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reworked nearly all of the OpenCV tests (except for opencv_gpu tests) - they now use the Google Test engine.

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This commit is contained in:
Vadim Pisarevsky
2011-02-09 20:55:11 +00:00
parent 63806c9ab9
commit 061b49e0b2
122 changed files with 39081 additions and 28854 deletions
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465
modules/objdetect/test/test_cascadeandhog.cpp Normal file
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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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*/
#include "test_precomp.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
using namespace std;
//#define GET_STAT
#define DIST_E "distE"
#define S_E "sE"
#define NO_PAIR_E "noPairE"
//#define TOTAL_NO_PAIR_E "totalNoPairE"
#define DETECTOR_NAMES "detector_names"
#define DETECTORS "detectors"
#define IMAGE_FILENAMES "image_filenames"
#define VALIDATION "validation"
#define FILENAME "fn"
#define C_SCALE_CASCADE "scale_cascade"
class CV_DetectorTest : public cvtest::BaseTest
{
public:
CV_DetectorTest();
protected:
virtual int prepareData( FileStorage& fs );
virtual void run( int startFrom );
virtual string& getValidationFilename();
virtual void readDetector( const FileNode& fn ) = 0;
virtual void writeDetector( FileStorage& fs, int di ) = 0;
int runTestCase( int detectorIdx, vector<vector<Rect> >& objects );
virtual int detectMultiScale( int di, const Mat& img, vector<Rect>& objects ) = 0;
int validate( int detectorIdx, vector<vector<Rect> >& objects );
struct
{
float dist;
float s;
float noPair;
//float totalNoPair;
} eps;
vector<string> detectorNames;
vector<string> detectorFilenames;
vector<string> imageFilenames;
vector<Mat> images;
string validationFilename;
FileStorage validationFS;
};
CV_DetectorTest::CV_DetectorTest()
{
}
string& CV_DetectorTest::getValidationFilename()
{
return validationFilename;
}
int CV_DetectorTest::prepareData( FileStorage& _fs )
{
if( !_fs.isOpened() )
test_case_count = -1;
else
{
FileNode fn = _fs.getFirstTopLevelNode();
fn[DIST_E] >> eps.dist;
fn[S_E] >> eps.s;
fn[NO_PAIR_E] >> eps.noPair;
// fn[TOTAL_NO_PAIR_E] >> eps.totalNoPair;
// read detectors
if( fn[DETECTOR_NAMES].node->data.seq != 0 )
{
FileNodeIterator it = fn[DETECTOR_NAMES].begin();
for( ; it != fn[DETECTOR_NAMES].end(); )
{
string name;
it >> name;
detectorNames.push_back(name);
readDetector(fn[DETECTORS][name]);
}
}
test_case_count = (int)detectorNames.size();
// read images filenames and images
string dataPath = ts->get_data_path();
if( fn[IMAGE_FILENAMES].node->data.seq != 0 )
{
for( FileNodeIterator it = fn[IMAGE_FILENAMES].begin(); it != fn[IMAGE_FILENAMES].end(); )
{
string filename;
it >> filename;
imageFilenames.push_back(filename);
Mat img = imread( dataPath+filename, 1 );
images.push_back( img );
}
}
}
return cvtest::TS::OK;
}
void CV_DetectorTest::run( int )
{
string dataPath = ts->get_data_path();
validationFS.open( dataPath + getValidationFilename(), FileStorage::READ );
int code = prepareData( validationFS );
if( code < 0 )
{
ts->set_failed_test_info( code );
return;
}
#ifdef GET_STAT
validationFS.release();
string filename = ts->get_data_path();
filename += getValidationFilename();
validationFS.open( filename, FileStorage::WRITE );
validationFS << FileStorage::getDefaultObjectName(validationFilename) << "{";
validationFS << DIST_E << eps.dist;
validationFS << S_E << eps.s;
validationFS << NO_PAIR_E << eps.noPair;
// validationFS << TOTAL_NO_PAIR_E << eps.totalNoPair;
// write detector names
validationFS << DETECTOR_NAMES << "[";
vector<string>::const_iterator nit = detectorNames.begin();
for( ; nit != detectorNames.end(); ++nit )
{
validationFS << *nit;
}
validationFS << "]"; // DETECTOR_NAMES
// write detectors
validationFS << DETECTORS << "{";
assert( detectorNames.size() == detectorFilenames.size() );
nit = detectorNames.begin();
for( int di = 0; di < detectorNames.size(), nit != detectorNames.end(); ++nit, di++ )
{
validationFS << *nit << "{";
writeDetector( validationFS, di );
validationFS << "}";
}
validationFS << "}";
// write image filenames
validationFS << IMAGE_FILENAMES << "[";
vector<string>::const_iterator it = imageFilenames.begin();
for( int ii = 0; it != imageFilenames.end(); ++it, ii++ )
{
char buf[10];
sprintf( buf, "%s%d", "img_", ii );
cvWriteComment( validationFS.fs, buf, 0 );
validationFS << *it;
}
validationFS << "]"; // IMAGE_FILENAMES
validationFS << VALIDATION << "{";
#endif
int progress = 0;
for( int di = 0; di < test_case_count; di++ )
{
progress = update_progress( progress, di, test_case_count, 0 );
#ifdef GET_STAT
validationFS << detectorNames[di] << "{";
#endif
vector<vector<Rect> > objects;
int temp_code = runTestCase( di, objects );
#ifndef GET_STAT
if (temp_code == cvtest::TS::OK)
temp_code = validate( di, objects );
#endif
if (temp_code != cvtest::TS::OK)
code = temp_code;
#ifdef GET_STAT
validationFS << "}"; // detectorNames[di]
#endif
}
#ifdef GET_STAT
validationFS << "}"; // VALIDATION
validationFS << "}"; // getDefaultObjectName
#endif
if ( test_case_count <= 0 || imageFilenames.size() <= 0 )
{
ts->printf( cvtest::TS::LOG, "validation file is not determined or not correct" );
code = cvtest::TS::FAIL_INVALID_TEST_DATA;
}
ts->set_failed_test_info( code );
}
int CV_DetectorTest::runTestCase( int detectorIdx, vector<vector<Rect> >& objects )
{
string dataPath = ts->get_data_path(), detectorFilename;
if( !detectorFilenames[detectorIdx].empty() )
detectorFilename = dataPath + detectorFilenames[detectorIdx];
for( int ii = 0; ii < (int)imageFilenames.size(); ++ii )
{
vector<Rect> imgObjects;
Mat image = images[ii];
if( image.empty() )
{
char msg[30];
sprintf( msg, "%s %d %s", "image ", ii, " can not be read" );
ts->printf( cvtest::TS::LOG, msg );
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
int code = detectMultiScale( detectorIdx, image, imgObjects );
if( code != cvtest::TS::OK )
return code;
objects.push_back( imgObjects );
#ifdef GET_STAT
char buf[10];
sprintf( buf, "%s%d", "img_", ii );
string imageIdxStr = buf;
validationFS << imageIdxStr << "[:";
for( vector<Rect>::const_iterator it = imgObjects.begin();
it != imgObjects.end(); ++it )
{
validationFS << it->x << it->y << it->width << it->height;
}
validationFS << "]"; // imageIdxStr
#endif
}
return cvtest::TS::OK;
}
bool isZero( uchar i ) {return i == 0;}
int CV_DetectorTest::validate( int detectorIdx, vector<vector<Rect> >& objects )
{
assert( imageFilenames.size() == objects.size() );
int imageIdx = 0;
int totalNoPair = 0, totalValRectCount = 0;
for( vector<vector<Rect> >::const_iterator it = objects.begin();
it != objects.end(); ++it, imageIdx++ ) // for image
{
Size imgSize = images[imageIdx].size();
float dist = min(imgSize.height, imgSize.width) * eps.dist;
float wDiff = imgSize.width * eps.s;
float hDiff = imgSize.height * eps.s;
int noPair = 0;
// read validation rectangles
char buf[10];
sprintf( buf, "%s%d", "img_", imageIdx );
string imageIdxStr = buf;
FileNode node = validationFS.getFirstTopLevelNode()[VALIDATION][detectorNames[detectorIdx]][imageIdxStr];
vector<Rect> valRects;
if( node.node->data.seq != 0 )
{
for( FileNodeIterator it = node.begin(); it != node.end(); )
{
Rect r;
it >> r.x >> r.y >> r.width >> r.height;
valRects.push_back(r);
}
}
totalValRectCount += (int)valRects.size();
// compare rectangles
vector<uchar> map(valRects.size(), 0);
for( vector<Rect>::const_iterator cr = it->begin();
cr != it->end(); ++cr )
{
// find nearest rectangle
Point2f cp1 = Point2f( cr->x + (float)cr->width/2.0f, cr->y + (float)cr->height/2.0f );
int minIdx = -1, vi = 0;
float minDist = (float)norm( Point(imgSize.width, imgSize.height) );
for( vector<Rect>::const_iterator vr = valRects.begin();
vr != valRects.end(); ++vr, vi++ )
{
Point2f cp2 = Point2f( vr->x + (float)vr->width/2.0f, vr->y + (float)vr->height/2.0f );
float curDist = (float)norm(cp1-cp2);
if( curDist < minDist )
{
minIdx = vi;
minDist = curDist;
}
}
if( minIdx == -1 )
{
noPair++;
}
else
{
Rect vr = valRects[minIdx];
if( map[minIdx] != 0 || (minDist > dist) || (abs(cr->width - vr.width) > wDiff) ||
(abs(cr->height - vr.height) > hDiff) )
noPair++;
else
map[minIdx] = 1;
}
}
noPair += (int)count_if( map.begin(), map.end(), isZero );
totalNoPair += noPair;
if( noPair > cvRound(valRects.size()*eps.noPair)+1 )
break;
}
if( imageIdx < (int)imageFilenames.size() )
{
char msg[500];
sprintf( msg, "detector %s has overrated count of rectangles without pair on %s-image\n",
detectorNames[detectorIdx].c_str(), imageFilenames[imageIdx].c_str() );
ts->printf( cvtest::TS::LOG, msg );
return cvtest::TS::FAIL_BAD_ACCURACY;
}
if ( totalNoPair > cvRound(totalValRectCount*eps./*total*/noPair)+1 )
{
ts->printf( cvtest::TS::LOG, "overrated count of rectangles without pair on all images set" );
return cvtest::TS::FAIL_BAD_ACCURACY;
}
return cvtest::TS::OK;
}
//----------------------------------------------- CascadeDetectorTest -----------------------------------
class CV_CascadeDetectorTest : public CV_DetectorTest
{
public:
CV_CascadeDetectorTest();
protected:
virtual void readDetector( const FileNode& fn );
virtual void writeDetector( FileStorage& fs, int di );
virtual int detectMultiScale( int di, const Mat& img, vector<Rect>& objects );
vector<int> flags;
};
CV_CascadeDetectorTest::CV_CascadeDetectorTest()
{
validationFilename = "cascadeandhog/cascade.xml";
}
void CV_CascadeDetectorTest::readDetector( const FileNode& fn )
{
string filename;
int flag;
fn[FILENAME] >> filename;
detectorFilenames.push_back(filename);
fn[C_SCALE_CASCADE] >> flag;
if( flag )
flags.push_back( 0 );
else
flags.push_back( CV_HAAR_SCALE_IMAGE );
}
void CV_CascadeDetectorTest::writeDetector( FileStorage& fs, int di )
{
int sc = flags[di] & CV_HAAR_SCALE_IMAGE ? 0 : 1;
fs << FILENAME << detectorFilenames[di];
fs << C_SCALE_CASCADE << sc;
}
int CV_CascadeDetectorTest::detectMultiScale( int di, const Mat& img,
vector<Rect>& objects)
{
string dataPath = ts->get_data_path(), filename;
filename = dataPath + detectorFilenames[di];
CascadeClassifier cascade( filename );
if( cascade.empty() )
{
ts->printf( cvtest::TS::LOG, "cascade %s can not be opened");
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
Mat grayImg;
cvtColor( img, grayImg, CV_BGR2GRAY );
equalizeHist( grayImg, grayImg );
cascade.detectMultiScale( grayImg, objects, 1.1, 3, flags[di] );
return cvtest::TS::OK;
}
//----------------------------------------------- HOGDetectorTest -----------------------------------
class CV_HOGDetectorTest : public CV_DetectorTest
{
public:
CV_HOGDetectorTest();
protected:
virtual void readDetector( const FileNode& fn );
virtual void writeDetector( FileStorage& fs, int di );
virtual int detectMultiScale( int di, const Mat& img, vector<Rect>& objects );
};
CV_HOGDetectorTest::CV_HOGDetectorTest()
{
validationFilename = "cascadeandhog/hog.xml";
}
void CV_HOGDetectorTest::readDetector( const FileNode& fn )
{
string filename;
if( fn[FILENAME].node->data.seq != 0 )
fn[FILENAME] >> filename;
detectorFilenames.push_back( filename);
}
void CV_HOGDetectorTest::writeDetector( FileStorage& fs, int di )
{
fs << FILENAME << detectorFilenames[di];
}
int CV_HOGDetectorTest::detectMultiScale( int di, const Mat& img,
vector<Rect>& objects)
{
HOGDescriptor hog;
if( detectorFilenames[di].empty() )
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
else
assert(0);
hog.detectMultiScale(img, objects);
return cvtest::TS::OK;
}
TEST(Objdetect_CascadeDetector, regression) { CV_CascadeDetectorTest test; test.safe_run(); }
TEST(Objdetect_HOGDetector, regression) { CV_HOGDetectorTest test; test.safe_run(); }

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modules/objdetect/test/test_latentsvmdetector.cpp Normal file
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/*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.
// 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*/
#include "test_precomp.hpp"
#include <string>
using namespace cv;
const int num_detections = 3;
const float true_scores[3] = {-0.383931f, -0.825876f, -0.959934f};
const float score_thr = 0.05f;
const CvRect true_bounding_boxes[3] = {cvRect(0, 45, 362, 452), cvRect(304, 0, 64, 80), cvRect(236, 0, 108, 59)};
class CV_LatentSVMDetectorTest : public cvtest::BaseTest
{
public:
CV_LatentSVMDetectorTest();
~CV_LatentSVMDetectorTest();
protected:
void run(int);
private:
bool isEqual(CvRect r1, CvRect r2);
};
CV_LatentSVMDetectorTest::CV_LatentSVMDetectorTest()
{
}
CV_LatentSVMDetectorTest::~CV_LatentSVMDetectorTest() {}
bool CV_LatentSVMDetectorTest::isEqual(CvRect r1, CvRect r2)
{
return ((r1.x == r2.x) && (r1.y == r2.y) && (r1.width == r2.width) && (r1.height == r2.height));
}
void CV_LatentSVMDetectorTest::run( int /* start_from */)
{
string img_path = string(ts->get_data_path()) + "latentsvmdetector/cat.jpg";
string model_path = string(ts->get_data_path()) + "latentsvmdetector/cat.xml";
IplImage* image = cvLoadImage(img_path.c_str());
if (!image)
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
CvLatentSvmDetector* detector = cvLoadLatentSvmDetector(model_path.c_str());
if (!detector)
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
cvReleaseImage(&image);
return;
}
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* detections = 0;
detections = cvLatentSvmDetectObjects(image, detector, storage);
if (detections->total != num_detections)
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
}
else
{
ts->set_failed_test_info(cvtest::TS::OK);
for (int i = 0; i < detections->total; i++)
{
CvObjectDetection detection = *(CvObjectDetection*)cvGetSeqElem( detections, i );
CvRect bounding_box = detection.rect;
float score = detection.score;
if ((!isEqual(bounding_box, true_bounding_boxes[i])) || (fabs(score - true_scores[i]) > score_thr))
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH );
break;
}
}
}
cvReleaseMemStorage( &storage );
cvReleaseLatentSvmDetector( &detector );
cvReleaseImage( &image );
}
TEST(Objdetect_LatentSVMDetector, regression) { CV_LatentSVMDetectorTest test; test.safe_run(); }

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#include "test_precomp.hpp"
CV_TEST_MAIN("cv")

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modules/objdetect/test/test_precomp.cpp Normal file
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#include "test_precomp.hpp"

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modules/objdetect/test/test_precomp.hpp Normal file
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#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include "opencv2/ts/ts.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#endif