generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

added saving of matches graph into opencv_stitching (in DOT format)

Browse Source
This commit is contained in:
Alexey Spizhevoy 2011-08-15 08:19:57 +00:00
parent 7820c343eb
commit f95e71ea3a
5 changed files with 282 additions and 191 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
modules/stitching/main.cpp
View File

@ -48,6 +48,7 @@
// 3) Automatic Panoramic Image Stitching using Invariant Features.
// Matthew Brown and David G. Lowe. 2007.
#include <fstream>
#include "precomp.hpp"
#include "util.hpp"
#include "warpers.hpp"
@ -83,6 +84,10 @@ void printUsage()
" Bundle adjustment cost function. The default is 'focal_ray'.\n"
" --wave_correct (no|yes)\n"
" Perform wave effect correction. The default is 'yes'.\n"
" --save_graph <file_name>\n"
" Save matches graph represented in DOT language to <file_name> file.\n"
" Labels description: Nm is number of matches, Ni is number of inliers,\n"
" C is confidence.\n"
"\nCompositing Flags:\n"
" --warp (plane|cylindrical|spherical)\n"
" Warp surface type. The default is 'spherical'.\n"
@ -114,6 +119,8 @@ double compose_megapix = -1;
int ba_space = BundleAdjuster::FOCAL_RAY_SPACE;
float conf_thresh = 1.f;
bool wave_correct = true;
bool save_graph = false;
std::string save_graph_to;
int warp_type = Warper::SPHERICAL;
int expos_comp_type = ExposureCompensator::GAIN_BLOCKS;
float match_conf = 0.65f;
@ -209,6 +216,12 @@ int parseCmdArgs(int argc, char** argv)
}
i++;
}
else if (string(argv[i]) == "--save_graph")
{
save_graph = true;
save_graph_to = argv[i + 1];
i++;
}
else if (string(argv[i]) == "--warp")
{
if (string(argv[i + 1]) == "plane")
@ -378,6 +391,14 @@ int main(int argc, char* argv[])
matcher.releaseMemory();
LOGLN("Pairwise matching, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
// Check if we should save matches graph
if (save_graph)
{
LOGLN("Saving matches graph...");
ofstream f(save_graph_to.c_str());
f << matchesGraphAsString(img_names, pairwise_matches, conf_thresh);
}
// Leave only images we are sure are from the same panorama
vector<int> indices = leaveBiggestComponent(features, pairwise_matches, conf_thresh);
vector<Mat> img_subset;

198
modules/stitching/matchers.hpp
View File

@ -38,102 +38,102 @@
// 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_MATCHERS_HPP__
#define __OPENCV_MATCHERS_HPP__
#include "precomp.hpp"
struct ImageFeatures
{
int img_idx;
cv::Size img_size;
std::vector<cv::KeyPoint> keypoints;
cv::Mat descriptors;
};
class FeaturesFinder
{
public:
virtual ~FeaturesFinder() {}
void operator ()(const cv::Mat &image, ImageFeatures &features);
virtual void releaseMemory() {}
protected:
virtual void find(const cv::Mat &image, ImageFeatures &features) = 0;
};
class SurfFeaturesFinder : public FeaturesFinder
{
public:
SurfFeaturesFinder(bool try_use_gpu = true, double hess_thresh = 300.0,
int num_octaves = 3, int num_layers = 4,
int num_octaves_descr = 4, int num_layers_descr = 2);
void releaseMemory();
protected:
void find(const cv::Mat &image, ImageFeatures &features);
cv::Ptr<FeaturesFinder> impl_;
};
struct MatchesInfo
{
MatchesInfo();
MatchesInfo(const MatchesInfo &other);
const MatchesInfo& operator =(const MatchesInfo &other);
int src_img_idx, dst_img_idx; // Images indices (optional)
std::vector<cv::DMatch> matches;
std::vector<uchar> inliers_mask; // Geometrically consistent matches mask
int num_inliers; // Number of geometrically consistent matches
cv::Mat H; // Estimated homography
double confidence; // Confidence two images are from the same panorama
};
class FeaturesMatcher
{
public:
virtual ~FeaturesMatcher() {}
void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
MatchesInfo& matches_info) { match(features1, features2, matches_info); }
void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches);
bool isThreadSafe() const { return is_thread_safe_; }
virtual void releaseMemory() {}
protected:
FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {}
virtual void match(const ImageFeatures &features1, const ImageFeatures &features2,
MatchesInfo& matches_info) = 0;
bool is_thread_safe_;
};
class BestOf2NearestMatcher : public FeaturesMatcher
{
public:
BestOf2NearestMatcher(bool try_use_gpu = true, float match_conf = 0.55f, int num_matches_thresh1 = 6,
int num_matches_thresh2 = 6);
void releaseMemory();
protected:
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info);
int num_matches_thresh1_;
int num_matches_thresh2_;
cv::Ptr<FeaturesMatcher> impl_;
};
#endif // __OPENCV_MATCHERS_HPP__
//M*/
#ifndef __OPENCV_MATCHERS_HPP__
#define __OPENCV_MATCHERS_HPP__
#include "precomp.hpp"
struct ImageFeatures
{
int img_idx;
cv::Size img_size;
std::vector<cv::KeyPoint> keypoints;
cv::Mat descriptors;
};
class FeaturesFinder
{
public:
virtual ~FeaturesFinder() {}
void operator ()(const cv::Mat &image, ImageFeatures &features);
virtual void releaseMemory() {}
protected:
virtual void find(const cv::Mat &image, ImageFeatures &features) = 0;
};
class SurfFeaturesFinder : public FeaturesFinder
{
public:
SurfFeaturesFinder(bool try_use_gpu = true, double hess_thresh = 300.0,
int num_octaves = 3, int num_layers = 4,
int num_octaves_descr = 4, int num_layers_descr = 2);
void releaseMemory();
protected:
void find(const cv::Mat &image, ImageFeatures &features);
cv::Ptr<FeaturesFinder> impl_;
};
struct MatchesInfo
{
MatchesInfo();
MatchesInfo(const MatchesInfo &other);
const MatchesInfo& operator =(const MatchesInfo &other);
int src_img_idx, dst_img_idx; // Images indices (optional)
std::vector<cv::DMatch> matches;
std::vector<uchar> inliers_mask; // Geometrically consistent matches mask
int num_inliers; // Number of geometrically consistent matches
cv::Mat H; // Estimated homography
double confidence; // Confidence two images are from the same panorama
};
class FeaturesMatcher
{
public:
virtual ~FeaturesMatcher() {}
void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
MatchesInfo& matches_info) { match(features1, features2, matches_info); }
void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches);
bool isThreadSafe() const { return is_thread_safe_; }
virtual void releaseMemory() {}
protected:
FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {}
virtual void match(const ImageFeatures &features1, const ImageFeatures &features2,
MatchesInfo& matches_info) = 0;
bool is_thread_safe_;
};
class BestOf2NearestMatcher : public FeaturesMatcher
{
public:
BestOf2NearestMatcher(bool try_use_gpu = true, float match_conf = 0.55f, int num_matches_thresh1 = 6,
int num_matches_thresh2 = 6);
void releaseMemory();
protected:
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info);
int num_matches_thresh1_;
int num_matches_thresh2_;
cv::Ptr<FeaturesMatcher> impl_;
};
#endif // __OPENCV_MATCHERS_HPP__

65
modules/stitching/motion_estimators.cpp
View File

@ -40,6 +40,7 @@
//
//M*/
#include <algorithm>
#include <sstream>
#include "autocalib.hpp"
#include "motion_estimators.hpp"
#include "util.hpp"
@ -407,6 +408,70 @@ void waveCorrect(vector<Mat> &rmats)
//////////////////////////////////////////////////////////////////////////////
string matchesGraphAsString(vector<string> &pathes, vector<MatchesInfo> &pairwise_matches,
float conf_threshold)
{
stringstream str;
str << "graph matches_graph{\n";
set<int> added_imgs;
// Add matches
for (size_t i = 0; i < pairwise_matches.size(); ++i)
{
if (pairwise_matches[i].src_img_idx < pairwise_matches[i].dst_img_idx &&
pairwise_matches[i].confidence > conf_threshold)
{
string name_src = pathes[pairwise_matches[i].src_img_idx];
size_t prefix_len = name_src.find_last_of("/\\");
if (prefix_len != string::npos) prefix_len++; else prefix_len = 0;
name_src = name_src.substr(prefix_len, name_src.size() - prefix_len);
string name_dst = pathes[pairwise_matches[i].dst_img_idx];
prefix_len = name_dst.find_last_of("/\\");
if (prefix_len != string::npos) prefix_len++; else prefix_len = 0;
name_dst = name_dst.substr(prefix_len, name_dst.size() - prefix_len);
added_imgs.insert(pairwise_matches[i].src_img_idx);
added_imgs.insert(pairwise_matches[i].dst_img_idx);
str << "\"" << name_src << "\" -- \"" << name_dst << "\""
<< "[label=\"Nm=" << pairwise_matches[i].matches.size()
<< ", Ni=" << pairwise_matches[i].num_inliers
<< ", C=" << pairwise_matches[i].confidence << "\"];\n";
}
}
// Add unmatched images
for (size_t i = 0; i < pairwise_matches.size(); ++i)
{
if (pairwise_matches[i].src_img_idx < pairwise_matches[i].dst_img_idx)
{
if (added_imgs.find(pairwise_matches[i].src_img_idx) == added_imgs.end())
{
added_imgs.insert(pairwise_matches[i].src_img_idx);
string name = pathes[pairwise_matches[i].src_img_idx];
size_t prefix_len = name.find_last_of("/\\");
if (prefix_len != string::npos) prefix_len++; else prefix_len = 0;
name = name.substr(prefix_len, name.size() - prefix_len);
str << "\"" << name << "\";\n";
}
if (added_imgs.find(pairwise_matches[i].dst_img_idx) == added_imgs.end())
{
added_imgs.insert(pairwise_matches[i].dst_img_idx);
string name = pathes[pairwise_matches[i].dst_img_idx];
size_t prefix_len = name.find_last_of("/\\");
if (prefix_len != string::npos) prefix_len++; else prefix_len = 0;
name = name.substr(prefix_len, name.size() - prefix_len);
str << "\"" << name << "\";\n";
}
}
}
str << "}";
return str.str();
}
vector<int> leaveBiggestComponent(vector<ImageFeatures> &features, vector<MatchesInfo> &pairwise_matches,
float conf_threshold)
{

186
modules/stitching/motion_estimators.hpp
View File

@ -38,94 +38,98 @@
// 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_MOTION_ESTIMATORS_HPP__
#define __OPENCV_MOTION_ESTIMATORS_HPP__
#include "precomp.hpp"
#include "matchers.hpp"
#include "util.hpp"
struct CameraParams
{
CameraParams();
CameraParams(const CameraParams& other);
const CameraParams& operator =(const CameraParams& other);
double focal; // Focal length
cv::Mat R; // Rotation
cv::Mat t; // Translation
};
class Estimator
{
public:
void operator ()(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras)
{
estimate(features, pairwise_matches, cameras);
}
protected:
virtual void estimate(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras) = 0;
};
class HomographyBasedEstimator : public Estimator
{
public:
HomographyBasedEstimator() : is_focals_estimated_(false) {}
bool isFocalsEstimated() const { return is_focals_estimated_; }
private:
void estimate(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras);
bool is_focals_estimated_;
};
class BundleAdjuster : public Estimator
{
public:
enum { RAY_SPACE, FOCAL_RAY_SPACE };
BundleAdjuster(int cost_space = FOCAL_RAY_SPACE, float conf_thresh = 1.f)
: cost_space_(cost_space), conf_thresh_(conf_thresh) {}
private:
void estimate(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras);
void calcError(cv::Mat &err);
void calcJacobian();
int num_images_;
int total_num_matches_;
const ImageFeatures *features_;
const MatchesInfo *pairwise_matches_;
cv::Mat cameras_;
std::vector<std::pair<int,int> > edges_;
int cost_space_;
float conf_thresh_;
cv::Mat err_, err1_, err2_;
cv::Mat J_;
};
void waveCorrect(std::vector<cv::Mat> &rmats);
//////////////////////////////////////////////////////////////////////////////
// Auxiliary functions
std::vector<int> leaveBiggestComponent(std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
float conf_threshold);
void findMaxSpanningTree(int num_images, const std::vector<MatchesInfo> &pairwise_matches,
Graph &span_tree, std::vector<int> &centers);
#endif // __OPENCV_MOTION_ESTIMATORS_HPP__
//M*/
#ifndef __OPENCV_MOTION_ESTIMATORS_HPP__
#define __OPENCV_MOTION_ESTIMATORS_HPP__
#include "precomp.hpp"
#include "matchers.hpp"
#include "util.hpp"
struct CameraParams
{
CameraParams();
CameraParams(const CameraParams& other);
const CameraParams& operator =(const CameraParams& other);
double focal; // Focal length
cv::Mat R; // Rotation
cv::Mat t; // Translation
};
class Estimator
{
public:
void operator ()(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras)
{
estimate(features, pairwise_matches, cameras);
}
protected:
virtual void estimate(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras) = 0;
};
class HomographyBasedEstimator : public Estimator
{
public:
HomographyBasedEstimator() : is_focals_estimated_(false) {}
bool isFocalsEstimated() const { return is_focals_estimated_; }
private:
void estimate(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras);
bool is_focals_estimated_;
};
class BundleAdjuster : public Estimator
{
public:
enum { RAY_SPACE, FOCAL_RAY_SPACE };
BundleAdjuster(int cost_space = FOCAL_RAY_SPACE, float conf_thresh = 1.f)
: cost_space_(cost_space), conf_thresh_(conf_thresh) {}
private:
void estimate(const std::vector<ImageFeatures> &features, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras);
void calcError(cv::Mat &err);
void calcJacobian();
int num_images_;
int total_num_matches_;
const ImageFeatures *features_;
const MatchesInfo *pairwise_matches_;
cv::Mat cameras_;
std::vector<std::pair<int,int> > edges_;
int cost_space_;
float conf_thresh_;
cv::Mat err_, err1_, err2_;
cv::Mat J_;
};
void waveCorrect(std::vector<cv::Mat> &rmats);
//////////////////////////////////////////////////////////////////////////////
// Auxiliary functions
// Returns matches graph representation in DOT language
std::string matchesGraphAsString(std::vector<std::string> &pathes, std::vector<MatchesInfo> &pairwise_matches,
float conf_threshold);
std::vector<int> leaveBiggestComponent(std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
float conf_threshold);
void findMaxSpanningTree(int num_images, const std::vector<MatchesInfo> &pairwise_matches,
Graph &span_tree, std::vector<int> &centers);
#endif // __OPENCV_MOTION_ESTIMATORS_HPP__

3
modules/stitching/util.hpp
View File

@ -108,8 +108,9 @@ bool overlapRoi(cv::Point tl1, cv::Point tl2, cv::Size sz1, cv::Size sz2, cv::Re
cv::Rect resultRoi(const std::vector<cv::Point> &corners, const std::vector<cv::Mat> &images);
cv::Rect resultRoi(const std::vector<cv::Point> &corners, const std::vector<cv::Size> &sizes);
cv::Point resultTl(const std::vector<cv::Point> &corners);
void selectRandomSubset(int count, int size, std::vector<int> &subset);
// Returns random 'count' element subset of the {0,1,...,size-1} set
void selectRandomSubset(int count, int size, std::vector<int> &subset);
#include "util_inl.hpp"