opencv-各模块待补充

参考：https://docs.opencv.org/3.2.0/

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1、highgui module

参考：https://docs.opencv.org/3.2.0/da/d6a/tutorial_trackbar.html

• 使用cv :: createTrackbar在OpenCV窗口中添加一个跟踪栏

C++

#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include <stdio.h>using namespace cv;const int alpha_slider_max = 100;int alpha_slider;double alpha;double beta;Mat src1;Mat src2;Mat dst;static void on_trackbar( int, void* ){   alpha = (double) alpha_slider/alpha_slider_max ;   beta = ( 1.0 - alpha );   addWeighted( src1, alpha, src2, beta, 0.0, dst);   imshow( "Linear Blend", dst );}int main( void ){   src1 = imread("../data/LinuxLogo.jpg");   src2 = imread("../data/WindowsLogo.jpg");   if( src1.empty() ) { printf("Error loading src1 \n"); return -1; }   if( src2.empty() ) { printf("Error loading src2 \n"); return -1; }   alpha_slider = 0;   namedWindow("Linear Blend", WINDOW_AUTOSIZE); // Create Window   char TrackbarName[50];   sprintf( TrackbarName, "Alpha x %d", alpha_slider_max );   createTrackbar( TrackbarName, "Linear Blend", &alpha_slider, alpha_slider_max, on_trackbar );   on_trackbar( alpha_slider, 0 );   waitKey(0);   return 0;}

附加 ：cvui

参考：http://blog.csdn.net/wc781708249/article/details/78501484

python

参考：http://blog.csdn.net/wc781708249/article/details/78296147

import cv2import numpy as npdef nothing(x):    pass# Create a black image, a windowimg = np.zeros((300,512,3), np.uint8)cv2.namedWindow('image')# create trackbars for color changecv2.createTrackbar('R','image',0,255,nothing) # 取值范围 0~255cv2.createTrackbar('G','image',0,255,nothing)cv2.createTrackbar('B','image',0,255,nothing)# create switch for ON/OFF functionalityswitch = '0 : OFF \n1 : ON'cv2.createTrackbar(switch, 'image',0,1,nothing) # 0~1 范围while(1):    cv2.imshow('image',img)    k = cv2.waitKey(1) & 0xFF    if k == 27:        break    # get current positions of four trackbars    r = cv2.getTrackbarPos('R','image')    g = cv2.getTrackbarPos('G','image')    b = cv2.getTrackbarPos('B','image')    s = cv2.getTrackbarPos(switch,'image')    if s == 0:        img[:] = 0    else:        img[:] = [b,g,r]cv2.destroyAllWindows()

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2、imgcodecs module

Reading Geospatial Raster files with GDAL

参考：http://blog.csdn.net/wc781708249/article/details/78479584

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3、videoio module

视频相似度测量

1、如何打开和读视频流
2、两种检查图像相似性的方法：PSNR和SSIM

参考：
https://docs.opencv.org/3.2.0/d5/dc4/tutorial_video_input_psnr_ssim.html

C++

#include <iostream> // for standard I/O#include <string>   // for strings#include <iomanip>  // for controlling float print precision#include <sstream>  // string to number conversion#include <opencv2/core.hpp>     // Basic OpenCV structures (cv::Mat, Scalar)#include <opencv2/imgproc.hpp>  // Gaussian Blur#include <opencv2/videoio.hpp>#include <opencv2/highgui.hpp>  // OpenCV window I/Ousing namespace std;using namespace cv;double getPSNR(const Mat& I1, const Mat& I2);Scalar getMSSIM(const Mat& I1, const Mat& I2);static void help(){    cout        << "------------------------------------------------------------------------------" << endl        << "This program shows how to read a video file with OpenCV. In addition, it "        << "tests the similarity of two input videos first with PSNR, and for the frames "        << "below a PSNR trigger value, also with MSSIM." << endl        << "Usage:" << endl        << "./video-input-psnr-ssim <referenceVideo> <useCaseTestVideo> <PSNR_Trigger_Value> <Wait_Between_Frames> " << endl        << "--------------------------------------------------------------------------" << endl        << endl;}int main(int argc, char *argv[]){    help();    if (argc != 5)    {        cout << "Not enough parameters" << endl;        return -1;    }    stringstream conv;    const string sourceReference = argv[1], sourceCompareWith = argv[2];    int psnrTriggerValue, delay;    conv << argv[3] << endl << argv[4];       // put in the strings    conv >> psnrTriggerValue >> delay;        // take out the numbers    int frameNum = -1;          // Frame counter    VideoCapture captRefrnc(sourceReference), captUndTst(sourceCompareWith);    if (!captRefrnc.isOpened())    {        cout << "Could not open reference " << sourceReference << endl;        return -1;    }    if (!captUndTst.isOpened())    {        cout << "Could not open case test " << sourceCompareWith << endl;        return -1;    }    Size refS = Size((int)captRefrnc.get(CAP_PROP_FRAME_WIDTH),        (int)captRefrnc.get(CAP_PROP_FRAME_HEIGHT)),        uTSi = Size((int)captUndTst.get(CAP_PROP_FRAME_WIDTH),        (int)captUndTst.get(CAP_PROP_FRAME_HEIGHT));    if (refS != uTSi)    {        cout << "Inputs have different size!!! Closing." << endl;        return -1;    }    const char* WIN_UT = "Under Test";    const char* WIN_RF = "Reference";    // Windows    namedWindow(WIN_RF, WINDOW_AUTOSIZE);    namedWindow(WIN_UT, WINDOW_AUTOSIZE);    moveWindow(WIN_RF, 400, 0);         //750,  2 (bernat =0)    moveWindow(WIN_UT, refS.width, 0);         //1500, 2    cout << "Reference frame resolution: Width=" << refS.width << "  Height=" << refS.height        << " of nr#: " << captRefrnc.get(CAP_PROP_FRAME_COUNT) << endl;    cout << "PSNR trigger value " << setiosflags(ios::fixed) << setprecision(3)        << psnrTriggerValue << endl;    Mat frameReference, frameUnderTest;    double psnrV;    Scalar mssimV;    for (;;) //Show the image captured in the window and repeat    {        captRefrnc >> frameReference;        captUndTst >> frameUnderTest;        if (frameReference.empty() || frameUnderTest.empty())        {            cout << " < < <  Game over!  > > > ";            break;        }        ++frameNum;        cout << "Frame: " << frameNum << "# ";        psnrV = getPSNR(frameReference, frameUnderTest);        cout << setiosflags(ios::fixed) << setprecision(3) << psnrV << "dB";        if (psnrV < psnrTriggerValue && psnrV)        {            mssimV = getMSSIM(frameReference, frameUnderTest);            cout << " MSSIM: "                << " R " << setiosflags(ios::fixed) << setprecision(2) << mssimV.val[2] * 100 << "%"                << " G " << setiosflags(ios::fixed) << setprecision(2) << mssimV.val[1] * 100 << "%"                << " B " << setiosflags(ios::fixed) << setprecision(2) << mssimV.val[0] * 100 << "%";        }        cout << endl;        imshow(WIN_RF, frameReference);        imshow(WIN_UT, frameUnderTest);        char c = (char)waitKey(delay);        if (c == 27) break;    }    return 0;}double getPSNR(const Mat& I1, const Mat& I2){    Mat s1;    absdiff(I1, I2, s1);       // |I1 - I2|    s1.convertTo(s1, CV_32F);  // cannot make a square on 8 bits    s1 = s1.mul(s1);           // |I1 - I2|^2    Scalar s = sum(s1);        // sum elements per channel    double sse = s.val[0] + s.val[1] + s.val[2]; // sum channels    if (sse <= 1e-10) // for small values return zero        return 0;    else    {        double mse = sse / (double)(I1.channels() * I1.total());        double psnr = 10.0 * log10((255 * 255) / mse);        return psnr;    }}Scalar getMSSIM(const Mat& i1, const Mat& i2){    const double C1 = 6.5025, C2 = 58.5225;    /***************************** INITS **********************************/    int d = CV_32F;    Mat I1, I2;    i1.convertTo(I1, d);            // cannot calculate on one byte large values    i2.convertTo(I2, d);    Mat I2_2 = I2.mul(I2);        // I2^2    Mat I1_2 = I1.mul(I1);        // I1^2    Mat I1_I2 = I1.mul(I2);        // I1 * I2                                   /*************************** END INITS **********************************/    Mat mu1, mu2;                   // PRELIMINARY COMPUTING    GaussianBlur(I1, mu1, Size(11, 11), 1.5);    GaussianBlur(I2, mu2, Size(11, 11), 1.5);    Mat mu1_2 = mu1.mul(mu1);    Mat mu2_2 = mu2.mul(mu2);    Mat mu1_mu2 = mu1.mul(mu2);    Mat sigma1_2, sigma2_2, sigma12;    GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);    sigma1_2 -= mu1_2;    GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);    sigma2_2 -= mu2_2;    GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);    sigma12 -= mu1_mu2;    Mat t1, t2, t3;    t1 = 2 * mu1_mu2 + C1;    t2 = 2 * sigma12 + C2;    t3 = t1.mul(t2);                 // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))    t1 = mu1_2 + mu2_2 + C1;    t2 = sigma1_2 + sigma2_2 + C2;    t1 = t1.mul(t2);                 // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))    Mat ssim_map;    divide(t3, t1, ssim_map);        // ssim_map =  t3./t1;    Scalar mssim = mean(ssim_map);   // mssim = average of ssim map    return mssim;}

Python

import cv2import numpy as np#########################################def getPSNR(I1,I2):    s1=cv2.absdiff(I1,I2) # |I1 - I2|    s1=np.float32(s1)  # cannot make a square on 8 bits    s1=s1*s1 # |I1 - I2|^2    s=np.sum(s1)    if s<1e-10: # for small values return zero        return 0    else:        mse=s/I1.size        psnr=10.0 * np.log10((255 * 255) / mse)        return psnrdef getMSSIM(I1,I2):    C1 = 6.5025; C2 = 58.5225    d = cv2.CV_32F    I1 = np.float32(I1)    I2 = np.float32(I2)    I2_2=I2*I2    I1_2=I1*I1    I1_I2=I1*I2    mu1=cv2.GaussianBlur(I1,(11,11),1.5)    mu2 = cv2.GaussianBlur(I2, (11, 11), 1.5)    mu1_2=mu1*mu1    mu2_2=mu2*mu2    mu1_mu2=mu1*mu2    sigma1_2=cv2.GaussianBlur(I1_2, (11, 11), 1.5)    sigma1_2 -= mu1_2    sigma2_2=cv2.GaussianBlur(I2_2, (11, 11), 1.5)    sigma2_2 -= mu2_2    sigma12=cv2.GaussianBlur(I1_I2, (11, 11), 1.5)    sigma12 -= mu1_mu2    t1 = 2 * mu1_mu2 + C1    t2 = 2 * sigma12 + C2    t3 = t1*t2 # t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))    t1 = mu1_2 + mu2_2 + C1;    t2 = sigma1_2 + sigma2_2 + C2;    t1 = t1*t2   # t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))    ssim_map=cv2.divide(t3,t1) # ssim_map =  t3./t1;    mssim = cv2.mean(ssim_map) # mssim = average of ssim map    return mssim########################################sourceReference="vtest.avi"sourceCompareWith="vtest.avi"psnrTriggerValue=10delay=60# 打开视频captRefrnc = cv2.VideoCapture(sourceReference)captUndTst = cv2.VideoCapture(sourceCompareWith)if not captRefrnc.isOpened():    print("Could not open reference", sourceReference);exit(-1)if not captUndTst.isOpened():    print("Could not open case test", sourceCompareWith);exit(-1)# 比较视频尺寸w1=int(captRefrnc.get(cv2.CAP_PROP_FRAME_WIDTH)) # 或 w1=captRefrnc.get(3) 宽h1=int(captRefrnc.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 或 h1=captRefrnc.get(4) 高# 补充 设置视频高 ret=cap.set(4,240)w2=int(captUndTst.get(cv2.CAP_PROP_FRAME_WIDTH))h2=int(captUndTst.get(cv2.CAP_PROP_FRAME_HEIGHT))if w1!=w2 or h1!=h2:    print("Inputs have different size!!! Closing.");exit(-1)# WindowsWIN_UT = "Under Test"WIN_RF = "Reference"cv2.namedWindow(WIN_RF,cv2.WINDOW_AUTOSIZE)cv2.namedWindow(WIN_UT,cv2.WINDOW_AUTOSIZE)cv2.moveWindow(WIN_RF, 400, 0)cv2.moveWindow(WIN_UT, w1, 0)print("Reference frame resolution: Width=",w1,"  Height=",h1," of nr#: ",      captRefrnc.get(cv2.CAP_PROP_FRAME_COUNT))frameNum = -1# Show the image captured in the window and repeatwhile(1):    ret1,frameReference = captRefrnc.read()    ret2,frameUnderTest = captUndTst.read()    if not ret1 or not ret2:        print( "Game over!" )        break    frameNum+=1    print("Frame: ",frameNum,"# ")    psnrV = getPSNR(frameReference, frameUnderTest)    if psnrV<psnrTriggerValue and psnrV:        mssimV = getMSSIM(frameReference, frameUnderTest)        print(" MSSIM:"," R ",mssimV[2] * 100," G ",mssimV[1] * 100," B ",mssimV[0] * 100)    cv2.imshow(WIN_RF, frameReference)    cv2.imshow(WIN_UT, frameUnderTest)    # c = ord(cv2.waitKey(delay)) # 转ASCII    c=cv2.waitKey(delay) & 0xff    if c == 27:break

OpenCV创建视频

• 如何使用OpenCV创建视频文件
• 什么类型的视频文件可以用OpenCV创建
• 如何从视频中提取给定的颜色通道

参考：https://docs.opencv.org/3.2.0/d7/d9e/tutorial_video_write.html

C++

#include <iostream> // for standard I/O#include <string>   // for strings#include <opencv2/core.hpp>     // Basic OpenCV structures (cv::Mat)#include <opencv2/videoio.hpp>  // Video writeusing namespace std;using namespace cv;static void help(){    cout        << "------------------------------------------------------------------------------" << endl        << "This program shows how to write video files."                                   << endl        << "You can extract the R or G or B color channel of the input video."              << endl        << "Usage:"                                                                         << endl        << "./video-write <input_video_name> [ R | G | B] [Y | N]"                          << endl        << "------------------------------------------------------------------------------" << endl        << endl;}int main(int argc, char *argv[]){    help();    if (argc != 4)    {        cout << "Not enough parameters" << endl;        return -1;    }    const string source      = argv[1];           // the source file name    const bool askOutputType = argv[3][0] =='Y';  // If false it will use the inputs codec type    VideoCapture inputVideo(source);              // Open input    if (!inputVideo.isOpened())    {        cout  << "Could not open the input video: " << source << endl;        return -1;    }    string::size_type pAt = source.find_last_of('.');                  // Find extension point    const string NAME = source.substr(0, pAt) + argv[2][0] + ".avi";   // Form the new name with container    int ex = static_cast<int>(inputVideo.get(CAP_PROP_FOURCC));     // Get Codec Type- Int form    // Transform from int to char via Bitwise operators    char EXT[] = {(char)(ex & 0XFF) , (char)((ex & 0XFF00) >> 8),(char)((ex & 0XFF0000) >> 16),(char)((ex & 0XFF000000) >> 24), 0};    Size S = Size((int) inputVideo.get(CAP_PROP_FRAME_WIDTH),    // Acquire input size                  (int) inputVideo.get(CAP_PROP_FRAME_HEIGHT));    VideoWriter outputVideo;                                        // Open the output    if (askOutputType)        outputVideo.open(NAME, ex=-1, inputVideo.get(CAP_PROP_FPS), S, true);    else        outputVideo.open(NAME, ex, inputVideo.get(CAP_PROP_FPS), S, true);    if (!outputVideo.isOpened())    {        cout  << "Could not open the output video for write: " << source << endl;        return -1;    }    cout << "Input frame resolution: Width=" << S.width << "  Height=" << S.height         << " of nr#: " << inputVideo.get(CAP_PROP_FRAME_COUNT) << endl;    cout << "Input codec type: " << EXT << endl;    int channel = 2; // Select the channel to save    switch(argv[2][0])    {    case 'R' : channel = 2; break;    case 'G' : channel = 1; break;    case 'B' : channel = 0; break;    }    Mat src, res;    vector<Mat> spl;    for(;;) //Show the image captured in the window and repeat    {        inputVideo >> src;              // read        if (src.empty()) break;         // check if at end        split(src, spl);                // process - extract only the correct channel        for (int i =0; i < 3; ++i)            if (i != channel)                spl[i] = Mat::zeros(S, spl[0].type());       merge(spl, res);       //outputVideo.write(res); //save or       outputVideo << res;    }    cout << "Finished writing" << endl;    return 0;}

python

# -*- coding: UTF-8 -*-import cv2import numpy as npimport ossource="./vtest.avi"askOutputType =FalseinputVideo=cv2.VideoCapture(source)if not inputVideo.isOpened():    print("Could not open the input video: ");exit(-1)pAt =source.split('/')[0]NAME =os.path.join(pAt,"R"+".avi") # Form the new name with containerex=int(inputVideo.get(cv2.CAP_PROP_FOURCC)) # Get Codec Type- Int form# Transform from int to char via Bitwise operatorsEXT=[(chr)(ex & 0XFF),(chr)((ex & 0XFF00) >> 8),(chr)((ex & 0XFF0000) >> 16),(chr)((ex & 0XFF000000) >> 24), 0]S=(int(inputVideo.get(cv2.CAP_PROP_FRAME_WIDTH)), # Acquire input size   int(inputVideo.get(cv2.CAP_PROP_FRAME_HEIGHT)))# outputVideo=cv2.VideoWriter()if askOutputType:    # outputVideo.open(NAME, ex=-1)    outputVideo = cv2.VideoWriter(NAME, -1, inputVideo.get(cv2.CAP_PROP_FPS), S)else:    # outputVideo.open(NAME, ex)    outputVideo = cv2.VideoWriter(NAME, ex, inputVideo.get(cv2.CAP_PROP_FPS), S)if not outputVideo.isOpened():    print("Could not open the output video for write: ",source)    exit(-1)print("Input frame resolution: Width=",S[0],"  Height=",S[1]," of nr#: ",      inputVideo.get(cv2.CAP_PROP_FRAME_COUNT))print("Input codec type: ",EXT)channel = 2while(1):    ret,src=inputVideo.read()    if not ret:break    spl=cv2.split(src)    for i in range(3):        if i !=channel:            spl[i]=np.zeros(spl[0].shape[:2],spl[0].dtype)    res=cv2.merge(spl)    # outputVideo.write(res); //save or    outputVideo.write(res)inputVideo.release()outputVideo.release()cv2.destroyAllWindows()print("Finished writing")exit(0)

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4、calib3d module

方形棋盘的摄像机校准

参考：http://blog.csdn.net/wc781708249/article/details/78528920#4221-%E8%AE%BE%E7%BD%AE-findchessboardcornerspy

• 通过在cmake配置中将BUILD_EXAMPLES设置为ON来编译opencv样本。
• 转到bin文件夹并使用imagelist_creator来创建图像的XML / YAML列表。
• 然后，运行校准样本来获取相机参数。 使用方形尺寸等于3厘米。

# -*- coding: utf-8 -*-# @Time    : 2017/7/13 下午6:21# @Author  : play4fun# @File    : 42.2.1-设置-findChessboardCorners.py# @Software: PyCharm"""42.2.1-设置-findChessboardCorners.py:径向畸变和切想畸变摄像机的内部和外部参数。 内部参数是摄像机特异的。它包括的信息有焦 ( fx, fy) 光学中心 (cx, cy)  等。 也 称为摄像机矩阵。它完全取决于摄像机自  只需要计算一次 以后就可以已知使用了。至少需要10张图案模式来进行摄像机标定3D 点 称为对象点， 2D 图像点 称为图像点除了使用棋盘之外 我们 可以使用环形格子使用函数 cv2.findCirclesGrid() 来找图案。据说使用环形格子只需要很少的图像 就可以了。"""import numpy as npimport cv2import glob# termination criteriacriteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)objp = np.zeros((6 * 7, 3), np.float32)objp[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2)# Arrays to store object points and image points from all the images.objpoints = []  # 3d point in real world spaceimgpoints = []  # 2d points in image plane.images = glob.glob('../data/left*.jpg')images += glob.glob('../data/right*.jpg')for fname in images:    img = cv2.imread(fname)    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)    # Find the chess board corners    ret, corners = cv2.findChessboardCorners(gray, (7, 6), None)    # If found, add object points, image points (after refining them)    if ret == True:        objpoints.append(objp)        corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)        imgpoints.append(corners)        # Draw and display the corners        cv2.drawChessboardCorners(img, (7, 6), corners2, ret)        cv2.imshow('img', img)        cv2.waitKey(500)cv2.destroyAllWindows()

Camera calibration With OpenCV

摄像机校准
参考：https://docs.opencv.org/3.2.0/d4/d94/tutorial_camera_calibration.html

• 确定失真矩阵
• 确定相机矩阵
• 从相机，视频和图像文件列表中进行输入
• 从XML / YAML文件读取配置
• 将结果保存到XML / YAML文件中
• 计算重新投影错误

C++

参考：https://stackoverflow.com/questions/8368255/camera-calibration-with-opencv-assertion-failed-fault

#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include "C:/opencv/include/opencv/cv.h"#include <iostream>#include <math.h>using namespace cv;using namespace std;std::vector<cv::Point2f> imageCorners;std::vector<cv::Point3f> objectCorners;std::vector<std::vector<cv::Point3f>> objectPoints;std::vector<std::vector<cv::Point2f>> imagePoints;void addPoints(const std::vector<cv::Point2f>&imageCorners, const std::vector<cv::Point3f>& objectCorners){// 2D image points from one viewimagePoints.push_back(imageCorners);// corresponding 3D scene pointsobjectPoints.push_back(objectCorners);}int main(){int key;cv::Mat   image;cv::Mat   gray_image;VideoCapture cap("here goes path of the file");    if (!cap.isOpened())  // check if we succeeded       cout<<"failed";else   cout<<"success";    cvNamedWindow( "video",0);cv::Size boardSize(8,6);// output Matricescv::Mat cameraMatrix;std::vector<cv::Mat> rvecs, tvecs;cv::Mat distCoeffs;for (int i=0; i<boardSize.height; i++) {    for (int j=0; j<boardSize.width; j++)     {        objectCorners.push_back(cv::Point3f(i, j, 0.0f));    }}int frame=1;int corner_count=0;while(1) {    if(cap.read(image))    {        frame++;        if(frame%20==0)        {            if(waitKey(30) >= 0) break;            bool found = cv::findChessboardCorners(image, boardSize, imageCorners);            cvtColor( image, gray_image, CV_RGB2GRAY );                addPoints(imageCorners, objectCorners);            //bool found = cv::findChessboardCorners(image,boardSize, imageCorners);            cv::drawChessboardCorners(gray_image,boardSize, imageCorners,found);            imshow( "video",  gray_image );        }    }    else        break;}int flag=0;std::string text="";for (int i=1; i<imagePoints.size();i++){    std::stringstream out;    out << imagePoints[i];    text=out.str();    cout<<text<<endl;}calibrateCamera(objectPoints,imagePoints,gray_image.size(), cameraMatrix, distCoeffs, rvecs, tvecs, flag);return 0;}

Python

参考：https://stackoverflow.com/questions/31249037/calibrating-webcam-using-python-and-opencv-error?rq=1

# -*- coding: UTF-8 -*-import numpy as npimport cv2import globcriteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)# Arrays to store object points and image points from all the images.objpoints = [] # 3d point in real worldimgpoints = [] # 2d points in image plane.# checkerboard Dimensionscbrow = 5cbcol = 7objp = np.zeros((cbrow*cbcol,3), np.float32)objp[:,:2] = np.mgrid[0:cbcol,0:cbrow].T.reshape(-1,2)objp = objp * 22"""inputVideo=cv2.VideoCapture("./vtest.avi")if not inputVideo.isOpened():    print("Could not open the input video: ");exit(-1)while(1):    ret, img = inputVideo.read()    if not ret: break"""images = glob.glob('./left/*.jpg')for fname in images:    img = cv2.imread(fname)    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)    ret = False    # Find the chess board corners    ret, corners = cv2.findChessboardCorners(gray, (cbcol,cbrow))    # If found, add object points, image points (after refining them)    if ret == True:        objpoints.append(objp)        cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)        imgpoints.append(corners)        # Draw and display the corners        cv2.drawChessboardCorners(img, (cbcol,cbrow), corners, ret)        cv2.imshow('img',img)        cv2.waitKey(0)cv2.waitKey(0)for i in range (1,5):    cv2.waitKey(1)    cv2.destroyAllWindows()    cv2.waitKey(1)ret, cameraMatrix, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)print ("camera matrix:\n", cameraMatrix)# pi camera intrinsic parametersay = cameraMatrix[1, 1]u0 = cameraMatrix[0, 2]v0 = cameraMatrix[1, 2]print ("Ay:", ay)print ("u0:", u0)print ("v0:", v0)

实时纹理对象姿态估计

Real Time pose estimation of a textured object
参考：https://docs.opencv.org/3.2.0/dc/d2c/tutorial_real_time_pose.html

• 阅读3D纹理对象模型和对象网格。
• 从相机或视频输入。
• 从场景中提取ORB特征和描述符。
• 使用Flann匹配器匹配场景描述符和模型描述符。
• 使用PnP + Ransac进行姿态估计。
• 线性卡尔曼滤波器的不良姿态抑制。

参考：http://blog.csdn.net/wc781708249/article/details/78528920#calib3dpy

交互式摄像机校准应用

参考：
https://docs.opencv.org/3.2.0/d7/d21/tutorial_interactive_calibration.html

• 确定每个元素的失真矩阵和置信区间
• 确定每个元素的相机矩阵和置信区间
• 从相机或视频文件输入
• 从XML文件读取配置
• 将结果保存到XML文件中
• 计算重新投影错误
• 以锐利的角度拒绝模式的观点，以防止出现病态的雅可比块
• 自动切换校准标志（如果需要，固定宽高比和失真矩阵元素）
• 使用多个标准进行校准时自动检测
• 自动捕捉静态模式（用户不需要按任何键捕捉帧，只是不要移动模式一秒钟）

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5、feature2d module

Harris 角点探测器

• 使用函数cv :: cornerHarris使用Harris-Stephens方法检测拐角。

C++

参考：https://docs.opencv.org/3.2.0/d4/d7d/tutorial_harris_detector.html

#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include "opencv2/imgproc.hpp"#include <iostream>using namespace cv;using namespace std;Mat src, src_gray;int thresh = 200;int max_thresh = 255;const char* source_window = "Source image";const char* corners_window = "Corners detected";void cornerHarris_demo( int, void* );int main( int, char** argv ){  src = imread( argv[1], IMREAD_COLOR );  cvtColor( src, src_gray, COLOR_BGR2GRAY );  namedWindow( source_window, WINDOW_AUTOSIZE );  createTrackbar( "Threshold: ", source_window, &thresh, max_thresh, cornerHarris_demo );  imshow( source_window, src );  cornerHarris_demo( 0, 0 );  waitKey(0);  return(0);}void cornerHarris_demo( int, void* ){  Mat dst, dst_norm, dst_norm_scaled;  dst = Mat::zeros( src.size(), CV_32FC1 );  int blockSize = 2;  int apertureSize = 3;  double k = 0.04;  cornerHarris( src_gray, dst, blockSize, apertureSize, k, BORDER_DEFAULT );  normalize( dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat() );  convertScaleAbs( dst_norm, dst_norm_scaled );  for( int j = 0; j < dst_norm.rows ; j++ )     { for( int i = 0; i < dst_norm.cols; i++ )          {            if( (int) dst_norm.at<float>(j,i) > thresh )              {               circle( dst_norm_scaled, Point( i, j ), 5,  Scalar(0), 2, 8, 0 );              }          }     }  namedWindow( corners_window, WINDOW_AUTOSIZE );  imshow( corners_window, dst_norm_scaled );}

python

参考：
http://blog.csdn.net/wc781708249/article/details/78524311#ch30-harris%E8%A7%92%E7%82%B9%E6%A3%80%E6%B5%8B

Shi-Tomasi角点探测器

• 使用函数cv :: goodFeaturesToTrack使用Shi-Tomasi方法检测拐角。

C++

参考：
https://docs.opencv.org/3.2.0/d8/dd8/tutorial_good_features_to_track.html

#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include "opencv2/imgproc.hpp"#include <iostream>using namespace cv;using namespace std;Mat src, src_gray;int maxCorners = 23;int maxTrackbar = 100;RNG rng(12345);const char* source_window = "Image";void goodFeaturesToTrack_Demo( int, void* );int main( int, char** argv ){  src = imread( argv[1], IMREAD_COLOR );  cvtColor( src, src_gray, COLOR_BGR2GRAY );  namedWindow( source_window, WINDOW_AUTOSIZE );  createTrackbar( "Max  corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );  imshow( source_window, src );  goodFeaturesToTrack_Demo( 0, 0 );  waitKey(0);  return(0);}void goodFeaturesToTrack_Demo( int, void* ){  if( maxCorners < 1 ) { maxCorners = 1; }  vector<Point2f> corners;  double qualityLevel = 0.01;  double minDistance = 10;  int blockSize = 3;  bool useHarrisDetector = false;  double k = 0.04;  Mat copy;  copy = src.clone();  goodFeaturesToTrack( src_gray,               corners,               maxCorners,               qualityLevel,               minDistance,               Mat(),               blockSize,               useHarrisDetector,               k );  cout<<"** Number of corners detected: "<<corners.size()<<endl;  int r = 4;  for( size_t i = 0; i < corners.size(); i++ )     { circle( copy, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255)), -1, 8, 0 ); }  namedWindow( source_window, WINDOW_AUTOSIZE );  imshow( source_window, copy );}

Python

参考：
http://blog.csdn.net/wc781708249/article/details/78528617#goodfeaturestotrackpy

Creating yor own corner detector

• 使用OpenCV函数cv :: cornerEigenValsAndVecs来查找特征值和特征向量，以确定像素是否是拐角。
• 使用OpenCV函数cv :: cornerMinEigenVal来查找拐角检测的最小特征值。
• 通过使用上述两个函数来实现我们自己的Harris探测器以及Shi-Tomasi探测器。

C++

参考：
https://docs.opencv.org/3.2.0/d9/dbc/tutorial_generic_corner_detector.html

#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include "opencv2/imgproc.hpp"#include <iostream>using namespace cv;using namespace std;Mat src, src_gray;Mat myHarris_dst; Mat myHarris_copy; Mat Mc;Mat myShiTomasi_dst; Mat myShiTomasi_copy;int myShiTomasi_qualityLevel = 50;int myHarris_qualityLevel = 50;int max_qualityLevel = 100;double myHarris_minVal; double myHarris_maxVal;double myShiTomasi_minVal; double myShiTomasi_maxVal;RNG rng(12345);const char* myHarris_window = "My Harris corner detector";const char* myShiTomasi_window = "My Shi Tomasi corner detector";void myShiTomasi_function( int, void* );void myHarris_function( int, void* );int main( int, char** argv ){  src = imread( argv[1], IMREAD_COLOR );  cvtColor( src, src_gray, COLOR_BGR2GRAY );  int blockSize = 3; int apertureSize = 3;  myHarris_dst = Mat::zeros( src_gray.size(), CV_32FC(6) );  Mc = Mat::zeros( src_gray.size(), CV_32FC1 );  cornerEigenValsAndVecs( src_gray, myHarris_dst, blockSize, apertureSize, BORDER_DEFAULT );  /* calculate Mc */  for( int j = 0; j < src_gray.rows; j++ )     { for( int i = 0; i < src_gray.cols; i++ )          {            float lambda_1 = myHarris_dst.at<Vec6f>(j, i)[0];            float lambda_2 = myHarris_dst.at<Vec6f>(j, i)[1];            Mc.at<float>(j,i) = lambda_1*lambda_2 - 0.04f*pow( ( lambda_1 + lambda_2 ), 2 );          }     }  minMaxLoc( Mc, &myHarris_minVal, &myHarris_maxVal, 0, 0, Mat() );  /* Create Window and Trackbar */  namedWindow( myHarris_window, WINDOW_AUTOSIZE );  createTrackbar( " Quality Level:", myHarris_window, &myHarris_qualityLevel, max_qualityLevel, myHarris_function );  myHarris_function( 0, 0 );  myShiTomasi_dst = Mat::zeros( src_gray.size(), CV_32FC1 );  cornerMinEigenVal( src_gray, myShiTomasi_dst, blockSize, apertureSize, BORDER_DEFAULT );  minMaxLoc( myShiTomasi_dst, &myShiTomasi_minVal, &myShiTomasi_maxVal, 0, 0, Mat() );  /* Create Window and Trackbar */  namedWindow( myShiTomasi_window, WINDOW_AUTOSIZE );  createTrackbar( " Quality Level:", myShiTomasi_window, &myShiTomasi_qualityLevel, max_qualityLevel, myShiTomasi_function );  myShiTomasi_function( 0, 0 );  waitKey(0);  return(0);}void myShiTomasi_function( int, void* ){  myShiTomasi_copy = src.clone();  if( myShiTomasi_qualityLevel < 1 ) { myShiTomasi_qualityLevel = 1; }  for( int j = 0; j < src_gray.rows; j++ )     { for( int i = 0; i < src_gray.cols; i++ )          {            if( myShiTomasi_dst.at<float>(j,i) > myShiTomasi_minVal + ( myShiTomasi_maxVal - myShiTomasi_minVal )*myShiTomasi_qualityLevel/max_qualityLevel )              { circle( myShiTomasi_copy, Point(i,j), 4, Scalar( rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255) ), -1, 8, 0 ); }          }     }  imshow( myShiTomasi_window, myShiTomasi_copy );}void myHarris_function( int, void* ){  myHarris_copy = src.clone();  if( myHarris_qualityLevel < 1 ) { myHarris_qualityLevel = 1; }  for( int j = 0; j < src_gray.rows; j++ )     { for( int i = 0; i < src_gray.cols; i++ )          {            if( Mc.at<float>(j,i) > myHarris_minVal + ( myHarris_maxVal - myHarris_minVal )*myHarris_qualityLevel/max_qualityLevel )              { circle( myHarris_copy, Point(i,j), 4, Scalar( rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255) ), -1, 8, 0 ); }          }     }  imshow( myHarris_window, myHarris_copy );}

检测子像素中的角落位置

参考：https://docs.opencv.org/3.2.0/d8/d5e/tutorial_corner_subpixeles.html

• 使用OpenCV函数cv :: cornerSubPix来查找更精确的角位置（比整数像素更精确）。

特征检测

• 使用cv :: FeatureDetector接口为了找到兴趣点。 特别：
  
  • 使用cv :: xfeatures2d :: SURF及其函数cv :: xfeatures2d :: SURF ::detect来执行检测过程
  • 使用函数cv :: drawKeypoints绘制检测到的关键点

C++

参考：https://docs.opencv.org/3.2.0/d7/d66/tutorial_feature_detection.html

#include <stdio.h>#include <iostream>#include "opencv2/core.hpp"#include "opencv2/features2d.hpp"#include "opencv2/xfeatures2d.hpp"#include "opencv2/highgui.hpp"using namespace cv;using namespace cv::xfeatures2d;void readme();/* @function main */int main( int argc, char** argv ){  if( argc != 3 )  { readme(); return -1; }  Mat img_1 = imread( argv[1], IMREAD_GRAYSCALE );  Mat img_2 = imread( argv[2], IMREAD_GRAYSCALE );  if( !img_1.data || !img_2.data )  { std::cout<< " --(!) Error reading images " << std::endl; return -1; }  //-- Step 1: Detect the keypoints using SURF Detector  int minHessian = 400;  Ptr<SURF> detector = SURF::create( minHessian );  std::vector<KeyPoint> keypoints_1, keypoints_2;  detector->detect( img_1, keypoints_1 );  detector->detect( img_2, keypoints_2 );  //-- Draw keypoints  Mat img_keypoints_1; Mat img_keypoints_2;  drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );  drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT );  //-- Show detected (drawn) keypoints  imshow("Keypoints 1", img_keypoints_1 );  imshow("Keypoints 2", img_keypoints_2 );  waitKey(0);  return 0;  }  /* @function readme */  void readme()  { std::cout << " Usage: ./SURF_detector <img1> <img2>" << std::endl; }

6、video module

7、objdetect module

8、ml module

9、photo module

10、stitching module

11、cuda module