OpenCV Dense feature detector

Question

I am using openCV to do some dense feature extraction. For example, The code

DenseFeatureDetector detector(12.f, 1, 0.1f, 10);

I don't really understand the parameters in the above constructor. What does it mean ? Reading the opencv documentation about it does not help much either. In the documentation the arguments are:

DenseFeatureDetector( float initFeatureScale=1.f, int featureScaleLevels=1,
                          float featureScaleMul=0.1f,
                          int initXyStep=6, int initImgBound=0,
                          bool varyXyStepWithScale=true,
                          bool varyImgBoundWithScale=false );

What are they supposed to do ? i.e. what is the meaning of scale, initFeatureScale, featureScaleLevels etc ? How do you know the grid or grid spacing etc for the dense sampling.

Have a look at my answer on the same question here: http://stackoverflow.com/questions/29970191/opencv-python-dense-sift-settings/32246964#32246964 — Seyeong Jeong, Aug 30 '15 at 03:22

score 0 · Answer 1 · answered Jan 03 '16 at 20:23

I'm using opencv with dense detector too and I think I can help you with something. I'm not sure about what I'm going to say but the experience learnt me that.

When I use Dense detector I pass there the gray scale image. The detector makes some threshold filters where opencv uses a gray minimum value with is used to transform the image. The píxels where have a more gray level than the threshold will be made like black points and the others are white point. This action is repeated in a loop where the threshold will be bigger and bigger. So the parameter initFeatureScale determine the first threshold you put to do this loop, the featureScaleLevels parameter indicates how much this threshold is bigger between one loop iteration and the next one and featureScaleMul is a multiply factor to calculate the next threshold.

Anyway if you are looking for a your optimal parameters to use Dense detector to detect any particular points You would offer a program I made for that. It is liberated in github. This is a program where you can test some detectors (Dense detector is one of them) and check how it works if you change their parameters thanks to a user interface that let you change the detectors parameters as long as you are executing the program. You will see how the detected points will be change. For try it just click on the link, and download the files. You might need almost all the files to execute the program.

Thank you very much for your time in explaining the parameters. However, I am no longer doing the project and currently working on a different field. Thank you for your time :) — tarmizi, Jan 05 '16 at 01:19

score 0 · Answer 2 · answered May 30 '17 at 19:44

Apologies in advance, i'm predominantly using Python so i'll avoid embarressing myself by referring to C++.

DenseFeatureDetector populates a vector with KeyPoints to pass to compute feature descriptors. These keypoints have a point vector and their scale set. In the documentation, scale is the pixel radius of the keypoint.

KeyPoints are evenly spaced across the width and height of the image matrix passed to DenseFeatureVector.

Now to the arguments:

initFeatureScale Set the initial KeyPoint feature radius in pixels (as far as I am aware this has no effect)

featureScaleLevels Number of scales overwhich we wish to make keypoints

featureScaleMuliplier Scale adjustment for initFeatureScale over featureScaleLevels, this scale adjustment can also be applied to the border (initImgBound) and the step size (initxystep). So when we set featureScaleLevels>1 then this multiplier will be applied to successive scales, to adjust feature scale, step and the boundary around the image.

initXyStep moving column and row step in pixels. Self explanatory I hope.

initImgBound row/col bounding region to ignore around the image (pixels), So a 100x100 image, with an initImgBound of 10, would create keypoints in the central 80x80 portion of the image.

varyXyStepWithScale Boolean, if we have multiple featureScaleLevels do we want to adjust the step size using featureScaleMultiplier.

varyImgBoundWithScale Boolean,as varyXyStepWithScale, but applied to the border.

Here is the DenseFeatureDetector source code from detectors.cpp in the OpenCV 2.4.3 source, which will probably explain better than my words:

DenseFeatureDetector::DenseFeatureDetector( float _initFeatureScale, int _featureScaleLevels,
                                      float _featureScaleMul, int _initXyStep,
                                      int _initImgBound, bool _varyXyStepWithScale,
                                      bool _varyImgBoundWithScale ) :
    initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels),
    featureScaleMul(_featureScaleMul), initXyStep(_initXyStep), initImgBound(_initImgBound),
    varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale)
{}


void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
    float curScale = static_cast<float>(initFeatureScale);
    int curStep = initXyStep;
    int curBound = initImgBound;
    for( int curLevel = 0; curLevel < featureScaleLevels; curLevel++ )
    {
        for( int x = curBound; x < image.cols - curBound; x += curStep )
        {
            for( int y = curBound; y < image.rows - curBound; y += curStep )
            {
                keypoints.push_back( KeyPoint(static_cast<float>(x), static_cast<float>(y), curScale) );
            }
        }

        curScale = static_cast<float>(curScale * featureScaleMul);
        if( varyXyStepWithScale ) curStep = static_cast<int>( curStep * featureScaleMul + 0.5f );
        if( varyImgBoundWithScale ) curBound = static_cast<int>( curBound * featureScaleMul + 0.5f );
    }

    KeyPointsFilter::runByPixelsMask( keypoints, mask );
}

You might expect a call to compute would calculate additional KeyPoint characteristics using the relevant keypoint detection algorithm (e.g. angle), based on the KeyPoints generated by DenseFeatureDetector. Unfortunately this isn't the case for SIFT under Python - i've not looked at at the other feature detectors, nor looked at the behaviour in C++.

Also note that DenseFeatureDetector is not in OpenCV 3.2 (unsure at which release it was removed).

OpenCV Dense feature detector

2 Answers2