android — несоответствия ORB / Bruteforce

Я использую ORB и Bruteforce для распознавания объектов как для Android, так и для C ++. Однако при использовании одного и того же потока на обоих языках результаты кажутся противоречивыми или разными. Например, используя C ++, он дает 21 совпадение, в то время как он имеет 15 совпадений в Android. Кстати, я использую два одинаковых изображения в обоих тестах.

Моя реализация с C ++:

Mat img_1 = imread( "Object.jpg");
Mat img_2 = imread( "Scene.jpg");

if( !img_1.data || !img_2.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }

//-- Step 1: Detect the keypoints using ORB Detector
OrbFeatureDetector detector;

std::vector<KeyPoint> keypoints_1, keypoints_2;

detector.detect( img_1, keypoints_1 );
detector.detect( img_2, keypoints_2 );

//-- Step 2: Calculate descriptors (feature vectors)
OrbDescriptorExtractor extractor;

Mat descriptors_1, descriptors_2;

extractor.compute( img_1, keypoints_1, descriptors_1 );
extractor.compute( img_2, keypoints_2, descriptors_2 );

//-- Step 3: Matching descriptor vectors using BF matcher
BFMatcher matcher;
std::vector< DMatch > matches;
matcher.match( descriptors_1, descriptors_2, matches );

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < descriptors_1.rows; i++ )
{ double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}

printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );

//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
//-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
//-- small)
//-- PS.- radiusMatch can also be used here.
std::vector< DMatch > good_matches;

for( int i = 0; i < descriptors_1.rows; i++ )
{ if( matches[i].distance <= 3*min_dist)
{ good_matches.push_back( matches[i]); }
}

Для Java:

       String path = Environment.getExternalStorageDirectory().getAbsolutePath();

Bitmap objectbmp = BitmapFactory.decodeFile(path+"/Sample/Object.jpg");
Bitmap scenebmp = BitmapFactory.decodeFile(path+"/Sample/Scene.jpg");

Mat object = new Mat(); //from the database
Mat scene = new Mat(); //user's input image

// convert bitmap to MAT
Utils.bitmapToMat(objectbmp, object);
Utils.bitmapToMat(scenebmp, scene);

//Feature Detection
FeatureDetector orbDetector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor orbextractor = DescriptorExtractor.create(DescriptorExtractor.ORB);

MatOfKeyPoint keypoints_object = new MatOfKeyPoint();
MatOfKeyPoint keypoints_scene = new MatOfKeyPoint();

Mat descriptors_object = new Mat();
Mat descriptors_scene = new Mat();

//Getting the keypoints
orbDetector.detect( object, keypoints_object );
orbDetector.detect( scene, keypoints_scene );

//Compute descriptors
orbextractor.compute( object, keypoints_object, descriptors_object );
orbextractor.compute( scene, keypoints_scene, descriptors_scene );

//Match with Brute Force
MatOfDMatch matches = new MatOfDMatch();
DescriptorMatcher matcher;
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
matcher.match( descriptors_object, descriptors_scene, matches );

double max_dist = 0;
double min_dist = 100;

List<DMatch> matchesList = matches.toList();

//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < descriptors_object.rows(); i++ )
{ double dist = matchesList.get(i).distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}

LinkedList<DMatch> good_matches = new LinkedList<DMatch>();

for( int i = 0; i < descriptors_object.rows(); i++ )
{ if( matchesList.get(i).distance <= 3*min_dist )
{ good_matches.addLast( matchesList.get(i));
}
}

Примечание: я считаю хорошие совпадения, которые дают разные результаты от одного и того же ввода.