Scalar(255, 0, 0), 1, 8, 0);
			}
		}
	}
	imshow("template matched result", src);

	waitKey(0);
	return 0;
}

模板匹配被称为最简单的模式识别方法、同时也被很多人认为是最没有用的模式识别方法。这里里面有很大的误区，就是模板匹配是工作条件限制比较严格，只有满足理论设置的条件以后，模板匹配才会比较好的开始工作，而且它不是基于特征的匹配，所以有很多弊端，但是不妨碍它成为入门级别模式识别的方法，通过它可以学习到很多相关的原理性内容，为后续学习打下良好的基础。