Mat RANSAC::compute(std::vector<Mat>& X, std::vector<Mat>& Y){
static int num_points = 5;
Mat T = Mat::eye(4, 4, CV_32FC1);
Mat best_T = Mat::eye(4, 4, CV_32FC1);
int max_inliers = 0;
std::srand(unsigned(std::time(0)));
std::vector<int> indices;
for (int i = 0; i < X.size(); ++i) indices.push_back(i);
for (int i = 0; i < m_max_iterations; i++){
//get random points
std::random_shuffle(indices.begin(), indices.end(), myrandom);
std::vector<Mat> new_X;
std::vector<Mat> new_Y;
for (int j = 0; j < num_points; ++j){
new_X.push_back(X[indices[j]]);
new_Y.push_back(Y[indices[j]]);
}
//compute T
T = getTransformationMatrix(new_X, new_Y);
int inliers = countInliers(T, X, Y, m_margin);
if (inliers > max_inliers){
best_T = T;
max_inliers = inliers;
}
}
static float percentage_outliers = 0;
percentage_outliers = (percentage_outliers + (X.size() - max_inliers)* 100.f / X.size()) / 2;
printf("Ouliers(Accumulated): %.2f%% Inliers: %i \n", percentage_outliers, max_inliers);
return best_T;
}
/******************* TO DO *********************
* alignPair:
* INPUT:
* f1, f2: source feature sets
* matches: correspondences between f1 and f2
* Each match in 'matches' contains two feature ids of
* Each match in 'matches' contains two feature ids of
* matching features, id1 (in f1) and id2 (in f2).
* m: motion model
* nRANSAC: number of RANSAC iterations
* RANSACthresh: RANSAC distance threshold
* M: transformation matrix (output)
*
* OUTPUT:
* repeat for nRANSAC iterations:
* choose a minimal set of feature matches
* estimate the transformation implied by these matches
* count the number of inliers
* for the transformation with the maximum number of inliers,
* compute the least squares motion estimate using the inliers,
* and store it in M
*/
int alignPair(const FeatureSet &f1, const FeatureSet &f2,
const vector<FeatureMatch> &matches, MotionModel m,
int nRANSAC, double RANSACthresh, CTransform3x3 &M)
{
// BEGIN TODO
// Write this entire method. You need to handle two types of
// motion models, pure translations (m == eTranslation) and
// full homographies (m == eHomography). However, you should
// only have one outer loop to perform the RANSAC code, as
// BEGIN TODO
// Write this entire method. You need to handle two types of
// motion models, pure translations (m == eTranslation) and
// full homographies (m == eHomography). However, you should
// only have one outer loop to perform the RANSAC code, as
// the use of RANSAC is almost identical for both cases.
//
// Your homography handling code should call ComputeHomography.
// This function should also call countInliers and, at the end,
// leastSquaresFit.
cout << "alignalignalignalign";
cout << f1.size();
cout << '\n';
cout << f2.size();
cout << '\n';
cout << matches.size();
cout << '\n';
int maxInliers = -1;
int sz = matches.size();
for (int i = 0; i < nRANSAC; i++) {
int n = rand() % sz;
FeatureMatch randomMatch = matches.at(n);
CTransform3x3 trans;
switch (m) {
case eTranslate: {
Feature first = f1[randomMatch.id1];
Feature second = f2[randomMatch.id2];
float xTranslation = (float)(second.x - first.x);
float yTranslation = (float)(second.y - first.y);
cout << "Translation: X "; cout << xTranslation;
cout << ", Y ";
cout << yTranslation; cout << '\n';
trans = CTransform3x3::Translation(xTranslation, yTranslation);
break;
}
case eHomography: {
int indices[] = {0, 0, 0, 0};
for (int chooseRand = 0; chooseRand < 4; chooseRand++)
{
indices[chooseRand] = rand() % sz;
for (int p = 0; p < chooseRand; p++)
{
if (indices[p] = indices[chooseRand]);
{
indices[chooseRand] = rand() % sz;
p--;
}
}
}
vector<FeatureMatch> selectedMatches;
selectedMatches.resize(4);
for (int c = 0; c < 4; c++)
{
int idx = indices[c];
FeatureMatch fm;
fm.id1 = matches.at(idx).id1;
fm.id2 = matches.at(idx).id2;
selectedMatches.push_back(fm);
}
trans = ComputeHomography(f1, f2, selectedMatches);
break;
}
}
vector<int> inliers;
countInliers(f1,f2,matches,m,trans,RANSACthresh,inliers);
cout << "Inliers: "; cout << inliers.size(); cout << '\n';
if (inliers.size() > maxInliers) {
maxInliers = inliers.size();
M = trans;
}
}
// END TODO
return 0;
}
