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some bugfixes & improvements in openfabmap code, docs and samples by Arren Glover

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This commit is contained in:
Vadim Pisarevsky 2012-09-23 20:57:34 +04:00
parent b7b32e74a5
commit 427ce1247a
3 changed files with 27 additions and 11 deletions
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2
modules/contrib/doc/openfabmap.rst
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@ -3,7 +3,7 @@ openFABMAP
.. highlight:: cpp
The openFABMAP package has been integrated into OpenCV from the openFABMAP <http://code.google.com/p/openfabmap/> project. OpenFABMAP is an open and modifiable code-source which implements the Fast Appearance-based Mapping algorithm (FAB-MAP) developed by Mark Cummins and Paul Newman. The algorithms used in openFABMAP were developed using only the relevant FAB-MAP publications.
The openFABMAP package has been integrated into OpenCV from the openFABMAP <http://code.google.com/p/openfabmap/> project [ICRA2011]_. OpenFABMAP is an open and modifiable code-source which implements the Fast Appearance-based Mapping algorithm (FAB-MAP) developed by Mark Cummins and Paul Newman. The algorithms used in openFABMAP were developed using only the relevant FAB-MAP publications.
FAB-MAP is an approach to appearance-based place recognition. FAB-MAP compares images of locations that have been visited and determines the probability of re-visiting a location, as well as providing a measure of the probability of being at a new, previously unvisited location. Camera images form the sole input to the system, from which visual bag-of-words models are formed through the extraction of appearance-based (e.g. SURF) features.

15
modules/contrib/src/openfabmap.cpp
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@ -202,14 +202,13 @@ void FabMap::compare(const vector<Mat>& queryImgDescriptors,
void FabMap::compare(const vector<Mat>& queryImgDescriptors,
const vector<Mat>& _testImgDescriptors,
vector<IMatch>& matches, const Mat& /*mask*/) {
if (_testImgDescriptors[0].data != this->testImgDescriptors[0].data) {
CV_Assert(!(flags & MOTION_MODEL));
for (size_t i = 0; i < _testImgDescriptors.size(); i++) {
CV_Assert(!_testImgDescriptors[i].empty());
CV_Assert(_testImgDescriptors[i].rows == 1);
CV_Assert(_testImgDescriptors[i].cols == clTree.cols);
CV_Assert(_testImgDescriptors[i].type() == CV_32F);
}
CV_Assert(!(flags & MOTION_MODEL));
for (size_t i = 0; i < _testImgDescriptors.size(); i++) {
CV_Assert(!_testImgDescriptors[i].empty());
CV_Assert(_testImgDescriptors[i].rows == 1);
CV_Assert(_testImgDescriptors[i].cols == clTree.cols);
CV_Assert(_testImgDescriptors[i].type() == CV_32F);
}
for (size_t i = 0; i < queryImgDescriptors.size(); i++) {

21
samples/cpp/fabmap_sample.cpp
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@ -58,6 +58,24 @@ using namespace std;
int main(int argc, char * argv[]) {
/*
Note: the vocabulary and training data is specifically made for this openCV
example. It is not reccomended for use with other datasets as it is
intentionally small to reduce baggage in the openCV project.
A new vocabulary can be generated using the supplied BOWMSCtrainer (or other
clustering method such as K-means
New training data can be generated by extracting bag-of-words using the
openCV BOWImgDescriptorExtractor class.
vocabulary, chow-liu tree, training data, and test data can all be saved and
loaded using openCV's FileStorage class and it is not necessary to generate
data each time as done in this example
*/
cout << "This sample program demonstrates the FAB-MAP image matching "
"algorithm" << endl << endl;
@ -188,10 +206,9 @@ int main(int argc, char * argv[]) {
Mat result_large(100, 100, CV_8UC1);
resize(result_small, result_large, Size(500, 500), 0, 0, CV_INTER_NN);
cout << endl << "Press any key to exit" << endl;
imshow("Confusion Matrix", result_large);
waitKey();
cout << endl << "Press any key to exit" << endl;
return 0;
}