目录

1、图像像素比较

1.1 比较函数

 1.2 图像最大值最小值寻找

2、图像像素逻辑操作

3、图像二值化

3.1 固定阈值二值化

3.2 自适应阈值二值化

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1、图像像素比较

1.1 比较函数

 1.2 图像最大值最小值寻找

 

	Mat img = imread("F:/testMap/bijiao.png");
	Mat white = imread("F:/testMap/white.png");
	Mat black = imread("F:/testMap/black.png");
	Mat Min, Max;
	min(img, white, Min);
	max(img, black, Max);

	Mat gray,gray_black;
	cvtColor(img,gray,COLOR_BGR2GRAY);
	cvtColor(black,gray_black,COLOR_BGR2GRAY);

	double minVal,maxVal;
	Point minLoc,maxLoc;
	minMaxLoc(gray,&minVal,&maxVal,&minLoc,&maxLoc,gray_black);

2、图像像素逻辑操作

 

 

 

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

using namespace std;
using namespace cv;

int main()
{
	Mat a = (Mat_<uchar>(1,2) << 0,5); 
	Mat b = (Mat_<uchar>(1,2) << 0,6);
	Mat Xor,Or,Not,And;
	bitwise_not(a,Not);
	cout << "a Not" << Not << endl;
	bitwise_and(a, b, And);
	bitwise_or(a, b, Or); bitwise_xor(a,b,Xor);
	cout << And << endl; cout << Or << endl; cout << Xor << endl;

	Mat img = imread("F:/testMap/bijiao.png");
	Mat mark = imread("F:/testMap/black.png");
	
	Mat result;
	bitwise_and(img, mark, result);

	Mat img_inv;
	//bitwise_not(img, img_inv);

	cvtColor(mark,mark,COLOR_BGR2GRAY); 
	bitwise_not(img,img_inv,mark);
	Mat mark_black = imread("F:/testMap/black.png");
	min(img,mark_black,img);
	img = img + img_inv;

	system("pause");
	return 0;
}

3、图像二值化

3.1 固定阈值二值化

 

 

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

using namespace std;
using namespace cv;

int main()
{
	Mat img = imread("F:/testMap/lena.png");
	if (img.empty()){
		cout << "请确认图像文件名称是否正确" << endl;
		return -1;
	}

	Mat gray;
	cvtColor(img, gray, COLOR_BGR2GRAY);
	Mat img_B, img_B_V, gray_B, gray_B_V, gray_T, gray_T_V, gray_TRUNC;

	//彩色图像二值化
	threshold(img, img_B, 125, 255, THRESH_BINARY);
	threshold(img, img_B_V, 125, 255, THRESH_BINARY_INV);

	//灰度图BINARY二值化
	threshold(gray,gray_B,125,255,THRESH_BINARY);
	threshold(gray,gray_B_V,125,255,THRESH_BINARY_INV); 

	//灰度图像TOZERO变换
	threshold(gray,gray_T,125,255,THRESH_TOZERO);
	threshold(gray,gray_T_V,125,255,THRESH_TOZERO_INV); 

	//灰度图像TRUNC变换
	threshold(gray,gray_TRUNC,125,255,THRESH_TRUNC); 

	//灰度图像大津法和三角形法二值化
	Mat img_Thr = imread("F:/testMap//threshold.jpg",IMREAD_GRAYSCALE); 
	Mat img_Thr_O, img_Thr_T;
	threshold(img_Thr,img_Thr_O,100,255,THRESH_BINARY | THRESH_OTSU);    //明暗渐变的图像得出的结果不理想
	threshold(img_Thr, img_Thr_T,125,255,THRESH_BINARY | THRESH_TRIANGLE); //明暗渐变的图像得出的结果不理想

	system("pause");
	return 0;
}

3.2 自适应阈值二值化

 

//自适应阈值二值化
Mat adaptive_mean,adaptive_gauss;
//均值法
adaptiveThreshold(img_Thr,adaptive_mean,255,ADAPTIVE_THRESH_MEAN_C,THRESH_BINARY,55,0);
//高斯法
adaptiveThreshold(img_Thr,adaptive_gauss,255,ADAPTIVE_THRESH_GAUSSIAN_C,THRESH_BINARY,55,0);