Opencv实现简单功能

1、利用数组中的数生成像素点。

std::vector<cv::Point> mypattern;
std::vector<cv::Point> mypattern2;
static int bit[10] 
={25,5,25,12,22,17,0,5,1,12};
const Point* pattern1 = (const Point*)bit;
std::copy(pattern1, pattern1 + 3, std::back_inserter(mypattern));

std::copy(pattern1+3, pattern1 + 5, std::back_inserter(mypattern2));

mypattern[0].x为25，mypattern[0].y为5。
mypattern2[0].x为0，mypattern2[0].y为5。

2、在图像上画网格

#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;

int main (){
   
   Mat image=imread("./1.png");
   int y=image.rows;
   int x=image.cols;
   int dx=x/20;//x轴间隔
   int dy=y/20;//y轴间隔
   for(int i=dx;i<x-1;i+=dx){//画竖线,第一个point是线的起始点，第二个是线的终点，2是线的粗细，LINE_8是线的形状
       line(image,Point(i,0),Point(i,y-1),Scalar(0,0,0),2,LINE_8);
    }
    for(int j=dy;j<y-1;j+=dy){//画横线
       line(image,Point(0,j),Point(x-1,j),Scalar(0,0,0),2,LINE_8);
    }
 
   imshow("image",image);
   waitKey(0);
   
    return 0;
}

3、创建高斯核
https://blog.csdn.net/u012633319/article/details/80921023
创建3*3，均方差为1.2的高斯核G

Mat kernelX = getGaussianKernel(3, 1.2);
cout << kernelX <<  endl;

Mat kernelY = getGaussianKernel(3, 1.2);
Mat G = kernelX * kernelY.t();
cout << G << endl;

2、二维高斯函数：

制作3*3高斯滤波器，把下图的坐标带入上式

得到：

这9个点的权重总和等于0.4787147，如果只计算这9个点的加权平均，还必须让它们的权重之和等于1，因此上面9个值还要分别除以0.4787147，得到最终的权重矩阵：

4、生成棋盘格

# -*- coding:utf-8 -*-
 
import cv2
import numpy as np
 
 
def generatePattern(CheckerboardSize, Nx_cor, Ny_cor):
    '''
    自定义生成棋盘
    :param CheckerboardSize: 棋盘格大小,此处100即可
    :param Nx_cor: 棋盘格横向内角数
    :param Ny_cor: 棋盘格纵向内角数
    :return:
    '''
    black = np.zeros((CheckerboardSize, CheckerboardSize, 3), np.uint8)
    white = np.zeros((CheckerboardSize, CheckerboardSize, 3), np.uint8)
    black[:] = [0, 0, 0]  # 纯黑色
    white[:] = [255, 255, 255]  # 纯白色
 
    black_white = np.concatenate([black, white], axis=1)
    black_white2 = black_white
    white_black = np.concatenate([white, black], axis=1)
    white_black2 = white_black
 
    # 横向连接
    if Nx_cor % 2 == 1:
        for i in range(1, (Nx_cor+1) // 2):
            black_white2 = np.concatenate([black_white2, black_white], axis=1)
            white_black2 = np.concatenate([white_black2, white_black], axis=1)
    else:
        for i in range(1, Nx_cor // 2):
            black_white2 = np.concatenate([black_white2, black_white], axis=1)
            white_black2 = np.concatenate([white_black2, white_black], axis=1)
        black_white2 = np.concatenate([black_white2, black], axis=1)
        white_black2 = np.concatenate([white_black2, white], axis=1)
 
    jj = 0
    black_white3 = black_white2
    for i in range(0, Ny_cor):
        jj += 1
        # 纵向连接
        if jj % 2 == 1:
            black_white3 = np.concatenate((black_white3, white_black2))  # =np.vstack((img1, img2))
        else:
            black_white3 = np.concatenate((black_white3, black_white2))  # =np.vstack((img1, img2))
 
    cv2.imshow('', black_white3)
    cv2.imwrite('pattern.jpg', black_white3)
    cv2.waitKey(5000)
    cv2.destroyAllWindows()
 
 
if __name__ == '__main__':
    generatePattern(100, 9, 6)

最后

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