opencv学习

Visual Studio 配置 OpenCv
我配置过程当中debug出问题按这位博主的方法解决的

加载图片

Example2-2

代码以下

#include<opencv2/opencv.hpp>
int main(int argc, char** argv) {
    cv::Mat img = cv::imread(argv[1], -1);
    if (img.empty()) {
        return -1;
    }
    cv::namedWindow("Example1", cv::WINDOW_AUTOSIZE);
    cv::imshow("Example1", img);
    cv::waitKey(0);
    cv::destroyWindow("Example1");
    return 0;
}

在 cv 命名空间下，等价于如下写法

#include<opencv2/opencv.hpp>
using namespace cv;
int main(int argc, char** argv) {
    Mat img = imread(argv[1], -1);
    if (img.empty()) {
        return -1;
    }
    namedWindow("Example1", WINDOW_AUTOSIZE);
    imshow("Example1", img);
    waitKey(0);
    destroyWindow("Example1");
    return 0;
}

而后就能够运行了，上面贴出的网页博主只设置了debug，由于个人代码是按照learning OpenCV3中来的，里面加载图片是在命令框中进行的，若是是在window下，还要再设置一下release，和配debug过程同样的。

找到有exe文件的对应文件夹，执行

如今我再去Ubuntu里试一遍,顺便使用一下吃灰好久的cmake
先建立一个.cpp文件,个人叫openpic
而后再写一个CMakeLists.txt
内容以下

cmake_minimum_required (VERSION 3.5.1)
project(openpic)
find_package( OpenCV REQUIRED )
add_executable(openpic openpic.cpp)
target_link_libraries( openpic ${OpenCV_LIBS} )

而后再命令框中依次执行

cmake .

make

加载视频

Example2-3

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
int main( int argc, char** argv ) {
cv::namedWindow( "Example3", cv::WINDOW_AUTOSIZE );
cv::VideoCapture cap;
cap.open( std::string(argv[1]) );
//string(argv[1])视频路径+名称
cv::Mat frame;
for(;;) {
cap >> frame;
//逐帧读取，直到读完全部帧
if( frame.empty() ) break; // Ran out of film
cv::imshow( "Example3", frame );
if( cv::waitKey(33) >= 0 ) break;//每帧展现33毫秒
}
return 0;
}

CMakeCache.txt

cmake_minimum_required (VERSION 3.5.1)
project(openvideo)
find_package( OpenCV REQUIRED )
add_executable(openvideo openvideo.cpp)
target_link_libraries( openvideo ${OpenCV_LIBS} )

cmake .

make

./openvideo red.avi

给视频加操做

• 加一个进度条slider trackbar
• 按S 单步调试-暂停
• 按R 开始
• 每当咱们进行进度调整操做时，执行S(暂停)

Example2-4

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <fstream>
using namespace std;

//有习惯使用g_表示全局变量
int g_slider_position = 0;
int g_run = 1;
//只要其不为零就显示新的帧
int g_dontset = 0; //start out in single step mode

cv::VideoCapture g_cap;

void onTrackbarSlide(int pos, void *) {
    g_cap.set(cv::CAP_PROP_POS_FRAMES, pos);
    if (!g_dontset){//被用户进行了操做
        g_run = 1;
        }
    g_dontset = 0;
}
int main(int argc, char** argv) {
    cv::namedWindow("Example2_4", cv::WINDOW_AUTOSIZE);
    g_cap.open(string(argv[1]));
    int frames = (int)g_cap.get(cv::CAP_PROP_FRAME_COUNT);//第几帧
    int tmpw = (int)g_cap.get(cv::CAP_PROP_FRAME_WIDTH);//长
    int tmph = (int)g_cap.get(cv::CAP_PROP_FRAME_HEIGHT);//高
    cout << "Video has " << frames << " frames of dimensions("
        << tmpw << ", " << tmph << ")." << endl;
        //calibrate the slider trackbar(校准滑块)
    cv::createTrackbar("Position"//进度条标签
    , "Example2_4"//放这个标签窗口的名称
    , &g_slider_position//一个和滑块绑定的变量
    , frames//进度最大值(视频总帧数)
    ,onTrackbarSlide//回调?(若是不想要,可设置为0)
    );
    cv::Mat frame;
    for (;;) {
        if (g_run != 0) {
            g_cap >> frame; if (frame.empty()) break;
            int current_pos = (int)g_cap.get(cv::CAP_PROP_POS_FRAMES);
            g_dontset = 1;
            cv::setTrackbarPos("Position", "Example2_4", current_pos);
            cv::imshow("Example2_4", frame);
            g_run -= 1;
        }
        char c = (char)cv::waitKey(10);
        if (c == 's') // single step
        {
            g_run = 1; cout << "Single step, run = " << g_run << endl;
        }
        if (c == 'r') // run mode
        {
            g_run = -1; cout << "Run mode, run = " << g_run << endl;
        }
        if (c == 27)
            break;
    }
    return(0);
}

增长一个全局变量表示进度条的位置
增长一个信号来更新这个变量并定位到须要的位置
当用进度条调整进度时,即设置g_run =1
功能需求:随着视频向前(as the video advances),进度条的位置也向前
咱们使用一个函数来避免(triggering single-step mode)触发单步模式
(callback routine)回调例程
void onTrackbarSlide(int pos, void *)
(passed a 32-bit integer)传递一个32位的整数pos

简单转换 平滑处理

Many basic vision tasks involve the application of filters to a video stream
不少视频将滤镜应用于视频流
稍后咱们将将视频的每帧都加以处理
smoothing an image 平滑处理

Example2-5

经过如高斯形式的卷积 或 相似函数 来减小图片中的信息

#include <opencv2/opencv.hpp>
int main(int argc, char** argv) {

     cv::Mat image = cv::imread(argv[1], -1);
    // Create some windows to show the input
    // and output images in.
    //
    cv::namedWindow("Example2_5-in", cv::WINDOW_AUTOSIZE);
    cv::namedWindow("Example2_5-out", cv::WINDOW_AUTOSIZE);
    // Create a window to show our input image
    //
    cv::imshow("Example2_5-in", image);
    // Create an image to hold the smoothed output
    //
    cv::Mat out;
    // Do the smoothing
    // ( Note: Could use GaussianBlur(), blur(), medianBlur() or bilateralFilter(). )
    //
    cv::GaussianBlur(image, out, cv::Size(5, 5), 3, 3);
    //blurred by a 5 × 5 Gaussian convolution filter
    //Gaussian kernel should always be given in odd numbers
    //input : image ; written to out
    cv::GaussianBlur(out, out, cv::Size(5, 5), 3, 3);
    // Show the smoothed image in the output window
    //
    cv::imshow("Example2_5-out", out);
    // Wait for the user to hit a key, windows will self destruct
    //
    cv::waitKey(0);
    return 0;
}

略复杂一点的

uses Gaussian blurring to downsample an image by a factor of 2
下采样 -- 缩小图像
一、使得图像符合显示区域的大小；
二、生成对应图像的缩略图
scale space(image pyramid)尺度空间
信号处理 和 Nyquist-Shannon Sampling Theorem
下采样一个信号
convolving with a series of delta functions
与一系列增量函数卷积
high-pass filter 高通滤波器
band-limit 带限

Example 2-6

cv::pyrDown()

#include <opencv2/opencv.hpp>
int main(int argc, char** argv) {
    cv::Mat img1, img2;
    cv::namedWindow("Example1", cv::WINDOW_AUTOSIZE);
    cv::namedWindow("Example2", cv::WINDOW_AUTOSIZE);
    img1 = cv::imread(argv[1]);
    cv::imshow("Example1", img1);
    cv::pyrDown(img1, img2);
    cv::imshow("Example2", img2);
    cv::waitKey(0);
    return 0;
};

边缘检测

Example2-7

• Cannyedge detector Cannyedge边缘检测

把图片转换成灰度图,单通道
cv::Canny()
cv::cvtColor() 将(BGR) 图片转换为灰度图
cv::COLOR_BGR2GRAY

#include <opencv2/opencv.hpp>
int main( int argc, char** argv ) {
cv::Mat img_rgb, img_gry, img_cny;
cv::namedWindow( "Example Gray", cv::WINDOW_AUTOSIZE );
cv::namedWindow( "Example Canny", cv::WINDOW_AUTOSIZE );
img_rgb = cv::imread( argv[1] );
cv::cvtColor( img_rgb, img_gry, cv::COLOR_BGR2GRAY);
cv::imshow( "Example Gray", img_gry );
cv::Canny( img_gry, img_cny, 10, 100, 3, true );
cv::imshow( "Example Canny", img_cny );
cv::waitKey(0);
}

Example2-8

下采样两次再Canny subroutine in a simple image pipeline

#include <opencv2/opencv.hpp>
int main(int argc, char** argv) {
    cv::Mat img_rgb, img_gry, img_cny, img_pyr, img_pyr2;
    cv::namedWindow("Example Gray", cv::WINDOW_AUTOSIZE);
    cv::namedWindow("Example Canny", cv::WINDOW_AUTOSIZE);
    img_rgb = cv::imread(argv[1]);
    cv::cvtColor(img_rgb, img_gry, cv::COLOR_BGR2GRAY);
    cv::pyrDown(img_gry, img_pyr);
    cv::pyrDown(img_pyr, img_pyr2);
    cv::imshow("Example Gray", img_gry);
    cv::Canny(img_pyr2, img_cny, 10, 100, 3, true);
    cv::imshow("Example Canny", img_cny);
    cv::waitKey(0);
}

Example 2-9

#include <opencv2/opencv.hpp>
int main(int argc, char** argv) {

    int x = 16, y = 32;

    cv::Mat img_rgb, img_gry, img_cny, img_pyr, img_pyr2,img_pyr3;
    cv::namedWindow("Example Gray", cv::WINDOW_AUTOSIZE);
    cv::namedWindow("Example Canny", cv::WINDOW_AUTOSIZE);
    cv::namedWindow("Example downsample", cv::WINDOW_AUTOSIZE);
    img_rgb = cv::imread(argv[1]);

    cv::Vec3b intensity = img_rgb.at< cv::Vec3b >(y, x);
    uchar blue = intensity[0];
    uchar green = intensity[1];
    uchar red = intensity[2];

    std::cout << "At (x,y) = (" << x << ", " << y <<
        "): (blue, green, red) = (" <<
        (unsigned int)blue <<
        ", " << (unsigned int)green << ", " <<
        (unsigned int)red << ")" << std::endl;

    cv::cvtColor(img_rgb, img_gry, cv::COLOR_BGR2GRAY);

    std::cout << "Gray pixel there is: " <<
        (unsigned int)img_gry.at<uchar>(y, x) << std::endl;

    cv::pyrDown(img_gry, img_pyr);
    cv::pyrDown(img_pyr, img_pyr2);
    cv::imshow("Example Gray", img_gry);
    cv::Canny(img_pyr2, img_cny, 10, 100, 3, true);

    x /= 4; y /= 4;
    std::cout << "Pyramid2 pixel there is: " <<
        (unsigned int)img_pyr2.at<uchar>(y, x) << std::endl;
    img_cny.at<uchar>(x, y) = 128; // Set the Canny pixel there to 128

    cv::pyrDown(img_rgb, img_pyr3);
    cv::pyrDown(img_pyr3, img_pyr3);
    cv::imshow("Example downsample", img_pyr3);
    cv::imshow("Example Canny", img_cny);
    cv::waitKey(0);
}

这个图我以为挺好看,若是侵权我就删了

从摄像头读入

analogous to 类同

Example 2-10

#include <opencv2/opencv.hpp>
#include <iostream>
int main(int argc, char* argv[]) {
    cv::namedWindow("Example2_11", cv::WINDOW_AUTOSIZE);
    cv::namedWindow("Log_Polar", cv::WINDOW_AUTOSIZE);
    // ( Note: could capture from a camera by giving a camera id as an int.)
    //
    cv::VideoCapture capture(argv[1]);
    double fps = capture.get(cv::CAP_PROP_FPS);//每秒读一帧?
    cv::Size size(
        (int)capture.get(cv::CAP_PROP_FRAME_WIDTH),
        (int)capture.get(cv::CAP_PROP_FRAME_HEIGHT)
    );

    //convert the frame to log-polar format

    cv::VideoWriter writer;
    writer.open(argv[2]//新文件的名字
        , CV_FOURCC('M', 'J', 'P', 'G')//视频编码形式
        , fps//重播帧率
        , size//图片大小
    );
    cv::Mat logpolar_frame, bgr_frame;

    for (;;) {
        capture >> bgr_frame;
        if (bgr_frame.empty()) break; // end if done
        cv::imshow("Example2_11", bgr_frame);
        cv::logPolar(
            bgr_frame, // Input color frame
            logpolar_frame, // Output log-polar frame
            cv::Point2f( // Centerpoint for log-polar transformation
                bgr_frame.cols / 2, // x
                bgr_frame.rows / 2 // y
            ),
            40, // Magnitude (scale parameter)
            cv::WARP_FILL_OUTLIERS // Fill outliers with 'zero'
        );
        cv::imshow("Log_Polar", logpolar_frame);
        writer << logpolar_frame;
        char c = cv::waitKey(10);
        if (c == 27) break; // allow the user to break out
    }
    capture.release();
}

argument 参数
replay frame rate 重播帧率