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rename SCascade -> SoftCascadeDetector

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marina.kolpakova
2013-01-29 13:09:49 +04:00
parent 1b43b0e2df
commit 61441a1014
6 changed files with 95 additions and 94 deletions
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modules/softcascade/include/opencv2/softcascade/softcascade.hpp
+49 -45
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@@ -56,50 +56,52 @@ protected:
enum {BINS = 10};
};
// Implementation of soft (stageless) cascaded detector.
class CV_EXPORTS_W SCascade : public Algorithm
// Representation of detectors result.
struct CV_EXPORTS Detection
{
// Default object type.
enum {PEDESTRIAN = 1};
// Creates Detection from an object bounding box and confidence.
// Param b is a bounding box
// Param c is a confidence that object belongs to class k
// Param k is an object class
Detection(const cv::Rect& b, const float c, int k = PEDESTRIAN) : bb(b), confidence(c), kind(k) {}
cv::Rect bb;
float confidence;
int kind;
};
// Create channel integrals for Soft Cascade detector.
class CV_EXPORTS Channels
{
public:
// constrictor form resizing factor.
// Param shrinkage is a resizing factor. Resize is applied before the computing integral sum
Channels(const int shrinkage);
// Representation of detectors result.
struct CV_EXPORTS Detection
{
// Default object type.
enum {PEDESTRIAN = 1};
// Appends specified number of HOG first-order features integrals into given vector.
// Param gray is an input 1-channel gray image.
// Param integrals is a vector of integrals. Hog-channels will be appended to it.
// Param bins is a number of hog-bins
void appendHogBins(const cv::Mat& gray, std::vector<cv::Mat>& integrals, int bins) const;
// Creates Detection from an object bounding box and confidence.
// Param b is a bounding box
// Param c is a confidence that object belongs to class k
// Param k is an object class
Detection(const cv::Rect& b, const float c, int k = PEDESTRIAN) : bb(b), confidence(c), kind(k) {}
// Converts 3-channel BGR input frame in Luv and appends each channel to the integrals.
// Param frame is an input 3-channel BGR colored image.
// Param integrals is a vector of integrals. Computed from the frame luv-channels will be appended to it.
void appendLuvBins(const cv::Mat& frame, std::vector<cv::Mat>& integrals) const;
cv::Rect bb;
float confidence;
int kind;
};
private:
int shrinkage;
};
// Create channel integrals for Soft Cascade detector.
class CV_EXPORTS Channels
{
public:
// constrictor form resizing factor.
// Param shrinkage is a resizing factor. Resize is applied before the computing integral sum
Channels(const int shrinkage);
// Appends specified number of HOG first-order features integrals into given vector.
// Param gray is an input 1-channel gray image.
// Param integrals is a vector of integrals. Hog-channels will be appended to it.
// Param bins is a number of hog-bins
void appendHogBins(const cv::Mat& gray, std::vector<cv::Mat>& integrals, int bins) const;
// Converts 3-channel BGR input frame in Luv and appends each channel to the integrals.
// Param frame is an input 3-channel BGR colored image.
// Param integrals is a vector of integrals. Computed from the frame luv-channels will be appended to it.
void appendLuvBins(const cv::Mat& frame, std::vector<cv::Mat>& integrals) const;
private:
int shrinkage;
};
// ========================================================================== //
// Implementation of soft (stageless) cascaded detector.
// ========================================================================== //
class CV_EXPORTS_W SoftCascadeDetector : public Algorithm
{
public:
enum { NO_REJECT = 1, DOLLAR = 2, /*PASCAL = 4,*/ DEFAULT = NO_REJECT};
@@ -108,24 +110,25 @@ public:
// Param minScale is a maximum scale relative to the original size of the image on which cascade will be applied.
// Param scales is a number of scales from minScale to maxScale.
// Param rejCriteria is used for NMS.
CV_WRAP SCascade(const double minScale = 0.4, const double maxScale = 5., const int scales = 55, const int rejCriteria = 1);
CV_WRAP SoftCascadeDetector(double minScale = 0.4, double maxScale = 5., int scales = 55, int rejCriteria = 1);
CV_WRAP virtual ~SCascade();
CV_WRAP virtual ~SoftCascadeDetector();
cv::AlgorithmInfo* info() const;
// Load cascade from FileNode.
// Param fn is a root node for cascade. Should be <cascade>.
CV_WRAP virtual bool load(const FileNode& fn);
// Load soft cascade from FileNode.
// Param fileNode is a root node for cascade.
CV_WRAP virtual bool load(const FileNode& fileNode);
// Load cascade config.
CV_WRAP virtual void read(const FileNode& fn);
// Load soft cascade config.
CV_WRAP virtual void read(const FileNode& fileNode);
// Return the vector of Detection objects.
// Param image is a frame on which detector will be applied.
// Param rois is a vector of regions of interest. Only the objects that fall into one of the regions will be returned.
// Param objects is an output array of Detections
virtual void detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const;
// Param rects is an output array of bounding rectangles for detected objects.
// Param confs is an output array of confidence for detected objects. i-th bounding rectangle corresponds i-th confidence.
CV_WRAP virtual void detect(InputArray image, InputArray rois, CV_OUT OutputArray rects, CV_OUT OutputArray confs) const;
@@ -144,6 +147,7 @@ private:
};
CV_EXPORTS bool initModule_softcascade(void);
}
#endif