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handling Ptr<> arguments and return values in Python wrappers

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partially wrapped features2d framework
added feature_homography.py sample
This commit is contained in:
Alexander Mordvintsev
2011-09-07 09:38:22 +00:00
parent 2ef4e2eeb7
commit d50cc51070
4 changed files with 161 additions and 34 deletions
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56
modules/features2d/include/opencv2/features2d/features2d.hpp
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@@ -1378,7 +1378,7 @@ protected:
/*
* Abstract base class for 2D image feature detectors.
*/
class CV_EXPORTS FeatureDetector
class CV_EXPORTS_W FeatureDetector
{
public:
virtual ~FeatureDetector();
@@ -1390,7 +1390,7 @@ public:
* mask Mask specifying where to look for keypoints (optional). Must be a char
* matrix with non-zero values in the region of interest.
*/
void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
CV_WRAP void detect( const Mat& image, CV_OUT vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
/*
* Detect keypoints in an image set.
@@ -1406,10 +1406,10 @@ public:
virtual void write( FileStorage& ) const;
// Return true if detector object is empty
virtual bool empty() const;
CV_WRAP virtual bool empty() const;
// Create feature detector by detector name.
static Ptr<FeatureDetector> create( const string& detectorType );
CV_WRAP static Ptr<FeatureDetector> create( const string& detectorType );
protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const = 0;
@@ -1422,10 +1422,10 @@ protected:
static void removeInvalidPoints( const Mat& mask, vector<KeyPoint>& keypoints );
};
class CV_EXPORTS FastFeatureDetector : public FeatureDetector
class CV_EXPORTS_W FastFeatureDetector : public FeatureDetector
{
public:
FastFeatureDetector( int threshold=10, bool nonmaxSuppression=true );
CV_WRAP FastFeatureDetector( int threshold=10, bool nonmaxSuppression=true );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
@@ -1673,11 +1673,11 @@ protected:
* Adapts a detector to detect points over multiple levels of a Gaussian
* pyramid. Useful for detectors that are not inherently scaled.
*/
class CV_EXPORTS PyramidAdaptedFeatureDetector : public FeatureDetector
class CV_EXPORTS_W PyramidAdaptedFeatureDetector : public FeatureDetector
{
public:
// maxLevel - The 0-based index of the last pyramid layer
PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxLevel=2 );
CV_WRAP PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxLevel=2 );
// TODO implement read/write
virtual bool empty() const;
@@ -1835,7 +1835,7 @@ CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const v
* distances between descriptors. Therefore we represent a collection of
* descriptors as a cv::Mat, where each row is one keypoint descriptor.
*/
class CV_EXPORTS DescriptorExtractor
class CV_EXPORTS_W DescriptorExtractor
{
public:
virtual ~DescriptorExtractor();
@@ -1846,7 +1846,7 @@ public:
* 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.
*/
void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
CV_WRAP void compute( const Mat& image, CV_IN_OUT vector<KeyPoint>& keypoints, CV_OUT Mat& descriptors ) const;
/*
* Compute the descriptors for a keypoints collection detected in image collection.
@@ -1860,12 +1860,12 @@ public:
virtual void read( const FileNode& );
virtual void write( FileStorage& ) const;
virtual int descriptorSize() const = 0;
virtual int descriptorType() const = 0;
CV_WRAP virtual int descriptorSize() const = 0;
CV_WRAP virtual int descriptorType() const = 0;
virtual bool empty() const;
CV_WRAP virtual bool empty() const;
static Ptr<DescriptorExtractor> create( const string& descriptorExtractorType );
CV_WRAP static Ptr<DescriptorExtractor> create( const string& descriptorExtractorType );
protected:
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const = 0;
@@ -2201,19 +2201,19 @@ struct CV_EXPORTS Hamming
/*
* Struct for matching: query descriptor index, train descriptor index, train image index and distance between descriptors.
*/
struct CV_EXPORTS DMatch
struct CV_EXPORTS_W_SIMPLE DMatch
{
DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(std::numeric_limits<float>::max()) {}
DMatch( int _queryIdx, int _trainIdx, float _distance ) :
CV_WRAP DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(std::numeric_limits<float>::max()) {}
CV_WRAP DMatch( int _queryIdx, int _trainIdx, float _distance ) :
queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {}
DMatch( int _queryIdx, int _trainIdx, int _imgIdx, float _distance ) :
CV_WRAP DMatch( int _queryIdx, int _trainIdx, int _imgIdx, float _distance ) :
queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {}
int queryIdx; // query descriptor index
int trainIdx; // train descriptor index
int imgIdx; // train image index
CV_PROP_RW int queryIdx; // query descriptor index
CV_PROP_RW int trainIdx; // train descriptor index
CV_PROP_RW int imgIdx; // train image index
float distance;
CV_PROP_RW float distance;
// less is better
bool operator<( const DMatch &m ) const
@@ -2228,7 +2228,7 @@ struct CV_EXPORTS DMatch
/*
* Abstract base class for matching two sets of descriptors.
*/
class CV_EXPORTS DescriptorMatcher
class CV_EXPORTS_W DescriptorMatcher
{
public:
virtual ~DescriptorMatcher();
@@ -2273,14 +2273,14 @@ public:
* Method train() is run in this methods.
*/
// Find one best match for each query descriptor (if mask is empty).
void match( const Mat& queryDescriptors, const Mat& trainDescriptors,
vector<DMatch>& matches, const Mat& mask=Mat() ) const;
CV_WRAP void match( const Mat& queryDescriptors, const Mat& trainDescriptors,
CV_OUT 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,
CV_WRAP void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
CV_OUT 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).
@@ -2308,7 +2308,7 @@ public:
// but with empty train data.
virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
static Ptr<DescriptorMatcher> create( const string& descriptorMatcherType );
CV_WRAP static Ptr<DescriptorMatcher> create( const string& descriptorMatcherType );
protected:
/*
* Class to work with descriptors from several images as with one merged matrix.