1.Threshold

#include <opencv2\opencv.hpp>
#include <iostream>
#include <vector>
using namespace std;
using namespace cv;
int main()
{
    Mat img = imread("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);
    imshow("img_B", img_B);
    imshow("img_B_V", img_B_V);
    //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);
    imshow("gray_B", gray_B);
    imshow("gray_B_V", gray_B_V);
    //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);
    imshow("gray_T", gray_T);
    imshow("gray_T_V", gray_T_V);
    //灰度图像TRUNC变换
    threshold(gray, gray_TRUNC, 125, 255, THRESH_TRUNC);
    imshow("gray_TRUNC", gray_TRUNC);
    //灰度图像大津法和三角形法二值化
    Mat img_Thr = imread("threshold.png", IMREAD_GRAYSCALE);
    Mat img_Thr_O, img_Thr_T;
    threshold(img_Thr, img_Thr_O, 100, 255, THRESH_BINARY | THRESH_OTSU);
    threshold(img_Thr, img_Thr_T, 125, 255, THRESH_BINARY | THRESH_TRIANGLE);
    imshow("img_Thr", img_Thr);
    imshow("img_Thr_O", img_Thr_O);
    imshow("img_Thr_T", img_Thr_T);
    //灰度图像自适应二值化
    Mat adaptive_mean, adaptive_gauss;
    adaptiveThreshold(img_Thr, adaptive_mean, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 55, 0);
    adaptiveThreshold(img_Thr, adaptive_gauss, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 55, 0);
    imshow("adaptive_mean", adaptive_mean);
    imshow("adaptive_gauss", adaptive_gauss);
    waitKey(0);
    return 0;
}

2.LUT

#include <opencv2\opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main()
{
    //LUT查找表第一层
    uchar lutFirst[256];
    for (int i = 0; i<256; i++)
    {
        if (i <= 100)
            lutFirst[i] = 0;
        if (i > 100 && i <= 200)
            lutFirst[i] = 100;
        if (i > 200)
            lutFirst[i] = 255;
    }
    Mat lutOne(1, 256, CV_8UC1, lutFirst);
    //LUT查找表第二层
    uchar lutSecond[256];
    for (int i = 0; i<256; i++)
    {
        if (i <= 100)
            lutSecond[i] = 0;
        if (i > 100 && i <= 150)
            lutSecond[i] = 100;
        if (i > 150 && i <= 200)
            lutSecond[i] = 150;
        if (i > 200)
            lutSecond[i] = 255;
    }
    Mat lutTwo(1, 256, CV_8UC1, lutSecond);
    //LUT查找表第三层
    uchar lutThird[256];
    for (int i = 0; i<256; i++)
    {
        if (i <= 100)
            lutThird[i] = 100;
        if (i > 100 && i <= 200)
            lutThird[i] = 200;
        if (i > 200)
            lutThird[i] = 255;
    }
    Mat lutThree(1, 256, CV_8UC1, lutThird);
    //拥有三通道的LUT查找表矩阵
    vector<Mat> mergeMats;
    mergeMats.push_back(lutOne);
    mergeMats.push_back(lutTwo);
    mergeMats.push_back(lutThree);
    Mat LutTree;
    merge(mergeMats, LutTree);
    //计算图像的查找表
    Mat img = imread("lena.png");
    if (img.empty())
    {
        cout << "请确认图像文件名称是否正确" << endl;
        return -1;
    }
    Mat gray, out0, out1, out2;
    cvtColor(img, gray, COLOR_BGR2GRAY);
    LUT(gray, lutOne, out0);
    LUT(img, lutOne, out1);
    LUT(img, LutTree, out2);
    imshow("out0", out0);
    imshow("out1", out1);
    imshow("out2", out2);
    waitKey(0);
    return 0;
}