计算机视觉（1）——卷积与opencv

这篇文章中我介绍了我初学opencv的知识与体会
文章开头，先放一张图!
[Canny,Sobel](https://img-blog.csdn.net/20161122090108624)

**详细代码：**

一、摄像机与视频的读取

VideoCapture cap(0);//有数字读摄像头，路径读视频
while (true)
{
Mat frame;
cap >> frame;//将读取的每一帧图片放入frame
namedWindow("123", 0);//0可改变大小，1不//创建窗口
imshow("123", frame);//在123窗口上输出图像
waitKey(30);//30代表30ms，用于增强视频的连贯性，0代表无限延迟。常用30到60
}

二、读取图片 与其中的像素值

//灰度图0到255，彩图比灰度图多三倍
Mat p1;
p1 = imread("1.jpg", 1);//1彩图，0灰度图
cvtColor(p1, p1, CV_RGB2GRAY);//颜色转换，cvtColor("输入素材Mat","输出素材Mat","颜色空间转换参数")
imshow("123", p1);
waitKey(0);

三、Mat对象一些操作

Mat image = Mat(5, 5, CV_64FC1);//5*5静态二维数组
Mat image = Mat::zeros(5, 5, CV_64FC1);//zeros,eye,ones//0,1,单位矩阵
cout << image << endl;
Mat image1 = Mat::ones(5,5, CV_64FC1);
cout << image1 << endl;
Mat image2 = image +image1;//+，-，*亦可(大小一样)
cout << image2 << endl;
cout << image.at<double>(0, 0) << endl;

image.copyTo.t;//t表示转置

四、图像x方向求导的卷积与非卷积操作

VideoCapture cop1(0);
while (true)
{
Mat frame;
cop1 >> frame;
cvtColor(frame, frame, CV_RGB2GRAY);
cout << "row" << frame.rows << "col" << frame.cols << endl;

/*对矩阵求导，公式如下：
Dx(x,y)=f(x+1,y)-f(x-1,y);
Dy(x,y)=f(x,y+1)-f(x,y-1);
*/
Mat dimg = Mat(frame.rows, frame.cols - 2, CV_8UC1);
for (int i = 0; i < fream.rows; i++)
{
for (int j = 1; j < fream.cols - 1; j++)
{
dimg.at<uchar>(i, j-1) = fream.at<uchar>(i, j - 1) - fream.at<uchar>(i, j + 1);
}
}
Mat dimg = Mat(frame.rows, frame.cols - 2, CV_8UC1);
Mat model = Mat(1, 3, CV_64FC1);//定义一个卷积模板

//初始化模板
model.at<double>(0, 0) = 1;
model.at<double>(0, 1) = 0;
model.at<double>(0, 2) = -1;

//卷积操作
for (int i = 0; i<frame.rows; i++)
{
for (int j = 1; j<frame.cols - 1; j++)
{
int half = model.cols / 2;
double sum = 0;
for (int m = 0; m<model.rows; m++)
{
for (int n = -half; n<model.cols - half; n++)
{
sum += (double)(frame.at<uchar>(i + m, j + n))*model.at<double>(m, n + half);//(double)为强制转化成double类型
}
}
dimg.at<uchar>(i, j - 1) = (uchar)sum;
}

}

imshow("【灰度图】", frame);
imshow("【求导后图】", dimg);

waitKey(30);
}

五、高斯模糊的核创建与卷积操作

//高斯卷积
double sigma = 50;
Mat gauss(5, 5, CV_64FC1);
for (int i = -2; i<3; i++)
{
for (int j = -2; j<3; j++)
{
gauss.at<double>(i + 2, j + 2) = exp(-(i*i + j*j) / (2 * sigma*sigma));//正态分布公式
}
}

double gssum = sum(gauss).val[0];//求和
//归一化（平均）
for (int i = -2; i<3; i++)
{
for (int j = -2; j<3; j++)
{
gauss.at<double>(i + 2, j + 2) /= gssum;
}
}

cout<<gauss<<endl;

VideoCapture cap2(0);

while (true)
{

Mat frame;
cap2 >> frame;
cvtColor(frame, frame, CV_RGB2GRAY);
Mat dimg = Mat(frame.rows - 4, frame.cols - 4, CV_8UC1);
//卷积操作
for (int i = 2; i<frame.rows - 2; i++)
{
for (int j = 2; j<frame.cols - 2; j++)
{
double sum = 0;

for (int m = 0; m<gauss.rows; m++)
{
for (int n = 0; n<gauss.cols; n++)
{
sum += (double)(frame.at<uchar>(i + m - 2, j + n - 2))*gauss.at<double>(m, n);
}
}
dimg.at<uchar>(i - 2, j - 2) = (uchar)sum;

}
}

imshow("【原图】", frame);
imshow("gauss", dimg);
waitKey(10);

}

六、相关API操作

VideoCapture cop3(0);
while (true)
{
Mat frame;
cop3 >> frame;
cvtColor(frame, frame, CV_RGB2GRAY);
imshow("【灰度图】", frame);
GaussianBlur(frame, frame, Size(5, 5), 0, 0);//高斯模糊
imshow("GaussianBlur", frame);
Canny(frame, frame, 100, 100);//Canny边缘检验算子
imshow("Canny", frame);
Sobel(frame, frame,0, 1, 1);//Sobl边缘检验算子
imshow("Sobel", frame);
waitKey(30);
}

[想看源码戳这里，不过你会失望的](http://download.csdn.net/detail/typedef_dc/9689477)