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CurveSignature.cpp
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CurveSignature.cpp
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/*
* CurveSignature.cpp
* CurveMatching
*
* Created by Roy Shilkrot on 12/7/12.
* Copyright 2012 MIT. All rights reserved.
*
*/
#include "std.h"
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/flann/flann.hpp>
#include <opencv2/flann/dist.h>
using namespace cv;
#include "CurveCSS.h"
#include "CurveSignature.h"
#pragma mark Utilities
bool fileExists(const std::string& filename)
{
struct stat buf;
if (stat(filename.c_str(), &buf) != -1)
{
return true;
}
return false;
}
void GetCurveForImage(const Mat& filename, vector<Point>& whole, vector<Point>& curve_upper, vector<Point>& curve_lower) {
assert(!filename.empty());
Mat tmp; filename.copyTo(tmp);
Mat gray;
if(tmp.type() == CV_8UC3)
cvtColor(tmp, gray, CV_BGR2GRAY);
else if(tmp.type() == CV_8UC1)
gray = tmp;
else
cvError(-1, "GetCurveForImage", "unsupported image format", __FILE__, __LINE__);
threshold(gray, gray, 128, 255, THRESH_BINARY);
// imshow("input",gray);
vector<vector<Point> > contours;
findContours( gray, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
if (contours.size()<=0) return;
vector<Point> upperCurve = contours[0];
if (upperCurve.size() <= 50) {
return;
}
//find minimal and maximal X coord
vector<double> x,y;
PolyLineSplit(contours[0], x, y);
Point minxp,maxxp;
minMaxLoc(x, 0, 0, &minxp, &maxxp);
int minx = minxp.x,maxx = maxxp.x;
if (minx > maxx) swap(minx, maxx);
//take lower and upper halves of the curve
vector<Point> upper,lower;
upper.insert(upper.begin(),contours[0].begin()+minx,contours[0].begin()+maxx);
lower.insert(lower.begin(),contours[0].begin()+maxx,contours[0].end());
lower.insert(lower.end(),contours[0].begin(),contours[0].begin()+minx);
//test which is really the upper part, by looking at the y-coord of the mid point
if (lower[lower.size()/2].y <= upper[upper.size()/2].y) {
curve_upper = lower;
curve_lower = upper;
} else {
curve_upper = upper;
curve_lower = lower;
}
//make sure it goes left-to-right
if (curve_upper.front().x > curve_upper.back().x) { //hmmm, need to flip
reverse(curve_upper.begin(), curve_upper.end());
}
whole.clear();
whole.insert(whole.begin(),curve_upper.rbegin(),curve_upper.rend());
whole.insert(whole.begin(),curve_lower.begin(),curve_lower.end());
}
void GetCurveForImage(const Mat& filename, vector<Point>& curve, bool onlyUpper, bool getLower) {
vector<Point> whole,upper,lower;
GetCurveForImage(filename,whole,upper,lower);
if (onlyUpper) {
if (getLower)
curve = lower;
else
curve = upper;
} else {
curve = whole;
}
}
#pragma mark Signature Database extracting and matching
void PrepareSignatureDB(const vector<Point2d>& curve_, vector<vector<double> >& DB, vector<Point>& DB_params) {
vector<Point2d> curve;
if (curve_.size() != 200) {
ResampleCurve(curve_, curve, 200, true);
}else {
curve = curve_;
}
vector<double> kappa;
vector<Point2d> smooth;
SimpleSmoothCurve(curve, smooth, 5.0, true);
vector<Point2d> small;
DB.clear(); DB_params.clear();
for (int len = 50; len < smooth.size() - 2; len+=5) {
//iterate different curve sizes, starting at 20 points
// cout << "len " << len << endl;
for (int off = (smooth.size() - len); off >= 0; off-=5) {
//iterate segments on A curve
vector<Point2d> small_smooth_input(smooth.begin()+off,smooth.begin()+off+len);
//resample to N points
ResampleCurve(small_smooth_input, small, 200, true);
//compute curvature
vector<Point2d> small_smooth;
ComputeCurveCSS(small, kappa, small_smooth, 0.66667, true);
vector<double> kappa_(kappa.begin()+1,kappa.end()-1);
DB.push_back(kappa_);
DB_params.push_back(Point(len,off));
}
}
cout << "DB size " << DB.size() << endl;
}
template<class T>
struct CrossCorrelationDistance
{
typedef cvflann::True is_kdtree_distance;
typedef cvflann::True is_vector_space_distance;
typedef T ElementType;
typedef typename Accumulator<T>::Type ResultType;
template <typename Iterator1, typename Iterator2>
ResultType operator()(Iterator1 x, Iterator2 y, size_t size, ResultType /*worst_dist*/ = -1) const
{
int i,n = size;
ResultType mx,my,sx,sy,sxy,denom,r;
/* Calculate the mean of the two series x[], y[] */
mx = 0;
my = 0;
for (i=0;i<n;i++) {
mx += x[i];
my += y[i];
}
mx /= n;
my /= n;
/* Calculate the denominator */
sx = 0;
sy = 0;
for (i=0;i<n;i++) {
sx += (x[i] - mx) * (x[i] - mx);
sy += (y[i] - my) * (y[i] - my);
}
denom = sqrt(sx*sy);
/* Calculate the correlation series */
sxy = 0;
for (i=0;i<n;i++) {
sxy += (x[i] - mx) * (y[i] - my);
}
r = sxy / denom;
return r;
}
template <typename U, typename V>
inline ResultType accum_dist(const U& a, const V& b, int) const
{
return a*b;
}
};
void CompareCurvesUsingFLANN(const vector<Mat>& DB,
const vector<vector<double> >& query_DB,
int& a_id,
int& a_subset_id,
int& b_subset_id)
{
Mat packed(DB.size()*DB[0].rows,DB[0].cols,CV_64FC1);
int rowstep = DB[0].rows;
for (int i=0; i<DB.size(); i++) {
DB[i].convertTo(packed(Range(i*rowstep,(i+1)*rowstep),Range::all()),CV_64F);
}
Mat packed_query(query_DB.size(),query_DB[0].size(),CV_64F);
for (int i=0; i<query_DB.size(); i++) {
Mat(Mat(query_DB[i]).t()).copyTo(packed_query.row(i));
}
cout << "training..";
cv::flann::GenericIndex<cvflann::L2<double> > gi(packed,cvflann::KDTreeIndexParams());
cout << "DONE\n";
Mat indices(packed_query.rows,1,CV_32SC1),dists(packed_query.rows,1,CV_64FC1);
CV_PROFILE(gi.knnSearch(packed_query, indices, dists, 1, cvflann::SearchParams());)
// cout << dists << endl;
// dists = Mat(abs(dists));
// minMaxIdx(dists, 0, 0, &b_subset_id);
vector<pair<double,int> > scores;
for (int i=0; i<indices.rows; i++) {
double d = dists.at<double>(i);
if (isnan(d)) continue;
scores.push_back(make_pair(d, i));
}
sort(scores.begin(),scores.end());
for (int i=0; i<20; i++) {
cout << scores[i].first << " = " << scores[i].second << "(" << indices.at<int>(scores[i].second) << ")" << endl;
}
b_subset_id = scores.front().second;
a_id = indices.at<int>(b_subset_id) / rowstep;
a_subset_id = indices.at<int>(b_subset_id) % rowstep;
cout << "minimal "<< indices.at<int>(b_subset_id) <<" = " << a_id << "(" << a_subset_id << ") -> " << b_subset_id << ": " << dists.at<double>(b_subset_id) << endl;
{
vector<double> a_sig(packed.cols);
memcpy(&(a_sig[0]), packed.row(indices.at<int>(b_subset_id)).data, sizeof(double)*a_sig.size());
ShowMathGLCurves(query_DB[b_subset_id], a_sig, "curvatures");
}
}
void CompareCurvesUsingSignatureDBMatcher(FlannBasedMatcher& matcher,
const vector<Point>& typical_params,
const vector<vector<double> >& b_DB,
int& a_id,
int& a_len,
int& a_off,
int& b_len,
int& b_off,
double& score
)
{
std::vector< DMatch > matches;
{
Mat_<float> b_DB_m = ConvertToMat<float,double>(b_DB);
CV_PROFILE(matcher.match( b_DB_m, matches );)
}
double min_dist = std::numeric_limits<double>::max();
DMatch min_match;
//-- Quick calculation of max and min distances between keypoints
for( int i = matches.size()-1; i >=0 ; i-- )
{
// int imgidx = matches[i].imgIdx;
double dist = (max(1.0,200.0 - (double)(typical_params[matches[i].queryIdx].x)) +
max(1.0,200.0 - (double)(typical_params[matches[i].trainIdx].x))) +
100.0*matches[i].distance;
if( dist < min_dist ) { min_dist = dist; min_match = matches[i]; }
// if( dist > max_dist ) max_dist = dist;
}
a_id = min_match.imgIdx;
// printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f, %d(%d,%d) -> %d(%d,%d) \n",
min_dist,
min_match.queryIdx,
typical_params[min_match.queryIdx].x,
typical_params[min_match.queryIdx].y,
min_match.trainIdx,
typical_params[min_match.trainIdx].x,
typical_params[min_match.trainIdx].y
);
// cout << Mat(a_DB[min_match.queryIdx]).t() << endl << Mat(b_DB[min_match.trainIdx]).t() << endl;
a_len = typical_params[min_match.queryIdx].x;
a_off = typical_params[min_match.queryIdx].y;
b_len = typical_params[min_match.trainIdx].x;
b_off = typical_params[min_match.trainIdx].y;
}
void CompareCurvesUsingSignatureDB(const vector<Point>& a_DB_params,
const vector<Point>& b_DB_params,
const vector<vector<double> >& a_DB,
const vector<vector<double> >& b_DB,
vector<pair<double,DMatch> >& scores_to_matches
)
{
FlannBasedMatcher matcher;
// BFMatcher matcher(NORM_L2);
std::vector< DMatch > matches;
{
Mat_<float> mnt_DB_m = ConvertToMat<float,double>(a_DB);
Mat_<float> obj_DB_m = ConvertToMat<float,double>(b_DB);
vector<Mat> obj_DB_mv(1,obj_DB_m);
matcher.add(obj_DB_mv);
CV_PROFILE(matcher.train();)
CV_PROFILE(matcher.match( mnt_DB_m, matches );)
}
DMatch min_match;
vector<pair<double,int> > scores;
for( int i = matches.size()-1; i >=0 ; i-- )
{
double d = //(max(1.0,200.0 - (double)(a_DB_params[matches[i].queryIdx].x)) +
// max(1.0,200.0 - (double)(b_DB_params[matches[i].trainIdx].x))) +
10000.0 * matches[i].distance;
if (isnan(d)) continue;
scores.push_back(make_pair(d, i));
}
sort(scores.begin(),scores.end());
scores_to_matches.clear();
for (int i=0; i<200; i++) {
// cout << scores[i].first << " = " << scores[i].second << endl;
scores_to_matches.push_back(make_pair(scores[i].first, matches[scores[i].second]));
}
min_match = matches[scores.front().second];
// double min_dist = scores.front().first;
// printf("-- Max dist : %f \n", max_dist );
// printf("-- Min dist : %f, %d(%d,%d) -> %d(%d,%d) \n",
// min_dist,
// min_match.queryIdx,
// a_DB_params[min_match.queryIdx].x,
// a_DB_params[min_match.queryIdx].y,
// min_match.trainIdx,
// b_DB_params[min_match.trainIdx].x,
// b_DB_params[min_match.trainIdx].y
// );
//
// cout << Mat(a_DB[min_match.queryIdx]).t() << endl << Mat(b_DB[min_match.trainIdx]).t() << endl;
// {
// vector<double> a_sig,b_sig;
// a_sig = a_DB[matches[scores.front().second].queryIdx],
// b_sig = b_DB[matches[scores.front().second].trainIdx];
// ShowMathMLCurves(a_sig, b_sig, "curvatures0");
//
// a_sig = a_DB[matches[scores[1].second].queryIdx];
// b_sig = b_DB[matches[scores[1].second].trainIdx];
// ShowMathMLCurves(a_sig, b_sig, "curvatures1");
//
// a_sig = a_DB[matches[scores[2].second].queryIdx];
// b_sig = b_DB[matches[scores[2].second].trainIdx];
// ShowMathMLCurves(a_sig, b_sig, "curvatures2");
//
// a_sig = a_DB[matches[scores[3].second].queryIdx];
// b_sig = b_DB[matches[scores[3].second].trainIdx];
// ShowMathMLCurves(a_sig, b_sig, "curvatures3");
//
// }
}
void CompareCurvesUsingSignatureDB(const vector<Point2d>& a,
const vector<Point2d>& b,
int& a_len,
int& a_off,
int& b_len,
int& b_off,
double& score
)
{
vector<Point> a_DB_params,b_DB_params;
vector<vector<double> > a_DB,b_DB;
PrepareSignatureDB(a, a_DB, a_DB_params);
PrepareSignatureDB(b, b_DB, b_DB_params);
vector<pair<double,DMatch> > scores_to_matches;
CompareCurvesUsingSignatureDB(a_DB_params,b_DB_params,a_DB,b_DB,scores_to_matches);
//re-rank results by RMSE measure after recovering rigid transformation
for (int i=0; i<scores_to_matches.size(); i++) {
int _a_len = a_DB_params[scores_to_matches[i].second.queryIdx].x;
int _a_off = a_DB_params[scores_to_matches[i].second.queryIdx].y;
int _b_len = b_DB_params[scores_to_matches[i].second.trainIdx].x;
int _b_off = b_DB_params[scores_to_matches[i].second.trainIdx].y;
vector<Point2d> a_subset(a.begin() + _a_off, a.begin() + _a_off + _a_len);
vector<Point2d> b_subset(b.begin() + _b_off, b.begin() + _b_off + _b_len);
ResampleCurve(a_subset, a_subset, 200, true);
ResampleCurve(b_subset, b_subset, 200, true);
Mat trans = Find2DRigidTransform(a_subset, b_subset);
// cout << trans << endl;
vector<Point2d> a_trans;
cv::transform(a_subset,a_trans,trans);
double rmse = 0;
for (int pt=0; pt<a_trans.size(); pt++) {
rmse += norm(a_trans[pt] - b_subset[pt]);
}
rmse = sqrt(rmse / (double)a_trans.size());
// cout << "("<<_a_len<<","<<_a_off<<") -> ("<<_b_len<<","<<_b_off<<") RMSE: " << rmse << endl;
scores_to_matches[i].first = rmse;
}
sort(scores_to_matches.begin(), scores_to_matches.end());
{
//Show curvatures
vector<double> a_sig,b_sig;
a_sig = a_DB[scores_to_matches.front().second.queryIdx],
b_sig = b_DB[scores_to_matches.front().second.trainIdx];
ShowMathGLCurves(a_sig, b_sig, "curvatures0");
}
a_len = a_DB_params[scores_to_matches.front().second.queryIdx].x;
a_off = a_DB_params[scores_to_matches.front().second.queryIdx].y;
b_len = b_DB_params[scores_to_matches.front().second.trainIdx].x;
b_off = b_DB_params[scores_to_matches.front().second.trainIdx].y;
score = scores_to_matches.front().first;
cout << "("<<a_len<<","<<a_off<<") -> ("<<b_len<<","<<b_off<<") RMSE: " << scores_to_matches.front().first << endl;
}
#pragma mark HSK extracting and matching
void CompareSignatures(const Mat_<double>& a, const Mat_<double>& b) {
double max_cc = 0.0;
int max_len,max_off,max_y,max_offb,max_yb;
for (int len = 20; len < a.cols; len++) {
//iterate different curve sizes, starting at 20 points
cout << "len " << len << endl;
for (int y = 0; y < a.rows; y++) {
//iterate sigma values on A (rows of A signature)
for (int yb = 0; yb<b.rows; yb++) {
//iterate sigma values on B (rows of B signature)
for (int off = 0; off < (a.cols - len); off++) {
//iterate segments on A curve
vector<double> templt; a(Range(y,y+1),Range(off,off+len)).copyTo(templt);
assert(templt.size() == len);
for (int offb = 0; offb < (b.cols - len); offb++) {
vector<double> bv; b(Range(yb,yb+1),Range(offb,offb+len)).copyTo(bv);
double cc = CalcCrossCorrelation(templt, bv) * (double)len;
if (cc > max_cc) {
max_cc = cc;
max_y = y; max_off = off; max_len = len; max_offb = offb; max_yb = yb;
}
}
}
}
}
}
}
#define STRINGIFY(A) #A
#pragma mark Curvature Extrema Matching
Mat_<double> GetSmithWatermanHMatrix(const vector<pair<char,int> >& a, const vector<pair<char,int> >& b) {
int M = a.size();
int N = b.size();
//Smith-Waterman
Mat_<double> H(M+1,N+1,0.0);
for (int i=1; i <= M; i++) {
for (int j=1; j <= N; j++) {
vector<double> v(4,0.0);
v[1] = H(i-1,j-1) + ((a[i-1].first == b[j-1].first) ? 2.0 : -1.0);
v[2] = H(i-1,j) - 1.0;
v[3] = H(i,j-1) - 1.0;
H(i,j) = *(max_element(v.begin(), v.end()));
}
}
// cout << H << endl;
return H;
}
/* original Smith Waterman algorithm */
double MatchSmithWaterman(const vector<pair<char,int> >& a, const vector<pair<char,int> >& b, vector<Point>& matching)
{
vector<Point> traceback;
Mat_<double> H = GetSmithWatermanHMatrix(a,b);
Point maxp; double maxval;
minMaxLoc(H, NULL, &maxval, NULL, &maxp);
vector<char> step;
while (H(maxp.y,maxp.x) != 0) {
// cout << "H(maxp.y-1,maxp.x-1) > H(maxp.y,maxp.x-1)" << H(maxp.y-1,maxp.x-1) << " > " << H(maxp.y,maxp.x-1) << endl;
if (H(maxp.y-1,maxp.x-1) > H(maxp.y,maxp.x-1) &&
H(maxp.y-1,maxp.x-1) > H(maxp.y-1,maxp.x))
{
traceback.push_back(maxp);
maxp = maxp - Point(1,1);
step.push_back('a');
} else
if (H(maxp.y-1,maxp.x) > H(maxp.y-1,maxp.x-1) &&
H(maxp.y-1,maxp.x) > H(maxp.y,maxp.x-1))
{
traceback.push_back(maxp);
maxp.y--;
step.push_back('d');
} else
if (H(maxp.y,maxp.x-1) > H(maxp.y-1,maxp.x-1) &&
H(maxp.y,maxp.x-1) > H(maxp.y-1,maxp.x))
{
traceback.push_back(maxp);
maxp.x--;
step.push_back('i');
}
else {
//default - go back on both
traceback.push_back(maxp);
maxp = maxp - Point(1,1);
step.push_back('a');
}
}
for (vector<Point>::reverse_iterator it = traceback.rbegin();
it != traceback.rend() - 1;
++it)
{
if((*it).y != (*(it+1)).y && (*it).x != (*(it+1)).x)
matching.push_back(Point((*it).y,(*it).x));
}
for (vector<Point>::reverse_iterator it = traceback.rbegin();
it != traceback.rend();
++it)
{
if(it==traceback.rend())
cout << a[(*it).y].first;
else {
if((*it).y == (*(it+1)).y)
cout << "-";
else {
cout << a[(*it).y].first;
}
}
}
cout << endl;
for (vector<Point>::reverse_iterator it = traceback.rbegin();
it != traceback.rend();
++it)
{
if(it==traceback.rend())
cout << b[(*it).x].first;
else {
if((*it).x == (*(it+1)).x)
cout << "-";
else
cout << b[(*it).x].first;
}
}
cout << endl;
for (int k=0; k<step.size(); k++) {
cout << step[k];
}
cout << endl;
return maxval;
}