Added ASIFT (to activate, check parameter Feature2D/Affine) (fixed issue 27)

git-svn-id: http://find-object.googlecode.com/svn/trunk/find_object@385 620bd6b2-0a58-f614-fd9a-1bd335dccda9

app/main.cpp

@@ -102,6 +102,7 @@ void showUsage()
 			"  --objects \"path\"       Directory of the objects to detect.\n"
 			"  --config \"path\"        Path to configuration file (default: %s).\n"
 			"  --scene \"path\"         Path to a scene image file.\n"
+			"  --debug                Show debug log.\n"
 			"  --params               Show all parameters.\n"
 			"  --My/Parameter \"value\" Set find-Object's parameter (look --params for parameters' name).\n"
 			"                           It will override the one in --config. Example to set 4 threads:\n"
@@ -209,6 +210,13 @@ int main(int argc, char* argv[])
 			guiMode = false;
 			continue;
 		}
+		if(strcmp(argv[i], "-debug") == 0 ||
+		   strcmp(argv[i], "--debug") == 0)
+		{
+			ULogger::setPrintWhere(true);
+			ULogger::setLevel(ULogger::kDebug);
+			continue;
+		}
 		if(strcmp(argv[i], "-help") == 0 ||
 		   strcmp(argv[i], "--help") == 0)
 		{

include/find_object/DetectionInfo.h

@@ -42,6 +42,7 @@ public:
 	enum TimeStamp{
 		kTimeKeypointDetection,
 		kTimeDescriptorExtraction,
+		kTimeSkewAffine,
 		kTimeIndexing,
 		kTimeMatching,
 		kTimeHomography,

include/find_object/FindObject.h

@@ -53,6 +53,13 @@ class DescriptorExtractor;
 class FINDOBJECT_EXP FindObject : public QObject
 {
 	Q_OBJECT;
+public:
+	static void affineSkew(float tilt,
+				float phi,
+				const cv::Mat & image,
+				cv::Mat & skewImage,
+				cv::Mat & skewMask,
+				cv::Mat & Ai);
 
 public:
 	FindObject(QObject * parent = 0);

include/find_object/Settings.h

@@ -97,6 +97,8 @@ class FINDOBJECT_EXP Settings
 	PARAMETER(Feature2D, 1Detector, QString, "7:Dense;Fast;GFTT;MSER;ORB;SIFT;Star;SURF;BRISK" , "Keypoint detector.");
 	PARAMETER(Feature2D, 2Descriptor, QString, "3:Brief;ORB;SIFT;SURF;BRISK;FREAK", "Keypoint descriptor.");
 	PARAMETER(Feature2D, 3MaxFeatures, int, 0, "Maximum features per image. If the number of features extracted is over this threshold, only X features with the highest response are kept. 0 means all features are kept.");
+	PARAMETER(Feature2D, 4Affine, bool, false, "(ASIFT) Extract features on multiple affine transformations of the image.");
+	PARAMETER(Feature2D, 5AffineCount, int, 6, "(ASIFT) Higher the value, more affine transformations will be done.");
 
 	PARAMETER(Feature2D, Brief_bytes, int, 32, "Bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.");
 
@@ -205,7 +207,7 @@ class FINDOBJECT_EXP Settings
 	PARAMETER(General, autoStartCamera, bool, false, "Automatically start the camera when the application is opened.");
 	PARAMETER(General, autoUpdateObjects, bool, true, "Automatically update objects on every parameter changes, otherwise you would need to press \"Update objects\" on the objects panel.");
 	PARAMETER(General, nextObjID, uint, 1, "Next object ID to use.");
-	PARAMETER(General, imageFormats, QString, "*.png *.jpg *.bmp *.tiff *.ppm", "Image formats supported.");
+	PARAMETER(General, imageFormats, QString, "*.png *.jpg *.bmp *.tiff *.ppm *.pgm", "Image formats supported.");
 	PARAMETER(General, videoFormats, QString, "*.avi *.m4v *.mp4", "Video formats supported.");
 	PARAMETER(General, mirrorView, bool, true, "Flip the camera image horizontally (like all webcam applications).");
 	PARAMETER(General, invertedSearch, bool, true, "Instead of matching descriptors from the objects to those in a vocabulary created with descriptors extracted from the scene, we create a vocabulary from all the objects' descriptors and we match scene's descriptors to this vocabulary. It is the inverted search mode.");
@@ -283,7 +285,9 @@ public:
 	KeypointDetector(cv::FeatureDetector * featureDetector);
 	KeypointDetector(GPUFeature2D * gpuFeature2D);
 
-	void detect(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints);
+	void detect(const cv::Mat & image,
+			std::vector<cv::KeyPoint> & keypoints,
+			const cv::Mat & mask = cv::Mat());
 
 private:
 	cv::FeatureDetector * featureDetector_;

src/FindObject.cpp

@@ -202,61 +202,275 @@ std::vector<cv::KeyPoint> limitKeypoints(const std::vector<cv::KeyPoint> & keypo
 	return kptsKept;
 }
 
+
+// taken from ASIFT example https://github.com/Itseez/opencv/blob/master/samples/python2/asift.py
+// affine - is an affine transform matrix from skew_img to img
+void FindObject::affineSkew(
+		float tilt,
+		float phi,
+		const cv::Mat & image,
+		cv::Mat & skewImage,
+		cv::Mat & skewMask,
+		cv::Mat & Ai)
+{
+    float h = image.rows;
+    float w = image.cols;
+    cv::Mat A = cv::Mat::zeros(2,3,CV_32FC1);
+    A.at<float>(0,0) = A.at<float>(1,1) = 1;
+    skewMask = cv::Mat::ones(h, w, CV_8U) * 255;
+    if(phi != 0.0)
+    {
+        phi = phi*CV_PI/180.0f; // deg2rad
+        float s = std::sin(phi);
+        float c = std::cos(phi);
+        cv::Mat A22 = (cv::Mat_<float>(2, 2) <<
+        		c, -s,
+        		s, c);
+        cv::Mat cornersIn = (cv::Mat_<float>(4, 2) <<
+        		0,0,
+        		w,0,
+        		w,h,
+        		0,h);
+        cv::Mat cornersOut = cornersIn * A22.t();
+        cv::Rect rect = cv::boundingRect(cornersOut.reshape(2,4));
+        A = (cv::Mat_<float>(2, 3) <<
+				c, -s, -rect.x,
+				s, c, -rect.y);
+        cv::warpAffine(image, skewImage, A, cv::Size(rect.width, rect.height), cv::INTER_LINEAR, cv::BORDER_REPLICATE);
+    }
+    else
+    {
+    	skewImage = image;
+    }
+    if(tilt != 1.0)
+    {
+        float s = 0.8*std::sqrt(tilt*tilt-1);
+        cv::Mat out, out2;
+        cv::GaussianBlur(skewImage, out, cv::Size(0, 0), s, 0.01);
+        cv::resize(out, out2, cv::Size(0, 0), 1.0/tilt, 1.0, cv::INTER_NEAREST);
+        skewImage = out2;
+        A.row(0) /= tilt;
+    }
+    if(phi != 0.0 || tilt != 1.0)
+    {
+    	cv::Mat mask = skewMask;
+        cv::warpAffine(mask, skewMask, A, skewImage.size(), cv::INTER_NEAREST);
+    }
+    cv::invertAffineTransform(A, Ai);
+}
+
+class AffineExtractionThread : public QThread
+{
+public:
+	AffineExtractionThread(
+			KeypointDetector * detector,
+			DescriptorExtractor * extractor,
+			const cv::Mat & image,
+			float tilt,
+			float phi) :
+		detector_(detector),
+		extractor_(extractor),
+		image_(image),
+		tilt_(tilt),
+		phi_(phi),
+		timeSkewAffine_(0),
+		timeDetection_(0),
+		timeExtraction_(0)
+	{
+		UASSERT(detector && extractor);
+	}
+	const cv::Mat & image() const {return image_;}
+	const std::vector<cv::KeyPoint> & keypoints() const {return keypoints_;}
+	const cv::Mat & descriptors() const {return descriptors_;}
+
+	int timeSkewAffine() const {return timeSkewAffine_;}
+	int timeDetection() const {return timeDetection_;}
+	int timeExtraction() const {return timeExtraction_;}
+
+protected:
+	virtual void run()
+	{
+		QTime timeStep;
+		timeStep.start();
+		cv::Mat skewImage, skewMask, Ai;
+		FindObject::affineSkew(tilt_, phi_, image_, skewImage, skewMask, Ai);
+		timeSkewAffine_=timeStep.restart();
+
+		//Detect features
+		detector_->detect(skewImage, keypoints_, skewMask);
+
+		if(keypoints_.size())
+		{
+			int maxFeatures = Settings::getFeature2D_3MaxFeatures();
+			if(maxFeatures > 0 && (int)keypoints_.size() > maxFeatures)
+			{
+				keypoints_ = limitKeypoints(keypoints_, maxFeatures);
+			}
+			timeDetection_=timeStep.restart();
+
+			//Extract descriptors
+			extractor_->compute(skewImage, keypoints_, descriptors_);
+			timeExtraction_=timeStep.restart();
+
+			// Transform points to original image coordinates
+			for(unsigned int i=0; i<keypoints_.size(); ++i)
+			{
+				cv::Mat p = (cv::Mat_<float>(3, 1) << keypoints_[i].pt.x, keypoints_[i].pt.y, 1);
+				cv::Mat pa = Ai * p;
+				keypoints_[i].pt.x = pa.at<float>(0,0);
+				keypoints_[i].pt.y = pa.at<float>(1,0);
+			}
+		}
+		else
+		{
+			timeDetection_=timeStep.restart();
+		}
+	}
+private:
+	KeypointDetector * detector_;
+	DescriptorExtractor * extractor_;
+	cv::Mat image_;
+	float tilt_;
+	float phi_;
+	std::vector<cv::KeyPoint> keypoints_;
+	cv::Mat descriptors_;
+
+	int timeSkewAffine_;
+	int timeDetection_;
+	int timeExtraction_;
+};
+
 class ExtractFeaturesThread : public QThread
 {
 public:
-	ExtractFeaturesThread(int objectId, const cv::Mat & image) :
+	ExtractFeaturesThread(
+			KeypointDetector * detector,
+			DescriptorExtractor * extractor,
+			int objectId,
+			const cv::Mat & image) :
+		detector_(detector),
+		extractor_(extractor),
 		objectId_(objectId),
-		image_(image)
+		image_(image),
+		timeSkewAffine_(0),
+		timeDetection_(0),
+		timeExtraction_(0)
 	{
-
+		UASSERT(detector && extractor);
 	}
 	int objectId() const {return objectId_;}
 	const cv::Mat & image() const {return image_;}
 	const std::vector<cv::KeyPoint> & keypoints() const {return keypoints_;}
 	const cv::Mat & descriptors() const {return descriptors_;}
 
+	int timeSkewAffine() const {return timeSkewAffine_;}
+	int timeDetection() const {return timeDetection_;}
+	int timeExtraction() const {return timeExtraction_;}
+
 protected:
 	virtual void run()
 	{
 		QTime time;
 		time.start();
 		UINFO("Extracting descriptors from object %d...", objectId_);
-		KeypointDetector * detector = Settings::createKeypointDetector();
-		keypoints_.clear();
-		descriptors_ = cv::Mat();
-		detector->detect(image_, keypoints_);
-		delete detector;
 
-		if(keypoints_.size())
+		QTime timeStep;
+		timeStep.start();
+
+		if(!Settings::getFeature2D_4Affine())
 		{
-			int maxFeatures = Settings::getFeature2D_3MaxFeatures();
+			keypoints_.clear();
-			if(maxFeatures > 0 && (int)keypoints_.size() > maxFeatures)
+			descriptors_ = cv::Mat();
-			{
+			detector_->detect(image_, keypoints_);
-				int previousCount = (int)keypoints_.size();
-				keypoints_ = limitKeypoints(keypoints_, maxFeatures);
-				UINFO("obj=%d, %d keypoints removed, (kept %d), min/max response=%f/%f", objectId_, previousCount-(int)keypoints_.size(), (int)keypoints_.size(), keypoints_.size()?keypoints_.back().response:0.0f, keypoints_.size()?keypoints_.front().response:0.0f);
-			}
 
-			DescriptorExtractor * extractor = Settings::createDescriptorExtractor();
+			if(keypoints_.size())
-			extractor->compute(image_, keypoints_, descriptors_);
-			delete extractor;
-			if((int)keypoints_.size() != descriptors_.rows)
 			{
-				UERROR("obj=%d kpt=%d != descriptors=%d", objectId_, (int)keypoints_.size(), descriptors_.rows);
+				int maxFeatures = Settings::getFeature2D_3MaxFeatures();
+				if(maxFeatures > 0 && (int)keypoints_.size() > maxFeatures)
+				{
+					int previousCount = (int)keypoints_.size();
+					keypoints_ = limitKeypoints(keypoints_, maxFeatures);
+					UDEBUG("obj=%d, %d keypoints removed, (kept %d), min/max response=%f/%f", objectId_, previousCount-(int)keypoints_.size(), (int)keypoints_.size(), keypoints_.size()?keypoints_.back().response:0.0f, keypoints_.size()?keypoints_.front().response:0.0f);
+				}
+				timeDetection_+=timeStep.restart();
+
+				extractor_->compute(image_, keypoints_, descriptors_);
+				timeExtraction_+=timeStep.restart();
+
+				if((int)keypoints_.size() != descriptors_.rows)
+				{
+					UERROR("obj=%d kpt=%d != descriptors=%d", objectId_, (int)keypoints_.size(), descriptors_.rows);
+				}
+			}
+			else
+			{
+				timeDetection_+=timeStep.restart();
+				UWARN("no features detected in object %d !?!", objectId_);
 			}
 		}
 		else
 		{
-			UWARN("no features detected in object %d !?!", objectId_);
+			//ASIFT
+			std::vector<float> tilts;
+			std::vector<float> phis;
+			tilts.push_back(1.0f);
+			phis.push_back(0.0f);
+			int nTilt = Settings::getFeature2D_5AffineCount();
+			for(int t=1; t<nTilt; ++t)
+			{
+				float tilt = std::pow(2.0f, 0.5f*float(t));
+				float inc = 72.0f / float(tilt);
+				for(float phi=0.0f; phi<180.0f; phi+=inc)
+				{
+					tilts.push_back(tilt);
+					phis.push_back(phi);
+				}
+			}
+
+			//multi-threaded
+			unsigned int threadCounts = Settings::getGeneral_threads();
+			if(threadCounts == 0)
+			{
+				threadCounts = tilts.size();
+			}
+
+			for(unsigned int i=0; i<tilts.size(); i+=threadCounts)
+			{
+				QVector<AffineExtractionThread*> threads;
+
+				for(unsigned int k=i; k<i+threadCounts && k<tilts.size(); ++k)
+				{
+					threads.push_back(new AffineExtractionThread(detector_, extractor_, image_, tilts[k], phis[k]));
+					threads.back()->start();
+				}
+
+				for(int k=0; k<threads.size(); ++k)
+				{
+					threads[k]->wait();
+
+					keypoints_.insert(keypoints_.end(), threads[k]->keypoints().begin(), threads[k]->keypoints().end());
+					descriptors_.push_back(threads[k]->descriptors());
+
+					timeSkewAffine_ += threads[k]->timeSkewAffine();
+					timeDetection_ += threads[k]->timeDetection();
+					timeExtraction_ += threads[k]->timeExtraction();
+				}
+			}
 		}
+
 		UINFO("%d descriptors extracted from object %d (in %d ms)", descriptors_.rows, objectId_, time.elapsed());
 	}
 private:
+	KeypointDetector * detector_;
+	DescriptorExtractor * extractor_;
 	int objectId_;
 	cv::Mat image_;
 	std::vector<cv::KeyPoint> keypoints_;
 	cv::Mat descriptors_;
+
+	int timeSkewAffine_;
+	int timeDetection_;
+	int timeExtraction_;
 };
 
 void FindObject::updateObjects()
@@ -278,7 +492,7 @@ void FindObject::updateObjects()
 			QVector<ExtractFeaturesThread*> threads;
 			for(int k=i; k<i+threadCounts && k<objectsList.size(); ++k)
 			{
-				threads.push_back(new ExtractFeaturesThread(objectsList.at(k)->id(), objectsList.at(k)->image()));
+				threads.push_back(new ExtractFeaturesThread(detector_, extractor_, objectsList.at(k)->id(), objectsList.at(k)->image()));
 				threads.back()->start();
 			}
 
@@ -677,30 +891,15 @@ bool FindObject::detect(const cv::Mat & image, find_object::DetectionInfo & info
 			grayscaleImg =  image;
 		}
 
-		QTime time;
+		// DETECT FEATURES AND EXTRACT DESCRIPTORS
-		time.start();
+		ExtractFeaturesThread extractThread(detector_, extractor_, -1, grayscaleImg);
-
+		extractThread.start();
-		// EXTRACT KEYPOINTS
+		extractThread.wait();
-		detector_->detect(grayscaleImg, info.sceneKeypoints_);
+		info.sceneKeypoints_ = extractThread.keypoints();
-		info.timeStamps_.insert(DetectionInfo::kTimeKeypointDetection, time.restart());
+		info.sceneDescriptors_ = extractThread.descriptors();
-
+		info.timeStamps_.insert(DetectionInfo::kTimeKeypointDetection, extractThread.timeDetection());
-		bool emptyScene = info.sceneKeypoints_.size() == 0;
+		info.timeStamps_.insert(DetectionInfo::kTimeDescriptorExtraction, extractThread.timeExtraction());
-		if(info.sceneKeypoints_.size())
+		info.timeStamps_.insert(DetectionInfo::kTimeSkewAffine, extractThread.timeSkewAffine());
-		{
-			int maxFeatures = Settings::getFeature2D_3MaxFeatures();
-			if(maxFeatures > 0 && (int)info.sceneKeypoints_.size() > maxFeatures)
-			{
-				info.sceneKeypoints_ = limitKeypoints(info.sceneKeypoints_, maxFeatures);
-			}
-
-			// EXTRACT DESCRIPTORS
-			extractor_->compute(grayscaleImg, info.sceneKeypoints_, info.sceneDescriptors_);
-			if((int)info.sceneKeypoints_.size() != info.sceneDescriptors_.rows)
-			{
-				UERROR("kpt=%d != descriptors=%d", (int)info.sceneKeypoints_.size(), info.sceneDescriptors_.rows);
-			}
-		}
-		info.timeStamps_.insert(DetectionInfo::kTimeDescriptorExtraction, time.restart());
 
 		bool consistentNNData = (vocabulary_->size()!=0 && vocabulary_->wordToObjects().begin().value()!=-1 && Settings::getGeneral_invertedSearch()) ||
 								((vocabulary_->size()==0 || vocabulary_->wordToObjects().begin().value()==-1) && !Settings::getGeneral_invertedSearch());
@@ -713,6 +912,8 @@ bool FindObject::detect(const cv::Mat & image, find_object::DetectionInfo & info
 		   objectsDescriptors_.begin().value().type() == info.sceneDescriptors_.type()) // binary descriptor issue, if the dataTree is not yet updated with modified settings
 		{
 			success = true;
+			QTime time;
+			time.start();
 
 			QMultiMap<int, int> words;
 
@@ -778,7 +979,10 @@ bool FindObject::detect(const cv::Mat & image, find_object::DetectionInfo & info
 							matched = false;
 						}
 					}
-					if(!matched && !Settings::getNearestNeighbor_3nndrRatioUsed() && !Settings::getNearestNeighbor_5minDistanceUsed())
+					if(!matched &&
+					   !Settings::getNearestNeighbor_3nndrRatioUsed() &&
+					   !Settings::getNearestNeighbor_5minDistanceUsed() &&
+					   dists.at<float>(i,0) >= 0.0f)
 					{
 						matched = true; // no criterion, match to the nearest descriptor
 					}
@@ -1024,7 +1228,7 @@ bool FindObject::detect(const cv::Mat & image, find_object::DetectionInfo & info
 		{
 			UWARN("Cannot search, objects must be updated");
 		}
-		else if(emptyScene)
+		else if(info.sceneKeypoints_.size() == 0)
 		{
 			// Accept but warn the user
 			UWARN("No features detected in the scene!?!");

src/MainWindow.cpp

@@ -786,6 +786,7 @@ void MainWindow::updateVocabulary()
 	QApplication::processEvents();
 
 	QTime time;
+	time.start();
 	findObject_->updateVocabulary();
 
 	if(findObject_->vocabulary()->size())
@@ -946,9 +947,13 @@ void MainWindow::update(const cv::Mat & image)
 	if(findObject_->detect(sceneImage_, info))
 	{
 		ui_->label_timeDetection->setNum(info.timeStamps_.value(DetectionInfo::kTimeKeypointDetection, 0));
+		ui_->label_timeSkewAffine->setNum(info.timeStamps_.value(DetectionInfo::kTimeSkewAffine, 0));
 		ui_->label_timeExtraction->setNum(info.timeStamps_.value(DetectionInfo::kTimeDescriptorExtraction, 0));
 		ui_->imageView_source->setData(info.sceneKeypoints_, cvtCvMat2QImage(sceneImage_));
-		ui_->label_timeIndexing->setNum(info.timeStamps_.value(DetectionInfo::kTimeIndexing, 0));
+		if(!findObject_->vocabulary()->size())
+		{
+			ui_->label_timeIndexing->setNum(info.timeStamps_.value(DetectionInfo::kTimeIndexing, 0));
+		}
 		ui_->label_timeMatching->setNum(info.timeStamps_.value(DetectionInfo::kTimeMatching, 0));
 		ui_->label_timeHomographies->setNum(info.timeStamps_.value(DetectionInfo::kTimeHomography, 0));
 
@@ -1135,7 +1140,13 @@ void MainWindow::update(const cv::Mat & image)
 	}
 	else
 	{
-		this->statusBar()->showMessage(tr("Cannot search, objects must be updated!"));
+		if(findObject_->vocabulary()->size())
+		{
+			this->statusBar()->showMessage(tr("Cannot search, objects must be updated!"));
+		}
+		ui_->label_timeDetection->setNum(info.timeStamps_.value(DetectionInfo::kTimeKeypointDetection, 0));
+		ui_->label_timeSkewAffine->setNum(info.timeStamps_.value(DetectionInfo::kTimeSkewAffine, 0));
+		ui_->label_timeExtraction->setNum(info.timeStamps_.value(DetectionInfo::kTimeDescriptorExtraction, 0));
 		ui_->imageView_source->setData(info.sceneKeypoints_, cvtCvMat2QImage(sceneImage_));
 	}
 

src/Settings.cpp

@@ -38,8 +38,6 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #include <opencv2/nonfree/gpu.hpp>
 #include <opencv2/gpu/gpu.hpp>
 
-#define VERBOSE 0
-
 namespace find_object {
 
 ParametersMap Settings::defaultParameters_;
@@ -199,7 +197,8 @@ public:
 	virtual ~GPUFeature2D() {}
 
 	virtual void detectKeypoints(const cv::Mat & image,
-			std::vector<cv::KeyPoint> & keypoints) = 0;
+			std::vector<cv::KeyPoint> & keypoints,
+			const cv::Mat & mask = cv::Mat()) = 0;
 
 	virtual void computeDescriptors(const cv::Mat & image,
 			std::vector<cv::KeyPoint> & keypoints,
@@ -225,12 +224,15 @@ public:
 	}
 	virtual ~GPUSURF() {}
 
-    void detectKeypoints(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints)
+    void detectKeypoints(const cv::Mat & image,
+    		std::vector<cv::KeyPoint> & keypoints,
+    		const cv::Mat & mask = cv::Mat())
     {
     	cv::gpu::GpuMat imgGpu(image);
+    	cv::gpu::GpuMat maskGpu(mask);
 		try
 		{
-			surf_(imgGpu, cv::gpu::GpuMat(), keypoints);
+			surf_(imgGpu, maskGpu, keypoints);
 		}
 		catch(cv::Exception &e)
 		{
@@ -292,10 +294,13 @@ public:
 	virtual ~GPUFAST() {}
 
 protected:
-	void detectKeypoints(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints)
+	void detectKeypoints(const cv::Mat & image,
+			std::vector<cv::KeyPoint> & keypoints,
+			const cv::Mat & mask = cv::Mat())
     {
     	cv::gpu::GpuMat imgGpu(image);
-    	fast_(imgGpu, cv::gpu::GpuMat(), keypoints);
+    	cv::gpu::GpuMat maskGpu(mask);
+    	fast_(imgGpu, maskGpu, keypoints);
     }
 	void computeDescriptors( const cv::Mat& image,
 		std::vector<cv::KeyPoint>& keypoints,
@@ -335,12 +340,15 @@ public:
 	virtual ~GPUORB() {}
 
 protected:
-	void detectKeypoints(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints)
+	void detectKeypoints(const cv::Mat & image,
+			std::vector<cv::KeyPoint> & keypoints,
+			const cv::Mat & mask = cv::Mat())
     {
     	cv::gpu::GpuMat imgGpu(image);
+    	cv::gpu::GpuMat maskGpu(mask);
     	try
     	{
-    		orb_(imgGpu, cv::gpu::GpuMat(), keypoints);
+    		orb_(imgGpu, maskGpu, keypoints);
     	}
     	catch(cv::Exception &e)
 		{
@@ -411,7 +419,7 @@ KeypointDetector * Settings::createKeypointDetector()
 								getFeature2D_Dense_initImgBound(),
 								getFeature2D_Dense_varyXyStepWithScale(),
 								getFeature2D_Dense_varyImgBoundWithScale());
-						if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				case 1:
@@ -422,14 +430,14 @@ KeypointDetector * Settings::createKeypointDetector()
 							detectorGPU = new GPUFAST(
 									getFeature2D_Fast_threshold(),
 									getFeature2D_Fast_nonmaxSuppression());
-							if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s GPU\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s GPU", strategies.at(index).toStdString().c_str());
 						}
 						else
 						{
 							detector = new cv::FastFeatureDetector(
 									getFeature2D_Fast_threshold(),
 									getFeature2D_Fast_nonmaxSuppression());
-							if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 						}
 					}
 					break;
@@ -443,7 +451,7 @@ KeypointDetector * Settings::createKeypointDetector()
 								getFeature2D_GFTT_blockSize(),
 								getFeature2D_GFTT_useHarrisDetector(),
 								getFeature2D_GFTT_k());
-						if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				case 3:
@@ -459,7 +467,7 @@ KeypointDetector * Settings::createKeypointDetector()
 								getFeature2D_MSER_areaThreshold(),
 								getFeature2D_MSER_minMargin(),
 								getFeature2D_MSER_edgeBlurSize());
-						if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				case 4:
@@ -478,7 +486,7 @@ KeypointDetector * Settings::createKeypointDetector()
 									getFeature2D_ORB_patchSize(),
 									getFeature2D_Fast_threshold(),
 									getFeature2D_Fast_nonmaxSuppression());
-							if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s (GPU)\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s (GPU)", strategies.at(index).toStdString().c_str());
 						}
 						else
 						{
@@ -491,7 +499,7 @@ KeypointDetector * Settings::createKeypointDetector()
 									getFeature2D_ORB_WTA_K(),
 									getFeature2D_ORB_scoreType(),
 									getFeature2D_ORB_patchSize());
-							if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 						}
 					}
 					break;
@@ -504,7 +512,7 @@ KeypointDetector * Settings::createKeypointDetector()
 								getFeature2D_SIFT_contrastThreshold(),
 								getFeature2D_SIFT_edgeThreshold(),
 								getFeature2D_SIFT_sigma());
-						if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				case 6:
@@ -516,7 +524,7 @@ KeypointDetector * Settings::createKeypointDetector()
 								getFeature2D_Star_lineThresholdProjected(),
 								getFeature2D_Star_lineThresholdBinarized(),
 								getFeature2D_Star_suppressNonmaxSize());
-						if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				case 7:
@@ -531,7 +539,7 @@ KeypointDetector * Settings::createKeypointDetector()
 									getFeature2D_SURF_extended(),
 									getFeature2D_SURF_keypointsRatio(),
 									getFeature2D_SURF_upright());
-							if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s (GPU)\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s (GPU)", strategies.at(index).toStdString().c_str());
 						}
 						else
 						{
@@ -541,7 +549,7 @@ KeypointDetector * Settings::createKeypointDetector()
 								getFeature2D_SURF_nOctaveLayers(),
 								getFeature2D_SURF_extended(),
 								getFeature2D_SURF_upright());
-							if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 						}
 					}
 					break;
@@ -552,7 +560,7 @@ KeypointDetector * Settings::createKeypointDetector()
 								getFeature2D_BRISK_thresh(),
 								getFeature2D_BRISK_octaves(),
 								getFeature2D_BRISK_patternScale());
-						if(VERBOSE)printf("Settings::createFeaturesDetector() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				default:
@@ -595,7 +603,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
 					{
 						extractor = new cv::BriefDescriptorExtractor(
 								getFeature2D_Brief_bytes());
-						if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				case 1:
@@ -614,7 +622,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
 									getFeature2D_ORB_patchSize(),
 									getFeature2D_Fast_threshold(),
 									getFeature2D_Fast_nonmaxSuppression());
-							if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s (GPU)\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s (GPU)", strategies.at(index).toStdString().c_str());
 						}
 						else
 						{
@@ -627,7 +635,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
 									getFeature2D_ORB_WTA_K(),
 									getFeature2D_ORB_scoreType(),
 									getFeature2D_ORB_patchSize());
-							if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 						}
 					}
 					break;
@@ -640,7 +648,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
 								getFeature2D_SIFT_contrastThreshold(),
 								getFeature2D_SIFT_edgeThreshold(),
 								getFeature2D_SIFT_sigma());
-						if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				case 3:
@@ -655,7 +663,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
 									getFeature2D_SURF_extended(),
 									getFeature2D_SURF_keypointsRatio(),
 									getFeature2D_SURF_upright());
-							if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s (GPU)\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s (GPU)", strategies.at(index).toStdString().c_str());
 						}
 						else
 						{
@@ -665,7 +673,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
 									getFeature2D_SURF_nOctaveLayers(),
 									getFeature2D_SURF_extended(),
 									getFeature2D_SURF_upright());
-							if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
+							UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 						}
 					}
 					break;
@@ -676,7 +684,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
 								getFeature2D_BRISK_thresh(),
 								getFeature2D_BRISK_octaves(),
 								getFeature2D_BRISK_patternScale());
-						if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				case 5:
@@ -687,7 +695,7 @@ DescriptorExtractor * Settings::createDescriptorExtractor()
 								getFeature2D_FREAK_scaleNormalized(),
 								getFeature2D_FREAK_patternScale(),
 								getFeature2D_FREAK_nOctaves());
-						if(VERBOSE)printf("Settings::createDescriptorsExtractor() type=%s\n", strategies.at(index).toStdString().c_str());
+						UDEBUG("type=%s", strategies.at(index).toStdString().c_str());
 					}
 					break;
 				default:
@@ -745,14 +753,14 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
 				case 0:
 					if(strategies.at(index).compare("Linear") == 0)
 					{
-						if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "Linear");
+						UDEBUG("type=%s", "Linear");
 						params = new cv::flann::LinearIndexParams();
 					}
 					break;
 				case 1:
 					if(strategies.at(index).compare("KDTree") == 0)
 					{
-						if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "KDTree");
+						UDEBUG("type=%s", "KDTree");
 						params = new cv::flann::KDTreeIndexParams(
 								getNearestNeighbor_KDTree_trees());
 					}
@@ -772,7 +780,7 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
 								centers_init = (cvflann::flann_centers_init_t)index;
 							}
 						}
-						if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "KMeans");
+						UDEBUG("type=%s", "KMeans");
 						params = new cv::flann::KMeansIndexParams(
 								getNearestNeighbor_KMeans_branching(),
 								getNearestNeighbor_KMeans_iterations(),
@@ -795,7 +803,7 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
 								centers_init = (cvflann::flann_centers_init_t)index;
 							}
 						}
-						if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "Composite");
+						UDEBUG("type=%s", "Composite");
 						params = new cv::flann::CompositeIndexParams(
 								getNearestNeighbor_Composite_trees(),
 								getNearestNeighbor_Composite_branching(),
@@ -807,7 +815,7 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
 				case 4:
 					if(strategies.at(index).compare("Autotuned") == 0)
 					{
-						if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "Autotuned");
+						UDEBUG("type=%s", "Autotuned");
 						params = new cv::flann::AutotunedIndexParams(
 								getNearestNeighbor_Autotuned_target_precision(),
 								getNearestNeighbor_Autotuned_build_weight(),
@@ -818,7 +826,7 @@ cv::flann::IndexParams * Settings::createFlannIndexParams()
 				case 5:
 					if(strategies.at(index).compare("Lsh") == 0)
 					{
-						if(VERBOSE)printf("Settings::getFlannIndexParams() type=%s\n", "Lsh");
+						UDEBUG("type=%s", "Lsh");
 						params = new cv::flann::LshIndexParams(
 								getNearestNeighbor_Lsh_table_number(),
 								getNearestNeighbor_Lsh_key_size(),
@@ -858,7 +866,6 @@ cvflann::flann_distance_t Settings::getFlannDistanceType()
 			}
 		}
 	}
-	if(VERBOSE)printf("Settings::getFlannDistanceType() distance=%d\n", distance);
 	return distance;
 }
 
@@ -896,7 +903,7 @@ int Settings::getHomographyMethod()
 			}
 		}
 	}
-	if(VERBOSE)printf("Settings::getHomographyMethod() method=%d\n", method);
+	UDEBUG("method=%d", method);
 	return method;
 }
 
@@ -912,15 +919,17 @@ KeypointDetector::KeypointDetector(GPUFeature2D * gpuFeature2D) :
 {
 	Q_ASSERT(gpuFeature2D_!=0);
 }
-void KeypointDetector::detect(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints)
+void KeypointDetector::detect(const cv::Mat & image,
+		std::vector<cv::KeyPoint> & keypoints,
+		const cv::Mat & mask)
 {
 	if(featureDetector_)
 	{
-		featureDetector_->detect(image, keypoints);
+		featureDetector_->detect(image, keypoints, mask);
 	}
 	else // assume GPU
 	{
-		gpuFeature2D_->detectKeypoints(image, keypoints);
+		gpuFeature2D_->detectKeypoints(image, keypoints, mask);
 	}
 }
 

src/ui/mainWindow.ui

@@ -7,7 +7,7 @@
     <x>0</x>
     <y>0</y>
     <width>826</width>
-    <height>506</height>
+    <height>523</height>
    </rect>
   </property>
   <property name="windowTitle">
@@ -344,7 +344,7 @@
           <x>0</x>
           <y>0</y>
           <width>198</width>
-          <height>376</height>
+          <height>393</height>
          </rect>
         </property>
         <layout class="QVBoxLayout" name="verticalLayout_objects">
@@ -449,83 +449,83 @@
         <number>0</number>
        </property>
        <item row="4" column="1">
-        <widget class="QLabel" name="label_timeMatching">
-         <property name="text">
-          <string>000</string>
-         </property>
-        </widget>
-       </item>
-       <item row="3" column="2">
-        <widget class="QLabel" name="label_9">
-         <property name="text">
-          <string>ms</string>
-         </property>
-        </widget>
-       </item>
-       <item row="4" column="2">
-        <widget class="QLabel" name="label_10">
-         <property name="text">
-          <string>ms</string>
-         </property>
-        </widget>
-       </item>
-       <item row="3" column="0">
-        <widget class="QLabel" name="label_8">
-         <property name="text">
-          <string>Descriptors indexing</string>
-         </property>
-        </widget>
-       </item>
-       <item row="3" column="1">
         <widget class="QLabel" name="label_timeIndexing">
          <property name="text">
           <string>000</string>
          </property>
         </widget>
        </item>
+       <item row="5" column="1">
+        <widget class="QLabel" name="label_timeMatching">
+         <property name="text">
+          <string>000</string>
+         </property>
+        </widget>
+       </item>
+       <item row="4" column="2">
+        <widget class="QLabel" name="label_9">
+         <property name="text">
+          <string>ms</string>
+         </property>
+        </widget>
+       </item>
        <item row="5" column="2">
+        <widget class="QLabel" name="label_10">
+         <property name="text">
+          <string>ms</string>
+         </property>
+        </widget>
+       </item>
+       <item row="4" column="0">
+        <widget class="QLabel" name="label_8">
+         <property name="text">
+          <string>Descriptors indexing</string>
+         </property>
+        </widget>
+       </item>
+       <item row="6" column="2">
         <widget class="QLabel" name="label_12">
          <property name="text">
           <string>ms</string>
          </property>
         </widget>
        </item>
-       <item row="7" column="1">
+       <item row="8" column="1">
         <widget class="QLabel" name="label_minMatchedDistance">
          <property name="text">
           <string>000</string>
          </property>
         </widget>
        </item>
-       <item row="7" column="0">
+       <item row="8" column="0">
         <widget class="QLabel" name="label_13">
          <property name="text">
           <string>Min matched distance</string>
          </property>
         </widget>
        </item>
-       <item row="8" column="0">
+       <item row="9" column="0">
         <widget class="QLabel" name="label_14">
          <property name="text">
           <string>Max matched distance</string>
          </property>
         </widget>
        </item>
-       <item row="8" column="1">
+       <item row="9" column="1">
         <widget class="QLabel" name="label_maxMatchedDistance">
          <property name="text">
           <string>000</string>
          </property>
         </widget>
        </item>
-       <item row="5" column="0">
+       <item row="6" column="0">
         <widget class="QLabel" name="label_11">
          <property name="text">
           <string>Homograhies</string>
          </property>
         </widget>
        </item>
-       <item row="2" column="0">
+       <item row="3" column="0">
         <widget class="QLabel" name="label_2">
          <property name="text">
           <string>Descriptors extraction</string>
@@ -539,14 +539,14 @@
          </property>
         </widget>
        </item>
-       <item row="2" column="1">
+       <item row="3" column="1">
         <widget class="QLabel" name="label_timeExtraction">
          <property name="text">
           <string>000</string>
          </property>
         </widget>
        </item>
-       <item row="2" column="2">
+       <item row="3" column="2">
         <widget class="QLabel" name="label_4">
          <property name="text">
           <string>ms</string>
@@ -567,7 +567,7 @@
          </property>
         </widget>
        </item>
-       <item row="4" column="0">
+       <item row="5" column="0">
         <widget class="QLabel" name="label_7">
          <property name="text">
           <string>Descriptors matching</string>
@@ -595,80 +595,101 @@
          </property>
         </widget>
        </item>
-       <item row="9" column="0">
+       <item row="10" column="0">
         <widget class="QLabel" name="label_17">
          <property name="text">
           <string>Vocabulary size</string>
          </property>
         </widget>
        </item>
-       <item row="9" column="1">
+       <item row="10" column="1">
         <widget class="QLabel" name="label_vocabularySize">
          <property name="text">
           <string>000</string>
          </property>
         </widget>
        </item>
-       <item row="10" column="0">
+       <item row="11" column="0">
         <widget class="QLabel" name="label_18">
          <property name="text">
           <string>IP address</string>
          </property>
         </widget>
        </item>
-       <item row="11" column="0">
+       <item row="12" column="0">
         <widget class="QLabel" name="label_19">
          <property name="text">
           <string>Output detection port</string>
          </property>
         </widget>
        </item>
-       <item row="10" column="1">
+       <item row="11" column="1">
         <widget class="QLabel" name="label_ipAddress">
          <property name="text">
           <string>0.0.0.0</string>
          </property>
         </widget>
        </item>
-       <item row="11" column="1">
+       <item row="12" column="1">
         <widget class="QLabel" name="label_port">
          <property name="text">
           <string>0</string>
          </property>
         </widget>
        </item>
-       <item row="6" column="0">
+       <item row="7" column="0">
         <widget class="QLabel" name="label_20">
          <property name="text">
           <string>Objects detected</string>
          </property>
         </widget>
        </item>
-       <item row="6" column="1">
+       <item row="7" column="1">
         <widget class="QLabel" name="label_objectsDetected">
          <property name="text">
           <string>000</string>
          </property>
         </widget>
        </item>
-       <item row="5" column="1">
+       <item row="6" column="1">
         <widget class="QLabel" name="label_timeHomographies">
          <property name="text">
           <string>000</string>
          </property>
         </widget>
        </item>
-       <item row="12" column="0">
+       <item row="13" column="0">
         <widget class="QLabel" name="label_21">
          <property name="text">
           <string>Input image port</string>
          </property>
         </widget>
        </item>
-       <item row="12" column="1">
+       <item row="13" column="1">
         <widget class="QLabel" name="label_port_image">
          <property name="text">
-          <string>NA</string>
+          <string>-</string>
+         </property>
+        </widget>
+       </item>
+       <item row="2" column="0">
+        <widget class="QLabel" name="label_22">
+         <property name="text">
+          <string>Affine transforms</string>
+         </property>
+        </widget>
+       </item>
+       <item row="2" column="1">
+        <widget class="QLabel" name="label_timeSkewAffine">
+         <property name="text">
+          <string>000</string>
+         </property>
+        </widget>
+       </item>
+       <item row="2" column="2">
+        <widget class="QLabel" name="label_23">
+         <property name="text">
+          <string>ms</string>
          </property>
         </widget>
        </item>