/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp

#include <stdio.h>
#include <iostream>

#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/core/utility.hpp>

using namespace cv;  // The new C++ interface API is inside this namespace. Import it.
using namespace std;

static void help( char* progName)
{
    cout << endl << progName
        << " shows how to use cv::Mat and IplImages together (converting back and forth)." << endl
        << "Also contains example for image read, spliting the planes, merging back and "  << endl
        << " color conversion, plus iterating through pixels. "                            << endl
        << "Usage:" << endl
        << progName << " [image-name Default: lena.jpg]"                           << endl << endl;
}

// comment out the define to use only the latest C++ API
#define DEMO_MIXED_API_USE

#ifdef DEMO_MIXED_API_USE
#  include <opencv2/highgui/highgui_c.h>
#  include <opencv2/imgcodecs/imgcodecs_c.h>
#endif

int main( int argc, char** argv )
{
    help(argv[0]);
    const char* imagename = argc > 1 ? argv[1] : "lena.jpg";

#ifdef DEMO_MIXED_API_USE
    Ptr<IplImage> IplI(cvLoadImage(imagename));      // Ptr<T> is a safe ref-counting pointer class
    if(!IplI)
    {
        cerr << "Can not load image " <<  imagename << endl;
        return -1;
    }
    Mat I = cv::cvarrToMat(IplI); // Convert to the new style container. Only header created. Image not copied.
#else
    Mat I = imread(imagename);        // the newer cvLoadImage alternative, MATLAB-style function
    if( I.empty() )                   // same as if( !I.data )
    {
        cerr << "Can not load image " <<  imagename << endl;
        return -1;
    }
#endif

    // convert image to YUV color space. The output image will be created automatically.
    Mat I_YUV;
    cvtColor(I, I_YUV, COLOR_BGR2YCrCb);

    vector<Mat> planes;    // Use the STL's vector structure to store multiple Mat objects
    split(I_YUV, planes);  // split the image into separate color planes (Y U V)

#if 1 // change it to 0 if you want to see a blurred and noisy version of this processing
    // Mat scanning
    // Method 1. process Y plane using an iterator
    MatIterator_<uchar> it = planes[0].begin<uchar>(), it_end = planes[0].end<uchar>();
    for(; it != it_end; ++it)
    {
        double v = *it * 1.7 + rand()%21 - 10;
        *it = saturate_cast<uchar>(v*v/255);
    }

    for( int y = 0; y < I_YUV.rows; y++ )
    {
        // Method 2. process the first chroma plane using pre-stored row pointer.
        uchar* Uptr = planes[1].ptr<uchar>(y);
        for( int x = 0; x < I_YUV.cols; x++ )
        {
            Uptr[x] = saturate_cast<uchar>((Uptr[x]-128)/2 + 128);

            // Method 3. process the second chroma plane using individual element access
            uchar& Vxy = planes[2].at<uchar>(y, x);
            Vxy =        saturate_cast<uchar>((Vxy-128)/2 + 128);
        }
    }

#else

    Mat noisyI(I.size(), CV_8U);           // Create a matrix of the specified size and type

    // Fills the matrix with normally distributed random values (around number with deviation off).
    // There is also randu() for uniformly distributed random number generation
    randn(noisyI, Scalar::all(128), Scalar::all(20));

    // blur the noisyI a bit, kernel size is 3x3 and both sigma's are set to 0.5
    GaussianBlur(noisyI, noisyI, Size(3, 3), 0.5, 0.5);

    const double brightness_gain = 0;
    const double contrast_gain = 1.7;

#ifdef DEMO_MIXED_API_USE
    // To pass the new matrices to the functions that only work with IplImage or CvMat do:
    // step 1) Convert the headers (tip: data will not be copied).
    // step 2) call the function   (tip: to pass a pointer do not forget unary "&" to form pointers)

    IplImage cv_planes_0 = planes[0], cv_noise = noisyI;
    cvAddWeighted(&cv_planes_0, contrast_gain, &cv_noise, 1, -128 + brightness_gain, &cv_planes_0);
#else
    addWeighted(planes[0], contrast_gain, noisyI, 1, -128 + brightness_gain, planes[0]);
#endif

    const double color_scale = 0.5;
    // Mat::convertTo() replaces cvConvertScale.
    // One must explicitly specify the output matrix type (we keep it intact - planes[1].type())
    planes[1].convertTo(planes[1], planes[1].type(), color_scale, 128*(1-color_scale));

    // alternative form of cv::convertScale if we know the datatype at compile time ("uchar" here).
    // This expression will not create any temporary arrays ( so should be almost as fast as above)
    planes[2] = Mat_<uchar>(planes[2]*color_scale + 128*(1-color_scale));

    // Mat::mul replaces cvMul(). Again, no temporary arrays are created in case of simple expressions.
    planes[0] = planes[0].mul(planes[0], 1./255);
#endif


    merge(planes, I_YUV);                // now merge the results back
    cvtColor(I_YUV, I, COLOR_YCrCb2BGR);  // and produce the output RGB image


    namedWindow("image with grain", WINDOW_AUTOSIZE);   // use this to create images

#ifdef DEMO_MIXED_API_USE
    // this is to demonstrate that I and IplI really share the data - the result of the above
    // processing is stored in I and thus in IplI too.
    cvShowImage("image with grain", IplI);
#else
    imshow("image with grain", I); // the new MATLAB style function show
#endif
    waitKey();

    // Tip: No memory freeing is required!
    //      All the memory will be automatically released by the Vector<>, Mat and Ptr<> destructor.
    return 0;
}