[人工智能]opencv图像操作——打开图像获得灰度图的反差图

本机环境为vs2017+opencv3.4.1。首先需要在vs中配置opencv，右击项目，点击属性，在VC++目录中的包含目录和库目录中添加如下信息：

包含目录中添加：(此处根据自己电脑中opencv的路径进行添加)

D:\opencv\build\include\opencv2
D:\opencv\build\include\opencv
D:\opencv\build\include

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库目录中添加：(此处根据自己电脑中opencv的路径进行添加)

D:\opencv\build\x64\vc15\lib

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在链接器的输入中添加：

opencv_world3410d.lib

进行图片的打开与显示，代码如下：

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

using namespace std;
using namespace cv;

int main(int arge, char**argv)
{
	Mat src, gray_src;
	src = imread("1.jpg");
	if (src.empty()) {
		cout << "找不到加载图片" << endl;
		return -1;
	}
	namedWindow("input", CV_WINDOW_AUTOSIZE);
	imshow("input", src);
	waitKey(0);
	return 0;

}

得到图片：

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获得单通道灰度图的反差图，代码如下：

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

using namespace std;
using namespace cv;

int main(int arge, char**argv)
{
	Mat src, gray_src;
	src = imread("1.jpg");
	if (src.empty()) {
		cout << "找不到加载图片" << endl;
		return -1;
	}
	namedWindow("input", CV_WINDOW_AUTOSIZE);
	imshow("input", src);

	
	cvtColor(src, gray_src, CV_BGR2GRAY);
	namedWindow("output", CV_WINDOW_AUTOSIZE);
	//imshow("output", gray_src);  //得到灰度图像

	int height = gray_src.rows;
	int width = gray_src.cols;
	
	//单通道
	for (int row = 0; row < height; row++) {
		for (int col = 0; col < width; col++) {
			int gray = gray_src.at<uchar>(row, col);  //获得灰度图像像素值
			gray_src.at<uchar>(row, col) = 255 - gray;   //灰度图像反差处理
		}
	}
	imshow("output", gray_src);
	
	waitKey(0);
	return 0;

}

效果如下：

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获得三通道图像的反差图：

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

using namespace std;
using namespace cv;

int main(int arge, char**argv)
{
	Mat src, gray_src;
	src = imread("1.jpg");
	if (src.empty()) {
		cout << "找不到加载图片" << endl;
		return -1;
	}
	namedWindow("input", CV_WINDOW_AUTOSIZE);
	imshow("input", src);

	
	cvtColor(src, gray_src, CV_BGR2GRAY);
	namedWindow("output", CV_WINDOW_AUTOSIZE);
	//imshow("output", gray_src);  //得到灰度图像

	int height = gray_src.rows;
	int width = gray_src.cols;
	
	//单通道
	for (int row = 0; row < height; row++) {
		for (int col = 0; col < width; col++) {
			int gray = gray_src.at<uchar>(row, col);  //获得灰度图像像素值
			gray_src.at<uchar>(row, col) = 255 - gray;   //灰度图像反差处理
		}
	}

	
	Mat dst;
	dst.create(src.size(), src.type());
	height = src.rows;
	width = src.cols;
	int nc = src.channels();
	
	for (int row = 0; row < height; row++) {
		for (int col = 0; col < width; col++) {
			if(nc==1){
				int gray = gray_src.at<uchar>(row, col);  //获得灰度图像像素值
				gray_src.at<uchar>(row, col) = 255 - gray;
			}
			else if (nc == 3) {   //彩色图像反差处理
				int b = src.at<Vec3b>(row, col)[0];
				int g = src.at<Vec3b>(row, col)[1];
				int r = src.at<Vec3b>(row, col)[2];
				
				dst.at<Vec3b>(row, col)[0] = 255 - b;
				dst.at<Vec3b>(row, col)[1] = 255 - g;
				dst.at<Vec3b>(row, col)[2] = 255 - r;

				//gray_src.at<uchar>(row, col) = min(r,min(b, g));
			}

		}
	}
	
	
	//bitwise_not(src, dst);   //彩色图像反差处理(快捷操作)

	imshow("output", dst);
	
	waitKey(0);
	return 0;

}

效果如下：

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