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All tests writing temporary files are updated to use cv::tempfile() function

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This commit is contained in:
Andrey Kamaev
2012-06-25 11:24:06 +00:00
parent ec3a7665b0
commit d9c74f63e1
26 changed files with 500 additions and 438 deletions
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78
modules/features2d/include/opencv2/features2d/features2d.hpp
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@@ -328,7 +328,7 @@ public:
float patternScale = 22.0f,
int nOctaves = 4,
const vector<int>& selectedPairs = vector<int>());
FREAK( const FREAK& rhs );
FREAK( const FREAK& rhs );
FREAK& operator=( const FREAK& );
virtual ~FREAK();
@@ -349,51 +349,51 @@ public:
vector<int> selectPairs( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints,
const double corrThresh = 0.7, bool verbose = true );
AlgorithmInfo* info() const;
AlgorithmInfo* info() const;
enum
{
NB_SCALES = 64, NB_PAIRS = 512, NB_ORIENPAIRS = 45
};
enum
{
NB_SCALES = 64, NB_PAIRS = 512, NB_ORIENPAIRS = 45
};
protected:
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
void buildPattern();
uchar meanIntensity( const Mat& image, const Mat& integral, const float kp_x, const float kp_y,
void buildPattern();
uchar meanIntensity( const Mat& image, const Mat& integral, const float kp_x, const float kp_y,
const unsigned int scale, const unsigned int rot, const unsigned int point ) const;
bool orientationNormalized; //true if the orientation is normalized, false otherwise
bool orientationNormalized; //true if the orientation is normalized, false otherwise
bool scaleNormalized; //true if the scale is normalized, false otherwise
double patternScale; //scaling of the pattern
int nOctaves; //number of octaves
bool extAll; // true if all pairs need to be extracted for pairs selection
double patternScale0;
int nOctaves0;
vector<int> selectedPairs0;
struct PatternPoint
{
float x; // x coordinate relative to center
float y; // x coordinate relative to center
float sigma; // Gaussian smoothing sigma
};
struct PatternPoint
{
float x; // x coordinate relative to center
float y; // x coordinate relative to center
float sigma; // Gaussian smoothing sigma
};
struct DescriptionPair
{
uchar i; // index of the first point
uchar j; // index of the second point
};
struct DescriptionPair
{
uchar i; // index of the first point
uchar j; // index of the second point
};
struct OrientationPair
{
uchar i; // index of the first point
uchar j; // index of the second point
int weight_dx; // dx/(norm_sq))*4096
int weight_dy; // dy/(norm_sq))*4096
};
vector<PatternPoint> patternLookup; // look-up table for the pattern points (position+sigma of all points at all scales and orientation)
struct OrientationPair
{
uchar i; // index of the first point
uchar j; // index of the second point
int weight_dx; // dx/(norm_sq))*4096
int weight_dy; // dy/(norm_sq))*4096
};
vector<PatternPoint> patternLookup; // look-up table for the pattern points (position+sigma of all points at all scales and orientation)
int patternSizes[NB_SCALES]; // size of the pattern at a specific scale (used to check if a point is within image boundaries)
DescriptionPair descriptionPairs[NB_PAIRS];
OrientationPair orientationPairs[NB_ORIENPAIRS];
@@ -603,7 +603,7 @@ public:
// TODO implement read/write
virtual bool empty() const;
AlgorithmInfo* info() const;
protected:
@@ -641,8 +641,8 @@ protected:
class CV_EXPORTS AdjusterAdapter: public FeatureDetector
{
public:
/** pure virtual interface
*/
/** pure virtual interface
*/
virtual ~AdjusterAdapter() {}
/** too few features were detected so, adjust the detector params accordingly
* \param min the minimum number of desired features
@@ -682,7 +682,7 @@ public:
/** \param adjuster an AdjusterAdapter that will do the detection and parameter adjustment
* \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
* for the FastAdjuster this can be high, but with Star or Surf this can get time consuming
* \param min_features the minimum desired features
*/
DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features=400, int max_features=500, int max_iters=5 );
@@ -693,8 +693,8 @@ protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
private:
DynamicAdaptedFeatureDetector& operator=(const DynamicAdaptedFeatureDetector&);
DynamicAdaptedFeatureDetector(const DynamicAdaptedFeatureDetector&);
DynamicAdaptedFeatureDetector& operator=(const DynamicAdaptedFeatureDetector&);
DynamicAdaptedFeatureDetector(const DynamicAdaptedFeatureDetector&);
int escape_iters_;
int min_features_, max_features_;
@@ -792,7 +792,7 @@ public:
virtual bool empty() const;
protected:
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
Ptr<DescriptorExtractor> descriptorExtractor;
};
@@ -962,7 +962,7 @@ class CV_EXPORTS_W DescriptorMatcher : public Algorithm
public:
virtual ~DescriptorMatcher();
/*
/*
* Add descriptors to train descriptor collection.
* descriptors Descriptors to add. Each descriptors[i] is a descriptors set from one image.
*/
@@ -1078,7 +1078,7 @@ protected:
static bool isMaskedOut( const vector<Mat>& masks, int queryIdx );
static Mat clone_op( Mat m ) { return m.clone(); }
void checkMasks( const vector<Mat>& masks, int queryDescriptorsCount ) const;
void checkMasks( const vector<Mat>& masks, int queryDescriptorsCount ) const;
// Collection of descriptors from train images.
vector<Mat> trainDescCollection;