347 lines
12 KiB
C++
347 lines
12 KiB
C++
/*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 "opencv2/core/utility.hpp"
|
|
#include "opencv2/imgproc.hpp"
|
|
#include "opencv2/highgui.hpp"
|
|
#include "opencv2/features2d.hpp"
|
|
|
|
#include <stdlib.h>
|
|
#include <stdio.h>
|
|
#include <sys/stat.h>
|
|
|
|
#include <limits>
|
|
#include <cstdio>
|
|
#include <iostream>
|
|
#include <fstream>
|
|
|
|
using namespace std;
|
|
using namespace cv;
|
|
|
|
/*
|
|
The algorithm:
|
|
|
|
for each tested combination of detector+descriptor+matcher:
|
|
|
|
create detector, descriptor and matcher,
|
|
load their params if they are there, otherwise use the default ones and save them
|
|
|
|
for each dataset:
|
|
|
|
load reference image
|
|
detect keypoints in it, compute descriptors
|
|
|
|
for each transformed image:
|
|
load the image
|
|
load the transformation matrix
|
|
detect keypoints in it too, compute descriptors
|
|
|
|
find matches
|
|
transform keypoints from the first image using the ground-truth matrix
|
|
|
|
compute the number of matched keypoints, i.e. for each pair (i,j) found by a matcher compare
|
|
j-th keypoint from the second image with the transformed i-th keypoint. If they are close, +1.
|
|
|
|
so, we have:
|
|
N - number of keypoints in the first image that are also visible
|
|
(after transformation) on the second image
|
|
|
|
N1 - number of keypoints in the first image that have been matched.
|
|
|
|
n - number of the correct matches found by the matcher
|
|
|
|
n/N1 - precision
|
|
n/N - recall (?)
|
|
|
|
we store (N, n/N1, n/N) (where N is stored primarily for tuning the detector's thresholds,
|
|
in order to semi-equalize their keypoints counts)
|
|
|
|
*/
|
|
|
|
typedef Vec3f TVec; // (N, n/N1, n/N) - see above
|
|
|
|
static void saveloadDDM( const string& params_filename,
|
|
Ptr<FeatureDetector>& detector,
|
|
Ptr<DescriptorExtractor>& descriptor,
|
|
Ptr<DescriptorMatcher>& matcher )
|
|
{
|
|
FileStorage fs(params_filename, FileStorage::READ);
|
|
if( fs.isOpened() )
|
|
{
|
|
detector->read(fs["detector"]);
|
|
descriptor->read(fs["descriptor"]);
|
|
matcher->read(fs["matcher"]);
|
|
}
|
|
else
|
|
{
|
|
fs.open(params_filename, FileStorage::WRITE);
|
|
fs << "detector" << "{";
|
|
detector->write(fs);
|
|
fs << "}" << "descriptor" << "{";
|
|
descriptor->write(fs);
|
|
fs << "}" << "matcher" << "{";
|
|
matcher->write(fs);
|
|
fs << "}";
|
|
}
|
|
}
|
|
|
|
static Mat loadMat(const string& fsname)
|
|
{
|
|
FileStorage fs(fsname, FileStorage::READ);
|
|
Mat m;
|
|
fs.getFirstTopLevelNode() >> m;
|
|
return m;
|
|
}
|
|
|
|
static void transformKeypoints( const vector<KeyPoint>& kp,
|
|
vector<vector<Point2f> >& contours,
|
|
const Mat& H )
|
|
{
|
|
const float scale = 256.f;
|
|
size_t i, n = kp.size();
|
|
contours.resize(n);
|
|
vector<Point> temp;
|
|
|
|
for( i = 0; i < n; i++ )
|
|
{
|
|
ellipse2Poly(Point2f(kp[i].pt.x*scale, kp[i].pt.y*scale),
|
|
Size2f(kp[i].size*scale, kp[i].size*scale),
|
|
0, 0, 360, 12, temp);
|
|
Mat(temp).convertTo(contours[i], CV_32F, 1./scale);
|
|
perspectiveTransform(contours[i], contours[i], H);
|
|
}
|
|
}
|
|
|
|
|
|
static TVec proccessMatches( Size imgsize,
|
|
const vector<DMatch>& matches,
|
|
const vector<vector<Point2f> >& kp1t_contours,
|
|
const vector<vector<Point2f> >& kp_contours,
|
|
double overlapThreshold )
|
|
{
|
|
const double visibilityThreshold = 0.6;
|
|
|
|
// 1. [preprocessing] find bounding rect for each element of kp1t_contours and kp_contours.
|
|
// 2. [cross-check] for each DMatch (iK, i1)
|
|
// update best_match[i1] using DMatch::distance.
|
|
// 3. [compute overlapping] for each i1 (keypoint from the first image) do:
|
|
// if i1-th keypoint is outside of image, skip it
|
|
// increment N
|
|
// if best_match[i1] is initialized, increment N1
|
|
// if kp_contours[best_match[i1]] and kp1t_contours[i1] overlap by overlapThreshold*100%,
|
|
// increment n. Use bounding rects to speedup this step
|
|
|
|
int i, size1 = (int)kp1t_contours.size(), size = (int)kp_contours.size(), msize = (int)matches.size();
|
|
vector<DMatch> best_match(size1);
|
|
vector<Rect> rects1(size1), rects(size);
|
|
|
|
// proprocess
|
|
for( i = 0; i < size1; i++ )
|
|
rects1[i] = boundingRect(kp1t_contours[i]);
|
|
|
|
for( i = 0; i < size; i++ )
|
|
rects[i] = boundingRect(kp_contours[i]);
|
|
|
|
// cross-check
|
|
for( i = 0; i < msize; i++ )
|
|
{
|
|
DMatch m = matches[i];
|
|
int i1 = m.trainIdx, iK = m.queryIdx;
|
|
CV_Assert( 0 <= i1 && i1 < size1 && 0 <= iK && iK < size );
|
|
if( best_match[i1].trainIdx < 0 || best_match[i1].distance > m.distance )
|
|
best_match[i1] = m;
|
|
}
|
|
|
|
int N = 0, N1 = 0, n = 0;
|
|
|
|
// overlapping
|
|
for( i = 0; i < size1; i++ )
|
|
{
|
|
int i1 = i, iK = best_match[i].queryIdx;
|
|
if( iK >= 0 )
|
|
N1++;
|
|
|
|
Rect r = rects1[i] & Rect(0, 0, imgsize.width, imgsize.height);
|
|
if( r.area() < visibilityThreshold*rects1[i].area() )
|
|
continue;
|
|
N++;
|
|
|
|
if( iK < 0 || (rects1[i1] & rects[iK]).area() == 0 )
|
|
continue;
|
|
|
|
double n_area = intersectConvexConvex(kp1t_contours[i1], kp_contours[iK], noArray(), true);
|
|
if( n_area == 0 )
|
|
continue;
|
|
|
|
double area1 = contourArea(kp1t_contours[i1], false);
|
|
double area = contourArea(kp_contours[iK], false);
|
|
|
|
double ratio = n_area/(area1 + area - n_area);
|
|
n += ratio >= overlapThreshold;
|
|
}
|
|
|
|
return TVec((float)N, (float)n/std::max(N1, 1), (float)n/std::max(N, 1));
|
|
}
|
|
|
|
|
|
static void saveResults(const string& dir, const string& name, const string& dsname,
|
|
const vector<TVec>& results, const int* xvals)
|
|
{
|
|
string fname1 = format("%s%s_%s_precision.csv", dir.c_str(), name.c_str(), dsname.c_str());
|
|
string fname2 = format("%s%s_%s_recall.csv", dir.c_str(), name.c_str(), dsname.c_str());
|
|
FILE* f1 = fopen(fname1.c_str(), "wt");
|
|
FILE* f2 = fopen(fname2.c_str(), "wt");
|
|
|
|
for( size_t i = 0; i < results.size(); i++ )
|
|
{
|
|
fprintf(f1, "%d, %.1f\n", xvals[i], results[i][1]*100);
|
|
fprintf(f2, "%d, %.1f\n", xvals[i], results[i][2]*100);
|
|
}
|
|
fclose(f1);
|
|
fclose(f2);
|
|
}
|
|
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
static const char* ddms[] =
|
|
{
|
|
"ORBX_BF", "ORB", "ORB", "BruteForce-Hamming",
|
|
//"ORB_BF", "ORB", "ORB", "BruteForce-Hamming",
|
|
//"ORB3_BF", "ORB", "ORB", "BruteForce-Hamming(2)",
|
|
//"ORB4_BF", "ORB", "ORB", "BruteForce-Hamming(2)",
|
|
//"ORB_LSH", "ORB", "ORB", "LSH"
|
|
//"SURF_BF", "SURF", "SURF", "BruteForce",
|
|
0
|
|
};
|
|
|
|
static const char* datasets[] =
|
|
{
|
|
"bark", "bikes", "boat", "graf", "leuven", "trees", "ubc", "wall", 0
|
|
};
|
|
|
|
static const int imgXVals[] = { 2, 3, 4, 5, 6 }; // if scale, blur or light changes
|
|
static const int viewpointXVals[] = { 20, 30, 40, 50, 60 }; // if viewpoint changes
|
|
static const int jpegXVals[] = { 60, 80, 90, 95, 98 }; // if jpeg compression
|
|
|
|
const double overlapThreshold = 0.6;
|
|
|
|
vector<vector<vector<TVec> > > results; // indexed as results[ddm][dataset][testcase]
|
|
|
|
string dataset_dir = string(getenv("OPENCV_TEST_DATA_PATH")) +
|
|
"/cv/detectors_descriptors_evaluation/images_datasets";
|
|
|
|
string dir=argc > 1 ? argv[1] : ".";
|
|
|
|
if( dir[dir.size()-1] != '\\' && dir[dir.size()-1] != '/' )
|
|
dir += "/";
|
|
|
|
int result = system(("mkdir " + dir).c_str());
|
|
CV_Assert(result == 0);
|
|
|
|
for( int i = 0; ddms[i*4] != 0; i++ )
|
|
{
|
|
const char* name = ddms[i*4];
|
|
const char* detector_name = ddms[i*4+1];
|
|
const char* descriptor_name = ddms[i*4+2];
|
|
const char* matcher_name = ddms[i*4+3];
|
|
string params_filename = dir + string(name) + "_params.yml";
|
|
|
|
cout << "Testing " << name << endl;
|
|
|
|
Ptr<FeatureDetector> detector = FeatureDetector::create(detector_name);
|
|
Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create(descriptor_name);
|
|
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create(matcher_name);
|
|
|
|
saveloadDDM( params_filename, detector, descriptor, matcher );
|
|
|
|
results.push_back(vector<vector<TVec> >());
|
|
|
|
for( int j = 0; datasets[j] != 0; j++ )
|
|
{
|
|
const char* dsname = datasets[j];
|
|
|
|
cout << "\ton " << dsname << " ";
|
|
cout.flush();
|
|
|
|
const int* xvals = strcmp(dsname, "ubc") == 0 ? jpegXVals :
|
|
strcmp(dsname, "graf") == 0 || strcmp(dsname, "wall") == 0 ? viewpointXVals : imgXVals;
|
|
|
|
vector<KeyPoint> kp1, kp;
|
|
vector<DMatch> matches;
|
|
vector<vector<Point2f> > kp1t_contours, kp_contours;
|
|
Mat desc1, desc;
|
|
|
|
Mat img1 = imread(format("%s/%s/img1.png", dataset_dir.c_str(), dsname), 0);
|
|
CV_Assert( !img1.empty() );
|
|
|
|
detector->detect(img1, kp1);
|
|
descriptor->compute(img1, kp1, desc1);
|
|
|
|
results[i].push_back(vector<TVec>());
|
|
|
|
for( int k = 2; ; k++ )
|
|
{
|
|
cout << ".";
|
|
cout.flush();
|
|
Mat imgK = imread(format("%s/%s/img%d.png", dataset_dir.c_str(), dsname, k), 0);
|
|
if( imgK.empty() )
|
|
break;
|
|
|
|
detector->detect(imgK, kp);
|
|
descriptor->compute(imgK, kp, desc);
|
|
matcher->match( desc, desc1, matches );
|
|
|
|
Mat H = loadMat(format("%s/%s/H1to%dp.xml", dataset_dir.c_str(), dsname, k));
|
|
|
|
transformKeypoints( kp1, kp1t_contours, H );
|
|
transformKeypoints( kp, kp_contours, Mat::eye(3, 3, CV_64F));
|
|
|
|
TVec r = proccessMatches( imgK.size(), matches, kp1t_contours, kp_contours, overlapThreshold );
|
|
results[i][j].push_back(r);
|
|
}
|
|
|
|
saveResults(dir, name, dsname, results[i][j], xvals);
|
|
cout << endl;
|
|
}
|
|
}
|
|
}
|