//#include "opencv2/imgproc.hpp"
//#include "opencv2/imgcodecs.hpp"
//#include "opencv2/highgui.hpp"
//#include <iostream>
//using namespace cv;
//using std::cout;
//int threshold_value = 0;
//int threshold_type = 3;
//int const max_value = 255;
//int const max_type = 4;
//int const max_binary_value = 255;
//Mat src, src_gray, dst;
//const char* window_name = "Threshold Demo";
//const char* trackbar_type = "Type: \n 0: Binary \n 1: Binary Inverted \n 2: Truncate \n 3: To Zero \n 4: To Zero Inverted";
//const char* trackbar_value = "Value";
//static void Threshold_Demo( int, void* )
//{
//    /* 0: Binary
//     1: Binary Inverted
//     2: Threshold Truncated
//     3: Threshold to Zero
//     4: Threshold to Zero Inverted
//    */
//    threshold( src_gray, dst, threshold_value, max_binary_value, threshold_type );
//    imshow( window_name, dst );
//}
//int main( int argc, char** argv )
//{
//    String imageName("C:\\Documents\\2.png"); // by default
//    if (argc > 1)
//    {
//        imageName = argv[1];
//    }
//    src = imread( samples::findFile( imageName ), IMREAD_COLOR ); // Load an image
//    if (src.empty())
//    {
//        cout << "Cannot read the image: " << imageName << std::endl;
//        return -1;
//    }
//    cvtColor( src, src_gray, COLOR_BGR2GRAY ); // Convert the image to Gray
//    namedWindow( window_name, WINDOW_AUTOSIZE ); // Create a window to display results
//    createTrackbar( trackbar_type,
//                   window_name, &threshold_type,
//                   max_type, Threshold_Demo ); // Create a Trackbar to choose type of Threshold
//    createTrackbar( trackbar_value,
//                   window_name, &threshold_value,
//                   max_value, Threshold_Demo ); // Create a Trackbar to choose Threshold value
//    Threshold_Demo( 0, 0 ); // Call the function to initialize
//    waitKey();
//    return 0;
//}


#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;

int main()
{
Mat src=imread("C:\\Documents\\1.png", 1);
Mat dst;//(src.rows,src.cols,CV_8UC4);
Mat tmp,alpha;

cvtColor(src,tmp,COLOR_BGR2GRAY);
threshold(tmp,alpha,254,255,THRESH_BINARY_INV | THRESH_OTSU);

Mat rgb[3];
split(src,rgb);

Mat rgba[4]={rgb[0],rgb[1],rgb[2],alpha};
merge(rgba,4,dst);


imwrite("C:\\Documents\\alpa.png",tmp);
imwrite("C:\\Documents\\dst9.png",dst);
    waitKey();

    return 0;
}


//#include <opencv2/imgproc.hpp>
//#include <opencv2/highgui.hpp>
//#include <opencv2/opencv.hpp>



//#include <vector>
//#include <algorithm>
//#include <tuple>
//#include <iostream>


//using namespace cv;


//struct comparator{
//    bool operator() (std::tuple<std::vector<cv::Point>, bool, double> t1,
//                     std::tuple<std::vector<cv::Point>, bool, double> t2) {
//        return std::get<2>(t1) > std::get<2>(t2);
//    }
//} comparator;

//int main(int, char**)
//{
//    // get image
//    cv::Mat image = cv::imread("C:\\Documents\\2.png");
//    cv::Mat grayImg;

//    // convert to greyscale
//    cv::cvtColor(image, grayImg, COLOR_BGRA2GRAY);

////    cv::Mat canny;
////    cv::Canny(grayImg,canny, 120, 255, 3);

//    // finding threshes
//    cv::Mat thresh;
//    cv::threshold(grayImg,thresh, 100, 255, THRESH_BINARY_INV| THRESH_OTSU);


//    // finding contours
//    std::vector<std::vector<cv::Point>> contours;
//    std::vector<cv::Vec4i> hierarchy;

//    findContours( thresh, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0) );


//    // finding max contour
//    std::vector<std::tuple<std::vector<cv::Point>, bool, double>> vec;


//    for(size_t i = 0; i < contours.size(); ++i){

//        vec.push_back(std::make_tuple(contours.at(i), cv::isContourConvex(contours.at(i)),cv::contourArea(contours.at(i))));
//    }

//    std::sort(vec.begin(), vec.end(), comparator);

//    std::tuple<std::vector<cv::Point>, bool, double> maxContour;
//    maxContour = vec.at(0);



//    // create mask
//    cv::Mat mask = Mat::zeros(thresh.size(), CV_8U);

////    for(size_t i = 0; i < contours.size(); ++i){
////        cv::fillConvexPoly(mask, std::get<0>(vec.at(i)), Scalar(255,0,0),8,0);
////    }

//    cv::fillConvexPoly(mask, std::get<0>(maxContour), Scalar(255,0,0),8,0);


//    // bitwise
//    cv::Mat res;
//    cv::bitwise_and(image, image, res, mask);

//    // show process
//    imshow("result", res);
//    imshow("mask", mask);
//    imshow("canny", thresh);
//    imshow("source", image);


//    // create transparent background
//    Mat dst;

//    Mat rgb[3];
//    split(image,rgb);

//    Mat rgba[4]={rgb[0],rgb[1],rgb[2], thresh};
//    merge(rgba,4,dst);


//    // save to file transparent and cropped images
//    imwrite("C:/Documents/21.png", res);
//    imwrite("C:/Documents/dst.png",dst);
//    imwrite("C:/Documents/mask.png",mask);
//    imwrite("C:/Documents/thresh.png",thresh);
//    imwrite("C:/Documents/src.png",image);

//    while (true) {
//        if (waitKey() == 27) { //wait for 'esc' key press for 30ms. If 'esc' key is pressed, break loop
//            std::cout << "esc key is pressed by user";
//            break;
//        }
//    }

//    return 0;

//}




//#include "opencv2/objdetect.hpp"
//#include "opencv2/highgui.hpp"
//#include "opencv2/imgproc.hpp"
//#include "opencv2/videoio.hpp"
//#include <iostream>
//using namespace std;
//using namespace cv;
//void detectAndDisplay( Mat frame );
//CascadeClassifier face_cascade;
//CascadeClassifier eyes_cascade;
//int main( int argc, const char** argv )
//{
//    CommandLineParser parser(argc, argv,
//                             "{help h||}"
//                             "{face_cascade|data/haarcascades/haarcascade_frontalface_alt.xml|Path to face cascade.}"
//                             "{eyes_cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|Path to eyes cascade.}"
//                             "{camera|0|Camera device number.}");
//    parser.about( "\nThis program demonstrates using the cv::CascadeClassifier class to detect objects (Face + eyes) in a video stream.\n"
//                 "You can use Haar or LBP features.\n\n" );
//    parser.printMessage();
//    String face_cascade_name = samples::findFile( parser.get<String>("face_cascade") );
//    String eyes_cascade_name = samples::findFile( parser.get<String>("eyes_cascade") );
//    //-- 1. Load the cascades
//    if( !face_cascade.load( face_cascade_name ) )
//    {
//        cout << "--(!)Error loading face cascade\n";
//        return -1;
//    };
//    if( !eyes_cascade.load( eyes_cascade_name ) )
//    {
//        cout << "--(!)Error loading eyes cascade\n";
//        return -1;
//    };
//    int camera_device = parser.get<int>("camera");
//    VideoCapture capture;
//    //-- 2. Read the video stream
//    capture.open( camera_device );
//    if ( ! capture.isOpened() )
//    {
//        cout << "--(!)Error opening video capture\n";
//        return -1;
//    }
//    Mat frame;
//    while ( capture.read(frame) )
//    {
//        if( frame.empty() )
//        {
//            cout << "--(!) No captured frame -- Break!\n";
//            break;
//        }
//        //-- 3. Apply the classifier to the frame
//        detectAndDisplay( frame );
//        if( waitKey(10) == 27 )
//        {
//            break; // escape
//        }
//    }
//    return 0;
//}
//void detectAndDisplay( Mat frame )
//{
//    Mat frame_gray;
//    cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
//    equalizeHist( frame_gray, frame_gray );
//    //-- Detect faces
//    std::vector<Rect> faces;
//    face_cascade.detectMultiScale( frame_gray, faces );
//    for ( size_t i = 0; i < faces.size(); i++ )
//    {
//        Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
//        ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2 ), 0, 0, 360, Scalar( 255, 0, 255 ), 4 );
//        Mat  faceROI = frame_gray( faces[i] );
//        //-- In each face, detect eyes
//        std::vector<Rect> eyes;
//        eyes_cascade.detectMultiScale( faceROI, eyes );
//        for ( size_t j = 0; j < eyes.size(); j++ )
//        {
//            Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
//            int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
//            circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 4 );
//        }
//    }
//    //-- Show what you got
//    imshow( "Capture - Face detection", frame );
//}