OpenCV画像の比較と類似性Android

Question

私はOpenCV学習者です。画像比較を試みていました。私はOpenCV 2.4.13.3を使用しましたこれらの2つの画像があります1.jpgおよびcam1.jpg。

OpenCVで次のコマンドを使用すると

File sdCard = Environment.getExternalStorageDirectory();
String path1, path2;
path1 = sdCard.getAbsolutePath() + "/1.jpg";
path2 = sdCard.getAbsolutePath() + "/cam1.jpg";

FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);

Mat img1 = Highgui.imread(path1);
Mat img2 = Highgui.imread(path2);

Mat descriptors1 = new Mat();
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
detector.detect(img1, keypoints1);
extractor.compute(img1, keypoints1, descriptors1);

//second image
// Mat img2 = Imgcodecs.imread(path2);
Mat descriptors2 = new Mat();
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
detector.detect(img2, keypoints2);
extractor.compute(img2, keypoints2, descriptors2);


//matcher image descriptors
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1,descriptors2,matches);

// Filter matches by distance
MatOfDMatch filtered = filterMatchesByDistance(matches);

int total = (int) matches.size().height;
int Match= (int) filtered.size().height;
Log.d("LOG", "total:" + total + " Match:"+Match);

メソッドfilterMatchesByDistance

    static MatOfDMatch filterMatchesByDistance(MatOfDMatch matches){
    List<DMatch> matches_original = matches.toList();
    List<DMatch> matches_filtered = new ArrayList<DMatch>();

    int DIST_LIMIT = 30;
    // Check all the matches distance and if it passes add to list of filtered matches
    Log.d("DISTFILTER", "ORG SIZE:" + matches_original.size() + "");
    for (int i = 0; i < matches_original.size(); i++) {
        DMatch d = matches_original.get(i);
        if (Math.abs(d.distance) <= DIST_LIMIT) {
            matches_filtered.add(d);
        }
    }
    Log.d("DISTFILTER", "FIL SIZE:" + matches_filtered.size() + "");

    MatOfDMatch mat = new MatOfDMatch();
    mat.fromList(matches_filtered);
    return mat;
}

ログ

total:122 Match:30

ログからわかるように、一致は30です。
しかし、ご覧のとおり、両方の画像の視覚要素は同じです（in）。
openCVを使用してmatch = 90を取得するにはどうすればよいですか？
誰かがコードスニペットを手伝ってくれるといいですね。
opencvを使用できない場合、他にどのような選択肢がありますか？

Answer 1

しかし、ご覧のとおり、両方の画像の視覚要素は同じです（in）。

したがって、画像全体ではなく、「同じ視覚要素」を比較する必要があります。 「テンプレート」と「カメラ」の画像自体を比較せずに、同じ方法で処理（たとえば、バイナリの白黒に変換）すると、「テンプレート」と「カメラ」の画像のMatch値をさらに改善できます。 。たとえば、両方の画像（「テンプレート」と「カメラ」）で青い（テンプレートロゴの背景）正方形を見つけて、その正方形（関心領域）を比較してみてください。コードは次のようになります。

_Bitmap bmImageTemplate = <get your template image Bitmap>;
Bitmap bmTemplate = findLogo(bmImageTemplate);  // process template image

Bitmap bmImage = <get your camera image Bitmap>;
Bitmap bmLogo = findLogo(bmImage); // process camera image same way

compareBitmaps(bmTemplate, bmLogo);
_

どこ

_private Bitmap findLogo(Bitmap sourceBitmap) {
    Bitmap roiBitmap = null;
    Mat sourceMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CvType.CV_8UC3);
    Utils.bitmapToMat(sourceBitmap, sourceMat);
    Mat roiTmp = sourceMat.clone();

    final Mat hsvMat = new Mat();
    sourceMat.copyTo(hsvMat);

    // convert mat to HSV format for Core.inRange()
    Imgproc.cvtColor(hsvMat, hsvMat, Imgproc.COLOR_RGB2HSV);

    Scalar lowerb = new Scalar(85, 50, 40);         // lower color border for BLUE
    Scalar upperb = new Scalar(135, 255, 255);      // upper color border for BLUE
    Core.inRange(hsvMat, lowerb, upperb, roiTmp);   // select only blue pixels

    // find contours
    List<MatOfPoint> contours = new ArrayList<>();
    List<Rect> squares = new ArrayList<>();
    Imgproc.findContours(roiTmp, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);

    // find appropriate bounding rectangles
    for (MatOfPoint contour : contours) {
        MatOfPoint2f areaPoints = new MatOfPoint2f(contour.toArray());
        RotatedRect boundingRect = Imgproc.minAreaRect(areaPoints);

        double rectangleArea = boundingRect.size.area();

        // test min ROI area in pixels
        if (rectangleArea > 400) {
            Point rotated_rect_points[] = new Point[4];
            boundingRect.points(rotated_rect_points);

            Rect rect = Imgproc.boundingRect(new MatOfPoint(rotated_rect_points));
            double aspectRatio = rect.width > rect.height ?
                    (double) rect.height / (double) rect.width : (double) rect.width / (double) rect.height;
            if (aspectRatio >= 0.9) {
                squares.add(rect);
            }
        }
    }

    Mat logoMat = extractSquareMat(roiTmp, getBiggestSquare(squares));

    roiBitmap = Bitmap.createBitmap(logoMat.cols(), logoMat.rows(), Bitmap.Config.ARGB_8888);
    Utils.matToBitmap(logoMat, roiBitmap);
    return roiBitmap;
}
_

メソッドextractSquareMat()画像全体から関心領域（ロゴ）を抽出するだけです

_public static Mat extractSquareMat(Mat sourceMat, Rect rect) {
    Mat squareMat = null;
    int padding = 50;

    if (rect != null) {
        Rect truncatedRect = new Rect((int) rect.tl().x + padding, (int) rect.tl().y + padding,
                rect.width - 2 * padding, rect.height - 2 * padding);
        squareMat = new Mat(sourceMat, truncatedRect);
    }

    return squareMat ;
}
_

およびcompareBitmaps()コードの単なるラッパー：

_private void compareBitmaps(Bitmap bitmap1, Bitmap bitmap2) {
    Mat mat1 = new Mat(bitmap1.getWidth(), bitmap1.getHeight(), CvType.CV_8UC3);
    Utils.bitmapToMat(bitmap1, mat1);

    Mat mat2 = new Mat(bitmap2.getWidth(), bitmap2.getHeight(), CvType.CV_8UC3);
    Utils.bitmapToMat(bitmap2, mat2);

    compareMats(mat1, mat2);
}
_

メソッドとしてのコード：

_private void compareMats(Mat img1, Mat img2) {
    FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
    DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
    DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);

    Mat descriptors1 = new Mat();
    MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
    detector.detect(img1, keypoints1);
    extractor.compute(img1, keypoints1, descriptors1);

    //second image
    // Mat img2 = Imgcodecs.imread(path2);
    Mat descriptors2 = new Mat();
    MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
    detector.detect(img2, keypoints2);
    extractor.compute(img2, keypoints2, descriptors2);


    //matcher image descriptors
    MatOfDMatch matches = new MatOfDMatch();
    matcher.match(descriptors1,descriptors2,matches);

    // Filter matches by distance
    MatOfDMatch filtered = filterMatchesByDistance(matches);

    int total = (int) matches.size().height;
    int Match= (int) filtered.size().height;
    Log.d("LOG", "total:" + total + " Match:" + Match);
}

static MatOfDMatch filterMatchesByDistance(MatOfDMatch matches){
    List<DMatch> matches_original = matches.toList();
    List<DMatch> matches_filtered = new ArrayList<DMatch>();

    int DIST_LIMIT = 30;
    // Check all the matches distance and if it passes add to list of filtered matches
    Log.d("DISTFILTER", "ORG SIZE:" + matches_original.size() + "");
    for (int i = 0; i < matches_original.size(); i++) {
        DMatch d = matches_original.get(i);
        if (Math.abs(d.distance) <= DIST_LIMIT) {
            matches_filtered.add(d);
        }
    }
    Log.d("DISTFILTER", "FIL SIZE:" + matches_filtered.size() + "");

    MatOfDMatch mat = new MatOfDMatch();
    mat.fromList(matches_filtered);
    return mat;
}
_

質問結果から保存されたサイズ変更（50％に拡大縮小）された画像の結果は次のとおりです。

_D/DISTFILTER: ORG SIZE:237
D/DISTFILTER: FIL SIZE:230
D/LOG: total:237 Match:230
_

NB！これは、特定のテンプレートに対してのみアプローチを示すための、簡単で汚い例です。

P.S. getBiggestSquare()は次のようになります（領域による比較に基づく）：

_public static Rect getBiggestSquare(List<Rect> squares) {
    Rect biggestSquare = null;

    if (squares != null && squares.size() >= 1) {
        Rect square;
        double maxArea;
        int ixMaxArea = 0;

        square = squares.get(ixMaxArea);
        maxArea = square.area();

        for (int ix = 1; ix < squares.size(); ix++) {
            square = squares.get(ix);
            if (square.area() > maxArea) {
                maxArea = square.area();
                ixMaxArea = ix;
            }
        }

        biggestSquare = squares.get(ixMaxArea);
    }

    return biggestSquare;
}
_