霍夫圆圈没有检测到眼睛虹膜

时间:2016-02-09 08:50:16

标签: android opencv image-processing hough-transform eye-detection

我想使用Hough Circle算法检测眼睛虹膜及其中心。

我正在使用此代码:

 private void houghCircle()
    {
        Bitmap obtainedBitmap = imagesList.getFirst();
                 /* convert bitmap to mat */
        Mat mat = new Mat(obtainedBitmap.getWidth(),obtainedBitmap.getHeight(),
                CvType.CV_8UC1);
        Mat grayMat = new Mat(obtainedBitmap.getWidth(), obtainedBitmap.getHeight(),
                CvType.CV_8UC1);


        Utils.bitmapToMat(obtainedBitmap, mat);

/* convert to grayscale */
        int colorChannels = (mat.channels() == 3) ? Imgproc.COLOR_BGR2GRAY : ((mat.channels() == 4) ? Imgproc.COLOR_BGRA2GRAY : 1);

        Imgproc.cvtColor(mat, grayMat, colorChannels);

/* reduce the noise so we avoid false circle detection */
        Imgproc.GaussianBlur(grayMat, grayMat, new Size(9, 9), 2, 2);

// accumulator value
        double dp = 1.2d;
// minimum distance between the center coordinates of detected circles in pixels
        double minDist = 100;

// min and max radii (set these values as you desire)
        int minRadius = 0, maxRadius = 1000;

// param1 = gradient value used to handle edge detection
// param2 = Accumulator threshold value for the
// cv2.CV_HOUGH_GRADIENT method.
// The smaller the threshold is, the more circles will be
// detected (including false circles).
// The larger the threshold is, the more circles will
// potentially be returned.
        double param1 = 70, param2 = 72;

/* create a Mat object to store the circles detected */
        Mat circles = new Mat(obtainedBitmap.getWidth(), obtainedBitmap.getHeight(), CvType.CV_8UC1);

/* find the circle in the image */
        Imgproc.HoughCircles(grayMat, circles, Imgproc.CV_HOUGH_GRADIENT, dp, minDist, param1, param2, minRadius, maxRadius);

/* get the number of circles detected */
        int numberOfCircles = (circles.rows() == 0) ? 0 : circles.cols();

/* draw the circles found on the image */
        for (int i=0; i<numberOfCircles; i++) {


/* get the circle details, circleCoordinates[0, 1, 2] = (x,y,r)
 * (x,y) are the coordinates of the circle's center
 */
            double[] circleCoordinates = circles.get(0, i);


            int x = (int) circleCoordinates[0], y = (int) circleCoordinates[1];

            Point center = new Point(x, y);

            int radius = (int) circleCoordinates[2];

    /* circle's outline */
            Core.circle(mat, center, radius, new Scalar(0,
                    255, 0), 4);

    /* circle's center outline */
            Core.rectangle(mat, new Point(x - 5, y - 5),
                    new Point(x + 5, y + 5),
                    new Scalar(0, 128, 255), -1);
        }

/* convert back to bitmap */
        Utils.matToBitmap(mat, obtainedBitmap);
        MediaStore.Images.Media.insertImage(getContentResolver(),obtainedBitmap, "testgray", "gray" );

    }

但它并没有正确检测所有图像中的虹膜。特别是,如果虹膜有深褐色的深色。如何修复此代码以正确检测虹膜及其中心?

编辑:以下是一些示例图片(我从网上获得),显示了算法的效果(请忽略红色方块所代表的地标):

在这些图像中,算法没有检测到所有虹膜:

enter image description here

enter image description here

此图显示算法根本无法检测虹膜:

enter image description here

编辑2:这是一个使用Canny边缘检测的代码,但它会导致应用程序崩溃:

 private void houghCircle()
    {
        Mat grayMat = new Mat();
        Mat cannyEdges = new Mat();
        Mat circles = new Mat();
        Bitmap obtainedBitmap = imagesList.getFirst();
         /* convert bitmap to mat */
        Mat originalBitmap = new Mat(obtainedBitmap.getWidth(),obtainedBitmap.getHeight(),
                CvType.CV_8UC1);
//Converting the image to grayscale
        Imgproc.cvtColor(originalBitmap,grayMat,Imgproc.COLOR_BGR2GRAY);
        Imgproc.Canny(grayMat, cannyEdges,10, 100);
        Imgproc.HoughCircles(cannyEdges, circles,
                Imgproc.CV_HOUGH_GRADIENT,1, cannyEdges.rows() / 15); //now circles is filled with detected circles.

//, grayMat.rows() / 8);
        Mat houghCircles = new Mat();
        houghCircles.create(cannyEdges.rows(),cannyEdges.cols()
                ,CvType.CV_8UC1);
//Drawing lines on the image
        for(int i = 0 ; i < circles.cols() ; i++)
        {
            double[] parameters = circles.get(0,i);
            double x, y;
            int r;
            x = parameters[0];
            y = parameters[1];
            r = (int)parameters[2];
            Point center = new Point(x, y);
//Drawing circles on an image
            Core.circle(houghCircles,center,r,
                    new Scalar(255,0,0),1);
        }
//Converting Mat back to Bitmap
        Utils.matToBitmap(houghCircles, obtainedBitmap);
        MediaStore.Images.Media.insertImage(getContentResolver(),obtainedBitmap, "testgray", "gray" );

    }

这是我在日志中得到的错误

FATAL EXCEPTION: Thread-28685
    CvException [org.opencv.core.CvException: cv::Exception: /hdd2/buildbot/slaves/slave_ardbeg1/50-SDK/opencv/modules/imgproc/src/color.cpp:3739: error: (-215) scn == 3 || scn == 4 in function void cv::cvtColor(cv::InputArray, cv::OutputArray, int, int)
    ]
            at org.opencv.imgproc.Imgproc.cvtColor_1(Native Method)
            at org.opencv.imgproc.Imgproc.cvtColor(Imgproc.java:4598)

这是由此行引起的:Imgproc.cvtColor(originalBitmap,grayMat,Imgproc.COLOR_BGR2GRAY);

有谁能告诉我这个错误怎么解决?也许添加精确边缘检测可以改善结果。

3 个答案:

答案 0 :(得分:2)

由于您希望使用霍夫变换检测虹膜(还有其他),您最好研究Canny边缘检测器及其参数。 cv::HoughCirclesparam1中的Canny-hysteresis阈值。单独调查Canny,您就会得到良好的阈值范围。

可能代替高斯模糊,您应用更好的去噪(非本地方式,例如h=32和窗口大小5和15),并尝试协调图像对比度,例如,使用对比度受限的自适应直方图均衡(cv::CLAHE)。

协调是为了确保所有(高光和阴影)眼睛都映射到相似的强度范围。

答案 1 :(得分:1)

我想知道这些图片是您处理过的图片,还是想拍摄手机屏幕的快照,然后将其上传到此处。因为光圈大于您在代码中设置的最大半径。因此,我不明白你怎么能找到任何虹膜。第一张图像中的光圈半径超过20.因此您无法检测到它们。 您应该将半径设置为您希望虹膜的半径范围。

答案 2 :(得分:1)

霍夫圈在定义明确的圈子上效果更好。他们对虹膜这样的东西不好。

经过一些阈值处理,形态学操作或canny边缘检测,MSER等特征检测方法对虹膜检测效果更好。

如果您正在寻找一些代码,

Here是一个类似的问题。