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2015-06-11 15:20:52 -0500 commented question Why does Python implementation and Java implementation of MSER create different output?

@berak: So if I understand correctly from the docs, both the java version and the python/C++ version have the features2d.FeatureDetector interface, but the python/C++ version has the additional MSER class to find regions, not just key points?

In that case, what do people do? Is it possible to add the C++ MSER class to the OpenCV manager, edit something like the javaFeatureDetector here here, and create a java wrapper for it? Thanks for any advice.

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2015-06-10 00:53:00 -0500 asked a question Why does Python implementation and Java implementation of MSER create different output?

I have been trying to use both the Python implementation (opencv 2.4.11) and the Java implementation (opencv 2.4.10) of OpenCV's MSER algorithm. Interestingly, I've noticed that MSER's detect returns different types of output in Python vs Java. In Python, detect returns a list of lists of points, where each list of points represents a blob detected. In Java, a Mat is returned, where each row is a single point with an associated diameter representing a blob detected. I would like to reproduce the Python behavior in Java, where blobs are defined by a set of points, not one point. Anyone know what's going on?

Python:

frame = cv2.imread('test.jpg')
mser = cv2.MSER(**dict((k, kw[k]) for k in MSER_KEYS))  
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  
regions = mser.detect(gray, None)
print("REGIONS ARE: " + str(regions))

where the dict given to cv2.MSER is
{'_delta':7, '_min_area': 2000, '_max_area': 20000, '_max_variation': .25, '_min_diversity': .2, '_max_evolution': 200, '_area_threshold': 1.01, '_min_margin': .003, '_edge_blur_size': 5}

Python output:

REGIONS ARE: [array([[197,  58],
   [197,  59],
   [197,  60],
    ..., 
    [143,  75],
    [167,  86],
    [172,  98]], dtype=int32), array([[114,   2],
    [114,   1],
    [114,   0],
    ..., 
    [144,  56],
    [ 84,  55],
    [ 83,  55]], dtype=int32)]

Java:

 Mat mat = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_16S, new Scalar(4));
 Mat gray = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_16S, new Scalar(4));
 Imgproc.cvtColor(mat, gray, Imgproc.COLOR_RGB2GRAY, 4);

 FeatureDetector fd = FeatureDetector.create(FeatureDetector.MSER);
 MatOfKeyPoint regions = new MatOfKeyPoint();
 fd.detect(gray, regions);
 System.out.println("REGIONS ARE: " + regions);

Java output:

 REGIONS ARE: Mat [ 10*1*CV_32FC(7), isCont=true, isSubmat=false, nativeObj=0x6702c688, dataAddr=0x59add760 ]

 where each row of the Mat looks like
 KeyPoint [pt={365.3387451171875, 363.75640869140625}, size=10.680443, angle=-1.0, response=0.0, octave=0, class_id=-1]