I got an issue with:
cv::FlannBasedMatcher
More precisely with the knnMatch method.
My program take as input and IplImage* and detect a face then it cut the faces and compare the face founded with an image stored on my computer. If i got more than 10 good match it writes on standard output Matched.
The images loaded aren't grayscale. Is that important?
My problem is that it works but for a random amount of time which vary from 1 minute to 3 minutes more or less.
The error messages always appears on the knnMatch methods. Here they are (Note that there is only one of them each time):
OpenCV Error: Assertion failed ((globalDescIdx>=0) && (globalDescIdx < size())) in getLocalIdx, file /opt/local/var/macports/build/_opt_mports_dports_graphics_opencv/opencv/work/opencv- 2.4.9/modules/features2d/src/matchers.cpp, line 163
libc++abi.dylib: terminating with uncaught exception of type cv::Exception: /opt/local/var/macports/build/_opt_mports_dports_graphics_opencv/opencv/work/opencv-2.4.9/modules/features2d/src/matchers.cpp:163: error: (-215) (globalDescIdx>=0) && (globalDescIdx < size()) in function getLocalIdx
I don't get why this exception is thrown...
Here is my code:
int DroneCV::matchFaces()
{
std::vector<cv::KeyPoint> keypointsO;
std::vector<cv::KeyPoint> keypointsS;
cv::Mat descriptors_object, descriptors_scene;
cv::Mat foundFaces(this->_faceCut);
cv::FlannBasedMatcher matcher;
std::vector<std::vector<cv::DMatch>> matches;
std::vector<cv::DMatch> good_matches;
cv::SurfDescriptorExtractor extractor;
cv::SurfFeatureDetector surf(this->_minHessian);
surf.detect(foundFaces,keypointsS);
surf.detect(this->_faceToRecognize,keypointsO);
if (!this->_faceToRecognize.data || !foundFaces.data)
{
this->log("Fail to init data in DronceCV::matchFaces");
return (0);
}
extractor.compute(foundFaces, keypointsS, descriptors_scene);
extractor.compute(this->_faceToRecognize, keypointsO, descriptors_object);
if(descriptors_scene.empty())//descriptors_scene.type()!=CV_32F)
{
this->log("Descriptor got wrong type");
descriptors_scene.convertTo(descriptors_scene, CV_32F);
return 0;
}
if(descriptors_object.type()!=CV_32F || descriptors_scene.type()!=CV_32F)
{
this->log("TYPE OBJECT " + std::to_string(descriptors_object.type()));
this->log("TYPE SCENE " + std::to_string(descriptors_scene.type()));
return (0);
}
//Both image must be in grayscale ???
try {
matcher.knnMatch( descriptors_object, descriptors_scene, matches, 5 ); // find the 2 nearest neighbors
} catch (cv::Exception e) {
this->log(e.err);
}
good_matches.reserve(matches.size());
for (size_t i = 0; i < matches.size(); ++i)
{
if (matches[i].size() < 2)
continue;
const cv::DMatch &m1 = matches[i][0];
const cv::DMatch &m2 = matches[i][1];
if(m1.distance <= this->_nndrRatio * m2.distance)
good_matches.push_back(m1);
}
this->log("Number of good matches" + std::to_string(good_matches.size()));
foundFaces.release();
if (good_matches.size() > 8)
return (1);
else
return (0);
}
void DroneCV::analyzeFrame(IplImage *img)
{
if (!img)
{
this->log("Frame empty");
return;
}
if (this->detectFaces(img) == 1)
{
if (this->matchFaces() == 1)
{
this->log("Matched");
cvReleaseImage(&this->_faceCut);
}
}
}
Thanks in advance for your help
I got stuck with this too and it took me almost 3-4 hours to figure this out. When you apply knn match , make sure that number of features in both test and query image is greater than or equal to number of nearest neighbors in knn match.
say for example, we have this code:
Mat img1,img2,desc1,desc2;
vector<KeyPoint> kpt1,kpt2;
FAST(img1,kpt1,30,true) ;
FAST(img2,kpt1,30,true) ;
SurfDescriptorExtractor sfdesc1,sfdesc2;
sfdesc1.compute(img1,kpt1,desc1);
sfdesc2.compute(img2,kpt2,desc2);
FlannBasedMatcher matcher;
vector< vector<DMatch> > matches1,matches2;
matcher.knnMatch(desc1,desc2,matches1,2);
this code will return exception as in the post, just modify the code as show below:
if(kpt1.size()>=2 && kpt2.size()>=2)
matcher.knnMatch(desc1,desc2,matches1,2);
this method worked for me ..!!
Related
I had a code which is getting ORB keypoints and descriptors of images and then it trains a bag of word using "BOWKMeansTrainer". the purpose of this code is to cluster photos using k-means. I don't have any problem using ORB.
Here is my problem:
I saw that openCV provides a VGG, You can find it HERE. I want to use VGG descriptor instead of ORB. Based on the documents, VGG doesn't have any function to make keypoints, so I used ORB keypoint and then used ORB keypoints to create VGG descriptors.
Ptr<ORB> detector = ORB::create();
Ptr<cv::xfeatures2d::VGG> detectorVGG = cv::xfeatures2d::VGG::create(VGG::VGG_120, 1.4f, true, true, 0.75f, true);
detector->detect(grayFrame, kp);
detectorVGG->compute(grayFrame, kp, descriptors);
This one also works and I can easily train bag of word and cluster them similar to the way I did using ORB. However when I want to test the bag of words, I'm getting this error:
error: (-215) _queryDescriptors.type() == trainDescType in function knnMatchImpl Aborttrap: 6
I tried to change the type of query descriptor Mat and descriptor Mat but I cannot solve it. In the best case , the error is changed to this:
(-215) (type == CV_8U && dtype == CV_32S) || dtype == CV_32F in function batchDistance
I am not sure if the problem is a small mistake in types or it is because of using ORB keypoints and VGG descriptors at the same time.
Part of my code is here:
vector<Mat> makeDescriptor(vector<Mat> frames)
{
int frameSize = frames.size();
vector<Mat> characteristics;
for (int i = 0 ; i < frameSize ; i++)
{
vector<KeyPoint> kp;
Mat descriptors;
Ptr<cv::xfeatures2d::VGG> detectorVGG = cv::xfeatures2d::VGG::create();
Ptr<ORB> detectorORB = ORB::create();
Mat frame = frames[i];
if (frame.empty()) {
cout << "Frame empty"<< "\n";
continue;
}
Mat grayFrame;
try
{
cvtColor(frame,grayFrame,CV_RGB2GRAY);
}
catch(Exception& e)
{
continue;
}
detectorORB->detect(grayFrame,kp);
detectorVGG->compute(grayFrame,kp,descriptors);
characteristics.push_back(descriptors);
}
return characteristics;
}
Mat makeDict(vector<Mat> characteristics, int _nCluster)
{
BOWKMeansTrainer bow(_nCluster);
for (int j = 0 ; j < characteristics.size() ; j++)
{
Mat descr = characteristics[j];
if (!descr.empty())
{
bow.add(descr);
}
}
Mat voc = bow.cluster();
return voc;
}
BOWImgDescriptorExtractor makeBow(Mat dict)
{
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
Ptr<DescriptorExtractor> extractor = VGG::create();
BOWImgDescriptorExtractor bowDE(extractor,matcher);
Mat voc;
dict.convertTo(voc,CV_32F);
bowDE.setVocabulary(voc);
return bowDE;
}
void testBow(BOWImgDescriptorExtractor bowDE, vector<Mat> frames)
{
Mat features;
vector< vector<KeyPoint> > keypoints = makeORBKeyPoints(frames);
for (int i = 0 ; i < frames.size() ; i++)
{
Mat bowDesc;
if (!keypoints[i].empty())
{
cout << "inside if" << '\n';
Mat frame;
frames[i].convertTo(frame, CV_8U);
bowDE.compute(frame, keypoints[i], bowDesc); //This Line
bowDesc.convertTo(bowDesc,CV_32F);
}
features.push_back(bowDesc);
}
}
The runtime error is happening at this line:
bowDE.compute(frame, keypoints[i], bowDesc); //This Line
I appreciate if someone helps me out to find a solution for it.
Thanks,
Below is my code , which is running fine but after a long processing it show me the run time error
// Initialize constant values
const int nb_cars = files.size();
const int not_cars = files_no.size();
const int num_img = nb_cars + not_cars; // Get the number of images
// Initialize your training set.
cv::Mat training_mat(num_img,dictionarySize,CV_32FC1);
cv::Mat labels(0,1,CV_32FC1);
std::vector<string> all_names;
all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());
// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);
Mat row_img = cv::imread( Dir +*i, 0 );
detector.detect( row_img, keypoints);
RetainBestKeypoints(keypoints, 20); // retain top 10 key points
extractor->compute( row_img, keypoints, descriptors_1);
//uncluster.push_back(descriptors_1);
descriptors.reshape(1,1);
bow.add(descriptors_1);
++count;
}
int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);
row_img = cv::imread( Dir +*k, 0 );
detector.detect( row_img, keypoints);
RetainBestKeypoints(keypoints, 20);
dextract.compute( row_img, keypoints, descriptors_1);
descriptors_1.reshape(1,1);
training_mat.push_back(descriptors_1);
labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1;
++count_2;
}
Error :
OpenCv Error : Formats of input argument do not match() in unknown function , file ..\..\..\src\opencv\modules\core\src\matrix.cpp, line 652
I made the descriptor_1 in second loop to reshape into row for SVM , but error is not solving
I think you are trying to cluster with less features then the number of classes.
You can take more images or more then 10 descriptors from each image.
As far i find out after 3 days that my error is in labeling , when i label the image i got the error there , and yes above answer is also relevant that using less number of images also cause the error , but in my case this is not the reason , When i start checking error line by line , the error starts from here :
labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1;
Because of the line :
Mat labels(0,1,CV_32FC1);
Instead of :
Mat labels(num_img,1,CV_32FC1);
and i should use
my_img.convertTo( training_mat.row(count_2), CV_32FC1 );
I'm trying to detect whether a template image (logo) is present in a pdf document. The document can be either a scan encapsulated in a pdf or a "pure" pdf document, but this is completely random.
First, I convert the pdf document to a png image using ImageMagick's convert tool, then I cut the output images in half because they're so big, and after that I try to match a logo from a database with any of the shapes present in the half-cut image.
To do so, I use an Orb Feature Detector with an Orb Descriptor, and a RobustMatcher (sort of improved BruteForce matcher, source code available here). Here is a snippet of code from my adaptation of it :
// Read input images
Mat image1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
Mat image2 = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
if (!image1.data || !image2.data) {
std::cout << " --(!) Error reading images " << std::endl;
exit(1);
}
// Setting up values for ORB Detector
int nfeatures = 800;
//float scaleFactor = 1.10;
int nlevels = 8;
int edgeThreshold = 12;
int firstLevel = 0;
int WTA_K = 2;
int scoreType = 0;
int patchSize = 31;
// Prepare the matcher
RobustMatcher rmatcher;
rmatcher.setConfidenceLevel(0.98);
rmatcher.setMinDistanceToEpipolar(1.0);
rmatcher.setRatio(0.80f);
cv::Ptr<cv::FeatureDetector> pfd = new cv::OrbFeatureDetector(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);
rmatcher.setFeatureDetector(pfd);
cv::Ptr<cv::DescriptorExtractor> pde = new cv::OrbDescriptorExtractor();
rmatcher.setDescriptorExtractor(pde);
// Match the two images
std::vector<cv::DMatch> matches;
std::vector<cv::KeyPoint> keypoints1, keypoints2;
cv::Mat fundemental = rmatcher.match(image1, image2, matches, keypoints1, keypoints2);
// If nothing could be matched, stop here
if(matches.size() < 4){
exit(2);
}
The code works great on some examples that I chose carefully, with a highly-recognizable logo and a clean image, with certain proportions... etc. But when I try to apply the process to random pdf files, I start to get this error from OpenCV :
OpenCV Error: Assertion failed (type == src2.type() && src1.cols == src2.cols && (type == CV_32F || type == CV_8U)) in batchDistance, file /home/das/Downloads/opencv-2.4.5/modules/core/src/stat.cpp, line 1797
terminate called after throwing an instance of 'cv::Exception'
what(): /home/das/Downloads/opencv-2.4.5/modules/core/src/stat.cpp:1797: error: (-215) type == src2.type() && src1.cols == src2.cols && (type == CV_32F || type == CV_8U) in function batchDistance
Aborted (core dumped)
I checked for this error, and it appeared that src1.cols != src2.cols, and a quick fix for it would be to test the condition before trying to match the images. The problem is that I miss a lot of images doing so, and this would be OK only if I were working on a video stream... but I'm not, and the next image has nothing in common with the previous one, and I can't determine whether my logo was present or not in the document.
Here is the code from stat.cpp, lines 1789 to 1826 : (assertion is at the beginning on line 1797)
void cv::batchDistance( InputArray _src1, InputArray _src2,
OutputArray _dist, int dtype, OutputArray _nidx,
int normType, int K, InputArray _mask,
int update, bool crosscheck )
{
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
int type = src1.type();
CV_Assert( type == src2.type() && src1.cols == src2.cols &&
(type == CV_32F || type == CV_8U));
CV_Assert( _nidx.needed() == (K > 0) );
if( dtype == -1 )
{
dtype = normType == NORM_HAMMING || normType == NORM_HAMMING2 ? CV_32S : CV_32F;
}
CV_Assert( (type == CV_8U && dtype == CV_32S) || dtype == CV_32F);
K = std::min(K, src2.rows);
_dist.create(src1.rows, (K > 0 ? K : src2.rows), dtype);
Mat dist = _dist.getMat(), nidx;
if( _nidx.needed() )
{
_nidx.create(dist.size(), CV_32S);
nidx = _nidx.getMat();
}
if( update == 0 && K > 0 )
{
dist = Scalar::all(dtype == CV_32S ? (double)INT_MAX : (double)FLT_MAX);
nidx = Scalar::all(-1);
}
if( crosscheck )
{
CV_Assert( K == 1 && update == 0 && mask.empty() );
Mat tdist, tidx;
batchDistance(src2, src1, tdist, dtype, tidx, normType, K, mask, 0, false);
So I'm wondering what does this assertion mean ? What are exactly the src1 and src2 files in stat.cpp ? Why do they need to have the same number of columns ?
I tried changing to a Surf detector and extractor, but I still get the error.
If anyone has an idea, do not hesitate to post, I welcome any advice or notice !
Thanks in advance.
EDIT
I have a more precise question now : how do I ensure that src1.cols == src2.cols ? To answer that question, I think I should know what are the transformations applied to my cv::Mat image1 and image2 before batchDistance(...) is called, in order to find a condition on image1 and image2 which will ensure that src1.cols == src2.cols, so my code would look like this :
// Match the two images
std::vector<cv::DMatch> matches;
std::vector<cv::KeyPoint> keypoints1, keypoints2;
if( CONDITION_ON_IMAGE1&IMAGE2_TO_ENSURE_SRC1.COLS==SRC2.COLS ){
cv::Mat fundemental = rmatcher.match(image1, image2, matches, keypoints1, keypoints2);
}
To get rid of errors, You can play with copying and pasting the images into empty one of required size, but this is only a quick and dirty solution for the assertion.
To make detector and descriptor work properly, You might have to get to know how it works. Maybe then You will be able to get images that will work. After reading this article, it looks that ORB will have problems with scaling (they mention it in the conclusion section). This means You will need to find a workaround for it (like image pyramids, or another way to check the image at multiple scales) or use another extractor and descriptor, which is scale and rotation invariant.
I am using FAST and FREAK to get the descriptors of a couple of images and then I apply knnMatch with a BruteForceMatcher matcher and next I am using a loop to separate the good matches:
float nndrRatio = 0.7f;
std::vector<KeyPoint> keypointsA, keypointsB;
Mat descriptorsA, descriptorsB;
std::vector< vector< DMatch > > matches;
int threshold=9;
// detect keypoints:
FAST(objectMat,keypointsA,threshold,true);
FAST(sceneMat,keypointsB,threshold,true);
FREAK extractor;
// extract descriptors:
extractor.compute( objectMat, keypointsA, descriptorsA );
extractor.compute( sceneMat, keypointsB, descriptorsB );
BruteForceMatcher<Hamming> matcher;
// match
matcher.knnMatch(descriptorsA, descriptorsB, matches, 2);
// good matches search:
vector< DMatch > good_matches;
for (size_t i = 0; i < matches.size(); ++i)
{
if (matches[i].size() < 2)
continue;
const DMatch &m1 = matches[i][0];
const DMatch &m2 = matches[i][1];
if(m1.distance <= nndrRatio * m2.distance)
good_matches.push_back(m1);
}
//If there are at least 7 good matches, then object has been found
if ( (good_matches.size() >=7))
{
cout << "OBJECT FOUND!" << endl;
}
I think the problem could be the good matches search method, because using it with the FlannBasedMatcher works fine but with the BruteForceMatcher very weirdly. I'm suspecting that I may be doing a nonsense with this method because the Hamming distance uses binary descriptors, but I can't think of a way to adapt it!
Any links, snippets, ideas,... please?
Your code is not bad, but I don't think it is what you want to do. Why did you choose this method?
If you want to detect an object in an image using OpenCV, you should maybe try the Cascade Classification. This link will explain how to train a classifier.
EDIT: If you think it is too complicated and if the object you want to detect is planar, you can try this tutorial (it basically computes the inliers by trying to find a homography transform between the object and the image). But the cascade classification is more general for object detection.
I am trying the quite new descriptor FREAK from the latest version of OpenCV following the freak_demo.cpp example. Instead of using SURF I use FAST. My basic code is something like this:
std::vector<KeyPoint> keypointsA, keypointsB;
Mat descriptorsA, descriptorsB;
std::vector<DMatch> matches;
FREAK extractor;
BruteForceMatcher<Hamming> matcher;
FAST(imgA,keypointsA,100);
FAST(imgB,keypointsB,20);
extractor.compute( imgA, keypointsA, descriptorsA );
extractor.compute( imgB, keypointsB, descriptorsB );
matcher.match(descriptorsA, descriptorsB, matches);
The algorithm finds a lot of matches, but there are a lot of outliers. Am I doing things right? Is there a way for tuning the algorithm?
When doing matching there are always some refinement steps for getting rid out of outliers.
What I usually do is discarding matches that have a distance over a threshold, for example:
for (int i = 0; i < matches.size(); i++ )
{
if(matches[i].distance > 200)
{
matches.erase(matches.begin()+i-1);
}
}
Then, I use RANSAC to see which matches fit the homography model. OpenCV has a function for this:
for( int i = 0; i < matches.size(); i++ )
{
trainMatches.push_back( cv::Point2f(keypointsB[ matches[i].trainIdx ].pt.x/500.0f, keypointsB[ matches[i].trainIdx ].pt.y/500.0f) );
queryMatches.push_back( cv::Point2f(keypointsA[ matches[i].queryIdx ].pt.x/500.0f, keypointsA[ matches[i].queryIdx ].pt.y/500.0f) );
}
Mat h = cv::findHomography(trainMatches,queryMatches,CV_RANSAC,0.005, status);
And I just draw the inliers:
for(size_t i = 0; i < queryMatches.size(); i++)
{
if(status.at<char>(i) != 0)
{
inliers.push_back(matches[i]);
}
}
Mat imgMatch;
drawMatches(imgA, keypointsA, imgB, keypointsB, inliers, imgMatch);
Just try different thresholds and distances until you get the desired resutls.
You can also train the descriptor by giving your own selected pairs. And tune the parameters in the constructor.
explicit FREAK( bool orientationNormalized = true
, bool scaleNormalized = true
, float patternScale = 22.0f
, int nbOctave = 4
, const vector<int>& selectedPairs = vector<int>()
);
BTW, a more efficient version of FREAK is on the way :-)