From e406dfee44a1d62dfd4617a9937ac66e7d232ae7 Mon Sep 17 00:00:00 2001
From: Maria Dimashova <no@email>
Date: Mon, 22 Nov 2010 18:27:08 +0000
Subject: [PATCH] refactored features2d and sample on matching to many images
---
.../include/opencv2/features2d/features2d.hpp | 916 +++++++++---------
modules/features2d/src/bagofwords.cpp | 37 +
modules/features2d/src/brief.cpp | 680 ++++++-------
modules/features2d/src/descriptors.cpp | 97 +-
modules/features2d/src/detectors.cpp | 146 ++-
modules/features2d/src/matchers.cpp | 455 +++++++--
samples/cpp/matching_to_many_images.cpp | 256 +++--
samples/cpp/matching_to_many_images/query.png | Bin 0 -> 121403 bytes
.../cpp/matching_to_many_images/train/1.png | Bin 0 -> 118730 bytes
.../cpp/matching_to_many_images/train/2.png | Bin 0 -> 114372 bytes
.../cpp/matching_to_many_images/train/3.png | Bin 0 -> 120917 bytes
.../train/trainImages.txt | 3 +
tests/cv/src/afeatures2d.cpp | 16 +-
13 files changed, 1600 insertions(+), 1006 deletions(-)
create mode 100755 samples/cpp/matching_to_many_images/query.png
create mode 100755 samples/cpp/matching_to_many_images/train/1.png
create mode 100755 samples/cpp/matching_to_many_images/train/2.png
create mode 100755 samples/cpp/matching_to_many_images/train/3.png
create mode 100755 samples/cpp/matching_to_many_images/train/trainImages.txt
diff --git a/modules/features2d/include/opencv2/features2d/features2d.hpp b/modules/features2d/include/opencv2/features2d/features2d.hpp
index d5243e0f88..0c686bfd1c 100644
--- a/modules/features2d/include/opencv2/features2d/features2d.hpp
+++ b/modules/features2d/include/opencv2/features2d/features2d.hpp
@@ -1231,34 +1231,34 @@ protected:
class CV_EXPORTS FeatureDetector
{
public:
- virtual ~FeatureDetector() {}
+ virtual ~FeatureDetector();
/*
- * Detect keypoints in an image. Must be implemented by the subclass.
- *
+ * Detect keypoints in an image.
* image The image.
* keypoints The detected keypoints.
* mask Mask specifying where to look for keypoints (optional). Must be a char
* matrix with non-zero values in the region of interest.
*/
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const = 0;
+ void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
/*
* Detect keypoints in an image set.
- *
- * images Image collection.
- * pointCollection Collection of keypoints detected in an input images.
- * masks Masks for each input image.
+ * images Image collection.
+ * keypoints Collection of keypoints detected in an input images. keypoints[i] is a set of keypoints detected in an images[i].
+ * masks Masks for image set. masks[i] is a mask for images[i].
*/
- void detect( const vector<Mat>& imageCollection, vector<vector<KeyPoint> >& pointCollection, const vector<Mat>& masks=vector<Mat>() ) const;
+ void detect( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, const vector<Mat>& masks=vector<Mat>() ) const;
- virtual void read( const FileNode& ) {}
- virtual void write( FileStorage& ) const {}
+ // Read detector object from a file node
+ virtual void read( const FileNode& );
+ // Read detector object from a file node
+ virtual void write( FileStorage& ) const;
protected:
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const = 0;
/*
* Remove keypoints that are not in the mask.
- *
* Helper function, useful when wrapping a library call for keypoint detection that
* does not support a mask argument.
*/
@@ -1268,13 +1268,13 @@ protected:
class CV_EXPORTS FastFeatureDetector : public FeatureDetector
{
public:
- FastFeatureDetector( int _threshold=1, bool _nonmaxSuppression=true );
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
-
+ FastFeatureDetector( int threshold=10, bool nonmaxSuppression=true );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+
int threshold;
bool nonmaxSuppression;
};
@@ -1283,102 +1283,123 @@ protected:
class CV_EXPORTS GoodFeaturesToTrackDetector : public FeatureDetector
{
public:
- GoodFeaturesToTrackDetector( int _maxCorners, double _qualityLevel, double _minDistance,
- int _blockSize=3, bool _useHarrisDetector=false, double _k=0.04 );
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+ class CV_EXPORTS Params
+ {
+ public:
+ Params( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1.,
+ int blockSize=3, bool useHarrisDetector=false, double k=0.04 );
+ void read( const FileNode& fn );
+ void write( FileStorage& fs ) const;
+
+ int maxCorners;
+ double qualityLevel;
+ double minDistance;
+ int blockSize;
+ bool useHarrisDetector;
+ double k;
+ };
+ GoodFeaturesToTrackDetector( const GoodFeaturesToTrackDetector::Params& params=GoodFeaturesToTrackDetector::Params() );
+ GoodFeaturesToTrackDetector( int maxCorners, double qualityLevel, double minDistance,
+ int blockSize=3, bool useHarrisDetector=false, double k=0.04 );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
- int maxCorners;
- double qualityLevel;
- double minDistance;
- int blockSize;
- bool useHarrisDetector;
- double k;
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+
+ Params params;
};
class CV_EXPORTS MserFeatureDetector : public FeatureDetector
{
public:
- MserFeatureDetector( CvMSERParams params=cvMSERParams () );
+ MserFeatureDetector( CvMSERParams params=cvMSERParams() );
MserFeatureDetector( int delta, int minArea, int maxArea, double maxVariation, double minDiversity,
int maxEvolution, double areaThreshold, double minMargin, int edgeBlurSize );
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
-
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+
MSER mser;
};
class CV_EXPORTS StarFeatureDetector : public FeatureDetector
{
public:
- StarFeatureDetector( int maxSize=16, int responseThreshold=30, int lineThresholdProjected = 10,
+ StarFeatureDetector( const CvStarDetectorParams& params=cvStarDetectorParams() );
+ StarFeatureDetector( int maxSize, int responseThreshold=30, int lineThresholdProjected = 10,
int lineThresholdBinarized=8, int suppressNonmaxSize=5 );
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
-
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+
StarDetector star;
};
class CV_EXPORTS SiftFeatureDetector : public FeatureDetector
{
public:
- SiftFeatureDetector( double threshold=SIFT::DetectorParams::GET_DEFAULT_THRESHOLD(),
- double edgeThreshold=SIFT::DetectorParams::GET_DEFAULT_EDGE_THRESHOLD(),
+ SiftFeatureDetector( const SIFT::DetectorParams& detectorParams=SIFT::DetectorParams(),
+ const SIFT::CommonParams& commonParams=SIFT::CommonParams() );
+ SiftFeatureDetector( double threshold, double edgeThreshold,
int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
int angleMode=SIFT::CommonParams::FIRST_ANGLE );
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
-
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+
SIFT sift;
};
class CV_EXPORTS SurfFeatureDetector : public FeatureDetector
{
public:
- SurfFeatureDetector( double hessianThreshold = 400., int octaves = 3, int octaveLayers = 4 );
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+ SurfFeatureDetector( double hessianThreshold=400., int octaves=3, int octaveLayers=4 );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+
SURF surf;
};
class CV_EXPORTS DenseFeatureDetector : public FeatureDetector
{
public:
- DenseFeatureDetector() : initFeatureScale(1), featureScaleLevels(1), featureScaleMul(0.1f),
- initXyStep(6), initImgBound(0), varyXyStepWithScale(true), varyImgBoundWithScale(false) {}
- DenseFeatureDetector( float _initFeatureScale, int _featureScaleLevels=1, float _featureScaleMul=0.1f,
- int _initXyStep=6, int _initImgBound=0, bool _varyXyStepWithScale=true, bool _varyImgBoundWithScale=false ) :
- initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels), featureScaleMul(_featureScaleMul),
- initXyStep(_initXyStep), initImgBound(_initImgBound), varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale) {}
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
- // todo read/write
-protected:
- float initFeatureScale;
- int featureScaleLevels;
- float featureScaleMul;
+ class CV_EXPORTS Params
+ {
+ public:
+ Params( float initFeatureScale=1.f, int featureScaleLevels=1, float featureScaleMul=0.1f,
+ int initXyStep=6, int initImgBound=0, bool varyXyStepWithScale=true, bool varyImgBoundWithScale=false );
+ float initFeatureScale;
+ int featureScaleLevels;
+ float featureScaleMul;
+
+ int initXyStep;
+ int initImgBound;
+
+ bool varyXyStepWithScale;
+ bool varyImgBoundWithScale;
+ };
+
+ DenseFeatureDetector( const DenseFeatureDetector::Params& params=DenseFeatureDetector::Params() );
+
+ // TODO implement read/write
- int initXyStep;
- int initImgBound;
+protected:
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
- bool varyXyStepWithScale;
- bool varyImgBoundWithScale;
+ Params params;
};
/*
@@ -1397,13 +1418,12 @@ public:
*/
GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxTotalKeypoints,
int gridRows=4, int gridCols=4 );
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
-
- // todo read/write
- virtual void read( const FileNode& ) {}
- virtual void write( FileStorage& ) const {}
+
+ // TODO implement read/write
protected:
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+
Ptr<FeatureDetector> detector;
int maxTotalKeypoints;
int gridRows;
@@ -1418,13 +1438,12 @@ class CV_EXPORTS PyramidAdaptedFeatureDetector : public FeatureDetector
{
public:
PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int levels=2 );
- virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
-
- // todo read/write
- virtual void read( const FileNode& ) {}
- virtual void write( FileStorage& ) const {}
+
+ // TODO implement read/write
protected:
+ virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
+
Ptr<FeatureDetector> detector;
int levels;
};
@@ -1450,38 +1469,39 @@ CV_EXPORTS Ptr<FeatureDetector> createFeatureDetector( const string& detectorTyp
class CV_EXPORTS DescriptorExtractor
{
public:
- virtual ~DescriptorExtractor() {}
+ virtual ~DescriptorExtractor();
+
/*
* Compute the descriptors for a set of keypoints in an image.
- * Must be implemented by the subclass.
- *
* image The image.
- * keypoints The keypoints. Keypoints for which a descriptor cannot be computed are removed.
- * descriptors The descriptors. Row i is the descriptor for keypoint i.
+ * keypoints The input keypoints. Keypoints for which a descriptor cannot be computed are removed.
+ * descriptors Copmputed descriptors. Row i is the descriptor for keypoint i.
*/
- virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const = 0;
+ void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
/*
* Compute the descriptors for a keypoints collection detected in image collection.
- *
- * imageCollection Image collection.
- * pointCollection Keypoints collection. pointCollection[i] is keypoints detected in imageCollection[i].
- * descCollection Descriptor collection. descCollection[i] is descriptors computed for pointCollection[i].
+ * images Image collection.
+ * keypoints Input keypoints collection. keypoints[i] is keypoints detected in images[i].
+ * Keypoints for which a descriptor cannot be computed are removed.
+ * descriptors Descriptor collection. descriptors[i] is descriptors computed for keypoints[i].
*/
- void compute( const vector<Mat>& imageCollection, vector<vector<KeyPoint> >& pointCollection, vector<Mat>& descCollection ) const;
+ void compute( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, vector<Mat>& descriptors ) const;
- virtual void read( const FileNode& ) {}
- virtual void write( FileStorage& ) const {}
+ virtual void read( const FileNode& );
+ virtual void write( FileStorage& ) const;
virtual int descriptorSize() const = 0;
virtual int descriptorType() const = 0;
protected:
+ virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const = 0;
+
/*
- * Remove keypoints within border_pixels of an image edge.
+ * Remove keypoints within borderPixels of an image edge.
*/
static void removeBorderKeypoints( vector<KeyPoint>& keypoints,
- Size imageSize, int borderPixels );
+ Size imageSize, int borderSize );
};
/*
@@ -1490,21 +1510,23 @@ protected:
class CV_EXPORTS SiftDescriptorExtractor : public DescriptorExtractor
{
public:
- SiftDescriptorExtractor( double magnification=SIFT::DescriptorParams::GET_DEFAULT_MAGNIFICATION(),
- bool isNormalize=true, bool recalculateAngles=true,
+ SiftDescriptorExtractor( const SIFT::DescriptorParams& descriptorParams=SIFT::DescriptorParams(),
+ const SIFT::CommonParams& commonParams=SIFT::CommonParams() );
+ SiftDescriptorExtractor( double magnification, bool isNormalize=true, bool recalculateAngles=true,
int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
int angleMode=SIFT::CommonParams::FIRST_ANGLE );
- virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
virtual void read( const FileNode &fn );
virtual void write( FileStorage &fs ) const;
- virtual int descriptorSize() const { return sift.descriptorSize(); }
- virtual int descriptorType() const { return CV_32FC1; }
+ virtual int descriptorSize() const;
+ virtual int descriptorType() const;
protected:
+ virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
+
SIFT sift;
};
@@ -1514,17 +1536,17 @@ protected:
class CV_EXPORTS SurfDescriptorExtractor : public DescriptorExtractor
{
public:
- SurfDescriptorExtractor( int nOctaves=4,
- int nOctaveLayers=2, bool extended=false );
+ SurfDescriptorExtractor( int nOctaves=4, int nOctaveLayers=2, bool extended=false );
- virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
virtual void read( const FileNode &fn );
virtual void write( FileStorage &fs ) const;
- virtual int descriptorSize() const { return surf.descriptorSize(); }
- virtual int descriptorType() const { return CV_32FC1; }
+ virtual int descriptorSize() const;
+ virtual int descriptorType() const;
protected:
+ virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
+
SURF surf;
};
@@ -1537,7 +1559,6 @@ class CV_EXPORTS CalonderDescriptorExtractor : public DescriptorExtractor
public:
CalonderDescriptorExtractor( const string& classifierFile );
- virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
virtual void read( const FileNode &fn );
virtual void write( FileStorage &fs ) const;
@@ -1545,6 +1566,8 @@ public:
virtual int descriptorType() const { return DataType<T>::type; }
protected:
+ virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
+
RTreeClassifier classifier_;
static const int BORDER_SIZE = 16;
};
@@ -1556,23 +1579,24 @@ CalonderDescriptorExtractor<T>::CalonderDescriptorExtractor(const std::string& c
}
template<typename T>
-void CalonderDescriptorExtractor<T>::compute( const cv::Mat& image,
+void CalonderDescriptorExtractor<T>::computeImpl( const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints,
cv::Mat& descriptors) const
{
- // Cannot compute descriptors for keypoints on the image border.
- removeBorderKeypoints(keypoints, image.size(), BORDER_SIZE);
+ // Cannot compute descriptors for keypoints on the image border.
+ removeBorderKeypoints(keypoints, image.size(), BORDER_SIZE);
- /// @todo Check 16-byte aligned
- descriptors.create(keypoints.size(), classifier_.classes(), cv::DataType<T>::type);
+ /// @todo Check 16-byte aligned
+ descriptors.create(keypoints.size(), classifier_.classes(), cv::DataType<T>::type);
- int patchSize = RandomizedTree::PATCH_SIZE;
- int offset = patchSize / 2;
- for (size_t i = 0; i < keypoints.size(); ++i) {
- cv::Point2f pt = keypoints[i].pt;
- IplImage ipl = image( Rect((int)(pt.x - offset), (int)(pt.y - offset), patchSize, patchSize) );
- classifier_.getSignature( &ipl, descriptors.ptr<T>(i));
- }
+ int patchSize = RandomizedTree::PATCH_SIZE;
+ int offset = patchSize / 2;
+ for (size_t i = 0; i < keypoints.size(); ++i)
+ {
+ cv::Point2f pt = keypoints[i].pt;
+ IplImage ipl = image( Rect((int)(pt.x - offset), (int)(pt.y - offset), patchSize, patchSize) );
+ classifier_.getSignature( &ipl, descriptors.ptr<T>(i));
+ }
}
template<typename T>
@@ -1595,18 +1619,18 @@ void CalonderDescriptorExtractor<T>::write( FileStorage& ) const
class CV_EXPORTS OpponentColorDescriptorExtractor : public DescriptorExtractor
{
public:
- OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor );
-
- virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
+ OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& descriptorExtractor );
virtual void read( const FileNode& );
virtual void write( FileStorage& ) const;
- virtual int descriptorSize() const { return 3*dextractor->descriptorSize(); }
- virtual int descriptorType() const { return dextractor->descriptorType(); }
+ virtual int descriptorSize() const;
+ virtual int descriptorType() const;
protected:
- Ptr<DescriptorExtractor> dextractor;
+ virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
+
+ Ptr<DescriptorExtractor> descriptorExtractor;
};
/*
@@ -1615,98 +1639,24 @@ protected:
class CV_EXPORTS BriefDescriptorExtractor : public DescriptorExtractor
{
public:
- BriefDescriptorExtractor(int bytes = 32);
+ static const int PATCH_SIZE = 48;
+ static const int KERNEL_SIZE = 9;
- virtual void compute(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors) const;
+ BriefDescriptorExtractor(int bytes = 32);
- virtual int descriptorSize() const
- {
- return bytes_;
- }
- virtual int descriptorType() const
- {
- return CV_8UC1;
- }
+ virtual int descriptorSize() const;
+ virtual int descriptorType() const;
- /// @todo read and write for brief
- //virtual void read(const FileNode& fn);
- //virtual void write(FileStorage& fs) const;
+ /// @todo read and write for brief
protected:
- static const int PATCH_SIZE = 48;
- static const int KERNEL_SIZE = 9;
-
- int bytes_;
- typedef void(*PixelTestFn)(const Mat&, const std::vector<KeyPoint>&, Mat&);
- PixelTestFn test_fn_;
-
- static int smoothedSum(const Mat& sum, const KeyPoint& pt, int y, int x);
- static void pixelTests16(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors);
- static void pixelTests32(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors);
- static void pixelTests64(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors);
-
-};
-
-inline int BriefDescriptorExtractor::smoothedSum(const Mat& sum, const KeyPoint& pt, int y, int x)
-{
- static const int HALF_KERNEL = KERNEL_SIZE / 2;
-
- int img_y = (int)(pt.pt.y + 0.5) + y;
- int img_x = (int)(pt.pt.x + 0.5) + x;
- return sum.at<int> (img_y + HALF_KERNEL + 1, img_x + HALF_KERNEL + 1) - sum.at<int> (img_y + HALF_KERNEL + 1,
- img_x - HALF_KERNEL)
- - sum.at<int> (img_y - HALF_KERNEL, img_x + HALF_KERNEL + 1) + sum.at<int> (img_y - HALF_KERNEL, img_x
- - HALF_KERNEL);
-}
-
-struct CV_EXPORTS HammingLUT
-{
- typedef unsigned char ValueType;
- typedef int ResultType;
-
- ResultType operator()(const unsigned char* a, const unsigned char* b, int size) const
- {
- ResultType result = 0;
- for (int i = 0; i < size; i++)
- {
- result += byteBitsLookUp(a[i] ^ b[i]);
- }
- return result;
- }
- /** \brief given a byte, count the bits using a compile time generated look up table
- * \param b the byte to count bits. The look up table has an entry for all
- * values of b, where that entry is the number of bits.
- * \return the number of bits in byte b
- */
- static unsigned char byteBitsLookUp(unsigned char b);
-};
+ virtual void computeImpl(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors) const;
-#if __GNUC__
-/// Hamming distance functor
-/// @todo Variable-length version, maybe default size=0 and specialize
-/// @todo Need to choose C/SSE4 at runtime, but amortize this at matcher level for efficiency...
-//template<int size>
-struct Hamming
-{
- typedef unsigned char ValueType;
- typedef int ResultType;
+ typedef void(*PixelTestFn)(const Mat&, const std::vector<KeyPoint>&, Mat&);
- ResultType operator()(const unsigned char* a, const unsigned char* b, int size) const
- {
- /// @todo Non-GCC-specific version
- ResultType result = 0;
- for (int i = 0; i < size; i += sizeof(unsigned long))
- {
- unsigned long a2 = *reinterpret_cast<const unsigned long*> (a + i);
- unsigned long b2 = *reinterpret_cast<const unsigned long*> (b + i);
- result += __builtin_popcountl(a2 ^ b2);
- }
- return result;
- }
+ int bytes_;
+ PixelTestFn test_fn_;
};
-#else
-typedef HammingLUT Hamming;
-#endif
CV_EXPORTS Ptr<DescriptorExtractor> createDescriptorExtractor( const string& descriptorExtractorType );
@@ -1766,6 +1716,57 @@ struct CV_EXPORTS L1
}
};
+/*
+ * Hamming distance (city block distance) functor
+ */
+struct CV_EXPORTS HammingLUT
+{
+ typedef unsigned char ValueType;
+ typedef int ResultType;
+
+ ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const
+ {
+ ResultType result = 0;
+ for (int i = 0; i < size; i++)
+ {
+ result += byteBitsLookUp(a[i] ^ b[i]);
+ }
+ return result;
+ }
+ /** \brief given a byte, count the bits using a compile time generated look up table
+ * \param b the byte to count bits. The look up table has an entry for all
+ * values of b, where that entry is the number of bits.
+ * \return the number of bits in byte b
+ */
+ static unsigned char byteBitsLookUp(unsigned char b);
+};
+
+#if __GNUC__
+/// Hamming distance functor
+/// @todo Variable-length version, maybe default size=0 and specialize
+/// @todo Need to choose C/SSE4 at runtime, but amortize this at matcher level for efficiency...
+
+struct Hamming
+{
+ typedef unsigned char ValueType;
+ typedef int ResultType;
+
+ ResultType operator()(const unsigned char* a, const unsigned char* b, int size) const
+ {
+ /// @todo Non-GCC-specific version
+ ResultType result = 0;
+ for (int i = 0; i < size; i += sizeof(unsigned long))
+ {
+ unsigned long a2 = *reinterpret_cast<const unsigned long*> (a + i);
+ unsigned long b2 = *reinterpret_cast<const unsigned long*> (b + i);
+ result += __builtin_popcountl(a2 ^ b2);
+ }
+ return result;
+ }
+};
+#else
+typedef HammingLUT Hamming;
+#endif
/****************************************************************************************\
* DMatch *
@@ -1783,7 +1784,7 @@ struct CV_EXPORTS DMatch
int queryIdx; // query descriptor index
int trainIdx; // train descriptor index
- int imgIdx; // train image index
+ int imgIdx; // train image index
float distance;
@@ -1803,111 +1804,126 @@ struct CV_EXPORTS DMatch
class CV_EXPORTS DescriptorMatcher
{
public:
- virtual ~DescriptorMatcher() {}
- /*
+ virtual ~DescriptorMatcher();
+
+ /*
* Add descriptors to train descriptor collection.
- * descCollection Descriptors to add. Each descCollection[i] is from one image.
+ * descriptors Descriptors to add. Each descriptors[i] is a descriptors set from one image.
*/
- virtual void add( const vector<Mat>& descCollection );
+ virtual void add( const vector<Mat>& descriptors );
/*
- * Get descriptor collection.
+ * Get train descriptors collection.
*/
- const vector<Mat>& getTrainDescCollection() const { return trainDescCollection; }
+ const vector<Mat>& getTrainDescriptors() const;
/*
- * Clear inner data (train image collection).
+ * Clear train descriptors collection.
*/
virtual void clear();
- virtual bool supportMask() = 0;
+ /*
+ * Return true if there are not train descriptors in collection.
+ */
+ bool empty() const;
+ /*
+ * Return true if the matcher supports mask in match methods.
+ */
+ virtual bool isMaskSupported() const = 0;
/*
- * Train matcher (e.g. train flann index)
+ * Train matcher (e.g. train flann index).
+ * In all methods to match the method train() is run every time before matching.
+ * Some descriptor matchers (e.g. BruteForceMatcher) have empty implementation
+ * of this method, other matchers realy train their inner structures
+ * (e.g. FlannBasedMatcher trains flann::Index). So nonempty implementation
+ * of train() should check the class object state and do traing/retraining
+ * only if the state requires that (e.g. FlannBasedMatcher trains flann::Index
+ * if it has not trained yet or if new descriptors have been added to the train
+ * collection).
*/
- virtual void train() = 0;
+ virtual void train();
/*
* Group of methods to match descriptors from image pair.
+ * Method train() is run in this methods.
*/
// Find one best match for each query descriptor (if mask is empty).
- void match( const Mat& queryDescs, const Mat& trainDescs, vector<DMatch>& matches,
- const Mat& mask=Mat() ) const;
- // Find knn best matches for each query descriptor (in increasing order of distances).
- // compactResult is used when mask is not empty. If compactResult is false matches vector will have the same size as queryDescs rows.
- // If compactResult is true matches vector will not contain matches for fully masked out query descriptors.
- void knnMatch( const Mat& queryDescs, const Mat& trainDescs, vector<vector<DMatch> >& matches, int knn,
+ void match( const Mat& queryDescriptors, const Mat& trainDescriptors,
+ vector<DMatch>& matches, const Mat& mask=Mat() ) const;
+ // Find k best matches for each query descriptor (in increasing order of distances).
+ // compactResult is used when mask is not empty. If compactResult is false matches
+ // vector will have the same size as queryDescriptors rows. If compactResult is true
+ // matches vector will not contain matches for fully masked out query descriptors.
+ void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
+ vector<vector<DMatch> >& matches, int k,
const Mat& mask=Mat(), bool compactResult=false ) const;
- // Find best matches for each query descriptor which have distance less than maxDistance (in increasing order of distances).
- void radiusMatch( const Mat& queryDescs, const Mat& trainDescs, vector<vector<DMatch> >& matches, float maxDistance,
+ // Find best matches for each query descriptor which have distance less than
+ // maxDistance (in increasing order of distances).
+ void radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
+ vector<vector<DMatch> >& matches, float maxDistance,
const Mat& mask=Mat(), bool compactResult=false ) const;
/*
* Group of methods to match descriptors from one image to image set.
* See description of similar methods for matching image pair above.
*/
- void match( const Mat& queryDescs, vector<DMatch>& matches,
+ void match( const Mat& queryDescriptors, vector<DMatch>& matches,
const vector<Mat>& masks=vector<Mat>() );
- void knnMatch( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
+ void knnMatch( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int k,
+ const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
+ void radiusMatch( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
- void radiusMatch( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
- const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
// Reads matcher object from a file node
- virtual void read( const FileNode& ) {}
-
+ virtual void read( const FileNode& );
// Writes matcher object to a file storage
- virtual void write( FileStorage& ) const {}
+ virtual void write( FileStorage& ) const;
+
+ // Clone the matcher. If emptyTrainData is false the method create deep copy of the object, i.e. copies
+ // both parameters and train data. If emptyTrainData is true the method create object copy with current parameters
+ // but with empty train data.
+ virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
protected:
/*
* Class to work with descriptors from several images as with one merged matrix.
- * It is used e.g. in FlannBasedMatcher
+ * It is used e.g. in FlannBasedMatcher.
*/
class CV_EXPORTS DescriptorCollection
{
public:
- DescriptorCollection() {}
- virtual ~DescriptorCollection() {}
+ DescriptorCollection();
+ DescriptorCollection( const DescriptorCollection& collection );
+ virtual ~DescriptorCollection();
- // descCollection will be merged to dmatrix here
- void set( const vector<Mat>& descCollection );
+ // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here.
+ void set( const vector<Mat>& descriptors );
virtual void clear();
- const Mat& getDescriptors() const { return dmatrix; }
+ const Mat& getDescriptors() const;
const Mat getDescriptor( int imgIdx, int localDescIdx ) const;
const Mat getDescriptor( int globalDescIdx ) const;
void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const;
- int size() const { return dmatrix.rows; }
+ int size() const;
protected:
- Mat dmatrix;
+ Mat mergedDescriptors;
vector<int> startIdxs;
};
- // create matcher clone with current parameters but with empty data
- virtual Ptr<DescriptorMatcher> cloneWithoutData() const = 0;
-
- virtual void knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
- const vector<Mat>& masks, bool compactResult ) = 0;
- virtual void radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
- const vector<Mat>& masks, bool compactResult ) = 0;
-
+ // In fact the matching is implemented only by the following two methods. These methods suppose
+ // that the class object has been trained already. Public match methods call these methods
+ // after calling train().
+ virtual void knnMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int k,
+ const vector<Mat>& masks=vector<Mat>(), bool compactResult=false ) = 0;
+ virtual void radiusMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
+ const vector<Mat>& masks=vector<Mat>(), bool compactResult=false ) = 0;
- static bool possibleMatch( const Mat& mask, int index_1, int index_2 )
- {
- return mask.empty() || mask.at<uchar>(index_1, index_2);
- }
-
- static bool maskedOut( const vector<Mat>& masks, int queryDescIdx )
- {
- size_t outCount = 0;
- for( size_t i = 0; i < masks.size(); i++ )
- {
- if( !masks[i].empty() && (countNonZero(masks[i].row(queryDescIdx)) == 0) )
- outCount++;
- }
+ static bool isPossibleMatch( const Mat& mask, int queryIdx, int trainIdx );
+ static bool isMaskedOut( const vector<Mat>& masks, int queryIdx );
- return !masks.empty() && outCount == masks.size() ;
- }
+ static Mat clone_op( Mat m ) { return m.clone(); }
+ void checkMasks( const vector<Mat>& masks, int queryDescriptorsCount ) const;
+ // Collection of descriptors from train images.
vector<Mat> trainDescCollection;
};
@@ -1927,64 +1943,78 @@ public:
BruteForceMatcher( Distance d = Distance() ) : distance(d) {}
virtual ~BruteForceMatcher() {}
- virtual void train() {}
- virtual bool supportMask() { return true; }
+ virtual bool isMaskSupported() const { return true; }
+
+ virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
- virtual Ptr<DescriptorMatcher> cloneWithoutData() const { return new BruteForceMatcher(distance); }
+ virtual void knnMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int k,
+ const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
+ virtual void radiusMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
+ const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
- virtual void knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
- const vector<Mat>& masks, bool compactResult );
- virtual void radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
- const vector<Mat>& masks, bool compactResult );
Distance distance;
private:
/*
- * Next two methods are used to implement specialization
+ * Next two methods are used to implement specialization.
*/
- static void bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
- const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
- const vector<Mat>& masks, bool compactResult );
- static void bfRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
- const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
- const vector<Mat>& masks, bool compactResult );
+ static void commonKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
+ const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int k,
+ const vector<Mat>& masks, bool compactResult );
+ static void commonRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
+ const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
+ const vector<Mat>& masks, bool compactResult );
};
template<class Distance>
-void BruteForceMatcher<Distance>::knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
+Ptr<DescriptorMatcher> BruteForceMatcher<Distance>::clone( bool emptyTrainData ) const
+{
+ BruteForceMatcher* matcher = new BruteForceMatcher(distance);
+ if( !emptyTrainData )
+ {
+ transform( trainDescCollection.begin(), trainDescCollection.end(),
+ matcher->trainDescCollection.begin(), clone_op );
+ }
+ return matcher;
+}
+
+template<class Distance>
+void BruteForceMatcher<Distance>::knnMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int k,
const vector<Mat>& masks, bool compactResult )
{
- bfKnnMatchImpl( *this, queryDescs, matches, knn, masks, compactResult );
+ commonKnnMatchImpl( *this, queryDescriptors, matches, k, masks, compactResult );
}
template<class Distance>
-void BruteForceMatcher<Distance>::radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
- const vector<Mat>& masks, bool compactResult )
+void BruteForceMatcher<Distance>::radiusMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches,
+ float maxDistance, const vector<Mat>& masks, bool compactResult )
{
- bfRadiusMatchImpl( *this, queryDescs, matches, maxDistance, masks, compactResult );
+ commonRadiusMatchImpl( *this, queryDescriptors, matches, maxDistance, masks, compactResult );
}
template<class Distance>
-inline void BruteForceMatcher<Distance>::bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
- const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
+inline void BruteForceMatcher<Distance>::commonKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
+ const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult )
{
typedef typename Distance::ValueType ValueType;
typedef typename Distance::ResultType DistanceType;
- CV_Assert( DataType<ValueType>::type == queryDescs.type() || queryDescs.empty() );
- CV_Assert( masks.empty() || masks.size() == matcher.trainDescCollection.size() );
- int dimension = queryDescs.cols;
- matches.reserve(queryDescs.rows);
+ CV_DbgAssert( !queryDescriptors.empty() );
+ CV_Assert( DataType<ValueType>::type == queryDescriptors.type() );
+
+ int dimension = queryDescriptors.cols;
+ matches.reserve(queryDescriptors.rows);
size_t imgCount = matcher.trainDescCollection.size();
vector<Mat> allDists( imgCount ); // distances between one query descriptor and all train descriptors
for( size_t i = 0; i < imgCount; i++ )
- allDists[i] = Mat( 1, matcher.trainDescCollection[i].rows, DataType<DistanceType>::type );
+ if( matcher.trainDescCollection[i].rows )
+ allDists[i] = Mat( 1, matcher.trainDescCollection[i].rows, DataType<DistanceType>::type );
- for( int qIdx = 0; qIdx < queryDescs.rows; qIdx++ )
+ for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
{
- if( matcher.maskedOut( masks, qIdx ) )
+ if( matcher.isMaskedOut( masks, qIdx ) )
{
if( !compactResult ) // push empty vector
matches.push_back( vector<DMatch>() );
@@ -1994,17 +2024,15 @@ inline void BruteForceMatcher<Distance>::bfKnnMatchImpl( BruteForceMatcher<Dista
// 1. compute distances between i-th query descriptor and all train descriptors
for( size_t iIdx = 0; iIdx < imgCount; iIdx++ )
{
- CV_Assert( masks.empty() || masks[iIdx].empty() ||
- ( masks[iIdx].rows == queryDescs.rows && masks[iIdx].cols == matcher.trainDescCollection[iIdx].rows &&
- masks[iIdx].type() == CV_8UC1 ) );
CV_Assert( DataType<ValueType>::type == matcher.trainDescCollection[iIdx].type() || matcher.trainDescCollection[iIdx].empty() );
- CV_Assert( queryDescs.cols == matcher.trainDescCollection[iIdx].cols );
+ CV_Assert( queryDescriptors.cols == matcher.trainDescCollection[iIdx].cols ||
+ matcher.trainDescCollection[iIdx].empty() );
- const ValueType* d1 = (const ValueType*)(queryDescs.data + queryDescs.step*qIdx);
+ const ValueType* d1 = (const ValueType*)(queryDescriptors.data + queryDescriptors.step*qIdx);
allDists[iIdx].setTo( Scalar::all(std::numeric_limits<DistanceType>::max()) );
for( int tIdx = 0; tIdx < matcher.trainDescCollection[iIdx].rows; tIdx++ )
{
- if( masks.empty() || matcher.possibleMatch(masks[iIdx], qIdx, tIdx) )
+ if( masks.empty() || matcher.isPossibleMatch(masks[iIdx], qIdx, tIdx) )
{
const ValueType* d2 = (const ValueType*)(matcher.trainDescCollection[iIdx].data +
matcher.trainDescCollection[iIdx].step*tIdx);
@@ -2013,20 +2041,23 @@ inline void BruteForceMatcher<Distance>::bfKnnMatchImpl( BruteForceMatcher<Dista
}
}
- // 2. choose knn nearest matches for query[i]
+ // 2. choose k nearest matches for query[i]
matches.push_back( vector<DMatch>() );
vector<vector<DMatch> >::reverse_iterator curMatches = matches.rbegin();
for( int k = 0; k < knn; k++ )
{
DMatch bestMatch;
- bestMatch.distance = std::numeric_limits<DistanceType>::max();
+ bestMatch.distance = std::numeric_limits<float>::max();
for( size_t iIdx = 0; iIdx < imgCount; iIdx++ )
{
- double minVal;
- Point minLoc;
- minMaxLoc( allDists[iIdx], &minVal, 0, &minLoc, 0 );
- if( minVal < bestMatch.distance )
- bestMatch = DMatch( qIdx, minLoc.x, iIdx, minVal );
+ if( !allDists[iIdx].empty() )
+ {
+ double minVal;
+ Point minLoc;
+ minMaxLoc( allDists[iIdx], &minVal, 0, &minLoc, 0 );
+ if( minVal < bestMatch.distance )
+ bestMatch = DMatch( qIdx, minLoc.x, iIdx, (float)minVal );
+ }
}
if( bestMatch.trainIdx == -1 )
break;
@@ -2041,22 +2072,22 @@ inline void BruteForceMatcher<Distance>::bfKnnMatchImpl( BruteForceMatcher<Dista
}
template<class Distance>
-inline void BruteForceMatcher<Distance>::bfRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
- const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
+inline void BruteForceMatcher<Distance>::commonRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
+ const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult )
{
typedef typename Distance::ValueType ValueType;
typedef typename Distance::ResultType DistanceType;
- CV_Assert( DataType<ValueType>::type == queryDescs.type() || queryDescs.empty() );
- CV_Assert( masks.empty() || masks.size() == matcher.trainDescCollection.size() );
-
- int dimension = queryDescs.cols;
- matches.reserve(queryDescs.rows);
+ CV_DbgAssert( !queryDescriptors.empty() );
+ CV_Assert( DataType<ValueType>::type == queryDescriptors.type() );
+
+ int dimension = queryDescriptors.cols;
+ matches.reserve(queryDescriptors.rows);
size_t imgCount = matcher.trainDescCollection.size();
- for( int qIdx = 0; qIdx < queryDescs.rows; qIdx++ )
+ for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
{
- if( matcher.maskedOut( masks, qIdx ) )
+ if( matcher.isMaskedOut( masks, qIdx ) )
{
if( !compactResult ) // push empty vector
matches.push_back( vector<DMatch>() );
@@ -2067,23 +2098,21 @@ inline void BruteForceMatcher<Distance>::bfRadiusMatchImpl( BruteForceMatcher<Di
vector<vector<DMatch> >::reverse_iterator curMatches = matches.rbegin();
for( size_t iIdx = 0; iIdx < imgCount; iIdx++ )
{
- CV_Assert( masks.empty() || masks[iIdx].empty() ||
- ( masks[iIdx].rows == queryDescs.rows && masks[iIdx].cols == matcher.trainDescCollection[iIdx].rows &&
- masks[iIdx].type() == CV_8UC1 ) );
CV_Assert( DataType<ValueType>::type == matcher.trainDescCollection[iIdx].type() ||
matcher.trainDescCollection[iIdx].empty() );
- CV_Assert( queryDescs.cols == matcher.trainDescCollection[iIdx].cols );
+ CV_Assert( queryDescriptors.cols == matcher.trainDescCollection[iIdx].cols ||
+ matcher.trainDescCollection[iIdx].empty() );
- const ValueType* d1 = (const ValueType*)(queryDescs.data + queryDescs.step*qIdx);
+ const ValueType* d1 = (const ValueType*)(queryDescriptors.data + queryDescriptors.step*qIdx);
for( int tIdx = 0; tIdx < matcher.trainDescCollection[iIdx].rows; tIdx++ )
{
- if( masks.empty() || matcher.possibleMatch(masks[iIdx], qIdx, tIdx) )
+ if( masks.empty() || matcher.isPossibleMatch(masks[iIdx], qIdx, tIdx) )
{
const ValueType* d2 = (const ValueType*)(matcher.trainDescCollection[iIdx].data +
matcher.trainDescCollection[iIdx].step*tIdx);
DistanceType d = matcher.distance(d1, d2, dimension);
if( d < maxDistance )
- curMatches->push_back( DMatch( qIdx, tIdx, iIdx, d ) );
+ curMatches->push_back( DMatch( qIdx, tIdx, iIdx, (float)d ) );
}
}
}
@@ -2096,11 +2125,11 @@ inline void BruteForceMatcher<Distance>::bfRadiusMatchImpl( BruteForceMatcher<Di
* BruteForceMatcher L2 specialization
*/
template<>
-void BruteForceMatcher<L2<float> >::knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
+void BruteForceMatcher<L2<float> >::knnMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int k,
const vector<Mat>& masks, bool compactResult );
template<>
-void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
- const vector<Mat>& masks, bool compactResult );
+void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches,
+ float maxDistance, const vector<Mat>& masks, bool compactResult );
/*
* Flann based matcher
@@ -2108,26 +2137,27 @@ void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescs, vect
class CV_EXPORTS FlannBasedMatcher : public DescriptorMatcher
{
public:
- FlannBasedMatcher( const Ptr<flann::IndexParams>& _indexParams=new flann::KDTreeIndexParams(),
- const Ptr<flann::SearchParams>& _searchParams=new flann::SearchParams() );
+ FlannBasedMatcher( const Ptr<flann::IndexParams>& indexParams=new flann::KDTreeIndexParams(),
+ const Ptr<flann::SearchParams>& searchParams=new flann::SearchParams() );
- virtual void add( const vector<Mat>& descCollection );
+ virtual void add( const vector<Mat>& descriptors );
virtual void clear();
virtual void train();
- virtual bool supportMask() { return false; }
-protected:
- virtual Ptr<DescriptorMatcher> cloneWithoutData() const { return new FlannBasedMatcher(indexParams, searchParams); }
-
- // masks is ignored (unsupported)
- virtual void knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
- const vector<Mat>& masks, bool compactResult );
- virtual void radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
- const vector<Mat>& masks, bool compactResult );
+ virtual bool isMaskSupported() const;
+
+ virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
- static void convertToDMatches( const DescriptorCollection& collection, const Mat& indices, const Mat& dists,
+protected:
+ static void convertToDMatches( const DescriptorCollection& descriptors,
+ const Mat& indices, const Mat& distances,
vector<vector<DMatch> >& matches );
+ virtual void knnMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int k,
+ const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
+ virtual void radiusMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
+ const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
+
Ptr<flann::IndexParams> indexParams;
Ptr<flann::SearchParams> searchParams;
Ptr<flann::Index> flannIndex;
@@ -2151,81 +2181,106 @@ typedef GenericDescriptorMatcher GenericDescriptorMatch;
class CV_EXPORTS GenericDescriptorMatcher
{
public:
- GenericDescriptorMatcher() {}
- virtual ~GenericDescriptorMatcher() {}
+ GenericDescriptorMatcher();
+ virtual ~GenericDescriptorMatcher();
/*
- * Set train collection: images and keypoints from them.
- * imgCollection Image collection.
- * pointCollection Keypoint collection detected on imgCollection.
+ * Add train collection: images and keypoints from them.
+ * images A set of train images.
+ * ketpoints Keypoint collection that have been detected on train images.
+ *
+ * Keypoints for which a descriptor cannot be computed are removed. Such keypoints
+ * must be filtered in this method befor adding keypoints to train collection "trainPointCollection".
+ * If inheritor class need perform such prefiltering the method add() must be overloaded.
+ * In the other class methods programmer has access to the train keypoints by a constant link.
*/
- virtual void add( const vector<Mat>& imgCollection,
- vector<vector<KeyPoint> >& pointCollection );
+ virtual void add( const vector<Mat>& images,
+ vector<vector<KeyPoint> >& keypoints );
- const vector<Mat>& getTrainImgCollection() const { return trainPointCollection.getImages(); }
- const vector<vector<KeyPoint> >& getTrainPointCollection() const { return trainPointCollection.getKeypoints(); }
+ const vector<Mat>& getTrainImages() const;
+ const vector<vector<KeyPoint> >& getTrainKeypoints() const;
- // Clears keypoints storing in collection
+ /*
+ * Clear images and keypoints storing in train collection.
+ */
virtual void clear();
-
- virtual void train() = 0;
-
- virtual bool supportMask() = 0;
+ /*
+ * Returns true if matcher supports mask to match descriptors.
+ */
+ virtual bool isMaskSupported() = 0;
+ /*
+ * Train some inner structures (e.g. flann index or decision trees).
+ * train() methods is run every time in matching methods. So the method implementation
+ * should has a check whether these inner structures need be trained/retrained or not.
+ */
+ virtual void train();
/*
* Classifies query keypoints.
* queryImage The query image
- * queryPoints Keypoints from the query image
+ * queryKeypoints Keypoints from the query image
* trainImage The train image
- * trainPoints Keypoints from the train image
+ * trainKeypoints Keypoints from the train image
*/
// Classify keypoints from query image under one train image.
- virtual void classify( const Mat& queryImage, vector<KeyPoint>& queryPoints,
- const Mat& trainImage, vector<KeyPoint>& trainPoints ) const;
+ virtual void classify( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ const Mat& trainImage, vector<KeyPoint>& trainKeypoints ) const;
// Classify keypoints from query image under train image collection.
- virtual void classify( const Mat& queryImage, vector<KeyPoint>& queryPoints );
+ virtual void classify( const Mat& queryImage, vector<KeyPoint>& queryKeypoints );
/*
* Group of methods to match keypoints from image pair.
+ * Keypoints for which a descriptor cannot be computed are removed.
+ * train() method is called here.
*/
// Find one best match for each query descriptor (if mask is empty).
- void match( const Mat& queryImg, vector<KeyPoint>& queryPoints,
- const Mat& trainImg, vector<KeyPoint>& trainPoints,
+ void match( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ const Mat& trainImage, vector<KeyPoint>& trainKeypoints,
vector<DMatch>& matches, const Mat& mask=Mat() ) const;
- // Find knn best matches for each query keypoint (in increasing order of distances).
- // compactResult is used when mask is not empty. If compactResult is false matches vector will have the same size as queryDescs rows.
+ // Find k best matches for each query keypoint (in increasing order of distances).
+ // compactResult is used when mask is not empty. If compactResult is false matches
+ // vector will have the same size as queryDescriptors rows.
// If compactResult is true matches vector will not contain matches for fully masked out query descriptors.
- void knnMatch( const Mat& queryImg, vector<KeyPoint>& queryPoints,
- const Mat& trainImg, vector<KeyPoint>& trainPoints,
- vector<vector<DMatch> >& matches, int knn, const Mat& mask=Mat(), bool compactResult=false ) const;
+ void knnMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ const Mat& trainImage, vector<KeyPoint>& trainKeypoints,
+ vector<vector<DMatch> >& matches, int k,
+ const Mat& mask=Mat(), bool compactResult=false ) const;
// Find best matches for each query descriptor which have distance less than maxDistance (in increasing order of distances).
- void radiusMatch( const Mat& queryImg, vector<KeyPoint>& queryPoints,
- const Mat& trainImg, vector<KeyPoint>& trainPoints,
- vector<vector<DMatch> >& matches, float maxDistance, const Mat& mask=Mat(), bool compactResult=false ) const;
+ void radiusMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ const Mat& trainImage, vector<KeyPoint>& trainKeypoints,
+ vector<vector<DMatch> >& matches, float maxDistance,
+ const Mat& mask=Mat(), bool compactResult=false ) const;
/*
* Group of methods to match keypoints from one image to image set.
* See description of similar methods for matching image pair above.
*/
- void match( const Mat& queryImg, vector<KeyPoint>& queryPoints,
+ void match( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
vector<DMatch>& matches, const vector<Mat>& masks=vector<Mat>() );
- void knnMatch( const Mat& queryImg, vector<KeyPoint>& queryPoints,
- vector<vector<DMatch> >& matches, int knn, const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
- void radiusMatch( const Mat& queryImg, vector<KeyPoint>& queryPoints,
- vector<vector<DMatch> >& matches, float maxDistance, const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
+ void knnMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ vector<vector<DMatch> >& matches, int k,
+ const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
+ void radiusMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ vector<vector<DMatch> >& matches, float maxDistance,
+ const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
// Reads matcher object from a file node
- virtual void read( const FileNode& ) {}
-
+ virtual void read( const FileNode& );
// Writes matcher object to a file storage
- virtual void write( FileStorage& ) const {}
+ virtual void write( FileStorage& ) const;
-protected:
- virtual Ptr<GenericDescriptorMatcher> createEmptyMatcherCopy() const = 0;
+ // Clone the matcher. If emptyTrainData is false the method create deep copy of the object, i.e. copies
+ // both parameters and train data. If emptyTrainData is true the method create object copy with current parameters
+ // but with empty train data.
+ virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
- virtual void knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
- vector<vector<DMatch> >& matches, int knn,
+protected:
+ // In fact the matching is implemented only by the following two methods. These methods suppose
+ // that the class object has been trained already. Public match methods call these methods
+ // after calling train().
+ virtual void knnMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ vector<vector<DMatch> >& matches, int k,
const vector<Mat>& masks, bool compactResult ) = 0;
- virtual void radiusMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
+ virtual void radiusMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult ) = 0;
/*
@@ -2234,32 +2289,34 @@ protected:
class CV_EXPORTS KeyPointCollection
{
public:
- KeyPointCollection() : size(0) {}
- void add( const vector<Mat>& _images, const vector<vector<KeyPoint> >& _points );
+ KeyPointCollection();
+ KeyPointCollection( const KeyPointCollection& collection );
+ void add( const vector<Mat>& images, const vector<vector<KeyPoint> >& keypoints );
void clear();
// Returns the total number of keypoints in the collection
- size_t pointCount() const { return size; }
- size_t imageCount() const { return images.size(); }
+ size_t keypointCount() const;
+ size_t imageCount() const;
- const vector<vector<KeyPoint> >& getKeypoints() const { return points; }
- const vector<KeyPoint>& getKeypoints( int imgIdx ) const { CV_Assert( imgIdx < (int)imageCount() ); return points[imgIdx]; }
+ const vector<vector<KeyPoint> >& getKeypoints() const;
+ const vector<KeyPoint>& getKeypoints( int imgIdx ) const;
const KeyPoint& getKeyPoint( int imgIdx, int localPointIdx ) const;
const KeyPoint& getKeyPoint( int globalPointIdx ) const;
void getLocalIdx( int globalPointIdx, int& imgIdx, int& localPointIdx ) const;
- const vector<Mat>& getImages() const { return images; }
- const Mat& getImage( int imgIdx ) const { CV_Assert( imgIdx < (int)imageCount() ); return images[imgIdx]; }
+ const vector<Mat>& getImages() const;
+ const Mat& getImage( int imgIdx ) const;
protected:
- int size;
+ int pointCount;
vector<Mat> images;
- vector<vector<KeyPoint> > points;
-
- // global indices of the first points in each image,
- // startIndices.size() = points.size()
+ vector<vector<KeyPoint> > keypoints;
+ // global indices of the first points in each image, startIndices.size() = keypoints.size()
vector<int> startIndices;
+
+ private:
+ static Mat clone_op( Mat m ) { return m.clone(); }
};
KeyPointCollection trainPointCollection;
@@ -2274,7 +2331,7 @@ typedef OneWayDescriptorMatcher OneWayDescriptorMatch;
class CV_EXPORTS OneWayDescriptorMatcher : public GenericDescriptorMatcher
{
public:
- class Params
+ class CV_EXPORTS Params
{
public:
static const int POSE_COUNT = 500;
@@ -2284,16 +2341,12 @@ public:
static float GET_MAX_SCALE() { return 1.5f; }
static float GET_STEP_SCALE() { return 1.2f; }
- Params( int _poseCount = POSE_COUNT,
- Size _patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
- string _pcaFilename = string(),
- string _trainPath = string(),
- string _trainImagesList = string(),
- float _minScale = GET_MIN_SCALE(), float _maxScale = GET_MAX_SCALE(),
- float _stepScale = GET_STEP_SCALE() ) :
- poseCount(_poseCount), patchSize(_patchSize), pcaFilename(_pcaFilename),
- trainPath(_trainPath), trainImagesList(_trainImagesList),
- minScale(_minScale), maxScale(_maxScale), stepScale(_stepScale) {}
+ Params( int poseCount = POSE_COUNT,
+ Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
+ string pcaFilename = string(),
+ string trainPath = string(), string trainImagesList = string(),
+ float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
+ float stepScale = GET_STEP_SCALE() );
int poseCount;
Size patchSize;
@@ -2304,38 +2357,34 @@ public:
float minScale, maxScale, stepScale;
};
- // Equivalent to calling PointMatchOneWay() followed by Initialize(_params)
- OneWayDescriptorMatcher( const Params& _params=Params() );
+ OneWayDescriptorMatcher( const Params& params=Params() );
virtual ~OneWayDescriptorMatcher();
- void initialize( const Params& _params, const Ptr<OneWayDescriptorBase>& _base=Ptr<OneWayDescriptorBase>() );
+ void initialize( const Params& params, const Ptr<OneWayDescriptorBase>& base=Ptr<OneWayDescriptorBase>() );
// Clears keypoints storing in collection and OneWayDescriptorBase
- virtual void clear ();
+ virtual void clear();
virtual void train();
- virtual bool supportMask() { return false; }
+ virtual bool isMaskSupported();
- // Reads match object from a file node
virtual void read( const FileNode &fn );
-
- // Writes match object to a file storage
virtual void write( FileStorage& fs ) const;
-protected:
- virtual Ptr<GenericDescriptorMatcher> createEmptyMatcherCopy() const { return new OneWayDescriptorMatcher( params ); }
+ virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
+protected:
// Matches a set of keypoints from a single image of the training set. A rectangle with a center in a keypoint
// and size (patch_width/2*scale, patch_height/2*scale) is cropped from the source image for each
// keypoint. scale is iterated from DescriptorOneWayParams::min_scale to DescriptorOneWayParams::max_scale.
// The minimum distance to each training patch with all its affine poses is found over all scales.
// The class ID of a match is returned for each keypoint. The distance is calculated over PCA components
// loaded with DescriptorOneWay::Initialize, kd tree is used for finding minimum distances.
- virtual void knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
- vector<vector<DMatch> >& matches, int knn,
+ virtual void knnMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ vector<vector<DMatch> >& matches, int k,
const vector<Mat>& masks, bool compactResult );
- virtual void radiusMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
+ virtual void radiusMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult );
@@ -2356,16 +2405,16 @@ public:
class CV_EXPORTS Params
{
public:
- Params( int _nclasses=0,
- int _patchSize=FernClassifier::PATCH_SIZE,
- int _signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
- int _nstructs=FernClassifier::DEFAULT_STRUCTS,
- int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
- int _nviews=FernClassifier::DEFAULT_VIEWS,
- int _compressionMethod=FernClassifier::COMPRESSION_NONE,
+ Params( int nclasses=0,
+ int patchSize=FernClassifier::PATCH_SIZE,
+ int signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
+ int nstructs=FernClassifier::DEFAULT_STRUCTS,
+ int structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
+ int nviews=FernClassifier::DEFAULT_VIEWS,
+ int compressionMethod=FernClassifier::COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator() );
- Params( const string& _filename );
+ Params( const string& filename );
int nclasses;
int patchSize;
@@ -2379,25 +2428,25 @@ public:
string filename;
};
- FernDescriptorMatcher( const Params& _params=Params() );
+ FernDescriptorMatcher( const Params& params=Params() );
virtual ~FernDescriptorMatcher();
virtual void clear();
virtual void train();
- virtual bool supportMask() { return false; }
+ virtual bool isMaskSupported();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
-protected:
- virtual Ptr<GenericDescriptorMatcher> createEmptyMatcherCopy() const { return new FernDescriptorMatcher( params ); }
+ virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
- virtual void knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
- vector<vector<DMatch> >& matches, int knn,
+protected:
+ virtual void knnMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ vector<vector<DMatch> >& matches, int k,
const vector<Mat>& masks, bool compactResult );
- virtual void radiusMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
+ virtual void radiusMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult );
@@ -2425,9 +2474,8 @@ typedef VectorDescriptorMatcher VectorDescriptorMatch;
class CV_EXPORTS VectorDescriptorMatcher : public GenericDescriptorMatcher
{
public:
- VectorDescriptorMatcher( const Ptr<DescriptorExtractor>& _extractor, const Ptr<DescriptorMatcher>& _matcher )
- : extractor( _extractor ), matcher( _matcher ) { CV_Assert( !extractor.empty() && !matcher.empty() ); }
- virtual ~VectorDescriptorMatcher() {}
+ VectorDescriptorMatcher( const Ptr<DescriptorExtractor>& extractor, const Ptr<DescriptorMatcher>& matcher );
+ virtual ~VectorDescriptorMatcher();
virtual void add( const vector<Mat>& imgCollection,
vector<vector<KeyPoint> >& pointCollection );
@@ -2436,18 +2484,18 @@ public:
virtual void train();
- virtual bool supportMask() { return matcher->supportMask(); }
+ virtual bool isMaskSupported();
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
-protected:
- virtual Ptr<GenericDescriptorMatcher> createEmptyMatcherCopy() const { return new VectorDescriptorMatcher(extractor, matcher); }
+ virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
- virtual void knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
- vector<vector<DMatch> >& matches, int knn,
+protected:
+ virtual void knnMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ vector<vector<DMatch> >& matches, int k,
const vector<Mat>& masks, bool compactResult );
- virtual void radiusMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
+ virtual void radiusMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult );
@@ -2520,12 +2568,12 @@ CV_EXPORTS void evaluateGenericDescriptorMatcher( const Mat& img1, const Mat& im
class CV_EXPORTS BOWTrainer
{
public:
- BOWTrainer(){}
- virtual ~BOWTrainer(){}
+ BOWTrainer();
+ virtual ~BOWTrainer();
void add( const Mat& descriptors );
- const vector<Mat>& getDescriptors() const { return descriptors; }
- int descripotorsCount() const { return descriptors.empty() ? 0 : size; }
+ const vector<Mat>& getDescriptors() const;
+ int descripotorsCount() const;
virtual void clear();
@@ -2552,7 +2600,7 @@ class CV_EXPORTS BOWKMeansTrainer : public BOWTrainer
public:
BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
int attempts=3, int flags=KMEANS_PP_CENTERS );
- virtual ~BOWKMeansTrainer(){}
+ virtual ~BOWKMeansTrainer();
// Returns trained vocabulary (i.e. cluster centers).
virtual Mat cluster() const;
@@ -2574,14 +2622,16 @@ class CV_EXPORTS BOWImgDescriptorExtractor
public:
BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
const Ptr<DescriptorMatcher>& dmatcher );
- virtual ~BOWImgDescriptorExtractor(){}
+ virtual ~BOWImgDescriptorExtractor();
void setVocabulary( const Mat& vocabulary );
- const Mat& getVocabulary() const { return vocabulary; }
+ const Mat& getVocabulary() const;
void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor,
- vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 ); //not constant because DescriptorMatcher::match is not constant
- int descriptorSize() const { return vocabulary.empty() ? 0 : vocabulary.rows; }
- int descriptorType() const { return CV_32FC1; }
+ vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 );
+ // compute() is not constant because DescriptorMatcher::match is not constant
+
+ int descriptorSize() const;
+ int descriptorType() const;
protected:
Mat vocabulary;
diff --git a/modules/features2d/src/bagofwords.cpp b/modules/features2d/src/bagofwords.cpp
index dc056cc92a..51daa892c6 100755
--- a/modules/features2d/src/bagofwords.cpp
+++ b/modules/features2d/src/bagofwords.cpp
@@ -46,6 +46,12 @@ using namespace std;
namespace cv
{
+BOWTrainer::BOWTrainer()
+{}
+
+BOWTrainer::~BOWTrainer()
+{}
+
void BOWTrainer::add( const Mat& _descriptors )
{
CV_Assert( !_descriptors.empty() );
@@ -63,6 +69,16 @@ void BOWTrainer::add( const Mat& _descriptors )
descriptors.push_back(_descriptors);
}
+const vector<Mat>& BOWTrainer::getDescriptors() const
+{
+ return descriptors;
+}
+
+int BOWTrainer::descripotorsCount() const
+{
+ return descriptors.empty() ? 0 : size;
+}
+
void BOWTrainer::clear()
{
descriptors.clear();
@@ -91,6 +107,9 @@ Mat BOWKMeansTrainer::cluster() const
return cluster( mergedDescriptors );
}
+BOWKMeansTrainer::~BOWKMeansTrainer()
+{}
+
Mat BOWKMeansTrainer::cluster( const Mat& descriptors ) const
{
Mat labels, vocabulary;
@@ -104,6 +123,9 @@ BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtrac
dextractor(_dextractor), dmatcher(_dmatcher)
{}
+BOWImgDescriptorExtractor::~BOWImgDescriptorExtractor()
+{}
+
void BOWImgDescriptorExtractor::setVocabulary( const Mat& _vocabulary )
{
dmatcher->clear();
@@ -111,6 +133,11 @@ void BOWImgDescriptorExtractor::setVocabulary( const Mat& _vocabulary )
dmatcher->add( vector<Mat>(1, vocabulary) );
}
+const Mat& BOWImgDescriptorExtractor::getVocabulary() const
+{
+ return vocabulary;
+}
+
void BOWImgDescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor,
vector<vector<int> >* pointIdxsOfClusters, Mat* _descriptors )
{
@@ -153,4 +180,14 @@ void BOWImgDescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& key
imgDescriptor /= descriptors.rows;
}
+int BOWImgDescriptorExtractor::descriptorSize() const
+{
+ return vocabulary.empty() ? 0 : vocabulary.rows;
+}
+
+int BOWImgDescriptorExtractor::descriptorType() const
+{
+ return CV_32FC1;
+}
+
}
diff --git a/modules/features2d/src/brief.cpp b/modules/features2d/src/brief.cpp
index e43efb3eaa..bc2978588c 100644
--- a/modules/features2d/src/brief.cpp
+++ b/modules/features2d/src/brief.cpp
@@ -44,78 +44,101 @@
#include <algorithm>
#include <vector>
-namespace cv
+using namespace cv;
+
+inline int smoothedSum(const Mat& sum, const KeyPoint& pt, int y, int x)
{
+ static const int HALF_KERNEL = BriefDescriptorExtractor::KERNEL_SIZE / 2;
-BriefDescriptorExtractor::BriefDescriptorExtractor(int bytes) :
- bytes_(bytes), test_fn_(NULL)
+ int img_y = (int)(pt.pt.y + 0.5) + y;
+ int img_x = (int)(pt.pt.x + 0.5) + x;
+ return sum.at<int>(img_y + HALF_KERNEL + 1, img_x + HALF_KERNEL + 1)
+ - sum.at<int>(img_y + HALF_KERNEL + 1, img_x - HALF_KERNEL)
+ - sum.at<int>(img_y - HALF_KERNEL, img_x + HALF_KERNEL + 1)
+ + sum.at<int>(img_y - HALF_KERNEL, img_x - HALF_KERNEL);
+}
+
+void pixelTests16(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
{
- switch (bytes)
- {
- case 16:
- test_fn_ = pixelTests16;
- break;
- case 32:
- test_fn_ = pixelTests32;
- break;
- case 64:
- test_fn_ = pixelTests64;
- break;
- default:
- CV_Error(CV_StsBadArg, "bytes must be 16, 32, or 64");
- }
+ for (int i = 0; i < (int)keypoints.size(); ++i)
+ {
+ uchar* desc = descriptors.ptr(i);
+ const KeyPoint& pt = keypoints[i];
+#include "generated_16.i"
+ }
}
-void BriefDescriptorExtractor::compute(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors) const
+void pixelTests32(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
{
- // Construct integral image for fast smoothing (box filter)
- Mat sum;
-
- ///TODO allow the user to pass in a precomputed integral image
- //if(image.type() == CV_32S)
- // sum = image;
- //else
-
- integral(image, sum, CV_32S);
+ for (int i = 0; i < (int)keypoints.size(); ++i)
+ {
+ uchar* desc = descriptors.ptr(i);
+ const KeyPoint& pt = keypoints[i];
- //Remove keypoints very close to the border
- removeBorderKeypoints(keypoints, image.size(), PATCH_SIZE/2 + KERNEL_SIZE/2);
+#include "generated_32.i"
+ }
+}
+
+void pixelTests64(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
+{
+ for (int i = 0; i < (int)keypoints.size(); ++i)
+ {
+ uchar* desc = descriptors.ptr(i);
+ const KeyPoint& pt = keypoints[i];
- descriptors = Mat::zeros(keypoints.size(), bytes_, CV_8U);
- test_fn_(sum, keypoints, descriptors);
+#include "generated_64.i"
+ }
}
-void BriefDescriptorExtractor::pixelTests16(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
+namespace cv
{
- for (int i = 0; i < (int)keypoints.size(); ++i)
- {
- uchar* desc = descriptors.ptr(i);
- const KeyPoint& pt = keypoints[i];
-#include "generated_16.i"
- }
+BriefDescriptorExtractor::BriefDescriptorExtractor(int bytes) :
+ bytes_(bytes), test_fn_(NULL)
+{
+ switch (bytes)
+ {
+ case 16:
+ test_fn_ = pixelTests16;
+ break;
+ case 32:
+ test_fn_ = pixelTests32;
+ break;
+ case 64:
+ test_fn_ = pixelTests64;
+ break;
+ default:
+ CV_Error(CV_StsBadArg, "bytes must be 16, 32, or 64");
+ }
}
-void BriefDescriptorExtractor::pixelTests32(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
+int BriefDescriptorExtractor::descriptorSize() const
{
- for (int i = 0; i < (int)keypoints.size(); ++i)
- {
- uchar* desc = descriptors.ptr(i);
- const KeyPoint& pt = keypoints[i];
+ return bytes_;
+}
-#include "generated_32.i"
- }
+int BriefDescriptorExtractor::descriptorType() const
+{
+ return CV_8UC1;
}
-void BriefDescriptorExtractor::pixelTests64(const Mat& sum, const std::vector<KeyPoint>& keypoints, Mat& descriptors)
+void BriefDescriptorExtractor::computeImpl(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors) const
{
- for (int i = 0; i < (int)keypoints.size(); ++i)
- {
- uchar* desc = descriptors.ptr(i);
- const KeyPoint& pt = keypoints[i];
+ // Construct integral image for fast smoothing (box filter)
+ Mat sum;
-#include "generated_64.i"
- }
+ ///TODO allow the user to pass in a precomputed integral image
+ //if(image.type() == CV_32S)
+ // sum = image;
+ //else
+
+ integral(image, sum, CV_32S);
+
+ //Remove keypoints very close to the border
+ removeBorderKeypoints(keypoints, image.size(), PATCH_SIZE/2 + KERNEL_SIZE/2);
+
+ descriptors = Mat::zeros(keypoints.size(), bytes_, CV_8U);
+ test_fn_(sum, keypoints, descriptors);
}
/**
@@ -125,282 +148,285 @@ void BriefDescriptorExtractor::pixelTests64(const Mat& sum, const std::vector<Ke
template<unsigned char b>
struct ByteBits
{
- /**
- * number of bits in the byte given by the template constant
- */
- enum
- {
- COUNT = ((b >> 0) & 1) +
- ((b >> 1) & 1) +
- ((b >> 2) & 1) +
- ((b >> 3) & 1) +
- ((b >> 4) & 1) +
- ((b >> 5) & 1) +
- ((b >> 6) & 1) +
- ((b >> 7) & 1)
- };
+ /**
+ * number of bits in the byte given by the template constant
+ */
+ enum
+ {
+ COUNT = ((b >> 0) & 1) +
+ ((b >> 1) & 1) +
+ ((b >> 2) & 1) +
+ ((b >> 3) & 1) +
+ ((b >> 4) & 1) +
+ ((b >> 5) & 1) +
+ ((b >> 6) & 1) +
+ ((b >> 7) & 1)
+ };
};
-unsigned char HammingLUT::byteBitsLookUp(unsigned char b){
- static const unsigned char table[256] =
- {
- ByteBits<0>::COUNT,
- ByteBits<1>::COUNT,
- ByteBits<2>::COUNT,
- ByteBits<3>::COUNT,
- ByteBits<4>::COUNT,
- ByteBits<5>::COUNT,
- ByteBits<6>::COUNT,
- ByteBits<7>::COUNT,
- ByteBits<8>::COUNT,
- ByteBits<9>::COUNT,
- ByteBits<10>::COUNT,
- ByteBits<11>::COUNT,
- ByteBits<12>::COUNT,
- ByteBits<13>::COUNT,
- ByteBits<14>::COUNT,
- ByteBits<15>::COUNT,
- ByteBits<16>::COUNT,
- ByteBits<17>::COUNT,
- ByteBits<18>::COUNT,
- ByteBits<19>::COUNT,
- ByteBits<20>::COUNT,
- ByteBits<21>::COUNT,
- ByteBits<22>::COUNT,
- ByteBits<23>::COUNT,
- ByteBits<24>::COUNT,
- ByteBits<25>::COUNT,
- ByteBits<26>::COUNT,
- ByteBits<27>::COUNT,
- ByteBits<28>::COUNT,
- ByteBits<29>::COUNT,
- ByteBits<30>::COUNT,
- ByteBits<31>::COUNT,
- ByteBits<32>::COUNT,
- ByteBits<33>::COUNT,
- ByteBits<34>::COUNT,
- ByteBits<35>::COUNT,
- ByteBits<36>::COUNT,
- ByteBits<37>::COUNT,
- ByteBits<38>::COUNT,
- ByteBits<39>::COUNT,
- ByteBits<40>::COUNT,
- ByteBits<41>::COUNT,
- ByteBits<42>::COUNT,
- ByteBits<43>::COUNT,
- ByteBits<44>::COUNT,
- ByteBits<45>::COUNT,
- ByteBits<46>::COUNT,
- ByteBits<47>::COUNT,
- ByteBits<48>::COUNT,
- ByteBits<49>::COUNT,
- ByteBits<50>::COUNT,
- ByteBits<51>::COUNT,
- ByteBits<52>::COUNT,
- ByteBits<53>::COUNT,
- ByteBits<54>::COUNT,
- ByteBits<55>::COUNT,
- ByteBits<56>::COUNT,
- ByteBits<57>::COUNT,
- ByteBits<58>::COUNT,
- ByteBits<59>::COUNT,
- ByteBits<60>::COUNT,
- ByteBits<61>::COUNT,
- ByteBits<62>::COUNT,
- ByteBits<63>::COUNT,
- ByteBits<64>::COUNT,
- ByteBits<65>::COUNT,
- ByteBits<66>::COUNT,
- ByteBits<67>::COUNT,
- ByteBits<68>::COUNT,
- ByteBits<69>::COUNT,
- ByteBits<70>::COUNT,
- ByteBits<71>::COUNT,
- ByteBits<72>::COUNT,
- ByteBits<73>::COUNT,
- ByteBits<74>::COUNT,
- ByteBits<75>::COUNT,
- ByteBits<76>::COUNT,
- ByteBits<77>::COUNT,
- ByteBits<78>::COUNT,
- ByteBits<79>::COUNT,
- ByteBits<80>::COUNT,
- ByteBits<81>::COUNT,
- ByteBits<82>::COUNT,
- ByteBits<83>::COUNT,
- ByteBits<84>::COUNT,
- ByteBits<85>::COUNT,
- ByteBits<86>::COUNT,
- ByteBits<87>::COUNT,
- ByteBits<88>::COUNT,
- ByteBits<89>::COUNT,
- ByteBits<90>::COUNT,
- ByteBits<91>::COUNT,
- ByteBits<92>::COUNT,
- ByteBits<93>::COUNT,
- ByteBits<94>::COUNT,
- ByteBits<95>::COUNT,
- ByteBits<96>::COUNT,
- ByteBits<97>::COUNT,
- ByteBits<98>::COUNT,
- ByteBits<99>::COUNT,
- ByteBits<100>::COUNT,
- ByteBits<101>::COUNT,
- ByteBits<102>::COUNT,
- ByteBits<103>::COUNT,
- ByteBits<104>::COUNT,
- ByteBits<105>::COUNT,
- ByteBits<106>::COUNT,
- ByteBits<107>::COUNT,
- ByteBits<108>::COUNT,
- ByteBits<109>::COUNT,
- ByteBits<110>::COUNT,
- ByteBits<111>::COUNT,
- ByteBits<112>::COUNT,
- ByteBits<113>::COUNT,
- ByteBits<114>::COUNT,
- ByteBits<115>::COUNT,
- ByteBits<116>::COUNT,
- ByteBits<117>::COUNT,
- ByteBits<118>::COUNT,
- ByteBits<119>::COUNT,
- ByteBits<120>::COUNT,
- ByteBits<121>::COUNT,
- ByteBits<122>::COUNT,
- ByteBits<123>::COUNT,
- ByteBits<124>::COUNT,
- ByteBits<125>::COUNT,
- ByteBits<126>::COUNT,
- ByteBits<127>::COUNT,
- ByteBits<128>::COUNT,
- ByteBits<129>::COUNT,
- ByteBits<130>::COUNT,
- ByteBits<131>::COUNT,
- ByteBits<132>::COUNT,
- ByteBits<133>::COUNT,
- ByteBits<134>::COUNT,
- ByteBits<135>::COUNT,
- ByteBits<136>::COUNT,
- ByteBits<137>::COUNT,
- ByteBits<138>::COUNT,
- ByteBits<139>::COUNT,
- ByteBits<140>::COUNT,
- ByteBits<141>::COUNT,
- ByteBits<142>::COUNT,
- ByteBits<143>::COUNT,
- ByteBits<144>::COUNT,
- ByteBits<145>::COUNT,
- ByteBits<146>::COUNT,
- ByteBits<147>::COUNT,
- ByteBits<148>::COUNT,
- ByteBits<149>::COUNT,
- ByteBits<150>::COUNT,
- ByteBits<151>::COUNT,
- ByteBits<152>::COUNT,
- ByteBits<153>::COUNT,
- ByteBits<154>::COUNT,
- ByteBits<155>::COUNT,
- ByteBits<156>::COUNT,
- ByteBits<157>::COUNT,
- ByteBits<158>::COUNT,
- ByteBits<159>::COUNT,
- ByteBits<160>::COUNT,
- ByteBits<161>::COUNT,
- ByteBits<162>::COUNT,
- ByteBits<163>::COUNT,
- ByteBits<164>::COUNT,
- ByteBits<165>::COUNT,
- ByteBits<166>::COUNT,
- ByteBits<167>::COUNT,
- ByteBits<168>::COUNT,
- ByteBits<169>::COUNT,
- ByteBits<170>::COUNT,
- ByteBits<171>::COUNT,
- ByteBits<172>::COUNT,
- ByteBits<173>::COUNT,
- ByteBits<174>::COUNT,
- ByteBits<175>::COUNT,
- ByteBits<176>::COUNT,
- ByteBits<177>::COUNT,
- ByteBits<178>::COUNT,
- ByteBits<179>::COUNT,
- ByteBits<180>::COUNT,
- ByteBits<181>::COUNT,
- ByteBits<182>::COUNT,
- ByteBits<183>::COUNT,
- ByteBits<184>::COUNT,
- ByteBits<185>::COUNT,
- ByteBits<186>::COUNT,
- ByteBits<187>::COUNT,
- ByteBits<188>::COUNT,
- ByteBits<189>::COUNT,
- ByteBits<190>::COUNT,
- ByteBits<191>::COUNT,
- ByteBits<192>::COUNT,
- ByteBits<193>::COUNT,
- ByteBits<194>::COUNT,
- ByteBits<195>::COUNT,
- ByteBits<196>::COUNT,
- ByteBits<197>::COUNT,
- ByteBits<198>::COUNT,
- ByteBits<199>::COUNT,
- ByteBits<200>::COUNT,
- ByteBits<201>::COUNT,
- ByteBits<202>::COUNT,
- ByteBits<203>::COUNT,
- ByteBits<204>::COUNT,
- ByteBits<205>::COUNT,
- ByteBits<206>::COUNT,
- ByteBits<207>::COUNT,
- ByteBits<208>::COUNT,
- ByteBits<209>::COUNT,
- ByteBits<210>::COUNT,
- ByteBits<211>::COUNT,
- ByteBits<212>::COUNT,
- ByteBits<213>::COUNT,
- ByteBits<214>::COUNT,
- ByteBits<215>::COUNT,
- ByteBits<216>::COUNT,
- ByteBits<217>::COUNT,
- ByteBits<218>::COUNT,
- ByteBits<219>::COUNT,
- ByteBits<220>::COUNT,
- ByteBits<221>::COUNT,
- ByteBits<222>::COUNT,
- ByteBits<223>::COUNT,
- ByteBits<224>::COUNT,
- ByteBits<225>::COUNT,
- ByteBits<226>::COUNT,
- ByteBits<227>::COUNT,
- ByteBits<228>::COUNT,
- ByteBits<229>::COUNT,
- ByteBits<230>::COUNT,
- ByteBits<231>::COUNT,
- ByteBits<232>::COUNT,
- ByteBits<233>::COUNT,
- ByteBits<234>::COUNT,
- ByteBits<235>::COUNT,
- ByteBits<236>::COUNT,
- ByteBits<237>::COUNT,
- ByteBits<238>::COUNT,
- ByteBits<239>::COUNT,
- ByteBits<240>::COUNT,
- ByteBits<241>::COUNT,
- ByteBits<242>::COUNT,
- ByteBits<243>::COUNT,
- ByteBits<244>::COUNT,
- ByteBits<245>::COUNT,
- ByteBits<246>::COUNT,
- ByteBits<247>::COUNT,
- ByteBits<248>::COUNT,
- ByteBits<249>::COUNT,
- ByteBits<250>::COUNT,
- ByteBits<251>::COUNT,
- ByteBits<252>::COUNT,
- ByteBits<253>::COUNT,
- ByteBits<254>::COUNT,
- ByteBits<255>::COUNT
- };
- return table[b];
+
+unsigned char HammingLUT::byteBitsLookUp(unsigned char b)
+{
+ static const unsigned char table[256] =
+ {
+ ByteBits<0>::COUNT,
+ ByteBits<1>::COUNT,
+ ByteBits<2>::COUNT,
+ ByteBits<3>::COUNT,
+ ByteBits<4>::COUNT,
+ ByteBits<5>::COUNT,
+ ByteBits<6>::COUNT,
+ ByteBits<7>::COUNT,
+ ByteBits<8>::COUNT,
+ ByteBits<9>::COUNT,
+ ByteBits<10>::COUNT,
+ ByteBits<11>::COUNT,
+ ByteBits<12>::COUNT,
+ ByteBits<13>::COUNT,
+ ByteBits<14>::COUNT,
+ ByteBits<15>::COUNT,
+ ByteBits<16>::COUNT,
+ ByteBits<17>::COUNT,
+ ByteBits<18>::COUNT,
+ ByteBits<19>::COUNT,
+ ByteBits<20>::COUNT,
+ ByteBits<21>::COUNT,
+ ByteBits<22>::COUNT,
+ ByteBits<23>::COUNT,
+ ByteBits<24>::COUNT,
+ ByteBits<25>::COUNT,
+ ByteBits<26>::COUNT,
+ ByteBits<27>::COUNT,
+ ByteBits<28>::COUNT,
+ ByteBits<29>::COUNT,
+ ByteBits<30>::COUNT,
+ ByteBits<31>::COUNT,
+ ByteBits<32>::COUNT,
+ ByteBits<33>::COUNT,
+ ByteBits<34>::COUNT,
+ ByteBits<35>::COUNT,
+ ByteBits<36>::COUNT,
+ ByteBits<37>::COUNT,
+ ByteBits<38>::COUNT,
+ ByteBits<39>::COUNT,
+ ByteBits<40>::COUNT,
+ ByteBits<41>::COUNT,
+ ByteBits<42>::COUNT,
+ ByteBits<43>::COUNT,
+ ByteBits<44>::COUNT,
+ ByteBits<45>::COUNT,
+ ByteBits<46>::COUNT,
+ ByteBits<47>::COUNT,
+ ByteBits<48>::COUNT,
+ ByteBits<49>::COUNT,
+ ByteBits<50>::COUNT,
+ ByteBits<51>::COUNT,
+ ByteBits<52>::COUNT,
+ ByteBits<53>::COUNT,
+ ByteBits<54>::COUNT,
+ ByteBits<55>::COUNT,
+ ByteBits<56>::COUNT,
+ ByteBits<57>::COUNT,
+ ByteBits<58>::COUNT,
+ ByteBits<59>::COUNT,
+ ByteBits<60>::COUNT,
+ ByteBits<61>::COUNT,
+ ByteBits<62>::COUNT,
+ ByteBits<63>::COUNT,
+ ByteBits<64>::COUNT,
+ ByteBits<65>::COUNT,
+ ByteBits<66>::COUNT,
+ ByteBits<67>::COUNT,
+ ByteBits<68>::COUNT,
+ ByteBits<69>::COUNT,
+ ByteBits<70>::COUNT,
+ ByteBits<71>::COUNT,
+ ByteBits<72>::COUNT,
+ ByteBits<73>::COUNT,
+ ByteBits<74>::COUNT,
+ ByteBits<75>::COUNT,
+ ByteBits<76>::COUNT,
+ ByteBits<77>::COUNT,
+ ByteBits<78>::COUNT,
+ ByteBits<79>::COUNT,
+ ByteBits<80>::COUNT,
+ ByteBits<81>::COUNT,
+ ByteBits<82>::COUNT,
+ ByteBits<83>::COUNT,
+ ByteBits<84>::COUNT,
+ ByteBits<85>::COUNT,
+ ByteBits<86>::COUNT,
+ ByteBits<87>::COUNT,
+ ByteBits<88>::COUNT,
+ ByteBits<89>::COUNT,
+ ByteBits<90>::COUNT,
+ ByteBits<91>::COUNT,
+ ByteBits<92>::COUNT,
+ ByteBits<93>::COUNT,
+ ByteBits<94>::COUNT,
+ ByteBits<95>::COUNT,
+ ByteBits<96>::COUNT,
+ ByteBits<97>::COUNT,
+ ByteBits<98>::COUNT,
+ ByteBits<99>::COUNT,
+ ByteBits<100>::COUNT,
+ ByteBits<101>::COUNT,
+ ByteBits<102>::COUNT,
+ ByteBits<103>::COUNT,
+ ByteBits<104>::COUNT,
+ ByteBits<105>::COUNT,
+ ByteBits<106>::COUNT,
+ ByteBits<107>::COUNT,
+ ByteBits<108>::COUNT,
+ ByteBits<109>::COUNT,
+ ByteBits<110>::COUNT,
+ ByteBits<111>::COUNT,
+ ByteBits<112>::COUNT,
+ ByteBits<113>::COUNT,
+ ByteBits<114>::COUNT,
+ ByteBits<115>::COUNT,
+ ByteBits<116>::COUNT,
+ ByteBits<117>::COUNT,
+ ByteBits<118>::COUNT,
+ ByteBits<119>::COUNT,
+ ByteBits<120>::COUNT,
+ ByteBits<121>::COUNT,
+ ByteBits<122>::COUNT,
+ ByteBits<123>::COUNT,
+ ByteBits<124>::COUNT,
+ ByteBits<125>::COUNT,
+ ByteBits<126>::COUNT,
+ ByteBits<127>::COUNT,
+ ByteBits<128>::COUNT,
+ ByteBits<129>::COUNT,
+ ByteBits<130>::COUNT,
+ ByteBits<131>::COUNT,
+ ByteBits<132>::COUNT,
+ ByteBits<133>::COUNT,
+ ByteBits<134>::COUNT,
+ ByteBits<135>::COUNT,
+ ByteBits<136>::COUNT,
+ ByteBits<137>::COUNT,
+ ByteBits<138>::COUNT,
+ ByteBits<139>::COUNT,
+ ByteBits<140>::COUNT,
+ ByteBits<141>::COUNT,
+ ByteBits<142>::COUNT,
+ ByteBits<143>::COUNT,
+ ByteBits<144>::COUNT,
+ ByteBits<145>::COUNT,
+ ByteBits<146>::COUNT,
+ ByteBits<147>::COUNT,
+ ByteBits<148>::COUNT,
+ ByteBits<149>::COUNT,
+ ByteBits<150>::COUNT,
+ ByteBits<151>::COUNT,
+ ByteBits<152>::COUNT,
+ ByteBits<153>::COUNT,
+ ByteBits<154>::COUNT,
+ ByteBits<155>::COUNT,
+ ByteBits<156>::COUNT,
+ ByteBits<157>::COUNT,
+ ByteBits<158>::COUNT,
+ ByteBits<159>::COUNT,
+ ByteBits<160>::COUNT,
+ ByteBits<161>::COUNT,
+ ByteBits<162>::COUNT,
+ ByteBits<163>::COUNT,
+ ByteBits<164>::COUNT,
+ ByteBits<165>::COUNT,
+ ByteBits<166>::COUNT,
+ ByteBits<167>::COUNT,
+ ByteBits<168>::COUNT,
+ ByteBits<169>::COUNT,
+ ByteBits<170>::COUNT,
+ ByteBits<171>::COUNT,
+ ByteBits<172>::COUNT,
+ ByteBits<173>::COUNT,
+ ByteBits<174>::COUNT,
+ ByteBits<175>::COUNT,
+ ByteBits<176>::COUNT,
+ ByteBits<177>::COUNT,
+ ByteBits<178>::COUNT,
+ ByteBits<179>::COUNT,
+ ByteBits<180>::COUNT,
+ ByteBits<181>::COUNT,
+ ByteBits<182>::COUNT,
+ ByteBits<183>::COUNT,
+ ByteBits<184>::COUNT,
+ ByteBits<185>::COUNT,
+ ByteBits<186>::COUNT,
+ ByteBits<187>::COUNT,
+ ByteBits<188>::COUNT,
+ ByteBits<189>::COUNT,
+ ByteBits<190>::COUNT,
+ ByteBits<191>::COUNT,
+ ByteBits<192>::COUNT,
+ ByteBits<193>::COUNT,
+ ByteBits<194>::COUNT,
+ ByteBits<195>::COUNT,
+ ByteBits<196>::COUNT,
+ ByteBits<197>::COUNT,
+ ByteBits<198>::COUNT,
+ ByteBits<199>::COUNT,
+ ByteBits<200>::COUNT,
+ ByteBits<201>::COUNT,
+ ByteBits<202>::COUNT,
+ ByteBits<203>::COUNT,
+ ByteBits<204>::COUNT,
+ ByteBits<205>::COUNT,
+ ByteBits<206>::COUNT,
+ ByteBits<207>::COUNT,
+ ByteBits<208>::COUNT,
+ ByteBits<209>::COUNT,
+ ByteBits<210>::COUNT,
+ ByteBits<211>::COUNT,
+ ByteBits<212>::COUNT,
+ ByteBits<213>::COUNT,
+ ByteBits<214>::COUNT,
+ ByteBits<215>::COUNT,
+ ByteBits<216>::COUNT,
+ ByteBits<217>::COUNT,
+ ByteBits<218>::COUNT,
+ ByteBits<219>::COUNT,
+ ByteBits<220>::COUNT,
+ ByteBits<221>::COUNT,
+ ByteBits<222>::COUNT,
+ ByteBits<223>::COUNT,
+ ByteBits<224>::COUNT,
+ ByteBits<225>::COUNT,
+ ByteBits<226>::COUNT,
+ ByteBits<227>::COUNT,
+ ByteBits<228>::COUNT,
+ ByteBits<229>::COUNT,
+ ByteBits<230>::COUNT,
+ ByteBits<231>::COUNT,
+ ByteBits<232>::COUNT,
+ ByteBits<233>::COUNT,
+ ByteBits<234>::COUNT,
+ ByteBits<235>::COUNT,
+ ByteBits<236>::COUNT,
+ ByteBits<237>::COUNT,
+ ByteBits<238>::COUNT,
+ ByteBits<239>::COUNT,
+ ByteBits<240>::COUNT,
+ ByteBits<241>::COUNT,
+ ByteBits<242>::COUNT,
+ ByteBits<243>::COUNT,
+ ByteBits<244>::COUNT,
+ ByteBits<245>::COUNT,
+ ByteBits<246>::COUNT,
+ ByteBits<247>::COUNT,
+ ByteBits<248>::COUNT,
+ ByteBits<249>::COUNT,
+ ByteBits<250>::COUNT,
+ ByteBits<251>::COUNT,
+ ByteBits<252>::COUNT,
+ ByteBits<253>::COUNT,
+ ByteBits<254>::COUNT,
+ ByteBits<255>::COUNT
+ };
+
+ return table[b];
}
} // namespace cv
diff --git a/modules/features2d/src/descriptors.cpp b/modules/features2d/src/descriptors.cpp
index 934d7b6e66..be27de48eb 100644
--- a/modules/features2d/src/descriptors.cpp
+++ b/modules/features2d/src/descriptors.cpp
@@ -67,6 +67,21 @@ struct RoiPredicate
float minX, minY, maxX, maxY;
};
+DescriptorExtractor::~DescriptorExtractor()
+{}
+
+void DescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const
+{
+ if( image.empty() || keypoints.empty() )
+ return;
+
+ // Check keypoints are in image. Do filter bad points here?
+ //for( size_t i = 0; i < keypoints.size(); i++ )
+ // CV_Assert( Rect(0,0, image.cols, image.rows).contains(keypoints[i].pt) );
+
+ computeImpl( image, keypoints, descriptors );
+}
+
void DescriptorExtractor::compute( const vector<Mat>& imageCollection, vector<vector<KeyPoint> >& pointCollection, vector<Mat>& descCollection ) const
{
descCollection.resize( imageCollection.size() );
@@ -74,27 +89,42 @@ void DescriptorExtractor::compute( const vector<Mat>& imageCollection, vector<ve
compute( imageCollection[i], pointCollection[i], descCollection[i] );
}
+void DescriptorExtractor::read( const FileNode& )
+{}
+
+void DescriptorExtractor::write( FileStorage& ) const
+{}
+
void DescriptorExtractor::removeBorderKeypoints( vector<KeyPoint>& keypoints,
- Size imageSize, int borderPixels )
+ Size imageSize, int borderSize )
{
- keypoints.erase( remove_if(keypoints.begin(), keypoints.end(),
- RoiPredicate((float)borderPixels, (float)borderPixels,
- (float)(imageSize.width - borderPixels),
- (float)(imageSize.height - borderPixels))),
- keypoints.end());
+ if( borderSize > 0)
+ {
+ keypoints.erase( remove_if(keypoints.begin(), keypoints.end(),
+ RoiPredicate((float)borderSize, (float)borderSize,
+ (float)(imageSize.width - borderSize),
+ (float)(imageSize.height - borderSize))),
+ keypoints.end() );
+ }
}
/****************************************************************************************\
* SiftDescriptorExtractor *
\****************************************************************************************/
+SiftDescriptorExtractor::SiftDescriptorExtractor(const SIFT::DescriptorParams& descriptorParams,
+ const SIFT::CommonParams& commonParams)
+ : sift( descriptorParams.magnification, descriptorParams.isNormalize, descriptorParams.recalculateAngles,
+ commonParams.nOctaves, commonParams.nOctaveLayers, commonParams.firstOctave, commonParams.angleMode )
+{}
+
SiftDescriptorExtractor::SiftDescriptorExtractor( double magnification, bool isNormalize, bool recalculateAngles,
int nOctaves, int nOctaveLayers, int firstOctave, int angleMode )
: sift( magnification, isNormalize, recalculateAngles, nOctaves, nOctaveLayers, firstOctave, angleMode )
{}
-void SiftDescriptorExtractor::compute( const Mat& image,
- vector<KeyPoint>& keypoints,
- Mat& descriptors) const
+void SiftDescriptorExtractor::computeImpl( const Mat& image,
+ vector<KeyPoint>& keypoints,
+ Mat& descriptors) const
{
bool useProvidedKeypoints = true;
Mat grayImage = image;
@@ -131,6 +161,16 @@ void SiftDescriptorExtractor::write (FileStorage &fs) const
fs << "angleMode" << commParams.angleMode;
}
+int SiftDescriptorExtractor::descriptorSize() const
+{
+ return sift.descriptorSize();
+}
+
+int SiftDescriptorExtractor::descriptorType() const
+{
+ return CV_32FC1;
+}
+
/****************************************************************************************\
* SurfDescriptorExtractor *
\****************************************************************************************/
@@ -139,9 +179,9 @@ SurfDescriptorExtractor::SurfDescriptorExtractor( int nOctaves,
: surf( 0.0, nOctaves, nOctaveLayers, extended )
{}
-void SurfDescriptorExtractor::compute( const Mat& image,
- vector<KeyPoint>& keypoints,
- Mat& descriptors) const
+void SurfDescriptorExtractor::computeImpl( const Mat& image,
+ vector<KeyPoint>& keypoints,
+ Mat& descriptors) const
{
// Compute descriptors for given keypoints
vector<float> _descriptors;
@@ -175,11 +215,21 @@ void SurfDescriptorExtractor::write( FileStorage &fs ) const
fs << "extended" << surf.extended;
}
+int SurfDescriptorExtractor::descriptorSize() const
+{
+ return surf.descriptorSize();
+}
+
+int SurfDescriptorExtractor::descriptorType() const
+{
+ return CV_32FC1;
+}
+
/****************************************************************************************\
* OpponentColorDescriptorExtractor *
\****************************************************************************************/
-OpponentColorDescriptorExtractor::OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& _dextractor ) :
- dextractor(_dextractor)
+OpponentColorDescriptorExtractor::OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& _descriptorExtractor ) :
+ descriptorExtractor(_descriptorExtractor)
{}
void convertBGRImageToOpponentColorSpace( const Mat& bgrImage, vector<Mat>& opponentChannels )
@@ -246,33 +296,42 @@ void convertBGRImageToOpponentColorSpace( const Mat& bgrImage, vector<Mat>& oppo
}
}
-void OpponentColorDescriptorExtractor::compute( const Mat& bgrImage, vector<KeyPoint>& keypoints, Mat& descriptors ) const
+void OpponentColorDescriptorExtractor::computeImpl( const Mat& bgrImage, vector<KeyPoint>& keypoints, Mat& descriptors ) const
{
vector<Mat> opponentChannels;
convertBGRImageToOpponentColorSpace( bgrImage, opponentChannels );
// Compute descriptors three times, once for each Opponent channel
// and concatenate into a single color surf descriptor
- int descriptorSize = dextractor->descriptorSize();
+ int descriptorSize = descriptorExtractor->descriptorSize();
descriptors.create( static_cast<int>(keypoints.size()), 3*descriptorSize, CV_32FC1 );
for( int i = 0; i < 3/*channel count*/; i++ )
{
CV_Assert( opponentChannels[i].type() == CV_8UC1 );
Mat opponentDescriptors = descriptors.colRange( i*descriptorSize, (i+1)*descriptorSize );
- dextractor->compute( opponentChannels[i], keypoints, opponentDescriptors );
+ descriptorExtractor->compute( opponentChannels[i], keypoints, opponentDescriptors );
}
}
void OpponentColorDescriptorExtractor::read( const FileNode& fn )
{
- dextractor->read( fn );
+ descriptorExtractor->read(fn);
}
void OpponentColorDescriptorExtractor::write( FileStorage& fs ) const
{
- dextractor->write( fs );
+ descriptorExtractor->write(fs);
+}
+
+int OpponentColorDescriptorExtractor::descriptorSize() const
+{
+ return 3*descriptorExtractor->descriptorSize();
}
+int OpponentColorDescriptorExtractor::descriptorType() const
+{
+ return descriptorExtractor->descriptorType();
+}
/****************************************************************************************\
* Factory function for descriptor extractor creating *
\****************************************************************************************/
diff --git a/modules/features2d/src/detectors.cpp b/modules/features2d/src/detectors.cpp
index 3dd999eae7..2fb086fc68 100644
--- a/modules/features2d/src/detectors.cpp
+++ b/modules/features2d/src/detectors.cpp
@@ -61,6 +61,21 @@ struct MaskPredicate
const Mat& mask;
};
+FeatureDetector::~FeatureDetector()
+{}
+
+void FeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+{
+ keypoints.clear();
+
+ if( image.empty() )
+ return;
+
+ CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) );
+
+ detectImpl( image, keypoints, mask );
+}
+
void FeatureDetector::detect(const vector<Mat>& imageCollection, vector<vector<KeyPoint> >& pointCollection, const vector<Mat>& masks ) const
{
pointCollection.resize( imageCollection.size() );
@@ -76,6 +91,12 @@ void FeatureDetector::removeInvalidPoints( const Mat& mask, vector<KeyPoint>& ke
keypoints.erase(remove_if(keypoints.begin(), keypoints.end(), MaskPredicate(mask)), keypoints.end());
};
+void FeatureDetector::read( const FileNode& )
+{}
+
+void FeatureDetector::write( FileStorage& ) const
+{}
+
/*
* FastFeatureDetector
*/
@@ -95,7 +116,7 @@ void FastFeatureDetector::write (FileStorage& fs) const
fs << "nonmaxSuppression" << nonmaxSuppression;
}
-void FastFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+void FastFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
@@ -106,14 +127,13 @@ void FastFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints,
/*
* GoodFeaturesToTrackDetector
*/
-GoodFeaturesToTrackDetector::GoodFeaturesToTrackDetector( int _maxCorners, double _qualityLevel, \
- double _minDistance, int _blockSize,
- bool _useHarrisDetector, double _k )
- : maxCorners(_maxCorners), qualityLevel(_qualityLevel), minDistance(_minDistance),
- blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
+GoodFeaturesToTrackDetector::Params::Params( int _maxCorners, double _qualityLevel, double _minDistance,
+ int _blockSize, bool _useHarrisDetector, double _k ) :
+ maxCorners(_maxCorners), qualityLevel(_qualityLevel), minDistance(_minDistance),
+ blockSize(_blockSize), useHarrisDetector(_useHarrisDetector), k(_k)
{}
-void GoodFeaturesToTrackDetector::read (const FileNode& fn)
+void GoodFeaturesToTrackDetector::Params::read (const FileNode& fn)
{
maxCorners = fn["maxCorners"];
qualityLevel = fn["qualityLevel"];
@@ -123,7 +143,7 @@ void GoodFeaturesToTrackDetector::read (const FileNode& fn)
k = fn["k"];
}
-void GoodFeaturesToTrackDetector::write (FileStorage& fs) const
+void GoodFeaturesToTrackDetector::Params::write (FileStorage& fs) const
{
fs << "maxCorners" << maxCorners;
fs << "qualityLevel" << qualityLevel;
@@ -133,20 +153,40 @@ void GoodFeaturesToTrackDetector::write (FileStorage& fs) const
fs << "k" << k;
}
-void GoodFeaturesToTrackDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
+GoodFeaturesToTrackDetector::GoodFeaturesToTrackDetector( const Params& _params ) : params(_params)
+{}
+
+GoodFeaturesToTrackDetector::GoodFeaturesToTrackDetector( int maxCorners, double qualityLevel,
+ double minDistance, int blockSize,
+ bool useHarrisDetector, double k )
+{
+ params = Params( maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, k );
+}
+
+void GoodFeaturesToTrackDetector::read (const FileNode& fn)
+{
+ params.read(fn);
+}
+
+void GoodFeaturesToTrackDetector::write (FileStorage& fs) const
+{
+ params.write(fs);
+}
+
+void GoodFeaturesToTrackDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
vector<Point2f> corners;
- goodFeaturesToTrack( grayImage, corners, maxCorners, qualityLevel, minDistance, mask,
- blockSize, useHarrisDetector, k );
+ goodFeaturesToTrack( grayImage, corners, params.maxCorners, params.qualityLevel, params.minDistance, mask,
+ params.blockSize, params.useHarrisDetector, params.k );
keypoints.resize(corners.size());
vector<Point2f>::const_iterator corner_it = corners.begin();
vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
for( ; corner_it != corners.end(); ++corner_it, ++keypoint_it )
{
- *keypoint_it = KeyPoint( *corner_it, (float)blockSize );
+ *keypoint_it = KeyPoint( *corner_it, (float)params.blockSize );
}
}
@@ -198,13 +238,12 @@ void MserFeatureDetector::write (FileStorage& fs) const
}
-void MserFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+void MserFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
vector<vector<Point> > msers;
mser(image, msers, mask);
- keypoints.clear();
vector<vector<Point> >::const_iterator contour_it = msers.begin();
for( ; contour_it != msers.end(); ++contour_it )
{
@@ -220,6 +259,12 @@ void MserFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints,
/*
* StarFeatureDetector
*/
+
+StarFeatureDetector::StarFeatureDetector( const CvStarDetectorParams& params )
+ : star( params.maxSize, params.responseThreshold, params.lineThresholdProjected,
+ params.lineThresholdBinarized, params.suppressNonmaxSize)
+{}
+
StarFeatureDetector::StarFeatureDetector(int maxSize, int responseThreshold,
int lineThresholdProjected,
int lineThresholdBinarized,
@@ -251,7 +296,7 @@ void StarFeatureDetector::write (FileStorage& fs) const
fs << "suppressNonmaxSize" << star.suppressNonmaxSize;
}
-void StarFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+void StarFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
@@ -263,13 +308,20 @@ void StarFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints,
/*
* SiftFeatureDetector
*/
-SiftFeatureDetector::SiftFeatureDetector(double threshold, double edgeThreshold,
- int nOctaves, int nOctaveLayers, int firstOctave, int angleMode) :
+SiftFeatureDetector::SiftFeatureDetector( const SIFT::DetectorParams &detectorParams,
+ const SIFT::CommonParams &commonParams )
+ : sift(detectorParams.threshold, detectorParams.edgeThreshold,
+ commonParams.nOctaves, commonParams.nOctaveLayers, commonParams.firstOctave, commonParams.angleMode)
+{
+}
+
+SiftFeatureDetector::SiftFeatureDetector( double threshold, double edgeThreshold,
+ int nOctaves, int nOctaveLayers, int firstOctave, int angleMode ) :
sift(threshold, edgeThreshold, nOctaves, nOctaveLayers, firstOctave, angleMode)
{
}
-void SiftFeatureDetector::read (const FileNode& fn)
+void SiftFeatureDetector::read( const FileNode& fn )
{
double threshold = fn["threshold"];
double edgeThreshold = fn["edgeThreshold"];
@@ -296,7 +348,7 @@ void SiftFeatureDetector::write (FileStorage& fs) const
}
-void SiftFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+void SiftFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
@@ -329,7 +381,7 @@ void SurfFeatureDetector::write (FileStorage& fs) const
fs << "octaveLayers" << surf.nOctaveLayers;
}
-void SurfFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+void SurfFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
@@ -340,14 +392,24 @@ void SurfFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints,
/*
* DenseFeatureDetector
*/
-void DenseFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
-{
- keypoints.clear();
+DenseFeatureDetector::Params::Params( float _initFeatureScale, int _featureScaleLevels,
+ float _featureScaleMul, int _initXyStep,
+ int _initImgBound, bool _varyXyStepWithScale,
+ bool _varyImgBoundWithScale ) :
+ initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels),
+ featureScaleMul(_featureScaleMul), initXyStep(_initXyStep), initImgBound(_initImgBound),
+ varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale)
+{}
+
+DenseFeatureDetector::DenseFeatureDetector(const DenseFeatureDetector::Params &_params) : params(_params)
+{}
- float curScale = initFeatureScale;
- int curStep = initXyStep;
- int curBound = initImgBound;
- for( int curLevel = 0; curLevel < featureScaleLevels; curLevel++ )
+void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+{
+ float curScale = params.initFeatureScale;
+ int curStep = params.initXyStep;
+ int curBound = params.initImgBound;
+ for( int curLevel = 0; curLevel < params.featureScaleLevels; curLevel++ )
{
for( int x = curBound; x < image.cols - curBound; x += curStep )
{
@@ -357,9 +419,9 @@ void DenseFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints
}
}
- curScale = curScale * featureScaleMul;
- if( varyXyStepWithScale ) curStep = static_cast<int>( curStep * featureScaleMul + 0.5f );
- if( varyImgBoundWithScale ) curBound = static_cast<int>( curBound * featureScaleMul + 0.5f );
+ curScale = curScale * params.featureScaleMul;
+ if( params.varyXyStepWithScale ) curStep = static_cast<int>( curStep * params.featureScaleMul + 0.5f );
+ if( params.varyImgBoundWithScale ) curBound = static_cast<int>( curBound * params.featureScaleMul + 0.5f );
}
removeInvalidPoints( mask, keypoints );
@@ -391,9 +453,8 @@ void keepStrongest( int N, vector<KeyPoint>& keypoints )
}
}
-void GridAdaptedFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+void GridAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
- keypoints.clear();
keypoints.reserve(maxTotalKeypoints);
int maxPerCell = maxTotalKeypoints / (gridRows * gridCols);
@@ -430,7 +491,7 @@ PyramidAdaptedFeatureDetector::PyramidAdaptedFeatureDetector( const Ptr<FeatureD
: detector(_detector), levels(_levels)
{}
-void PyramidAdaptedFeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
+void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
Mat src = image;
for( int l = 0, multiplier = 1; l <= levels; ++l, multiplier *= 2 )
@@ -463,12 +524,11 @@ Ptr<FeatureDetector> createFeatureDetector( const string& detectorType )
FeatureDetector* fd = 0;
if( !detectorType.compare( "FAST" ) )
{
- fd = new FastFeatureDetector( 10/*threshold*/, true/*nonmax_suppression*/ );
+ fd = new FastFeatureDetector();
}
else if( !detectorType.compare( "STAR" ) )
{
- fd = new StarFeatureDetector( 16/*max_size*/, 5/*response_threshold*/, 10/*line_threshold_projected*/,
- 8/*line_threshold_binarized*/, 5/*suppress_nonmax_size*/ );
+ fd = new StarFeatureDetector();
}
else if( !detectorType.compare( "SIFT" ) )
{
@@ -477,23 +537,21 @@ Ptr<FeatureDetector> createFeatureDetector( const string& detectorType )
}
else if( !detectorType.compare( "SURF" ) )
{
- fd = new SurfFeatureDetector( 400./*hessian_threshold*/, 3 /*octaves*/, 4/*octave_layers*/ );
+ fd = new SurfFeatureDetector();
}
else if( !detectorType.compare( "MSER" ) )
{
- fd = new MserFeatureDetector( 5/*delta*/, 60/*min_area*/, 14400/*_max_area*/, 0.25f/*max_variation*/,
- 0.2/*min_diversity*/, 200/*max_evolution*/, 1.01/*area_threshold*/, 0.003/*min_margin*/,
- 5/*edge_blur_size*/ );
+ fd = new MserFeatureDetector();
}
else if( !detectorType.compare( "GFTT" ) )
{
- fd = new GoodFeaturesToTrackDetector( 1000/*maxCorners*/, 0.01/*qualityLevel*/, 1./*minDistance*/,
- 3/*int _blockSize*/, false/*useHarrisDetector*/, 0.04/*k*/ );
+ fd = new GoodFeaturesToTrackDetector();
}
else if( !detectorType.compare( "HARRIS" ) )
{
- fd = new GoodFeaturesToTrackDetector( 1000/*maxCorners*/, 0.01/*qualityLevel*/, 1./*minDistance*/,
- 3/*int _blockSize*/, true/*useHarrisDetector*/, 0.04/*k*/ );
+ GoodFeaturesToTrackDetector::Params params;
+ params.useHarrisDetector = true;
+ fd = new GoodFeaturesToTrackDetector(params);
}
return fd;
}
diff --git a/modules/features2d/src/matchers.cpp b/modules/features2d/src/matchers.cpp
index 3f374d479f..3be8febd84 100755
--- a/modules/features2d/src/matchers.cpp
+++ b/modules/features2d/src/matchers.cpp
@@ -71,11 +71,23 @@ Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPo
/****************************************************************************************\
* DescriptorMatcher *
\****************************************************************************************/
-void DescriptorMatcher::DescriptorCollection::set( const vector<Mat>& descCollection )
+DescriptorMatcher::DescriptorCollection::DescriptorCollection()
+{}
+
+DescriptorMatcher::DescriptorCollection::DescriptorCollection( const DescriptorCollection& collection )
+{
+ mergedDescriptors = collection.mergedDescriptors.clone();
+ copy( collection.startIdxs.begin(), collection.startIdxs.begin(), startIdxs.begin() );
+}
+
+DescriptorMatcher::DescriptorCollection::~DescriptorCollection()
+{}
+
+void DescriptorMatcher::DescriptorCollection::set( const vector<Mat>& descriptors )
{
clear();
- size_t imageCount = descCollection.size();
+ size_t imageCount = descriptors.size();
CV_Assert( imageCount > 0 );
startIdxs.resize( imageCount );
@@ -86,35 +98,35 @@ void DescriptorMatcher::DescriptorCollection::set( const vector<Mat>& descCollec
for( size_t i = 1; i < imageCount; i++ )
{
int s = 0;
- if( !descCollection[i-1].empty() )
+ if( !descriptors[i-1].empty() )
{
- dim = descCollection[i-1].cols;
- type = descCollection[i-1].type();
- s = descCollection[i-1].rows;
+ dim = descriptors[i-1].cols;
+ type = descriptors[i-1].type();
+ s = descriptors[i-1].rows;
}
startIdxs[i] = startIdxs[i-1] + s;
}
if( imageCount == 1 )
{
- if( descCollection[0].empty() ) return;
+ if( descriptors[0].empty() ) return;
- dim = descCollection[0].cols;
- type = descCollection[0].type();
+ dim = descriptors[0].cols;
+ type = descriptors[0].type();
}
assert( dim > 0 );
- int count = startIdxs[imageCount-1] + descCollection[imageCount-1].rows;
+ int count = startIdxs[imageCount-1] + descriptors[imageCount-1].rows;
if( count > 0 )
{
- dmatrix.create( count, dim, type );
+ mergedDescriptors.create( count, dim, type );
for( size_t i = 0; i < imageCount; i++ )
{
- if( !descCollection[i].empty() )
+ if( !descriptors[i].empty() )
{
- CV_Assert( descCollection[i].cols == dim && descCollection[i].type() == type );
- Mat m = dmatrix.rowRange( startIdxs[i], startIdxs[i] + descCollection[i].rows );
- descCollection[i].copyTo(m);
+ CV_Assert( descriptors[i].cols == dim && descriptors[i].type() == type );
+ Mat m = mergedDescriptors.rowRange( startIdxs[i], startIdxs[i] + descriptors[i].rows );
+ descriptors[i].copyTo(m);
}
}
}
@@ -123,7 +135,7 @@ void DescriptorMatcher::DescriptorCollection::set( const vector<Mat>& descCollec
void DescriptorMatcher::DescriptorCollection::clear()
{
startIdxs.clear();
- dmatrix.release();
+ mergedDescriptors.release();
}
const Mat DescriptorMatcher::DescriptorCollection::getDescriptor( int imgIdx, int localDescIdx ) const
@@ -135,10 +147,15 @@ const Mat DescriptorMatcher::DescriptorCollection::getDescriptor( int imgIdx, in
return getDescriptor( globalIdx );
}
+const Mat& DescriptorMatcher::DescriptorCollection::getDescriptors() const
+{
+ return mergedDescriptors;
+}
+
const Mat DescriptorMatcher::DescriptorCollection::getDescriptor( int globalDescIdx ) const
{
CV_Assert( globalDescIdx < size() );
- return dmatrix.row( globalDescIdx );
+ return mergedDescriptors.row( globalDescIdx );
}
void DescriptorMatcher::DescriptorCollection::getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const
@@ -157,6 +174,11 @@ void DescriptorMatcher::DescriptorCollection::getLocalIdx( int globalDescIdx, in
localDescIdx = globalDescIdx - startIdxs[imgIdx];
}
+int DescriptorMatcher::DescriptorCollection::size() const
+{
+ return mergedDescriptors.rows;
+}
+
/*
* DescriptorMatcher
*/
@@ -172,9 +194,17 @@ void convertMatches( const vector<vector<DMatch> >& knnMatches, vector<DMatch>&
}
}
-void DescriptorMatcher::add( const vector<Mat>& descCollection )
+DescriptorMatcher::~DescriptorMatcher()
+{}
+
+void DescriptorMatcher::add( const vector<Mat>& descriptors )
+{
+ trainDescCollection.insert( trainDescCollection.end(), descriptors.begin(), descriptors.end() );
+}
+
+const vector<Mat>& DescriptorMatcher::getTrainDescriptors() const
{
- trainDescCollection.insert( trainDescCollection.end(), descCollection.begin(), descCollection.end() );
+ return trainDescCollection;
}
void DescriptorMatcher::clear()
@@ -182,67 +212,134 @@ void DescriptorMatcher::clear()
trainDescCollection.clear();
}
-void DescriptorMatcher::match( const Mat& queryDescs, const Mat& trainDescs, vector<DMatch>& matches, const Mat& mask ) const
+bool DescriptorMatcher::empty() const
+{
+ return trainDescCollection.size() == 0;
+}
+
+void DescriptorMatcher::train()
+{}
+
+void DescriptorMatcher::match( const Mat& queryDescriptors, const Mat& trainDescriptors, vector<DMatch>& matches, const Mat& mask ) const
{
- Ptr<DescriptorMatcher> tempMatcher = cloneWithoutData();
- tempMatcher->add( vector<Mat>(1, trainDescs) );
- tempMatcher->match( queryDescs, matches, vector<Mat>(1, mask) );
+ Ptr<DescriptorMatcher> tempMatcher = clone(true);
+ tempMatcher->add( vector<Mat>(1, trainDescriptors) );
+ tempMatcher->match( queryDescriptors, matches, vector<Mat>(1, mask) );
}
-void DescriptorMatcher::knnMatch( const Mat& queryDescs, const Mat& trainDescs, vector<vector<DMatch> >& matches, int knn,
+void DescriptorMatcher::knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, vector<vector<DMatch> >& matches, int knn,
const Mat& mask, bool compactResult ) const
{
- Ptr<DescriptorMatcher> tempMatcher = cloneWithoutData();
- tempMatcher->add( vector<Mat>(1, trainDescs) );
- tempMatcher->knnMatch( queryDescs, matches, knn, vector<Mat>(1, mask), compactResult );
+ Ptr<DescriptorMatcher> tempMatcher = clone(true);
+ tempMatcher->add( vector<Mat>(1, trainDescriptors) );
+ tempMatcher->knnMatch( queryDescriptors, matches, knn, vector<Mat>(1, mask), compactResult );
}
-void DescriptorMatcher::radiusMatch( const Mat& queryDescs, const Mat& trainDescs, vector<vector<DMatch> >& matches, float maxDistance,
+void DescriptorMatcher::radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
const Mat& mask, bool compactResult ) const
{
- Ptr<DescriptorMatcher> tempMatcher = cloneWithoutData();
- tempMatcher->add( vector<Mat>(1, trainDescs) );
- tempMatcher->radiusMatch( queryDescs, matches, maxDistance, vector<Mat>(1, mask), compactResult );
+ Ptr<DescriptorMatcher> tempMatcher = clone(true);
+ tempMatcher->add( vector<Mat>(1, trainDescriptors) );
+ tempMatcher->radiusMatch( queryDescriptors, matches, maxDistance, vector<Mat>(1, mask), compactResult );
}
-void DescriptorMatcher::match( const Mat& queryDescs, vector<DMatch>& matches, const vector<Mat>& masks )
+void DescriptorMatcher::match( const Mat& queryDescriptors, vector<DMatch>& matches, const vector<Mat>& masks )
{
vector<vector<DMatch> > knnMatches;
- knnMatch( queryDescs, knnMatches, 1, masks, true /*compactResult*/ );
+ knnMatch( queryDescriptors, knnMatches, 1, masks, true /*compactResult*/ );
convertMatches( knnMatches, matches );
}
-void DescriptorMatcher::knnMatch( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
+void DescriptorMatcher::checkMasks( const vector<Mat>& masks, int queryDescriptorsCount ) const
+{
+ if( isMaskSupported() && !masks.empty() )
+ {
+ // Check masks
+ size_t imageCount = trainDescCollection.size();
+ CV_Assert( masks.size() == imageCount );
+ for( size_t i = 0; i < imageCount; i++ )
+ {
+ if( !masks[i].empty() && !trainDescCollection[i].empty() )
+ {
+ CV_Assert( masks[i].rows == queryDescriptorsCount &&
+ masks[i].cols == trainDescCollection[i].rows &&
+ masks[i].type() == CV_8UC1 );
+ }
+ }
+ }
+}
+
+void DescriptorMatcher::knnMatch( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult )
{
+ matches.empty();
+ if( empty() || queryDescriptors.empty() )
+ return;
+
+ CV_Assert( knn > 0 );
+
+ checkMasks( masks, queryDescriptors.rows );
+
train();
- knnMatchImpl( queryDescs, matches, knn, masks, compactResult );
+ knnMatchImpl( queryDescriptors, matches, knn, masks, compactResult );
}
-void DescriptorMatcher::radiusMatch( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
+void DescriptorMatcher::radiusMatch( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult )
{
+ matches.empty();
+ if( empty() || queryDescriptors.empty() )
+ return;
+
+ CV_Assert( maxDistance > std::numeric_limits<float>::epsilon() );
+
+ checkMasks( masks, queryDescriptors.rows );
+
train();
- radiusMatchImpl( queryDescs, matches, maxDistance, masks, compactResult );
+ radiusMatchImpl( queryDescriptors, matches, maxDistance, masks, compactResult );
+}
+
+void DescriptorMatcher::read( const FileNode& )
+{}
+
+void DescriptorMatcher::write( FileStorage& ) const
+{}
+
+bool DescriptorMatcher::isPossibleMatch( const Mat& mask, int queryIdx, int trainIdx )
+{
+ return mask.empty() || mask.at<uchar>(queryIdx, trainIdx);
+}
+
+bool DescriptorMatcher::isMaskedOut( const vector<Mat>& masks, int queryIdx )
+{
+ size_t outCount = 0;
+ for( size_t i = 0; i < masks.size(); i++ )
+ {
+ if( !masks[i].empty() && (countNonZero(masks[i].row(queryIdx)) == 0) )
+ outCount++;
+ }
+
+ return !masks.empty() && outCount == masks.size() ;
}
+
template<>
-void BruteForceMatcher<L2<float> >::knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
- const vector<Mat>& masks, bool compactResult )
+void BruteForceMatcher<L2<float> >::knnMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int knn,
+ const vector<Mat>& masks, bool compactResult )
{
#ifndef HAVE_EIGEN2
- bfKnnMatchImpl( *this, queryDescs, matches, knn, masks, compactResult );
+ commonKnnMatchImpl( *this, queryDescriptors, matches, knn, masks, compactResult );
#else
- CV_Assert( queryDescs.type() == CV_32FC1 || queryDescs.empty() );
+ CV_Assert( queryDescriptors.type() == CV_32FC1 || queryDescriptors.empty() );
CV_Assert( masks.empty() || masks.size() == trainDescCollection.size() );
- matches.reserve(queryDescs.rows);
+ matches.reserve(queryDescriptors.rows);
size_t imgCount = trainDescCollection.size();
Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> e_query_t;
vector<Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> > e_trainCollection(trainDescCollection.size());
vector<Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> > e_trainNorms2(trainDescCollection.size());
- cv2eigen( queryDescs.t(), e_query_t);
+ cv2eigen( queryDescriptors.t(), e_query_t);
for( size_t i = 0; i < trainDescCollection.size(); i++ )
{
cv2eigen( trainDescCollection[i], e_trainCollection[i] );
@@ -251,7 +348,7 @@ void BruteForceMatcher<L2<float> >::knnMatchImpl( const Mat& queryDescs, vector<
vector<Eigen::Matrix<float, Eigen::Dynamic, 1> > e_allDists( imgCount ); // distances between one query descriptor and all train descriptors
- for( int qIdx = 0; qIdx < queryDescs.rows; qIdx++ )
+ for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
{
if( maskedOut( masks, qIdx ) )
{
@@ -265,10 +362,10 @@ void BruteForceMatcher<L2<float> >::knnMatchImpl( const Mat& queryDescs, vector<
for( size_t iIdx = 0; iIdx < imgCount; iIdx++ )
{
CV_Assert( masks.empty() || masks[iIdx].empty() ||
- ( masks[iIdx].rows == queryDescs.rows && masks[iIdx].cols == trainDescCollection[iIdx].rows &&
+ ( masks[iIdx].rows == queryDescriptors.rows && masks[iIdx].cols == trainDescCollection[iIdx].rows &&
masks[iIdx].type() == CV_8UC1 ) );
CV_Assert( trainDescCollection[iIdx].type() == CV_32FC1 || trainDescCollection[iIdx].empty() );
- CV_Assert( queryDescs.cols == trainDescCollection[iIdx].cols );
+ CV_Assert( queryDescriptors.cols == trainDescCollection[iIdx].cols );
e_allDists[iIdx] = e_trainCollection[iIdx] *e_query_t.col(qIdx);
e_allDists[iIdx] -= e_trainNorms2[iIdx];
@@ -315,22 +412,22 @@ void BruteForceMatcher<L2<float> >::knnMatchImpl( const Mat& queryDescs, vector<
}
template<>
-void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
+void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult )
{
#ifndef HAVE_EIGEN2
- bfRadiusMatchImpl( *this, queryDescs, matches, maxDistance, masks, compactResult );
+ commonRadiusMatchImpl( *this, queryDescriptors, matches, maxDistance, masks, compactResult );
#else
- CV_Assert( queryDescs.type() == CV_32FC1 || queryDescs.empty() );
+ CV_Assert( queryDescriptors.type() == CV_32FC1 || queryDescriptors.empty() );
CV_Assert( masks.empty() || masks.size() == trainDescCollection.size() );
- matches.reserve(queryDescs.rows);
+ matches.reserve(queryDescriptors.rows);
size_t imgCount = trainDescCollection.size();
Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> e_query_t;
vector<Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> > e_trainCollection(trainDescCollection.size());
vector<Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic> > e_trainNorms2(trainDescCollection.size());
- cv2eigen( queryDescs.t(), e_query_t);
+ cv2eigen( queryDescriptors.t(), e_query_t);
for( size_t i = 0; i < trainDescCollection.size(); i++ )
{
cv2eigen( trainDescCollection[i], e_trainCollection[i] );
@@ -339,7 +436,7 @@ void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescs, vect
vector<Eigen::Matrix<float, Eigen::Dynamic, 1> > e_allDists( imgCount ); // distances between one query descriptor and all train descriptors
- for( int qIdx = 0; qIdx < queryDescs.rows; qIdx++ )
+ for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
{
if( maskedOut( masks, qIdx ) )
{
@@ -353,10 +450,10 @@ void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescs, vect
for( size_t iIdx = 0; iIdx < imgCount; iIdx++ )
{
CV_Assert( masks.empty() || masks[iIdx].empty() ||
- ( masks[iIdx].rows == queryDescs.rows && masks[iIdx].cols == trainDescCollection[iIdx].rows &&
+ ( masks[iIdx].rows == queryDescriptors.rows && masks[iIdx].cols == trainDescCollection[iIdx].rows &&
masks[iIdx].type() == CV_8UC1 ) );
CV_Assert( trainDescCollection[iIdx].type() == CV_32FC1 || trainDescCollection[iIdx].empty() );
- CV_Assert( queryDescs.cols == trainDescCollection[iIdx].cols );
+ CV_Assert( queryDescriptors.cols == trainDescCollection[iIdx].cols );
e_allDists[iIdx] = e_trainCollection[iIdx] *e_query_t.col(qIdx);
e_allDists[iIdx] -= e_trainNorms2[iIdx];
@@ -393,12 +490,12 @@ FlannBasedMatcher::FlannBasedMatcher( const Ptr<flann::IndexParams>& _indexParam
CV_Assert( !_searchParams.empty() );
}
-void FlannBasedMatcher::add( const vector<Mat>& descCollection )
+void FlannBasedMatcher::add( const vector<Mat>& descriptors )
{
- DescriptorMatcher::add( descCollection );
- for( size_t i = 0; i < descCollection.size(); i++ )
+ DescriptorMatcher::add( descriptors );
+ for( size_t i = 0; i < descriptors.size(); i++ )
{
- addedDescCount += descCollection[i].rows;
+ addedDescCount += descriptors[i].rows;
}
}
@@ -421,6 +518,27 @@ void FlannBasedMatcher::train()
}
}
+bool FlannBasedMatcher::isMaskSupported() const
+{
+ return false;
+}
+
+Ptr<DescriptorMatcher> FlannBasedMatcher::clone( bool emptyTrainData ) const
+{
+ FlannBasedMatcher* matcher = new FlannBasedMatcher(indexParams, searchParams);
+ if( !emptyTrainData )
+ {
+ CV_Error( CV_StsNotImplemented, "deep clone functionality is not implemented, because "
+ "Flann::Index has not copy constructor or clone method ");
+ //matcher->flannIndex;
+ matcher->addedDescCount = addedDescCount;
+ matcher->mergedDescriptors = DescriptorCollection( mergedDescriptors );
+ transform( trainDescCollection.begin(), trainDescCollection.end(),
+ matcher->trainDescCollection.begin(), clone_op );
+ }
+ return matcher;
+}
+
void FlannBasedMatcher::convertToDMatches( const DescriptorCollection& collection, const Mat& indices, const Mat& dists,
vector<vector<DMatch> >& matches )
{
@@ -440,28 +558,28 @@ void FlannBasedMatcher::convertToDMatches( const DescriptorCollection& collectio
}
}
-void FlannBasedMatcher::knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
+void FlannBasedMatcher::knnMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& /*masks*/, bool /*compactResult*/ )
{
- Mat indices( queryDescs.rows, knn, CV_32SC1 );
- Mat dists( queryDescs.rows, knn, CV_32FC1);
- flannIndex->knnSearch( queryDescs, indices, dists, knn, *searchParams );
+ Mat indices( queryDescriptors.rows, knn, CV_32SC1 );
+ Mat dists( queryDescriptors.rows, knn, CV_32FC1);
+ flannIndex->knnSearch( queryDescriptors, indices, dists, knn, *searchParams );
convertToDMatches( mergedDescriptors, indices, dists, matches );
}
-void FlannBasedMatcher::radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
+void FlannBasedMatcher::radiusMatchImpl( const Mat& queryDescriptors, vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& /*masks*/, bool /*compactResult*/ )
{
const int count = mergedDescriptors.size(); // TODO do count as param?
- Mat indices( queryDescs.rows, count, CV_32SC1, Scalar::all(-1) );
- Mat dists( queryDescs.rows, count, CV_32FC1, Scalar::all(-1) );
- for( int qIdx = 0; qIdx < queryDescs.rows; qIdx++ )
+ Mat indices( queryDescriptors.rows, count, CV_32SC1, Scalar::all(-1) );
+ Mat dists( queryDescriptors.rows, count, CV_32FC1, Scalar::all(-1) );
+ for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
{
- Mat queryDescsRow = queryDescs.row(qIdx);
+ Mat queryDescriptorsRow = queryDescriptors.row(qIdx);
Mat indicesRow = indices.row(qIdx);
Mat distsRow = dists.row(qIdx);
- flannIndex->radiusSearch( queryDescsRow, indicesRow, distsRow, maxDistance*maxDistance, *searchParams );
+ flannIndex->radiusSearch( queryDescriptorsRow, indicesRow, distsRow, maxDistance*maxDistance, *searchParams );
}
convertToDMatches( mergedDescriptors, indices, dists, matches );
@@ -507,6 +625,22 @@ Ptr<DescriptorMatcher> createDescriptorMatcher( const string& descriptorMatcherT
/*
* KeyPointCollection
*/
+GenericDescriptorMatcher::KeyPointCollection::KeyPointCollection() : pointCount(0)
+{}
+
+GenericDescriptorMatcher::KeyPointCollection::KeyPointCollection( const KeyPointCollection& collection )
+{
+ pointCount = collection.pointCount;
+
+ transform( collection.images.begin(), collection.images.end(), images.begin(), clone_op );
+
+ keypoints.resize( collection.keypoints.size() );
+ for( size_t i = 0; i < keypoints.size(); i++ )
+ copy( collection.keypoints[i].begin(), collection.keypoints[i].end(), keypoints[i].begin() );
+
+ copy( collection.startIndices.begin(), collection.startIndices.end(), startIndices.begin() );
+}
+
void GenericDescriptorMatcher::KeyPointCollection::add( const vector<Mat>& _images,
const vector<vector<KeyPoint> >& _points )
{
@@ -514,9 +648,9 @@ void GenericDescriptorMatcher::KeyPointCollection::add( const vector<Mat>& _imag
CV_Assert( _images.size() == _points.size() );
images.insert( images.end(), _images.begin(), _images.end() );
- points.insert( points.end(), _points.begin(), _points.end() );
+ keypoints.insert( keypoints.end(), _points.begin(), _points.end() );
for( size_t i = 0; i < _points.size(); i++ )
- size += _points[i].size();
+ pointCount += _points[i].size();
size_t prevSize = startIndices.size(), addSize = _images.size();
startIndices.resize( prevSize + addSize );
@@ -524,37 +658,58 @@ void GenericDescriptorMatcher::KeyPointCollection::add( const vector<Mat>& _imag
if( prevSize == 0 )
startIndices[prevSize] = 0; //first
else
- startIndices[prevSize] = startIndices[prevSize-1] + points[prevSize-1].size();
+ startIndices[prevSize] = startIndices[prevSize-1] + keypoints[prevSize-1].size();
for( size_t i = prevSize + 1; i < prevSize + addSize; i++ )
{
- startIndices[i] = startIndices[i - 1] + points[i - 1].size();
+ startIndices[i] = startIndices[i - 1] + keypoints[i - 1].size();
}
}
void GenericDescriptorMatcher::KeyPointCollection::clear()
{
- points.clear();
+ keypoints.clear();
+}
+
+size_t GenericDescriptorMatcher::KeyPointCollection::keypointCount() const
+{
+ return pointCount;
+}
+
+size_t GenericDescriptorMatcher::KeyPointCollection::imageCount() const
+{
+ return images.size();
+}
+
+const vector<vector<KeyPoint> >& GenericDescriptorMatcher::KeyPointCollection::getKeypoints() const
+{
+ return keypoints;
+}
+
+const vector<KeyPoint>& GenericDescriptorMatcher::KeyPointCollection::getKeypoints( int imgIdx ) const
+{
+ CV_Assert( imgIdx < (int)imageCount() );
+ return keypoints[imgIdx];
}
const KeyPoint& GenericDescriptorMatcher::KeyPointCollection::getKeyPoint( int imgIdx, int localPointIdx ) const
{
CV_Assert( imgIdx < (int)images.size() );
- CV_Assert( localPointIdx < (int)points[imgIdx].size() );
- return points[imgIdx][localPointIdx];
+ CV_Assert( localPointIdx < (int)keypoints[imgIdx].size() );
+ return keypoints[imgIdx][localPointIdx];
}
const KeyPoint& GenericDescriptorMatcher::KeyPointCollection::getKeyPoint( int globalPointIdx ) const
{
int imgIdx, localPointIdx;
getLocalIdx( globalPointIdx, imgIdx, localPointIdx );
- return points[imgIdx][localPointIdx];
+ return keypoints[imgIdx][localPointIdx];
}
void GenericDescriptorMatcher::KeyPointCollection::getLocalIdx( int globalPointIdx, int& imgIdx, int& localPointIdx ) const
{
imgIdx = -1;
- CV_Assert( globalPointIdx < (int)pointCount() );
+ CV_Assert( globalPointIdx < (int)keypointCount() );
for( size_t i = 1; i < startIndices.size(); i++ )
{
if( globalPointIdx < startIndices[i] )
@@ -567,20 +722,50 @@ void GenericDescriptorMatcher::KeyPointCollection::getLocalIdx( int globalPointI
localPointIdx = globalPointIdx - startIndices[imgIdx];
}
+const vector<Mat>& GenericDescriptorMatcher::KeyPointCollection::getImages() const
+{
+ return images;
+}
+
+const Mat& GenericDescriptorMatcher::KeyPointCollection::getImage( int imgIdx ) const
+{
+ CV_Assert( imgIdx < (int)imageCount() );
+ return images[imgIdx];
+}
+
/*
* GenericDescriptorMatcher
*/
+GenericDescriptorMatcher::GenericDescriptorMatcher()
+{}
+
+GenericDescriptorMatcher::~GenericDescriptorMatcher()
+{}
+
void GenericDescriptorMatcher::add( const vector<Mat>& imgCollection,
vector<vector<KeyPoint> >& pointCollection )
{
trainPointCollection.add( imgCollection, pointCollection );
}
+const vector<Mat>& GenericDescriptorMatcher::getTrainImages() const
+{
+ return trainPointCollection.getImages();
+}
+
+const vector<vector<KeyPoint> >& GenericDescriptorMatcher::getTrainKeypoints() const
+{
+ return trainPointCollection.getKeypoints();
+}
+
void GenericDescriptorMatcher::clear()
{
trainPointCollection.clear();
}
+void GenericDescriptorMatcher::train()
+{}
+
void GenericDescriptorMatcher::classify( const Mat& queryImage, vector<KeyPoint>& queryPoints,
const Mat& trainImage, vector<KeyPoint>& trainPoints ) const
{
@@ -606,7 +791,7 @@ void GenericDescriptorMatcher::match( const Mat& queryImg, vector<KeyPoint>& que
const Mat& trainImg, vector<KeyPoint>& trainPoints,
vector<DMatch>& matches, const Mat& mask ) const
{
- Ptr<GenericDescriptorMatcher> tempMatcher = createEmptyMatcherCopy();
+ Ptr<GenericDescriptorMatcher> tempMatcher = clone( true );
vector<vector<KeyPoint> > vecTrainPoints(1, trainPoints);
tempMatcher->add( vector<Mat>(1, trainImg), vecTrainPoints );
tempMatcher->match( queryImg, queryPoints, matches, vector<Mat>(1, mask) );
@@ -617,7 +802,7 @@ void GenericDescriptorMatcher::knnMatch( const Mat& queryImg, vector<KeyPoint>&
const Mat& trainImg, vector<KeyPoint>& trainPoints,
vector<vector<DMatch> >& matches, int knn, const Mat& mask, bool compactResult ) const
{
- Ptr<GenericDescriptorMatcher> tempMatcher = createEmptyMatcherCopy();
+ Ptr<GenericDescriptorMatcher> tempMatcher = clone( true );
vector<vector<KeyPoint> > vecTrainPoints(1, trainPoints);
tempMatcher->add( vector<Mat>(1, trainImg), vecTrainPoints );
tempMatcher->knnMatch( queryImg, queryPoints, matches, knn, vector<Mat>(1, mask), compactResult );
@@ -629,7 +814,7 @@ void GenericDescriptorMatcher::radiusMatch( const Mat& queryImg, vector<KeyPoint
vector<vector<DMatch> >& matches, float maxDistance,
const Mat& mask, bool compactResult ) const
{
- Ptr<GenericDescriptorMatcher> tempMatcher = createEmptyMatcherCopy();
+ Ptr<GenericDescriptorMatcher> tempMatcher = clone( true );
vector<vector<KeyPoint> > vecTrainPoints(1, trainPoints);
tempMatcher->add( vector<Mat>(1, trainImg), vecTrainPoints );
tempMatcher->radiusMatch( queryImg, queryPoints, matches, maxDistance, vector<Mat>(1, mask), compactResult );
@@ -660,9 +845,25 @@ void GenericDescriptorMatcher::radiusMatch( const Mat& queryImg, vector<KeyPoint
radiusMatchImpl( queryImg, queryPoints, matches, maxDistance, masks, compactResult );
}
+void GenericDescriptorMatcher::read( const FileNode& )
+{}
+
+void GenericDescriptorMatcher::write( FileStorage& ) const
+{}
+
/****************************************************************************************\
* OneWayDescriptorMatcher *
\****************************************************************************************/
+
+OneWayDescriptorMatcher::Params::Params( int _poseCount, Size _patchSize, string _pcaFilename,
+ string _trainPath, string _trainImagesList,
+ float _minScale, float _maxScale, float _stepScale ) :
+ poseCount(_poseCount), patchSize(_patchSize), pcaFilename(_pcaFilename),
+ trainPath(_trainPath), trainImagesList(_trainImagesList),
+ minScale(_minScale), maxScale(_maxScale), stepScale(_stepScale)
+{}
+
+
OneWayDescriptorMatcher::OneWayDescriptorMatcher( const Params& _params)
{
initialize(_params);
@@ -691,13 +892,13 @@ void OneWayDescriptorMatcher::clear()
void OneWayDescriptorMatcher::train()
{
- if( base.empty() || prevTrainCount < (int)trainPointCollection.pointCount() )
+ if( base.empty() || prevTrainCount < (int)trainPointCollection.keypointCount() )
{
base = new OneWayDescriptorObject( params.patchSize, params.poseCount, params.pcaFilename,
params.trainPath, params.trainImagesList, params.minScale, params.maxScale, params.stepScale );
- base->Allocate( trainPointCollection.pointCount() );
- prevTrainCount = trainPointCollection.pointCount();
+ base->Allocate( trainPointCollection.keypointCount() );
+ prevTrainCount = trainPointCollection.keypointCount();
const vector<vector<KeyPoint> >& points = trainPointCollection.getKeypoints();
int count = 0;
@@ -714,6 +915,11 @@ void OneWayDescriptorMatcher::train()
}
}
+bool OneWayDescriptorMatcher::isMaskSupported()
+{
+ return false;
+}
+
void OneWayDescriptorMatcher::knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& /*masks*/, bool /*compactResult*/ )
@@ -763,6 +969,23 @@ void OneWayDescriptorMatcher::write( FileStorage& fs ) const
base->Write (fs);
}
+Ptr<GenericDescriptorMatcher> OneWayDescriptorMatcher::clone( bool emptyTrainData ) const
+{
+ OneWayDescriptorMatcher* matcher = new OneWayDescriptorMatcher( params );
+
+ if( !emptyTrainData )
+ {
+ CV_Error( CV_StsNotImplemented, "deep clone dunctionality is not implemented, because "
+ "OneWayDescriptorBase has not copy constructor or clone method ");
+
+ //matcher->base;
+ matcher->params = params;
+ matcher->prevTrainCount = prevTrainCount;
+ matcher->trainPointCollection = trainPointCollection;
+ }
+ return matcher;
+}
+
/****************************************************************************************\
* FernDescriptorMatcher *
\****************************************************************************************/
@@ -805,7 +1028,7 @@ void FernDescriptorMatcher::clear()
void FernDescriptorMatcher::train()
{
- if( classifier.empty() || prevTrainCount < (int)trainPointCollection.pointCount() )
+ if( classifier.empty() || prevTrainCount < (int)trainPointCollection.keypointCount() )
{
assert( params.filename.empty() );
@@ -819,6 +1042,11 @@ void FernDescriptorMatcher::train()
}
}
+bool FernDescriptorMatcher::isMaskSupported()
+{
+ return false;
+}
+
void FernDescriptorMatcher::calcBestProbAndMatchIdx( const Mat& image, const Point2f& pt,
float& bestProb, int& bestMatchIdx, vector<float>& signature )
{
@@ -921,16 +1149,42 @@ void FernDescriptorMatcher::write( FileStorage& fs ) const
// classifier->write(fs);
}
+Ptr<GenericDescriptorMatcher> FernDescriptorMatcher::clone( bool emptyTrainData ) const
+{
+ FernDescriptorMatcher* matcher = new FernDescriptorMatcher( params );
+ if( !emptyTrainData )
+ {
+ CV_Error( CV_StsNotImplemented, "deep clone dunctionality is not implemented, because "
+ "FernClassifier has not copy constructor or clone method ");
+
+ //matcher->classifier;
+ matcher->params = params;
+ matcher->prevTrainCount = prevTrainCount;
+ matcher->trainPointCollection = trainPointCollection;
+ }
+ return matcher;
+}
+
/****************************************************************************************\
-* VectorDescriptorMatcher *
+* VectorDescriptorMatcher *
\****************************************************************************************/
+VectorDescriptorMatcher::VectorDescriptorMatcher( const Ptr<DescriptorExtractor>& _extractor,
+ const Ptr<DescriptorMatcher>& _matcher )
+ : extractor( _extractor ), matcher( _matcher )
+{
+ CV_Assert( !extractor.empty() && !matcher.empty() );
+}
+
+VectorDescriptorMatcher::~VectorDescriptorMatcher()
+{}
+
void VectorDescriptorMatcher::add( const vector<Mat>& imgCollection,
vector<vector<KeyPoint> >& pointCollection )
{
- vector<Mat> descCollection;
- extractor->compute( imgCollection, pointCollection, descCollection );
+ vector<Mat> descriptors;
+ extractor->compute( imgCollection, pointCollection, descriptors );
- matcher->add( descCollection );
+ matcher->add( descriptors );
trainPointCollection.add( imgCollection, pointCollection );
}
@@ -947,28 +1201,33 @@ void VectorDescriptorMatcher::train()
matcher->train();
}
+bool VectorDescriptorMatcher::isMaskSupported()
+{
+ return matcher->isMaskSupported();
+}
+
void VectorDescriptorMatcher::knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
vector<vector<DMatch> >& matches, int knn,
const vector<Mat>& masks, bool compactResult )
{
- Mat queryDescs;
- extractor->compute( queryImg, queryPoints, queryDescs );
- matcher->knnMatch( queryDescs, matches, knn, masks, compactResult );
+ Mat queryDescriptors;
+ extractor->compute( queryImg, queryPoints, queryDescriptors );
+ matcher->knnMatch( queryDescriptors, matches, knn, masks, compactResult );
}
void VectorDescriptorMatcher::radiusMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult )
{
- Mat queryDescs;
- extractor->compute( queryImg, queryPoints, queryDescs );
- matcher->radiusMatch( queryDescs, matches, maxDistance, masks, compactResult );
+ Mat queryDescriptors;
+ extractor->compute( queryImg, queryPoints, queryDescriptors );
+ matcher->radiusMatch( queryDescriptors, matches, maxDistance, masks, compactResult );
}
void VectorDescriptorMatcher::read( const FileNode& fn )
{
GenericDescriptorMatcher::read(fn);
- extractor->read (fn);
+ extractor->read(fn);
}
void VectorDescriptorMatcher::write (FileStorage& fs) const
@@ -977,6 +1236,12 @@ void VectorDescriptorMatcher::write (FileStorage& fs) const
extractor->write (fs);
}
+Ptr<GenericDescriptorMatcher> VectorDescriptorMatcher::clone( bool emptyTrainData ) const
+{
+ // TODO clone extractor
+ return new VectorDescriptorMatcher( extractor, matcher->clone(emptyTrainData) );
+}
+
/*
* Factory function for GenericDescriptorMatch creating
*/
diff --git a/samples/cpp/matching_to_many_images.cpp b/samples/cpp/matching_to_many_images.cpp
index f235fdd1dd..69da1e134f 100644
--- a/samples/cpp/matching_to_many_images.cpp
+++ b/samples/cpp/matching_to_many_images.cpp
@@ -7,132 +7,228 @@
using namespace cv;
using namespace std;
-void maskMatchesByTrainImgIdx( const vector<DMatch>& matches, int trainImgIdx, vector<char>& mask );
-void readTrainFilenames( const string& filename, string& dirName, vector<string>& trainFilenames );
+/*
+ * This is a sample on matching descriptors detected on one image to descriptors detected in image set.
+ * So we have one query image and several train images. For each keypoint descriptor of query image
+ * the one nearest train descriptor is found the entire collection of train images. To visualize the result
+ * of matching we save images, each of which combines query and train image with matches between them (if they exist).
+ * Match is drawn as line between corresponding points. Count of all matches is equel to count of
+ * query keypoints, so we have the same count of lines in all set of result images (but not for each result
+ * (train) image).
+ */
+
+const string defaultDetectorType = "SURF";
+const string defaultDescriptorType = "SURF";
+const string defaultMatcherType = "FlannBased";
+const string defaultQueryImageName = "../../opencv/samples/cpp/matching_to_many_images/query.png";
+const string defaultFileWithTrainImages = "../../opencv/samples/cpp/matching_to_many_images/train/trainImages.txt";
+const string defaultDirToSaveResImages = "../../opencv/samples/cpp/matching_to_many_images/results";
-int main(int argc, char** argv)
+void maskMatchesByTrainImgIdx( const vector<DMatch>& matches, int trainImgIdx, vector<char>& mask )
+{
+ mask.resize( matches.size() );
+ fill( mask.begin(), mask.end(), 0 );
+ for( size_t i = 0; i < matches.size(); i++ )
+ {
+ if( matches[i].imgIdx == trainImgIdx )
+ mask[i] = 1;
+ }
+}
+
+void readTrainFilenames( const string& filename, string& dirName, vector<string>& trainFilenames )
{
- Mat queryImg;
- vector<KeyPoint> queryPoints;
- Mat queryDescs;
+ const char dlmtr = '/';
- vector<Mat> trainImgCollection;
- vector<vector<KeyPoint> > trainPointCollection;
- vector<Mat> trainDescCollection;
+ trainFilenames.clear();
- vector<DMatch> matches;
+ ifstream file( filename.c_str() );
+ if ( !file.is_open() )
+ return;
- if( argc != 7 )
+ size_t pos = filename.rfind(dlmtr);
+ dirName = pos == string::npos ? "" : filename.substr(0, pos) + dlmtr;
+ while( !file.eof() )
{
- cout << "Format:" << endl;
- cout << argv[0] << "[detectorType] [descriptorType] [matcherType] [queryImage] [fileWithTrainImages] [dirToSaveResImages]" << endl;
- return -1;
+ string str; getline( file, str );
+ if( str.empty() ) break;
+ trainFilenames.push_back(str);
}
+ file.close();
+}
- cout << "< 1.) Creating feature detector, descriptor extractor and descriptor matcher ..." << endl;
- Ptr<FeatureDetector> detector = createFeatureDetector( argv[1] );
- Ptr<DescriptorExtractor> descriptorExtractor = createDescriptorExtractor( argv[2] );
- Ptr<DescriptorMatcher> descriptorMatcher = createDescriptorMatcher( argv[3] );
+bool createDetectorDescriptorMatcher( const string& detectorType, const string& descriptorType, const string& matcherType,
+ Ptr<FeatureDetector>& featureDetector,
+ Ptr<DescriptorExtractor>& descriptorExtractor,
+ Ptr<DescriptorMatcher>& descriptorMatcher )
+{
+ cout << "< Creating feature detector, descriptor extractor and descriptor matcher ..." << endl;
+ featureDetector = createFeatureDetector( detectorType );
+ descriptorExtractor = createDescriptorExtractor( descriptorType );
+ descriptorMatcher = createDescriptorMatcher( matcherType );
cout << ">" << endl;
- if( detector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() )
- {
+
+ bool isCreated = !( featureDetector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() );
+ if( !isCreated )
cout << "Can not create feature detector or descriptor exstractor or descriptor matcher of given types." << endl << ">" << endl;
- return -1;
- }
- cout << "< 2.) Reading the images..." << endl;
- queryImg = imread( argv[4], CV_LOAD_IMAGE_GRAYSCALE);
- if( queryImg.empty() )
+ return isCreated;
+}
+
+bool readImages( const string& queryImageName, const string& trainFilename,
+ Mat& queryImage, vector <Mat>& trainImages, vector<string>& trainImageNames )
+{
+ cout << "< Reading the images..." << endl;
+ queryImage = imread( queryImageName, CV_LOAD_IMAGE_GRAYSCALE);
+ if( queryImage.empty() )
{
cout << "Query image can not be read." << endl << ">" << endl;
- return -1;
+ return false;
}
string trainDirName;
- vector<string> trainFilenames;
- vector<int> usedTrainImgIdxs;
- readTrainFilenames( argv[5], trainDirName, trainFilenames );
- if( trainFilenames.empty() )
+ readTrainFilenames( trainFilename, trainDirName, trainImageNames );
+ if( trainImageNames.empty() )
{
cout << "Train image filenames can not be read." << endl << ">" << endl;
- return -1;
+ return false;
}
- for( size_t i = 0; i < trainFilenames.size(); i++ )
+ int readImageCount = 0;
+ for( size_t i = 0; i < trainImageNames.size(); i++ )
{
- Mat img = imread( trainDirName + trainFilenames[i], CV_LOAD_IMAGE_GRAYSCALE );
- if( img.empty() ) cout << "Train image " << trainDirName + trainFilenames[i] << " can not be read." << endl;
- trainImgCollection.push_back( img );
- usedTrainImgIdxs.push_back( i );
+ string filename = trainDirName + trainImageNames[i];
+ Mat img = imread( filename, CV_LOAD_IMAGE_GRAYSCALE );
+ if( img.empty() )
+ cout << "Train image " << filename << " can not be read." << endl;
+ else
+ readImageCount++;
+ trainImages.push_back( img );
}
- if( trainImgCollection.empty() )
+ if( !readImageCount )
{
cout << "All train images can not be read." << endl << ">" << endl;
- return -1;
+ return false;
}
else
- cout << trainImgCollection.size() << " train images were read." << endl;
+ cout << readImageCount << " train images were read." << endl;
cout << ">" << endl;
- cout << endl << "< 3.) Extracting keypoints from images..." << endl;
- detector->detect( queryImg, queryPoints );
- detector->detect( trainImgCollection, trainPointCollection );
+ return true;
+}
+
+void detectKeypoints( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
+ const vector<Mat>& trainImages, vector<vector<KeyPoint> >& trainKeypoints,
+ Ptr<FeatureDetector>& featureDetector )
+{
+ cout << endl << "< Extracting keypoints from images..." << endl;
+ featureDetector->detect( queryImage, queryKeypoints );
+ featureDetector->detect( trainImages, trainKeypoints );
cout << ">" << endl;
+}
- cout << "< 4.) Computing descriptors for keypoints..." << endl;
- descriptorExtractor->compute( queryImg, queryPoints, queryDescs );
- descriptorExtractor->compute( trainImgCollection, trainPointCollection, trainDescCollection );
+void computeDescriptors( const Mat& queryImage, vector<KeyPoint>& queryKeypoints, Mat& queryDescriptors,
+ const vector<Mat>& trainImages, vector<vector<KeyPoint> >& trainKeypoints, vector<Mat>& trainDescriptors,
+ Ptr<DescriptorExtractor>& descriptorExtractor )
+{
+ cout << "< Computing descriptors for keypoints..." << endl;
+ descriptorExtractor->compute( queryImage, queryKeypoints, queryDescriptors );
+ descriptorExtractor->compute( trainImages, trainKeypoints, trainDescriptors );
cout << ">" << endl;
+}
- cout << "< 5.) Set train descriptors collection in the matcher and match query descriptors to them..." << endl;
- descriptorMatcher->add( trainDescCollection );
- descriptorMatcher->match( queryDescs, matches );
- CV_Assert( queryPoints.size() == matches.size() );
+void matchDescriptors( const Mat& queryDescriptors, const vector<Mat>& trainDescriptors,
+ vector<DMatch>& matches, Ptr<DescriptorMatcher>& descriptorMatcher )
+{
+ cout << "< Set train descriptors collection in the matcher and match query descriptors to them..." << endl;
+ descriptorMatcher->add( trainDescriptors );
+ descriptorMatcher->match( queryDescriptors, matches );
+ CV_Assert( queryDescriptors.rows == (int)matches.size() || matches.empty() );
cout << ">" << endl;
+}
- cout << "< 6.) Save results..." << endl;
+void saveResultImages( const Mat& queryImage, const vector<KeyPoint>& queryKeypoints,
+ const vector<Mat>& trainImages, const vector<vector<KeyPoint> >& trainKeypoints,
+ const vector<DMatch>& matches, const vector<string>& trainImagesNames, const string& resultDir )
+{
+ cout << "< Save results..." << endl;
Mat drawImg;
vector<char> mask;
- for( size_t i = 0; i < trainImgCollection.size(); i++ )
+ for( size_t i = 0; i < trainImages.size(); i++ )
{
- maskMatchesByTrainImgIdx( matches, i, mask );
- drawMatches( queryImg, queryPoints, trainImgCollection[i], trainPointCollection[i],
- matches, drawImg, Scalar::all(-1), Scalar::all(-1), mask );
-
- imwrite( string(argv[6]) + "/res_" + trainFilenames[usedTrainImgIdxs[i]] + ".png", drawImg );
+ if( !trainImages[i].empty() )
+ {
+ maskMatchesByTrainImgIdx( matches, i, mask );
+ drawMatches( queryImage, queryKeypoints, trainImages[i], trainKeypoints[i],
+ matches, drawImg, Scalar::all(-1), Scalar::all(-1), mask );
+ string filename = resultDir + "/res_" + trainImagesNames[i];
+ if( !imwrite( filename, drawImg ) )
+ cout << "Image " << filename << " can not be saved (may be because directory " << resultDir << " does not exist)." << endl;
+ }
}
cout << ">" << endl;
-
- return 0;
}
-
-void maskMatchesByTrainImgIdx( const vector<DMatch>& matches, int trainImgIdx, vector<char>& mask )
+void printPrompt( const string& applName )
{
- mask.resize( matches.size() );
- fill( mask.begin(), mask.end(), 0 );
- for( size_t i = 0; i < matches.size(); i++ )
- {
- if( matches[i].imgIdx == trainImgIdx )
- mask[i] = 1;
- }
+ cout << endl << "Format:" << endl;
+ cout << applName << " [detectorType] [descriptorType] [matcherType] [queryImage] [fileWithTrainImages] [dirToSaveResImages]" << endl;
+ cout << endl << "Example:" << endl
+ << defaultDetectorType << " " << defaultDescriptorType << " " << defaultMatcherType << " "
+ << defaultQueryImageName << " " << defaultFileWithTrainImages << " " << defaultDirToSaveResImages << endl;
}
-void readTrainFilenames( const string& filename, string& dirName, vector<string>& trainFilenames )
+int main(int argc, char** argv)
{
- const char dlmtr = '/';
+ string detectorType = defaultDetectorType;
+ string descriptorType = defaultDescriptorType;
+ string matcherType = defaultMatcherType;
+ string queryImageName = defaultQueryImageName;
+ string fileWithTrainImages = defaultFileWithTrainImages;
+ string dirToSaveResImages = defaultDirToSaveResImages;
+
+ if( argc != 7 && argc != 1 )
+ {
+ printPrompt( argv[0] );
+ return -1;
+ }
- trainFilenames.clear();
+ if( argc != 1 )
+ {
+ detectorType = argv[1]; descriptorType = argv[2]; matcherType = argv[3];
+ queryImageName = argv[4]; fileWithTrainImages = argv[5];
+ dirToSaveResImages = argv[6];
+ }
- ifstream file( filename.c_str() );
- if ( !file.is_open() )
- return;
+ Ptr<FeatureDetector> featureDetector;
+ Ptr<DescriptorExtractor> descriptorExtractor;
+ Ptr<DescriptorMatcher> descriptorMatcher;
+ if( !createDetectorDescriptorMatcher( detectorType, descriptorType, matcherType, featureDetector, descriptorExtractor, descriptorMatcher ) )
+ {
+ printPrompt( argv[0] );
+ return -1;
+ }
- size_t pos = filename.rfind(dlmtr);
- dirName = pos == string::npos ? "" : filename.substr(0, pos) + dlmtr;
- while( !file.eof() )
+ Mat queryImage;
+ vector<Mat> trainImages;
+ vector<string> trainImagesNames;
+ if( !readImages( queryImageName, fileWithTrainImages, queryImage, trainImages, trainImagesNames ) )
{
- string str; getline( file, str );
- if( str.empty() ) break;
- trainFilenames.push_back(str);
+ printPrompt( argv[0] );
+ return -1;
}
- file.close();
+
+ vector<KeyPoint> queryKeypoints;
+ vector<vector<KeyPoint> > trainKeypoints;
+ detectKeypoints( queryImage, queryKeypoints, trainImages, trainKeypoints, featureDetector );
+
+ Mat queryDescriptors;
+ vector<Mat> trainDescriptors;
+ computeDescriptors( queryImage, queryKeypoints, queryDescriptors,
+ trainImages, trainKeypoints, trainDescriptors,
+ descriptorExtractor );
+
+ vector<DMatch> matches;
+ matchDescriptors( queryDescriptors, trainDescriptors, matches, descriptorMatcher );
+
+ saveResultImages( queryImage, queryKeypoints, trainImages, trainKeypoints,
+ matches, trainImagesNames, dirToSaveResImages );
+ return 0;
}
diff --git a/samples/cpp/matching_to_many_images/query.png b/samples/cpp/matching_to_many_images/query.png
new file mode 100755
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HcmV?d00001
diff --git a/samples/cpp/matching_to_many_images/train/trainImages.txt b/samples/cpp/matching_to_many_images/train/trainImages.txt
new file mode 100755
index 0000000000..b37663950f
--- /dev/null
+++ b/samples/cpp/matching_to_many_images/train/trainImages.txt
@@ -0,0 +1,3 @@
+1.png
+2.png
+3.png
diff --git a/tests/cv/src/afeatures2d.cpp b/tests/cv/src/afeatures2d.cpp
index 4b36c4e9cd..d8a1cb5f9f 100644
--- a/tests/cv/src/afeatures2d.cpp
+++ b/tests/cv/src/afeatures2d.cpp
@@ -60,7 +60,7 @@ public:
CvTest( testName, "cv::FeatureDetector::detect"), fdetector(_fdetector) {}
protected:
- virtual void run( int start_from )
+ virtual void run( int /*start_from*/ )
{
const float maxPtDif = 1.f;
const float maxSizeDif = 1.f;
@@ -112,7 +112,7 @@ protected:
for( size_t c = 0; c < calcKeypoints.size(); c++ )
{
progress = update_progress( progress, v*calcKeypoints.size() + c, progressCount, 0 );
- float curDist = norm( calcKeypoints[c].pt - validKeypoints[v].pt );
+ float curDist = (float)norm( calcKeypoints[c].pt - validKeypoints[v].pt );
if( curDist < minDist )
{
minDist = curDist;
@@ -434,7 +434,7 @@ int CV_DescriptorMatcherTest::testMatch( const Mat& query, const Mat& train )
for( size_t i = 0; i < matches.size(); i++ )
{
DMatch match = matches[i];
- int shift = dmatcher->supportMask() ? 1 : 0;
+ int shift = dmatcher->isMaskSupported() ? 1 : 0;
{
if( i < queryDescCount/2 )
{
@@ -533,7 +533,7 @@ int CV_DescriptorMatcherTest::testKnnMatch( const Mat& query, const Mat& train )
else
{
int badCount = 0;
- int shift = dmatcher->supportMask() ? 1 : 0;
+ int shift = dmatcher->isMaskSupported() ? 1 : 0;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != knn )
@@ -641,8 +641,8 @@ int CV_DescriptorMatcherTest::testRadiusMatch( const Mat& query, const Mat& trai
res = curRes != CvTS::OK ? curRes : res;
int badCount = 0;
- int shift = dmatcher->supportMask() ? 1 : 0;
- int needMatchCount = dmatcher->supportMask() ? n-1 : n;
+ int shift = dmatcher->isMaskSupported() ? 1 : 0;
+ int needMatchCount = dmatcher->isMaskSupported() ? n-1 : n;
for( size_t i = 0; i < matches.size(); i++ )
{
if( (int)matches[i].size() != needMatchCount )
@@ -741,6 +741,6 @@ CV_CalonderDescriptorExtractorTest<float> floatCalonderTest( "descriptor-calonde
* Matchers
*/
CV_DescriptorMatcherTest bruteForceMatcherTest( "descriptor-matcher-brute-force",
- new BruteForceMatcher<L2<float> >, 0.01 );
+ new BruteForceMatcher<L2<float> >, 0.01f );
CV_DescriptorMatcherTest flannBasedMatcherTest( "descriptor-matcher-flann-based",
- new FlannBasedMatcher, 0.04 );
+ new FlannBasedMatcher, 0.04f );