// Perform a query on the database. This simply runs matchFeatures on
// each image in the database, and returns the feature set of the best
// matching image.
bool performQuery(const FeatureSet &f, const ImageDatabase &db, int &bestIndex, vector<FeatureMatch> &bestMatches, double &bestDistance, int matchType) {
vector<FeatureMatch> tempMatches;
for (unsigned int i=0; i<db.size(); i++) {
if (!matchFeatures(f, db[i].features, tempMatches, matchType)) {
return false;
}
bestIndex = i;
bestMatches = tempMatches;
}
return true;
}
int main()
{
vector<string> path;
path.push_back("C:\\WORK\\Matchmover\\Core\\desk\\_DSC2192.JPG");
path.push_back("C:\\WORK\\Matchmover\\Core\\desk\\_DSC2193.JPG");
path.push_back("C:\\WORK\\Matchmover\\Core\\desk\\_DSC2194.JPG");
path.push_back("C:\\WORK\\Matchmover\\Core\\desk\\_DSC2195.JPG");
initialize(path);
matchFeatures();
reconstruct(27.0, 36.0, 24.0);
toMax(camList, points3D, "Camera.ms");
return 0;
}
// Perform a query on the database. This simply runs matchFeatures on
// each image in the database, and returns the feature set of the best
// matching image.
bool performQuery(const FeatureSet &f, const ImageDatabase &db, int &bestIndex, vector<FeatureMatch> &bestMatches, double &bestScore, int matchType) {
// Here's a nice low number.
bestScore = -1e100;
vector<FeatureMatch> tempMatches;
double tempScore;
for (unsigned int i=0; i<db.size(); i++) {
if (!matchFeatures(f, db[i].features, tempMatches, tempScore, matchType)) {
return false;
}
if (tempScore > bestScore) {
bestIndex = i;
bestScore = tempScore;
bestMatches = tempMatches;
}
}
return true;
}
// Match _features.
JNIEXPORT jint JNICALL
Java_fr_ensicaen_panandroid_stitcher_StitcherWrapper_matchFeatures
(JNIEnv* env, jobject obj)
{
return matchFeatures();
}
// Initialize global alignment of all images.
int initGlobalAlign(const vector<FeatureSet> &fs, int minMatches, MotionModel m, float f, int width, int height, int nRANSAC, double RANSACthresh, AlignMatrix &am, vector<CTransform3x3> &ms) {
int n = fs.size();
// create the n-by-n alignment matrix
am.resize(n);
for (int i=0; i<n; i++)
am[i].resize(n);
for (int i=0; i<n; i++) {
for (int j=0; j<n; j++) {
if (i != j) {
printf("matching image %d with image %d \n", i, j);
// BEGIN TODO
// write code to fill in the information for am[i][j]
//
// you'll need to call your feature matching routine,
// then your pair alignment routine
am[i][j].matches = matchFeatures(fs[i], fs[j]);
alignImagePair(fs[i], fs[j], am[i][j].matches, m, f, width, height, nRANSAC, RANSACthresh, am[i][j].r, am[i][j].inliers);
// END TODO
printf("%d inliers\n", am[i][j].inliers.size());
if ((int) am[i][j].inliers.size() < minMatches)
am[i][j].inliers.clear();
}
}
}
printf("done..\n");
vector<AlignmentImage> nodes(n);
for (int i=1; i<n; i++) {
nodes[i].added = false;
nodes[i].nBest = 0;
nodes[i].imageID = i;
nodes[i].parentID = -1;
}
// create the image heap
ImageHeap heap(nodes);
// add the first image and update the match quality of
// its neighbors
nodes[0].added = true;
for (int j=1; j<n; j++) {
int nMatches = am[0][j].inliers.size();
if (nodes[j].nBest < nMatches) {
heap.increaseKey(nodes[j].heapIndex, nMatches);
nodes[j].parentID = 0;
}
}
AlignmentImage *nextImage;
// add the rest of the images
for (int i=0; i<n-1; i++) {
nextImage = heap.extractMax();
if (nextImage->nBest == 0) {
// image set seems to be disconnected
return -1;
}
nextImage->added = true;
int id = nextImage->imageID;
int pid = nextImage->parentID;
// compute the global alignment of the extracted image
nextImage->r = am[pid][id].r * nodes[pid].r;
// update the match quality for its neighbor images
for (int j=0; j<n; j++) {
if ((id != j) && (!nodes[j].added)) {
int nMatches = am[id][j].inliers.size();
if (nodes[j].nBest < nMatches) {
heap.increaseKey(nodes[j].heapIndex, nMatches);
nodes[j].parentID = id;
}
}
}
}
ms.clear();
// put the global transformations into the output array
for (int i=0; i<n; i++) {
ms.push_back(nodes[i].r);
}
return 0;
}
