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Remove all using directives for STL namespace and members

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Made all STL usages explicit to be able automatically find all usages of
particular class or function.
This commit is contained in:
Andrey Kamaev
2013-02-24 20:14:01 +04:00
parent f783f34e0b
commit 2a6fb2867e
310 changed files with 5744 additions and 5964 deletions
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146
modules/legacy/include/opencv2/legacy/legacy.hpp
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@@ -1877,17 +1877,17 @@ public:
LDetector(int _radius, int _threshold, int _nOctaves,
int _nViews, double _baseFeatureSize, double _clusteringDistance);
void operator()(const Mat& image,
CV_OUT vector<KeyPoint>& keypoints,
CV_OUT std::vector<KeyPoint>& keypoints,
int maxCount=0, bool scaleCoords=true) const;
void operator()(const vector<Mat>& pyr,
CV_OUT vector<KeyPoint>& keypoints,
void operator()(const std::vector<Mat>& pyr,
CV_OUT std::vector<KeyPoint>& keypoints,
int maxCount=0, bool scaleCoords=true) const;
void getMostStable2D(const Mat& image, CV_OUT vector<KeyPoint>& keypoints,
void getMostStable2D(const Mat& image, CV_OUT std::vector<KeyPoint>& keypoints,
int maxCount, const PatchGenerator& patchGenerator) const;
void setVerbose(bool verbose);
void read(const FileNode& node);
void write(FileStorage& fs, const String& name=String()) const;
void write(FileStorage& fs, const std::string& name=std::string()) const;
int radius;
int threshold;
@@ -1906,9 +1906,9 @@ class CV_EXPORTS FernClassifier
public:
FernClassifier();
FernClassifier(const FileNode& node);
FernClassifier(const vector<vector<Point2f> >& points,
const vector<Mat>& refimgs,
const vector<vector<int> >& labels=vector<vector<int> >(),
FernClassifier(const std::vector<std::vector<Point2f> >& points,
const std::vector<Mat>& refimgs,
const std::vector<std::vector<int> >& labels=std::vector<std::vector<int> >(),
int _nclasses=0, int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
@@ -1918,9 +1918,9 @@ public:
const PatchGenerator& patchGenerator=PatchGenerator());
virtual ~FernClassifier();
virtual void read(const FileNode& n);
virtual void write(FileStorage& fs, const String& name=String()) const;
virtual void write(FileStorage& fs, const std::string& name=std::string()) const;
virtual void trainFromSingleView(const Mat& image,
const vector<KeyPoint>& keypoints,
const std::vector<KeyPoint>& keypoints,
int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
@@ -1928,9 +1928,9 @@ public:
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual void train(const vector<vector<Point2f> >& points,
const vector<Mat>& refimgs,
const vector<vector<int> >& labels=vector<vector<int> >(),
virtual void train(const std::vector<std::vector<Point2f> >& points,
const std::vector<Mat>& refimgs,
const std::vector<std::vector<int> >& labels=std::vector<std::vector<int> >(),
int _nclasses=0, int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
@@ -1938,8 +1938,8 @@ public:
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual int operator()(const Mat& img, Point2f kpt, vector<float>& signature) const;
virtual int operator()(const Mat& patch, vector<float>& signature) const;
virtual int operator()(const Mat& img, Point2f kpt, std::vector<float>& signature) const;
virtual int operator()(const Mat& patch, std::vector<float>& signature) const;
virtual void clear();
virtual bool empty() const;
void setVerbose(bool verbose);
@@ -1990,9 +1990,9 @@ protected:
int compressionMethod;
int leavesPerStruct;
Size patchSize;
vector<Feature> features;
vector<int> classCounters;
vector<float> posteriors;
std::vector<Feature> features;
std::vector<int> classCounters;
std::vector<float> posteriors;
};
@@ -2032,9 +2032,9 @@ public:
RandomizedTree();
~RandomizedTree();
void train(vector<BaseKeypoint> const& base_set, RNG &rng,
void train(std::vector<BaseKeypoint> const& base_set, RNG &rng,
int depth, int views, size_t reduced_num_dim, int num_quant_bits);
void train(vector<BaseKeypoint> const& base_set, RNG &rng,
void train(std::vector<BaseKeypoint> const& base_set, RNG &rng,
PatchGenerator &make_patch, int depth, int views, size_t reduced_num_dim,
int num_quant_bits);
@@ -2069,10 +2069,10 @@ private:
int classes_;
int depth_;
int num_leaves_;
vector<RTreeNode> nodes_;
std::vector<RTreeNode> nodes_;
float **posteriors_; // 16-bytes aligned posteriors
uchar **posteriors2_; // 16-bytes aligned posteriors
vector<int> leaf_counts_;
std::vector<int> leaf_counts_;
void createNodes(int num_nodes, RNG &rng);
void allocPosteriorsAligned(int num_leaves, int num_classes);
@@ -2142,14 +2142,14 @@ public:
static const size_t DEFAULT_NUM_QUANT_BITS = 4;
RTreeClassifier();
void train(vector<BaseKeypoint> const& base_set,
void train(std::vector<BaseKeypoint> const& base_set,
RNG &rng,
int num_trees = RTreeClassifier::DEFAULT_TREES,
int depth = RandomizedTree::DEFAULT_DEPTH,
int views = RandomizedTree::DEFAULT_VIEWS,
size_t reduced_num_dim = RandomizedTree::DEFAULT_REDUCED_NUM_DIM,
int num_quant_bits = DEFAULT_NUM_QUANT_BITS);
void train(vector<BaseKeypoint> const& base_set,
void train(std::vector<BaseKeypoint> const& base_set,
RNG &rng,
PatchGenerator &make_patch,
int num_trees = RTreeClassifier::DEFAULT_TREES,
@@ -2186,7 +2186,7 @@ public:
void setFloatPosteriorsFromTextfile_176(std::string url);
float countZeroElements();
vector<RandomizedTree> trees_;
std::vector<RandomizedTree> trees_;
private:
int classes_;
@@ -2356,7 +2356,7 @@ protected:
CvAffinePose* m_affine_poses; // an array of poses
CvMat** m_transforms; // an array of affine transforms corresponding to poses
string m_feature_name; // the name of the feature associated with the descriptor
std::string m_feature_name; // the name of the feature associated with the descriptor
CvPoint m_center; // the coordinates of the feature (the center of the input image ROI)
int m_pca_dim_high; // the number of descriptor pca components to use for generating affine poses
@@ -2382,7 +2382,7 @@ public:
const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1,
int pca_dim_high = 100, int pca_dim_low = 100);
OneWayDescriptorBase(CvSize patch_size, int pose_count, const string &pca_filename, const string &train_path = string(), const string &images_list = string(),
OneWayDescriptorBase(CvSize patch_size, int pose_count, const std::string &pca_filename, const std::string &train_path = std::string(), const std::string &images_list = std::string(),
float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1,
int pca_dim_high = 100, int pca_dim_low = 100);
@@ -2412,7 +2412,7 @@ public:
// - src: input image
// - features: input features
// - pyr_levels: the number of pyramid levels
void CreateDescriptorsFromImage(IplImage* src, const vector<KeyPoint>& features);
void CreateDescriptorsFromImage(IplImage* src, const std::vector<KeyPoint>& features);
// CreatePCADescriptors: generates descriptors for PCA components, needed for fast generation of feature descriptors
void CreatePCADescriptors();
@@ -2436,8 +2436,8 @@ public:
// - distances: distance from the input patch to the closest feature pose (n)
// - _scales: scales of the input patch
// - scale_ranges: input scales variation (float[2])
void FindDescriptor(IplImage* patch, int n, vector<int>& desc_idxs, vector<int>& pose_idxs,
vector<float>& distances, vector<float>& _scales, float* scale_ranges = 0) const;
void FindDescriptor(IplImage* patch, int n, std::vector<int>& desc_idxs, std::vector<int>& pose_idxs,
std::vector<float>& distances, std::vector<float>& _scales, float* scale_ranges = 0) const;
// FindDescriptor: finds the closest descriptor
// - src: input image
@@ -2465,7 +2465,7 @@ public:
void InitializeDescriptor(int desc_idx, IplImage* train_image, const KeyPoint& keypoint, const char* feature_label);
// InitializeDescriptors: load features from an image and create descriptors for each of them
void InitializeDescriptors(IplImage* train_image, const vector<KeyPoint>& features,
void InitializeDescriptors(IplImage* train_image, const std::vector<KeyPoint>& features,
const char* feature_label = "", int desc_start_idx = 0);
// Write: writes this object to a file storage
@@ -2516,7 +2516,7 @@ public:
void ConvertDescriptorsArrayToTree(); // Converting pca_descriptors array to KD tree
// GetPCAFilename: get default PCA filename
static string GetPCAFilename () { return "pca.yml"; }
static std::string GetPCAFilename () { return "pca.yml"; }
virtual bool empty() const { return m_train_feature_count <= 0 ? true : false; }
@@ -2568,8 +2568,8 @@ public:
OneWayDescriptorObject(CvSize patch_size, int pose_count, const char* train_path, const char* pca_config,
const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1);
OneWayDescriptorObject(CvSize patch_size, int pose_count, const string &pca_filename,
const string &train_path = string (), const string &images_list = string (),
OneWayDescriptorObject(CvSize patch_size, int pose_count, const std::string &pca_filename,
const std::string &train_path = std::string (), const std::string &images_list = std::string (),
float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1);
@@ -2581,10 +2581,10 @@ public:
void Allocate(int train_feature_count, int object_feature_count);
void SetLabeledFeatures(const vector<KeyPoint>& features) {m_train_features = features;};
vector<KeyPoint>& GetLabeledFeatures() {return m_train_features;};
const vector<KeyPoint>& GetLabeledFeatures() const {return m_train_features;};
vector<KeyPoint> _GetLabeledFeatures() const;
void SetLabeledFeatures(const std::vector<KeyPoint>& features) {m_train_features = features;};
std::vector<KeyPoint>& GetLabeledFeatures() {return m_train_features;};
const std::vector<KeyPoint>& GetLabeledFeatures() const {return m_train_features;};
std::vector<KeyPoint> _GetLabeledFeatures() const;
// IsDescriptorObject: returns 1 if descriptor with specified index is positive, otherwise 0
int IsDescriptorObject(int desc_idx) const;
@@ -2597,7 +2597,7 @@ public:
int GetDescriptorPart(int desc_idx) const;
void InitializeObjectDescriptors(IplImage* train_image, const vector<KeyPoint>& features,
void InitializeObjectDescriptors(IplImage* train_image, const std::vector<KeyPoint>& features,
const char* feature_label, int desc_start_idx = 0, float scale = 1.0f,
int is_background = 0);
@@ -2606,7 +2606,7 @@ public:
protected:
int* m_part_id; // contains part id for each of object descriptors
vector<KeyPoint> m_train_features; // train features
std::vector<KeyPoint> m_train_features; // train features
int m_object_feature_count; // the number of the positive features
};
@@ -2633,16 +2633,16 @@ public:
Params( int poseCount = POSE_COUNT,
Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
string pcaFilename = string(),
string trainPath = string(), string trainImagesList = string(),
std::string pcaFilename = std::string(),
std::string trainPath = std::string(), std::string trainImagesList = std::string(),
float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
float stepScale = GET_STEP_SCALE() );
int poseCount;
Size patchSize;
string pcaFilename;
string trainPath;
string trainImagesList;
std::string pcaFilename;
std::string trainPath;
std::string trainImagesList;
float minScale, maxScale, stepScale;
};
@@ -2673,12 +2673,12 @@ protected:
// 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& queryImage, vector<KeyPoint>& queryKeypoints,
vector<vector<DMatch> >& matches, int k,
const vector<Mat>& masks, bool compactResult );
virtual void radiusMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult );
virtual void knnMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
std::vector<std::vector<DMatch> >& matches, int k,
const std::vector<Mat>& masks, bool compactResult );
virtual void radiusMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
std::vector<std::vector<DMatch> >& matches, float maxDistance,
const std::vector<Mat>& masks, bool compactResult );
Ptr<OneWayDescriptorBase> base;
Params params;
@@ -2706,7 +2706,7 @@ public:
int compressionMethod=FernClassifier::COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator() );
Params( const string& filename );
Params( const std::string& filename );
int nclasses;
int patchSize;
@@ -2717,7 +2717,7 @@ public:
int compressionMethod;
PatchGenerator patchGenerator;
string filename;
std::string filename;
};
FernDescriptorMatcher( const Params& params=Params() );
@@ -2736,16 +2736,16 @@ public:
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
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& queryImage, vector<KeyPoint>& queryKeypoints,
vector<vector<DMatch> >& matches, float maxDistance,
const vector<Mat>& masks, bool compactResult );
virtual void knnMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
std::vector<std::vector<DMatch> >& matches, int k,
const std::vector<Mat>& masks, bool compactResult );
virtual void radiusMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
std::vector<std::vector<DMatch> >& matches, float maxDistance,
const std::vector<Mat>& masks, bool compactResult );
void trainFernClassifier();
void calcBestProbAndMatchIdx( const Mat& image, const Point2f& pt,
float& bestProb, int& bestMatchIdx, vector<float>& signature );
float& bestProb, int& bestMatchIdx, std::vector<float>& signature );
Ptr<FernClassifier> classifier;
Params params;
int prevTrainCount;
@@ -2759,7 +2759,7 @@ template<typename T>
class CV_EXPORTS CalonderDescriptorExtractor : public DescriptorExtractor
{
public:
CalonderDescriptorExtractor( const string& classifierFile );
CalonderDescriptorExtractor( const std::string& classifierFile );
virtual void read( const FileNode &fn );
virtual void write( FileStorage &fs ) const;
@@ -2770,7 +2770,7 @@ public:
virtual bool empty() const;
protected:
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
virtual void computeImpl( const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors ) const;
RTreeClassifier classifier_;
static const int BORDER_SIZE = 16;
@@ -2784,7 +2784,7 @@ CalonderDescriptorExtractor<T>::CalonderDescriptorExtractor(const std::string& c
template<typename T>
void CalonderDescriptorExtractor<T>::computeImpl( const Mat& image,
vector<KeyPoint>& keypoints,
std::vector<KeyPoint>& keypoints,
Mat& descriptors) const
{
// Cannot compute descriptors for keypoints on the image border.
@@ -2838,7 +2838,7 @@ class CV_EXPORTS PlanarObjectDetector
public:
PlanarObjectDetector();
PlanarObjectDetector(const FileNode& node);
PlanarObjectDetector(const vector<Mat>& pyr, int _npoints=300,
PlanarObjectDetector(const std::vector<Mat>& pyr, int _npoints=300,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
@@ -2846,14 +2846,14 @@ public:
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
virtual ~PlanarObjectDetector();
virtual void train(const vector<Mat>& pyr, int _npoints=300,
virtual void train(const std::vector<Mat>& pyr, int _npoints=300,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
virtual void train(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
virtual void train(const std::vector<Mat>& pyr, const std::vector<KeyPoint>& keypoints,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
@@ -2861,22 +2861,22 @@ public:
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
Rect getModelROI() const;
vector<KeyPoint> getModelPoints() const;
std::vector<KeyPoint> getModelPoints() const;
const LDetector& getDetector() const;
const FernClassifier& getClassifier() const;
void setVerbose(bool verbose);
void read(const FileNode& node);
void write(FileStorage& fs, const String& name=String()) const;
bool operator()(const Mat& image, CV_OUT Mat& H, CV_OUT vector<Point2f>& corners) const;
bool operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
CV_OUT Mat& H, CV_OUT vector<Point2f>& corners,
CV_OUT vector<int>* pairs=0) const;
void write(FileStorage& fs, const std::string& name=std::string()) const;
bool operator()(const Mat& image, CV_OUT Mat& H, CV_OUT std::vector<Point2f>& corners) const;
bool operator()(const std::vector<Mat>& pyr, const std::vector<KeyPoint>& keypoints,
CV_OUT Mat& H, CV_OUT std::vector<Point2f>& corners,
CV_OUT std::vector<int>* pairs=0) const;
protected:
bool verbose;
Rect modelROI;
vector<KeyPoint> modelPoints;
std::vector<KeyPoint> modelPoints;
LDetector ldetector;
FernClassifier fernClassifier;
};