mirror of
https://github.com/opencv/opencv.git
synced 2026-07-30 15:53:03 +04:00
merged all the latest changes from 2.4 to trunk
This commit is contained in:
Vadim Pisarevsky
@@ -500,7 +500,7 @@ The constructor
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:param max_features: Maximum desired number of features.
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:param max_iters: Maximum number of times to try adjusting the feature detector parameters. For :ocv:class:`FastAdjuster` , this number can be high, but with ``Star`` or ``Surf`` many iterations can be time-comsuming. At each iteration the detector is rerun.
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:param max_iters: Maximum number of times to try adjusting the feature detector parameters. For :ocv:class:`FastAdjuster` , this number can be high, but with ``Star`` or ``Surf`` many iterations can be time-consuming. At each iteration the detector is rerun.
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AdjusterAdapter
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---------------
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@@ -196,7 +196,7 @@ Finds the ``k`` best matches for each query keypoint.
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.. ocv:function:: void GenericDescriptorMatcher::knnMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints, vector<vector<DMatch> >& matches, int k, const vector<Mat>& masks=vector<Mat>(), bool compactResult=false )
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The methods are extended variants of ``GenericDescriptorMatch::match``. The parameters are similar, and the the semantics is similar to ``DescriptorMatcher::knnMatch``. But this class does not require explicitly computed keypoint descriptors.
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The methods are extended variants of ``GenericDescriptorMatch::match``. The parameters are similar, and the semantics is similar to ``DescriptorMatcher::knnMatch``. But this class does not require explicitly computed keypoint descriptors.
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@@ -15,7 +15,7 @@ Detects corners using the FAST algorithm
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:param threshold: Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel. See the algorithm description below.
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:param nonmaxSupression: If it is true, non-maximum supression is applied to detected corners (keypoints).
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:param nonmaxSupression: If it is true, non-maximum suppression is applied to detected corners (keypoints).
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Detects corners using the FAST algorithm by E. Rosten (*Machine Learning for High-speed Corner Detection*, 2006).
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@@ -43,7 +43,7 @@ Maximally stable extremal region extractor. ::
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};
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The class encapsulates all the parameters of the MSER extraction algorithm (see
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http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER for usefull comments and parameters description.
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http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER for useful comments and parameters description.
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StarDetector
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@@ -186,7 +186,7 @@ Computes an image descriptor using the set visual vocabulary.
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BOWImgDescriptorExtractor::descriptorSize
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---------------------------------------------
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Returns an image discriptor size if the vocabulary is set. Otherwise, it returns 0.
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Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.
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.. ocv:function:: int BOWImgDescriptorExtractor::descriptorSize() const
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@@ -51,6 +51,8 @@
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namespace cv
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{
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CV_EXPORTS bool initModule_features2d();
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/*!
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The Keypoint Class
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@@ -172,20 +172,4 @@ void BriefDescriptorExtractor::computeImpl(const Mat& image, std::vector<KeyPoin
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test_fn_(sum, keypoints, descriptors);
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}
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static Algorithm* createBRIEF() { return new BriefDescriptorExtractor; }
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static AlgorithmInfo brief_info("Feature2D.BRIEF", createBRIEF);
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AlgorithmInfo* BriefDescriptorExtractor::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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BriefDescriptorExtractor brief;
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brief_info.addParam(brief, "bytes", brief.bytes_);
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initialized = true;
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}
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return &brief_info;
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}
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} // namespace cv
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@@ -195,7 +195,7 @@ DenseFeatureDetector::DenseFeatureDetector( float _initFeatureScale, int _featur
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void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
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{
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float curScale = initFeatureScale;
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float curScale = static_cast<float>(initFeatureScale);
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int curStep = initXyStep;
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int curBound = initImgBound;
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for( int curLevel = 0; curLevel < featureScaleLevels; curLevel++ )
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@@ -208,7 +208,7 @@ void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypo
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}
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}
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curScale = curScale * featureScaleMul;
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curScale = static_cast<float>(curScale * featureScaleMul);
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if( varyXyStepWithScale ) curStep = static_cast<int>( curStep * featureScaleMul + 0.5f );
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if( varyImgBoundWithScale ) curBound = static_cast<int>( curBound * featureScaleMul + 0.5f );
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}
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@@ -359,5 +359,161 @@ void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoin
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if( !mask.empty() )
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KeyPointsFilter::runByPixelsMask( keypoints, mask );
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}
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/////////////////////// AlgorithmInfo for various detector & descriptors ////////////////////////////
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/* NOTE!!!
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All the AlgorithmInfo-related stuff should be in the same file as initModule_features2d().
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Otherwise, linker may throw away some seemingly unused stuff.
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*/
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static Algorithm* createBRIEF() { return new BriefDescriptorExtractor; }
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static AlgorithmInfo& brief_info()
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{
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static AlgorithmInfo brief_info_var("Feature2D.BRIEF", createBRIEF);
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return brief_info_var;
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}
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static AlgorithmInfo& brief_info_auto = brief_info();
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AlgorithmInfo* BriefDescriptorExtractor::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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BriefDescriptorExtractor brief;
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brief_info().addParam(brief, "bytes", brief.bytes_);
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initialized = true;
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}
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return &brief_info();
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////
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static Algorithm* createFAST() { return new FastFeatureDetector; }
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static AlgorithmInfo& fast_info()
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{
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static AlgorithmInfo fast_info_var("Feature2D.FAST", createFAST);
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return fast_info_var;
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}
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static AlgorithmInfo& fast_info_auto = fast_info();
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AlgorithmInfo* FastFeatureDetector::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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FastFeatureDetector obj;
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fast_info().addParam(obj, "threshold", obj.threshold);
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fast_info().addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression);
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initialized = true;
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}
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return &fast_info();
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////
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static Algorithm* createStarDetector() { return new StarDetector; }
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static AlgorithmInfo& star_info()
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{
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static AlgorithmInfo star_info_var("Feature2D.STAR", createStarDetector);
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return star_info_var;
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}
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static AlgorithmInfo& star_info_auto = star_info();
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AlgorithmInfo* StarDetector::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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StarDetector obj;
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star_info().addParam(obj, "maxSize", obj.maxSize);
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star_info().addParam(obj, "responseThreshold", obj.responseThreshold);
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star_info().addParam(obj, "lineThresholdProjected", obj.lineThresholdProjected);
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star_info().addParam(obj, "lineThresholdBinarized", obj.lineThresholdBinarized);
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star_info().addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize);
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initialized = true;
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}
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return &star_info();
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////
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static Algorithm* createMSER() { return new MSER; }
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static AlgorithmInfo& mser_info()
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{
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static AlgorithmInfo mser_info_var("Feature2D.MSER", createMSER);
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return mser_info_var;
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}
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static AlgorithmInfo& mser_info_auto = mser_info();
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AlgorithmInfo* MSER::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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MSER obj;
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mser_info().addParam(obj, "delta", obj.delta);
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mser_info().addParam(obj, "minArea", obj.minArea);
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mser_info().addParam(obj, "maxArea", obj.maxArea);
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mser_info().addParam(obj, "maxVariation", obj.maxVariation);
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mser_info().addParam(obj, "minDiversity", obj.minDiversity);
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mser_info().addParam(obj, "maxEvolution", obj.maxEvolution);
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mser_info().addParam(obj, "areaThreshold", obj.areaThreshold);
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mser_info().addParam(obj, "minMargin", obj.minMargin);
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mser_info().addParam(obj, "edgeBlurSize", obj.edgeBlurSize);
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initialized = true;
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}
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return &mser_info();
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////
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static Algorithm* createORB() { return new ORB; }
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static AlgorithmInfo& orb_info()
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{
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static AlgorithmInfo orb_info_var("Feature2D.ORB", createORB);
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return orb_info_var;
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}
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static AlgorithmInfo& orb_info_auto = orb_info();
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AlgorithmInfo* ORB::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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ORB obj;
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orb_info().addParam(obj, "nFeatures", obj.nfeatures);
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orb_info().addParam(obj, "scaleFactor", obj.scaleFactor);
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orb_info().addParam(obj, "nLevels", obj.nlevels);
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orb_info().addParam(obj, "firstLevel", obj.firstLevel);
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orb_info().addParam(obj, "edgeThreshold", obj.edgeThreshold);
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orb_info().addParam(obj, "patchSize", obj.patchSize);
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orb_info().addParam(obj, "WTA_K", obj.WTA_K);
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orb_info().addParam(obj, "scoreType", obj.scoreType);
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initialized = true;
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}
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return &orb_info();
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}
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bool initModule_features2d(void)
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{
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Ptr<Algorithm> brief = createBRIEF(), orb = createORB(),
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star = createStarDetector(), fastd = createFAST(), mser = createMSER();
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return brief->info() != 0 && orb->info() != 0 && star->info() != 0 &&
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fastd->info() != 0 && mser->info() != 0;
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}
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}
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@@ -391,22 +391,4 @@ void FastFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoi
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KeyPointsFilter::runByPixelsMask( keypoints, mask );
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}
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static Algorithm* createFAST() { return new FastFeatureDetector; }
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static AlgorithmInfo fast_info("Feature2D.FAST", createFAST);
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AlgorithmInfo* FastFeatureDetector::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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FastFeatureDetector obj;
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fast_info.addParam(obj, "threshold", obj.threshold);
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fast_info.addParam(obj, "nonmaxSuppression", obj.nonmaxSuppression);
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initialized = true;
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}
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return &fast_info;
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}
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}
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@@ -383,7 +383,7 @@ void BFMatcher::knnMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch>
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vector<DMatch>& mq = matches.back();
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mq.reserve(knn);
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for( int k = 0; k < knn; k++ )
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for( int k = 0; k < nidx.cols; k++ )
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{
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if( nidxptr[k] < 0 )
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break;
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@@ -1299,28 +1299,4 @@ void MserFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoi
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}
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}
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static Algorithm* createMSER() { return new MSER; }
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static AlgorithmInfo mser_info("Feature2D.MSER", createMSER);
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AlgorithmInfo* MSER::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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MSER obj;
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mser_info.addParam(obj, "delta", obj.delta);
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mser_info.addParam(obj, "minArea", obj.minArea);
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mser_info.addParam(obj, "maxArea", obj.maxArea);
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mser_info.addParam(obj, "maxVariation", obj.maxVariation);
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mser_info.addParam(obj, "minDiversity", obj.minDiversity);
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mser_info.addParam(obj, "maxEvolution", obj.maxEvolution);
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mser_info.addParam(obj, "areaThreshold", obj.areaThreshold);
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mser_info.addParam(obj, "minMargin", obj.minMargin);
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mser_info.addParam(obj, "edgeBlurSize", obj.edgeBlurSize);
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initialized = true;
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}
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return &mser_info;
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}
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}
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@@ -546,31 +546,6 @@ static void makeRandomPattern(int patchSize, Point* pattern, int npoints)
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////////////
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static Algorithm* createORB() { return new ORB; }
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static AlgorithmInfo orb_info("Feature2D.ORB", createORB);
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AlgorithmInfo* ORB::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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ORB obj;
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orb_info.addParam(obj, "nFeatures", obj.nfeatures);
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orb_info.addParam(obj, "scaleFactor", obj.scaleFactor);
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orb_info.addParam(obj, "nLevels", obj.nlevels);
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orb_info.addParam(obj, "firstLevel", obj.firstLevel);
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orb_info.addParam(obj, "edgeThreshold", obj.edgeThreshold);
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orb_info.addParam(obj, "patchSize", obj.patchSize);
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orb_info.addParam(obj, "WTA_K", obj.WTA_K);
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orb_info.addParam(obj, "scoreType", obj.scoreType);
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initialized = true;
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}
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return &orb_info;
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}
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static inline float getScale(int level, int firstLevel, double scaleFactor)
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{
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return (float)std::pow(scaleFactor, (double)(level - firstLevel));
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@@ -447,25 +447,4 @@ void StarDetector::operator()(const Mat& img, vector<KeyPoint>& keypoints) const
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lineThresholdBinarized, suppressNonmaxSize );
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}
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static Algorithm* createStarDetector() { return new StarDetector; }
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static AlgorithmInfo star_info("Feature2D.STAR", createStarDetector);
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AlgorithmInfo* StarDetector::info() const
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{
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static volatile bool initialized = false;
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if( !initialized )
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{
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StarDetector obj;
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star_info.addParam(obj, "maxSize", obj.maxSize);
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star_info.addParam(obj, "responseThreshold", obj.responseThreshold);
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star_info.addParam(obj, "lineThresholdProjected", obj.lineThresholdProjected);
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star_info.addParam(obj, "lineThresholdBinarized", obj.lineThresholdBinarized);
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star_info.addParam(obj, "suppressNonmaxSize", obj.suppressNonmaxSize);
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initialized = true;
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}
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return &star_info;
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}
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}
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@@ -269,13 +269,15 @@ static Mat readMatFromBin( const string& filename )
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if( f )
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{
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int rows, cols, type, dataSize;
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fread( (void*)&rows, sizeof(int), 1, f );
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fread( (void*)&cols, sizeof(int), 1, f );
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fread( (void*)&type, sizeof(int), 1, f );
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fread( (void*)&dataSize, sizeof(int), 1, f );
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size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
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size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
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size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
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size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
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CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
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uchar* data = (uchar*)cvAlloc(dataSize);
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fread( (void*)data, 1, dataSize, f );
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size_t elements_read = fread( (void*)data, 1, dataSize, f );
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CV_Assert(elements_read == (size_t)(dataSize));
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fclose(f);
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return Mat( rows, cols, type, data );
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@@ -75,7 +75,8 @@ int CV_MserTest::LoadBoxes(const char* path, vector<CvBox2D>& boxes)
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while (!feof(f))
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{
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CvBox2D box;
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fscanf(f,"%f,%f,%f,%f,%f\n",&box.angle,&box.center.x,&box.center.y,&box.size.width,&box.size.height);
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int values_read = fscanf(f,"%f,%f,%f,%f,%f\n",&box.angle,&box.center.x,&box.center.y,&box.size.width,&box.size.height);
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CV_Assert(values_read == 5);
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boxes.push_back(box);
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}
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fclose(f);
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