Move C API of opencv_objdetect to separate file
Also move cv::linemod to own header
This commit is contained in:
Andrey Kamaev
@@ -64,11 +64,10 @@
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#include "opencv2/imgproc/imgproc_c.h"
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#include "opencv2/imgproc/imgproc_c.h"
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#include "opencv2/photo/photo_c.h"
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#include "opencv2/photo/photo_c.h"
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#include "opencv2/video/tracking_c.h"
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#include "opencv2/video/tracking_c.h"
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#include "opencv2/objdetect/objdetect_c.h"
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#include "opencv2/legacy.hpp"
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#include "opencv2/legacy.hpp"
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#include "opencv2/legacy/compat.hpp"
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#include "opencv2/legacy/compat.hpp"
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#include "opencv2/objdetect.hpp"
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#if !defined(CV_IMPL)
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#if !defined(CV_IMPL)
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#define CV_IMPL extern "C"
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#define CV_IMPL extern "C"
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#endif //CV_IMPL
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#endif //CV_IMPL
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@@ -55,5 +55,6 @@
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#include "opencv2/highgui.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/calib3d.hpp"
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#include "opencv2/calib3d.hpp"
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#include "opencv2/objdetect.hpp"
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#endif
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#endif
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@@ -50,12 +50,11 @@
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#include "opencv2/imgproc/imgproc_c.h"
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#include "opencv2/imgproc/imgproc_c.h"
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#include "opencv2/photo/photo_c.h"
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#include "opencv2/photo/photo_c.h"
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#include "opencv2/video/tracking_c.h"
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#include "opencv2/video/tracking_c.h"
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#include "opencv2/objdetect/objdetect_c.h"
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#include "opencv2/legacy.hpp"
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#include "opencv2/legacy.hpp"
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#include "opencv2/legacy/compat.hpp"
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#include "opencv2/legacy/compat.hpp"
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#include "opencv2/legacy/blobtrack.hpp"
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#include "opencv2/legacy/blobtrack.hpp"
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#include "opencv2/objdetect.hpp"
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#include "opencv2/contrib.hpp"
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#include "opencv2/contrib.hpp"
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#endif
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#endif
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@@ -48,6 +48,8 @@
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#include "opencv2/features2d.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/objdetect.hpp"
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#include "opencv2/objdetect.hpp"
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#include "opencv2/core/core_c.h"
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#include <ostream>
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#include <ostream>
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#ifdef __cplusplus
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#ifdef __cplusplus
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@@ -7,11 +7,12 @@
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// copy or use the software.
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// copy or use the software.
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//
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//
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//
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//
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// License Agreement
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// License Agreement
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// For Open Source Computer Vision Library
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// For Open Source Computer Vision Library
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//
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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// Third party copyrights are property of their respective owners.
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//
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// Redistribution and use in source and binary forms, with or without modification,
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@@ -43,248 +44,10 @@
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#ifndef __OPENCV_OBJDETECT_HPP__
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#ifndef __OPENCV_OBJDETECT_HPP__
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#define __OPENCV_OBJDETECT_HPP__
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#define __OPENCV_OBJDETECT_HPP__
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#ifdef __cplusplus
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#include "opencv2/core.hpp"
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# include "opencv2/core.hpp"
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#endif
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#include "opencv2/core/core_c.h"
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#ifdef __cplusplus
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typedef struct CvLatentSvmDetector CvLatentSvmDetector;
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#include <map>
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typedef struct CvHaarClassifierCascade CvHaarClassifierCascade;
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#include <deque>
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extern "C" {
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#endif
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/****************************************************************************************\
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* Haar-like Object Detection functions *
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\****************************************************************************************/
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#define CV_HAAR_MAGIC_VAL 0x42500000
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#define CV_TYPE_NAME_HAAR "opencv-haar-classifier"
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#define CV_IS_HAAR_CLASSIFIER( haar ) \
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((haar) != NULL && \
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(((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL)
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#define CV_HAAR_FEATURE_MAX 3
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typedef struct CvHaarFeature
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{
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int tilted;
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struct
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{
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CvRect r;
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float weight;
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} rect[CV_HAAR_FEATURE_MAX];
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} CvHaarFeature;
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typedef struct CvHaarClassifier
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{
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int count;
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CvHaarFeature* haar_feature;
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float* threshold;
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int* left;
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int* right;
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float* alpha;
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} CvHaarClassifier;
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typedef struct CvHaarStageClassifier
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{
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int count;
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float threshold;
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CvHaarClassifier* classifier;
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int next;
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int child;
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int parent;
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} CvHaarStageClassifier;
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typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade;
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typedef struct CvHaarClassifierCascade
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{
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int flags;
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int count;
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CvSize orig_window_size;
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CvSize real_window_size;
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double scale;
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CvHaarStageClassifier* stage_classifier;
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CvHidHaarClassifierCascade* hid_cascade;
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} CvHaarClassifierCascade;
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typedef struct CvAvgComp
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{
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CvRect rect;
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int neighbors;
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} CvAvgComp;
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/* Loads haar classifier cascade from a directory.
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It is obsolete: convert your cascade to xml and use cvLoad instead */
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CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade(
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const char* directory, CvSize orig_window_size);
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CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
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#define CV_HAAR_DO_CANNY_PRUNING 1
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#define CV_HAAR_SCALE_IMAGE 2
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#define CV_HAAR_FIND_BIGGEST_OBJECT 4
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#define CV_HAAR_DO_ROUGH_SEARCH 8
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//CVAPI(CvSeq*) cvHaarDetectObjectsForROC( const CvArr* image,
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// CvHaarClassifierCascade* cascade, CvMemStorage* storage,
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// CvSeq** rejectLevels, CvSeq** levelWeightds,
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// double scale_factor CV_DEFAULT(1.1),
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// int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
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// CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)),
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// bool outputRejectLevels = false );
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CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image,
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CvHaarClassifierCascade* cascade, CvMemStorage* storage,
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double scale_factor CV_DEFAULT(1.1),
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int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
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CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)));
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/* sets images for haar classifier cascade */
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CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade,
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const CvArr* sum, const CvArr* sqsum,
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const CvArr* tilted_sum, double scale );
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/* runs the cascade on the specified window */
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CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade,
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CvPoint pt, int start_stage CV_DEFAULT(0));
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/****************************************************************************************\
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* Latent SVM Object Detection functions *
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\****************************************************************************************/
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// DataType: STRUCT position
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// Structure describes the position of the filter in the feature pyramid
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// l - level in the feature pyramid
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// (x, y) - coordinate in level l
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typedef struct CvLSVMFilterPosition
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{
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int x;
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int y;
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int l;
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} CvLSVMFilterPosition;
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// DataType: STRUCT filterObject
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// Description of the filter, which corresponds to the part of the object
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// V - ideal (penalty = 0) position of the partial filter
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// from the root filter position (V_i in the paper)
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// penaltyFunction - vector describes penalty function (d_i in the paper)
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// pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
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// FILTER DESCRIPTION
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// Rectangular map (sizeX x sizeY),
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// every cell stores feature vector (dimension = p)
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// H - matrix of feature vectors
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// to set and get feature vectors (i,j)
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// used formula H[(j * sizeX + i) * p + k], where
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// k - component of feature vector in cell (i, j)
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// END OF FILTER DESCRIPTION
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typedef struct CvLSVMFilterObject{
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CvLSVMFilterPosition V;
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float fineFunction[4];
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int sizeX;
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int sizeY;
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int numFeatures;
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float *H;
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} CvLSVMFilterObject;
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// data type: STRUCT CvLatentSvmDetector
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// structure contains internal representation of trained Latent SVM detector
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// num_filters - total number of filters (root plus part) in model
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// num_components - number of components in model
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// num_part_filters - array containing number of part filters for each component
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// filters - root and part filters for all model components
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// b - biases for all model components
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// score_threshold - confidence level threshold
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typedef struct CvLatentSvmDetector
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{
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int num_filters;
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int num_components;
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int* num_part_filters;
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CvLSVMFilterObject** filters;
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float* b;
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float score_threshold;
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}
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CvLatentSvmDetector;
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// data type: STRUCT CvObjectDetection
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// structure contains the bounding box and confidence level for detected object
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// rect - bounding box for a detected object
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// score - confidence level
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typedef struct CvObjectDetection
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{
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CvRect rect;
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float score;
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} CvObjectDetection;
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//////////////// Object Detection using Latent SVM //////////////
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/*
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// load trained detector from a file
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//
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// API
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// CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename);
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// INPUT
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// filename - path to the file containing the parameters of
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- trained Latent SVM detector
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// OUTPUT
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// trained Latent SVM detector in internal representation
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*/
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CVAPI(CvLatentSvmDetector*) cvLoadLatentSvmDetector(const char* filename);
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/*
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// release memory allocated for CvLatentSvmDetector structure
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//
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// API
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// void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
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// INPUT
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// detector - CvLatentSvmDetector structure to be released
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// OUTPUT
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*/
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CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
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/*
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// find rectangular regions in the given image that are likely
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// to contain objects and corresponding confidence levels
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//
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// API
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// CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
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// CvLatentSvmDetector* detector,
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// CvMemStorage* storage,
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// float overlap_threshold = 0.5f,
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// int numThreads = -1);
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// INPUT
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// image - image to detect objects in
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// detector - Latent SVM detector in internal representation
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// storage - memory storage to store the resultant sequence
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// of the object candidate rectangles
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// overlap_threshold - threshold for the non-maximum suppression algorithm
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= 0.5f [here will be the reference to original paper]
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// OUTPUT
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// sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures)
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*/
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CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
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CvLatentSvmDetector* detector,
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CvMemStorage* storage,
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float overlap_threshold CV_DEFAULT(0.5f),
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int numThreads CV_DEFAULT(-1));
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#ifdef __cplusplus
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}
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CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image,
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CvHaarClassifierCascade* cascade, CvMemStorage* storage,
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std::vector<int>& rejectLevels, std::vector<double>& levelWeightds,
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double scale_factor CV_DEFAULT(1.1),
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int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
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CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)),
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bool outputRejectLevels = false );
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namespace cv
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namespace cv
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{
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{
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@@ -303,24 +66,24 @@ public:
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struct CV_EXPORTS ObjectDetection
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struct CV_EXPORTS ObjectDetection
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{
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{
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ObjectDetection();
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ObjectDetection();
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ObjectDetection( const Rect& rect, float score, int classID=-1 );
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ObjectDetection( const Rect& rect, float score, int classID = -1 );
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Rect rect;
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Rect rect;
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float score;
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float score;
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int classID;
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int classID;
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};
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};
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LatentSvmDetector();
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LatentSvmDetector();
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LatentSvmDetector( const std::vector<String>& filenames, const std::vector<String>& classNames=std::vector<String>() );
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LatentSvmDetector( const std::vector<String>& filenames, const std::vector<String>& classNames = std::vector<String>() );
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virtual ~LatentSvmDetector();
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virtual ~LatentSvmDetector();
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virtual void clear();
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virtual void clear();
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virtual bool empty() const;
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virtual bool empty() const;
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bool load( const std::vector<String>& filenames, const std::vector<String>& classNames=std::vector<String>() );
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bool load( const std::vector<String>& filenames, const std::vector<String>& classNames = std::vector<String>() );
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virtual void detect( const Mat& image,
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virtual void detect( const Mat& image,
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std::vector<ObjectDetection>& objectDetections,
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std::vector<ObjectDetection>& objectDetections,
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float overlapThreshold=0.5f,
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float overlapThreshold = 0.5f,
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int numThreads=-1 );
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int numThreads = -1 );
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const std::vector<String>& getClassNames() const;
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const std::vector<String>& getClassNames() const;
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size_t getClassCount() const;
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size_t getClassCount() const;
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@@ -330,19 +93,22 @@ private:
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std::vector<String> classNames;
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std::vector<String> classNames;
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};
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};
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CV_EXPORTS void groupRectangles(CV_OUT CV_IN_OUT std::vector<Rect>& rectList, int groupThreshold, double eps=0.2);
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CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps = 0.2);
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CV_EXPORTS_W void groupRectangles(CV_OUT CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights, int groupThreshold, double eps=0.2);
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CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights, int groupThreshold, double eps = 0.2);
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CV_EXPORTS void groupRectangles( std::vector<Rect>& rectList, int groupThreshold, double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
|
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
|
||||||
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
|
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
|
||||||
std::vector<double>& levelWeights, int groupThreshold, double eps=0.2);
|
std::vector<double>& levelWeights, int groupThreshold, double eps = 0.2);
|
||||||
CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights, std::vector<double>& foundScales,
|
CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights, std::vector<double>& foundScales,
|
||||||
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
|
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
|
||||||
|
|
||||||
|
|
||||||
class CV_EXPORTS FeatureEvaluator
|
class CV_EXPORTS FeatureEvaluator
|
||||||
{
|
{
|
||||||
public:
|
public:
|
||||||
enum { HAAR = 0, LBP = 1, HOG = 2 };
|
enum { HAAR = 0,
|
||||||
|
LBP = 1,
|
||||||
|
HOG = 2
|
||||||
|
};
|
||||||
|
|
||||||
virtual ~FeatureEvaluator();
|
virtual ~FeatureEvaluator();
|
||||||
|
|
||||||
virtual bool read(const FileNode& node);
|
virtual bool read(const FileNode& node);
|
||||||
@@ -360,13 +126,11 @@ public:
|
|||||||
|
|
||||||
template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj();
|
template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj();
|
||||||
|
|
||||||
enum
|
enum { CASCADE_DO_CANNY_PRUNING = 1,
|
||||||
{
|
CASCADE_SCALE_IMAGE = 2,
|
||||||
CASCADE_DO_CANNY_PRUNING=1,
|
CASCADE_FIND_BIGGEST_OBJECT = 4,
|
||||||
CASCADE_SCALE_IMAGE=2,
|
CASCADE_DO_ROUGH_SEARCH = 8
|
||||||
CASCADE_FIND_BIGGEST_OBJECT=4,
|
};
|
||||||
CASCADE_DO_ROUGH_SEARCH=8
|
|
||||||
};
|
|
||||||
|
|
||||||
class CV_EXPORTS_W CascadeClassifier
|
class CV_EXPORTS_W CascadeClassifier
|
||||||
{
|
{
|
||||||
@@ -380,20 +144,20 @@ public:
|
|||||||
virtual bool read( const FileNode& node );
|
virtual bool read( const FileNode& node );
|
||||||
CV_WRAP virtual void detectMultiScale( const Mat& image,
|
CV_WRAP virtual void detectMultiScale( const Mat& image,
|
||||||
CV_OUT std::vector<Rect>& objects,
|
CV_OUT std::vector<Rect>& objects,
|
||||||
double scaleFactor=1.1,
|
double scaleFactor = 1.1,
|
||||||
int minNeighbors=3, int flags=0,
|
int minNeighbors = 3, int flags = 0,
|
||||||
Size minSize=Size(),
|
Size minSize = Size(),
|
||||||
Size maxSize=Size() );
|
Size maxSize = Size() );
|
||||||
|
|
||||||
CV_WRAP virtual void detectMultiScale( const Mat& image,
|
CV_WRAP virtual void detectMultiScale( const Mat& image,
|
||||||
CV_OUT std::vector<Rect>& objects,
|
CV_OUT std::vector<Rect>& objects,
|
||||||
CV_OUT std::vector<int>& rejectLevels,
|
CV_OUT std::vector<int>& rejectLevels,
|
||||||
CV_OUT std::vector<double>& levelWeights,
|
CV_OUT std::vector<double>& levelWeights,
|
||||||
double scaleFactor=1.1,
|
double scaleFactor = 1.1,
|
||||||
int minNeighbors=3, int flags=0,
|
int minNeighbors = 3, int flags = 0,
|
||||||
Size minSize=Size(),
|
Size minSize = Size(),
|
||||||
Size maxSize=Size(),
|
Size maxSize = Size(),
|
||||||
bool outputRejectLevels=false );
|
bool outputRejectLevels = false );
|
||||||
|
|
||||||
|
|
||||||
bool isOldFormatCascade() const;
|
bool isOldFormatCascade() const;
|
||||||
@@ -402,17 +166,18 @@ public:
|
|||||||
bool setImage( const Mat& );
|
bool setImage( const Mat& );
|
||||||
|
|
||||||
protected:
|
protected:
|
||||||
//virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
|
|
||||||
// int stripSize, int yStep, double factor, std::vector<Rect>& candidates );
|
|
||||||
|
|
||||||
virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
|
virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
|
||||||
int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
|
int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
|
||||||
std::vector<int>& rejectLevels, std::vector<double>& levelWeights, bool outputRejectLevels=false);
|
std::vector<int>& rejectLevels, std::vector<double>& levelWeights, bool outputRejectLevels = false);
|
||||||
|
|
||||||
protected:
|
protected:
|
||||||
enum { BOOST = 0 };
|
enum { BOOST = 0
|
||||||
enum { DO_CANNY_PRUNING = 1, SCALE_IMAGE = 2,
|
};
|
||||||
FIND_BIGGEST_OBJECT = 4, DO_ROUGH_SEARCH = 8 };
|
enum { DO_CANNY_PRUNING = CASCADE_DO_CANNY_PRUNING,
|
||||||
|
SCALE_IMAGE = CASCADE_SCALE_IMAGE,
|
||||||
|
FIND_BIGGEST_OBJECT = CASCADE_FIND_BIGGEST_OBJECT,
|
||||||
|
DO_ROUGH_SEARCH = CASCADE_DO_ROUGH_SEARCH
|
||||||
|
};
|
||||||
|
|
||||||
friend class CascadeClassifierInvoker;
|
friend class CascadeClassifierInvoker;
|
||||||
|
|
||||||
@@ -507,8 +272,10 @@ struct DetectionROI
|
|||||||
struct CV_EXPORTS_W HOGDescriptor
|
struct CV_EXPORTS_W HOGDescriptor
|
||||||
{
|
{
|
||||||
public:
|
public:
|
||||||
enum { L2Hys=0 };
|
enum { L2Hys = 0
|
||||||
enum { DEFAULT_NLEVELS=64 };
|
};
|
||||||
|
enum { DEFAULT_NLEVELS = 64
|
||||||
|
};
|
||||||
|
|
||||||
CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
|
CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
|
||||||
cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
|
cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
|
||||||
@@ -548,38 +315,38 @@ public:
|
|||||||
virtual bool read(FileNode& fn);
|
virtual bool read(FileNode& fn);
|
||||||
virtual void write(FileStorage& fs, const String& objname) const;
|
virtual void write(FileStorage& fs, const String& objname) const;
|
||||||
|
|
||||||
CV_WRAP virtual bool load(const String& filename, const String& objname=String());
|
CV_WRAP virtual bool load(const String& filename, const String& objname = String());
|
||||||
CV_WRAP virtual void save(const String& filename, const String& objname=String()) const;
|
CV_WRAP virtual void save(const String& filename, const String& objname = String()) const;
|
||||||
virtual void copyTo(HOGDescriptor& c) const;
|
virtual void copyTo(HOGDescriptor& c) const;
|
||||||
|
|
||||||
CV_WRAP virtual void compute(const Mat& img,
|
CV_WRAP virtual void compute(const Mat& img,
|
||||||
CV_OUT std::vector<float>& descriptors,
|
CV_OUT std::vector<float>& descriptors,
|
||||||
Size winStride=Size(), Size padding=Size(),
|
Size winStride = Size(), Size padding = Size(),
|
||||||
const std::vector<Point>& locations=std::vector<Point>()) const;
|
const std::vector<Point>& locations = std::vector<Point>()) const;
|
||||||
//with found weights output
|
//with found weights output
|
||||||
CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
|
CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
|
||||||
CV_OUT std::vector<double>& weights,
|
CV_OUT std::vector<double>& weights,
|
||||||
double hitThreshold=0, Size winStride=Size(),
|
double hitThreshold = 0, Size winStride = Size(),
|
||||||
Size padding=Size(),
|
Size padding = Size(),
|
||||||
const std::vector<Point>& searchLocations=std::vector<Point>()) const;
|
const std::vector<Point>& searchLocations = std::vector<Point>()) const;
|
||||||
//without found weights output
|
//without found weights output
|
||||||
virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
|
virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
|
||||||
double hitThreshold=0, Size winStride=Size(),
|
double hitThreshold = 0, Size winStride = Size(),
|
||||||
Size padding=Size(),
|
Size padding = Size(),
|
||||||
const std::vector<Point>& searchLocations=std::vector<Point>()) const;
|
const std::vector<Point>& searchLocations=std::vector<Point>()) const;
|
||||||
//with result weights output
|
//with result weights output
|
||||||
CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT std::vector<Rect>& foundLocations,
|
CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT std::vector<Rect>& foundLocations,
|
||||||
CV_OUT std::vector<double>& foundWeights, double hitThreshold=0,
|
CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
|
||||||
Size winStride=Size(), Size padding=Size(), double scale=1.05,
|
Size winStride = Size(), Size padding = Size(), double scale = 1.05,
|
||||||
double finalThreshold=2.0,bool useMeanshiftGrouping = false) const;
|
double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
|
||||||
//without found weights output
|
//without found weights output
|
||||||
virtual void detectMultiScale(const Mat& img, CV_OUT std::vector<Rect>& foundLocations,
|
virtual void detectMultiScale(const Mat& img, CV_OUT std::vector<Rect>& foundLocations,
|
||||||
double hitThreshold=0, Size winStride=Size(),
|
double hitThreshold = 0, Size winStride = Size(),
|
||||||
Size padding=Size(), double scale=1.05,
|
Size padding = Size(), double scale = 1.05,
|
||||||
double finalThreshold=2.0, bool useMeanshiftGrouping = false) const;
|
double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
|
||||||
|
|
||||||
CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
|
CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
|
||||||
Size paddingTL=Size(), Size paddingBR=Size()) const;
|
Size paddingTL = Size(), Size paddingBR = Size()) const;
|
||||||
|
|
||||||
CV_WRAP static std::vector<float> getDefaultPeopleDetector();
|
CV_WRAP static std::vector<float> getDefaultPeopleDetector();
|
||||||
CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
|
CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
|
||||||
@@ -618,430 +385,14 @@ public:
|
|||||||
|
|
||||||
CV_EXPORTS_W void findDataMatrix(InputArray image,
|
CV_EXPORTS_W void findDataMatrix(InputArray image,
|
||||||
CV_OUT std::vector<String>& codes,
|
CV_OUT std::vector<String>& codes,
|
||||||
OutputArray corners=noArray(),
|
OutputArray corners = noArray(),
|
||||||
OutputArrayOfArrays dmtx=noArray());
|
OutputArrayOfArrays dmtx = noArray());
|
||||||
|
|
||||||
CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image,
|
CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image,
|
||||||
const std::vector<String>& codes,
|
const std::vector<String>& codes,
|
||||||
InputArray corners);
|
InputArray corners);
|
||||||
}
|
}
|
||||||
|
|
||||||
/****************************************************************************************\
|
#include "opencv2/objdetect/linemod.hpp"
|
||||||
* Datamatrix *
|
|
||||||
\****************************************************************************************/
|
|
||||||
|
|
||||||
struct CV_EXPORTS CvDataMatrixCode {
|
|
||||||
char msg[4];
|
|
||||||
CvMat *original;
|
|
||||||
CvMat *corners;
|
|
||||||
};
|
|
||||||
|
|
||||||
CV_EXPORTS std::deque<CvDataMatrixCode> cvFindDataMatrix(CvMat *im);
|
|
||||||
|
|
||||||
/****************************************************************************************\
|
|
||||||
* LINE-MOD *
|
|
||||||
\****************************************************************************************/
|
|
||||||
|
|
||||||
namespace cv {
|
|
||||||
namespace linemod {
|
|
||||||
|
|
||||||
/// @todo Convert doxy comments to rst
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Discriminant feature described by its location and label.
|
|
||||||
*/
|
|
||||||
struct CV_EXPORTS Feature
|
|
||||||
{
|
|
||||||
int x; ///< x offset
|
|
||||||
int y; ///< y offset
|
|
||||||
int label; ///< Quantization
|
|
||||||
|
|
||||||
Feature() : x(0), y(0), label(0) {}
|
|
||||||
Feature(int x, int y, int label);
|
|
||||||
|
|
||||||
void read(const FileNode& fn);
|
|
||||||
void write(FileStorage& fs) const;
|
|
||||||
};
|
|
||||||
|
|
||||||
inline Feature::Feature(int _x, int _y, int _label) : x(_x), y(_y), label(_label) {}
|
|
||||||
|
|
||||||
struct CV_EXPORTS Template
|
|
||||||
{
|
|
||||||
int width;
|
|
||||||
int height;
|
|
||||||
int pyramid_level;
|
|
||||||
std::vector<Feature> features;
|
|
||||||
|
|
||||||
void read(const FileNode& fn);
|
|
||||||
void write(FileStorage& fs) const;
|
|
||||||
};
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Represents a modality operating over an image pyramid.
|
|
||||||
*/
|
|
||||||
class QuantizedPyramid
|
|
||||||
{
|
|
||||||
public:
|
|
||||||
// Virtual destructor
|
|
||||||
virtual ~QuantizedPyramid() {}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Compute quantized image at current pyramid level for online detection.
|
|
||||||
*
|
|
||||||
* \param[out] dst The destination 8-bit image. For each pixel at most one bit is set,
|
|
||||||
* representing its classification.
|
|
||||||
*/
|
|
||||||
virtual void quantize(Mat& dst) const =0;
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Extract most discriminant features at current pyramid level to form a new template.
|
|
||||||
*
|
|
||||||
* \param[out] templ The new template.
|
|
||||||
*/
|
|
||||||
virtual bool extractTemplate(Template& templ) const =0;
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Go to the next pyramid level.
|
|
||||||
*
|
|
||||||
* \todo Allow pyramid scale factor other than 2
|
|
||||||
*/
|
|
||||||
virtual void pyrDown() =0;
|
|
||||||
|
|
||||||
protected:
|
|
||||||
/// Candidate feature with a score
|
|
||||||
struct Candidate
|
|
||||||
{
|
|
||||||
Candidate(int x, int y, int label, float score);
|
|
||||||
|
|
||||||
/// Sort candidates with high score to the front
|
|
||||||
bool operator<(const Candidate& rhs) const
|
|
||||||
{
|
|
||||||
return score > rhs.score;
|
|
||||||
}
|
|
||||||
|
|
||||||
Feature f;
|
|
||||||
float score;
|
|
||||||
};
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Choose candidate features so that they are not bunched together.
|
|
||||||
*
|
|
||||||
* \param[in] candidates Candidate features sorted by score.
|
|
||||||
* \param[out] features Destination vector of selected features.
|
|
||||||
* \param[in] num_features Number of candidates to select.
|
|
||||||
* \param[in] distance Hint for desired distance between features.
|
|
||||||
*/
|
|
||||||
static void selectScatteredFeatures(const std::vector<Candidate>& candidates,
|
|
||||||
std::vector<Feature>& features,
|
|
||||||
size_t num_features, float distance);
|
|
||||||
};
|
|
||||||
|
|
||||||
inline QuantizedPyramid::Candidate::Candidate(int x, int y, int label, float _score) : f(x, y, label), score(_score) {}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Interface for modalities that plug into the LINE template matching representation.
|
|
||||||
*
|
|
||||||
* \todo Max response, to allow optimization of summing (255/MAX) features as uint8
|
|
||||||
*/
|
|
||||||
class CV_EXPORTS Modality
|
|
||||||
{
|
|
||||||
public:
|
|
||||||
// Virtual destructor
|
|
||||||
virtual ~Modality() {}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Form a quantized image pyramid from a source image.
|
|
||||||
*
|
|
||||||
* \param[in] src The source image. Type depends on the modality.
|
|
||||||
* \param[in] mask Optional mask. If not empty, unmasked pixels are set to zero
|
|
||||||
* in quantized image and cannot be extracted as features.
|
|
||||||
*/
|
|
||||||
Ptr<QuantizedPyramid> process(const Mat& src,
|
|
||||||
const Mat& mask = Mat()) const
|
|
||||||
{
|
|
||||||
return processImpl(src, mask);
|
|
||||||
}
|
|
||||||
|
|
||||||
virtual String name() const =0;
|
|
||||||
|
|
||||||
virtual void read(const FileNode& fn) =0;
|
|
||||||
virtual void write(FileStorage& fs) const =0;
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Create modality by name.
|
|
||||||
*
|
|
||||||
* The following modality types are supported:
|
|
||||||
* - "ColorGradient"
|
|
||||||
* - "DepthNormal"
|
|
||||||
*/
|
|
||||||
static Ptr<Modality> create(const String& modality_type);
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Load a modality from file.
|
|
||||||
*/
|
|
||||||
static Ptr<Modality> create(const FileNode& fn);
|
|
||||||
|
|
||||||
protected:
|
|
||||||
// Indirection is because process() has a default parameter.
|
|
||||||
virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
|
|
||||||
const Mat& mask) const =0;
|
|
||||||
};
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Modality that computes quantized gradient orientations from a color image.
|
|
||||||
*/
|
|
||||||
class CV_EXPORTS ColorGradient : public Modality
|
|
||||||
{
|
|
||||||
public:
|
|
||||||
/**
|
|
||||||
* \brief Default constructor. Uses reasonable default parameter values.
|
|
||||||
*/
|
|
||||||
ColorGradient();
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Constructor.
|
|
||||||
*
|
|
||||||
* \param weak_threshold When quantizing, discard gradients with magnitude less than this.
|
|
||||||
* \param num_features How many features a template must contain.
|
|
||||||
* \param strong_threshold Consider as candidate features only gradients whose norms are
|
|
||||||
* larger than this.
|
|
||||||
*/
|
|
||||||
ColorGradient(float weak_threshold, size_t num_features, float strong_threshold);
|
|
||||||
|
|
||||||
virtual String name() const;
|
|
||||||
|
|
||||||
virtual void read(const FileNode& fn);
|
|
||||||
virtual void write(FileStorage& fs) const;
|
|
||||||
|
|
||||||
float weak_threshold;
|
|
||||||
size_t num_features;
|
|
||||||
float strong_threshold;
|
|
||||||
|
|
||||||
protected:
|
|
||||||
virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
|
|
||||||
const Mat& mask) const;
|
|
||||||
};
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Modality that computes quantized surface normals from a dense depth map.
|
|
||||||
*/
|
|
||||||
class CV_EXPORTS DepthNormal : public Modality
|
|
||||||
{
|
|
||||||
public:
|
|
||||||
/**
|
|
||||||
* \brief Default constructor. Uses reasonable default parameter values.
|
|
||||||
*/
|
|
||||||
DepthNormal();
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Constructor.
|
|
||||||
*
|
|
||||||
* \param distance_threshold Ignore pixels beyond this distance.
|
|
||||||
* \param difference_threshold When computing normals, ignore contributions of pixels whose
|
|
||||||
* depth difference with the central pixel is above this threshold.
|
|
||||||
* \param num_features How many features a template must contain.
|
|
||||||
* \param extract_threshold Consider as candidate feature only if there are no differing
|
|
||||||
* orientations within a distance of extract_threshold.
|
|
||||||
*/
|
|
||||||
DepthNormal(int distance_threshold, int difference_threshold, size_t num_features,
|
|
||||||
int extract_threshold);
|
|
||||||
|
|
||||||
virtual String name() const;
|
|
||||||
|
|
||||||
virtual void read(const FileNode& fn);
|
|
||||||
virtual void write(FileStorage& fs) const;
|
|
||||||
|
|
||||||
int distance_threshold;
|
|
||||||
int difference_threshold;
|
|
||||||
size_t num_features;
|
|
||||||
int extract_threshold;
|
|
||||||
|
|
||||||
protected:
|
|
||||||
virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
|
|
||||||
const Mat& mask) const;
|
|
||||||
};
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Debug function to colormap a quantized image for viewing.
|
|
||||||
*/
|
|
||||||
void colormap(const Mat& quantized, Mat& dst);
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Represents a successful template match.
|
|
||||||
*/
|
|
||||||
struct CV_EXPORTS Match
|
|
||||||
{
|
|
||||||
Match()
|
|
||||||
{
|
|
||||||
}
|
|
||||||
|
|
||||||
Match(int x, int y, float similarity, const String& class_id, int template_id);
|
|
||||||
|
|
||||||
/// Sort matches with high similarity to the front
|
|
||||||
bool operator<(const Match& rhs) const
|
|
||||||
{
|
|
||||||
// Secondarily sort on template_id for the sake of duplicate removal
|
|
||||||
if (similarity != rhs.similarity)
|
|
||||||
return similarity > rhs.similarity;
|
|
||||||
else
|
|
||||||
return template_id < rhs.template_id;
|
|
||||||
}
|
|
||||||
|
|
||||||
bool operator==(const Match& rhs) const
|
|
||||||
{
|
|
||||||
return x == rhs.x && y == rhs.y && similarity == rhs.similarity && class_id == rhs.class_id;
|
|
||||||
}
|
|
||||||
|
|
||||||
int x;
|
|
||||||
int y;
|
|
||||||
float similarity;
|
|
||||||
String class_id;
|
|
||||||
int template_id;
|
|
||||||
};
|
|
||||||
|
|
||||||
inline Match::Match(int _x, int _y, float _similarity, const String& _class_id, int _template_id)
|
|
||||||
: x(_x), y(_y), similarity(_similarity), class_id(_class_id), template_id(_template_id)
|
|
||||||
{
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Object detector using the LINE template matching algorithm with any set of
|
|
||||||
* modalities.
|
|
||||||
*/
|
|
||||||
class CV_EXPORTS Detector
|
|
||||||
{
|
|
||||||
public:
|
|
||||||
/**
|
|
||||||
* \brief Empty constructor, initialize with read().
|
|
||||||
*/
|
|
||||||
Detector();
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Constructor.
|
|
||||||
*
|
|
||||||
* \param modalities Modalities to use (color gradients, depth normals, ...).
|
|
||||||
* \param T_pyramid Value of the sampling step T at each pyramid level. The
|
|
||||||
* number of pyramid levels is T_pyramid.size().
|
|
||||||
*/
|
|
||||||
Detector(const std::vector< Ptr<Modality> >& modalities, const std::vector<int>& T_pyramid);
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Detect objects by template matching.
|
|
||||||
*
|
|
||||||
* Matches globally at the lowest pyramid level, then refines locally stepping up the pyramid.
|
|
||||||
*
|
|
||||||
* \param sources Source images, one for each modality.
|
|
||||||
* \param threshold Similarity threshold, a percentage between 0 and 100.
|
|
||||||
* \param[out] matches Template matches, sorted by similarity score.
|
|
||||||
* \param class_ids If non-empty, only search for the desired object classes.
|
|
||||||
* \param[out] quantized_images Optionally return vector<Mat> of quantized images.
|
|
||||||
* \param masks The masks for consideration during matching. The masks should be CV_8UC1
|
|
||||||
* where 255 represents a valid pixel. If non-empty, the vector must be
|
|
||||||
* the same size as sources. Each element must be
|
|
||||||
* empty or the same size as its corresponding source.
|
|
||||||
*/
|
|
||||||
void match(const std::vector<Mat>& sources, float threshold, std::vector<Match>& matches,
|
|
||||||
const std::vector<String>& class_ids = std::vector<String>(),
|
|
||||||
OutputArrayOfArrays quantized_images = noArray(),
|
|
||||||
const std::vector<Mat>& masks = std::vector<Mat>()) const;
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Add new object template.
|
|
||||||
*
|
|
||||||
* \param sources Source images, one for each modality.
|
|
||||||
* \param class_id Object class ID.
|
|
||||||
* \param object_mask Mask separating object from background.
|
|
||||||
* \param[out] bounding_box Optionally return bounding box of the extracted features.
|
|
||||||
*
|
|
||||||
* \return Template ID, or -1 if failed to extract a valid template.
|
|
||||||
*/
|
|
||||||
int addTemplate(const std::vector<Mat>& sources, const String& class_id,
|
|
||||||
const Mat& object_mask, Rect* bounding_box = NULL);
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Add a new object template computed by external means.
|
|
||||||
*/
|
|
||||||
int addSyntheticTemplate(const std::vector<Template>& templates, const String& class_id);
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Get the modalities used by this detector.
|
|
||||||
*
|
|
||||||
* You are not permitted to add/remove modalities, but you may dynamic_cast them to
|
|
||||||
* tweak parameters.
|
|
||||||
*/
|
|
||||||
const std::vector< Ptr<Modality> >& getModalities() const { return modalities; }
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Get sampling step T at pyramid_level.
|
|
||||||
*/
|
|
||||||
int getT(int pyramid_level) const { return T_at_level[pyramid_level]; }
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Get number of pyramid levels used by this detector.
|
|
||||||
*/
|
|
||||||
int pyramidLevels() const { return pyramid_levels; }
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Get the template pyramid identified by template_id.
|
|
||||||
*
|
|
||||||
* For example, with 2 modalities (Gradient, Normal) and two pyramid levels
|
|
||||||
* (L0, L1), the order is (GradientL0, NormalL0, GradientL1, NormalL1).
|
|
||||||
*/
|
|
||||||
const std::vector<Template>& getTemplates(const String& class_id, int template_id) const;
|
|
||||||
|
|
||||||
int numTemplates() const;
|
|
||||||
int numTemplates(const String& class_id) const;
|
|
||||||
int numClasses() const { return static_cast<int>(class_templates.size()); }
|
|
||||||
|
|
||||||
std::vector<String> classIds() const;
|
|
||||||
|
|
||||||
void read(const FileNode& fn);
|
|
||||||
void write(FileStorage& fs) const;
|
|
||||||
|
|
||||||
String readClass(const FileNode& fn, const String &class_id_override = "");
|
|
||||||
void writeClass(const String& class_id, FileStorage& fs) const;
|
|
||||||
|
|
||||||
void readClasses(const std::vector<String>& class_ids,
|
|
||||||
const String& format = "templates_%s.yml.gz");
|
|
||||||
void writeClasses(const String& format = "templates_%s.yml.gz") const;
|
|
||||||
|
|
||||||
protected:
|
|
||||||
std::vector< Ptr<Modality> > modalities;
|
|
||||||
int pyramid_levels;
|
|
||||||
std::vector<int> T_at_level;
|
|
||||||
|
|
||||||
typedef std::vector<Template> TemplatePyramid;
|
|
||||||
typedef std::map<String, std::vector<TemplatePyramid> > TemplatesMap;
|
|
||||||
TemplatesMap class_templates;
|
|
||||||
|
|
||||||
typedef std::vector<Mat> LinearMemories;
|
|
||||||
// Indexed as [pyramid level][modality][quantized label]
|
|
||||||
typedef std::vector< std::vector<LinearMemories> > LinearMemoryPyramid;
|
|
||||||
|
|
||||||
void matchClass(const LinearMemoryPyramid& lm_pyramid,
|
|
||||||
const std::vector<Size>& sizes,
|
|
||||||
float threshold, std::vector<Match>& matches,
|
|
||||||
const String& class_id,
|
|
||||||
const std::vector<TemplatePyramid>& template_pyramids) const;
|
|
||||||
};
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Factory function for detector using LINE algorithm with color gradients.
|
|
||||||
*
|
|
||||||
* Default parameter settings suitable for VGA images.
|
|
||||||
*/
|
|
||||||
CV_EXPORTS Ptr<Detector> getDefaultLINE();
|
|
||||||
|
|
||||||
/**
|
|
||||||
* \brief Factory function for detector using LINE-MOD algorithm with color gradients
|
|
||||||
* and depth normals.
|
|
||||||
*
|
|
||||||
* Default parameter settings suitable for VGA images.
|
|
||||||
*/
|
|
||||||
CV_EXPORTS Ptr<Detector> getDefaultLINEMOD();
|
|
||||||
|
|
||||||
} // namespace linemod
|
|
||||||
} // namespace cv
|
|
||||||
|
|
||||||
#endif
|
|
||||||
|
|
||||||
#endif
|
#endif
|
||||||
|
|||||||
455
modules/objdetect/include/opencv2/objdetect/linemod.hpp
Normal file
455
modules/objdetect/include/opencv2/objdetect/linemod.hpp
Normal file
@@ -0,0 +1,455 @@
|
|||||||
|
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||||
|
//
|
||||||
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||||
|
//
|
||||||
|
// By downloading, copying, installing or using the software you agree to this license.
|
||||||
|
// If you do not agree to this license, do not download, install,
|
||||||
|
// copy or use the software.
|
||||||
|
//
|
||||||
|
//
|
||||||
|
// License Agreement
|
||||||
|
// For Open Source Computer Vision Library
|
||||||
|
//
|
||||||
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||||
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||||
|
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
|
||||||
|
// Third party copyrights are property of their respective owners.
|
||||||
|
//
|
||||||
|
// Redistribution and use in source and binary forms, with or without modification,
|
||||||
|
// are permitted provided that the following conditions are met:
|
||||||
|
//
|
||||||
|
// * Redistribution's of source code must retain the above copyright notice,
|
||||||
|
// this list of conditions and the following disclaimer.
|
||||||
|
//
|
||||||
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||||
|
// this list of conditions and the following disclaimer in the documentation
|
||||||
|
// and/or other materials provided with the distribution.
|
||||||
|
//
|
||||||
|
// * The name of the copyright holders may not be used to endorse or promote products
|
||||||
|
// derived from this software without specific prior written permission.
|
||||||
|
//
|
||||||
|
// This software is provided by the copyright holders and contributors "as is" and
|
||||||
|
// any express or implied warranties, including, but not limited to, the implied
|
||||||
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||||
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||||
|
// indirect, incidental, special, exemplary, or consequential damages
|
||||||
|
// (including, but not limited to, procurement of substitute goods or services;
|
||||||
|
// loss of use, data, or profits; or business interruption) however caused
|
||||||
|
// and on any theory of liability, whether in contract, strict liability,
|
||||||
|
// or tort (including negligence or otherwise) arising in any way out of
|
||||||
|
// the use of this software, even if advised of the possibility of such damage.
|
||||||
|
//
|
||||||
|
//M*/
|
||||||
|
|
||||||
|
#ifndef __OPENCV_OBJDETECT_LINEMOD_HPP__
|
||||||
|
#define __OPENCV_OBJDETECT_LINEMOD_HPP__
|
||||||
|
|
||||||
|
#include "opencv2/core.hpp"
|
||||||
|
#include <map>
|
||||||
|
|
||||||
|
/****************************************************************************************\
|
||||||
|
* LINE-MOD *
|
||||||
|
\****************************************************************************************/
|
||||||
|
|
||||||
|
namespace cv {
|
||||||
|
namespace linemod {
|
||||||
|
|
||||||
|
/// @todo Convert doxy comments to rst
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Discriminant feature described by its location and label.
|
||||||
|
*/
|
||||||
|
struct CV_EXPORTS Feature
|
||||||
|
{
|
||||||
|
int x; ///< x offset
|
||||||
|
int y; ///< y offset
|
||||||
|
int label; ///< Quantization
|
||||||
|
|
||||||
|
Feature() : x(0), y(0), label(0) {}
|
||||||
|
Feature(int x, int y, int label);
|
||||||
|
|
||||||
|
void read(const FileNode& fn);
|
||||||
|
void write(FileStorage& fs) const;
|
||||||
|
};
|
||||||
|
|
||||||
|
inline Feature::Feature(int _x, int _y, int _label) : x(_x), y(_y), label(_label) {}
|
||||||
|
|
||||||
|
struct CV_EXPORTS Template
|
||||||
|
{
|
||||||
|
int width;
|
||||||
|
int height;
|
||||||
|
int pyramid_level;
|
||||||
|
std::vector<Feature> features;
|
||||||
|
|
||||||
|
void read(const FileNode& fn);
|
||||||
|
void write(FileStorage& fs) const;
|
||||||
|
};
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Represents a modality operating over an image pyramid.
|
||||||
|
*/
|
||||||
|
class QuantizedPyramid
|
||||||
|
{
|
||||||
|
public:
|
||||||
|
// Virtual destructor
|
||||||
|
virtual ~QuantizedPyramid() {}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Compute quantized image at current pyramid level for online detection.
|
||||||
|
*
|
||||||
|
* \param[out] dst The destination 8-bit image. For each pixel at most one bit is set,
|
||||||
|
* representing its classification.
|
||||||
|
*/
|
||||||
|
virtual void quantize(Mat& dst) const =0;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Extract most discriminant features at current pyramid level to form a new template.
|
||||||
|
*
|
||||||
|
* \param[out] templ The new template.
|
||||||
|
*/
|
||||||
|
virtual bool extractTemplate(Template& templ) const =0;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Go to the next pyramid level.
|
||||||
|
*
|
||||||
|
* \todo Allow pyramid scale factor other than 2
|
||||||
|
*/
|
||||||
|
virtual void pyrDown() =0;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
/// Candidate feature with a score
|
||||||
|
struct Candidate
|
||||||
|
{
|
||||||
|
Candidate(int x, int y, int label, float score);
|
||||||
|
|
||||||
|
/// Sort candidates with high score to the front
|
||||||
|
bool operator<(const Candidate& rhs) const
|
||||||
|
{
|
||||||
|
return score > rhs.score;
|
||||||
|
}
|
||||||
|
|
||||||
|
Feature f;
|
||||||
|
float score;
|
||||||
|
};
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Choose candidate features so that they are not bunched together.
|
||||||
|
*
|
||||||
|
* \param[in] candidates Candidate features sorted by score.
|
||||||
|
* \param[out] features Destination vector of selected features.
|
||||||
|
* \param[in] num_features Number of candidates to select.
|
||||||
|
* \param[in] distance Hint for desired distance between features.
|
||||||
|
*/
|
||||||
|
static void selectScatteredFeatures(const std::vector<Candidate>& candidates,
|
||||||
|
std::vector<Feature>& features,
|
||||||
|
size_t num_features, float distance);
|
||||||
|
};
|
||||||
|
|
||||||
|
inline QuantizedPyramid::Candidate::Candidate(int x, int y, int label, float _score) : f(x, y, label), score(_score) {}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Interface for modalities that plug into the LINE template matching representation.
|
||||||
|
*
|
||||||
|
* \todo Max response, to allow optimization of summing (255/MAX) features as uint8
|
||||||
|
*/
|
||||||
|
class CV_EXPORTS Modality
|
||||||
|
{
|
||||||
|
public:
|
||||||
|
// Virtual destructor
|
||||||
|
virtual ~Modality() {}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Form a quantized image pyramid from a source image.
|
||||||
|
*
|
||||||
|
* \param[in] src The source image. Type depends on the modality.
|
||||||
|
* \param[in] mask Optional mask. If not empty, unmasked pixels are set to zero
|
||||||
|
* in quantized image and cannot be extracted as features.
|
||||||
|
*/
|
||||||
|
Ptr<QuantizedPyramid> process(const Mat& src,
|
||||||
|
const Mat& mask = Mat()) const
|
||||||
|
{
|
||||||
|
return processImpl(src, mask);
|
||||||
|
}
|
||||||
|
|
||||||
|
virtual String name() const =0;
|
||||||
|
|
||||||
|
virtual void read(const FileNode& fn) =0;
|
||||||
|
virtual void write(FileStorage& fs) const =0;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Create modality by name.
|
||||||
|
*
|
||||||
|
* The following modality types are supported:
|
||||||
|
* - "ColorGradient"
|
||||||
|
* - "DepthNormal"
|
||||||
|
*/
|
||||||
|
static Ptr<Modality> create(const String& modality_type);
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Load a modality from file.
|
||||||
|
*/
|
||||||
|
static Ptr<Modality> create(const FileNode& fn);
|
||||||
|
|
||||||
|
protected:
|
||||||
|
// Indirection is because process() has a default parameter.
|
||||||
|
virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
|
||||||
|
const Mat& mask) const =0;
|
||||||
|
};
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Modality that computes quantized gradient orientations from a color image.
|
||||||
|
*/
|
||||||
|
class CV_EXPORTS ColorGradient : public Modality
|
||||||
|
{
|
||||||
|
public:
|
||||||
|
/**
|
||||||
|
* \brief Default constructor. Uses reasonable default parameter values.
|
||||||
|
*/
|
||||||
|
ColorGradient();
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Constructor.
|
||||||
|
*
|
||||||
|
* \param weak_threshold When quantizing, discard gradients with magnitude less than this.
|
||||||
|
* \param num_features How many features a template must contain.
|
||||||
|
* \param strong_threshold Consider as candidate features only gradients whose norms are
|
||||||
|
* larger than this.
|
||||||
|
*/
|
||||||
|
ColorGradient(float weak_threshold, size_t num_features, float strong_threshold);
|
||||||
|
|
||||||
|
virtual String name() const;
|
||||||
|
|
||||||
|
virtual void read(const FileNode& fn);
|
||||||
|
virtual void write(FileStorage& fs) const;
|
||||||
|
|
||||||
|
float weak_threshold;
|
||||||
|
size_t num_features;
|
||||||
|
float strong_threshold;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
|
||||||
|
const Mat& mask) const;
|
||||||
|
};
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Modality that computes quantized surface normals from a dense depth map.
|
||||||
|
*/
|
||||||
|
class CV_EXPORTS DepthNormal : public Modality
|
||||||
|
{
|
||||||
|
public:
|
||||||
|
/**
|
||||||
|
* \brief Default constructor. Uses reasonable default parameter values.
|
||||||
|
*/
|
||||||
|
DepthNormal();
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Constructor.
|
||||||
|
*
|
||||||
|
* \param distance_threshold Ignore pixels beyond this distance.
|
||||||
|
* \param difference_threshold When computing normals, ignore contributions of pixels whose
|
||||||
|
* depth difference with the central pixel is above this threshold.
|
||||||
|
* \param num_features How many features a template must contain.
|
||||||
|
* \param extract_threshold Consider as candidate feature only if there are no differing
|
||||||
|
* orientations within a distance of extract_threshold.
|
||||||
|
*/
|
||||||
|
DepthNormal(int distance_threshold, int difference_threshold, size_t num_features,
|
||||||
|
int extract_threshold);
|
||||||
|
|
||||||
|
virtual String name() const;
|
||||||
|
|
||||||
|
virtual void read(const FileNode& fn);
|
||||||
|
virtual void write(FileStorage& fs) const;
|
||||||
|
|
||||||
|
int distance_threshold;
|
||||||
|
int difference_threshold;
|
||||||
|
size_t num_features;
|
||||||
|
int extract_threshold;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
virtual Ptr<QuantizedPyramid> processImpl(const Mat& src,
|
||||||
|
const Mat& mask) const;
|
||||||
|
};
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Debug function to colormap a quantized image for viewing.
|
||||||
|
*/
|
||||||
|
void colormap(const Mat& quantized, Mat& dst);
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Represents a successful template match.
|
||||||
|
*/
|
||||||
|
struct CV_EXPORTS Match
|
||||||
|
{
|
||||||
|
Match()
|
||||||
|
{
|
||||||
|
}
|
||||||
|
|
||||||
|
Match(int x, int y, float similarity, const String& class_id, int template_id);
|
||||||
|
|
||||||
|
/// Sort matches with high similarity to the front
|
||||||
|
bool operator<(const Match& rhs) const
|
||||||
|
{
|
||||||
|
// Secondarily sort on template_id for the sake of duplicate removal
|
||||||
|
if (similarity != rhs.similarity)
|
||||||
|
return similarity > rhs.similarity;
|
||||||
|
else
|
||||||
|
return template_id < rhs.template_id;
|
||||||
|
}
|
||||||
|
|
||||||
|
bool operator==(const Match& rhs) const
|
||||||
|
{
|
||||||
|
return x == rhs.x && y == rhs.y && similarity == rhs.similarity && class_id == rhs.class_id;
|
||||||
|
}
|
||||||
|
|
||||||
|
int x;
|
||||||
|
int y;
|
||||||
|
float similarity;
|
||||||
|
String class_id;
|
||||||
|
int template_id;
|
||||||
|
};
|
||||||
|
|
||||||
|
inline
|
||||||
|
Match::Match(int _x, int _y, float _similarity, const String& _class_id, int _template_id)
|
||||||
|
: x(_x), y(_y), similarity(_similarity), class_id(_class_id), template_id(_template_id)
|
||||||
|
{}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Object detector using the LINE template matching algorithm with any set of
|
||||||
|
* modalities.
|
||||||
|
*/
|
||||||
|
class CV_EXPORTS Detector
|
||||||
|
{
|
||||||
|
public:
|
||||||
|
/**
|
||||||
|
* \brief Empty constructor, initialize with read().
|
||||||
|
*/
|
||||||
|
Detector();
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Constructor.
|
||||||
|
*
|
||||||
|
* \param modalities Modalities to use (color gradients, depth normals, ...).
|
||||||
|
* \param T_pyramid Value of the sampling step T at each pyramid level. The
|
||||||
|
* number of pyramid levels is T_pyramid.size().
|
||||||
|
*/
|
||||||
|
Detector(const std::vector< Ptr<Modality> >& modalities, const std::vector<int>& T_pyramid);
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Detect objects by template matching.
|
||||||
|
*
|
||||||
|
* Matches globally at the lowest pyramid level, then refines locally stepping up the pyramid.
|
||||||
|
*
|
||||||
|
* \param sources Source images, one for each modality.
|
||||||
|
* \param threshold Similarity threshold, a percentage between 0 and 100.
|
||||||
|
* \param[out] matches Template matches, sorted by similarity score.
|
||||||
|
* \param class_ids If non-empty, only search for the desired object classes.
|
||||||
|
* \param[out] quantized_images Optionally return vector<Mat> of quantized images.
|
||||||
|
* \param masks The masks for consideration during matching. The masks should be CV_8UC1
|
||||||
|
* where 255 represents a valid pixel. If non-empty, the vector must be
|
||||||
|
* the same size as sources. Each element must be
|
||||||
|
* empty or the same size as its corresponding source.
|
||||||
|
*/
|
||||||
|
void match(const std::vector<Mat>& sources, float threshold, std::vector<Match>& matches,
|
||||||
|
const std::vector<String>& class_ids = std::vector<String>(),
|
||||||
|
OutputArrayOfArrays quantized_images = noArray(),
|
||||||
|
const std::vector<Mat>& masks = std::vector<Mat>()) const;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Add new object template.
|
||||||
|
*
|
||||||
|
* \param sources Source images, one for each modality.
|
||||||
|
* \param class_id Object class ID.
|
||||||
|
* \param object_mask Mask separating object from background.
|
||||||
|
* \param[out] bounding_box Optionally return bounding box of the extracted features.
|
||||||
|
*
|
||||||
|
* \return Template ID, or -1 if failed to extract a valid template.
|
||||||
|
*/
|
||||||
|
int addTemplate(const std::vector<Mat>& sources, const String& class_id,
|
||||||
|
const Mat& object_mask, Rect* bounding_box = NULL);
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Add a new object template computed by external means.
|
||||||
|
*/
|
||||||
|
int addSyntheticTemplate(const std::vector<Template>& templates, const String& class_id);
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Get the modalities used by this detector.
|
||||||
|
*
|
||||||
|
* You are not permitted to add/remove modalities, but you may dynamic_cast them to
|
||||||
|
* tweak parameters.
|
||||||
|
*/
|
||||||
|
const std::vector< Ptr<Modality> >& getModalities() const { return modalities; }
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Get sampling step T at pyramid_level.
|
||||||
|
*/
|
||||||
|
int getT(int pyramid_level) const { return T_at_level[pyramid_level]; }
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Get number of pyramid levels used by this detector.
|
||||||
|
*/
|
||||||
|
int pyramidLevels() const { return pyramid_levels; }
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Get the template pyramid identified by template_id.
|
||||||
|
*
|
||||||
|
* For example, with 2 modalities (Gradient, Normal) and two pyramid levels
|
||||||
|
* (L0, L1), the order is (GradientL0, NormalL0, GradientL1, NormalL1).
|
||||||
|
*/
|
||||||
|
const std::vector<Template>& getTemplates(const String& class_id, int template_id) const;
|
||||||
|
|
||||||
|
int numTemplates() const;
|
||||||
|
int numTemplates(const String& class_id) const;
|
||||||
|
int numClasses() const { return static_cast<int>(class_templates.size()); }
|
||||||
|
|
||||||
|
std::vector<String> classIds() const;
|
||||||
|
|
||||||
|
void read(const FileNode& fn);
|
||||||
|
void write(FileStorage& fs) const;
|
||||||
|
|
||||||
|
String readClass(const FileNode& fn, const String &class_id_override = "");
|
||||||
|
void writeClass(const String& class_id, FileStorage& fs) const;
|
||||||
|
|
||||||
|
void readClasses(const std::vector<String>& class_ids,
|
||||||
|
const String& format = "templates_%s.yml.gz");
|
||||||
|
void writeClasses(const String& format = "templates_%s.yml.gz") const;
|
||||||
|
|
||||||
|
protected:
|
||||||
|
std::vector< Ptr<Modality> > modalities;
|
||||||
|
int pyramid_levels;
|
||||||
|
std::vector<int> T_at_level;
|
||||||
|
|
||||||
|
typedef std::vector<Template> TemplatePyramid;
|
||||||
|
typedef std::map<String, std::vector<TemplatePyramid> > TemplatesMap;
|
||||||
|
TemplatesMap class_templates;
|
||||||
|
|
||||||
|
typedef std::vector<Mat> LinearMemories;
|
||||||
|
// Indexed as [pyramid level][modality][quantized label]
|
||||||
|
typedef std::vector< std::vector<LinearMemories> > LinearMemoryPyramid;
|
||||||
|
|
||||||
|
void matchClass(const LinearMemoryPyramid& lm_pyramid,
|
||||||
|
const std::vector<Size>& sizes,
|
||||||
|
float threshold, std::vector<Match>& matches,
|
||||||
|
const String& class_id,
|
||||||
|
const std::vector<TemplatePyramid>& template_pyramids) const;
|
||||||
|
};
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Factory function for detector using LINE algorithm with color gradients.
|
||||||
|
*
|
||||||
|
* Default parameter settings suitable for VGA images.
|
||||||
|
*/
|
||||||
|
CV_EXPORTS Ptr<Detector> getDefaultLINE();
|
||||||
|
|
||||||
|
/**
|
||||||
|
* \brief Factory function for detector using LINE-MOD algorithm with color gradients
|
||||||
|
* and depth normals.
|
||||||
|
*
|
||||||
|
* Default parameter settings suitable for VGA images.
|
||||||
|
*/
|
||||||
|
CV_EXPORTS Ptr<Detector> getDefaultLINEMOD();
|
||||||
|
|
||||||
|
} // namespace linemod
|
||||||
|
} // namespace cv
|
||||||
|
|
||||||
|
#endif // __OPENCV_OBJDETECT_LINEMOD_HPP__
|
||||||
289
modules/objdetect/include/opencv2/objdetect/objdetect_c.h
Normal file
289
modules/objdetect/include/opencv2/objdetect/objdetect_c.h
Normal file
@@ -0,0 +1,289 @@
|
|||||||
|
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||||
|
//
|
||||||
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||||
|
//
|
||||||
|
// By downloading, copying, installing or using the software you agree to this license.
|
||||||
|
// If you do not agree to this license, do not download, install,
|
||||||
|
// copy or use the software.
|
||||||
|
//
|
||||||
|
//
|
||||||
|
// License Agreement
|
||||||
|
// For Open Source Computer Vision Library
|
||||||
|
//
|
||||||
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||||
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||||
|
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
|
||||||
|
// Third party copyrights are property of their respective owners.
|
||||||
|
//
|
||||||
|
// Redistribution and use in source and binary forms, with or without modification,
|
||||||
|
// are permitted provided that the following conditions are met:
|
||||||
|
//
|
||||||
|
// * Redistribution's of source code must retain the above copyright notice,
|
||||||
|
// this list of conditions and the following disclaimer.
|
||||||
|
//
|
||||||
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||||
|
// this list of conditions and the following disclaimer in the documentation
|
||||||
|
// and/or other materials provided with the distribution.
|
||||||
|
//
|
||||||
|
// * The name of the copyright holders may not be used to endorse or promote products
|
||||||
|
// derived from this software without specific prior written permission.
|
||||||
|
//
|
||||||
|
// This software is provided by the copyright holders and contributors "as is" and
|
||||||
|
// any express or implied warranties, including, but not limited to, the implied
|
||||||
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||||
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||||
|
// indirect, incidental, special, exemplary, or consequential damages
|
||||||
|
// (including, but not limited to, procurement of substitute goods or services;
|
||||||
|
// loss of use, data, or profits; or business interruption) however caused
|
||||||
|
// and on any theory of liability, whether in contract, strict liability,
|
||||||
|
// or tort (including negligence or otherwise) arising in any way out of
|
||||||
|
// the use of this software, even if advised of the possibility of such damage.
|
||||||
|
//
|
||||||
|
//M*/
|
||||||
|
|
||||||
|
#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__ */
|
||||||
@@ -1,6 +1,8 @@
|
|||||||
#ifndef _LSVM_ROUTINE_H_
|
#ifndef _LSVM_ROUTINE_H_
|
||||||
#define _LSVM_ROUTINE_H_
|
#define _LSVM_ROUTINE_H_
|
||||||
|
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
|
|
||||||
#include "_lsvm_types.h"
|
#include "_lsvm_types.h"
|
||||||
#include "_lsvm_error.h"
|
#include "_lsvm_error.h"
|
||||||
|
|
||||||
|
|||||||
@@ -1,5 +1,6 @@
|
|||||||
#ifndef LSVM_PARSER
|
#ifndef LSVM_PARSER
|
||||||
#define LSVM_PARSER
|
#define LSVM_PARSER
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
|
|
||||||
#include "_lsvm_types.h"
|
#include "_lsvm_types.h"
|
||||||
|
|
||||||
|
|||||||
@@ -43,6 +43,7 @@
|
|||||||
#include <cstdio>
|
#include <cstdio>
|
||||||
|
|
||||||
#include "cascadedetect.hpp"
|
#include "cascadedetect.hpp"
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
|
|
||||||
#if defined (LOG_CASCADE_STATISTIC)
|
#if defined (LOG_CASCADE_STATISTIC)
|
||||||
struct Logger
|
struct Logger
|
||||||
|
|||||||
@@ -1,7 +1,7 @@
|
|||||||
#include "precomp.hpp"
|
#include "precomp.hpp"
|
||||||
#include "opencv2/imgproc/imgproc_c.h"
|
#include "opencv2/imgproc/imgproc_c.h"
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
|
|
||||||
#include <deque>
|
|
||||||
#include <algorithm>
|
#include <algorithm>
|
||||||
|
|
||||||
class Sampler {
|
class Sampler {
|
||||||
|
|||||||
@@ -43,6 +43,7 @@
|
|||||||
|
|
||||||
#include "precomp.hpp"
|
#include "precomp.hpp"
|
||||||
#include "opencv2/imgproc/imgproc_c.h"
|
#include "opencv2/imgproc/imgproc_c.h"
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
#include <stdio.h>
|
#include <stdio.h>
|
||||||
|
|
||||||
#if CV_SSE2
|
#if CV_SSE2
|
||||||
|
|||||||
@@ -41,6 +41,7 @@
|
|||||||
//M*/
|
//M*/
|
||||||
|
|
||||||
#include "precomp.hpp"
|
#include "precomp.hpp"
|
||||||
|
#include "opencv2/core/core_c.h"
|
||||||
|
|
||||||
#include <cstdio>
|
#include <cstdio>
|
||||||
#include <iterator>
|
#include <iterator>
|
||||||
@@ -2862,7 +2863,7 @@ void HOGDescriptor::readALTModel(String modelfile)
|
|||||||
String eerr("file not exist");
|
String eerr("file not exist");
|
||||||
String efile(__FILE__);
|
String efile(__FILE__);
|
||||||
String efunc(__FUNCTION__);
|
String efunc(__FUNCTION__);
|
||||||
throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
|
throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
|
||||||
}
|
}
|
||||||
char version_buffer[10];
|
char version_buffer[10];
|
||||||
if (!fread (&version_buffer,sizeof(char),10,modelfl))
|
if (!fread (&version_buffer,sizeof(char),10,modelfl))
|
||||||
@@ -2870,13 +2871,13 @@ void HOGDescriptor::readALTModel(String modelfile)
|
|||||||
String eerr("version?");
|
String eerr("version?");
|
||||||
String efile(__FILE__);
|
String efile(__FILE__);
|
||||||
String efunc(__FUNCTION__);
|
String efunc(__FUNCTION__);
|
||||||
throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
|
throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
|
||||||
}
|
}
|
||||||
if(strcmp(version_buffer,"V6.01")) {
|
if(strcmp(version_buffer,"V6.01")) {
|
||||||
String eerr("version doesnot match");
|
String eerr("version doesnot match");
|
||||||
String efile(__FILE__);
|
String efile(__FILE__);
|
||||||
String efunc(__FUNCTION__);
|
String efunc(__FUNCTION__);
|
||||||
throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
|
throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
|
||||||
}
|
}
|
||||||
/* read version number */
|
/* read version number */
|
||||||
int version = 0;
|
int version = 0;
|
||||||
|
|||||||
@@ -1,5 +1,6 @@
|
|||||||
#include "precomp.hpp"
|
#include "precomp.hpp"
|
||||||
#include "opencv2/imgproc/imgproc_c.h"
|
#include "opencv2/imgproc/imgproc_c.h"
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
#include "_lsvmparser.h"
|
#include "_lsvmparser.h"
|
||||||
#include "_lsvm_matching.h"
|
#include "_lsvm_matching.h"
|
||||||
|
|
||||||
|
|||||||
@@ -66,7 +66,7 @@ static inline int getLabel(int quantized)
|
|||||||
case 64: return 6;
|
case 64: return 6;
|
||||||
case 128: return 7;
|
case 128: return 7;
|
||||||
default:
|
default:
|
||||||
CV_Error(CV_StsBadArg, "Invalid value of quantized parameter");
|
CV_Error(Error::StsBadArg, "Invalid value of quantized parameter");
|
||||||
return -1; //avoid warning
|
return -1; //avoid warning
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
@@ -1398,17 +1398,17 @@ void Detector::match(const std::vector<Mat>& sources, float threshold, std::vect
|
|||||||
if (quantized_images.needed())
|
if (quantized_images.needed())
|
||||||
quantized_images.create(1, static_cast<int>(pyramid_levels * modalities.size()), CV_8U);
|
quantized_images.create(1, static_cast<int>(pyramid_levels * modalities.size()), CV_8U);
|
||||||
|
|
||||||
assert(sources.size() == modalities.size());
|
CV_Assert(sources.size() == modalities.size());
|
||||||
// Initialize each modality with our sources
|
// Initialize each modality with our sources
|
||||||
std::vector< Ptr<QuantizedPyramid> > quantizers;
|
std::vector< Ptr<QuantizedPyramid> > quantizers;
|
||||||
for (int i = 0; i < (int)modalities.size(); ++i){
|
for (int i = 0; i < (int)modalities.size(); ++i){
|
||||||
Mat mask, source;
|
Mat mask, source;
|
||||||
source = sources[i];
|
source = sources[i];
|
||||||
if(!masks.empty()){
|
if(!masks.empty()){
|
||||||
assert(masks.size() == modalities.size());
|
CV_Assert(masks.size() == modalities.size());
|
||||||
mask = masks[i];
|
mask = masks[i];
|
||||||
}
|
}
|
||||||
assert(mask.empty() || mask.size() == source.size());
|
CV_Assert(mask.empty() || mask.size() == source.size());
|
||||||
quantizers.push_back(modalities[i]->process(source, mask));
|
quantizers.push_back(modalities[i]->process(source, mask));
|
||||||
}
|
}
|
||||||
// pyramid level -> modality -> quantization
|
// pyramid level -> modality -> quantization
|
||||||
|
|||||||
@@ -1,4 +1,5 @@
|
|||||||
#include "precomp.hpp"
|
#include "precomp.hpp"
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
#include "_lsvm_matching.h"
|
#include "_lsvm_matching.h"
|
||||||
#include <stdio.h>
|
#include <stdio.h>
|
||||||
|
|
||||||
|
|||||||
@@ -41,6 +41,7 @@
|
|||||||
|
|
||||||
#include "test_precomp.hpp"
|
#include "test_precomp.hpp"
|
||||||
#include "opencv2/imgproc.hpp"
|
#include "opencv2/imgproc.hpp"
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
|
|
||||||
using namespace cv;
|
using namespace cv;
|
||||||
using namespace std;
|
using namespace std;
|
||||||
@@ -117,7 +118,7 @@ int CV_DetectorTest::prepareData( FileStorage& _fs )
|
|||||||
// fn[TOTAL_NO_PAIR_E] >> eps.totalNoPair;
|
// fn[TOTAL_NO_PAIR_E] >> eps.totalNoPair;
|
||||||
|
|
||||||
// read detectors
|
// read detectors
|
||||||
if( fn[DETECTOR_NAMES].node->data.seq != 0 )
|
if( fn[DETECTOR_NAMES].size() != 0 )
|
||||||
{
|
{
|
||||||
FileNodeIterator it = fn[DETECTOR_NAMES].begin();
|
FileNodeIterator it = fn[DETECTOR_NAMES].begin();
|
||||||
for( ; it != fn[DETECTOR_NAMES].end(); )
|
for( ; it != fn[DETECTOR_NAMES].end(); )
|
||||||
@@ -132,7 +133,7 @@ int CV_DetectorTest::prepareData( FileStorage& _fs )
|
|||||||
|
|
||||||
// read images filenames and images
|
// read images filenames and images
|
||||||
string dataPath = ts->get_data_path();
|
string dataPath = ts->get_data_path();
|
||||||
if( fn[IMAGE_FILENAMES].node->data.seq != 0 )
|
if( fn[IMAGE_FILENAMES].size() != 0 )
|
||||||
{
|
{
|
||||||
for( FileNodeIterator it = fn[IMAGE_FILENAMES].begin(); it != fn[IMAGE_FILENAMES].end(); )
|
for( FileNodeIterator it = fn[IMAGE_FILENAMES].begin(); it != fn[IMAGE_FILENAMES].end(); )
|
||||||
{
|
{
|
||||||
@@ -210,7 +211,7 @@ void CV_DetectorTest::run( int )
|
|||||||
{
|
{
|
||||||
char buf[10];
|
char buf[10];
|
||||||
sprintf( buf, "%s%d", "img_", ii );
|
sprintf( buf, "%s%d", "img_", ii );
|
||||||
cvWriteComment( validationFS.fs, buf, 0 );
|
//cvWriteComment( validationFS.fs, buf, 0 );
|
||||||
validationFS << *it;
|
validationFS << *it;
|
||||||
}
|
}
|
||||||
validationFS << "]"; // IMAGE_FILENAMES
|
validationFS << "]"; // IMAGE_FILENAMES
|
||||||
@@ -316,7 +317,7 @@ int CV_DetectorTest::validate( int detectorIdx, vector<vector<Rect> >& objects )
|
|||||||
string imageIdxStr = buf;
|
string imageIdxStr = buf;
|
||||||
FileNode node = validationFS.getFirstTopLevelNode()[VALIDATION][detectorNames[detectorIdx]][imageIdxStr];
|
FileNode node = validationFS.getFirstTopLevelNode()[VALIDATION][detectorNames[detectorIdx]][imageIdxStr];
|
||||||
vector<Rect> valRects;
|
vector<Rect> valRects;
|
||||||
if( node.node->data.seq != 0 )
|
if( node.size() != 0 )
|
||||||
{
|
{
|
||||||
for( FileNodeIterator it2 = node.begin(); it2 != node.end(); )
|
for( FileNodeIterator it2 = node.begin(); it2 != node.end(); )
|
||||||
{
|
{
|
||||||
@@ -410,12 +411,12 @@ void CV_CascadeDetectorTest::readDetector( const FileNode& fn )
|
|||||||
if( flag )
|
if( flag )
|
||||||
flags.push_back( 0 );
|
flags.push_back( 0 );
|
||||||
else
|
else
|
||||||
flags.push_back( CV_HAAR_SCALE_IMAGE );
|
flags.push_back( CASCADE_SCALE_IMAGE );
|
||||||
}
|
}
|
||||||
|
|
||||||
void CV_CascadeDetectorTest::writeDetector( FileStorage& fs, int di )
|
void CV_CascadeDetectorTest::writeDetector( FileStorage& fs, int di )
|
||||||
{
|
{
|
||||||
int sc = flags[di] & CV_HAAR_SCALE_IMAGE ? 0 : 1;
|
int sc = flags[di] & CASCADE_SCALE_IMAGE ? 0 : 1;
|
||||||
fs << FILENAME << detectorFilenames[di];
|
fs << FILENAME << detectorFilenames[di];
|
||||||
fs << C_SCALE_CASCADE << sc;
|
fs << C_SCALE_CASCADE << sc;
|
||||||
}
|
}
|
||||||
@@ -439,7 +440,7 @@ int CV_CascadeDetectorTest::detectMultiScale_C( const string& filename,
|
|||||||
|
|
||||||
CvMat c_gray = grayImg;
|
CvMat c_gray = grayImg;
|
||||||
CvSeq* rs = cvHaarDetectObjects(&c_gray, c_cascade, storage, 1.1, 3, flags[di] );
|
CvSeq* rs = cvHaarDetectObjects(&c_gray, c_cascade, storage, 1.1, 3, flags[di] );
|
||||||
|
|
||||||
objects.clear();
|
objects.clear();
|
||||||
for( int i = 0; i < rs->total; i++ )
|
for( int i = 0; i < rs->total; i++ )
|
||||||
{
|
{
|
||||||
@@ -494,7 +495,7 @@ CV_HOGDetectorTest::CV_HOGDetectorTest()
|
|||||||
void CV_HOGDetectorTest::readDetector( const FileNode& fn )
|
void CV_HOGDetectorTest::readDetector( const FileNode& fn )
|
||||||
{
|
{
|
||||||
String filename;
|
String filename;
|
||||||
if( fn[FILENAME].node->data.seq != 0 )
|
if( fn[FILENAME].size() != 0 )
|
||||||
fn[FILENAME] >> filename;
|
fn[FILENAME] >> filename;
|
||||||
detectorFilenames.push_back( filename);
|
detectorFilenames.push_back( filename);
|
||||||
}
|
}
|
||||||
@@ -1085,7 +1086,7 @@ void HOGDescriptorTester::detect(const Mat& img,
|
|||||||
}
|
}
|
||||||
|
|
||||||
const double eps = 0.0;
|
const double eps = 0.0;
|
||||||
double diff_norm = norm(Mat(actual_weights) - Mat(weights), CV_L2);
|
double diff_norm = norm(Mat(actual_weights) - Mat(weights), NORM_L2);
|
||||||
if (diff_norm > eps)
|
if (diff_norm > eps)
|
||||||
{
|
{
|
||||||
ts->printf(cvtest::TS::SUMMARY, "Weights for found locations aren't equal.\n"
|
ts->printf(cvtest::TS::SUMMARY, "Weights for found locations aren't equal.\n"
|
||||||
@@ -1164,7 +1165,7 @@ void HOGDescriptorTester::compute(const Mat& img, vector<float>& descriptors,
|
|||||||
std::vector<float> actual_descriptors;
|
std::vector<float> actual_descriptors;
|
||||||
actual_hog->compute(img, actual_descriptors, winStride, padding, locations);
|
actual_hog->compute(img, actual_descriptors, winStride, padding, locations);
|
||||||
|
|
||||||
double diff_norm = cv::norm(Mat(actual_descriptors) - Mat(descriptors), CV_L2);
|
double diff_norm = cv::norm(Mat(actual_descriptors) - Mat(descriptors), NORM_L2);
|
||||||
const double eps = 0.0;
|
const double eps = 0.0;
|
||||||
if (diff_norm > eps)
|
if (diff_norm > eps)
|
||||||
{
|
{
|
||||||
@@ -1314,7 +1315,7 @@ void HOGDescriptorTester::computeGradient(const Mat& img, Mat& grad, Mat& qangle
|
|||||||
const double eps = 0.0;
|
const double eps = 0.0;
|
||||||
for (i = 0; i < 2; ++i)
|
for (i = 0; i < 2; ++i)
|
||||||
{
|
{
|
||||||
double diff_norm = norm(reference_mats[i] - actual_mats[i], CV_L2);
|
double diff_norm = norm(reference_mats[i] - actual_mats[i], NORM_L2);
|
||||||
if (diff_norm > eps)
|
if (diff_norm > eps)
|
||||||
{
|
{
|
||||||
ts->printf(cvtest::TS::LOG, "%s matrices are not equal\n"
|
ts->printf(cvtest::TS::LOG, "%s matrices are not equal\n"
|
||||||
|
|||||||
@@ -41,6 +41,7 @@
|
|||||||
//M*/
|
//M*/
|
||||||
|
|
||||||
#include "test_precomp.hpp"
|
#include "test_precomp.hpp"
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
#include <string>
|
#include <string>
|
||||||
|
|
||||||
#ifdef HAVE_TBB
|
#ifdef HAVE_TBB
|
||||||
|
|||||||
@@ -18,14 +18,14 @@
|
|||||||
#include "opencv2/features2d.hpp"
|
#include "opencv2/features2d.hpp"
|
||||||
#include "opencv2/objdetect.hpp"
|
#include "opencv2/objdetect.hpp"
|
||||||
#include "opencv2/softcascade.hpp"
|
#include "opencv2/softcascade.hpp"
|
||||||
#include "opencv2/video/tracking.hpp"
|
#include "opencv2/video.hpp"
|
||||||
#include "opencv2/video/background_segm.hpp"
|
|
||||||
#include "opencv2/photo.hpp"
|
#include "opencv2/photo.hpp"
|
||||||
#include "opencv2/highgui.hpp"
|
#include "opencv2/highgui.hpp"
|
||||||
|
|
||||||
#include "opencv2/highgui/highgui_c.h"
|
#include "opencv2/highgui/highgui_c.h"
|
||||||
#include "opencv2/photo/photo_c.h"
|
#include "opencv2/photo/photo_c.h"
|
||||||
#include "opencv2/video/tracking_c.h"
|
#include "opencv2/video/tracking_c.h"
|
||||||
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
|
|
||||||
#include "opencv2/opencv_modules.hpp"
|
#include "opencv2/opencv_modules.hpp"
|
||||||
|
|
||||||
|
|||||||
@@ -54,7 +54,7 @@ using namespace cv::superres::detail;
|
|||||||
|
|
||||||
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1_GPU()
|
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1_GPU()
|
||||||
{
|
{
|
||||||
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
|
CV_Error(Error::StsNotImplemented, "The called functionality is disabled for current build or platform");
|
||||||
return Ptr<SuperResolution>();
|
return Ptr<SuperResolution>();
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|||||||
@@ -1,4 +1,4 @@
|
|||||||
#include "opencv2/objdetect/objdetect.hpp"
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
#include "opencv2/highgui/highgui_c.h"
|
#include "opencv2/highgui/highgui_c.h"
|
||||||
|
|
||||||
#include <ctype.h>
|
#include <ctype.h>
|
||||||
|
|||||||
@@ -216,9 +216,9 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
|
|||||||
t = (double)cvGetTickCount();
|
t = (double)cvGetTickCount();
|
||||||
cascade.detectMultiScale( smallImg, faces,
|
cascade.detectMultiScale( smallImg, faces,
|
||||||
1.1, 2, 0
|
1.1, 2, 0
|
||||||
//|CV_HAAR_FIND_BIGGEST_OBJECT
|
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||||
//|CV_HAAR_DO_ROUGH_SEARCH
|
//|CASCADE_DO_ROUGH_SEARCH
|
||||||
|CV_HAAR_SCALE_IMAGE
|
|CASCADE_SCALE_IMAGE
|
||||||
,
|
,
|
||||||
Size(30, 30) );
|
Size(30, 30) );
|
||||||
if( tryflip )
|
if( tryflip )
|
||||||
@@ -226,9 +226,9 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
|
|||||||
flip(smallImg, smallImg, 1);
|
flip(smallImg, smallImg, 1);
|
||||||
cascade.detectMultiScale( smallImg, faces2,
|
cascade.detectMultiScale( smallImg, faces2,
|
||||||
1.1, 2, 0
|
1.1, 2, 0
|
||||||
//|CV_HAAR_FIND_BIGGEST_OBJECT
|
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||||
//|CV_HAAR_DO_ROUGH_SEARCH
|
//|CASCADE_DO_ROUGH_SEARCH
|
||||||
|CV_HAAR_SCALE_IMAGE
|
|CASCADE_SCALE_IMAGE
|
||||||
,
|
,
|
||||||
Size(30, 30) );
|
Size(30, 30) );
|
||||||
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
|
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
|
||||||
@@ -263,10 +263,10 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
|
|||||||
smallImgROI = smallImg(*r);
|
smallImgROI = smallImg(*r);
|
||||||
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
|
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
|
||||||
1.1, 2, 0
|
1.1, 2, 0
|
||||||
//|CV_HAAR_FIND_BIGGEST_OBJECT
|
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||||
//|CV_HAAR_DO_ROUGH_SEARCH
|
//|CASCADE_DO_ROUGH_SEARCH
|
||||||
//|CV_HAAR_DO_CANNY_PRUNING
|
//|CASCADE_DO_CANNY_PRUNING
|
||||||
|CV_HAAR_SCALE_IMAGE
|
|CASCADE_SCALE_IMAGE
|
||||||
,
|
,
|
||||||
Size(30, 30) );
|
Size(30, 30) );
|
||||||
for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
|
for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
|
||||||
|
|||||||
@@ -1,4 +1,4 @@
|
|||||||
#include "opencv2/objdetect.hpp"
|
#include "opencv2/objdetect/objdetect_c.h"
|
||||||
#include "opencv2/highgui/highgui_c.h"
|
#include "opencv2/highgui/highgui_c.h"
|
||||||
#include <stdio.h>
|
#include <stdio.h>
|
||||||
|
|
||||||
|
|||||||
@@ -175,9 +175,9 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
|
|||||||
|
|
||||||
cascade.detectMultiScale( smallImg, faces,
|
cascade.detectMultiScale( smallImg, faces,
|
||||||
1.1, 2, 0
|
1.1, 2, 0
|
||||||
//|CV_HAAR_FIND_BIGGEST_OBJECT
|
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||||
//|CV_HAAR_DO_ROUGH_SEARCH
|
//|CASCADE_DO_ROUGH_SEARCH
|
||||||
|CV_HAAR_SCALE_IMAGE
|
|CASCADE_SCALE_IMAGE
|
||||||
,
|
,
|
||||||
Size(30, 30) );
|
Size(30, 30) );
|
||||||
if( tryflip )
|
if( tryflip )
|
||||||
@@ -185,9 +185,9 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
|
|||||||
flip(smallImg, smallImg, 1);
|
flip(smallImg, smallImg, 1);
|
||||||
cascade.detectMultiScale( smallImg, faces2,
|
cascade.detectMultiScale( smallImg, faces2,
|
||||||
1.1, 2, 0
|
1.1, 2, 0
|
||||||
//|CV_HAAR_FIND_BIGGEST_OBJECT
|
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||||
//|CV_HAAR_DO_ROUGH_SEARCH
|
//|CASCADE_DO_ROUGH_SEARCH
|
||||||
|CV_HAAR_SCALE_IMAGE
|
|CASCADE_SCALE_IMAGE
|
||||||
,
|
,
|
||||||
Size(30, 30) );
|
Size(30, 30) );
|
||||||
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
|
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
|
||||||
@@ -223,10 +223,10 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
|
|||||||
smallImgROI = smallImg(*r);
|
smallImgROI = smallImg(*r);
|
||||||
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
|
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
|
||||||
1.1, 0, 0
|
1.1, 0, 0
|
||||||
//|CV_HAAR_FIND_BIGGEST_OBJECT
|
//|CASCADE_FIND_BIGGEST_OBJECT
|
||||||
//|CV_HAAR_DO_ROUGH_SEARCH
|
//|CASCADE_DO_ROUGH_SEARCH
|
||||||
//|CV_HAAR_DO_CANNY_PRUNING
|
//|CASCADE_DO_CANNY_PRUNING
|
||||||
|CV_HAAR_SCALE_IMAGE
|
|CASCADE_SCALE_IMAGE
|
||||||
,
|
,
|
||||||
Size(30, 30) );
|
Size(30, 30) );
|
||||||
|
|
||||||
|
|||||||
@@ -79,7 +79,7 @@ int main(int , char** )
|
|||||||
|
|
||||||
for (size_t i = 0; i < Faces.size(); i++)
|
for (size_t i = 0; i < Faces.size(); i++)
|
||||||
{
|
{
|
||||||
rectangle(ReferenceFrame, Faces[i], CV_RGB(0,255,0));
|
rectangle(ReferenceFrame, Faces[i], Scalar(0,255,0));
|
||||||
}
|
}
|
||||||
|
|
||||||
imshow(WindowName, ReferenceFrame);
|
imshow(WindowName, ReferenceFrame);
|
||||||
|
|||||||
@@ -141,7 +141,7 @@ int main(int argc, char** argv)
|
|||||||
conf << d.confidence;
|
conf << d.confidence;
|
||||||
|
|
||||||
cv::rectangle(frame, cv::Rect((int)d.x, (int)d.y, (int)d.w, (int)d.h), cv::Scalar(b, 0, 255 - b, 255), 2);
|
cv::rectangle(frame, cv::Rect((int)d.x, (int)d.y, (int)d.w, (int)d.h), cv::Scalar(b, 0, 255 - b, 255), 2);
|
||||||
cv::putText(frame, conf.str() , cv::Point((int)d.x + 10, (int)d.y - 5),1, 1.1, cv::Scalar(25, 133, 255, 0), 1, CV_AA);
|
cv::putText(frame, conf.str() , cv::Point((int)d.x + 10, (int)d.y - 5),1, 1.1, cv::Scalar(25, 133, 255, 0), 1, cv::LINE_AA);
|
||||||
|
|
||||||
if (wf)
|
if (wf)
|
||||||
myfile << d.x << "," << d.y << "," << d.w << "," << d.h << "," << d.confidence << "\n";
|
myfile << d.x << "," << d.y << "," << d.w << "," << d.h << "," << d.confidence << "\n";
|
||||||
|
|||||||
@@ -73,7 +73,7 @@ void detectAndDisplay( Mat frame )
|
|||||||
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
|
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
|
||||||
equalizeHist( frame_gray, frame_gray );
|
equalizeHist( frame_gray, frame_gray );
|
||||||
//-- Detect faces
|
//-- Detect faces
|
||||||
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
|
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );
|
||||||
|
|
||||||
for( size_t i = 0; i < faces.size(); i++ )
|
for( size_t i = 0; i < faces.size(); i++ )
|
||||||
{
|
{
|
||||||
@@ -84,7 +84,7 @@ void detectAndDisplay( Mat frame )
|
|||||||
std::vector<Rect> eyes;
|
std::vector<Rect> eyes;
|
||||||
|
|
||||||
//-- In each face, detect eyes
|
//-- In each face, detect eyes
|
||||||
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
|
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
|
||||||
|
|
||||||
for( size_t j = 0; j < eyes.size(); j++ )
|
for( size_t j = 0; j < eyes.size(); j++ )
|
||||||
{
|
{
|
||||||
|
|||||||
@@ -82,7 +82,7 @@ void detectAndDisplay( Mat frame )
|
|||||||
std::vector<Rect> eyes;
|
std::vector<Rect> eyes;
|
||||||
|
|
||||||
//-- In each face, detect eyes
|
//-- In each face, detect eyes
|
||||||
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
|
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
|
||||||
if( eyes.size() == 2)
|
if( eyes.size() == 2)
|
||||||
{
|
{
|
||||||
//-- Draw the face
|
//-- Draw the face
|
||||||
|
|||||||
@@ -228,8 +228,8 @@ int main(int argc, const char *argv[])
|
|||||||
Size minSize = cascade_gpu.getClassifierSize();
|
Size minSize = cascade_gpu.getClassifierSize();
|
||||||
cascade_cpu.detectMultiScale(resized_cpu, facesBuf_cpu, 1.2,
|
cascade_cpu.detectMultiScale(resized_cpu, facesBuf_cpu, 1.2,
|
||||||
(filterRects || findLargestObject) ? 4 : 0,
|
(filterRects || findLargestObject) ? 4 : 0,
|
||||||
(findLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
|
(findLargestObject ? CASCADE_FIND_BIGGEST_OBJECT : 0)
|
||||||
| CV_HAAR_SCALE_IMAGE,
|
| CASCADE_SCALE_IMAGE,
|
||||||
minSize);
|
minSize);
|
||||||
detections_num = (int)facesBuf_cpu.size();
|
detections_num = (int)facesBuf_cpu.size();
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -326,7 +326,7 @@ void App::run()
|
|||||||
for (size_t i = 0; i < found.size(); i++)
|
for (size_t i = 0; i < found.size(); i++)
|
||||||
{
|
{
|
||||||
Rect r = found[i];
|
Rect r = found[i];
|
||||||
rectangle(img_to_show, r.tl(), r.br(), CV_RGB(0, 255, 0), 3);
|
rectangle(img_to_show, r.tl(), r.br(), Scalar(0, 255, 0), 3);
|
||||||
}
|
}
|
||||||
|
|
||||||
if (use_gpu)
|
if (use_gpu)
|
||||||
|
|||||||
@@ -51,7 +51,7 @@ int main(int argc, const char* argv[])
|
|||||||
for (size_t i = 0; i < lines_cpu.size(); ++i)
|
for (size_t i = 0; i < lines_cpu.size(); ++i)
|
||||||
{
|
{
|
||||||
Vec4i l = lines_cpu[i];
|
Vec4i l = lines_cpu[i];
|
||||||
line(dst_cpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, CV_AA);
|
line(dst_cpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, LINE_AA);
|
||||||
}
|
}
|
||||||
|
|
||||||
GpuMat d_src(mask);
|
GpuMat d_src(mask);
|
||||||
@@ -77,7 +77,7 @@ int main(int argc, const char* argv[])
|
|||||||
for (size_t i = 0; i < lines_gpu.size(); ++i)
|
for (size_t i = 0; i < lines_gpu.size(); ++i)
|
||||||
{
|
{
|
||||||
Vec4i l = lines_gpu[i];
|
Vec4i l = lines_gpu[i];
|
||||||
line(dst_gpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, CV_AA);
|
line(dst_gpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, LINE_AA);
|
||||||
}
|
}
|
||||||
|
|
||||||
imshow("source", src);
|
imshow("source", src);
|
||||||
|
|||||||
@@ -144,7 +144,7 @@ string abspath(const string& relpath)
|
|||||||
}
|
}
|
||||||
|
|
||||||
|
|
||||||
static int CV_CDECL cvErrorCallback(int /*status*/, const char* /*func_name*/,
|
static int cvErrorCallback(int /*status*/, const char* /*func_name*/,
|
||||||
const char* err_msg, const char* /*file_name*/,
|
const char* err_msg, const char* /*file_name*/,
|
||||||
int /*line*/, void* /*userdata*/)
|
int /*line*/, void* /*userdata*/)
|
||||||
{
|
{
|
||||||
|
|||||||
Reference in New Issue
Block a user