some snippet of js with opencv matching

gistfile1.txt

        let MAX_FEATURES = 500;
        let imSceneGray = new cv.Mat();
        let imObjectGray = new cv.Mat();

        let orb = new cv.ORB();

        let kpScene = new cv.KeyPointVector();
        let descriptorsScene = new cv.Mat();
        let kpObject = new cv.KeyPointVector();
        let descriptorsObject = new cv.Mat();
        let bf;
        let matches = new cv.DMatchVectorVector();
        let mask = new cv.Mat();

        // Convert images to grayscale
        cv.cvtColor(imScene, imSceneGray, cv.COLOR_RGBA2GRAY, 0);
        cv.cvtColor(imObject, imObjectGray, cv.COLOR_RGBA2GRAY, 0);

        // Detect ORB features and compute descriptors.
        orb.setMaxFeatures(MAX_FEATURES);
        orb.detectAndCompute(imScene, new cv.Mat(), kpScene, descriptorsScene)
        orb.detectAndCompute(imObject, new cv.Mat(), kpObject, descriptorsObject)

        // create BFMatcher object
        bf = new cv.BFMatcher(cv.NORM_HAMMING2, true);

        // Match descriptors.
        // bf.match(descriptorsScene, descriptorsObject, matches, mask)
        if (descriptorsScene.size().width < 1 && descriptorsScene.size().height < 1 )
        {
            return;
        }

        bf.knnMatch(descriptorsObject, descriptorsScene, matches, 1)
        
        // filtering bad matches...
        for (let i = 0; i < this.size(); i++) {
            // CPP:  if (knn_matches[i][0].distance < ratio_thresh * knn_matches[i][1].distance)
            if (this.get(i).get(0) !== undefined)
            {
                debugger;
            }
        }