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soft cascade become Algorithm

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pull/137/head
marina.kolpakova 12 years ago
parent ff8417db00
commit 4a1c4a9862
5 changed files with 122 additions and 107 deletions
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  1. 63
      modules/objdetect/include/opencv2/objdetect/objdetect.hpp
  2. 17
      modules/objdetect/perf/perf_cascadeclassifier.cpp
  3. 23
      modules/objdetect/src/objdetect_init.cpp
  4. 32
      modules/objdetect/src/softcascade.cpp
  5. 94
      modules/objdetect/test/test_softcascade.cpp

63
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
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@ -488,52 +488,52 @@ protected:
Ptr<MaskGenerator> maskGenerator;
};
/**
* \class SoftCascade
* \brief Implement soft (stageless) cascade.
*/
class CV_EXPORTS SoftCascade
// Implementation of soft (stageless) cascaded detector.
class CV_EXPORTS SCascade : public Algorithm
{
public:
/**
* \class Detection
* \brief Soft cascade detector result represintation.
*/
// Representation of detectors result.
struct CV_EXPORTS Detection
{
// Default object type.
enum {PEDESTRIAN = 1};
//! Create detection from an object bounding rectangle and confidence. Only PEDESTRIAN type carrently supported.
//! Param r is a boundinf rectangle
//! param c is a confidence that object belongs to class k
//! Paral k is an object class
// Creates Detection from an object bounding box and confidence.
// Param b is a bounding box
// Param c is a confidence that object belongs to class k
// Paral k is an object class
Detection(const cv::Rect& b, const float c, int k = PEDESTRIAN) : bb(b), confidence(c), kind(k) {}
Detection(const cv::Rect& r, const float c, int k = PEDESTRIAN) : rect(r), confidence(c), kind(k) {}
cv::Rect rect;
cv::Rect bb;
float confidence;
int kind;
};
//! An empty cascade will be created.
//! Param minScale is a minimum scale relative to the original size of the image on which cascade will be applyed.
//! Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
//! Param scales is a number of scales from minScale to maxScale.
SoftCascade( const float minScale = 0.4f, const float maxScale = 5.f, const int scales = 55);
// An empty cascade will be created.
// Param minScale is a minimum scale relative to the original size of the image on which cascade will be applyed.
// Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
// Param scales is a number of scales from minScale to maxScale.
// Param rejfactor is used for NMS.
SCascade(const float minScale = 0.4f, const float maxScale = 5.f, const int scales = 55, const int rejfactor = 1);
//! Cascade will be created for scales from minScale to maxScale.
//! Param fs is a serialized sacsade.
SoftCascade( const cv::FileStorage& fs);
virtual ~SCascade();
//! cascade will be loaded. The previous cascade will be destroyed.
//! Param fs is a serialized sacsade.
bool read( const cv::FileStorage& fs);
cv::AlgorithmInfo* info() const;
virtual ~SoftCascade();
// Load cascade from FileNode.
// Param fn is a root node for cascade. Should be <cascade>.
virtual bool load(const FileNode& fn);
//! return vector of bounding boxes. Each box contains one detected object
virtual void detectMultiScale(const Mat& image, const std::vector<cv::Rect>& rois, std::vector<Detection>& objects,
int rejectfactor = 1) const;
// Load cascade config.
virtual void read(const FileNode& fn);
// Return the vector of Decection objcts.
// Param image is a frame on which detector will be applied.
// Param rois is a vector of regions of interest. Only the objects that fall into one of the regions will be returned.
// Param objects is an output array of Detections
virtual void detect(const Mat& image, const std::vector<cv::Rect>& rois, std::vector<Detection>& objects) const;
private:
struct Filds;
@ -542,8 +542,11 @@ private:
float minScale;
float maxScale;
int scales;
int rejfactor;
};
CV_EXPORTS bool initModule_objdetect(void);
/**
* \class IntegralChannels
* \brief Create channel integrals for Soft Cascade detector.

17
modules/objdetect/perf/perf_cascadeclassifier.cpp
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@ -58,35 +58,36 @@ typedef perf::TestBaseWithParam<fixture> detect;
namespace {
typedef cv::SoftCascade::Detection detection_t;
typedef cv::SCascade::Detection detection_t;
void extractRacts(std::vector<detection_t> objectBoxes, vector<Rect> rects)
{
rects.clear();
for (int i = 0; i < (int)objectBoxes.size(); ++i)
rects.push_back(objectBoxes[i].rect);
rects.push_back(objectBoxes[i].bb);
}
}
PERF_TEST_P(detect, SoftCascade,
PERF_TEST_P(detect, SCascade,
testing::Combine(testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
testing::Values(std::string("cv/cascadeandhog/bahnhof/image_00000000_0.png"))))
{
typedef cv::SoftCascade::Detection detection_t;
typedef cv::SCascade::Detection Detection;
cv::Mat colored = imread(getDataPath(get<1>(GetParam())));
ASSERT_FALSE(colored.empty());
cv::SoftCascade cascade;
cv::SCascade cascade;
cv::FileStorage fs(getDataPath(get<0>(GetParam())), cv::FileStorage::READ);
ASSERT_TRUE(cascade.read(fs));
ASSERT_TRUE(fs.isOpened());
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
std::vector<cv::Rect> rois;
std::vector<detection_t> objectBoxes;
cascade.detectMultiScale(colored, rois, objectBoxes);
cascade.detect(colored, rois, objectBoxes);
TEST_CYCLE()
{
cascade.detectMultiScale(colored, rois, objectBoxes);
cascade.detect(colored, rois, objectBoxes);
}
vector<Rect> rects;

23
modules/objdetect/src/objdetect_init.cpp
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@ -7,11 +7,11 @@
// copy or use the software.
//
//
// License Agreement
// 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) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
@ -40,4 +40,21 @@
//
//M*/
#include "precomp.hpp"
#include <precomp.hpp>
namespace cv
{
CV_INIT_ALGORITHM(SCascade, "CascadeDetector.SCascade",
obj.info()->addParam(obj, "minScale", obj.minScale));
// obj.info()->addParam(obj, "maxScale", obj.maxScale);
// obj.info()->addParam(obj, "scales", obj.scales);
// obj.info()->addParam(obj, "rejfactor", obj.rejfactor));
bool initModule_objdetect(void)
{
Ptr<Algorithm> sc = createSCascade();
return sc->info() != 0;
}
}

32
modules/objdetect/src/softcascade.cpp
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@ -175,7 +175,7 @@ struct Level
enum { R_SHIFT = 1 << 15 };
float scaling[2];
typedef cv::SoftCascade::Detection detection_t;
typedef cv::SCascade::Detection detection_t;
Level(const Octave& oct, const float scale, const int shrinkage, const int w, const int h)
: octave(&oct), origScale(scale), relScale(scale / oct.scale),
@ -252,7 +252,7 @@ struct ChannelStorage
}
struct cv::SoftCascade::Filds
struct cv::SCascade::Filds
{
float minScale;
float maxScale;
@ -491,33 +491,25 @@ struct cv::SoftCascade::Filds
}
};
cv::SoftCascade::SoftCascade(const float mins, const float maxs, const int nsc)
: filds(0), minScale(mins), maxScale(maxs), scales(nsc) {}
cv::SCascade::SCascade(const float mins, const float maxs, const int nsc, const int rej)
: filds(0), minScale(mins), maxScale(maxs), scales(nsc), rejfactor(rej) {}
cv::SoftCascade::SoftCascade(const cv::FileStorage& fs) : filds(0)
{
read(fs);
}
cv::SoftCascade::~SoftCascade()
cv::SCascade::~SCascade() { delete filds;}
void cv::SCascade::read(const FileNode& fn)
{
delete filds;
Algorithm::read(fn);
}
bool cv::SoftCascade::read( const cv::FileStorage& fs)
bool cv::SCascade::load(const FileNode& fn)
{
if (!fs.isOpened()) return false;
if (filds)
delete filds;
filds = 0;
if (filds) delete filds;
filds = new Filds;
Filds& flds = *filds;
return flds.fill(fs.getFirstTopLevelNode(), minScale, maxScale);
return filds->fill(fn, minScale, maxScale);
}
void cv::SoftCascade::detectMultiScale(const Mat& image, const std::vector<cv::Rect>& /*rois*/,
std::vector<Detection>& objects, const int /*rejectfactor*/) const
void cv::SCascade::detect(const Mat& image, const std::vector<cv::Rect>& /*rois*/, std::vector<Detection>& objects) const
{
// only color images are supperted
CV_Assert(image.type() == CV_8UC3);

94
modules/objdetect/test/test_softcascade.cpp
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@ -1,31 +1,31 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
// 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.
// 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
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., 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:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistributions 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.
// * 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.
// * 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
@ -37,60 +37,62 @@
// 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*/
#include "test_precomp.hpp"
TEST(SoftCascade, readCascade)
TEST(SCascade, readCascade)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/icf-template.xml";
cv::SoftCascade cascade;
cv::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
ASSERT_TRUE(cascade.read(fs));
ASSERT_TRUE(fs.isOpened());
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
}
TEST(SoftCascade, detect)
TEST(SCascade, detect)
{
typedef cv::SoftCascade::Detection detection_t;
typedef cv::SCascade::Detection Detection;
std::string xml = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::SoftCascade cascade;
cv::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
ASSERT_TRUE(cascade.read(fs));
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
cv::Mat colored = cv::imread(cvtest::TS::ptr()->get_data_path() + "cascadeandhog/bahnhof/image_00000000_0.png");
ASSERT_FALSE(colored.empty());
std::vector<detection_t> objects;
std::vector<Detection> objects;
std::vector<cv::Rect> rois;
rois.push_back(cv::Rect(0, 0, 640, 480));
cascade.detectMultiScale(colored, rois, objects);
cascade.detect(colored, rois, objects);
// cv::Mat out = colored.clone();
// int level = 0, total = 0;
// int levelWidth = objects[0].rect.width;
cv::Mat out = colored.clone();
int level = 0, total = 0;
int levelWidth = objects[0].bb.width;
// for(int i = 0 ; i < (int)objects.size(); ++i)
// {
// if (objects[i].rect.width != levelWidth)
// {
// std::cout << "Level: " << level << " total " << total << std::endl;
// cv::imshow("out", out);
// cv::waitKey(0);
// out = colored.clone();
// levelWidth = objects[i].rect.width;
// total = 0;
// level++;
// }
// cv::rectangle(out, objects[i].rect, cv::Scalar(255, 0, 0, 255), 1);
// std::cout << "detection: " << objects[i].rect.x
// << " " << objects[i].rect.y
// << " " << objects[i].rect.width
// << " " << objects[i].rect.height << std::endl;
// total++;
// }
// std::cout << "detected: " << (int)objects.size() << std::endl;
for(int i = 0 ; i < (int)objects.size(); ++i)
{
if (objects[i].bb.width != levelWidth)
{
std::cout << "Level: " << level << " total " << total << std::endl;
cv::imshow("out", out);
cv::waitKey(0);
out = colored.clone();
levelWidth = objects[i].bb.width;
total = 0;
level++;
}
cv::rectangle(out, objects[i].bb, cv::Scalar(255, 0, 0, 255), 1);
std::cout << "detection: " << objects[i].bb.x
<< " " << objects[i].bb.y
<< " " << objects[i].bb.width
<< " " << objects[i].bb.height << std::endl;
total++;
}
std::cout << "detected: " << (int)objects.size() << std::endl;
ASSERT_EQ((int)objects.size(), 3668);
}
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