Andrey Kamaev 5e048d1fa5 Move C API of opencv_objdetect to separate file
Also move cv::linemod to own header
2013-04-12 14:37:01 +04:00

290 lines
10 KiB
C++

/*M///////////////////////////////////////////////////////////////////////////////////////
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#ifndef __OPENCV_OBJDETECT_C_H__
#define __OPENCV_OBJDETECT_C_H__
#include "opencv2/core/core_c.h"
#ifdef __cplusplus
#include <deque>
#include <vector>
extern "C" {
#endif
/****************************************************************************************\
* Haar-like Object Detection functions *
\****************************************************************************************/
#define CV_HAAR_MAGIC_VAL 0x42500000
#define CV_TYPE_NAME_HAAR "opencv-haar-classifier"
#define CV_IS_HAAR_CLASSIFIER( haar ) \
((haar) != NULL && \
(((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL)
#define CV_HAAR_FEATURE_MAX 3
typedef struct CvHaarFeature
{
int tilted;
struct
{
CvRect r;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
} CvHaarFeature;
typedef struct CvHaarClassifier
{
int count;
CvHaarFeature* haar_feature;
float* threshold;
int* left;
int* right;
float* alpha;
} CvHaarClassifier;
typedef struct CvHaarStageClassifier
{
int count;
float threshold;
CvHaarClassifier* classifier;
int next;
int child;
int parent;
} CvHaarStageClassifier;
typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade;
typedef struct CvHaarClassifierCascade
{
int flags;
int count;
CvSize orig_window_size;
CvSize real_window_size;
double scale;
CvHaarStageClassifier* stage_classifier;
CvHidHaarClassifierCascade* hid_cascade;
} CvHaarClassifierCascade;
typedef struct CvAvgComp
{
CvRect rect;
int neighbors;
} CvAvgComp;
/* Loads haar classifier cascade from a directory.
It is obsolete: convert your cascade to xml and use cvLoad instead */
CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade(
const char* directory, CvSize orig_window_size);
CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
#define CV_HAAR_DO_CANNY_PRUNING 1
#define CV_HAAR_SCALE_IMAGE 2
#define CV_HAAR_FIND_BIGGEST_OBJECT 4
#define CV_HAAR_DO_ROUGH_SEARCH 8
CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
double scale_factor CV_DEFAULT(1.1),
int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)));
/* sets images for haar classifier cascade */
CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade,
const CvArr* sum, const CvArr* sqsum,
const CvArr* tilted_sum, double scale );
/* runs the cascade on the specified window */
CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade,
CvPoint pt, int start_stage CV_DEFAULT(0));
/****************************************************************************************\
* Latent SVM Object Detection functions *
\****************************************************************************************/
// DataType: STRUCT position
// Structure describes the position of the filter in the feature pyramid
// l - level in the feature pyramid
// (x, y) - coordinate in level l
typedef struct CvLSVMFilterPosition
{
int x;
int y;
int l;
} CvLSVMFilterPosition;
// DataType: STRUCT filterObject
// Description of the filter, which corresponds to the part of the object
// V - ideal (penalty = 0) position of the partial filter
// from the root filter position (V_i in the paper)
// penaltyFunction - vector describes penalty function (d_i in the paper)
// pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
// FILTER DESCRIPTION
// Rectangular map (sizeX x sizeY),
// every cell stores feature vector (dimension = p)
// H - matrix of feature vectors
// to set and get feature vectors (i,j)
// used formula H[(j * sizeX + i) * p + k], where
// k - component of feature vector in cell (i, j)
// END OF FILTER DESCRIPTION
typedef struct CvLSVMFilterObject{
CvLSVMFilterPosition V;
float fineFunction[4];
int sizeX;
int sizeY;
int numFeatures;
float *H;
} CvLSVMFilterObject;
// data type: STRUCT CvLatentSvmDetector
// structure contains internal representation of trained Latent SVM detector
// num_filters - total number of filters (root plus part) in model
// num_components - number of components in model
// num_part_filters - array containing number of part filters for each component
// filters - root and part filters for all model components
// b - biases for all model components
// score_threshold - confidence level threshold
typedef struct CvLatentSvmDetector
{
int num_filters;
int num_components;
int* num_part_filters;
CvLSVMFilterObject** filters;
float* b;
float score_threshold;
} CvLatentSvmDetector;
// data type: STRUCT CvObjectDetection
// structure contains the bounding box and confidence level for detected object
// rect - bounding box for a detected object
// score - confidence level
typedef struct CvObjectDetection
{
CvRect rect;
float score;
} CvObjectDetection;
//////////////// Object Detection using Latent SVM //////////////
/*
// load trained detector from a file
//
// API
// CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename);
// INPUT
// filename - path to the file containing the parameters of
- trained Latent SVM detector
// OUTPUT
// trained Latent SVM detector in internal representation
*/
CVAPI(CvLatentSvmDetector*) cvLoadLatentSvmDetector(const char* filename);
/*
// release memory allocated for CvLatentSvmDetector structure
//
// API
// void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
// INPUT
// detector - CvLatentSvmDetector structure to be released
// OUTPUT
*/
CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
/*
// find rectangular regions in the given image that are likely
// to contain objects and corresponding confidence levels
//
// API
// CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
// CvLatentSvmDetector* detector,
// CvMemStorage* storage,
// float overlap_threshold = 0.5f,
// int numThreads = -1);
// INPUT
// image - image to detect objects in
// detector - Latent SVM detector in internal representation
// storage - memory storage to store the resultant sequence
// of the object candidate rectangles
// overlap_threshold - threshold for the non-maximum suppression algorithm
= 0.5f [here will be the reference to original paper]
// OUTPUT
// sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures)
*/
CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
CvLatentSvmDetector* detector,
CvMemStorage* storage,
float overlap_threshold CV_DEFAULT(0.5f),
int numThreads CV_DEFAULT(-1));
#ifdef __cplusplus
}
CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image,
CvHaarClassifierCascade* cascade, CvMemStorage* storage,
std::vector<int>& rejectLevels, std::vector<double>& levelWeightds,
double scale_factor = 1.1,
int min_neighbors = 3, int flags = 0,
CvSize min_size = cvSize(0, 0), CvSize max_size = cvSize(0, 0),
bool outputRejectLevels = false );
struct CvDataMatrixCode
{
char msg[4];
CvMat* original;
CvMat* corners;
};
CV_EXPORTS std::deque<CvDataMatrixCode> cvFindDataMatrix(CvMat *im);
#endif
#endif /* __OPENCV_OBJDETECT_C_H__ */