C++ opencv图像处理实现灰度变换示例

#include<iostream>
#include<opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
	Mat img, img2;
	img = imread("猫1.jpg");
	imshow("原图", img);
	img2.create(img.size(), 0);
	for (int i = 0; i < img.rows; i++)
	{
		for (int j = 0; j < img.cols; j++)
		{
			img2.at<uchar>(i, j) = saturate_cast<uchar>(0.114*img.at<Vec3b>(i, j)[0] + 0.587*img.at<Vec3b>(i, j)[1] + 0.299*img.at<Vec3b>(i, j)[2]);
		}
	}
	imshow("经验公式", img2);
	waitKey(0);
}

int main()
{
	Mat img, img2;
	img = imread("猫1.jpg");
	imshow("原图", img);
	img2.create(img.size(), 0);
	for (int i = 0; i < img.rows; i++)
	{
		for (int j = 0; j < img.cols; j++)
		{
			int max = img.at<Vec3b>(i, j)[0];
			for (int x = 0; x < 3; x++)
			{
				if (max < img.at<Vec3b>(i, j)[x])
				{
					max = img.at<Vec3b>(i, j)[x];
				}
			}
			img2.at<uchar>(i, j) = saturate_cast<uchar>(max);
		}
	}
	imshow("最大值", img2);
	waitKey(0);
}

int main()
{
	Mat img, img2;
	img = imread("猫1.jpg");
	imshow("原图", img);
	img2.create(img.size(), 0);
	for (int i = 0; i < img.rows; i++)
	{
		for (int j = 0; j < img.cols; j++)
		{
			img2.at<uchar>(i, j) = saturate_cast<uchar>((img.at<Vec3b>(i, j)[0] + img.at<Vec3b>(i, j)[1] + img.at<Vec3b>(i, j)[2])/3);
		}
	}
	imshow("平均值", img2);
	waitKey(0);
}

int main()
{
	Mat img1, img2;
	img1 = imread("猫1.jpg", 1);
	imshow("原图", img1);
	img2 = Mat::zeros(img1.size(), 0);
	for (int i = 0; i < img1.rows; i++)
	{
		for (int j = 0; j < img1.cols; j++)
		{
			for (int s = 0; s < 3; s++)
			{
				img2.at<uchar>(i, j) = saturate_cast<uchar>(1.1*img1.at<Vec3b>(i, j)[s] + 20);
			}
		}
	}
	imshow("线性", img2);
	waitKey(0);
}

int main()
{
	Mat img1, img2;
	img1 = imread("猫1.jpg", 0);
	imshow("原图", img1);
	img2 = Mat::zeros(img1.size(), 0);
	for (int i = 0; i < img1.rows; i++)
	{
		for (int j = 0; j < img1.cols; j++)
		{
			uchar temp = img1.at<uchar>(i, j);
			if (temp <=70)
			{
				img2.at<uchar>(i, j) = saturate_cast<uchar>(0.5*temp + 20);
			}
			else if (temp > 70 && temp <= 150)
			{
				img2.at<uchar>(i, j) = saturate_cast<uchar>(1.2*temp + 100);
			}
			else if (temp > 150 && temp <= 255)
			{
				img2.at<uchar>(i, j) = saturate_cast<uchar>(0.9*temp + 55);
			}
		}
	}
	imshow("分段线性", img2);
	waitKey(0);
}

int main()
{
	double c = 1.2;
	Mat img1, img2, img3;
	img1 = imread("猫1.jpg",0);
	img3 = Mat::ones(img1.size(), CV_32FC3);
	add(img1, Scalar(1.0), img1);
	img1.convertTo(img1, CV_32F);
	log(img1, img3);
	img3 = c*img3;
	normalize(img3, img3, 0, 255, NORM_MINMAX);//归一化到0-255 NORM_MINMAX 线性归一化
	convertScaleAbs(img3, img3);//转换成8bit通道显示
	imshow("对数变换", img3);
	waitKey(0);
}

int main()
{
	Mat img1, img2;
	img1 = imread("猫1.jpg",0);
	img2.create(img1.size(), img1.type());
	for (int i = 0; i < img1.rows; i++)
	{
		for (int j = 0; j < img1.cols; j++)
		{
			int gray = img1.at<uchar>(i, j);
			img2.at<uchar>(i, j) = saturate_cast<uchar>(pow(gray,0.5));
		}
	}
	normalize(img2, img2, 0, 255, NORM_MINMAX);
	imshow("幂律变换", img2);
	waitKey(0);
}