Basic image processing in opencv

基本图像操作

方法	描述	示例
rect	Set the rectangle frame	Rect rect(100, 100, 200, 200); Mat roi = src(rect);//提取srcThe data marked by the rectangle in the data,此时roi指向了srcThe rectangle data corresponding to the picture,是指针！
cvtColor	图片类型转换	cvtColor(src, gray, COLOR_BGR2GRAY); //BGR转灰度图
threshold	二进制阈值化	threshold(gray, bin, 100, 255, THRESH_BINARY); //大于100全部设为255：白色; 小于100的设为0：黑色; threshold(gray,ibin, 100, 255, THRESH_BINARY_INV);//反二进制阈值化
resize	图片尺寸调整	resize(src, des1024, Size(1024, 1024), 0, 0, INTER_LINEAR);
pyrDown	高斯金字塔,下采样	pyrDown(src, gsrc);高斯金字塔(Gaussian pyramid):用来向下采样–下采样：The image is scaled down exponentially
pyrUp	拉普拉斯金字塔,上采样	pyrUp(src, lsrc);//拉普拉斯金字塔(Laplacian pyramid):用来从金字塔低层图像重建上层未采样图像 — 上采样：The image is multiplied
addWeighted	图像混合	addWeighted(img1, a, img2, 1 - a, 80, dst); // addWeighted：加权; //a:img1的权重,取值范围[0,1]; dst:生成图像
rotate	图像旋转	cv::rotate(img, rot, ROTATE_90_CLOCKWISE); // cv::rotate cv命名空间下的rotate函数,So functions are too easily conflicted by function names in other libraries.
flip	图像镜像	cv::flip(img, fl, 1); // type:0（围绕x轴镜像）,1（围绕y轴镜像）,-1（围绕x和y轴做镜像）

ROI区域

#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main(int argc, char *argv[])
{
    
	Mat src = imread("1.png");
	Rect rect(100, 100, 200, 200);
	Mat roi = src(rect);  //浅拷贝,Only the matrix header is assigned

	namedWindow("src");
	namedWindow("roi");
	moveWindow("roi", 512, 0);  //The position of the image display window is moved
	imshow("src", src);
	imshow("roi", roi);
	waitKey(0);
	return 0;
}

//图像ROI提取 、 放回
Mat binNowX = matFenge(rectBinX).clone();  //获取对应的数据
binNowX.copyTo(BinaryTemplete(rectBinX));  //Local pixel copy.原理是将（Mat *）binNowX拷贝到BinaryTemplete的rectBinX区域.

灰度图转换

void RGBToGray(Mat &src, Mat &des)
{
    
	// GRay = (R*30 + G*59 + B*11 +50)/100
	des.create(src.rows,src.cols,CV_8UC1);
	for (int r = 0; r < src.rows; r++)
	{
    
		for (int c = 0; c < src.cols; c++)
		{
    
			Vec3b &m = src.at<Vec3b>(r, c);    //3位的向量, Vec3b： BGR
			int gray = (m[2] * 30 + m[1] * 59 + m[0] * 11 + 50) / 100;   //Mat:BGR
			des.at<uchar>(r, c) = gray;
		}
	}
}
int main(int argc, char *argv[])
{
    
	Mat src = imread("1.png");
	src.create(3000, 4000, CV_8UC3);   //重新创建src内容, imread内容被删除了;In order to reflect the advantages of multi-threaded conversion,A large image is used.
	Mat gray;
	PrintMs("");
	cvtColor(src, gray, COLOR_BGR2GRAY);  //采用了多线程,But the first time due to starting threads etc,耗时较久.424ms
	PrintMs("cvtColor1");

	cvtColor(src, gray, COLOR_BGR2GRAY);  //The second conversion is the advantage of multithreading,Conversion is very fast.17ms
	PrintMs("cvtColor2");
	Mat mygray;
	RGBToGray(src, mygray);    //自己转换,单个函数,耗时77ms
	PrintMs("RGBToGray");

	namedWindow("src");
	namedWindow("gray");
	namedWindow("mygray");

	imshow("src", src);
	imshow("gray", gray);
	imshow("mygray", mygray);

	waitKey(0);
	return 0;
}

Image gain design

//
///@para a float 对比度 1.0~3.0
///@para b int 亮度 0~100
void ChangeGain(Mat &src, Mat &des, float a, int b)
{
    
	//g(r,c) = a*f(r,c) + b
	des.create(src.rows, src.cols, src.type());
	for (int r = 0; r < src.rows; r++)
	{
    
		for (int c = 0; c < src.cols; c++)
		{
    
			for (int i = 0; i < 3; i++)
			{
    
				des.at<Vec3b>(r, c)[i] = saturate_cast<uchar>(a * src.at<Vec3b>(r, c)[i] + b);   //saturate_cast<uchar>：防止溢出;超过uchar的值255就设定为255
			}
		}
	}
}
int main(int argc, char *argv[])
{
    
	//调整对比度和亮度 ; gain：增益 
	Mat src = imread("2.jpg");
	Mat des;
	PrintMs("");
	ChangeGain(src,des, 2.0,50);  //对比度：2.0; 亮度：+50;
	PrintMs("ChangeGain");
	Mat des2;
	src.convertTo(des2, -1, 2.0, 50);  //opencv提供的函数,-1：Negative numbers represent the same type as the original image; Performance is better than writing it yourself Function performance is high.
	PrintMs("convertTo");

	namedWindow("src");
	namedWindow("des");
	namedWindow("des2");
	imshow("src", src);
	imshow("des", des);
	imshow("des2", des2);
	waitKey(0);
	return 0;
}

图像尺寸调整

void xresize(Mat &src, Mat &des, Size size)  // Size：是cv命名空间的Size,Types that are prone to conflict.
{
    
	des.create(size, src.type());
	//The original image coordinates of the map
	int sx, sy = 0;
	float fx = (float)src.cols / des.cols;   // Original image columns(宽度)/The number of target graph columns 
	float fy = (float)src.rows / des.rows;  // The number of lines in the original image(高度)/The number of rows in the target graph
	for (int x = 0; x < des.cols; x++)
	{
    
		sx = fx * x + 0.5;   // int类型,四舍五入
		for (int y = 0; y <des.rows; y++)
		{
    
			sy = fy * y + 0.5;
			des.at<Vec3b>(y, x) = src.at<Vec3b>(sy, sx);  // The content value of the target coordinate is equal to the content value of the corresponding coordinate of the original image
		}
	}
}
int main(int argc, char *argv[])
{
    
	//Image resizing algorithm,Implement the nearest neighbor algorithm manually;放大/缩小
	Mat src = imread("1.png"); //512*512 256 1024
	Mat img256,img1024,des256,des1024;
	resize(src, des256, Size(256, 256), 0, 0, INTER_NEAREST);

	PrintMs();
	//xresize(src, img256, Size(256, 256));
	//PrintMs("img256");
	xresize(src, img1024, Size(1024, 1024));
	//resize(src, img1024, Size(4024, 4024), 0, 0, INTER_NEAREST);
	PrintMs("img1024");
	//resize(src, des256, Size(1024, 1024), 0, 0, INTER_NEAREST); // 近邻算法：INTER_NEAREST , Simple copy of adjacent value：Mosaics are prone to appear in the corners when zoomed in.The original one pixel is copied into a large range of pixels;There will be a loss of content if you shrink it.
	resize(src, des1024, Size(1024, 1024), 0, 0, INTER_LINEAR);   //Bilinear interpolation algorithm（The algorithm used by default）,不是简单的拷贝,Instead, it does an adjacent operation;The output pixel is a weighted sum of the input neighborhood pixels.
	//The source image position is near it2*2区域4The values ​​of adjacent pixels are calculated by weighted average.Low-pass filtering properties,使高频分量受损,Image outlines may be blurred a bit.

	PrintMs("des1024"); 

	namedWindow("src");
	//namedWindow("img256");
	namedWindow("des1024");
	namedWindow("img1024");
	imshow("src", src);
	imshow("img1024", img1024);
	imshow("des1024", des1024);
	moveWindow("des1024", 512, 0);  //The position of the image display window is moved

	//imshow("img1024", img1024);
	waitKey(0);
	return 0;
}

图像混合,设定透明度

int main(int argc, char *argv[])
{
    
	Mat img1 = imread("1.png");
	Mat img2 = imread("2.png");
	resize(img2, img2, img1.size());  //！！！The images must be the same size;
	Mat dst;
	float a = 0.8;
	addWeighted(img1, a, img2, 1 - a, 80, dst);   // addWeighted：加权; //a:img1的权重,取值范围[0,1]; dst:生成图像

	namedWindow("blending");
	imshow("blending", dst);
	waitKey(0);
	return 0;
}

图像合并,并列显示

/************************************************************************ 注意： Mat r1 = des(Rect(0, 0, width1, height)); //1号图,！！！： des(Rect())This is not newly createdMat r1数据,而是将r1This pointer object points todes这个图,对desmake direct changes. **********************************************************************/
#include <opencv2/imgproc.hpp>
int main(int argc, char *argv[])
{
    
	Mat img1 = imread("1.png");
	Mat img2 = imread("2.png");
	int height = img1.rows;  //Set the height of the two images to be uniform, The width is not uniform
	int width1 = img1.cols;
	int width2 = img2.cols;
	// Scale taller images to match the height of lower images
	if (img1.rows > img2.rows)
	{
    
		height = img2.rows;
		width1 = img1.cols * ((float)img2.rows / (float)img1.rows);  //等比缩放,高度一致,The width is proportionally changed.
		resize(img1, img1, Size(width1, height));
	}
	else if(img1.rows < img2.rows)
	{
    
		width2 = img2.cols * ((float)img1.rows / (float)img2.rows);
		resize(img2, img2, Size(width2, height));
	}
	//创建目标Mat
	Mat des;
	des.create(height, width1 + width2, img1.type());  //创建, 谨记！！！The multiplexing function,At this point the first parameter is high,第二个是宽
	Mat r1 = des(Rect(0, 0, width1, height));  //1号图,！！！： des(Rect())This is not newly createdMat r1数据,而是将r1This pointer object points todes这个图,对desmake direct changes.
	img1.copyTo(r1);  // 将img1拷贝到r1区域,r1The area pointer points todes,So it was finally copieddesin the defined data variable.
	Mat r2 = des(Rect(width1, 0, width2, height));
	img2.copyTo(r2);

	namedWindow("des");
	imshow("des", des);
	waitKey(0);
	destroyAllWindows();
	return 0;
}