使用 OpenCV 移除圆形

Question

我正在处理一个关于opencv的问题，想要找出哪些圆是填充的。不过，有时候圆的边缘会导致错误的判断。这让我想知道，是否可以通过把所有RGB中红色值高的像素变成白色来去掉这些圆。我打算先创建一个粉色像素的遮罩，然后从原始图像中减去这个遮罩，以去掉圆。目前我得到的是黑色的遮罩，看来我做错了什么。请给我一些指导。

    rgb = cv2.imread(img, cv2.CV_LOAD_IMAGE_COLOR)
    rgb_filtered = cv2.inRange(rgb, (200, 0, 90), (255, 110, 255))
    cv2.imwrite('mask.png',rgb_filtered) 

Answer 1

这是我完成这个任务的方法：

1. 先把图片转换成灰度图，然后用高斯模糊来去掉噪点。
2. 接着使用Otsu阈值处理，这个方法能很好地区分前景和背景，建议你了解一下。
3. 然后使用霍夫圆变换来找出可能的圆形，遗憾的是这个步骤需要很多调试。也许使用分水岭分割会是个更好的选择。
4. 从找到的圆形中提取感兴趣区域（ROI），并计算黑白像素的比例。

这是我的样本结果：

当我们把结果画到原始图片上时：

这是我的示例代码（抱歉用的是C++）：

void findFilledCircles( Mat& img ){
    Mat gray;
    cvtColor( img, gray, CV_BGR2GRAY );

    /* Apply some blurring to remove some noises */
    GaussianBlur( gray, gray, Size(5, 5), 1, 1);

    /* Otsu thresholding maximizes inter class variance, pretty good in separating background from foreground */
    threshold( gray, gray, 0.0, 255.0, CV_THRESH_OTSU );
    erode( gray, gray, Mat(), Point(-1, -1), 1 );

    /* Sadly, this is tuning heavy, adjust the params for Hough Circles */
    double dp       = 1.0;
    double min_dist = 15.0;
    double param1   = 40.0;
    double param2   = 10.0;
    int min_radius  = 15;
    int max_radius  = 22;

    /* Use hough circles to find the circles, maybe we could use watershed for segmentation instead(?) */
    vector<Vec3f> found_circles;
    HoughCircles( gray, found_circles, CV_HOUGH_GRADIENT, dp, min_dist, param1, param2, min_radius, max_radius );

    /* This is just to draw coloured circles on the 'originally' gray image */
    vector<Mat> out = { gray, gray, gray };
    Mat output;
    merge( out, output );

    float diameter  = max_radius * 2;
    float area      = diameter * diameter;

    Mat roi( max_radius, max_radius, CV_8UC3, Scalar(255, 255, 255) );
    for( Vec3f circ: found_circles ) {
    /* Basically we extract the region of the circles, and count the ratio of black pixels (0) and white pixels (255) */
        Mat( gray, Rect( circ[0] - max_radius, circ[1] - max_radius, diameter, diameter ) ).copyTo( roi );
        float filled_percentage = 1.0 - 1.0 * countNonZero( roi ) / area;

        /* If more than half is filled, then maybe it's filled */
        if( filled_percentage > 0.5 )
            circle( output, Point2f( circ[0], circ[1] ), max_radius, Scalar( 0, 0, 255), 3 );
        else
            circle( output, Point2f( circ[0], circ[1] ), max_radius, Scalar( 255, 255, 0), 3 );
    }


    namedWindow("");
    moveWindow("", 0, 0);
    imshow("", output );
    waitKey();
}

Answer 2

我尝试用Python来找出一个解决方案。基本的步骤如下：

• 先用高斯模糊来减少噪声。
• 然后使用Otsu的阈值方法。
• 接着找到没有父级的轮廓，这些轮廓应该是圆形。
• 最后检查每个轮廓内部白色像素和黑色像素的比例。

你可能需要调整白色像素的比例阈值，以适应你的应用。我用的是0.7，这个值看起来比较合理。

import cv2
import numpy

# Read image and apply gaussian blur

img = cv2.imread("circles.png", cv2.CV_LOAD_IMAGE_GRAYSCALE)
img = cv2.GaussianBlur(img, (5, 5), 0)

# Apply OTSU thresholding and reverse it so the circles are in the foreground (white)

_, otsu = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
otsu = cv2.bitwise_not(otsu).astype("uint8")

# Find contours that have no parent

contours, hierarchy = cv2.findContours(numpy.copy(otsu), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
parent_contours = [contours[idx] for idx, val in enumerate(hierarchy[0]) if val[3] == -1]

# Loop through all contours to check the ratio of white to black pixels inside each one

filled_circles_contours = list()

for contour in parent_contours:

    contour_mask = numpy.zeros(img.shape).astype("uint8")
    cv2.drawContours(contour_mask, [contour], -1, 1, thickness=-1)
    white_len_mask = len(cv2.findNonZero(contour_mask))
    white_len_thresholded = len(cv2.findNonZero(contour_mask * otsu))
    white_ratio = float(white_len_thresholded) / white_len_mask

    if white_ratio > 0.7:
        filled_circles_contours.append(contour)

# Show image with detected circles

cv2.drawContours(img, filled_circles_contours, -1, (0, 0, 0), thickness=2)
cv2.namedWindow("Result")
cv2.imshow("Result", img)
cv2.waitKey(0)

这是我把上面的代码应用到你的图片后得到的结果：

Answer 3

这是我的解决方案。不幸的是，它也是用C++写的，下面是它的工作原理：

1. 对图像进行阈值处理，以找出哪些部分是背景（白纸）。
2. 通过提取轮廓来找到圆形。
3. 现在每个轮廓都被认为是一个圆，所以计算包围这个轮廓的最小圆。如果输入数据没问题，就不需要调整参数（这意味着每个圆都是一个单独的轮廓，比如说圆可能不会因为绘制而连接在一起）。

4. 检查每个圆形内部，看看里面是前景（绘画）像素多，还是背景（白纸）像素多（通过某个比例阈值）。

   int main()
   {
   cv::Mat colorImage = cv::imread("countFilledCircles.png");
   cv::Mat image = cv::imread("countFilledCircles.png", CV_LOAD_IMAGE_GRAYSCALE);
   
   // threshold the image!
   cv::Mat thresholded;
   cv::threshold(image,thresholded,0,255,CV_THRESH_BINARY_INV | CV_THRESH_OTSU);
   
   // save threshold image for demonstration:
   cv::imwrite("countFilledCircles_threshold.png", thresholded);
   
   
   // find outer-contours in the image these should be the circles!
   cv::Mat conts = thresholded.clone();
   std::vector<std::vector<cv::Point> > contours;
   std::vector<cv::Vec4i> hierarchy;
   
   cv::findContours(conts,contours,hierarchy, CV_RETR_EXTERNAL, CV_C    HAIN_APPROX_SIMPLE, cv::Point(0,0));
   
   
   
   // colors in which marked/unmarked circle outlines will be drawn:
   cv::Scalar colorMarked(0,255,0);
   cv::Scalar colorUnMarked(0,0,255);
   
   // each outer contour is assumed to be a circle
   // TODO: you could first find the mean radius of all assumed circles and try to find outlier (dirt etc in the image)
   for(unsigned int i=0; i<contours.size(); ++i)
   {
           cv::Point2f center;
           float radius;
           // find minimum circle enclosing the contour
           cv::minEnclosingCircle(contours[i],center,radius);
   
           bool marked = false;
   
       cv::Rect circleROI(center.x-radius, center.y-radius, center.x+radius, center.y+radius);
       //circleROI = circleROI & cv::Rect(0,0,image.cols, image.rows);
   
       // count pixel inside the circle
       float sumCirclePixel = 0;
       float sumCirclePixelMarked = 0;
       for(int j=circleROI.y; j<circleROI.y+circleROI.height; ++j)
           for(int i=circleROI.x; i<circleROI.x+circleROI.width; ++i)
           {
               cv::Point2f current(i,j);
               // test if pixel really inside the circle:
           if(cv::norm(current-center) < radius)
               {
                   // count total number of pixel in the circle
                   sumCirclePixel = sumCirclePixel+1.0f;
                   // and count all pixel in the circle which hold the segmentation threshold
                   if(thresholded.at<unsigned char>(j,i))
                       sumCirclePixelMarked = sumCirclePixelMarked + 1.0f;
               }
           }
   
       const float ratioThreshold = 0.5f;
       if(sumCirclePixel)
           if(sumCirclePixelMarked/sumCirclePixel > ratioThreshold) marked = true;
   
       // draw the circle for demonstration
       if(marked)
           cv::circle(colorImage,center,radius,colorMarked,1);
       else
           cv::circle(colorImage,center,radius,colorUnMarked,1);
       }
   
   
   cv::imshow("thres", thresholded);
   cv::imshow("colorImage", colorImage);
   cv::imwrite("countFilledCircles_output.png", colorImage);
   
   cv::waitKey(-1);
   }
   

这样我得到了这些结果：

经过Otsu阈值处理后：

最终图像：