Updated sample files documentation inclusions

samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp

@@ -47,6 +47,7 @@ int main( int argc, char* argv[])
         return -1;
     }
 
+    //! [dividewith]
     int divideWith = 0; // convert our input string to number - C++ style
     stringstream s;
     s << argv[2];
@@ -60,6 +61,7 @@ int main( int argc, char* argv[])
     uchar table[256];
     for (int i = 0; i < 256; ++i)
        table[i] = (uchar)(divideWith * (i/divideWith));
+    //! [dividewith]
 
     const int times = 100;
     double t;
@@ -106,15 +108,19 @@ int main( int argc, char* argv[])
     cout << "Time of reducing with the on-the-fly address generation - at function (averaged for "
         << times << " runs): " << t << " milliseconds."<< endl;
 
+    //! [table-init]
     Mat lookUpTable(1, 256, CV_8U);
     uchar* p = lookUpTable.ptr();
     for( int i = 0; i < 256; ++i)
         p[i] = table[i];
+    //! [table-init]
 
     t = (double)getTickCount();
 
     for (int i = 0; i < times; ++i)
+        //! [table-use]
         LUT(I, lookUpTable, J);
+        //! [table-use]
 
     t = 1000*((double)getTickCount() - t)/getTickFrequency();
     t /= times;
@@ -124,6 +130,7 @@ int main( int argc, char* argv[])
     return 0;
 }
 
+//! [scan-c]
 Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)
 {
     // accept only char type matrices
@@ -152,7 +159,9 @@ Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)
     }
     return I;
 }
+//! [scan-c]
 
+//! [scan-iterator]
 Mat& ScanImageAndReduceIterator(Mat& I, const uchar* const table)
 {
     // accept only char type matrices
@@ -182,7 +191,9 @@ Mat& ScanImageAndReduceIterator(Mat& I, const uchar* const table)
 
     return I;
 }
+//! [scan-iterator]
 
+//! [scan-random]
 Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar* const table)
 {
     // accept only char type matrices
@@ -216,3 +227,4 @@ Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar* const table)
 
     return I;
 }
+//! [scan-random]

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

@@ -1,3 +1,4 @@
+//! [head]
 #include <stdio.h>
 #include <iostream>
 
@@ -9,6 +10,7 @@
 
 using namespace cv;  // The new C++ interface API is inside this namespace. Import it.
 using namespace std;
+//! [head]
 
 static void help( char* progName)
 {
@@ -20,6 +22,7 @@ static void help( char* progName)
         << progName << " [image-name Default: ../data/lena.jpg]"                   << endl << endl;
 }
 
+//! [start]
 // comment out the define to use only the latest C++ API
 #define DEMO_MIXED_API_USE
 
@@ -49,15 +52,19 @@ int main( int argc, char** argv )
         return -1;
     }
 #endif
+//! [start]
 
+    //! [new]
     // 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)
+    //! [new]
 
 #if 1 // change it to 0 if you want to see a blurred and noisy version of this processing
+    //! [scanning]
     // Mat scanning
     // Method 1. process Y plane using an iterator
     MatIterator_<uchar> it = planes[0].begin<uchar>(), it_end = planes[0].end<uchar>();
@@ -80,9 +87,11 @@ int main( int argc, char** argv )
             Vxy =        saturate_cast<uchar>((Vxy-128)/2 + 128);
         }
     }
+    //! [scanning]
 
 #else
 
+    //! [noisy]
     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).
@@ -117,13 +126,14 @@ int main( int argc, char** argv )
 
     // Mat::mul replaces cvMul(). Again, no temporary arrays are created in case of simple expressions.
     planes[0] = planes[0].mul(planes[0], 1./255);
+    //! [noisy]
 #endif
 
 
+    //! [end]
     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
@@ -133,6 +143,7 @@ int main( int argc, char** argv )
 #else
     imshow("image with grain", I); // the new MATLAB style function show
 #endif
+    //! [end]
     waitKey();
 
     // Tip: No memory freeing is required!

samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp

@@ -24,62 +24,90 @@ int main(int,char**)
 {
     help();
     // create by using the constructor
+    //! [constructor]
     Mat M(2,2, CV_8UC3, Scalar(0,0,255));
     cout << "M = " << endl << " " << M << endl << endl;
+    //! [constructor]
 
     // create by using the create function()
+    //! [create]
     M.create(4,4, CV_8UC(2));
     cout << "M = "<< endl << " "  << M << endl << endl;
+    //! [create]
 
     // create multidimensional matrices
+    //! [init]
     int sz[3] = {2,2,2};
     Mat L(3,sz, CV_8UC(1), Scalar::all(0));
+    //! [init]
+
     // Cannot print via operator <<
 
     // Create using MATLAB style eye, ones or zero matrix
+    //! [matlab]
     Mat E = Mat::eye(4, 4, CV_64F);
     cout << "E = " << endl << " " << E << endl << endl;
-
     Mat O = Mat::ones(2, 2, CV_32F);
     cout << "O = " << endl << " " << O << endl << endl;
-
     Mat Z = Mat::zeros(3,3, CV_8UC1);
     cout << "Z = " << endl << " " << Z << endl << endl;
+    //! [matlab]
 
     // create a 3x3 double-precision identity matrix
+    //! [comma]
     Mat C = (Mat_<double>(3,3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
     cout << "C = " << endl << " " << C << endl << endl;
+    //! [comma]
 
+    //! [clone]
     Mat RowClone = C.row(1).clone();
     cout << "RowClone = " << endl << " " << RowClone << endl << endl;
+    //! [clone]
 
     // Fill a matrix with random values
+    //! [random]
     Mat R = Mat(3, 2, CV_8UC3);
     randu(R, Scalar::all(0), Scalar::all(255));
+    //! [random]
 
     // Demonstrate the output formating options
+    //! [out-default]
     cout << "R (default) = " << endl <<        R           << endl << endl;
+    //! [out-default]
+    //! [out-python]
     cout << "R (python)  = " << endl << format(R, Formatter::FMT_PYTHON) << endl << endl;
+    //! [out-python]
+    //! [out-numpy]
     cout << "R (numpy)   = " << endl << format(R, Formatter::FMT_NUMPY ) << endl << endl;
+    //! [out-numpy]
+    //! [out-csv]
     cout << "R (csv)     = " << endl << format(R, Formatter::FMT_CSV   ) << endl << endl;
+    //! [out-csv]
+    //! [out-c]
     cout << "R (c)       = " << endl << format(R, Formatter::FMT_C     ) << endl << endl;
+    //! [out-c]
 
+    //! [out-point2]
     Point2f P(5, 1);
     cout << "Point (2D) = " << P << endl << endl;
+    //! [out-point2]
 
+    //! [out-point3]
     Point3f P3f(2, 6, 7);
     cout << "Point (3D) = " << P3f << endl << endl;
+    //! [out-point3]
 
-
+    //! [out-vector]
     vector<float> v;
     v.push_back( (float)CV_PI);   v.push_back(2);    v.push_back(3.01f);
-
     cout << "Vector of floats via Mat = " << Mat(v) << endl << endl;
+    //! [out-vector]
 
+    //! [out-vector-points]
     vector<Point2f> vPoints(20);
     for (size_t i = 0; i < vPoints.size(); ++i)
         vPoints[i] = Point2f((float)(i * 5), (float)(i % 7));
-
     cout << "A vector of 2D Points = " << vPoints << endl << endl;
+    //! [out-vector-points]
     return 0;
 }

samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp

@@ -19,14 +19,17 @@ Scalar getMSSIM( const Mat& I1, const Mat& I2);
 double getPSNR_CUDA(const Mat& I1, const Mat& I2);  // Basic CUDA versions
 Scalar getMSSIM_CUDA( const Mat& I1, const Mat& I2);
 
+//! [psnr]
 struct BufferPSNR                                     // Optimized CUDA versions
 {   // Data allocations are very expensive on CUDA. Use a buffer to solve: allocate once reuse later.
     cuda::GpuMat gI1, gI2, gs, t1,t2;
 
     cuda::GpuMat buf;
 };
+//! [psnr]
 double getPSNR_CUDA_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b);
 
+//! [ssim]
 struct BufferMSSIM                                     // Optimized CUDA versions
 {   // Data allocations are very expensive on CUDA. Use a buffer to solve: allocate once reuse later.
     cuda::GpuMat gI1, gI2, gs, t1,t2;
@@ -44,6 +47,7 @@ struct BufferMSSIM                                     // Optimized CUDA version
 
     cuda::GpuMat buf;
 };
+//! [ssim]
 Scalar getMSSIM_CUDA_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b);
 
 static void help()
@@ -165,7 +169,7 @@ int main(int, char *argv[])
     return 0;
 }
 
-
+//! [getpsnr]
 double getPSNR(const Mat& I1, const Mat& I2)
 {
     Mat s1;
@@ -186,9 +190,9 @@ double getPSNR(const Mat& I1, const Mat& I2)
         return psnr;
     }
 }
+//! [getpsnr]
 
-
-
+//! [getpsnropt]
 double getPSNR_CUDA_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b)
 {
     b.gI1.upload(I1);
@@ -211,7 +215,9 @@ double getPSNR_CUDA_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b)
         return psnr;
     }
 }
+//! [getpsnropt]
 
+//! [getpsnrcuda]
 double getPSNR_CUDA(const Mat& I1, const Mat& I2)
 {
     cuda::GpuMat gI1, gI2, gs, t1,t2;
@@ -237,7 +243,9 @@ double getPSNR_CUDA(const Mat& I1, const Mat& I2)
         return psnr;
     }
 }
+//! [getpsnrcuda]
 
+//! [getssim]
 Scalar getMSSIM( const Mat& i1, const Mat& i2)
 {
     const double C1 = 6.5025, C2 = 58.5225;
@@ -290,7 +298,9 @@ Scalar getMSSIM( const Mat& i1, const Mat& i2)
     Scalar mssim = mean( ssim_map ); // mssim = average of ssim map
     return mssim;
 }
+//! [getssim]
 
+//! [getssimcuda]
 Scalar getMSSIM_CUDA( const Mat& i1, const Mat& i2)
 {
     const float C1 = 6.5025f, C2 = 58.5225f;
@@ -359,7 +369,9 @@ Scalar getMSSIM_CUDA( const Mat& i1, const Mat& i2)
     }
     return mssim;
 }
+//! [getssimcuda]
 
+//! [getssimopt]
 Scalar getMSSIM_CUDA_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
 {
     const float C1 = 6.5025f, C2 = 58.5225f;
@@ -430,3 +442,4 @@ Scalar getMSSIM_CUDA_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
     }
     return mssim;
 }
+//! [getssimopt]

samples/cpp/tutorial_code/introduction/display_image/display_image.cpp

@@ -1,23 +1,35 @@
+//! [includes]
 #include <opencv2/core/core.hpp>
 #include <opencv2/imgcodecs.hpp>
 #include <opencv2/highgui/highgui.hpp>
 
 #include <iostream>
 #include <string>
+//! [includes]
 
+//! [namespace]
 using namespace cv;
+//! [namespace]
+
 using namespace std;
 
 int main( int argc, char** argv )
 {
+    //! [load]
     string imageName("../data/HappyFish.jpg"); // by default
     if( argc > 1)
     {
         imageName = argv[1];
     }
+    //! [load]
 
+    //! [mat]
     Mat image;
+    //! [mat]
+
+    //! [imread]
     image = imread(imageName.c_str(), IMREAD_COLOR); // Read the file
+    //! [imread]
 
     if( image.empty() )                      // Check for invalid input
     {
@@ -25,9 +37,16 @@ int main( int argc, char** argv )
         return -1;
     }
 
+    //! [window]
     namedWindow( "Display window", WINDOW_AUTOSIZE ); // Create a window for display.
-    imshow( "Display window", image );                // Show our image inside it.
+    //! [window]
 
+    //! [imshow]
+    imshow( "Display window", image );                // Show our image inside it.
+    //! [imshow]
+
+    //! [wait]
     waitKey(0); // Wait for a keystroke in the window
+    //! [wait]
     return 0;
 }