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Mergin itseez

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This commit is contained in:
Fedor Morozov
2013-09-18 18:55:12 +04:00
parent 90b6af1496 99a43257d5
commit f99be6bda6
1586 changed files with 85437 additions and 29692 deletions
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3
samples/cpp/CMakeLists.txt
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@@ -5,7 +5,7 @@
SET(OPENCV_CPP_SAMPLES_REQUIRED_DEPS opencv_core opencv_flann opencv_imgproc
opencv_highgui opencv_ml opencv_video opencv_objdetect opencv_photo opencv_nonfree opencv_softcascade
opencv_features2d opencv_calib3d opencv_legacy opencv_contrib opencv_stitching opencv_videostab)
opencv_features2d opencv_calib3d opencv_legacy opencv_contrib opencv_stitching opencv_videostab opencv_bioinspired)
ocv_check_dependencies(${OPENCV_CPP_SAMPLES_REQUIRED_DEPS})
@@ -95,4 +95,3 @@ if (INSTALL_C_EXAMPLES AND NOT WIN32)
DESTINATION share/OpenCV/samples/cpp
PERMISSIONS OWNER_READ GROUP_READ WORLD_READ)
endif()

74
samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp
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@@ -10,8 +10,9 @@
#include <iostream>
#include <cstring>
#include "opencv2/contrib.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/bioinspired.hpp" // retina based algorithms
#include "opencv2/imgproc.hpp" // cvCvtcolor function
#include "opencv2/highgui.hpp" // display
static void help(std::string errorMessage)
{
@@ -127,7 +128,7 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
normalize(imageInputRescaled, imageInputRescaled, 0.0, 255.0, cv::NORM_MINMAX);
}
cv::Ptr<cv::Retina> retina;
cv::Ptr<cv::bioinspired::Retina> retina;
int retinaHcellsGain;
int localAdaptation_photoreceptors, localAdaptation_Gcells;
static void callBack_updateRetinaParams(int, void*)
@@ -175,6 +176,12 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
}
bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
int chosenMethod=0;
if (!strcmp(argv[argc-1], "fast"))
{
chosenMethod=1;
std::cout<<"Using fast method (no spectral whithning), adaptation of Meylan&al 2008 method"<<std::endl;
}
std::string inputImageName=argv[1];
@@ -210,17 +217,22 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
* -> if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
*/
if (useLogSampling)
{
retina = cv::createRetina(inputImage.size(),true, cv::RETINA_COLOR_BAYER, true, 2.0, 10.0);
{
retina = cv::bioinspired::createRetina(inputImage.size(),true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
}
else// -> else allocate "classical" retina :
retina = cv::createRetina(inputImage.size());
retina = cv::bioinspired::createRetina(inputImage.size());
// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
retina->write("RetinaDefaultParameters.xml");
// create a fast retina tone mapper (Meyla&al algorithm)
std::cout<<"Allocating fast tone mapper..."<<std::endl;
//cv::Ptr<cv::RetinaFastToneMapping> fastToneMapper=createRetinaFastToneMapping(inputImage.size());
std::cout<<"Fast tone mapper allocated"<<std::endl;
// desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here
retina->activateMovingContoursProcessing(false);
// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
retina->write("RetinaDefaultParameters.xml");
// desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here
retina->activateMovingContoursProcessing(false);
// declare retina output buffers
cv::Mat retinaOutput_parvo;
@@ -230,20 +242,19 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
histogramClippingValue=0; // default value... updated with interface slider
//inputRescaleMat = inputImage;
//outputRescaleMat = imageInputRescaled;
cv::namedWindow("Retina input image (with cut edges histogram for basic pixels error avoidance)",1);
cv::createTrackbar("histogram edges clipping limit", "Retina input image (with cut edges histogram for basic pixels error avoidance)",&histogramClippingValue,50,callBack_rescaleGrayLevelMat);
cv::namedWindow("Processing configuration",1);
cv::createTrackbar("histogram edges clipping limit", "Processing configuration",&histogramClippingValue,50,callBack_rescaleGrayLevelMat);
cv::namedWindow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", 1);
colorSaturationFactor=3;
cv::createTrackbar("Color saturation", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &colorSaturationFactor,5,callback_saturateColors);
cv::createTrackbar("Color saturation", "Processing configuration", &colorSaturationFactor,5,callback_saturateColors);
retinaHcellsGain=40;
cv::createTrackbar("Hcells gain", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping",&retinaHcellsGain,100,callBack_updateRetinaParams);
cv::createTrackbar("Hcells gain", "Processing configuration",&retinaHcellsGain,100,callBack_updateRetinaParams);
localAdaptation_photoreceptors=197;
localAdaptation_Gcells=190;
cv::createTrackbar("Ph sensitivity", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams);
cv::createTrackbar("Gcells sensitivity", "Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", &localAdaptation_Gcells,199,callBack_updateRetinaParams);
cv::createTrackbar("Ph sensitivity", "Processing configuration", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams);
cv::createTrackbar("Gcells sensitivity", "Processing configuration", &localAdaptation_Gcells,199,callBack_updateRetinaParams);
/////////////////////////////////////////////
@@ -257,11 +268,28 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
while(continueProcessing)
{
// run retina filter
retina->run(imageInputRescaled);
// Retrieve and display retina output
retina->getParvo(retinaOutput_parvo);
cv::imshow("Retina input image (with cut edges histogram for basic pixels error avoidance)", imageInputRescaled/255.0);
cv::imshow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", retinaOutput_parvo);
if (!chosenMethod)
{
retina->run(imageInputRescaled);
// Retrieve and display retina output
retina->getParvo(retinaOutput_parvo);
cv::imshow("Retina input image (with cut edges histogram for basic pixels error avoidance)", imageInputRescaled/255.0);
cv::imshow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", retinaOutput_parvo);
cv::imwrite("HDRinput.jpg",imageInputRescaled/255.0);
cv::imwrite("RetinaToneMapping.jpg",retinaOutput_parvo);
}
else
{
// apply the simplified hdr tone mapping method
cv::Mat fastToneMappingOutput;
retina->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput);
cv::imshow("Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput);
}
/*cv::Mat fastToneMappingOutput_specificObject;
fastToneMapper->setup(3.f, 1.5f, 1.f);
fastToneMapper->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput_specificObject);
cv::imshow("### Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput_specificObject);
*/
cv::waitKey(10);
}
}catch(cv::Exception e)
@@ -274,5 +302,3 @@ static void drawPlot(const cv::Mat curve, const std::string figureTitle, const i
return 0;
}

13
samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping_video.cpp
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@@ -14,8 +14,9 @@
#include <stdio.h>
#include <cstring>
#include "opencv2/contrib.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/bioinspired.hpp" // retina based algorithms
#include "opencv2/imgproc.hpp" // cvCvtcolor function
#include "opencv2/highgui.hpp" // display
static void help(std::string errorMessage)
{
@@ -160,7 +161,7 @@ static void rescaleGrayLevelMat(const cv::Mat &inputMat, cv::Mat &outputMat, con
}
cv::Ptr<cv::Retina> retina;
cv::Ptr<cv::bioinspired::Retina> retina;
int retinaHcellsGain;
int localAdaptation_photoreceptors, localAdaptation_Gcells;
static void callBack_updateRetinaParams(int, void*)
@@ -280,10 +281,10 @@ static void loadNewFrame(const std::string filenamePrototype, const int currentF
*/
if (useLogSampling)
{
retina = cv::createRetina(inputImage.size(),true, cv::RETINA_COLOR_BAYER, true, 2.0, 10.0);
retina = cv::bioinspired::createRetina(inputImage.size(),true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
}
else// -> else allocate "classical" retina :
retina = cv::createRetina(inputImage.size());
retina = cv::bioinspired::createRetina(inputImage.size());
// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
retina->write("RetinaDefaultParameters.xml");
@@ -358,5 +359,3 @@ static void loadNewFrame(const std::string filenamePrototype, const int currentF
return 0;
}

6
samples/cpp/bagofwords_classification.cpp
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@@ -2563,19 +2563,19 @@ int main(int argc, char** argv)
Ptr<FeatureDetector> featureDetector = FeatureDetector::create( ddmParams.detectorType );
Ptr<DescriptorExtractor> descExtractor = DescriptorExtractor::create( ddmParams.descriptorType );
Ptr<BOWImgDescriptorExtractor> bowExtractor;
if( featureDetector.empty() || descExtractor.empty() )
if( !featureDetector || !descExtractor )
{
cout << "featureDetector or descExtractor was not created" << endl;
return -1;
}
{
Ptr<DescriptorMatcher> descMatcher = DescriptorMatcher::create( ddmParams.matcherType );
if( featureDetector.empty() || descExtractor.empty() || descMatcher.empty() )
if( !featureDetector || !descExtractor || !descMatcher )
{
cout << "descMatcher was not created" << endl;
return -1;
}
bowExtractor = new BOWImgDescriptorExtractor( descExtractor, descMatcher );
bowExtractor = makePtr<BOWImgDescriptorExtractor>( descExtractor, descMatcher );
}
// Print configuration to screen

3
samples/cpp/bgfg_gmg.cpp
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@@ -35,7 +35,7 @@ int main(int argc, char** argv)
setNumThreads(8);
Ptr<BackgroundSubtractor> fgbg = createBackgroundSubtractorGMG(20, 0.7);
if (fgbg.empty())
if (!fgbg)
{
std::cerr << "Failed to create BackgroundSubtractor.GMG Algorithm." << std::endl;
return -1;
@@ -78,4 +78,3 @@ int main(int argc, char** argv)
return 0;
}

1
samples/cpp/calibration_artificial.cpp
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@@ -332,4 +332,3 @@ Mat cv::ChessBoardGenerator::operator ()(const Mat& bg, const Mat& camMat, const
return generageChessBoard(bg, camMat, distCoeffs, zero, pb1, pb2, sqWidth, sqHeight, pts3d, corners);
}

10
samples/cpp/dbt_face_detection.cpp
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@@ -22,7 +22,7 @@ class CascadeDetectorAdapter: public DetectionBasedTracker::IDetector
IDetector(),
Detector(detector)
{
CV_Assert(!detector.empty());
CV_Assert(detector);
}
void detect(const cv::Mat &Image, std::vector<cv::Rect> &objects)
@@ -51,11 +51,11 @@ int main(int , char** )
}
std::string cascadeFrontalfilename = "../../data/lbpcascades/lbpcascade_frontalface.xml";
cv::Ptr<cv::CascadeClassifier> cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> MainDetector = new CascadeDetectorAdapter(cascade);
cv::Ptr<cv::CascadeClassifier> cascade = makePtr<cv::CascadeClassifier>(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> MainDetector = makePtr<CascadeDetectorAdapter>(cascade);
cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> TrackingDetector = new CascadeDetectorAdapter(cascade);
cascade = makePtr<cv::CascadeClassifier>(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> TrackingDetector = makePtr<CascadeDetectorAdapter>(cascade);
DetectionBasedTracker::Parameters params;
DetectionBasedTracker Detector(MainDetector, TrackingDetector, params);

4
samples/cpp/descriptor_extractor_matcher.cpp
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@@ -153,7 +153,7 @@ static void doIteration( const Mat& img1, Mat& img2, bool isWarpPerspective,
{
cout << "< Evaluate descriptor matcher..." << endl;
vector<Point2f> curve;
Ptr<GenericDescriptorMatcher> gdm = new VectorDescriptorMatcher( descriptorExtractor, descriptorMatcher );
Ptr<GenericDescriptorMatcher> gdm = makePtr<VectorDescriptorMatcher>( descriptorExtractor, descriptorMatcher );
evaluateGenericDescriptorMatcher( img1, img2, H12, keypoints1, keypoints2, 0, 0, curve, gdm );
Point2f firstPoint = *curve.begin();
@@ -253,7 +253,7 @@ int main(int argc, char** argv)
int mactherFilterType = getMatcherFilterType( argv[4] );
bool eval = !isWarpPerspective ? false : (atoi(argv[6]) == 0 ? false : true);
cout << ">" << endl;
if( detector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() )
if( !detector || !descriptorExtractor || !descriptorMatcher )
{
cout << "Can not create detector or descriptor exstractor or descriptor matcher of given types" << endl;
return -1;

10
samples/cpp/detection_based_tracker_sample.cpp
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@@ -67,7 +67,7 @@ class CascadeDetectorAdapter: public DetectionBasedTracker::IDetector
CascadeDetectorAdapter(cv::Ptr<cv::CascadeClassifier> detector):
Detector(detector)
{
CV_Assert(!detector.empty());
CV_Assert(detector);
}
void detect(const cv::Mat &Image, std::vector<cv::Rect> &objects)
@@ -117,11 +117,11 @@ static int test_FaceDetector(int argc, char *argv[])
}
std::string cascadeFrontalfilename=cascadefile;
cv::Ptr<cv::CascadeClassifier> cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> MainDetector = new CascadeDetectorAdapter(cascade);
cv::Ptr<cv::CascadeClassifier> cascade = makePtr<cv::CascadeClassifier>(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> MainDetector = makePtr<CascadeDetectorAdapter>(cascade);
cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> TrackingDetector = new CascadeDetectorAdapter(cascade);
cascade = makePtr<cv::CascadeClassifier>(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> TrackingDetector = makePtr<CascadeDetectorAdapter>(cascade);
DetectionBasedTracker::Parameters params;
DetectionBasedTracker fd(MainDetector, TrackingDetector, params);

44
samples/cpp/detector_descriptor_evaluation.cpp
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@@ -535,7 +535,7 @@ void DetectorQualityEvaluator::readAlgorithm ()
{
defaultDetector = FeatureDetector::create( algName );
specificDetector = FeatureDetector::create( algName );
if( defaultDetector.empty() )
if( !defaultDetector )
{
printf( "Algorithm can not be read\n" );
exit(-1);
@@ -769,14 +769,14 @@ void DescriptorQualityEvaluator::readAlgorithm( )
defaultDescMatcher = GenericDescriptorMatcher::create( algName );
specificDescMatcher = GenericDescriptorMatcher::create( algName );
if( defaultDescMatcher.empty() )
if( !defaultDescMatcher )
{
Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create( algName );
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create( matcherName );
defaultDescMatcher = new VectorDescriptorMatch( extractor, matcher );
specificDescMatcher = new VectorDescriptorMatch( extractor, matcher );
defaultDescMatcher = makePtr<VectorDescriptorMatch>( extractor, matcher );
specificDescMatcher = makePtr<VectorDescriptorMatch>( extractor, matcher );
if( extractor.empty() || matcher.empty() )
if( !extractor || !matcher )
{
printf("Algorithm can not be read\n");
exit(-1);
@@ -881,8 +881,9 @@ public:
virtual void readAlgorithm( )
{
string classifierFile = data_path + "/features2d/calonder_classifier.rtc";
defaultDescMatcher = new VectorDescriptorMatch( new CalonderDescriptorExtractor<float>( classifierFile ),
new BFMatcher(NORM_L2) );
defaultDescMatcher = makePtr<VectorDescriptorMatch>(
makePtr<CalonderDescriptorExtractor<float> >( classifierFile ),
makePtr<BFMatcher>(int(NORM_L2)));
specificDescMatcher = defaultDescMatcher;
}
};
@@ -922,10 +923,11 @@ void OneWayDescriptorQualityTest::processRunParamsFile ()
readAllDatasetsRunParams();
OneWayDescriptorBase *base = new OneWayDescriptorBase(patchSize, poseCount, pcaFilename,
trainPath, trainImagesList);
Ptr<OneWayDescriptorBase> base(
new OneWayDescriptorBase(patchSize, poseCount, pcaFilename,
trainPath, trainImagesList));
OneWayDescriptorMatch *match = new OneWayDescriptorMatch ();
Ptr<OneWayDescriptorMatch> match = makePtr<OneWayDescriptorMatch>();
match->initialize( OneWayDescriptorMatch::Params (), base );
defaultDescMatcher = match;
writeAllDatasetsRunParams();
@@ -958,18 +960,18 @@ int main( int argc, char** argv )
Ptr<BaseQualityEvaluator> evals[] =
{
new DetectorQualityEvaluator( "FAST", "quality-detector-fast" ),
new DetectorQualityEvaluator( "GFTT", "quality-detector-gftt" ),
new DetectorQualityEvaluator( "HARRIS", "quality-detector-harris" ),
new DetectorQualityEvaluator( "MSER", "quality-detector-mser" ),
new DetectorQualityEvaluator( "STAR", "quality-detector-star" ),
new DetectorQualityEvaluator( "SIFT", "quality-detector-sift" ),
new DetectorQualityEvaluator( "SURF", "quality-detector-surf" ),
makePtr<DetectorQualityEvaluator>( "FAST", "quality-detector-fast" ),
makePtr<DetectorQualityEvaluator>( "GFTT", "quality-detector-gftt" ),
makePtr<DetectorQualityEvaluator>( "HARRIS", "quality-detector-harris" ),
makePtr<DetectorQualityEvaluator>( "MSER", "quality-detector-mser" ),
makePtr<DetectorQualityEvaluator>( "STAR", "quality-detector-star" ),
makePtr<DetectorQualityEvaluator>( "SIFT", "quality-detector-sift" ),
makePtr<DetectorQualityEvaluator>( "SURF", "quality-detector-surf" ),
new DescriptorQualityEvaluator( "SIFT", "quality-descriptor-sift", "BruteForce" ),
new DescriptorQualityEvaluator( "SURF", "quality-descriptor-surf", "BruteForce" ),
new DescriptorQualityEvaluator( "FERN", "quality-descriptor-fern"),
new CalonderDescriptorQualityEvaluator()
makePtr<DescriptorQualityEvaluator>( "SIFT", "quality-descriptor-sift", "BruteForce" ),
makePtr<DescriptorQualityEvaluator>( "SURF", "quality-descriptor-surf", "BruteForce" ),
makePtr<DescriptorQualityEvaluator>( "FERN", "quality-descriptor-fern"),
makePtr<CalonderDescriptorQualityEvaluator>()
};
for( size_t i = 0; i < sizeof(evals)/sizeof(evals[0]); i++ )

1
samples/cpp/dft.cpp
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@@ -80,4 +80,3 @@ int main(int argc, const char ** argv)
waitKey();
return 0;
}

120
samples/cpp/erfilter.cpp Normal file
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@@ -0,0 +1,120 @@
//--------------------------------------------------------------------------------------------------
// A demo program of the Extremal Region Filter algorithm described in
// Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
//--------------------------------------------------------------------------------------------------
#include "opencv2/opencv.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <vector>
#include <iostream>
#include <iomanip>
using namespace std;
using namespace cv;
void er_draw(Mat &src, Mat &dst, ERStat& er);
void er_draw(Mat &src, Mat &dst, ERStat& er)
{
if (er.parent != NULL) // deprecate the root region
{
int newMaskVal = 255;
int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
floodFill(src,dst,Point(er.pixel%src.cols,er.pixel/src.cols),Scalar(255),0,Scalar(er.level),Scalar(0),flags);
}
}
int main(int argc, const char * argv[])
{
vector<ERStat> regions;
if (argc < 2) {
cout << "Demo program of the Extremal Region Filter algorithm described in " << endl;
cout << "Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012" << endl << endl;
cout << " Usage: " << argv[0] << " input_image <optional_groundtruth_image>" << endl;
cout << " Default classifier files (trained_classifierNM*.xml) should be in ./" << endl;
return -1;
}
Mat original = imread(argv[1]);
Mat gt;
if (argc > 2)
{
gt = imread(argv[2]);
cvtColor(gt, gt, COLOR_RGB2GRAY);
threshold(gt, gt, 254, 255, THRESH_BINARY);
}
Mat grey(original.size(),CV_8UC1);
cvtColor(original,grey,COLOR_RGB2GRAY);
double t = (double)getTickCount();
// Build ER tree and filter with the 1st stage default classifier
Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"));
er_filter1->run(grey, regions);
t = (double)getTickCount() - t;
cout << " --------------------------------------------------------------------------------------------------" << endl;
cout << "\t FIRST STAGE CLASSIFIER done in " << t * 1000. / getTickFrequency() << " ms." << endl;
cout << " --------------------------------------------------------------------------------------------------" << endl;
cout << setw(9) << regions.size()+er_filter1->getNumRejected() << "\t Extremal Regions extracted " << endl;
cout << setw(9) << regions.size() << "\t Extremal Regions selected by the first stage of the sequential classifier." << endl;
cout << "\t \t (saving into out_second_stage.jpg)" << endl;
cout << " --------------------------------------------------------------------------------------------------" << endl;
er_filter1.release();
// draw regions
Mat mask = Mat::zeros(grey.rows+2,grey.cols+2,CV_8UC1);
for (int r=0; r<(int)regions.size(); r++)
er_draw(grey, mask, regions.at(r));
mask = 255-mask;
imwrite("out_first_stage.jpg", mask);
if (argc > 2)
{
Mat tmp_mask = (255-gt) & (255-mask(Rect(Point(1,1),Size(mask.cols-2,mask.rows-2))));
cout << "Recall for the 1st stage filter = " << (float)countNonZero(tmp_mask) / countNonZero(255-gt) << endl;
}
t = (double)getTickCount();
// Default second stage classifier
Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"));
er_filter2->run(grey, regions);
t = (double)getTickCount() - t;
cout << " --------------------------------------------------------------------------------------------------" << endl;
cout << "\t SECOND STAGE CLASSIFIER done in " << t * 1000. / getTickFrequency() << " ms." << endl;
cout << " --------------------------------------------------------------------------------------------------" << endl;
cout << setw(9) << regions.size() << "\t Extremal Regions selected by the second stage of the sequential classifier." << endl;
cout << "\t \t (saving into out_second_stage.jpg)" << endl;
cout << " --------------------------------------------------------------------------------------------------" << endl;
er_filter2.release();
// draw regions
mask = mask*0;
for (int r=0; r<(int)regions.size(); r++)
er_draw(grey, mask, regions.at(r));
mask = 255-mask;
imwrite("out_second_stage.jpg", mask);
if (argc > 2)
{
Mat tmp_mask = (255-gt) & (255-mask(Rect(Point(1,1),Size(mask.cols-2,mask.rows-2))));
cout << "Recall for the 2nd stage filter = " << (float)countNonZero(tmp_mask) / countNonZero(255-gt) << endl;
}
regions.clear();
}

12
samples/cpp/fabmap_sample.cpp
View File

@@ -131,11 +131,11 @@ int main(int argc, char * argv[]) {
//generate test data
cout << "Extracting Test Data from images" << endl <<
endl;
Ptr<FeatureDetector> detector =
Ptr<FeatureDetector> detector(
new DynamicAdaptedFeatureDetector(
AdjusterAdapter::create("STAR"), 130, 150, 5);
Ptr<DescriptorExtractor> extractor =
new SurfDescriptorExtractor(1000, 4, 2, false, true);
AdjusterAdapter::create("STAR"), 130, 150, 5));
Ptr<DescriptorExtractor> extractor(
new SurfDescriptorExtractor(1000, 4, 2, false, true));
Ptr<DescriptorMatcher> matcher =
DescriptorMatcher::create("FlannBased");
@@ -183,8 +183,8 @@ int main(int argc, char * argv[]) {
endl;
Ptr<of2::FabMap> fabmap;
fabmap = new of2::FabMap2(tree, 0.39, 0, of2::FabMap::SAMPLED |
of2::FabMap::CHOW_LIU);
fabmap.reset(new of2::FabMap2(tree, 0.39, 0, of2::FabMap::SAMPLED |
of2::FabMap::CHOW_LIU));
fabmap->addTraining(trainData);
vector<of2::IMatch> matches;

2
samples/cpp/freak_demo.cpp
View File

@@ -56,7 +56,7 @@ static void help( char** argv )
}
int main( int argc, char** argv ) {
// check http://opencv.itseez.com/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.html
// check http://docs.opencv.org/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.html
// for OpenCV general detection/matching framework details
if( argc != 3 ) {

2
samples/cpp/generic_descriptor_match.cpp
View File

@@ -33,7 +33,7 @@ int main(int argc, char** argv)
std::string params_filename = std::string(argv[4]);
Ptr<GenericDescriptorMatcher> descriptorMatcher = GenericDescriptorMatcher::create(alg_name, params_filename);
if( descriptorMatcher.empty() )
if( !descriptorMatcher )
{
printf ("Cannot create descriptor\n");
return 0;

1
samples/cpp/houghlines.cpp
View File

@@ -61,4 +61,3 @@ int main(int argc, char** argv)
return 0;
}

4
samples/cpp/image.cpp
View File

@@ -31,8 +31,8 @@ int main( int argc, char** argv )
help();
const char* imagename = argc > 1 ? argv[1] : "lena.jpg";
#if DEMO_MIXED_API_USE
Ptr<IplImage> iplimg = cvLoadImage(imagename); // Ptr<T> is safe ref-conting pointer class
if(iplimg.empty())
Ptr<IplImage> iplimg(cvLoadImage(imagename)); // Ptr<T> is safe ref-counting pointer class
if(!iplimg)
{
fprintf(stderr, "Can not load image %s\n", imagename);
return -1;

57
samples/cpp/image_sequence.cpp Normal file
View File

@@ -0,0 +1,57 @@
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
using namespace cv;
using namespace std;
static void help(char** argv)
{
cout << "\nThis sample shows you how to read a sequence of images using the VideoCapture interface.\n"
<< "Usage: " << argv[0] << " <image_mask> (example mask: example_%%02d.jpg)\n"
<< "Image mask defines the name variation for the input images that have to be read as a sequence. \n"
<< "Using the mask example_%%02d.jpg will read in images labeled as 'example_00.jpg', 'example_01.jpg', etc."
<< endl;
}
int main(int argc, char** argv)
{
if(argc != 2)
{
help(argv);
return 1;
}
string first_file = argv[1];
VideoCapture sequence(first_file);
if (!sequence.isOpened())
{
cerr << "Failed to open the image sequence!\n" << endl;
return 1;
}
Mat image;
namedWindow("Image sequence | press ESC to close", 1);
for(;;)
{
// Read in image from sequence
sequence >> image;
// If no image was retrieved -> end of sequence
if(image.empty())
{
cout << "End of Sequence" << endl;
break;
}
imshow("Image sequence | press ESC to close", image);
if(waitKey(500) == 27)
break;
}
return 0;
}

4
samples/cpp/latentsvm_multidetect.cpp
View File

@@ -3,7 +3,7 @@
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/contrib/contrib.hpp"
#ifdef WIN32
#if defined(WIN32) || defined(_WIN32)
#include <io.h>
#else
#include <dirent.h>
@@ -59,7 +59,7 @@ static void readDirectory( const string& directoryName, vector<String>& filename
{
filenames.clear();
#ifdef WIN32
#if defined(WIN32) | defined(_WIN32)
struct _finddata_t s_file;
string str = directoryName + "\\*.*";

2
samples/cpp/linemod.cpp
View File

@@ -114,7 +114,7 @@ private:
// Functions to store detector and templates in single XML/YAML file
static cv::Ptr<cv::linemod::Detector> readLinemod(const std::string& filename)
{
cv::Ptr<cv::linemod::Detector> detector = new cv::linemod::Detector;
cv::Ptr<cv::linemod::Detector> detector = cv::makePtr<cv::linemod::Detector>();
cv::FileStorage fs(filename, cv::FileStorage::READ);
detector->read(fs.root());

54
samples/cpp/lsd_lines.cpp Normal file
View File

@@ -0,0 +1,54 @@
#include <iostream>
#include <string>
#include "opencv2/core/core.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace std;
using namespace cv;
int main(int argc, char** argv)
{
std::string in;
if (argc != 2)
{
std::cout << "Usage: lsd_lines [input image]. Now loading building.jpg" << std::endl;
in = "building.jpg";
}
else
{
in = argv[1];
}
Mat image = imread(in, IMREAD_GRAYSCALE);
#if 0
Canny(image, image, 50, 200, 3); // Apply canny edge
#endif
// Create and LSD detector with standard or no refinement.
#if 1
Ptr<LineSegmentDetector> ls = createLineSegmentDetectorPtr(LSD_REFINE_STD);
#else
Ptr<LineSegmentDetector> ls = createLineSegmentDetectorPtr(LSD_REFINE_NONE);
#endif
double start = double(getTickCount());
vector<Vec4i> lines_std;
// Detect the lines
ls->detect(image, lines_std);
double duration_ms = (double(getTickCount()) - start) * 1000 / getTickFrequency();
std::cout << "It took " << duration_ms << " ms." << std::endl;
// Show found lines
Mat drawnLines(image);
ls->drawSegments(drawnLines, lines_std);
imshow("Standard refinement", drawnLines);
waitKey();
return 0;
}

2
samples/cpp/matching_to_many_images.cpp
View File

@@ -84,7 +84,7 @@ static bool createDetectorDescriptorMatcher( const string& detectorType, const s
descriptorMatcher = DescriptorMatcher::create( matcherType );
cout << ">" << endl;
bool isCreated = !( featureDetector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() );
bool isCreated = featureDetector && descriptorExtractor && descriptorMatcher;
if( !isCreated )
cout << "Can not create feature detector or descriptor extractor or descriptor matcher of given types." << endl << ">" << endl;

4
samples/cpp/phase_corr.cpp
View File

@@ -43,7 +43,3 @@ int main(int, char* [])
return 0;
}

12
samples/cpp/retinaDemo.cpp
View File

@@ -9,7 +9,7 @@
#include <iostream>
#include <cstring>
#include "opencv2/contrib.hpp"
#include "opencv2/bioinspired.hpp"
#include "opencv2/highgui.hpp"
static void help(std::string errorMessage)
@@ -106,15 +106,15 @@ int main(int argc, char* argv[]) {
try
{
// create a retina instance with default parameters setup, uncomment the initialisation you wanna test
cv::Ptr<cv::Retina> myRetina;
cv::Ptr<cv::bioinspired::Retina> myRetina;
// if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
if (useLogSampling)
{
myRetina = cv::createRetina(inputFrame.size(), true, cv::RETINA_COLOR_BAYER, true, 2.0, 10.0);
myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
}
else// -> else allocate "classical" retina :
myRetina = cv::createRetina(inputFrame.size());
myRetina = cv::bioinspired::createRetina(inputFrame.size());
// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
myRetina->write("RetinaDefaultParameters.xml");
@@ -143,7 +143,8 @@ int main(int argc, char* argv[]) {
cv::imshow("retina input", inputFrame);
cv::imshow("Retina Parvo", retinaOutput_parvo);
cv::imshow("Retina Magno", retinaOutput_magno);
cv::waitKey(10);
cv::waitKey(5);
}
}catch(cv::Exception e)
{
@@ -155,4 +156,3 @@ int main(int argc, char* argv[]) {
return 0;
}

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6
samples/cpp/starter_video.cpp
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@@ -41,15 +41,15 @@ namespace {
cout << "press space to save a picture. q or esc to quit" << endl;
namedWindow(window_name, WINDOW_KEEPRATIO); //resizable window;
Mat frame;
for (;;) {
capture >> frame;
if (frame.empty())
break;
imshow(window_name, frame);
char key = (char)waitKey(30); //delay N millis, usually long enough to display and capture input
switch (key) {
case 'q':
case 'Q':

16
samples/cpp/stereo_calib.cpp
View File

@@ -14,15 +14,12 @@
Or: http://oreilly.com/catalog/9780596516130/
ISBN-10: 0596516134 or: ISBN-13: 978-0596516130
OTHER OPENCV SITES:
* The source code is on sourceforge at:
http://sourceforge.net/projects/opencvlibrary/
* The OpenCV wiki page (As of Oct 1, 2008 this is down for changing over servers, but should come back):
http://opencvlibrary.sourceforge.net/
* An active user group is at:
http://tech.groups.yahoo.com/group/OpenCV/
* The minutes of weekly OpenCV development meetings are at:
http://code.opencv.org/projects/opencv/wiki/Meeting_notes
OPENCV WEBSITES:
Homepage: http://opencv.org
Online docs: http://docs.opencv.org
Q&A forum: http://answers.opencv.org
Issue tracker: http://code.opencv.org
GitHub: https://github.com/Itseez/opencv/
************************************************** */
#include "opencv2/calib3d/calib3d.hpp"
@@ -404,4 +401,3 @@ int main(int argc, char** argv)
StereoCalib(imagelist, boardSize, true, showRectified);
return 0;
}

2
samples/cpp/stitching.cpp
View File

@@ -134,5 +134,3 @@ int parseCmdArgs(int argc, char** argv)
}
return 0;
}

104
samples/cpp/stitching_detailed.cpp
View File

@@ -358,14 +358,14 @@ int main(int argc, char* argv[])
{
#ifdef HAVE_OPENCV_NONFREE
if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
finder = new SurfFeaturesFinderGpu();
finder = makePtr<SurfFeaturesFinderGpu>();
else
#endif
finder = new SurfFeaturesFinder();
finder = makePtr<SurfFeaturesFinder>();
}
else if (features_type == "orb")
{
finder = new OrbFeaturesFinder();
finder = makePtr<OrbFeaturesFinder>();
}
else
{
@@ -469,7 +469,11 @@ int main(int argc, char* argv[])
HomographyBasedEstimator estimator;
vector<CameraParams> cameras;
estimator(features, pairwise_matches, cameras);
if (!estimator(features, pairwise_matches, cameras))
{
cout << "Homography estimation failed.\n";
return -1;
}
for (size_t i = 0; i < cameras.size(); ++i)
{
@@ -480,8 +484,8 @@ int main(int argc, char* argv[])
}
Ptr<detail::BundleAdjusterBase> adjuster;
if (ba_cost_func == "reproj") adjuster = new detail::BundleAdjusterReproj();
else if (ba_cost_func == "ray") adjuster = new detail::BundleAdjusterRay();
if (ba_cost_func == "reproj") adjuster = makePtr<detail::BundleAdjusterReproj>();
else if (ba_cost_func == "ray") adjuster = makePtr<detail::BundleAdjusterRay>();
else
{
cout << "Unknown bundle adjustment cost function: '" << ba_cost_func << "'.\n";
@@ -495,7 +499,11 @@ int main(int argc, char* argv[])
if (ba_refine_mask[3] == 'x') refine_mask(1,1) = 1;
if (ba_refine_mask[4] == 'x') refine_mask(1,2) = 1;
adjuster->setRefinementMask(refine_mask);
(*adjuster)(features, pairwise_matches, cameras);
if (!(*adjuster)(features, pairwise_matches, cameras))
{
cout << "Camera parameters adjusting failed.\n";
return -1;
}
// Find median focal length
@@ -547,31 +555,49 @@ int main(int argc, char* argv[])
#ifdef HAVE_OPENCV_GPUWARPING
if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
{
if (warp_type == "plane") warper_creator = new cv::PlaneWarperGpu();
else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarperGpu();
else if (warp_type == "spherical") warper_creator = new cv::SphericalWarperGpu();
if (warp_type == "plane")
warper_creator = makePtr<cv::PlaneWarperGpu>();
else if (warp_type == "cylindrical")
warper_creator = makePtr<cv::CylindricalWarperGpu>();
else if (warp_type == "spherical")
warper_creator = makePtr<cv::SphericalWarperGpu>();
}
else
#endif
{
if (warp_type == "plane") warper_creator = new cv::PlaneWarper();
else if (warp_type == "cylindrical") warper_creator = new cv::CylindricalWarper();
else if (warp_type == "spherical") warper_creator = new cv::SphericalWarper();
else if (warp_type == "fisheye") warper_creator = new cv::FisheyeWarper();
else if (warp_type == "stereographic") warper_creator = new cv::StereographicWarper();
else if (warp_type == "compressedPlaneA2B1") warper_creator = new cv::CompressedRectilinearWarper(2, 1);
else if (warp_type == "compressedPlaneA1.5B1") warper_creator = new cv::CompressedRectilinearWarper(1.5, 1);
else if (warp_type == "compressedPlanePortraitA2B1") warper_creator = new cv::CompressedRectilinearPortraitWarper(2, 1);
else if (warp_type == "compressedPlanePortraitA1.5B1") warper_creator = new cv::CompressedRectilinearPortraitWarper(1.5, 1);
else if (warp_type == "paniniA2B1") warper_creator = new cv::PaniniWarper(2, 1);
else if (warp_type == "paniniA1.5B1") warper_creator = new cv::PaniniWarper(1.5, 1);
else if (warp_type == "paniniPortraitA2B1") warper_creator = new cv::PaniniPortraitWarper(2, 1);
else if (warp_type == "paniniPortraitA1.5B1") warper_creator = new cv::PaniniPortraitWarper(1.5, 1);
else if (warp_type == "mercator") warper_creator = new cv::MercatorWarper();
else if (warp_type == "transverseMercator") warper_creator = new cv::TransverseMercatorWarper();
if (warp_type == "plane")
warper_creator = makePtr<cv::PlaneWarper>();
else if (warp_type == "cylindrical")
warper_creator = makePtr<cv::CylindricalWarper>();
else if (warp_type == "spherical")
warper_creator = makePtr<cv::SphericalWarper>();
else if (warp_type == "fisheye")
warper_creator = makePtr<cv::FisheyeWarper>();
else if (warp_type == "stereographic")
warper_creator = makePtr<cv::StereographicWarper>();
else if (warp_type == "compressedPlaneA2B1")
warper_creator = makePtr<cv::CompressedRectilinearWarper>(2.0f, 1.0f);
else if (warp_type == "compressedPlaneA1.5B1")
warper_creator = makePtr<cv::CompressedRectilinearWarper>(1.5f, 1.0f);
else if (warp_type == "compressedPlanePortraitA2B1")
warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(2.0f, 1.0f);
else if (warp_type == "compressedPlanePortraitA1.5B1")
warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(1.5f, 1.0f);
else if (warp_type == "paniniA2B1")
warper_creator = makePtr<cv::PaniniWarper>(2.0f, 1.0f);
else if (warp_type == "paniniA1.5B1")
warper_creator = makePtr<cv::PaniniWarper>(1.5f, 1.0f);
else if (warp_type == "paniniPortraitA2B1")
warper_creator = makePtr<cv::PaniniPortraitWarper>(2.0f, 1.0f);
else if (warp_type == "paniniPortraitA1.5B1")
warper_creator = makePtr<cv::PaniniPortraitWarper>(1.5f, 1.0f);
else if (warp_type == "mercator")
warper_creator = makePtr<cv::MercatorWarper>();
else if (warp_type == "transverseMercator")
warper_creator = makePtr<cv::TransverseMercatorWarper>();
}
if (warper_creator.empty())
if (!warper_creator)
{
cout << "Can't create the following warper '" << warp_type << "'\n";
return 1;
@@ -604,32 +630,32 @@ int main(int argc, char* argv[])
Ptr<SeamFinder> seam_finder;
if (seam_find_type == "no")
seam_finder = new detail::NoSeamFinder();
seam_finder = makePtr<detail::NoSeamFinder>();
else if (seam_find_type == "voronoi")
seam_finder = new detail::VoronoiSeamFinder();
seam_finder = makePtr<detail::VoronoiSeamFinder>();
else if (seam_find_type == "gc_color")
{
#ifdef HAVE_OPENCV_GPU
if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR);
seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR);
else
#endif
seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR);
seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR);
}
else if (seam_find_type == "gc_colorgrad")
{
#ifdef HAVE_OPENCV_GPU
if (try_gpu && gpu::getCudaEnabledDeviceCount() > 0)
seam_finder = new detail::GraphCutSeamFinderGpu(GraphCutSeamFinderBase::COST_COLOR_GRAD);
seam_finder = makePtr<detail::GraphCutSeamFinderGpu>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
else
#endif
seam_finder = new detail::GraphCutSeamFinder(GraphCutSeamFinderBase::COST_COLOR_GRAD);
seam_finder = makePtr<detail::GraphCutSeamFinder>(GraphCutSeamFinderBase::COST_COLOR_GRAD);
}
else if (seam_find_type == "dp_color")
seam_finder = new detail::DpSeamFinder(DpSeamFinder::COLOR);
seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR);
else if (seam_find_type == "dp_colorgrad")
seam_finder = new detail::DpSeamFinder(DpSeamFinder::COLOR_GRAD);
if (seam_finder.empty())
seam_finder = makePtr<detail::DpSeamFinder>(DpSeamFinder::COLOR_GRAD);
if (!seam_finder)
{
cout << "Can't create the following seam finder '" << seam_find_type << "'\n";
return 1;
@@ -727,7 +753,7 @@ int main(int argc, char* argv[])
resize(dilated_mask, seam_mask, mask_warped.size());
mask_warped = seam_mask & mask_warped;
if (blender.empty())
if (!blender)
{
blender = Blender::createDefault(blend_type, try_gpu);
Size dst_sz = resultRoi(corners, sizes).size();
@@ -736,13 +762,13 @@ int main(int argc, char* argv[])
blender = Blender::createDefault(Blender::NO, try_gpu);
else if (blend_type == Blender::MULTI_BAND)
{
MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(static_cast<Blender*>(blender));
MultiBandBlender* mb = dynamic_cast<MultiBandBlender*>(blender.get());
mb->setNumBands(static_cast<int>(ceil(log(blend_width)/log(2.)) - 1.));
LOGLN("Multi-band blender, number of bands: " << mb->numBands());
}
else if (blend_type == Blender::FEATHER)
{
FeatherBlender* fb = dynamic_cast<FeatherBlender*>(static_cast<Blender*>(blender));
FeatherBlender* fb = dynamic_cast<FeatherBlender*>(blender.get());
fb->setSharpness(1.f/blend_width);
LOGLN("Feather blender, sharpness: " << fb->sharpness());
}
@@ -763,5 +789,3 @@ int main(int argc, char* argv[])
LOGLN("Finished, total time: " << ((getTickCount() - app_start_time) / getTickFrequency()) << " sec");
return 0;
}

4046
samples/cpp/trained_classifierNM1.xml Normal file
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4046
samples/cpp/trained_classifierNM2.xml Normal file
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2
samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp
View File

@@ -76,5 +76,3 @@ void Morphology_Operations( int, void* )
morphologyEx( src, dst, operation, element );
imshow( window_name, dst );
}

7
samples/cpp/tutorial_code/ImgProc/Pyramids.cpp
View File

@@ -66,10 +66,3 @@ int main( void )
return 0;
}

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

@@ -61,5 +61,3 @@ int main( int, char** argv )
return 0;
}

5
samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp
View File

@@ -15,7 +15,6 @@ using namespace cv;
Mat src, dst;
int top, bottom, left, right;
int borderType;
Scalar value;
const char* window_name = "copyMakeBorder Demo";
RNG rng(12345);
@@ -64,7 +63,7 @@ int main( int, char** argv )
else if( (char)c == 'r' )
{ borderType = BORDER_REPLICATE; }
value = Scalar( rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255) );
Scalar value( rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255) );
copyMakeBorder( src, dst, top, bottom, left, right, borderType, value );
imshow( window_name, dst );
@@ -72,5 +71,3 @@ int main( int, char** argv )
return 0;
}

1
samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp
View File

@@ -92,4 +92,3 @@ void thresh_callback(int, void* )
circle( drawing, mc[i], 4, color, -1, 8, 0 );
}
}

2
samples/cpp/tutorial_code/ShapeDescriptors/pointPolygonTest_demo.cpp
View File

@@ -79,5 +79,3 @@ int main( void )
waitKey(0);
return(0);
}

1
samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp
View File

@@ -120,4 +120,3 @@ void myHarris_function( int, void* )
}
imshow( myHarris_window, myHarris_copy );
}

1
samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp
View File

@@ -102,4 +102,3 @@ void goodFeaturesToTrack_Demo( int, void* )
for( size_t i = 0; i < corners.size(); i++ )
{ cout<<" -- Refined Corner ["<<i<<"] ("<<corners[i].x<<","<<corners[i].y<<")"<<endl; }
}

1
samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp
View File

@@ -90,4 +90,3 @@ void goodFeaturesToTrack_Demo( int, void* )
namedWindow( source_window, WINDOW_AUTOSIZE );
imshow( source_window, copy );
}

8
samples/cpp/tutorial_code/contrib/retina_tutorial.cpp → samples/cpp/tutorial_code/bioinspired/retina_tutorial.cpp
View File

@@ -9,7 +9,7 @@
#include <iostream>
#include <cstring>
#include "opencv2/contrib.hpp"
#include "opencv2/bioinspired.hpp"
#include "opencv2/highgui.hpp"
static void help(std::string errorMessage)
@@ -95,16 +95,16 @@ int main(int argc, char* argv[]) {
try
{
// create a retina instance with default parameters setup, uncomment the initialisation you wanna test
cv::Ptr<cv::Retina> myRetina;
cv::Ptr<cv::bioinspired::Retina> myRetina;
// if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
if (useLogSampling)
{
myRetina = cv::createRetina(inputFrame.size(), true, cv::RETINA_COLOR_BAYER, true, 2.0, 10.0);
myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
}
else// -> else allocate "classical" retina :
{
myRetina = cv::createRetina(inputFrame.size());
myRetina = cv::bioinspired::createRetina(inputFrame.size());
}
// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"

2
samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp
View File

@@ -168,5 +168,3 @@ void MyLine( Mat img, Point start, Point end )
thickness,
lineType );
}

2
samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp
View File

@@ -75,4 +75,4 @@ int main(int argc, char ** argv)
waitKey();
return 0;
}
}

2
samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp
View File

@@ -151,4 +151,4 @@ int main(int ac, char** av)
<< "Tip: Open up " << filename << " with a text editor to see the serialized data." << endl;
return 0;
}
}

2
samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
View File

@@ -214,4 +214,4 @@ Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar* const table)
}
return I;
}
}

4
samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp
View File

@@ -32,8 +32,8 @@ int main( int argc, char** argv )
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())
Ptr<IplImage> IplI(cvLoadImage(imagename)); // Ptr<T> is a safe ref-counting pointer class
if(!IplI)
{
cerr << "Can not load image " << imagename << endl;
return -1;

2
samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp
View File

@@ -84,4 +84,4 @@ void Sharpen(const Mat& myImage,Mat& Result)
Result.row(Result.rows-1).setTo(Scalar(0));
Result.col(0).setTo(Scalar(0));
Result.col(Result.cols-1).setTo(Scalar(0));
}
}

2
samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp
View File

@@ -82,4 +82,4 @@ int main(int,char**)
cout << "A vector of 2D Points = " << vPoints << endl << endl;
return 0;
}
}

1
samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp
View File

@@ -430,4 +430,3 @@ Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
}
return mssim;
}

2
samples/cpp/tutorial_code/introduction/display_image/display_image.cpp
View File

@@ -27,4 +27,4 @@ int main( int argc, char** argv )
waitKey(0); // Wait for a keystroke in the window
return 0;
}
}

2
samples/cpp/tutorial_code/introduction/windows_visual_studio_Opencv/Test.cpp
View File

@@ -203,4 +203,4 @@ Scalar getMSSIM( const Mat& i1, const Mat& i2)
Scalar mssim = mean( ssim_map ); // mssim = average of ssim map
return mssim;
}
}

2
samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
View File

@@ -127,4 +127,4 @@ int main()
imwrite("result.png", I); // save the Image
imshow("SVM for Non-Linear Training Data", I); // show it to the user
waitKey(0);
}
}

114
samples/cpp/tutorial_code/objectDetection/objectDetection.cpp
View File

@@ -1,14 +1,6 @@
/**
* @file objectDetection.cpp
* @author A. Huaman ( based in the classic facedetect.cpp in samples/c )
* @brief A simplified version of facedetect.cpp, show how to load a cascade classifier and how to find objects (Face + eyes) in a video stream
*/
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/objdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
#include <stdio.h>
@@ -20,79 +12,73 @@ using namespace cv;
void detectAndDisplay( Mat frame );
/** Global variables */
//-- Note, either copy these two files from opencv/data/haarscascades to your current folder, or change these locations
string face_cascade_name = "haarcascade_frontalface_alt.xml";
string eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
String face_cascade_name = "haarcascade_frontalface_alt.xml";
String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
string window_name = "Capture - Face detection";
RNG rng(12345);
String window_name = "Capture - Face detection";
/**
* @function main
*/
/** @function main */
int main( void )
{
CvCapture* capture;
Mat frame;
VideoCapture capture;
Mat frame;
//-- 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; };
//-- 1. Load the cascades
if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading face cascade\n"); return -1; };
if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading eyes cascade\n"); return -1; };
//-- 2. Read the video stream
capture = cvCaptureFromCAM( -1 );
if( capture )
{
for(;;)
//-- 2. Read the video stream
capture.open( -1 );
if ( ! capture.isOpened() ) { printf("--(!)Error opening video capture\n"); return -1; }
while ( capture.read(frame) )
{
frame = cv::cvarrToMat(cvQueryFrame( capture ));
if( frame.empty() )
{
printf(" --(!) No captured frame -- Break!");
break;
}
//-- 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; }
//-- 3. Apply the classifier to the frame
detectAndDisplay( frame );
int c = waitKey(10);
if( (char)c == 27 ) { break; } // escape
}
}
return 0;
return 0;
}
/**
* @function detectAndDisplay
*/
/** @function detectAndDisplay */
void detectAndDisplay( Mat frame )
{
std::vector<Rect> faces;
Mat frame_gray;
std::vector<Rect> faces;
Mat frame_gray;
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
//-- Detect faces
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
for( size_t i = 0; i < faces.size(); i++ )
//-- Detect faces
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );
for ( size_t i = 0; i < faces.size(); i++ )
{
Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2), 0, 0, 360, Scalar( 255, 0, 255 ), 2, 8, 0 );
Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2 ), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
Mat faceROI = frame_gray( faces[i] );
std::vector<Rect> eyes;
Mat faceROI = frame_gray( faces[i] );
std::vector<Rect> eyes;
//-- In each face, detect eyes
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
//-- In each face, detect eyes
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
for( size_t j = 0; j < eyes.size(); j++ )
{
Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 3, 8, 0 );
}
for ( size_t j = 0; j < eyes.size(); j++ )
{
Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
}
}
//-- Show what you got
imshow( window_name, frame );
//-- Show what you got
imshow( window_name, frame );
}

110
samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp
View File

@@ -3,12 +3,9 @@
* @author A. Huaman ( based in the classic facedetect.cpp in samples/c )
* @brief A simplified version of facedetect.cpp, show how to load a cascade classifier and how to find objects (Face + eyes) in a video stream - Using LBP here
*/
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/objdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
#include <stdio.h>
@@ -20,46 +17,43 @@ using namespace cv;
void detectAndDisplay( Mat frame );
/** Global variables */
string face_cascade_name = "lbpcascade_frontalface.xml";
string eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
String face_cascade_name = "lbpcascade_frontalface.xml";
String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
string window_name = "Capture - Face detection";
RNG rng(12345);
String window_name = "Capture - Face detection";
/**
* @function main
*/
int main( void )
{
CvCapture* capture;
Mat frame;
VideoCapture capture;
Mat frame;
//-- 1. Load the cascade
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; };
//-- 1. Load the cascade
if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading face cascade\n"); return -1; };
if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading eyes cascade\n"); return -1; };
//-- 2. Read the video stream
capture = cvCaptureFromCAM( -1 );
if( capture )
{
for(;;)
//-- 2. Read the video stream
capture.open( -1 );
if ( ! capture.isOpened() ) { printf("--(!)Error opening video capture\n"); return -1; }
while ( capture.read(frame) )
{
frame = cv::cvarrToMat(cvQueryFrame( capture ));
if( frame.empty() )
{
printf(" --(!) No captured frame -- Break!");
break;
}
//-- 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; }
//-- 3. Apply the classifier to the frame
detectAndDisplay( frame );
//-- bail out if escape was pressed
int c = waitKey(10);
if( (char)c == 27 ) { break; }
}
}
return 0;
return 0;
}
/**
@@ -67,37 +61,37 @@ int main( void )
*/
void detectAndDisplay( Mat frame )
{
std::vector<Rect> faces;
Mat frame_gray;
std::vector<Rect> faces;
Mat frame_gray;
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
//-- Detect faces
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0, Size(80, 80) );
//-- Detect faces
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0, Size(80, 80) );
for( size_t i = 0; i < faces.size(); i++ )
for( size_t i = 0; i < faces.size(); i++ )
{
Mat faceROI = frame_gray( faces[i] );
std::vector<Rect> eyes;
Mat faceROI = frame_gray( faces[i] );
std::vector<Rect> eyes;
//-- In each face, detect eyes
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
if( eyes.size() == 2)
{
//-- Draw the face
Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2), 0, 0, 360, Scalar( 255, 0, 0 ), 2, 8, 0 );
//-- In each face, detect eyes
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
if( eyes.size() == 2)
{
//-- Draw the face
Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2 ), 0, 0, 360, Scalar( 255, 0, 0 ), 2, 8, 0 );
for( size_t j = 0; j < eyes.size(); j++ )
{ //-- Draw the eyes
Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
circle( frame, eye_center, radius, Scalar( 255, 0, 255 ), 3, 8, 0 );
}
}
for( size_t j = 0; j < eyes.size(); j++ )
{ //-- Draw the eyes
Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
circle( frame, eye_center, radius, Scalar( 255, 0, 255 ), 3, 8, 0 );
}
}
}
//-- Show what you got
imshow( window_name, frame );
//-- Show what you got
imshow( window_name, frame );
}

2
samples/cpp/video_homography.cpp
View File

@@ -152,7 +152,7 @@ int main(int ac, char ** av)
Mat train_desc, query_desc;
const int DESIRED_FTRS = 500;
GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
GridAdaptedFeatureDetector detector(makePtr<FastFeatureDetector>(10, true), DESIRED_FTRS, 4, 4);
Mat H_prev = Mat::eye(3, 3, CV_32FC1);
for (;;)

70
samples/cpp/videostab.cpp
View File

@@ -193,7 +193,7 @@ public:
virtual Ptr<ImageMotionEstimatorBase> build()
{
MotionEstimatorRansacL2 *est = new MotionEstimatorRansacL2(motionModel(arg(prefix + "model")));
Ptr<MotionEstimatorRansacL2> est = makePtr<MotionEstimatorRansacL2>(motionModel(arg(prefix + "model")));
RansacParams ransac = est->ransacParams();
if (arg(prefix + "subset") != "auto")
@@ -205,10 +205,10 @@ public:
est->setMinInlierRatio(argf(prefix + "min-inlier-ratio"));
Ptr<IOutlierRejector> outlierRejector = new NullOutlierRejector();
Ptr<IOutlierRejector> outlierRejector = makePtr<NullOutlierRejector>();
if (arg(prefix + "local-outlier-rejection") == "yes")
{
TranslationBasedLocalOutlierRejector *tblor = new TranslationBasedLocalOutlierRejector();
Ptr<TranslationBasedLocalOutlierRejector> tblor = makePtr<TranslationBasedLocalOutlierRejector>();
RansacParams ransacParams = tblor->ransacParams();
if (arg(prefix + "thresh") != "auto")
ransacParams.thresh = argf(prefix + "thresh");
@@ -219,14 +219,14 @@ public:
#if defined(HAVE_OPENCV_GPUIMGPROC) && defined(HAVE_OPENCV_GPU) && defined(HAVE_OPENCV_GPUOPTFLOW)
if (gpu)
{
KeypointBasedMotionEstimatorGpu *kbest = new KeypointBasedMotionEstimatorGpu(est);
Ptr<KeypointBasedMotionEstimatorGpu> kbest = makePtr<KeypointBasedMotionEstimatorGpu>(est);
kbest->setOutlierRejector(outlierRejector);
return kbest;
}
#endif
KeypointBasedMotionEstimator *kbest = new KeypointBasedMotionEstimator(est);
kbest->setDetector(new GoodFeaturesToTrackDetector(argi(prefix + "nkps")));
Ptr<KeypointBasedMotionEstimator> kbest = makePtr<KeypointBasedMotionEstimator>(est);
kbest->setDetector(makePtr<GoodFeaturesToTrackDetector>(argi(prefix + "nkps")));
kbest->setOutlierRejector(outlierRejector);
return kbest;
}
@@ -244,12 +244,12 @@ public:
virtual Ptr<ImageMotionEstimatorBase> build()
{
MotionEstimatorL1 *est = new MotionEstimatorL1(motionModel(arg(prefix + "model")));
Ptr<MotionEstimatorL1> est = makePtr<MotionEstimatorL1>(motionModel(arg(prefix + "model")));
Ptr<IOutlierRejector> outlierRejector = new NullOutlierRejector();
Ptr<IOutlierRejector> outlierRejector = makePtr<NullOutlierRejector>();
if (arg(prefix + "local-outlier-rejection") == "yes")
{
TranslationBasedLocalOutlierRejector *tblor = new TranslationBasedLocalOutlierRejector();
Ptr<TranslationBasedLocalOutlierRejector> tblor = makePtr<TranslationBasedLocalOutlierRejector>();
RansacParams ransacParams = tblor->ransacParams();
if (arg(prefix + "thresh") != "auto")
ransacParams.thresh = argf(prefix + "thresh");
@@ -260,14 +260,14 @@ public:
#if defined(HAVE_OPENCV_GPUIMGPROC) && defined(HAVE_OPENCV_GPU) && defined(HAVE_OPENCV_GPUOPTFLOW)
if (gpu)
{
KeypointBasedMotionEstimatorGpu *kbest = new KeypointBasedMotionEstimatorGpu(est);
Ptr<KeypointBasedMotionEstimatorGpu> kbest = makePtr<KeypointBasedMotionEstimatorGpu>(est);
kbest->setOutlierRejector(outlierRejector);
return kbest;
}
#endif
KeypointBasedMotionEstimator *kbest = new KeypointBasedMotionEstimator(est);
kbest->setDetector(new GoodFeaturesToTrackDetector(argi(prefix + "nkps")));
Ptr<KeypointBasedMotionEstimator> kbest = makePtr<KeypointBasedMotionEstimator>(est);
kbest->setDetector(makePtr<GoodFeaturesToTrackDetector>(argi(prefix + "nkps")));
kbest->setOutlierRejector(outlierRejector);
return kbest;
}
@@ -363,7 +363,7 @@ int main(int argc, const char **argv)
// get source video parameters
VideoFileSource *source = new VideoFileSource(inputPath);
Ptr<VideoFileSource> source = makePtr<VideoFileSource>(inputPath);
cout << "frame count (rough): " << source->count() << endl;
if (arg("fps") == "auto")
outputFps = source->fps();
@@ -374,15 +374,15 @@ int main(int argc, const char **argv)
Ptr<IMotionEstimatorBuilder> motionEstBuilder;
if (arg("lin-prog-motion-est") == "yes")
motionEstBuilder = new MotionEstimatorL1Builder(cmd, arg("gpu") == "yes");
motionEstBuilder.reset(new MotionEstimatorL1Builder(cmd, arg("gpu") == "yes"));
else
motionEstBuilder = new MotionEstimatorRansacL2Builder(cmd, arg("gpu") == "yes");
motionEstBuilder.reset(new MotionEstimatorRansacL2Builder(cmd, arg("gpu") == "yes"));
Ptr<IMotionEstimatorBuilder> wsMotionEstBuilder;
if (arg("ws-lp") == "yes")
wsMotionEstBuilder = new MotionEstimatorL1Builder(cmd, arg("gpu") == "yes", "ws-");
wsMotionEstBuilder.reset(new MotionEstimatorL1Builder(cmd, arg("gpu") == "yes", "ws-"));
else
wsMotionEstBuilder = new MotionEstimatorRansacL2Builder(cmd, arg("gpu") == "yes", "ws-");
wsMotionEstBuilder.reset(new MotionEstimatorRansacL2Builder(cmd, arg("gpu") == "yes", "ws-"));
// determine whether we must use one pass or two pass stabilizer
bool isTwoPass =
@@ -400,7 +400,7 @@ int main(int argc, const char **argv)
if (arg("lin-prog-stab") == "yes")
{
LpMotionStabilizer *stab = new LpMotionStabilizer();
Ptr<LpMotionStabilizer> stab = makePtr<LpMotionStabilizer>();
stab->setFrameSize(Size(source->width(), source->height()));
stab->setTrimRatio(arg("lps-trim-ratio") == "auto" ? argf("trim-ratio") : argf("lps-trim-ratio"));
stab->setWeight1(argf("lps-w1"));
@@ -410,18 +410,18 @@ int main(int argc, const char **argv)
twoPassStabilizer->setMotionStabilizer(stab);
}
else if (arg("stdev") == "auto")
twoPassStabilizer->setMotionStabilizer(new GaussianMotionFilter(argi("radius")));
twoPassStabilizer->setMotionStabilizer(makePtr<GaussianMotionFilter>(argi("radius")));
else
twoPassStabilizer->setMotionStabilizer(new GaussianMotionFilter(argi("radius"), argf("stdev")));
twoPassStabilizer->setMotionStabilizer(makePtr<GaussianMotionFilter>(argi("radius"), argf("stdev")));
// init wobble suppressor if necessary
if (arg("wobble-suppress") == "yes")
{
MoreAccurateMotionWobbleSuppressorBase *ws = new MoreAccurateMotionWobbleSuppressor();
Ptr<MoreAccurateMotionWobbleSuppressorBase> ws = makePtr<MoreAccurateMotionWobbleSuppressor>();
if (arg("gpu") == "yes")
#ifdef HAVE_OPENCV_GPU
ws = new MoreAccurateMotionWobbleSuppressorGpu();
ws = makePtr<MoreAccurateMotionWobbleSuppressorGpu>();
#else
throw runtime_error("OpenCV is built without GPU support");
#endif
@@ -433,12 +433,12 @@ int main(int argc, const char **argv)
MotionModel model = ws->motionEstimator()->motionModel();
if (arg("load-motions2") != "no")
{
ws->setMotionEstimator(new FromFileMotionReader(arg("load-motions2")));
ws->setMotionEstimator(makePtr<FromFileMotionReader>(arg("load-motions2")));
ws->motionEstimator()->setMotionModel(model);
}
if (arg("save-motions2") != "no")
{
ws->setMotionEstimator(new ToFileMotionWriter(arg("save-motions2"), ws->motionEstimator()));
ws->setMotionEstimator(makePtr<ToFileMotionWriter>(arg("save-motions2"), ws->motionEstimator()));
ws->motionEstimator()->setMotionModel(model);
}
}
@@ -450,26 +450,26 @@ int main(int argc, const char **argv)
OnePassStabilizer *onePassStabilizer = new OnePassStabilizer();
stabilizer = onePassStabilizer;
if (arg("stdev") == "auto")
onePassStabilizer->setMotionFilter(new GaussianMotionFilter(argi("radius")));
onePassStabilizer->setMotionFilter(makePtr<GaussianMotionFilter>(argi("radius")));
else
onePassStabilizer->setMotionFilter(new GaussianMotionFilter(argi("radius"), argf("stdev")));
onePassStabilizer->setMotionFilter(makePtr<GaussianMotionFilter>(argi("radius"), argf("stdev")));
}
stabilizer->setFrameSource(source);
stabilizer->setMotionEstimator(motionEstBuilder->build());
// cast stabilizer to simple frame source interface to read stabilized frames
stabilizedFrames = dynamic_cast<IFrameSource*>(stabilizer);
stabilizedFrames.reset(dynamic_cast<IFrameSource*>(stabilizer));
MotionModel model = stabilizer->motionEstimator()->motionModel();
if (arg("load-motions") != "no")
{
stabilizer->setMotionEstimator(new FromFileMotionReader(arg("load-motions")));
stabilizer->setMotionEstimator(makePtr<FromFileMotionReader>(arg("load-motions")));
stabilizer->motionEstimator()->setMotionModel(model);
}
if (arg("save-motions") != "no")
{
stabilizer->setMotionEstimator(new ToFileMotionWriter(arg("save-motions"), stabilizer->motionEstimator()));
stabilizer->setMotionEstimator(makePtr<ToFileMotionWriter>(arg("save-motions"), stabilizer->motionEstimator()));
stabilizer->motionEstimator()->setMotionModel(model);
}
@@ -478,7 +478,7 @@ int main(int argc, const char **argv)
// init deblurer
if (arg("deblur") == "yes")
{
WeightingDeblurer *deblurer = new WeightingDeblurer();
Ptr<WeightingDeblurer> deblurer = makePtr<WeightingDeblurer>();
deblurer->setRadius(argi("radius"));
deblurer->setSensitivity(argf("deblur-sens"));
stabilizer->setDeblurer(deblurer);
@@ -503,22 +503,22 @@ int main(int argc, const char **argv)
Ptr<InpainterBase> inpainters_(inpainters);
if (arg("mosaic") == "yes")
{
ConsistentMosaicInpainter *inp = new ConsistentMosaicInpainter();
Ptr<ConsistentMosaicInpainter> inp = makePtr<ConsistentMosaicInpainter>();
inp->setStdevThresh(argf("mosaic-stdev"));
inpainters->pushBack(inp);
}
if (arg("motion-inpaint") == "yes")
{
MotionInpainter *inp = new MotionInpainter();
Ptr<MotionInpainter> inp = makePtr<MotionInpainter>();
inp->setDistThreshold(argf("mi-dist-thresh"));
inpainters->pushBack(inp);
}
if (arg("color-inpaint") == "average")
inpainters->pushBack(new ColorAverageInpainter());
inpainters->pushBack(makePtr<ColorAverageInpainter>());
else if (arg("color-inpaint") == "ns")
inpainters->pushBack(new ColorInpainter(INPAINT_NS, argd("ci-radius")));
inpainters->pushBack(makePtr<ColorInpainter>(int(INPAINT_NS), argd("ci-radius")));
else if (arg("color-inpaint") == "telea")
inpainters->pushBack(new ColorInpainter(INPAINT_TELEA, argd("ci-radius")));
inpainters->pushBack(makePtr<ColorInpainter>(int(INPAINT_TELEA), argd("ci-radius")));
else if (arg("color-inpaint") != "no")
throw runtime_error("unknown color inpainting method: " + arg("color-inpaint"));
if (!inpainters->empty())