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This commit is contained in:
OpenCV Buildbot
2012-10-17 11:12:04 +04:00
committed by Andrey Kamaev
parent 0442bca235
commit 81f826db2b
1511 changed files with 258678 additions and 258624 deletions
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16
samples/cpp/tutorial_code/HighGUI/AddingImagesTrackbar.cpp
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@@ -11,9 +11,9 @@ using namespace cv;
/** Global Variables */
const int alpha_slider_max = 100;
int alpha_slider;
int alpha_slider;
double alpha;
double beta;
double beta;
/** Matrices to store images */
Mat src1;
@@ -25,20 +25,20 @@ Mat dst;
* @brief Callback for trackbar
*/
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 );
}
/**
* @function main
* @brief Main function
* @brief Main function
*/
int main( int argc, char** argv )
{
@@ -49,7 +49,7 @@ int main( int argc, char** argv )
if( !src1.data ) { printf("Error loading src1 \n"); return -1; }
if( !src2.data ) { printf("Error loading src2 \n"); return -1; }
/// Initialize values
/// Initialize values
alpha_slider = 0;
/// Create Windows
@@ -57,12 +57,12 @@ int main( int argc, char** argv )
/// Create Trackbars
char TrackbarName[50];
sprintf( TrackbarName, "Alpha x %d", alpha_slider_max );
sprintf( TrackbarName, "Alpha x %d", alpha_slider_max );
createTrackbar( TrackbarName, "Linear Blend", &alpha_slider, alpha_slider_max, on_trackbar );
/// Show some stuff
on_trackbar( alpha_slider, 0 );
/// Wait until user press some key
waitKey(0);
return 0;

14
samples/cpp/tutorial_code/HighGUI/BasicLinearTransformsTrackbar.cpp
View File

@@ -25,16 +25,16 @@ Mat new_image;
* @brief Called whenever any of alpha or beta changes
*/
void on_trackbar( int, void* )
{
{
Mat new_image = Mat::zeros( image.size(), image.type() );
for( int y = 0; y < image.rows; y++ )
{ for( int x = 0; x < image.cols; x++ )
{ for( int c = 0; c < 3; c++ )
{
new_image.at<Vec3b>(y,x)[c] = saturate_cast<uchar>( alpha*( image.at<Vec3b>(y,x)[c] ) + beta );
new_image.at<Vec3b>(y,x)[c] = saturate_cast<uchar>( alpha*( image.at<Vec3b>(y,x)[c] ) + beta );
}
}
}
}
imshow("New Image", new_image);
}
@@ -42,14 +42,14 @@ void on_trackbar( int, void* )
/**
* @function main
* @brief Main function
* @brief Main function
*/
int main( int argc, char** argv )
{
/// Read image given by user
image = imread( argv[1] );
/// Initialize values
/// Initialize values
alpha = 1;
beta = 0;
@@ -59,12 +59,12 @@ int main( int argc, char** argv )
/// Create Trackbars
createTrackbar( "Contrast Trackbar", "New Image", &alpha, alpha_max, on_trackbar );
createTrackbar( "Brightness Trackbar", "New Image", &beta, beta_max, on_trackbar );
createTrackbar( "Brightness Trackbar", "New Image", &beta, beta_max, on_trackbar );
/// Show some stuff
imshow("Original Image", image);
imshow("New Image", image);
/// Wait until user press some key
waitKey();
return 0;

408
samples/cpp/tutorial_code/HighGUI/video-input-psnr-ssim/video-input-psnr-ssim.cpp
View File

@@ -1,205 +1,205 @@
#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/imgproc/imgproc.hpp> // Gaussian Blur
#include <opencv2/core/core.hpp> // Basic OpenCV structures (cv::Mat, Scalar)
#include <opencv2/highgui/highgui.hpp> // OpenCV window I/O
using namespace std;
using namespace cv;
double getPSNR ( const Mat& I1, const Mat& I2);
Scalar getMSSIM( const Mat& I1, const Mat& I2);
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << 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 " << endl
<< "trigger value, also with MSSIM."<< endl
<< "Usage:" << endl
<< "./video-source referenceVideo useCaseTestVideo PSNR_Trigger_Value Wait_Between_Frames " << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int argc, char *argv[], char *window_name)
{
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
char c;
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(CV_CAP_PROP_FRAME_WIDTH),
(int) captRefrnc.get(CV_CAP_PROP_FRAME_HEIGHT)),
uTSi = Size((int) captUndTst.get(CV_CAP_PROP_FRAME_WIDTH),
(int) captUndTst.get(CV_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, CV_WINDOW_AUTOSIZE );
namedWindow(WIN_UT, CV_WINDOW_AUTOSIZE );
cvMoveWindow(WIN_RF, 400 , 0); //750, 2 (bernat =0)
cvMoveWindow(WIN_UT, refS.width, 0); //1500, 2
cout << "Reference frame resolution: Width=" << refS.width << " Height=" << refS.height
<< " of nr#: " << captRefrnc.get(CV_CAP_PROP_FRAME_COUNT) << endl;
cout << "PSNR trigger value " <<
setiosflags(ios::fixed) << setprecision(3) << psnrTriggerValue << endl;
Mat frameReference, frameUnderTest;
double psnrV;
Scalar mssimV;
while( true) //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 <<"# ";
///////////////////////////////// PSNR ////////////////////////////////////////////////////
psnrV = getPSNR(frameReference,frameUnderTest); //get PSNR
cout << setiosflags(ios::fixed) << setprecision(3) << psnrV << "dB";
//////////////////////////////////// MSSIM /////////////////////////////////////////////////
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;
////////////////////////////////// Show Image /////////////////////////////////////////////
imshow( WIN_RF, frameReference);
imshow( WIN_UT, frameUnderTest);
c = cvWaitKey(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;
///////////////////////////////// FORMULA ////////////////////////////////
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;
#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/imgproc/imgproc.hpp> // Gaussian Blur
#include <opencv2/core/core.hpp> // Basic OpenCV structures (cv::Mat, Scalar)
#include <opencv2/highgui/highgui.hpp> // OpenCV window I/O
using namespace std;
using namespace cv;
double getPSNR ( const Mat& I1, const Mat& I2);
Scalar getMSSIM( const Mat& I1, const Mat& I2);
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << 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 " << endl
<< "trigger value, also with MSSIM."<< endl
<< "Usage:" << endl
<< "./video-source referenceVideo useCaseTestVideo PSNR_Trigger_Value Wait_Between_Frames " << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int argc, char *argv[], char *window_name)
{
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
char c;
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(CV_CAP_PROP_FRAME_WIDTH),
(int) captRefrnc.get(CV_CAP_PROP_FRAME_HEIGHT)),
uTSi = Size((int) captUndTst.get(CV_CAP_PROP_FRAME_WIDTH),
(int) captUndTst.get(CV_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, CV_WINDOW_AUTOSIZE );
namedWindow(WIN_UT, CV_WINDOW_AUTOSIZE );
cvMoveWindow(WIN_RF, 400 , 0); //750, 2 (bernat =0)
cvMoveWindow(WIN_UT, refS.width, 0); //1500, 2
cout << "Reference frame resolution: Width=" << refS.width << " Height=" << refS.height
<< " of nr#: " << captRefrnc.get(CV_CAP_PROP_FRAME_COUNT) << endl;
cout << "PSNR trigger value " <<
setiosflags(ios::fixed) << setprecision(3) << psnrTriggerValue << endl;
Mat frameReference, frameUnderTest;
double psnrV;
Scalar mssimV;
while( true) //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 <<"# ";
///////////////////////////////// PSNR ////////////////////////////////////////////////////
psnrV = getPSNR(frameReference,frameUnderTest); //get PSNR
cout << setiosflags(ios::fixed) << setprecision(3) << psnrV << "dB";
//////////////////////////////////// MSSIM /////////////////////////////////////////////////
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;
////////////////////////////////// Show Image /////////////////////////////////////////////
imshow( WIN_RF, frameReference);
imshow( WIN_UT, frameUnderTest);
c = cvWaitKey(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;
///////////////////////////////// FORMULA ////////////////////////////////
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;
}

192
samples/cpp/tutorial_code/HighGUI/video-write/video-write.cpp
View File

@@ -1,97 +1,97 @@
#include <iostream> // for standard I/O
#include <string> // for strings
#include <opencv2/core/core.hpp> // Basic OpenCV structures (cv::Mat)
#include <opencv2/highgui/highgui.hpp> // Video write
using namespace std;
using namespace cv;
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << endl
<< "This program shows how to write video files. You can extract the R or G or B color channel "
<< " of the input video.write " << endl
<< "Usage:" << endl
<< "./video-write inputvideoName [ R | G | B] [Y | N]" << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int argc, char *argv[], char *window_name)
{
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(CV_CAP_PROP_FOURCC)); // Get Codec Type- Int form
// Transform from int to char via Bitwise operators
char EXT[] = {ex & 0XFF , (ex & 0XFF00) >> 8,(ex & 0XFF0000) >> 16,(ex & 0XFF000000) >> 24, 0};
Size S = Size((int) inputVideo.get(CV_CAP_PROP_FRAME_WIDTH), //Acquire input size
(int) inputVideo.get(CV_CAP_PROP_FRAME_HEIGHT));
VideoWriter outputVideo; // Open the output
if (askOutputType)
outputVideo.open(NAME , ex=-1, inputVideo.get(CV_CAP_PROP_FPS),S, true);
else
outputVideo.open(NAME , ex, inputVideo.get(CV_CAP_PROP_FPS),S, true);
if (!outputVideo.isOpened())
{
cout << "Could not open the output video for write: " << source << endl;
return -1;
}
union { int v; char c[5];} uEx ;
uEx.v = ex; // From Int to char via union
uEx.c[4]='\0';
cout << "Input frame resolution: Width=" << S.width << " Height=" << S.height
<< " of nr#: " << inputVideo.get(CV_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;
while( true) //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;
#include <iostream> // for standard I/O
#include <string> // for strings
#include <opencv2/core/core.hpp> // Basic OpenCV structures (cv::Mat)
#include <opencv2/highgui/highgui.hpp> // Video write
using namespace std;
using namespace cv;
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << endl
<< "This program shows how to write video files. You can extract the R or G or B color channel "
<< " of the input video.write " << endl
<< "Usage:" << endl
<< "./video-write inputvideoName [ R | G | B] [Y | N]" << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int argc, char *argv[], char *window_name)
{
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(CV_CAP_PROP_FOURCC)); // Get Codec Type- Int form
// Transform from int to char via Bitwise operators
char EXT[] = {ex & 0XFF , (ex & 0XFF00) >> 8,(ex & 0XFF0000) >> 16,(ex & 0XFF000000) >> 24, 0};
Size S = Size((int) inputVideo.get(CV_CAP_PROP_FRAME_WIDTH), //Acquire input size
(int) inputVideo.get(CV_CAP_PROP_FRAME_HEIGHT));
VideoWriter outputVideo; // Open the output
if (askOutputType)
outputVideo.open(NAME , ex=-1, inputVideo.get(CV_CAP_PROP_FPS),S, true);
else
outputVideo.open(NAME , ex, inputVideo.get(CV_CAP_PROP_FPS),S, true);
if (!outputVideo.isOpened())
{
cout << "Could not open the output video for write: " << source << endl;
return -1;
}
union { int v; char c[5];} uEx ;
uEx.v = ex; // From Int to char via union
uEx.c[4]='\0';
cout << "Input frame resolution: Width=" << S.width << " Height=" << S.height
<< " of nr#: " << inputVideo.get(CV_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;
while( true) //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;
}

8
samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp
View File

@@ -27,7 +27,7 @@ int main( int argc, char** argv )
if( !src.data )
{ cout<<"Usage: ./Histogram_Demo <path_to_image>"<<endl;
return -1;
return -1;
}
/// Convert to grayscale
@@ -35,15 +35,15 @@ int main( int argc, char** argv )
/// Apply Histogram Equalization
equalizeHist( src, dst );
/// Display results
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
namedWindow( equalized_window, CV_WINDOW_AUTOSIZE );
imshow( source_window, src );
imshow( equalized_window, dst );
/// Wait until user exits the program
/// Wait until user exits the program
waitKey(0);
return 0;

16
samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp
View File

@@ -35,7 +35,7 @@ int main( int argc, char** argv )
/// Create windows
namedWindow( image_window, CV_WINDOW_AUTOSIZE );
namedWindow( result_window, CV_WINDOW_AUTOSIZE );
/// Create Trackbar
char* trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );
@@ -55,11 +55,11 @@ void MatchingMethod( int, void* )
/// Source image to display
Mat img_display;
img.copyTo( img_display );
/// Create the result matrix
int result_cols = img.cols - templ.cols + 1;
int result_rows = img.rows - templ.rows + 1;
int result_rows = img.rows - templ.rows + 1;
result.create( result_cols, result_rows, CV_32FC1 );
/// Do the Matching and Normalize
@@ -69,19 +69,19 @@ void MatchingMethod( int, void* )
/// Localizing the best match with minMaxLoc
double minVal; double maxVal; Point minLoc; Point maxLoc;
Point matchLoc;
minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
if( match_method == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
{ matchLoc = minLoc; }
else
else
{ matchLoc = maxLoc; }
/// Show me what you got
rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
imshow( image_window, img_display );
imshow( result_window, result );

12
samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp
View File

@@ -13,7 +13,7 @@ using namespace cv;
using namespace std;
/// Global Variables
Mat src; Mat hsv; Mat hue;
Mat src; Mat hsv; Mat hue;
int bins = 25;
/// Function Headers
@@ -33,7 +33,7 @@ int main( int argc, char** argv )
/// Use only the Hue value
hue.create( hsv.size(), hsv.depth() );
int ch[] = { 0, 0 };
mixChannels( &hsv, 1, &hue, 1, ch, 1 );
mixChannels( &hsv, 1, &hue, 1, ch, 1 );
/// Create Trackbar to enter the number of bins
char* window_image = "Source image";
@@ -57,7 +57,7 @@ int main( int argc, char** argv )
void Hist_and_Backproj(int, void* )
{
MatND hist;
int histSize = MAX( bins, 2 );
int histSize = MAX( bins, 2 );
float hue_range[] = { 0, 180 };
const float* ranges = { hue_range };
@@ -68,16 +68,16 @@ void Hist_and_Backproj(int, void* )
/// Get Backprojection
MatND backproj;
calcBackProject( &hue, 1, 0, hist, backproj, &ranges, 1, true );
/// Draw the backproj
imshow( "BackProj", backproj );
/// Draw the histogram
int w = 400; int h = 400;
int bin_w = cvRound( (double) w / histSize );
int bin_w = cvRound( (double) w / histSize );
Mat histImg = Mat::zeros( w, h, CV_8UC3 );
for( int i = 0; i < bins; i ++ )
for( int i = 0; i < bins; i ++ )
{ rectangle( histImg, Point( i*bin_w, h ), Point( (i+1)*bin_w, h - cvRound( hist.at<float>(i)*h/255.0 ) ), Scalar( 0, 0, 255 ), -1 ); }
imshow( "Histogram", histImg );

12
samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp
View File

@@ -13,7 +13,7 @@ using namespace cv;
using namespace std;
/// Global Variables
Mat src; Mat hsv;
Mat src; Mat hsv;
Mat mask;
int lo = 20; int up = 20;
@@ -33,7 +33,7 @@ int main( int argc, char** argv )
/// Transform it to HSV
cvtColor( src, hsv, CV_BGR2HSV );
/// Show the image
/// Show the image
namedWindow( window_image, CV_WINDOW_AUTOSIZE );
imshow( window_image, src );
@@ -57,7 +57,7 @@ void pickPoint (int event, int x, int y, int, void* )
// Fill and get the mask
Point seed = Point( x, y );
int newMaskVal = 255;
Scalar newVal = Scalar( 120, 120, 120 );
@@ -65,7 +65,7 @@ void pickPoint (int event, int x, int y, int, void* )
int flags = connectivity + (newMaskVal << 8 ) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
Mat mask2 = Mat::zeros( src.rows + 2, src.cols + 2, CV_8UC1 );
floodFill( src, mask2, seed, newVal, 0, Scalar( lo, lo, lo ), Scalar( up, up, up), flags );
floodFill( src, mask2, seed, newVal, 0, Scalar( lo, lo, lo ), Scalar( up, up, up), flags );
mask = mask2( Range( 1, mask2.rows - 1 ), Range( 1, mask2.cols - 1 ) );
imshow( "Mask", mask );
@@ -80,7 +80,7 @@ void Hist_and_Backproj( )
{
MatND hist;
int h_bins = 30; int s_bins = 32;
int histSize[] = { h_bins, s_bins };
int histSize[] = { h_bins, s_bins };
float h_range[] = { 0, 179 };
float s_range[] = { 0, 255 };
@@ -96,7 +96,7 @@ void Hist_and_Backproj( )
/// Get Backprojection
MatND backproj;
calcBackProject( &hsv, 1, channels, hist, backproj, ranges, 1, true );
/// Draw the backproj
imshow( "BackProj", backproj );

10
samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp
View File

@@ -25,7 +25,7 @@ int main( int argc, char** argv )
/// Load three images with different environment settings
if( argc < 4 )
{ printf("** Error. Usage: ./compareHist_Demo <image_settings0> <image_setting1> <image_settings2>\n");
return -1;
return -1;
}
src_base = imread( argv[1], 1 );
@@ -37,7 +37,7 @@ int main( int argc, char** argv )
cvtColor( src_test1, hsv_test1, CV_BGR2HSV );
cvtColor( src_test2, hsv_test2, CV_BGR2HSV );
hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );
hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );
/// Using 30 bins for hue and 32 for saturation
int h_bins = 50; int s_bins = 60;
@@ -74,14 +74,14 @@ int main( int argc, char** argv )
/// Apply the histogram comparison methods
for( int i = 0; i < 4; i++ )
{ int compare_method = i;
{ int compare_method = i;
double base_base = compareHist( hist_base, hist_base, compare_method );
double base_half = compareHist( hist_base, hist_half_down, compare_method );
double base_test1 = compareHist( hist_base, hist_test1, compare_method );
double base_test2 = compareHist( hist_base, hist_test2, compare_method );
printf( " Method [%d] Perfect, Base-Half, Base-Test(1), Base-Test(2) : %f, %f, %f, %f \n", i, base_base, base_half , base_test1, base_test2 );
}
}
printf( "Done \n" );

6
samples/cpp/tutorial_code/ImgProc/AddingImages.cpp
View File

@@ -12,12 +12,12 @@ using namespace cv;
/**
* @function main
* @brief Main function
* @brief Main function
*/
int main( int argc, char** argv )
{
double alpha = 0.5; double beta; double input;
double alpha = 0.5; double beta; double input;
Mat src1, src2, dst;
@@ -43,7 +43,7 @@ int main( int argc, char** argv )
beta = ( 1.0 - alpha );
addWeighted( src1, alpha, src2, beta, 0.0, dst);
imshow( "Linear Blend", dst );

10
samples/cpp/tutorial_code/ImgProc/BasicLinearTransforms.cpp
View File

@@ -15,7 +15,7 @@ int beta; /**< Simple brightness control */
/**
* @function main
* @brief Main function
* @brief Main function
*/
int main( int argc, char** argv )
{
@@ -23,7 +23,7 @@ int main( int argc, char** argv )
Mat image = imread( argv[1] );
Mat new_image = Mat::zeros( image.size(), image.type() );
/// Initialize values
/// Initialize values
std::cout<<" Basic Linear Transforms "<<std::endl;
std::cout<<"-------------------------"<<std::endl;
std::cout<<"* Enter the alpha value [1.0-3.0]: ";std::cin>>alpha;
@@ -38,9 +38,9 @@ int main( int argc, char** argv )
{ for( int x = 0; x < image.cols; x++ )
{ for( int c = 0; c < 3; c++ )
{
new_image.at<Vec3b>(y,x)[c] = saturate_cast<uchar>( alpha*( image.at<Vec3b>(y,x)[c] ) + beta );
new_image.at<Vec3b>(y,x)[c] = saturate_cast<uchar>( alpha*( image.at<Vec3b>(y,x)[c] ) + beta );
}
}
}
}
/// Create Windows
@@ -51,7 +51,7 @@ int main( int argc, char** argv )
imshow("Original Image", image);
imshow("New Image", new_image);
/// Wait until user press some key
waitKey();
return 0;

46
samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp
View File

@@ -36,29 +36,29 @@ int main( int argc, char** argv )
if( !src.data )
{ return -1; }
/// Create windows
namedWindow( "Erosion Demo", CV_WINDOW_AUTOSIZE );
namedWindow( "Dilation Demo", CV_WINDOW_AUTOSIZE );
cvMoveWindow( "Dilation Demo", src.cols, 0 );
/// Create Erosion Trackbar
createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Erosion Demo",
&erosion_elem, max_elem,
Erosion );
createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Erosion Demo",
&erosion_elem, max_elem,
Erosion );
createTrackbar( "Kernel size:\n 2n +1", "Erosion Demo",
&erosion_size, max_kernel_size,
Erosion );
createTrackbar( "Kernel size:\n 2n +1", "Erosion Demo",
&erosion_size, max_kernel_size,
Erosion );
/// Create Dilation Trackbar
createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Dilation Demo",
&dilation_elem, max_elem,
Dilation );
createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Dilation Demo",
&dilation_elem, max_elem,
Dilation );
createTrackbar( "Kernel size:\n 2n +1", "Dilation Demo",
&dilation_size, max_kernel_size,
Dilation );
createTrackbar( "Kernel size:\n 2n +1", "Dilation Demo",
&dilation_size, max_kernel_size,
Dilation );
/// Default start
Erosion( 0, 0 );
@@ -68,7 +68,7 @@ int main( int argc, char** argv )
return 0;
}
/**
/**
* @function Erosion
*/
void Erosion( int, void* )
@@ -78,15 +78,15 @@ void Erosion( int, void* )
else if( erosion_elem == 1 ){ erosion_type = MORPH_CROSS; }
else if( erosion_elem == 2) { erosion_type = MORPH_ELLIPSE; }
Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
/// Apply the erosion operation
erode( src, erosion_dst, element );
imshow( "Erosion Demo", erosion_dst );
imshow( "Erosion Demo", erosion_dst );
}
/**
/**
* @function Dilation
*/
void Dilation( int, void* )
@@ -96,10 +96,10 @@ void Dilation( int, void* )
else if( dilation_elem == 1 ){ dilation_type = MORPH_CROSS; }
else if( dilation_elem == 2) { dilation_type = MORPH_ELLIPSE; }
Mat element = getStructuringElement( dilation_type,
Size( 2*dilation_size + 1, 2*dilation_size+1 ),
Point( dilation_size, dilation_size ) );
Mat element = getStructuringElement( dilation_type,
Size( 2*dilation_size + 1, 2*dilation_size+1 ),
Point( dilation_size, dilation_size ) );
/// Apply the dilation operation
dilate( src, dilation_dst, element );
imshow( "Dilation Demo", dilation_dst );
imshow( "Dilation Demo", dilation_dst );
}

20
samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp
View File

@@ -37,7 +37,7 @@ int main( int argc, char** argv )
if( !src.data )
{ return -1; }
/// Create window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
@@ -45,14 +45,14 @@ int main( int argc, char** argv )
createTrackbar("Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat", window_name, &morph_operator, max_operator, Morphology_Operations );
/// Create Trackbar to select kernel type
createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
&morph_elem, max_elem,
Morphology_Operations );
createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
&morph_elem, max_elem,
Morphology_Operations );
/// Create Trackbar to choose kernel size
createTrackbar( "Kernel size:\n 2n +1", window_name,
&morph_size, max_kernel_size,
Morphology_Operations );
createTrackbar( "Kernel size:\n 2n +1", window_name,
&morph_size, max_kernel_size,
Morphology_Operations );
/// Default start
Morphology_Operations( 0, 0 );
@@ -61,7 +61,7 @@ int main( int argc, char** argv )
return 0;
}
/**
/**
* @function Morphology_Operations
*/
void Morphology_Operations( int, void* )
@@ -70,11 +70,11 @@ void Morphology_Operations( int, void* )
// Since MORPH_X : 2,3,4,5 and 6
int operation = morph_operator + 2;
Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
/// Apply the specified morphology operation
morphologyEx( src, dst, operation, element );
imshow( window_name, dst );
imshow( window_name, dst );
}

10
samples/cpp/tutorial_code/ImgProc/Pyramids.cpp
View File

@@ -36,7 +36,7 @@ int main( int argc, char** argv )
{ printf(" No data! -- Exiting the program \n");
return -1; }
tmp = src;
tmp = src;
dst = tmp;
/// Create window
@@ -45,7 +45,7 @@ int main( int argc, char** argv )
/// Loop
while( true )
{
{
int c;
c = waitKey(10);
@@ -53,7 +53,7 @@ int main( int argc, char** argv )
{ break; }
if( (char)c == 'u' )
{ pyrUp( tmp, dst, Size( tmp.cols*2, tmp.rows*2 ) );
printf( "** Zoom In: Image x 2 \n" );
printf( "** Zoom In: Image x 2 \n" );
}
else if( (char)c == 'd' )
{ pyrDown( tmp, dst, Size( tmp.cols/2, tmp.rows/2 ) );
@@ -62,8 +62,8 @@ int main( int argc, char** argv )
imshow( window_name, dst );
tmp = dst;
}
}
return 0;
}

16
samples/cpp/tutorial_code/ImgProc/Smoothing.cpp
View File

@@ -26,15 +26,15 @@ int display_caption( char* caption );
int display_dst( int delay );
/**
* function main
/**
* function main
*/
int main( int argc, char** argv )
{
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Load the source image
src = imread( "../images/lena.png", 1 );
src = imread( "../images/lena.png", 1 );
if( display_caption( "Original Image" ) != 0 ) { return 0; }
@@ -42,7 +42,7 @@ int main( int argc, char** argv )
if( display_dst( DELAY_CAPTION ) != 0 ) { return 0; }
/// Applying Homogeneous blur
/// Applying Homogeneous blur
if( display_caption( "Homogeneous Blur" ) != 0 ) { return 0; }
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
@@ -50,7 +50,7 @@ int main( int argc, char** argv )
if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } }
/// Applying Gaussian blur
/// Applying Gaussian blur
if( display_caption( "Gaussian Blur" ) != 0 ) { return 0; }
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
@@ -87,8 +87,8 @@ int main( int argc, char** argv )
int display_caption( char* caption )
{
dst = Mat::zeros( src.size(), src.type() );
putText( dst, caption,
Point( src.cols/4, src.rows/2),
putText( dst, caption,
Point( src.cols/4, src.rows/2),
CV_FONT_HERSHEY_COMPLEX, 1, Scalar(255, 255, 255) );
imshow( window_name, dst );
@@ -105,5 +105,5 @@ int display_dst( int delay )
imshow( window_name, dst );
int c = waitKey ( delay );
if( c >= 0 ) { return -1; }
return 0;
return 0;
}

12
samples/cpp/tutorial_code/ImgProc/Threshold.cpp
View File

@@ -43,13 +43,13 @@ int main( int argc, char** argv )
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Create Trackbar to choose type of Threshold
createTrackbar( trackbar_type,
window_name, &threshold_type,
max_type, Threshold_Demo );
createTrackbar( trackbar_type,
window_name, &threshold_type,
max_type, Threshold_Demo );
createTrackbar( trackbar_value,
window_name, &threshold_value,
max_value, Threshold_Demo );
window_name, &threshold_value,
max_value, Threshold_Demo );
/// Call the function to initialize
Threshold_Demo( 0, 0 );
@@ -60,7 +60,7 @@ int main( int argc, char** argv )
int c;
c = waitKey( 20 );
if( (char)c == 27 )
{ break; }
{ break; }
}
}

2
samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp
View File

@@ -37,7 +37,7 @@ void CannyThreshold(int, void*)
/// Using Canny's output as a mask, we display our result
dst = Scalar::all(0);
src.copyTo( dst, detected_edges);
imshow( window_name, dst );
}

6
samples/cpp/tutorial_code/ImgTrans/Geometric_Transforms_Demo.cpp
View File

@@ -32,14 +32,14 @@ int main( int argc, char** argv )
/// Load the image
src = imread( argv[1], 1 );
/// Set the dst image the same type and size as src
/// Set the dst image the same type and size as src
warp_dst = Mat::zeros( src.rows, src.cols, src.type() );
/// Set your 3 points to calculate the Affine Transform
srcTri[0] = Point2f( 0,0 );
srcTri[1] = Point2f( src.cols - 1, 0 );
srcTri[2] = Point2f( 0, src.rows - 1 );
dstTri[0] = Point2f( src.cols*0.0, src.rows*0.33 );
dstTri[1] = Point2f( src.cols*0.85, src.rows*0.25 );
dstTri[2] = Point2f( src.cols*0.15, src.rows*0.7 );
@@ -62,7 +62,7 @@ int main( int argc, char** argv )
/// Rotate the warped image
warpAffine( warp_dst, warp_rotate_dst, rot_mat, warp_dst.size() );
/// Show what you got
namedWindow( source_window, CV_WINDOW_AUTOSIZE );

4
samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp
View File

@@ -24,7 +24,7 @@ int main(int argc, char** argv)
if( !src.data )
{ return -1; }
/// Convert it to gray
/// Convert it to gray
cvtColor( src, src_gray, CV_BGR2GRAY );
/// Reduce the noise so we avoid false circle detection
@@ -46,7 +46,7 @@ int main(int argc, char** argv)
circle( src, center, radius, Scalar(0,0,255), 3, 8, 0 );
}
/// Show your results
/// Show your results
namedWindow( "Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE );
imshow( "Hough Circle Transform Demo", src );

12
samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp
View File

@@ -15,8 +15,8 @@ using namespace std;
/// Global variables
/** General variables */
Mat src, edges;
Mat src_gray;
Mat src, edges;
Mat src_gray;
Mat standard_hough, probabilistic_hough;
int min_threshold = 50;
int max_trackbar = 150;
@@ -47,7 +47,7 @@ int main( int argc, char** argv )
/// Pass the image to gray
cvtColor( src, src_gray, CV_RGB2GRAY );
/// Apply Canny edge detector
Canny( src_gray, edges, 50, 200, 3 );
@@ -88,7 +88,7 @@ void Standard_Hough( int, void* )
cvtColor( edges, standard_hough, CV_GRAY2BGR );
/// 1. Use Standard Hough Transform
HoughLines( edges, s_lines, 1, CV_PI/180, min_threshold + s_trackbar, 0, 0 );
HoughLines( edges, s_lines, 1, CV_PI/180, min_threshold + s_trackbar, 0, 0 );
/// Show the result
for( int i = 0; i < s_lines.size(); i++ )
@@ -100,8 +100,8 @@ void Standard_Hough( int, void* )
Point pt1( cvRound(x0 + alpha*(-sin_t)), cvRound(y0 + alpha*cos_t) );
Point pt2( cvRound(x0 - alpha*(-sin_t)), cvRound(y0 - alpha*cos_t) );
line( standard_hough, pt1, pt2, Scalar(255,0,0), 3, CV_AA);
}
line( standard_hough, pt1, pt2, Scalar(255,0,0), 3, CV_AA);
}
imshow( standard_name, standard_hough );
}

2
samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp
View File

@@ -18,7 +18,7 @@ int main( int argc, char** argv )
{
Mat src, src_gray, dst;
int kernel_size = 3;
int kernel_size = 3;
int scale = 1;
int delta = 0;
int ddepth = CV_16S;

46
samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp
View File

@@ -32,7 +32,7 @@ int main( int argc, char** argv )
dst.create( src.size(), src.type() );
map_x.create( src.size(), CV_32FC1 );
map_y.create( src.size(), CV_32FC1 );
/// Create window
namedWindow( remap_window, CV_WINDOW_AUTOSIZE );
@@ -47,7 +47,7 @@ int main( int argc, char** argv )
/// Update map_x & map_y. Then apply remap
update_map();
remap( src, dst, map_x, map_y, CV_INTER_LINEAR, BORDER_CONSTANT, Scalar(0, 0, 0) );
remap( src, dst, map_x, map_y, CV_INTER_LINEAR, BORDER_CONSTANT, Scalar(0, 0, 0) );
// Display results
imshow( remap_window, dst );
@@ -65,34 +65,34 @@ void update_map( void )
for( int j = 0; j < src.rows; j++ )
{ for( int i = 0; i < src.cols; i++ )
{
{
switch( ind )
{
case 0:
if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
{
case 0:
if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
{
map_x.at<float>(j,i) = 2*( i - src.cols*0.25 ) + 0.5 ;
map_y.at<float>(j,i) = 2*( j - src.rows*0.25 ) + 0.5 ;
}
else
{ map_x.at<float>(j,i) = 0 ;
map_y.at<float>(j,i) = 0 ;
map_x.at<float>(j,i) = 2*( i - src.cols*0.25 ) + 0.5 ;
map_y.at<float>(j,i) = 2*( j - src.rows*0.25 ) + 0.5 ;
}
else
{ map_x.at<float>(j,i) = 0 ;
map_y.at<float>(j,i) = 0 ;
}
break;
case 1:
map_x.at<float>(j,i) = i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
case 1:
map_x.at<float>(j,i) = i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
case 2:
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = j ;
break;
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = j ;
break;
case 3:
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
} // end of switch
}
}
}
ind++;
}

10
samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp
View File

@@ -18,7 +18,7 @@ int main( int argc, char** argv )
{
Mat src, src_gray;
Mat grad;
Mat grad;
char* window_name = "Sobel Demo - Simple Edge Detector";
int scale = 1;
int delta = 0;
@@ -43,15 +43,15 @@ int main( int argc, char** argv )
/// Generate grad_x and grad_y
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
/// Gradient X
//Scharr( src_gray, grad_x, ddepth, 1, 0, scale, delta, BORDER_DEFAULT );
Sobel( src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT );
Sobel( src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT );
convertScaleAbs( grad_x, abs_grad_x );
/// Gradient Y
/// Gradient Y
//Scharr( src_gray, grad_y, ddepth, 0, 1, scale, delta, BORDER_DEFAULT );
Sobel( src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT );
Sobel( src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT );
convertScaleAbs( grad_y, abs_grad_y );
/// Total Gradient (approximate)

10
samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
View File

@@ -11,7 +11,7 @@
using namespace cv;
/// Global Variables
/// Global Variables
Mat src, dst;
int top, bottom, left, right;
int borderType;
@@ -29,10 +29,10 @@ int main( int argc, char** argv )
/// Load an image
src = imread( argv[1] );
if( !src.data )
{ return -1;
printf(" No data entered, please enter the path to an image file \n");
printf(" No data entered, please enter the path to an image file \n");
}
/// Brief how-to for this program
@@ -46,12 +46,12 @@ int main( int argc, char** argv )
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Initialize arguments for the filter
top = (int) (0.05*src.rows); bottom = (int) (0.05*src.rows);
top = (int) (0.05*src.rows); bottom = (int) (0.05*src.rows);
left = (int) (0.05*src.cols); right = (int) (0.05*src.cols);
dst = src;
imshow( window_name, dst );
while( true )
{
c = waitKey(500);

6
samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp
View File

@@ -22,7 +22,7 @@ int main ( int argc, char** argv )
Mat kernel;
Point anchor;
double delta;
int ddepth;
int ddepth;
int kernel_size;
char* window_name = "filter2D Demo";
@@ -36,7 +36,7 @@ int main ( int argc, char** argv )
/// Create window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Initialize arguments for the filter
anchor = Point( -1, -1 );
delta = 0;
@@ -60,6 +60,6 @@ int main ( int argc, char** argv )
imshow( window_name, dst );
ind++;
}
return 0;
}

6
samples/cpp/tutorial_code/ShapeDescriptors/findContours_demo.cpp
View File

@@ -56,15 +56,15 @@ void thresh_callback(int, void* )
/// Detect edges using canny
Canny( src_gray, canny_output, thresh, thresh*2, 3 );
/// Find contours
/// Find contours
findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Draw contours
Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
{
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
}
/// Show in a window

12
samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp
View File

@@ -56,7 +56,7 @@ void thresh_callback(int, void* )
/// Detect edges using Threshold
threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
/// Find contours
/// Find contours
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Approximate contours to polygons + get bounding rects and circles
@@ -66,18 +66,18 @@ void thresh_callback(int, void* )
vector<float>radius( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
boundRect[i] = boundingRect( Mat(contours_poly[i]) );
{ approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
boundRect[i] = boundingRect( Mat(contours_poly[i]) );
minEnclosingCircle( contours_poly[i], center[i], radius[i] );
}
}
/// Draw polygonal contour + bonding rects + circles
Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
{
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours_poly, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
drawContours( drawing, contours_poly, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
rectangle( drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0 );
circle( drawing, center[i], (int)radius[i], color, 2, 8, 0 );
}

14
samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo2.cpp
View File

@@ -56,7 +56,7 @@ void thresh_callback(int, void* )
/// Detect edges using Threshold
threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
/// Find contours
/// Find contours
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Find the rotated rectangles and ellipses for each contour
@@ -64,24 +64,24 @@ void thresh_callback(int, void* )
vector<RotatedRect> minEllipse( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ minRect[i] = minAreaRect( Mat(contours[i]) );
{ minRect[i] = minAreaRect( Mat(contours[i]) );
if( contours[i].size() > 5 )
{ minEllipse[i] = fitEllipse( Mat(contours[i]) ); }
}
}
/// Draw contours + rotated rects + ellipses
Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
{
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
// contour
drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
// ellipse
ellipse( drawing, minEllipse[i], color, 2, 8 );
// rotated rectangle
// rotated rectangle
Point2f rect_points[4]; minRect[i].points( rect_points );
for( int j = 0; j < 4; j++ )
line( drawing, rect_points[j], rect_points[(j+1)%4], color, 1, 8 );
line( drawing, rect_points[j], rect_points[(j+1)%4], color, 1, 8 );
}
/// Show in a window

10
samples/cpp/tutorial_code/ShapeDescriptors/hull_demo.cpp
View File

@@ -58,21 +58,21 @@ void thresh_callback(int, void* )
/// Detect edges using Threshold
threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
/// Find contours
/// Find contours
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Find the convex hull object for each contour
vector<vector<Point> >hull( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ convexHull( Mat(contours[i]), hull[i], false ); }
{ convexHull( Mat(contours[i]), hull[i], false ); }
/// Draw contours + hull results
Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
{
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
drawContours( drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
drawContours( drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
}
/// Show in a window

12
samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp
View File

@@ -56,7 +56,7 @@ void thresh_callback(int, void* )
/// Detect edges using canny
Canny( src_gray, canny_output, thresh, thresh*2, 3 );
/// Find contours
/// Find contours
findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Get the moments
@@ -64,7 +64,7 @@ void thresh_callback(int, void* )
for( int i = 0; i < contours.size(); i++ )
{ mu[i] = moments( contours[i], false ); }
/// Get the mass centers:
/// Get the mass centers:
vector<Point2f> mc( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }
@@ -72,9 +72,9 @@ void thresh_callback(int, void* )
/// Draw contours
Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
{
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
circle( drawing, mc[i], 4, color, -1, 8, 0 );
}
@@ -86,9 +86,9 @@ void thresh_callback(int, void* )
printf("\t Info: Area and Contour Length \n");
for( int i = 0; i< contours.size(); i++ )
{
printf(" * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f \n", i, mu[i].m00, contourArea(contours[i]), arcLength( contours[i], true ) );
printf(" * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f \n", i, mu[i].m00, contourArea(contours[i]), arcLength( contours[i], true ) );
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
circle( drawing, mc[i], 4, color, -1, 8, 0 );
}
}

12
samples/cpp/tutorial_code/ShapeDescriptors/pointPolygonTest_demo.cpp
View File

@@ -34,13 +34,13 @@ int main( int argc, char** argv )
/// Draw it in src
for( int j = 0; j < 6; j++ )
{ line( src, vert[j], vert[(j+1)%6], Scalar( 255 ), 3, 8 ); }
{ line( src, vert[j], vert[(j+1)%6], Scalar( 255 ), 3, 8 ); }
/// Get the contours
vector<vector<Point> > contours; vector<Vec4i> hierarchy;
Mat src_copy = src.clone();
findContours( src_copy, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
findContours( src_copy, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
/// Calculate the distances to the contour
Mat raw_dist( src.size(), CV_32FC1 );
@@ -53,19 +53,19 @@ int main( int argc, char** argv )
double minVal; double maxVal;
minMaxLoc( raw_dist, &minVal, &maxVal, 0, 0, Mat() );
minVal = abs(minVal); maxVal = abs(maxVal);
/// Depicting the distances graphically
Mat drawing = Mat::zeros( src.size(), CV_8UC3 );
for( int j = 0; j < src.rows; j++ )
{ for( int i = 0; i < src.cols; i++ )
{
{
if( raw_dist.at<float>(j,i) < 0 )
{ drawing.at<Vec3b>(j,i)[0] = 255 - (int) abs(raw_dist.at<float>(j,i))*255/minVal; }
else if( raw_dist.at<float>(j,i) > 0 )
{ drawing.at<Vec3b>(j,i)[2] = 255 - (int) raw_dist.at<float>(j,i)*255/maxVal; }
{ drawing.at<Vec3b>(j,i)[2] = 255 - (int) raw_dist.at<float>(j,i)*255/maxVal; }
else
{ drawing.at<Vec3b>(j,i)[0] = 255; drawing.at<Vec3b>(j,i)[1] = 255; drawing.at<Vec3b>(j,i)[2] = 255; }
{ drawing.at<Vec3b>(j,i)[0] = 255; drawing.at<Vec3b>(j,i)[1] = 255; drawing.at<Vec3b>(j,i)[2] = 255; }
}
}

16
samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp
View File

@@ -37,7 +37,7 @@ int main( int argc, char** argv )
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
createTrackbar( "Threshold: ", source_window, &thresh, max_thresh, cornerHarris_demo );
imshow( source_window, src );
cornerHarris_demo( 0, 0 );
waitKey(0);
@@ -58,25 +58,25 @@ void cornerHarris_demo( int, void* )
int blockSize = 2;
int apertureSize = 3;
double k = 0.04;
/// Detecting corners
cornerHarris( src_gray, dst, blockSize, apertureSize, k, BORDER_DEFAULT );
/// Normalizing
normalize( dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat() );
convertScaleAbs( dst_norm, dst_norm_scaled );
convertScaleAbs( dst_norm, dst_norm_scaled );
/// Drawing a circle around corners
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 );
{
circle( dst_norm_scaled, Point( i, j ), 5, Scalar(0), 2, 8, 0 );
}
}
}
/// Showing the result
}
}
/// Showing the result
namedWindow( corners_window, CV_WINDOW_AUTOSIZE );
imshow( corners_window, dst_norm_scaled );
}

28
samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp
View File

@@ -38,7 +38,7 @@ int main( int argc, char** argv )
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
/// Create Trackbar to set the number of corners
createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );
createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );
imshow( source_window, src );
@@ -55,7 +55,7 @@ int main( int argc, char** argv )
void goodFeaturesToTrack_Demo( int, void* )
{
if( maxCorners < 1 ) { maxCorners = 1; }
/// Parameters for Shi-Tomasi algorithm
vector<Point2f> corners;
double qualityLevel = 0.01;
@@ -69,16 +69,16 @@ void goodFeaturesToTrack_Demo( int, void* )
copy = src.clone();
/// Apply corner detection
goodFeaturesToTrack( src_gray,
corners,
maxCorners,
qualityLevel,
minDistance,
Mat(),
blockSize,
useHarrisDetector,
k );
goodFeaturesToTrack( src_gray,
corners,
maxCorners,
qualityLevel,
minDistance,
Mat(),
blockSize,
useHarrisDetector,
k );
/// Draw corners detected
cout<<"** Number of corners detected: "<<corners.size()<<endl;
@@ -88,7 +88,7 @@ void goodFeaturesToTrack_Demo( int, void* )
/// Show what you got
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
imshow( source_window, copy );
imshow( source_window, copy );
/// Set the neeed parameters to find the refined corners
Size winSize = Size( 5, 5 );
@@ -100,6 +100,6 @@ void goodFeaturesToTrack_Demo( int, void* )
/// Write them down
for( int i = 0; i < corners.size(); i++ )
{ cout<<" -- Refined Corner ["<<i<<"] ("<<corners[i].x<<","<<corners[i].y<<")"<<endl; }
{ cout<<" -- Refined Corner ["<<i<<"] ("<<corners[i].x<<","<<corners[i].y<<")"<<endl; }
}

26
samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp
View File

@@ -38,7 +38,7 @@ int main( int argc, char** argv )
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
/// Create Trackbar to set the number of corners
createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );
createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );
imshow( source_window, src );
@@ -55,7 +55,7 @@ int main( int argc, char** argv )
void goodFeaturesToTrack_Demo( int, void* )
{
if( maxCorners < 1 ) { maxCorners = 1; }
/// Parameters for Shi-Tomasi algorithm
vector<Point2f> corners;
double qualityLevel = 0.01;
@@ -69,16 +69,16 @@ void goodFeaturesToTrack_Demo( int, void* )
copy = src.clone();
/// Apply corner detection
goodFeaturesToTrack( src_gray,
corners,
maxCorners,
qualityLevel,
minDistance,
Mat(),
blockSize,
useHarrisDetector,
k );
goodFeaturesToTrack( src_gray,
corners,
maxCorners,
qualityLevel,
minDistance,
Mat(),
blockSize,
useHarrisDetector,
k );
/// Draw corners detected
cout<<"** Number of corners detected: "<<corners.size()<<endl;
@@ -88,6 +88,6 @@ void goodFeaturesToTrack_Demo( int, void* )
/// Show what you got
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
imshow( source_window, copy );
imshow( source_window, copy );
}

24
samples/cpp/tutorial_code/calib3d/camera_calibration/VID5.xml
View File

@@ -1,13 +1,13 @@
<?xml version="1.0"?>
<opencv_storage>
<images>
images/CameraCalibraation/VID5/xx1.jpg
images/CameraCalibraation/VID5/xx2.jpg
images/CameraCalibraation/VID5/xx3.jpg
images/CameraCalibraation/VID5/xx4.jpg
images/CameraCalibraation/VID5/xx5.jpg
images/CameraCalibraation/VID5/xx6.jpg
images/CameraCalibraation/VID5/xx7.jpg
images/CameraCalibraation/VID5/xx8.jpg
</images>
<?xml version="1.0"?>
<opencv_storage>
<images>
images/CameraCalibraation/VID5/xx1.jpg
images/CameraCalibraation/VID5/xx2.jpg
images/CameraCalibraation/VID5/xx3.jpg
images/CameraCalibraation/VID5/xx4.jpg
images/CameraCalibraation/VID5/xx5.jpg
images/CameraCalibraation/VID5/xx6.jpg
images/CameraCalibraation/VID5/xx7.jpg
images/CameraCalibraation/VID5/xx8.jpg
</images>
</opencv_storage>

1102
samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp
View File

File diff suppressed because it is too large Load Diff

90
samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml
View File

@@ -1,46 +1,46 @@
<?xml version="1.0"?>
<opencv_storage>
<Settings>
<!-- Number of inner corners per a item row and column. (square, circle) -->
<BoardSize_Width> 9</BoardSize_Width>
<BoardSize_Height>6</BoardSize_Height>
<!-- The size of a square in some user defined metric system (pixel, millimeter)-->
<Square_Size>50</Square_Size>
<!-- The type of input used for camera calibration. One of: CHESSBOARD CIRCLES_GRID ASYMMETRIC_CIRCLES_GRID -->
<Calibrate_Pattern>"CHESSBOARD"</Calibrate_Pattern>
<!-- The input to use for calibration.
To use an input camera -> give the ID of the camera, like "1"
To use an input video -> give the path of the input video, like "/tmp/x.avi"
To use an image list -> give the path to the XML or YAML file containing the list of the images, like "/tmp/circles_list.xml"
-->
<Input>"images/CameraCalibraation/VID5/VID5.xml"</Input>
<!-- If true (non-zero) we flip the input images around the horizontal axis.-->
<Input_FlipAroundHorizontalAxis>0</Input_FlipAroundHorizontalAxis>
<!-- Time delay between frames in case of camera. -->
<Input_Delay>100</Input_Delay>
<!-- How many frames to use, for calibration. -->
<Calibrate_NrOfFrameToUse>25</Calibrate_NrOfFrameToUse>
<!-- Consider only fy as a free parameter, the ratio fx/fy stays the same as in the input cameraMatrix.
Use or not setting. 0 - False Non-Zero - True-->
<Calibrate_FixAspectRatio> 1 </Calibrate_FixAspectRatio>
<!-- If true (non-zero) tangential distortion coefficients are set to zeros and stay zero.-->
<Calibrate_AssumeZeroTangentialDistortion>1</Calibrate_AssumeZeroTangentialDistortion>
<!-- If true (non-zero) the principal point is not changed during the global optimization.-->
<Calibrate_FixPrincipalPointAtTheCenter> 1 </Calibrate_FixPrincipalPointAtTheCenter>
<!-- The name of the output log file. -->
<Write_outputFileName>"out_camera_data.xml"</Write_outputFileName>
<!-- If true (non-zero) we write to the output file the feature points.-->
<Write_DetectedFeaturePoints>1</Write_DetectedFeaturePoints>
<!-- If true (non-zero) we write to the output file the extrinsic camera parameters.-->
<Write_extrinsicParameters>1</Write_extrinsicParameters>
<!-- If true (non-zero) we show after calibration the undistorted images.-->
<Show_UndistortedImage>1</Show_UndistortedImage>
</Settings>
<?xml version="1.0"?>
<opencv_storage>
<Settings>
<!-- Number of inner corners per a item row and column. (square, circle) -->
<BoardSize_Width> 9</BoardSize_Width>
<BoardSize_Height>6</BoardSize_Height>
<!-- The size of a square in some user defined metric system (pixel, millimeter)-->
<Square_Size>50</Square_Size>
<!-- The type of input used for camera calibration. One of: CHESSBOARD CIRCLES_GRID ASYMMETRIC_CIRCLES_GRID -->
<Calibrate_Pattern>"CHESSBOARD"</Calibrate_Pattern>
<!-- The input to use for calibration.
To use an input camera -> give the ID of the camera, like "1"
To use an input video -> give the path of the input video, like "/tmp/x.avi"
To use an image list -> give the path to the XML or YAML file containing the list of the images, like "/tmp/circles_list.xml"
-->
<Input>"images/CameraCalibraation/VID5/VID5.xml"</Input>
<!-- If true (non-zero) we flip the input images around the horizontal axis.-->
<Input_FlipAroundHorizontalAxis>0</Input_FlipAroundHorizontalAxis>
<!-- Time delay between frames in case of camera. -->
<Input_Delay>100</Input_Delay>
<!-- How many frames to use, for calibration. -->
<Calibrate_NrOfFrameToUse>25</Calibrate_NrOfFrameToUse>
<!-- Consider only fy as a free parameter, the ratio fx/fy stays the same as in the input cameraMatrix.
Use or not setting. 0 - False Non-Zero - True-->
<Calibrate_FixAspectRatio> 1 </Calibrate_FixAspectRatio>
<!-- If true (non-zero) tangential distortion coefficients are set to zeros and stay zero.-->
<Calibrate_AssumeZeroTangentialDistortion>1</Calibrate_AssumeZeroTangentialDistortion>
<!-- If true (non-zero) the principal point is not changed during the global optimization.-->
<Calibrate_FixPrincipalPointAtTheCenter> 1 </Calibrate_FixPrincipalPointAtTheCenter>
<!-- The name of the output log file. -->
<Write_outputFileName>"out_camera_data.xml"</Write_outputFileName>
<!-- If true (non-zero) we write to the output file the feature points.-->
<Write_DetectedFeaturePoints>1</Write_DetectedFeaturePoints>
<!-- If true (non-zero) we write to the output file the extrinsic camera parameters.-->
<Write_extrinsicParameters>1</Write_extrinsicParameters>
<!-- If true (non-zero) we show after calibration the undistorted images.-->
<Show_UndistortedImage>1</Show_UndistortedImage>
</Settings>
</opencv_storage>

704
samples/cpp/tutorial_code/calib3d/camera_calibration/out_camera_data.yml
View File

@@ -1,352 +1,352 @@
%YAML:1.0
calibration_Time: "08/19/11 20:44:38"
nrOfFrames: 8
image_Width: 640
image_Height: 480
board_Width: 9
board_Height: 6
square_Size: 50.
FixAspectRatio: 1.
# flags: +fix_aspectRatio +fix_principal_point +zero_tangent_dist
flagValue: 14
Camera_Matrix: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 6.5746697810243404e+002, 0., 3.1950000000000000e+002, 0.,
6.5746697810243404e+002, 2.3950000000000000e+002, 0., 0., 1. ]
Distortion_Coefficients: !!opencv-matrix
rows: 5
cols: 1
dt: d
data: [ -4.1802327018241026e-001, 5.0715243805833121e-001, 0., 0.,
-5.7843596847939704e-001 ]
Avg_Reprojection_Error: 3.8441346462381665e-001
Per_View_Reprojection_Errors: !!opencv-matrix
rows: 8
cols: 1
dt: f
data: [ 5.04357755e-001, 4.85754758e-001, 3.99563968e-001,
4.13829178e-001, 3.53570908e-001, 3.21116358e-001,
2.74473161e-001, 2.39761785e-001 ]
# a set of 6-tuples (rotation vector + translation vector) for each view
Extrinsic_Parameters: !!opencv-matrix
rows: 8
cols: 6
dt: d
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6.8105689976949157e-002, 6.4426739692440949e-002,
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-1.5876137659782963e+000, -1.0242234847115104e+002,
2.2583088401423066e+002, 1.1125972190244058e+003 ]
Image_points: !!opencv-matrix
rows: 8
cols: 54
dt: "2f"
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2.22421921e+002, 4.50077850e+002, 2.20683517e+002,
2.11095444e+002, 2.66940186e+002, 2.40241348e+002,
2.64970093e+002, 2.69563019e+002, 2.63153290e+002,
2.99863464e+002, 2.60983551e+002, 3.30282440e+002,
2.58911560e+002, 3.60724792e+002, 2.56935730e+002,
3.91487915e+002, 2.54799423e+002, 4.21789093e+002,
2.52929688e+002, 4.51818481e+002, 2.51059357e+002,
2.13829117e+002, 2.96591217e+002, 2.42742859e+002,
2.94884583e+002, 2.72209076e+002, 2.93215668e+002,
3.02402985e+002, 2.91230591e+002, 3.32536072e+002,
2.89165192e+002, 3.62860901e+002, 2.87413605e+002,
3.93481842e+002, 2.85199615e+002, 4.23728851e+002,
2.83277496e+002, 4.53453094e+002, 2.81229309e+002,
2.16799316e+002, 3.25975220e+002, 2.45605515e+002,
3.24619904e+002, 2.74777344e+002, 3.22958679e+002,
3.04762817e+002, 3.21008057e+002, 3.34797150e+002,
3.19291443e+002, 3.65005798e+002, 3.17295044e+002,
3.95311981e+002, 3.15296021e+002, 4.25312592e+002,
3.13086945e+002, 4.54931152e+002, 3.11027130e+002,
2.60550232e+002, 3.70739563e+002, 2.59674011e+002,
3.42115936e+002, 2.58910492e+002, 3.13278015e+002,
2.58195618e+002, 2.84013580e+002, 2.57727173e+002,
2.55017166e+002, 2.57326263e+002, 2.25760986e+002,
2.57096619e+002, 1.96577972e+002, 2.57031860e+002,
1.68026199e+002, 2.56873383e+002, 1.39550308e+002,
2.89019318e+002, 3.70481354e+002, 2.88355560e+002,
3.41833252e+002, 2.87601471e+002, 3.12872925e+002,
2.87057190e+002, 2.83485535e+002, 2.86599762e+002,
2.54255096e+002, 2.86174438e+002, 2.25023285e+002,
2.85775940e+002, 1.95715347e+002, 2.85577087e+002,
1.66989502e+002, 2.85395477e+002, 1.38597382e+002,
3.18072754e+002, 3.70118317e+002, 3.17432709e+002,
3.41398743e+002, 3.16917267e+002, 3.12476044e+002,
3.16284363e+002, 2.83001587e+002, 3.15799072e+002,
2.53725845e+002, 3.15411957e+002, 2.24337708e+002,
3.15070374e+002, 1.95034119e+002, 3.14736847e+002,
1.66195313e+002, 3.14439789e+002, 1.37797058e+002,
3.47083588e+002, 3.69678101e+002, 3.46717987e+002,
3.40949524e+002, 3.46185303e+002, 3.12009857e+002,
3.45728088e+002, 2.82454071e+002, 3.45226624e+002,
2.53109863e+002, 3.44883606e+002, 2.23839539e+002,
3.44373535e+002, 1.94399933e+002, 3.43879852e+002,
1.65690643e+002, 3.43438629e+002, 1.37252930e+002,
3.76341522e+002, 3.68972321e+002, 3.76086884e+002,
3.40412842e+002, 3.75708893e+002, 3.11398376e+002,
3.75143494e+002, 2.81901520e+002, 3.74762970e+002,
2.52577988e+002, 3.74223969e+002, 2.23348221e+002,
3.73600891e+002, 1.93979538e+002, 3.72983917e+002,
1.65201294e+002, 3.72517273e+002, 1.36871033e+002,
4.05512115e+002, 3.68243225e+002, 4.05366333e+002,
3.39678650e+002, 4.05090027e+002, 3.10679108e+002,
4.04612366e+002, 2.81203522e+002, 4.04152649e+002,
2.52051605e+002, 4.03539703e+002, 2.22930420e+002,
4.02903351e+002, 1.93625381e+002, 4.02272827e+002,
1.65004440e+002, 4.01353333e+002, 1.36796814e+002 ]
%YAML:1.0
calibration_Time: "08/19/11 20:44:38"
nrOfFrames: 8
image_Width: 640
image_Height: 480
board_Width: 9
board_Height: 6
square_Size: 50.
FixAspectRatio: 1.
# flags: +fix_aspectRatio +fix_principal_point +zero_tangent_dist
flagValue: 14
Camera_Matrix: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 6.5746697810243404e+002, 0., 3.1950000000000000e+002, 0.,
6.5746697810243404e+002, 2.3950000000000000e+002, 0., 0., 1. ]
Distortion_Coefficients: !!opencv-matrix
rows: 5
cols: 1
dt: d
data: [ -4.1802327018241026e-001, 5.0715243805833121e-001, 0., 0.,
-5.7843596847939704e-001 ]
Avg_Reprojection_Error: 3.8441346462381665e-001
Per_View_Reprojection_Errors: !!opencv-matrix
rows: 8
cols: 1
dt: f
data: [ 5.04357755e-001, 4.85754758e-001, 3.99563968e-001,
4.13829178e-001, 3.53570908e-001, 3.21116358e-001,
2.74473161e-001, 2.39761785e-001 ]
# a set of 6-tuples (rotation vector + translation vector) for each view
Extrinsic_Parameters: !!opencv-matrix
rows: 8
cols: 6
dt: d
data: [ -7.8704123655486097e-002, -1.5922384772614945e-001,
3.1166227207451498e+000, 2.4224388101960471e+002,
1.1795590397660339e+002, 6.2576484126093249e+002,
-1.4117480285164308e-001, -1.7917415443804836e-002,
3.1333182268743949e+000, 2.5943034781849354e+002,
1.4039780562976958e+002, 6.3848706527260981e+002,
7.2230525186138789e-002, -7.5445981266787754e-002,
1.5712860749221762e+000, 1.7426560451795339e+002,
-1.9309240362258871e+002, 7.0891416556762647e+002,
2.0367310600105853e-002, 6.8565520026996951e-002,
-5.4313033031644169e-004, -2.0146314940404827e+002,
-1.3305643514116997e+002, 7.4933554744027231e+002,
-3.4468530027734055e-002, 2.1921265175331925e-002,
-1.5731053528054522e+000, -1.1155718744299284e+002,
2.0307615364261443e+002, 8.4915903914333899e+002,
3.7425562109513817e-002, 7.4883169379022230e-002,
-3.6031632305130512e-002, -2.0094505419395196e+002,
-1.1627359108310560e+002, 9.2021583518760133e+002,
6.8105689976949157e-002, 6.4426739692440949e-002,
-7.0967130057087435e-002, -1.9233852871740035e+002,
-1.0334652096641923e+002, 1.0755293563503658e+003,
-5.8017546499862287e-002, -1.6909812666033443e-003,
-1.5876137659782963e+000, -1.0242234847115104e+002,
2.2583088401423066e+002, 1.1125972190244058e+003 ]
Image_points: !!opencv-matrix
rows: 8
cols: 54
dt: "2f"
data: [ 5.58494690e+002, 3.55650085e+002, 5.13314697e+002,
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3.67339203e+002, 3.12101196e+002, 3.69211914e+002,
2.59208405e+002, 3.70413513e+002, 2.07456192e+002,
3.71175995e+002, 1.56619507e+002, 3.72176544e+002,
5.60868713e+002, 3.08104828e+002, 5.15191772e+002,
3.10485626e+002, 4.67032959e+002, 3.12660004e+002,
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3.16825775e+002, 3.10712372e+002, 3.18640808e+002,
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3.20908417e+002, 1.52469528e+002, 3.22105377e+002,
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2.59919281e+002, 4.67907654e+002, 2.61257874e+002,
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2.08424667e+002, 4.68059296e+002, 2.08661697e+002,
4.16216431e+002, 2.09268982e+002, 3.62888763e+002,
2.10013397e+002, 3.08458557e+002, 2.11074738e+002,
2.53267990e+002, 2.12496582e+002, 1.99121384e+002,
2.14005814e+002, 1.46551376e+002, 2.15851318e+002,
5.62997437e+002, 1.57966492e+002, 5.16406494e+002,
1.56580688e+002, 4.67334900e+002, 1.55756500e+002,
4.15378235e+002, 1.55492874e+002, 3.62096710e+002,
1.55498734e+002, 3.07522827e+002, 1.56133240e+002,
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3.29362366e+002, 4.81278229e+002, 3.31532928e+002,
4.32783203e+002, 3.33552185e+002, 3.82408234e+002,
3.35186554e+002, 3.30427399e+002, 3.36404053e+002,
2.77138519e+002, 3.37450958e+002, 2.24525131e+002,
3.37957092e+002, 1.72285507e+002, 3.38503540e+002,
5.70942749e+002, 2.79243713e+002, 5.27789307e+002,
2.80073486e+002, 4.82146576e+002, 2.81226410e+002,
4.33247375e+002, 2.82237427e+002, 3.82503662e+002,
2.83062286e+002, 3.30138885e+002, 2.83794434e+002,
2.76433228e+002, 2.84549286e+002, 2.23158783e+002,
2.84981049e+002, 1.70520218e+002, 2.85720886e+002,
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2.30519928e+002, 4.82114563e+002, 2.30268906e+002,
4.33114563e+002, 2.30243515e+002, 3.82384857e+002,
2.30311340e+002, 3.29870392e+002, 2.30454620e+002,
2.76012634e+002, 2.30882156e+002, 2.22529434e+002,
2.31355362e+002, 1.69742065e+002, 2.32063004e+002,
5.70199036e+002, 1.82609772e+002, 5.27030884e+002,
1.80973267e+002, 4.81193573e+002, 1.79573792e+002,
4.32409821e+002, 1.78475616e+002, 3.81855530e+002,
1.77680283e+002, 3.29641937e+002, 1.77092087e+002,
2.75895782e+002, 1.77155502e+002, 2.22438889e+002,
1.77605667e+002, 1.69884583e+002, 1.78365585e+002,
5.69026245e+002, 1.34654831e+002, 5.26171570e+002,
1.31798691e+002, 4.80653503e+002, 1.29171509e+002,
4.31869904e+002, 1.27280067e+002, 3.81419739e+002,
1.25591202e+002, 3.29466644e+002, 1.24407089e+002,
2.76225342e+002, 1.24174736e+002, 2.23024109e+002,
1.24463333e+002, 1.70838898e+002, 1.25398903e+002,
4.73812897e+002, 6.94673386e+001, 4.74245453e+002,
1.12387466e+002, 4.74243347e+002, 1.56034164e+002,
4.73834778e+002, 2.00523651e+002, 4.72891602e+002,
2.44457306e+002, 4.71412811e+002, 2.87981171e+002,
4.69708252e+002, 3.30783173e+002, 4.67558228e+002,
3.71818420e+002, 4.65495667e+002, 4.11996979e+002,
4.31027649e+002, 6.75546722e+001, 4.31269440e+002,
1.10960022e+002, 4.31185486e+002, 1.55113556e+002,
4.30830139e+002, 2.00061066e+002, 4.30168427e+002,
2.44456863e+002, 4.29107544e+002, 2.88479645e+002,
4.27829071e+002, 3.31813507e+002, 4.26131653e+002,
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3.84405273e+002, 3.32408020e+002, 3.83303772e+002,
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1.09505333e+002, 2.95704895e+002, 1.54202652e+002,
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2.96138733e+002, 3.32610931e+002, 2.96520905e+002,
3.74608551e+002, 2.96987091e+002, 4.14774902e+002,
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1.10189331e+002, 2.50183380e+002, 1.54658005e+002,
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2.44719513e+002, 2.51083817e+002, 2.88868286e+002,
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3.74022491e+002, 2.54404007e+002, 4.14018066e+002,
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1.48706085e+002, 1.69077423e+002, 1.88827805e+002,
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3.87030273e+002, 1.64203415e+002, 3.86741211e+002,
1.26606262e+002, 3.86337311e+002, 8.99655075e+001,
4.24534088e+002, 3.92702545e+002, 4.25310822e+002,
3.55900452e+002, 4.25869019e+002, 3.18160614e+002,
4.25909790e+002, 2.79615753e+002, 4.25977295e+002,
2.41165100e+002, 4.25826477e+002, 2.02876389e+002,
4.25331665e+002, 1.64527618e+002, 4.24775787e+002,
1.27097328e+002, 4.23985138e+002, 9.08176651e+001,
1.79142670e+002, 1.58573654e+002, 2.12791580e+002,
1.56291031e+002, 2.47140106e+002, 1.54265656e+002,
2.82607300e+002, 1.52373688e+002, 3.18175507e+002,
1.50692184e+002, 3.54185852e+002, 1.49404175e+002,
3.90455200e+002, 1.48229370e+002, 4.26106689e+002,
1.47507843e+002, 4.61576141e+002, 1.46712479e+002,
1.80388336e+002, 1.93027603e+002, 2.14026459e+002,
1.91128204e+002, 2.48376541e+002, 1.89414978e+002,
2.83795807e+002, 1.87720856e+002, 3.19472473e+002,
1.86192383e+002, 3.55483826e+002, 1.84929199e+002,
3.91970764e+002, 1.83747040e+002, 4.27654572e+002,
1.82931534e+002, 4.63295227e+002, 1.81977234e+002,
1.81914261e+002, 2.27955460e+002, 2.15291260e+002,
2.26512482e+002, 2.49628265e+002, 2.25067520e+002,
2.85066406e+002, 2.23593185e+002, 3.20846680e+002,
2.22337708e+002, 3.56862885e+002, 2.21191040e+002,
3.93279907e+002, 2.19905640e+002, 4.29202271e+002,
2.18870361e+002, 4.64728424e+002, 2.17972977e+002,
1.83496948e+002, 2.62963226e+002, 2.16930527e+002,
2.61755219e+002, 2.51115829e+002, 2.60777222e+002,
2.86553406e+002, 2.59500336e+002, 3.22299896e+002,
2.58380737e+002, 3.58307648e+002, 2.57236694e+002,
3.94551819e+002, 2.56009125e+002, 4.30358948e+002,
2.54925797e+002, 4.65684998e+002, 2.54021484e+002,
1.85461685e+002, 2.97687378e+002, 2.18712234e+002,
2.96999207e+002, 2.52770218e+002, 2.96270752e+002,
2.88213776e+002, 2.95168213e+002, 3.23698334e+002,
2.94233032e+002, 3.59477722e+002, 2.93170715e+002,
3.95647766e+002, 2.91897400e+002, 4.31309845e+002,
2.90856995e+002, 4.66494110e+002, 2.89726410e+002,
1.87661331e+002, 3.32186188e+002, 2.20767746e+002,
3.31906250e+002, 2.54839096e+002, 3.31398651e+002,
2.89963745e+002, 3.30524139e+002, 3.25207642e+002,
3.29771820e+002, 3.60686035e+002, 3.28762695e+002,
3.96576447e+002, 3.27542206e+002, 4.31994415e+002,
3.26294189e+002, 4.66894653e+002, 3.24949921e+002,
2.03543015e+002, 1.77473557e+002, 2.32777847e+002,
1.74712509e+002, 2.62628723e+002, 1.72331970e+002,
2.93045898e+002, 1.69686768e+002, 3.23527618e+002,
1.67496246e+002, 3.54206787e+002, 1.65446075e+002,
3.85180176e+002, 1.63360580e+002, 4.15484253e+002,
1.61536423e+002, 4.45720947e+002, 1.59896164e+002,
2.05864395e+002, 2.07228104e+002, 2.35242096e+002,
2.04699326e+002, 2.64853973e+002, 2.02407455e+002,
2.95353882e+002, 1.99972321e+002, 3.25811890e+002,
1.97671921e+002, 3.56471252e+002, 1.95763168e+002,
3.87280548e+002, 1.93597977e+002, 4.17615814e+002,
1.91867371e+002, 4.48018677e+002, 1.90067413e+002,
2.08421249e+002, 2.37166977e+002, 2.37513824e+002,
2.34982773e+002, 2.67261261e+002, 2.32802841e+002,
2.97555817e+002, 2.30466080e+002, 3.28118103e+002,
2.28462463e+002, 3.58699707e+002, 2.26417038e+002,
3.89468842e+002, 2.24356827e+002, 4.19895996e+002,
2.22421921e+002, 4.50077850e+002, 2.20683517e+002,
2.11095444e+002, 2.66940186e+002, 2.40241348e+002,
2.64970093e+002, 2.69563019e+002, 2.63153290e+002,
2.99863464e+002, 2.60983551e+002, 3.30282440e+002,
2.58911560e+002, 3.60724792e+002, 2.56935730e+002,
3.91487915e+002, 2.54799423e+002, 4.21789093e+002,
2.52929688e+002, 4.51818481e+002, 2.51059357e+002,
2.13829117e+002, 2.96591217e+002, 2.42742859e+002,
2.94884583e+002, 2.72209076e+002, 2.93215668e+002,
3.02402985e+002, 2.91230591e+002, 3.32536072e+002,
2.89165192e+002, 3.62860901e+002, 2.87413605e+002,
3.93481842e+002, 2.85199615e+002, 4.23728851e+002,
2.83277496e+002, 4.53453094e+002, 2.81229309e+002,
2.16799316e+002, 3.25975220e+002, 2.45605515e+002,
3.24619904e+002, 2.74777344e+002, 3.22958679e+002,
3.04762817e+002, 3.21008057e+002, 3.34797150e+002,
3.19291443e+002, 3.65005798e+002, 3.17295044e+002,
3.95311981e+002, 3.15296021e+002, 4.25312592e+002,
3.13086945e+002, 4.54931152e+002, 3.11027130e+002,
2.60550232e+002, 3.70739563e+002, 2.59674011e+002,
3.42115936e+002, 2.58910492e+002, 3.13278015e+002,
2.58195618e+002, 2.84013580e+002, 2.57727173e+002,
2.55017166e+002, 2.57326263e+002, 2.25760986e+002,
2.57096619e+002, 1.96577972e+002, 2.57031860e+002,
1.68026199e+002, 2.56873383e+002, 1.39550308e+002,
2.89019318e+002, 3.70481354e+002, 2.88355560e+002,
3.41833252e+002, 2.87601471e+002, 3.12872925e+002,
2.87057190e+002, 2.83485535e+002, 2.86599762e+002,
2.54255096e+002, 2.86174438e+002, 2.25023285e+002,
2.85775940e+002, 1.95715347e+002, 2.85577087e+002,
1.66989502e+002, 2.85395477e+002, 1.38597382e+002,
3.18072754e+002, 3.70118317e+002, 3.17432709e+002,
3.41398743e+002, 3.16917267e+002, 3.12476044e+002,
3.16284363e+002, 2.83001587e+002, 3.15799072e+002,
2.53725845e+002, 3.15411957e+002, 2.24337708e+002,
3.15070374e+002, 1.95034119e+002, 3.14736847e+002,
1.66195313e+002, 3.14439789e+002, 1.37797058e+002,
3.47083588e+002, 3.69678101e+002, 3.46717987e+002,
3.40949524e+002, 3.46185303e+002, 3.12009857e+002,
3.45728088e+002, 2.82454071e+002, 3.45226624e+002,
2.53109863e+002, 3.44883606e+002, 2.23839539e+002,
3.44373535e+002, 1.94399933e+002, 3.43879852e+002,
1.65690643e+002, 3.43438629e+002, 1.37252930e+002,
3.76341522e+002, 3.68972321e+002, 3.76086884e+002,
3.40412842e+002, 3.75708893e+002, 3.11398376e+002,
3.75143494e+002, 2.81901520e+002, 3.74762970e+002,
2.52577988e+002, 3.74223969e+002, 2.23348221e+002,
3.73600891e+002, 1.93979538e+002, 3.72983917e+002,
1.65201294e+002, 3.72517273e+002, 1.36871033e+002,
4.05512115e+002, 3.68243225e+002, 4.05366333e+002,
3.39678650e+002, 4.05090027e+002, 3.10679108e+002,
4.04612366e+002, 2.81203522e+002, 4.04152649e+002,
2.52051605e+002, 4.03539703e+002, 2.22930420e+002,
4.02903351e+002, 1.93625381e+002, 4.02272827e+002,
1.65004440e+002, 4.01353333e+002, 1.36796814e+002 ]

4
samples/cpp/tutorial_code/calib3d/stereoBM/SBM_Sample.cpp
View File

@@ -31,7 +31,7 @@ int main( int argc, char** argv )
//-- And create the image in which we will save our disparities
Mat imgDisparity16S = Mat( imgLeft.rows, imgLeft.cols, CV_16S );
Mat imgDisparity8U = Mat( imgLeft.rows, imgLeft.cols, CV_8UC1 );
if( !imgLeft.data || !imgRight.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
@@ -40,7 +40,7 @@ int main( int argc, char** argv )
int SADWindowSize = 21; /**< Size of the block window. Must be odd */
StereoBM sbm( StereoBM::BASIC_PRESET,
ndisparities,
ndisparities,
SADWindowSize );
//-- 3. Calculate the disparity image

64
samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp
View File

@@ -26,14 +26,14 @@ int main( int argc, char **argv ){
char atom_window[] = "Drawing 1: Atom";
char rook_window[] = "Drawing 2: Rook";
/// Create black empty images
/// Create black empty images
Mat atom_image = Mat::zeros( w, w, CV_8UC3 );
Mat rook_image = Mat::zeros( w, w, CV_8UC3 );
/// 1. Draw a simple atom:
/// -----------------------
/// 1.a. Creating ellipses
/// 1.a. Creating ellipses
MyEllipse( atom_image, 90 );
MyEllipse( atom_image, 0 );
MyEllipse( atom_image, 45 );
@@ -50,13 +50,13 @@ int main( int argc, char **argv ){
/// 2.b. Creating rectangles
rectangle( rook_image,
Point( 0, 7*w/8.0 ),
Point( w, w),
Scalar( 0, 255, 255 ),
-1,
8 );
Point( 0, 7*w/8.0 ),
Point( w, w),
Scalar( 0, 255, 255 ),
-1,
8 );
/// 2.c. Create a few lines
/// 2.c. Create a few lines
MyLine( rook_image, Point( 0, 15*w/16 ), Point( w, 15*w/16 ) );
MyLine( rook_image, Point( w/4, 7*w/8 ), Point( w/4, w ) );
MyLine( rook_image, Point( w/2, 7*w/8 ), Point( w/2, w ) );
@@ -84,14 +84,14 @@ void MyEllipse( Mat img, double angle )
int lineType = 8;
ellipse( img,
Point( w/2.0, w/2.0 ),
Size( w/4.0, w/16.0 ),
angle,
0,
360,
Scalar( 255, 0, 0 ),
thickness,
lineType );
Point( w/2.0, w/2.0 ),
Size( w/4.0, w/16.0 ),
angle,
0,
360,
Scalar( 255, 0, 0 ),
thickness,
lineType );
}
/**
@@ -103,12 +103,12 @@ void MyFilledCircle( Mat img, Point center )
int thickness = -1;
int lineType = 8;
circle( img,
center,
w/32.0,
Scalar( 0, 0, 255 ),
thickness,
lineType );
circle( img,
center,
w/32.0,
Scalar( 0, 0, 255 ),
thickness,
lineType );
}
/**
@@ -146,11 +146,11 @@ void MyPolygon( Mat img )
int npt[] = { 20 };
fillPoly( img,
ppt,
npt,
ppt,
npt,
1,
Scalar( 255, 255, 255 ),
lineType );
Scalar( 255, 255, 255 ),
lineType );
}
/**
@@ -161,12 +161,12 @@ void MyLine( Mat img, Point start, Point end )
{
int thickness = 2;
int lineType = 8;
line( img,
start,
end,
Scalar( 0, 0, 0 ),
thickness,
lineType );
line( img,
start,
end,
Scalar( 0, 0, 0 ),
thickness,
lineType );
}

58
samples/cpp/tutorial_code/core/Matrix/Drawing_2.cpp
View File

@@ -11,7 +11,7 @@
using namespace cv;
/// Global Variables
const int NUMBER = 100;
const int NUMBER = 100;
const int DELAY = 5;
const int window_width = 900;
@@ -44,14 +44,14 @@ int main( int argc, char** argv )
char window_name[] = "Drawing_2 Tutorial";
/// Also create a random object (RNG)
RNG rng( 0xFFFFFFFF );
RNG rng( 0xFFFFFFFF );
/// Initialize a matrix filled with zeros
Mat image = Mat::zeros( window_height, window_width, CV_8UC3 );
/// Show it in a window during DELAY ms
imshow( window_name, image );
imshow( window_name, image );
waitKey( DELAY );
/// Now, let's draw some lines
c = Drawing_Random_Lines(image, window_name, rng);
if( c != 0 ) return 0;
@@ -145,7 +145,7 @@ int Drawing_Random_Rectangles( Mat image, char* window_name, RNG rng )
imshow( window_name, image );
if( waitKey( DELAY ) >= 0 )
{ return -1; }
{ return -1; }
}
return 0;
@@ -163,7 +163,7 @@ int Drawing_Random_Ellipses( Mat image, char* window_name, RNG rng )
Point center;
center.x = rng.uniform(x_1, x_2);
center.y = rng.uniform(y_1, y_2);
Size axes;
axes.width = rng.uniform(0, 200);
axes.height = rng.uniform(0, 200);
@@ -172,10 +172,10 @@ int Drawing_Random_Ellipses( Mat image, char* window_name, RNG rng )
ellipse( image, center, axes, angle, angle - 100, angle + 200,
randomColor(rng), rng.uniform(-1,9), lineType );
imshow( window_name, image );
if( waitKey(DELAY) >= 0 )
if( waitKey(DELAY) >= 0 )
{ return -1; }
}
@@ -194,28 +194,28 @@ int Drawing_Random_Polylines( Mat image, char* window_name, RNG rng )
Point pt[2][3];
pt[0][0].x = rng.uniform(x_1, x_2);
pt[0][0].y = rng.uniform(y_1, y_2);
pt[0][1].x = rng.uniform(x_1, x_2);
pt[0][1].y = rng.uniform(y_1, y_2);
pt[0][1].x = rng.uniform(x_1, x_2);
pt[0][1].y = rng.uniform(y_1, y_2);
pt[0][2].x = rng.uniform(x_1, x_2);
pt[0][2].y = rng.uniform(y_1, y_2);
pt[1][0].x = rng.uniform(x_1, x_2);
pt[1][0].x = rng.uniform(x_1, x_2);
pt[1][0].y = rng.uniform(y_1, y_2);
pt[1][1].x = rng.uniform(x_1, x_2);
pt[1][1].x = rng.uniform(x_1, x_2);
pt[1][1].y = rng.uniform(y_1, y_2);
pt[1][2].x = rng.uniform(x_1, x_2);
pt[1][2].x = rng.uniform(x_1, x_2);
pt[1][2].y = rng.uniform(y_1, y_2);
const Point* ppt[2] = {pt[0], pt[1]};
int npt[] = {3, 3};
polylines(image, ppt, npt, 2, true, randomColor(rng), rng.uniform(1,10), lineType);
imshow( window_name, image );
if( waitKey(DELAY) >= 0 )
{ return -1; }
}
return 0;
}
}
/**
* @function Drawing_Random_Filled_Polygons
@@ -229,22 +229,22 @@ int Drawing_Random_Filled_Polygons( Mat image, char* window_name, RNG rng )
Point pt[2][3];
pt[0][0].x = rng.uniform(x_1, x_2);
pt[0][0].y = rng.uniform(y_1, y_2);
pt[0][1].x = rng.uniform(x_1, x_2);
pt[0][1].y = rng.uniform(y_1, y_2);
pt[0][1].x = rng.uniform(x_1, x_2);
pt[0][1].y = rng.uniform(y_1, y_2);
pt[0][2].x = rng.uniform(x_1, x_2);
pt[0][2].y = rng.uniform(y_1, y_2);
pt[1][0].x = rng.uniform(x_1, x_2);
pt[1][0].x = rng.uniform(x_1, x_2);
pt[1][0].y = rng.uniform(y_1, y_2);
pt[1][1].x = rng.uniform(x_1, x_2);
pt[1][1].x = rng.uniform(x_1, x_2);
pt[1][1].y = rng.uniform(y_1, y_2);
pt[1][2].x = rng.uniform(x_1, x_2);
pt[1][2].x = rng.uniform(x_1, x_2);
pt[1][2].y = rng.uniform(y_1, y_2);
const Point* ppt[2] = {pt[0], pt[1]};
int npt[] = {3, 3};
fillPoly( image, ppt, npt, 2, randomColor(rng), lineType );
imshow( window_name, image );
if( waitKey(DELAY) >= 0 )
{ return -1; }
@@ -264,10 +264,10 @@ int Drawing_Random_Circles( Mat image, char* window_name, RNG rng )
Point center;
center.x = rng.uniform(x_1, x_2);
center.y = rng.uniform(y_1, y_2);
circle( image, center, rng.uniform(0, 300), randomColor(rng),
rng.uniform(-1, 9), lineType );
imshow( window_name, image );
if( waitKey(DELAY) >= 0 )
{ return -1; }
@@ -291,7 +291,7 @@ int Displaying_Random_Text( Mat image, char* window_name, RNG rng )
putText( image, "Testing text rendering", org, rng.uniform(0,8),
rng.uniform(0,100)*0.05+0.1, randomColor(rng), rng.uniform(1, 10), lineType);
imshow( window_name, image );
if( waitKey(DELAY) >= 0 )
{ return -1; }
@@ -308,7 +308,7 @@ int Displaying_Big_End( Mat image, char* window_name, RNG rng )
Size textsize = getTextSize("OpenCV forever!", CV_FONT_HERSHEY_COMPLEX, 3, 5, 0);
Point org((window_width - textsize.width)/2, (window_height - textsize.height)/2);
int lineType = 8;
Mat image2;
for( int i = 0; i < 255; i += 2 )
@@ -316,7 +316,7 @@ int Displaying_Big_End( Mat image, char* window_name, RNG rng )
image2 = image - Scalar::all(i);
putText( image2, "OpenCV forever!", org, CV_FONT_HERSHEY_COMPLEX, 3,
Scalar(i, i, 255), 5, lineType );
imshow( window_name, image2 );
if( waitKey(DELAY) >= 0 )
{ return -1; }

154
samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp
View File

@@ -1,78 +1,78 @@
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
using namespace cv;
using namespace std;
void help(char* progName)
{
cout << endl
<< "This program demonstrated the use of the discrete Fourier transform (DFT). " << endl
<< "The dft of an image is taken and it's power spectrum is displayed." << endl
<< "Usage:" << endl
<< progName << " [image_name -- default lena.jpg] " << endl << endl;
}
int main(int argc, char ** argv)
{
help(argv[0]);
const char* filename = argc >=2 ? argv[1] : "lena.jpg";
Mat I = imread(filename, CV_LOAD_IMAGE_GRAYSCALE);
if( I.empty())
return -1;
Mat padded; //expand input image to optimal size
int m = getOptimalDFTSize( I.rows );
int n = getOptimalDFTSize( I.cols ); // on the border add zero values
copyMakeBorder(I, padded, 0, m - I.rows, 0, n - I.cols, BORDER_CONSTANT, Scalar::all(0));
Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)};
Mat complexI;
merge(planes, 2, complexI); // Add to the expanded another plane with zeros
dft(complexI, complexI); // this way the result may fit in the source matrix
// compute the magnitude and switch to logarithmic scale
// => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
split(complexI, planes); // planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
magnitude(planes[0], planes[1], planes[0]);// planes[0] = magnitude
Mat magI = planes[0];
magI += Scalar::all(1); // switch to logarithmic scale
log(magI, magI);
// crop the spectrum, if it has an odd number of rows or columns
magI = magI(Rect(0, 0, magI.cols & -2, magI.rows & -2));
// rearrange the quadrants of Fourier image so that the origin is at the image center
int cx = magI.cols/2;
int cy = magI.rows/2;
Mat q0(magI, Rect(0, 0, cx, cy)); // Top-Left - Create a ROI per quadrant
Mat q1(magI, Rect(cx, 0, cx, cy)); // Top-Right
Mat q2(magI, Rect(0, cy, cx, cy)); // Bottom-Left
Mat q3(magI, Rect(cx, cy, cx, cy)); // Bottom-Right
Mat tmp; // swap quadrants (Top-Left with Bottom-Right)
q0.copyTo(tmp);
q3.copyTo(q0);
tmp.copyTo(q3);
q1.copyTo(tmp); // swap quadrant (Top-Right with Bottom-Left)
q2.copyTo(q1);
tmp.copyTo(q2);
normalize(magI, magI, 0, 1, CV_MINMAX); // Transform the matrix with float values into a
// viewable image form (float between values 0 and 1).
imshow("Input Image" , I ); // Show the result
imshow("spectrum magnitude", magI);
waitKey();
return 0;
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
using namespace cv;
using namespace std;
void help(char* progName)
{
cout << endl
<< "This program demonstrated the use of the discrete Fourier transform (DFT). " << endl
<< "The dft of an image is taken and it's power spectrum is displayed." << endl
<< "Usage:" << endl
<< progName << " [image_name -- default lena.jpg] " << endl << endl;
}
int main(int argc, char ** argv)
{
help(argv[0]);
const char* filename = argc >=2 ? argv[1] : "lena.jpg";
Mat I = imread(filename, CV_LOAD_IMAGE_GRAYSCALE);
if( I.empty())
return -1;
Mat padded; //expand input image to optimal size
int m = getOptimalDFTSize( I.rows );
int n = getOptimalDFTSize( I.cols ); // on the border add zero values
copyMakeBorder(I, padded, 0, m - I.rows, 0, n - I.cols, BORDER_CONSTANT, Scalar::all(0));
Mat planes[] = {Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F)};
Mat complexI;
merge(planes, 2, complexI); // Add to the expanded another plane with zeros
dft(complexI, complexI); // this way the result may fit in the source matrix
// compute the magnitude and switch to logarithmic scale
// => log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))
split(complexI, planes); // planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
magnitude(planes[0], planes[1], planes[0]);// planes[0] = magnitude
Mat magI = planes[0];
magI += Scalar::all(1); // switch to logarithmic scale
log(magI, magI);
// crop the spectrum, if it has an odd number of rows or columns
magI = magI(Rect(0, 0, magI.cols & -2, magI.rows & -2));
// rearrange the quadrants of Fourier image so that the origin is at the image center
int cx = magI.cols/2;
int cy = magI.rows/2;
Mat q0(magI, Rect(0, 0, cx, cy)); // Top-Left - Create a ROI per quadrant
Mat q1(magI, Rect(cx, 0, cx, cy)); // Top-Right
Mat q2(magI, Rect(0, cy, cx, cy)); // Bottom-Left
Mat q3(magI, Rect(cx, cy, cx, cy)); // Bottom-Right
Mat tmp; // swap quadrants (Top-Left with Bottom-Right)
q0.copyTo(tmp);
q3.copyTo(q0);
tmp.copyTo(q3);
q1.copyTo(tmp); // swap quadrant (Top-Right with Bottom-Left)
q2.copyTo(q1);
tmp.copyTo(q2);
normalize(magI, magI, 0, 1, CV_MINMAX); // Transform the matrix with float values into a
// viewable image form (float between values 0 and 1).
imshow("Input Image" , I ); // Show the result
imshow("spectrum magnitude", magI);
waitKey();
return 0;
}

30
samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp
View File

@@ -7,7 +7,7 @@ using namespace std;
void help(char** av)
{
cout << endl
cout << endl
<< av[0] << " shows the usage of the OpenCV serialization functionality." << endl
<< "usage: " << endl
<< av[0] << " outputfile.yml.gz" << endl
@@ -54,8 +54,8 @@ void read(const FileNode& node, MyData& x, const MyData& default_value = MyData(
}
// This function will print our custom class to the console
ostream& operator<<(ostream& out, const MyData& m)
{
ostream& operator<<(ostream& out, const MyData& m)
{
out << "{ id = " << m.id << ", ";
out << "X = " << m.X << ", ";
out << "A = " << m.A << "}";
@@ -82,10 +82,10 @@ int main(int ac, char** av)
fs << "strings" << "["; // text - string sequence
fs << "image1.jpg" << "Awesomeness" << "baboon.jpg";
fs << "]"; // close sequence
fs << "Mapping"; // text - mapping
fs << "{" << "One" << 1;
fs << "Two" << 2 << "}";
fs << "Two" << 2 << "}";
fs << "R" << R; // cv::Mat
fs << "T" << T;
@@ -98,10 +98,10 @@ int main(int ac, char** av)
{//read
cout << endl << "Reading: " << endl;
FileStorage fs;
FileStorage fs;
fs.open(filename, FileStorage::READ);
int itNr;
int itNr;
//fs["iterationNr"] >> itNr;
itNr = (int) fs["iterationNr"];
cout << itNr;
@@ -122,12 +122,12 @@ int main(int ac, char** av)
FileNodeIterator it = n.begin(), it_end = n.end(); // Go through the node
for (; it != it_end; ++it)
cout << (string)*it << endl;
n = fs["Mapping"]; // Read mappings from a sequence
cout << "Two " << (int)(n["Two"]) << "; ";
cout << "One " << (int)(n["One"]) << endl << endl;
cout << "Two " << (int)(n["Two"]) << "; ";
cout << "One " << (int)(n["One"]) << endl << endl;
MyData m;
Mat R, T;
@@ -136,18 +136,18 @@ int main(int ac, char** av)
fs["T"] >> T;
fs["MyData"] >> m; // Read your own structure_
cout << endl
cout << endl
<< "R = " << R << endl;
cout << "T = " << T << endl << endl;
cout << "MyData = " << endl << m << endl << endl;
//Show default behavior for non existing nodes
cout << "Attempt to read NonExisting (should initialize the data structure with its default).";
cout << "Attempt to read NonExisting (should initialize the data structure with its default).";
fs["NonExisting"] >> m;
cout << endl << "NonExisting = " << endl << m << endl;
}
cout << endl
cout << endl
<< "Tip: Open up " << filename << " with a text editor to see the serialized data." << endl;
return 0;

430
samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
View File

@@ -1,216 +1,216 @@
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <sstream>
using namespace std;
using namespace cv;
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << endl
<< "This program shows how to scan image objects in OpenCV (cv::Mat). As use case"
<< " we take an input image and divide the native color palette (255) with the " << endl
<< "input. Shows C operator[] method, iterators and at function for on-the-fly item address calculation."<< endl
<< "Usage:" << endl
<< "./howToScanImages imageNameToUse divideWith [G]" << endl
<< "if you add a G parameter the image is processed in gray scale" << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
Mat& ScanImageAndReduceC(Mat& I, const uchar* table);
Mat& ScanImageAndReduceIterator(Mat& I, const uchar* table);
Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar * table);
int main( int argc, char* argv[])
{
help();
if (argc < 3)
{
cout << "Not enough parameters" << endl;
return -1;
}
Mat I, J;
if( argc == 4 && !strcmp(argv[3],"G") )
I = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
else
I = imread(argv[1], CV_LOAD_IMAGE_COLOR);
if (!I.data)
{
cout << "The image" << argv[1] << " could not be loaded." << endl;
return -1;
}
int divideWith; // convert our input string to number - C++ style
stringstream s;
s << argv[2];
s >> divideWith;
if (!s)
{
cout << "Invalid number entered for dividing. " << endl;
return -1;
}
uchar table[256];
for (int i = 0; i < 256; ++i)
table[i] = divideWith* (i/divideWith);
const int times = 100;
double t;
t = (double)getTickCount();
for (int i = 0; i < times; ++i)
{
cv::Mat clone_i = I.clone();
J = ScanImageAndReduceC(clone_i, table);
}
t = 1000*((double)getTickCount() - t)/getTickFrequency();
t /= times;
cout << "Time of reducing with the C operator [] (averaged for "
<< times << " runs): " << t << " milliseconds."<< endl;
t = (double)getTickCount();
for (int i = 0; i < times; ++i)
{
cv::Mat clone_i = I.clone();
J = ScanImageAndReduceIterator(clone_i, table);
}
t = 1000*((double)getTickCount() - t)/getTickFrequency();
t /= times;
cout << "Time of reducing with the iterator (averaged for "
<< times << " runs): " << t << " milliseconds."<< endl;
t = (double)getTickCount();
for (int i = 0; i < times; ++i)
{
cv::Mat clone_i = I.clone();
ScanImageAndReduceRandomAccess(clone_i, table);
}
t = 1000*((double)getTickCount() - t)/getTickFrequency();
t /= times;
cout << "Time of reducing with the on-the-fly address generation - at function (averaged for "
<< times << " runs): " << t << " milliseconds."<< endl;
Mat lookUpTable(1, 256, CV_8U);
uchar* p = lookUpTable.data;
for( int i = 0; i < 256; ++i)
p[i] = table[i];
t = (double)getTickCount();
for (int i = 0; i < times; ++i)
LUT(I, lookUpTable, J);
t = 1000*((double)getTickCount() - t)/getTickFrequency();
t /= times;
cout << "Time of reducing with the LUT function (averaged for "
<< times << " runs): " << t << " milliseconds."<< endl;
return 0;
}
Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
int channels = I.channels();
int nRows = I.rows;
int nCols = I.cols * channels;
if (I.isContinuous())
{
nCols *= nRows;
nRows = 1;
}
int i,j;
uchar* p;
for( i = 0; i < nRows; ++i)
{
p = I.ptr<uchar>(i);
for ( j = 0; j < nCols; ++j)
{
p[j] = table[p[j]];
}
}
return I;
}
Mat& ScanImageAndReduceIterator(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
const int channels = I.channels();
switch(channels)
{
case 1:
{
MatIterator_<uchar> it, end;
for( it = I.begin<uchar>(), end = I.end<uchar>(); it != end; ++it)
*it = table[*it];
break;
}
case 3:
{
MatIterator_<Vec3b> it, end;
for( it = I.begin<Vec3b>(), end = I.end<Vec3b>(); it != end; ++it)
{
(*it)[0] = table[(*it)[0]];
(*it)[1] = table[(*it)[1]];
(*it)[2] = table[(*it)[2]];
}
}
}
return I;
}
Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
const int channels = I.channels();
switch(channels)
{
case 1:
{
for( int i = 0; i < I.rows; ++i)
for( int j = 0; j < I.cols; ++j )
I.at<uchar>(i,j) = table[I.at<uchar>(i,j)];
break;
}
case 3:
{
Mat_<Vec3b> _I = I;
for( int i = 0; i < I.rows; ++i)
for( int j = 0; j < I.cols; ++j )
{
_I(i,j)[0] = table[_I(i,j)[0]];
_I(i,j)[1] = table[_I(i,j)[1]];
_I(i,j)[2] = table[_I(i,j)[2]];
}
I = _I;
break;
}
}
return I;
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <sstream>
using namespace std;
using namespace cv;
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << endl
<< "This program shows how to scan image objects in OpenCV (cv::Mat). As use case"
<< " we take an input image and divide the native color palette (255) with the " << endl
<< "input. Shows C operator[] method, iterators and at function for on-the-fly item address calculation."<< endl
<< "Usage:" << endl
<< "./howToScanImages imageNameToUse divideWith [G]" << endl
<< "if you add a G parameter the image is processed in gray scale" << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
Mat& ScanImageAndReduceC(Mat& I, const uchar* table);
Mat& ScanImageAndReduceIterator(Mat& I, const uchar* table);
Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar * table);
int main( int argc, char* argv[])
{
help();
if (argc < 3)
{
cout << "Not enough parameters" << endl;
return -1;
}
Mat I, J;
if( argc == 4 && !strcmp(argv[3],"G") )
I = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
else
I = imread(argv[1], CV_LOAD_IMAGE_COLOR);
if (!I.data)
{
cout << "The image" << argv[1] << " could not be loaded." << endl;
return -1;
}
int divideWith; // convert our input string to number - C++ style
stringstream s;
s << argv[2];
s >> divideWith;
if (!s)
{
cout << "Invalid number entered for dividing. " << endl;
return -1;
}
uchar table[256];
for (int i = 0; i < 256; ++i)
table[i] = divideWith* (i/divideWith);
const int times = 100;
double t;
t = (double)getTickCount();
for (int i = 0; i < times; ++i)
{
cv::Mat clone_i = I.clone();
J = ScanImageAndReduceC(clone_i, table);
}
t = 1000*((double)getTickCount() - t)/getTickFrequency();
t /= times;
cout << "Time of reducing with the C operator [] (averaged for "
<< times << " runs): " << t << " milliseconds."<< endl;
t = (double)getTickCount();
for (int i = 0; i < times; ++i)
{
cv::Mat clone_i = I.clone();
J = ScanImageAndReduceIterator(clone_i, table);
}
t = 1000*((double)getTickCount() - t)/getTickFrequency();
t /= times;
cout << "Time of reducing with the iterator (averaged for "
<< times << " runs): " << t << " milliseconds."<< endl;
t = (double)getTickCount();
for (int i = 0; i < times; ++i)
{
cv::Mat clone_i = I.clone();
ScanImageAndReduceRandomAccess(clone_i, table);
}
t = 1000*((double)getTickCount() - t)/getTickFrequency();
t /= times;
cout << "Time of reducing with the on-the-fly address generation - at function (averaged for "
<< times << " runs): " << t << " milliseconds."<< endl;
Mat lookUpTable(1, 256, CV_8U);
uchar* p = lookUpTable.data;
for( int i = 0; i < 256; ++i)
p[i] = table[i];
t = (double)getTickCount();
for (int i = 0; i < times; ++i)
LUT(I, lookUpTable, J);
t = 1000*((double)getTickCount() - t)/getTickFrequency();
t /= times;
cout << "Time of reducing with the LUT function (averaged for "
<< times << " runs): " << t << " milliseconds."<< endl;
return 0;
}
Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
int channels = I.channels();
int nRows = I.rows;
int nCols = I.cols * channels;
if (I.isContinuous())
{
nCols *= nRows;
nRows = 1;
}
int i,j;
uchar* p;
for( i = 0; i < nRows; ++i)
{
p = I.ptr<uchar>(i);
for ( j = 0; j < nCols; ++j)
{
p[j] = table[p[j]];
}
}
return I;
}
Mat& ScanImageAndReduceIterator(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
const int channels = I.channels();
switch(channels)
{
case 1:
{
MatIterator_<uchar> it, end;
for( it = I.begin<uchar>(), end = I.end<uchar>(); it != end; ++it)
*it = table[*it];
break;
}
case 3:
{
MatIterator_<Vec3b> it, end;
for( it = I.begin<Vec3b>(), end = I.end<Vec3b>(); it != end; ++it)
{
(*it)[0] = table[(*it)[0]];
(*it)[1] = table[(*it)[1]];
(*it)[2] = table[(*it)[2]];
}
}
}
return I;
}
Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
const int channels = I.channels();
switch(channels)
{
case 1:
{
for( int i = 0; i < I.rows; ++i)
for( int j = 0; j < I.cols; ++j )
I.at<uchar>(i,j) = table[I.at<uchar>(i,j)];
break;
}
case 3:
{
Mat_<Vec3b> _I = I;
for( int i = 0; i < I.rows; ++i)
for( int j = 0; j < I.cols; ++j )
{
_I(i,j)[0] = table[_I(i,j)[0]];
_I(i,j)[1] = table[_I(i,j)[1]];
_I(i,j)[2] = table[_I(i,j)[2]];
}
I = _I;
break;
}
}
return I;
}

268
samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp
View File

@@ -1,134 +1,134 @@
#include <stdio.h>
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace cv; // The new C++ interface API is inside this namespace. Import it.
using namespace std;
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
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 safe ref-counting pointer class
if(IplI.empty())
{
cerr << "Can not load image " << imagename << endl;
return -1;
}
Mat I(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, CV_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, CV_YCrCb2BGR); // and produce the output RGB image
namedWindow("image with grain", CV_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;
}
#include <stdio.h>
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace cv; // The new C++ interface API is inside this namespace. Import it.
using namespace std;
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
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 safe ref-counting pointer class
if(IplI.empty())
{
cerr << "Can not load image " << imagename << endl;
return -1;
}
Mat I(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, CV_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, CV_YCrCb2BGR); // and produce the output RGB image
namedWindow("image with grain", CV_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;
}

170
samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp
View File

@@ -1,86 +1,86 @@
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
using namespace std;
using namespace cv;
void help(char* progName)
{
cout << endl
<< "This program shows how to filter images with mask: the write it yourself and the"
<< "filter2d way. " << endl
<< "Usage:" << endl
<< progName << " [image_name -- default lena.jpg] [G -- grayscale] " << endl << endl;
}
void Sharpen(const Mat& myImage,Mat& Result);
int main( int argc, char* argv[])
{
help(argv[0]);
const char* filename = argc >=2 ? argv[1] : "lena.jpg";
Mat I, J, K;
if (argc >= 3 && !strcmp("G", argv[2]))
I = imread( filename, CV_LOAD_IMAGE_GRAYSCALE);
else
I = imread( filename, CV_LOAD_IMAGE_COLOR);
namedWindow("Input", CV_WINDOW_AUTOSIZE);
namedWindow("Output", CV_WINDOW_AUTOSIZE);
imshow("Input", I);
double t = (double)getTickCount();
Sharpen(I, J);
t = ((double)getTickCount() - t)/getTickFrequency();
cout << "Hand written function times passed in seconds: " << t << endl;
imshow("Output", J);
cvWaitKey(0);
Mat kern = (Mat_<char>(3,3) << 0, -1, 0,
-1, 5, -1,
0, -1, 0);
t = (double)getTickCount();
filter2D(I, K, I.depth(), kern );
t = ((double)getTickCount() - t)/getTickFrequency();
cout << "Built-in filter2D time passed in seconds: " << t << endl;
imshow("Output", K);
cvWaitKey(0);
return 0;
}
void Sharpen(const Mat& myImage,Mat& Result)
{
CV_Assert(myImage.depth() == CV_8U); // accept only uchar images
const int nChannels = myImage.channels();
Result.create(myImage.size(),myImage.type());
for(int j = 1 ; j < myImage.rows-1; ++j)
{
const uchar* previous = myImage.ptr<uchar>(j - 1);
const uchar* current = myImage.ptr<uchar>(j );
const uchar* next = myImage.ptr<uchar>(j + 1);
uchar* output = Result.ptr<uchar>(j);
for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)
{
*output++ = saturate_cast<uchar>(5*current[i]
-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);
}
}
Result.row(0).setTo(Scalar(0));
Result.row(Result.rows-1).setTo(Scalar(0));
Result.col(0).setTo(Scalar(0));
Result.col(Result.cols-1).setTo(Scalar(0));
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
using namespace std;
using namespace cv;
void help(char* progName)
{
cout << endl
<< "This program shows how to filter images with mask: the write it yourself and the"
<< "filter2d way. " << endl
<< "Usage:" << endl
<< progName << " [image_name -- default lena.jpg] [G -- grayscale] " << endl << endl;
}
void Sharpen(const Mat& myImage,Mat& Result);
int main( int argc, char* argv[])
{
help(argv[0]);
const char* filename = argc >=2 ? argv[1] : "lena.jpg";
Mat I, J, K;
if (argc >= 3 && !strcmp("G", argv[2]))
I = imread( filename, CV_LOAD_IMAGE_GRAYSCALE);
else
I = imread( filename, CV_LOAD_IMAGE_COLOR);
namedWindow("Input", CV_WINDOW_AUTOSIZE);
namedWindow("Output", CV_WINDOW_AUTOSIZE);
imshow("Input", I);
double t = (double)getTickCount();
Sharpen(I, J);
t = ((double)getTickCount() - t)/getTickFrequency();
cout << "Hand written function times passed in seconds: " << t << endl;
imshow("Output", J);
cvWaitKey(0);
Mat kern = (Mat_<char>(3,3) << 0, -1, 0,
-1, 5, -1,
0, -1, 0);
t = (double)getTickCount();
filter2D(I, K, I.depth(), kern );
t = ((double)getTickCount() - t)/getTickFrequency();
cout << "Built-in filter2D time passed in seconds: " << t << endl;
imshow("Output", K);
cvWaitKey(0);
return 0;
}
void Sharpen(const Mat& myImage,Mat& Result)
{
CV_Assert(myImage.depth() == CV_8U); // accept only uchar images
const int nChannels = myImage.channels();
Result.create(myImage.size(),myImage.type());
for(int j = 1 ; j < myImage.rows-1; ++j)
{
const uchar* previous = myImage.ptr<uchar>(j - 1);
const uchar* current = myImage.ptr<uchar>(j );
const uchar* next = myImage.ptr<uchar>(j + 1);
uchar* output = Result.ptr<uchar>(j);
for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)
{
*output++ = saturate_cast<uchar>(5*current[i]
-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);
}
}
Result.row(0).setTo(Scalar(0));
Result.row(Result.rows-1).setTo(Scalar(0));
Result.col(0).setTo(Scalar(0));
Result.col(Result.cols-1).setTo(Scalar(0));
}

24
samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp
View File

@@ -16,38 +16,38 @@ void help()
<< "Shows how output can be formated to OpenCV, python, numpy, csv and C styles." << endl
<< "Usage:" << endl
<< "./cvout_sample" << endl
<< "--------------------------------------------------------------------------" << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int,char**)
{
help();
help();
// create by using the constructor
Mat M(2,2, CV_8UC3, Scalar(0,0,255));
cout << "M = " << endl << " " << M << endl << endl;
Mat M(2,2, CV_8UC3, Scalar(0,0,255));
cout << "M = " << endl << " " << M << endl << endl;
// create by using the create function()
M.create(4,4, CV_8UC(2));
cout << "M = "<< endl << " " << M << endl << endl;
// create multidimensional matrices
int sz[3] = {2,2,2};
int sz[3] = {2,2,2};
Mat L(3,sz, CV_8UC(1), Scalar::all(0));
// Cannot print via operator <<
// Create using MATLAB style eye, ones or zero matrix
Mat E = Mat::eye(4, 4, CV_64F);
Mat E = Mat::eye(4, 4, CV_64F);
cout << "E = " << endl << " " << E << endl << endl;
Mat O = Mat::ones(2, 2, CV_32F);
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;
// create a 3x3 double-precision identity matrix
Mat C = (Mat_<double>(3,3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
Mat C = (Mat_<double>(3,3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
cout << "C = " << endl << " " << C << endl << endl;
Mat RowClone = C.row(1).clone();
@@ -73,9 +73,9 @@ int main(int,char**)
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;
vector<Point2f> vPoints(20);
for (size_t E = 0; E < vPoints.size(); ++E)
vPoints[E] = Point2f((float)(E * 5), (float)(E % 7));

12
samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp
View File

@@ -26,7 +26,7 @@ int main( int argc, char** argv )
Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
if( !img_1.data || !img_2.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
@@ -64,7 +64,7 @@ int main( int argc, char** argv )
printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );
//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )
//-- PS.- radiusMatch can also be used here.
std::vector< DMatch > good_matches;
@@ -72,13 +72,13 @@ int main( int argc, char** argv )
for( int i = 0; i < descriptors_1.rows; i++ )
{ if( matches[i].distance < 2*min_dist )
{ good_matches.push_back( matches[i]); }
}
}
//-- Draw only "good" matches
Mat img_matches;
drawMatches( img_1, keypoints_1, img_2, keypoints_2,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
drawMatches( img_1, keypoints_1, img_2, keypoints_2,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
//-- Show detected matches
imshow( "Good Matches", img_matches );

18
samples/cpp/tutorial_code/features2D/SURF_Homography.cpp
View File

@@ -27,7 +27,7 @@ int main( int argc, char** argv )
Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
if( !img_object.data || !img_scene.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
@@ -65,22 +65,22 @@ int main( int argc, char** argv )
printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );
//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
std::vector< DMatch > good_matches;
for( int i = 0; i < descriptors_object.rows; i++ )
{ if( matches[i].distance < 3*min_dist )
{ good_matches.push_back( matches[i]); }
}
}
Mat img_matches;
drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
//-- Localize the object from img_1 in img_2
//-- Localize the object from img_1 in img_2
std::vector<Point2f> obj;
std::vector<Point2f> scene;
@@ -88,7 +88,7 @@ int main( int argc, char** argv )
{
//-- Get the keypoints from the good matches
obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
}
Mat H = findHomography( obj, scene, CV_RANSAC );
@@ -101,7 +101,7 @@ int main( int argc, char** argv )
perspectiveTransform( obj_corners, scene_corners, H);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );

4
samples/cpp/tutorial_code/features2D/SURF_descriptor.cpp
View File

@@ -26,7 +26,7 @@ int main( int argc, char** argv )
Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
if( !img_1.data || !img_2.data )
{ return -1; }
@@ -55,7 +55,7 @@ int main( int argc, char** argv )
//-- Draw matches
Mat img_matches;
drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
//-- Show detected matches
imshow("Matches", img_matches );

6
samples/cpp/tutorial_code/features2D/SURF_detector.cpp
View File

@@ -26,7 +26,7 @@ int main( int argc, char** argv )
Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
if( !img_1.data || !img_2.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
@@ -43,8 +43,8 @@ int main( int argc, char** argv )
//-- 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 );
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 );

858
samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp
View File

@@ -1,429 +1,429 @@
#include <iostream> // Console I/O
#include <sstream> // String to number conversion
#include <opencv2/core/core.hpp> // Basic OpenCV structures
#include <opencv2/imgproc/imgproc.hpp>// Image processing methods for the CPU
#include <opencv2/highgui/highgui.hpp>// Read images
#include <opencv2/gpu/gpu.hpp> // GPU structures and methods
using namespace std;
using namespace cv;
double getPSNR(const Mat& I1, const Mat& I2); // CPU versions
Scalar getMSSIM( const Mat& I1, const Mat& I2);
double getPSNR_GPU(const Mat& I1, const Mat& I2); // Basic GPU versions
Scalar getMSSIM_GPU( const Mat& I1, const Mat& I2);
struct BufferPSNR // Optimized GPU versions
{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
gpu::GpuMat gI1, gI2, gs, t1,t2;
gpu::GpuMat buf;
};
double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b);
struct BufferMSSIM // Optimized GPU versions
{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
gpu::GpuMat gI1, gI2, gs, t1,t2;
gpu::GpuMat I1_2, I2_2, I1_I2;
vector<gpu::GpuMat> vI1, vI2;
gpu::GpuMat mu1, mu2;
gpu::GpuMat mu1_2, mu2_2, mu1_mu2;
gpu::GpuMat sigma1_2, sigma2_2, sigma12;
gpu::GpuMat t3;
gpu::GpuMat ssim_map;
gpu::GpuMat buf;
};
Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b);
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << endl
<< "This program shows how to port your CPU code to GPU or write that from scratch." << endl
<< "You can see the performance improvement for the similarity check methods (PSNR and SSIM)." << endl
<< "Usage:" << endl
<< "./gpu-basics-similarity referenceImage comparedImage numberOfTimesToRunTest(like 10)." << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int argc, char *argv[])
{
help();
Mat I1 = imread(argv[1]); // Read the two images
Mat I2 = imread(argv[2]);
if (!I1.data || !I2.data) // Check for success
{
cout << "Couldn't read the image";
return 0;
}
BufferPSNR bufferPSNR;
BufferMSSIM bufferMSSIM;
int TIMES;
stringstream sstr(argv[3]);
sstr >> TIMES;
double time, result;
//------------------------------- PSNR CPU ----------------------------------------------------
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
result = getPSNR(I1,I2);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of PSNR CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of: " << result << endl;
//------------------------------- PSNR GPU ----------------------------------------------------
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
result = getPSNR_GPU(I1,I2);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of PSNR GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of: " << result << endl;
//------------------------------- PSNR GPU Optimized--------------------------------------------
time = (double)getTickCount(); // Initial call
result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
cout << "Initial call GPU optimized: " << time <<" milliseconds."
<< " With result of: " << result << endl;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of PSNR GPU OPTIMIZED ( / " << TIMES << " runs): " << time
<< " milliseconds." << " With result of: " << result << endl << endl;
//------------------------------- SSIM CPU -----------------------------------------------------
Scalar x;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
x = getMSSIM(I1,I2);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of MSSIM CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
//------------------------------- SSIM GPU -----------------------------------------------------
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
x = getMSSIM_GPU(I1,I2);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of MSSIM GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
//------------------------------- SSIM GPU Optimized--------------------------------------------
time = (double)getTickCount();
x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
cout << "Time of MSSIM GPU Initial Call " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of MSSIM GPU OPTIMIZED ( / " << TIMES << " runs): " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl << endl;
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;
}
}
double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b)
{
b.gI1.upload(I1);
b.gI2.upload(I2);
b.gI1.convertTo(b.t1, CV_32F);
b.gI2.convertTo(b.t2, CV_32F);
gpu::absdiff(b.t1.reshape(1), b.t2.reshape(1), b.gs);
gpu::multiply(b.gs, b.gs, b.gs);
double sse = gpu::sum(b.gs, b.buf)[0];
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;
}
}
double getPSNR_GPU(const Mat& I1, const Mat& I2)
{
gpu::GpuMat gI1, gI2, gs, t1,t2;
gI1.upload(I1);
gI2.upload(I2);
gI1.convertTo(t1, CV_32F);
gI2.convertTo(t2, CV_32F);
gpu::absdiff(t1.reshape(1), t2.reshape(1), gs);
gpu::multiply(gs, gs, gs);
Scalar s = gpu::sum(gs);
double sse = s.val[0] + s.val[1] + s.val[2];
if( sse <= 1e-10) // for small values return zero
return 0;
else
{
double mse =sse /(double)(gI1.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;
///////////////////////////////// FORMULA ////////////////////////////////
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;
}
Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
{
const float C1 = 6.5025f, C2 = 58.5225f;
/***************************** INITS **********************************/
gpu::GpuMat gI1, gI2, gs1, t1,t2;
gI1.upload(i1);
gI2.upload(i2);
gI1.convertTo(t1, CV_MAKE_TYPE(CV_32F, gI1.channels()));
gI2.convertTo(t2, CV_MAKE_TYPE(CV_32F, gI2.channels()));
vector<gpu::GpuMat> vI1, vI2;
gpu::split(t1, vI1);
gpu::split(t2, vI2);
Scalar mssim;
for( int i = 0; i < gI1.channels(); ++i )
{
gpu::GpuMat I2_2, I1_2, I1_I2;
gpu::multiply(vI2[i], vI2[i], I2_2); // I2^2
gpu::multiply(vI1[i], vI1[i], I1_2); // I1^2
gpu::multiply(vI1[i], vI2[i], I1_I2); // I1 * I2
/*************************** END INITS **********************************/
gpu::GpuMat mu1, mu2; // PRELIMINARY COMPUTING
gpu::GaussianBlur(vI1[i], mu1, Size(11, 11), 1.5);
gpu::GaussianBlur(vI2[i], mu2, Size(11, 11), 1.5);
gpu::GpuMat mu1_2, mu2_2, mu1_mu2;
gpu::multiply(mu1, mu1, mu1_2);
gpu::multiply(mu2, mu2, mu2_2);
gpu::multiply(mu1, mu2, mu1_mu2);
gpu::GpuMat sigma1_2, sigma2_2, sigma12;
gpu::GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
gpu::subtract(sigma1_2, mu1_2, sigma1_2); // sigma1_2 -= mu1_2;
gpu::GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
gpu::subtract(sigma2_2, mu2_2, sigma2_2); // sigma2_2 -= mu2_2;
gpu::GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);
gpu::subtract(sigma12, mu1_mu2, sigma12); // sigma12 -= mu1_mu2;
///////////////////////////////// FORMULA ////////////////////////////////
gpu::GpuMat t1, t2, t3;
mu1_mu2.convertTo(t1, -1, 2, C1); // t1 = 2 * mu1_mu2 + C1;
sigma12.convertTo(t2, -1, 2, C2); // t2 = 2 * sigma12 + C2;
gpu::multiply(t1, t2, t3); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
gpu::addWeighted(mu1_2, 1.0, mu2_2, 1.0, C1, t1); // t1 = mu1_2 + mu2_2 + C1;
gpu::addWeighted(sigma1_2, 1.0, sigma2_2, 1.0, C2, t2); // t2 = sigma1_2 + sigma2_2 + C2;
gpu::multiply(t1, t2, t1); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
gpu::GpuMat ssim_map;
gpu::divide(t3, t1, ssim_map); // ssim_map = t3./t1;
Scalar s = gpu::sum(ssim_map);
mssim.val[i] = s.val[0] / (ssim_map.rows * ssim_map.cols);
}
return mssim;
}
Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
{
int cn = i1.channels();
const float C1 = 6.5025f, C2 = 58.5225f;
/***************************** INITS **********************************/
b.gI1.upload(i1);
b.gI2.upload(i2);
gpu::Stream stream;
stream.enqueueConvert(b.gI1, b.t1, CV_32F);
stream.enqueueConvert(b.gI2, b.t2, CV_32F);
gpu::split(b.t1, b.vI1, stream);
gpu::split(b.t2, b.vI2, stream);
Scalar mssim;
gpu::GpuMat buf;
for( int i = 0; i < b.gI1.channels(); ++i )
{
gpu::multiply(b.vI2[i], b.vI2[i], b.I2_2, stream); // I2^2
gpu::multiply(b.vI1[i], b.vI1[i], b.I1_2, stream); // I1^2
gpu::multiply(b.vI1[i], b.vI2[i], b.I1_I2, stream); // I1 * I2
gpu::GaussianBlur(b.vI1[i], b.mu1, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::GaussianBlur(b.vI2[i], b.mu2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::multiply(b.mu1, b.mu1, b.mu1_2, stream);
gpu::multiply(b.mu2, b.mu2, b.mu2_2, stream);
gpu::multiply(b.mu1, b.mu2, b.mu1_mu2, stream);
gpu::GaussianBlur(b.I1_2, b.sigma1_2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, gpu::GpuMat(), -1, stream);
//b.sigma1_2 -= b.mu1_2; - This would result in an extra data transfer operation
gpu::GaussianBlur(b.I2_2, b.sigma2_2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, gpu::GpuMat(), -1, stream);
//b.sigma2_2 -= b.mu2_2;
gpu::GaussianBlur(b.I1_I2, b.sigma12, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::subtract(b.sigma12, b.mu1_mu2, b.sigma12, gpu::GpuMat(), -1, stream);
//b.sigma12 -= b.mu1_mu2;
//here too it would be an extra data transfer due to call of operator*(Scalar, Mat)
gpu::multiply(b.mu1_mu2, 2, b.t1, 1, -1, stream); //b.t1 = 2 * b.mu1_mu2 + C1;
gpu::add(b.t1, C1, b.t1, gpu::GpuMat(), -1, stream);
gpu::multiply(b.sigma12, 2, b.t2, 1, -1, stream); //b.t2 = 2 * b.sigma12 + C2;
gpu::add(b.t2, C2, b.t2, gpu::GpuMat(), -12, stream);
gpu::multiply(b.t1, b.t2, b.t3, 1, -1, stream); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
gpu::add(b.mu1_2, b.mu2_2, b.t1, gpu::GpuMat(), -1, stream);
gpu::add(b.t1, C1, b.t1, gpu::GpuMat(), -1, stream);
gpu::add(b.sigma1_2, b.sigma2_2, b.t2, gpu::GpuMat(), -1, stream);
gpu::add(b.t2, C2, b.t2, gpu::GpuMat(), -1, stream);
gpu::multiply(b.t1, b.t2, b.t1, 1, -1, stream); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
gpu::divide(b.t3, b.t1, b.ssim_map, 1, -1, stream); // ssim_map = t3./t1;
stream.waitForCompletion();
Scalar s = gpu::sum(b.ssim_map, b.buf);
mssim.val[i] = s.val[0] / (b.ssim_map.rows * b.ssim_map.cols);
}
return mssim;
}
#include <iostream> // Console I/O
#include <sstream> // String to number conversion
#include <opencv2/core/core.hpp> // Basic OpenCV structures
#include <opencv2/imgproc/imgproc.hpp>// Image processing methods for the CPU
#include <opencv2/highgui/highgui.hpp>// Read images
#include <opencv2/gpu/gpu.hpp> // GPU structures and methods
using namespace std;
using namespace cv;
double getPSNR(const Mat& I1, const Mat& I2); // CPU versions
Scalar getMSSIM( const Mat& I1, const Mat& I2);
double getPSNR_GPU(const Mat& I1, const Mat& I2); // Basic GPU versions
Scalar getMSSIM_GPU( const Mat& I1, const Mat& I2);
struct BufferPSNR // Optimized GPU versions
{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
gpu::GpuMat gI1, gI2, gs, t1,t2;
gpu::GpuMat buf;
};
double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b);
struct BufferMSSIM // Optimized GPU versions
{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
gpu::GpuMat gI1, gI2, gs, t1,t2;
gpu::GpuMat I1_2, I2_2, I1_I2;
vector<gpu::GpuMat> vI1, vI2;
gpu::GpuMat mu1, mu2;
gpu::GpuMat mu1_2, mu2_2, mu1_mu2;
gpu::GpuMat sigma1_2, sigma2_2, sigma12;
gpu::GpuMat t3;
gpu::GpuMat ssim_map;
gpu::GpuMat buf;
};
Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b);
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << endl
<< "This program shows how to port your CPU code to GPU or write that from scratch." << endl
<< "You can see the performance improvement for the similarity check methods (PSNR and SSIM)." << endl
<< "Usage:" << endl
<< "./gpu-basics-similarity referenceImage comparedImage numberOfTimesToRunTest(like 10)." << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int argc, char *argv[])
{
help();
Mat I1 = imread(argv[1]); // Read the two images
Mat I2 = imread(argv[2]);
if (!I1.data || !I2.data) // Check for success
{
cout << "Couldn't read the image";
return 0;
}
BufferPSNR bufferPSNR;
BufferMSSIM bufferMSSIM;
int TIMES;
stringstream sstr(argv[3]);
sstr >> TIMES;
double time, result;
//------------------------------- PSNR CPU ----------------------------------------------------
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
result = getPSNR(I1,I2);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of PSNR CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of: " << result << endl;
//------------------------------- PSNR GPU ----------------------------------------------------
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
result = getPSNR_GPU(I1,I2);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of PSNR GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of: " << result << endl;
//------------------------------- PSNR GPU Optimized--------------------------------------------
time = (double)getTickCount(); // Initial call
result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
cout << "Initial call GPU optimized: " << time <<" milliseconds."
<< " With result of: " << result << endl;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of PSNR GPU OPTIMIZED ( / " << TIMES << " runs): " << time
<< " milliseconds." << " With result of: " << result << endl << endl;
//------------------------------- SSIM CPU -----------------------------------------------------
Scalar x;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
x = getMSSIM(I1,I2);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of MSSIM CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
//------------------------------- SSIM GPU -----------------------------------------------------
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
x = getMSSIM_GPU(I1,I2);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of MSSIM GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
//------------------------------- SSIM GPU Optimized--------------------------------------------
time = (double)getTickCount();
x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
cout << "Time of MSSIM GPU Initial Call " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM);
time = 1000*((double)getTickCount() - time)/getTickFrequency();
time /= TIMES;
cout << "Time of MSSIM GPU OPTIMIZED ( / " << TIMES << " runs): " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl << endl;
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;
}
}
double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b)
{
b.gI1.upload(I1);
b.gI2.upload(I2);
b.gI1.convertTo(b.t1, CV_32F);
b.gI2.convertTo(b.t2, CV_32F);
gpu::absdiff(b.t1.reshape(1), b.t2.reshape(1), b.gs);
gpu::multiply(b.gs, b.gs, b.gs);
double sse = gpu::sum(b.gs, b.buf)[0];
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;
}
}
double getPSNR_GPU(const Mat& I1, const Mat& I2)
{
gpu::GpuMat gI1, gI2, gs, t1,t2;
gI1.upload(I1);
gI2.upload(I2);
gI1.convertTo(t1, CV_32F);
gI2.convertTo(t2, CV_32F);
gpu::absdiff(t1.reshape(1), t2.reshape(1), gs);
gpu::multiply(gs, gs, gs);
Scalar s = gpu::sum(gs);
double sse = s.val[0] + s.val[1] + s.val[2];
if( sse <= 1e-10) // for small values return zero
return 0;
else
{
double mse =sse /(double)(gI1.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;
///////////////////////////////// FORMULA ////////////////////////////////
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;
}
Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
{
const float C1 = 6.5025f, C2 = 58.5225f;
/***************************** INITS **********************************/
gpu::GpuMat gI1, gI2, gs1, t1,t2;
gI1.upload(i1);
gI2.upload(i2);
gI1.convertTo(t1, CV_MAKE_TYPE(CV_32F, gI1.channels()));
gI2.convertTo(t2, CV_MAKE_TYPE(CV_32F, gI2.channels()));
vector<gpu::GpuMat> vI1, vI2;
gpu::split(t1, vI1);
gpu::split(t2, vI2);
Scalar mssim;
for( int i = 0; i < gI1.channels(); ++i )
{
gpu::GpuMat I2_2, I1_2, I1_I2;
gpu::multiply(vI2[i], vI2[i], I2_2); // I2^2
gpu::multiply(vI1[i], vI1[i], I1_2); // I1^2
gpu::multiply(vI1[i], vI2[i], I1_I2); // I1 * I2
/*************************** END INITS **********************************/
gpu::GpuMat mu1, mu2; // PRELIMINARY COMPUTING
gpu::GaussianBlur(vI1[i], mu1, Size(11, 11), 1.5);
gpu::GaussianBlur(vI2[i], mu2, Size(11, 11), 1.5);
gpu::GpuMat mu1_2, mu2_2, mu1_mu2;
gpu::multiply(mu1, mu1, mu1_2);
gpu::multiply(mu2, mu2, mu2_2);
gpu::multiply(mu1, mu2, mu1_mu2);
gpu::GpuMat sigma1_2, sigma2_2, sigma12;
gpu::GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
gpu::subtract(sigma1_2, mu1_2, sigma1_2); // sigma1_2 -= mu1_2;
gpu::GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
gpu::subtract(sigma2_2, mu2_2, sigma2_2); // sigma2_2 -= mu2_2;
gpu::GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);
gpu::subtract(sigma12, mu1_mu2, sigma12); // sigma12 -= mu1_mu2;
///////////////////////////////// FORMULA ////////////////////////////////
gpu::GpuMat t1, t2, t3;
mu1_mu2.convertTo(t1, -1, 2, C1); // t1 = 2 * mu1_mu2 + C1;
sigma12.convertTo(t2, -1, 2, C2); // t2 = 2 * sigma12 + C2;
gpu::multiply(t1, t2, t3); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
gpu::addWeighted(mu1_2, 1.0, mu2_2, 1.0, C1, t1); // t1 = mu1_2 + mu2_2 + C1;
gpu::addWeighted(sigma1_2, 1.0, sigma2_2, 1.0, C2, t2); // t2 = sigma1_2 + sigma2_2 + C2;
gpu::multiply(t1, t2, t1); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
gpu::GpuMat ssim_map;
gpu::divide(t3, t1, ssim_map); // ssim_map = t3./t1;
Scalar s = gpu::sum(ssim_map);
mssim.val[i] = s.val[0] / (ssim_map.rows * ssim_map.cols);
}
return mssim;
}
Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
{
int cn = i1.channels();
const float C1 = 6.5025f, C2 = 58.5225f;
/***************************** INITS **********************************/
b.gI1.upload(i1);
b.gI2.upload(i2);
gpu::Stream stream;
stream.enqueueConvert(b.gI1, b.t1, CV_32F);
stream.enqueueConvert(b.gI2, b.t2, CV_32F);
gpu::split(b.t1, b.vI1, stream);
gpu::split(b.t2, b.vI2, stream);
Scalar mssim;
gpu::GpuMat buf;
for( int i = 0; i < b.gI1.channels(); ++i )
{
gpu::multiply(b.vI2[i], b.vI2[i], b.I2_2, stream); // I2^2
gpu::multiply(b.vI1[i], b.vI1[i], b.I1_2, stream); // I1^2
gpu::multiply(b.vI1[i], b.vI2[i], b.I1_I2, stream); // I1 * I2
gpu::GaussianBlur(b.vI1[i], b.mu1, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::GaussianBlur(b.vI2[i], b.mu2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::multiply(b.mu1, b.mu1, b.mu1_2, stream);
gpu::multiply(b.mu2, b.mu2, b.mu2_2, stream);
gpu::multiply(b.mu1, b.mu2, b.mu1_mu2, stream);
gpu::GaussianBlur(b.I1_2, b.sigma1_2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, gpu::GpuMat(), -1, stream);
//b.sigma1_2 -= b.mu1_2; - This would result in an extra data transfer operation
gpu::GaussianBlur(b.I2_2, b.sigma2_2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, gpu::GpuMat(), -1, stream);
//b.sigma2_2 -= b.mu2_2;
gpu::GaussianBlur(b.I1_I2, b.sigma12, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::subtract(b.sigma12, b.mu1_mu2, b.sigma12, gpu::GpuMat(), -1, stream);
//b.sigma12 -= b.mu1_mu2;
//here too it would be an extra data transfer due to call of operator*(Scalar, Mat)
gpu::multiply(b.mu1_mu2, 2, b.t1, 1, -1, stream); //b.t1 = 2 * b.mu1_mu2 + C1;
gpu::add(b.t1, C1, b.t1, gpu::GpuMat(), -1, stream);
gpu::multiply(b.sigma12, 2, b.t2, 1, -1, stream); //b.t2 = 2 * b.sigma12 + C2;
gpu::add(b.t2, C2, b.t2, gpu::GpuMat(), -12, stream);
gpu::multiply(b.t1, b.t2, b.t3, 1, -1, stream); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
gpu::add(b.mu1_2, b.mu2_2, b.t1, gpu::GpuMat(), -1, stream);
gpu::add(b.t1, C1, b.t1, gpu::GpuMat(), -1, stream);
gpu::add(b.sigma1_2, b.sigma2_2, b.t2, gpu::GpuMat(), -1, stream);
gpu::add(b.t2, C2, b.t2, gpu::GpuMat(), -1, stream);
gpu::multiply(b.t1, b.t2, b.t1, 1, -1, stream); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
gpu::divide(b.t3, b.t1, b.ssim_map, 1, -1, stream); // ssim_map = t3./t1;
stream.waitForCompletion();
Scalar s = gpu::sum(b.ssim_map, b.buf);
mssim.val[i] = s.val[0] / (b.ssim_map.rows * b.ssim_map.cols);
}
return mssim;
}

58
samples/cpp/tutorial_code/introduction/display_image/display_image.cpp
View File

@@ -1,30 +1,30 @@
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main( int argc, char** argv )
{
if( argc != 2)
{
cout <<" Usage: display_image ImageToLoadAndDisplay" << endl;
return -1;
}
Mat image;
image = imread(argv[1], CV_LOAD_IMAGE_COLOR); // Read the file
if(! image.data ) // Check for invalid input
{
cout << "Could not open or find the image" << std::endl ;
return -1;
}
namedWindow( "Display window", CV_WINDOW_AUTOSIZE );// Create a window for display.
imshow( "Display window", image ); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
return 0;
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main( int argc, char** argv )
{
if( argc != 2)
{
cout <<" Usage: display_image ImageToLoadAndDisplay" << endl;
return -1;
}
Mat image;
image = imread(argv[1], CV_LOAD_IMAGE_COLOR); // Read the file
if(! image.data ) // Check for invalid input
{
cout << "Could not open or find the image" << std::endl ;
return -1;
}
namedWindow( "Display window", CV_WINDOW_AUTOSIZE );// Create a window for display.
imshow( "Display window", image ); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
return 0;
}

410
samples/cpp/tutorial_code/introduction/windows_visual_studio_Opencv/Test.cpp
View File

@@ -1,206 +1,206 @@
// Video Image PSNR and SSIM
#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/imgproc/imgproc.hpp> // Gaussian Blur
#include <opencv2/core/core.hpp> // Basic OpenCV structures (cv::Mat, Scalar)
#include <opencv2/highgui/highgui.hpp> // OpenCV window I/O
using namespace std;
using namespace cv;
double getPSNR ( const Mat& I1, const Mat& I2);
Scalar getMSSIM( const Mat& I1, const Mat& I2);
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << 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 " << endl
<< "trigger value, also with MSSIM."<< endl
<< "Usage:" << endl
<< "./video-source referenceVideo useCaseTestVideo PSNR_Trigger_Value Wait_Between_Frames " << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int argc, char *argv[], char *window_name)
{
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] << argv[4]; // put in the strings
conv >> psnrTriggerValue >> delay;// take out the numbers
char c;
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(CV_CAP_PROP_FRAME_WIDTH),
(int) captRefrnc.get(CV_CAP_PROP_FRAME_HEIGHT)),
uTSi = Size((int) captUndTst.get(CV_CAP_PROP_FRAME_WIDTH),
(int) captUndTst.get(CV_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, CV_WINDOW_AUTOSIZE );
namedWindow(WIN_UT, CV_WINDOW_AUTOSIZE );
cvMoveWindow(WIN_RF, 400 , 0); //750, 2 (bernat =0)
cvMoveWindow(WIN_UT, refS.width, 0); //1500, 2
cout << "Frame resolution: Width=" << refS.width << " Height=" << refS.height
<< " of nr#: " << captRefrnc.get(CV_CAP_PROP_FRAME_COUNT) << endl;
cout << "PSNR trigger value " <<
setiosflags(ios::fixed) << setprecision(3) << psnrTriggerValue << endl;
Mat frameReference, frameUnderTest;
double psnrV;
Scalar mssimV;
while( true) //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;
///////////////////////////////// PSNR ////////////////////////////////////////////////////
psnrV = getPSNR(frameReference,frameUnderTest); //get PSNR
cout << setiosflags(ios::fixed) << setprecision(3) << psnrV << "dB";
//////////////////////////////////// MSSIM /////////////////////////////////////////////////
if (psnrV < psnrTriggerValue)
{
mssimV = getMSSIM(frameReference,frameUnderTest);
cout << " MSSIM: "
<< "R" << setiosflags(ios::fixed) << setprecision(3) << mssimV.val[2] * 100
<< "G" << setiosflags(ios::fixed) << setprecision(3) << mssimV.val[1] * 100
<< "B" << setiosflags(ios::fixed) << setprecision(3) << mssimV.val[0] * 100;
}
cout << endl;
////////////////////////////////// Show Image /////////////////////////////////////////////
imshow( WIN_RF, frameReference);
imshow( WIN_UT, frameUnderTest);
c = cvWaitKey(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;
///////////////////////////////// FORMULA ////////////////////////////////
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;
// Video Image PSNR and SSIM
#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/imgproc/imgproc.hpp> // Gaussian Blur
#include <opencv2/core/core.hpp> // Basic OpenCV structures (cv::Mat, Scalar)
#include <opencv2/highgui/highgui.hpp> // OpenCV window I/O
using namespace std;
using namespace cv;
double getPSNR ( const Mat& I1, const Mat& I2);
Scalar getMSSIM( const Mat& I1, const Mat& I2);
void help()
{
cout
<< "\n--------------------------------------------------------------------------" << 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 " << endl
<< "trigger value, also with MSSIM."<< endl
<< "Usage:" << endl
<< "./video-source referenceVideo useCaseTestVideo PSNR_Trigger_Value Wait_Between_Frames " << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int argc, char *argv[], char *window_name)
{
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] << argv[4]; // put in the strings
conv >> psnrTriggerValue >> delay;// take out the numbers
char c;
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(CV_CAP_PROP_FRAME_WIDTH),
(int) captRefrnc.get(CV_CAP_PROP_FRAME_HEIGHT)),
uTSi = Size((int) captUndTst.get(CV_CAP_PROP_FRAME_WIDTH),
(int) captUndTst.get(CV_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, CV_WINDOW_AUTOSIZE );
namedWindow(WIN_UT, CV_WINDOW_AUTOSIZE );
cvMoveWindow(WIN_RF, 400 , 0); //750, 2 (bernat =0)
cvMoveWindow(WIN_UT, refS.width, 0); //1500, 2
cout << "Frame resolution: Width=" << refS.width << " Height=" << refS.height
<< " of nr#: " << captRefrnc.get(CV_CAP_PROP_FRAME_COUNT) << endl;
cout << "PSNR trigger value " <<
setiosflags(ios::fixed) << setprecision(3) << psnrTriggerValue << endl;
Mat frameReference, frameUnderTest;
double psnrV;
Scalar mssimV;
while( true) //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;
///////////////////////////////// PSNR ////////////////////////////////////////////////////
psnrV = getPSNR(frameReference,frameUnderTest); //get PSNR
cout << setiosflags(ios::fixed) << setprecision(3) << psnrV << "dB";
//////////////////////////////////// MSSIM /////////////////////////////////////////////////
if (psnrV < psnrTriggerValue)
{
mssimV = getMSSIM(frameReference,frameUnderTest);
cout << " MSSIM: "
<< "R" << setiosflags(ios::fixed) << setprecision(3) << mssimV.val[2] * 100
<< "G" << setiosflags(ios::fixed) << setprecision(3) << mssimV.val[1] * 100
<< "B" << setiosflags(ios::fixed) << setprecision(3) << mssimV.val[0] * 100;
}
cout << endl;
////////////////////////////////// Show Image /////////////////////////////////////////////
imshow( WIN_RF, frameReference);
imshow( WIN_UT, frameUnderTest);
c = cvWaitKey(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;
///////////////////////////////// FORMULA ////////////////////////////////
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;
}

12
samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
View File

@@ -30,7 +30,7 @@ int main()
//--------------------- 1. Set up training data randomly ---------------------------------------
Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1);
Mat labels (2*NTRAINING_SAMPLES, 1, CV_32FC1);
RNG rng(100); // Random value generation class
// Set up the linearly separable part of the training data
@@ -48,7 +48,7 @@ int main()
// Generate random points for the class 2
trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES);
// The x coordinate of the points is in [0.6, 1]
c = trainClass.colRange(0 , 1);
c = trainClass.colRange(0 , 1);
rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH));
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1,2);
@@ -60,11 +60,11 @@ int main()
trainClass = trainData.rowRange( nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples);
// The x coordinate of the points is in [0.4, 0.6)
c = trainClass.colRange(0,1);
rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));
rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1,2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
//------------------------- Set up the labels for the classes ---------------------------------
labels.rowRange( 0, NTRAINING_SAMPLES).setTo(1); // Class 1
labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2); // Class 2
@@ -81,9 +81,9 @@ int main()
CvSVM svm;
svm.train(trainData, labels, Mat(), Mat(), params);
cout << "Finished training process" << endl;
//------------------------ 4. Show the decision regions ----------------------------------------
Vec3b green(0,100,0), blue (100,0,0);
Vec3b green(0,100,0), blue (100,0,0);
for (int i = 0; i < I.rows; ++i)
for (int j = 0; j < I.cols; ++j)
{

12
samples/cpp/tutorial_code/objectDetection/objectDetection.cpp
View File

@@ -36,7 +36,7 @@ int main( int argc, const char** argv )
//-- 1. Load the cascades
if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
//-- 2. Read the video stream
capture = cvCaptureFromCAM( -1 );
if( capture )
@@ -44,15 +44,15 @@ int main( int argc, const char** argv )
while( true )
{
frame = cvQueryFrame( capture );
//-- 3. Apply the classifier to the frame
if( !frame.empty() )
{ detectAndDisplay( frame ); }
else
{ printf(" --(!) No captured frame -- Break!"); break; }
int c = waitKey(10);
if( (char)c == 'c' ) { break; }
if( (char)c == 'c' ) { break; }
}
}
@@ -85,11 +85,11 @@ void detectAndDisplay( Mat frame )
for( int j = 0; j < eyes.size(); j++ )
{
Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
circle( frame, center, radius, Scalar( 255, 0, 0 ), 3, 8, 0 );
}
}
}
//-- Show what you got
imshow( window_name, frame );
}

10
samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp
View File

@@ -44,15 +44,15 @@ int main( int argc, const char** argv )
while( true )
{
frame = cvQueryFrame( capture );
//-- 3. Apply the classifier to the frame
if( !frame.empty() )
{ detectAndDisplay( frame ); }
else
{ printf(" --(!) No captured frame -- Break!"); break; }
int c = waitKey(10);
if( (char)c == 'c' ) { break; }
if( (char)c == 'c' ) { break; }
}
}
@@ -88,13 +88,13 @@ void detectAndDisplay( Mat frame )
for( int j = 0; j < eyes.size(); j++ )
{ //-- Draw the eyes
Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
circle( frame, center, radius, Scalar( 255, 0, 255 ), 3, 8, 0 );
}
}
}
}
//-- Show what you got
imshow( window_name, frame );
}