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refactoring dynamic detectors

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This commit is contained in:
Ethan Rublee
2010-11-23 22:26:36 +00:00
parent c6e43c385d
commit f6b0818996
4 changed files with 247 additions and 148 deletions
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215
modules/features2d/include/opencv2/features2d/features2d.hpp
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@@ -1451,148 +1451,97 @@ protected:
/*
* Dynamic Feature Detectors
*/
/** \brief A feature detector parameter adjuster, this is used by the DynamicDetector
* and is a wrapper for FeatureDetector that allow them to be adjusted after a detection
*/
class CV_EXPORTS AdjusterAdapter: public FeatureDetector {
public:
/** pure virtual interface
*/
virtual ~AdjusterAdapter() {
}
/** too few features were detected so, adjust the detector params accordingly
* \param min the minimum number of desired features
* \param n_detected the number previously detected
*/
virtual void tooFew(int min, int n_detected) = 0;
/** too many features were detected so, adjust the detector params accordingly
* \param max the maximum number of desired features
* \param n_detected the number previously detected
*/
virtual void tooMany(int max, int n_detected) = 0;
/** are params maxed out or still valid?
* \return false if the parameters can't be adjusted any more
*/
virtual bool good() const = 0;
};
/** \brief an adaptively adjusting detector that iteratively detects until the desired number
* of features are detected.
* Beware that this is not thread safe - as the adjustment of parameters breaks the const
* of the detection routine...
* /TODO Make this const correct and thread safe
*/
template<typename Adjuster>
class DynamicDetectorAdaptor: public FeatureDetector {
class CV_EXPORTS DynamicDetector: public FeatureDetector {
public:
/** \param min_features the minimum desired features
* \param max_features the maximum desired number of features
* \param max_iters the maximum number of times to try to adjust the feature detector params
* for the FastAdjuster this can be high, but with Star or Surf this can get time consuming
* \param a a copy of an Adjuster that will do the detection and parameter adjustment
* \param a an AdjusterAdapter that will do the detection and parameter adjustment
*/
DynamicDetectorAdaptor(int min_features, int max_features,
int max_iters, const Adjuster& a = Adjuster()) :
escape_iters_(max_iters), min_features_(min_features), max_features_(
max_features), adjuster_(a) {
}
DynamicDetector(int min_features, int max_features, int max_iters,
const Ptr<AdjusterAdapter>& a);
protected:
virtual void detectImpl(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
cv::Mat()) const {
//for oscillation testing
bool down = false;
bool up = false;
//flag for whether the correct threshhold has been reached
bool thresh_good = false;
//this is bad but adjuster should persist from detection to detection
Adjuster& adjuster = const_cast<Adjuster&> (adjuster_);
//break if the desired number hasn't been reached.
int iter_count = escape_iters_;
do {
keypoints.clear();
//the adjuster takes care of calling the detector with updated parameters
adjuster.detect(image, mask, keypoints);
if (int(keypoints.size()) < min_features_) {
down = true;
adjuster.tooFew(min_features_, keypoints.size());
} else if (int(keypoints.size()) > max_features_) {
up = true;
adjuster.tooMany(max_features_, keypoints.size());
} else
thresh_good = true;
} while (--iter_count >= 0 && !(down && up) && !thresh_good
&& adjuster.good());
}
virtual void detectImpl(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
cv::Mat()) const;
private:
int escape_iters_;
int min_features_, max_features_;
Adjuster adjuster_;
Ptr<AdjusterAdapter> adjuster_;
};
struct FastAdjuster {
FastAdjuster() :
thresh_(20) {
}
void detect(const Mat& img, const Mat& mask, std::vector<
KeyPoint>& keypoints) const {
FastFeatureDetector(thresh_, true).detect(img, keypoints, mask);
}
void tooFew(int min, int n_detected) {
//fast is easy to adjust
thresh_--;
}
void tooMany(int max, int n_detected) {
//fast is easy to adjust
thresh_++;
}
//return whether or not the threshhold is beyond
//a useful point
bool good() const {
return (thresh_ > 1) && (thresh_ < 200);
}
class FastAdjuster: public AdjusterAdapter {
public:
FastAdjuster(int init_thresh = 20, bool nonmax = true);
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
protected:
virtual void detectImpl(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
cv::Mat()) const;
int thresh_;
bool nonmax_;
};
struct StarAdjuster {
StarAdjuster() :
thresh_(30) {
}
void detect(const Mat& img, const Mat& mask, std::vector<
KeyPoint>& keypoints) const {
StarFeatureDetector detector_tmp(16, thresh_, 10, 8, 3);
detector_tmp.detect(img, keypoints, mask);
}
void tooFew(int min, int n_detected) {
thresh_ *= 0.9;
if (thresh_ < 1.1)
thresh_ = 1.1;
}
void tooMany(int max, int n_detected) {
thresh_ *= 1.1;
}
//return whether or not the threshhold is beyond
//a useful point
bool good() const {
return (thresh_ > 2) && (thresh_ < 200);
}
struct StarAdjuster: public AdjusterAdapter {
StarAdjuster(double initial_thresh = 30.0);
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
protected:
virtual void detectImpl(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
cv::Mat()) const;
double thresh_;
};
struct SurfAdjuster {
SurfAdjuster() :
thresh_(400.0) {
}
void detect(const Mat& img, const Mat& mask, std::vector<
KeyPoint>& keypoints) const {
SurfFeatureDetector detector_tmp(thresh_);
detector_tmp.detect(img, keypoints, mask);
}
void tooFew(int min, int n_detected) {
thresh_ *= 0.9;
if (thresh_ < 1.1)
thresh_ = 1.1;
}
void tooMany(int max, int n_detected) {
thresh_ *= 1.1;
}
//return whether or not the threshhold is beyond
//a useful point
bool good() const {
return (thresh_ > 2) && (thresh_ < 1000);
}
struct SurfAdjuster: public AdjusterAdapter {
SurfAdjuster();
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
protected:
virtual void detectImpl(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
cv::Mat()) const;
double thresh_;
};
typedef DynamicDetectorAdaptor<FastAdjuster> FASTDynamicDetector;
typedef DynamicDetectorAdaptor<StarAdjuster> StarDynamicDetector;
typedef DynamicDetectorAdaptor<SurfAdjuster> SurfDynamicDetector;
CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
float maxDeltaX, float maxDeltaY );
@@ -1865,22 +1814,15 @@ struct CV_EXPORTS L1
/*
* Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
* bit count of A exclusive ored with B
* bit count of A exclusive XOR'ed with B
*/
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;
}
ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const;
/** \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.
@@ -1889,32 +1831,17 @@ struct CV_EXPORTS HammingLUT
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...
/// Hamming distance functor, this one will try to use gcc's __builtin_popcountl
/// but will fall back on HammingLUT if not available
/// bit count of A exclusive XOR'ed with B
struct CV_EXPORTS 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;
}
ResultType operator()(const unsigned char* a, const unsigned char* b, int size) const;
};
#else
typedef HammingLUT Hamming;
#endif
/****************************************************************************************\
* DMatch *