检测图像中呈灰色的缺陷

发布于 2025-01-20 20:58:28 字数 1183 浏览 2 评论 0原文

我需要检测颜色偏灰色的缺陷。我尝试从图像中去除噪声，对图像进行阈值处理，但由于缺陷的灰色，它变得不可见或仅保留边界。我不知道如何检测缺陷，如果我应用扩张，它会与周围的圆圈混合。有缺陷的图像：

无缺陷的图像：

我用红笔标记缺陷的原始图像是：

我已尝试以下代码：

//convert to grayscale
    Mat gray_img;
    cv::cvtColor(r_img, gray_img, COLOR_BGR2GRAY);

    
    Mat dst1, dst2, dst;
    cv::blur(gray_img, dst1, Size(3, 3));
    cv::blur(gray_img, dst2, Size(7, 7)); 
    cv::subtract(dst1, dst2, dst);

    //thresholding
    Mat thresh;
    adaptiveThreshold(dst, thresh, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 5, 5);

    Mat inv_thresh;
    bitwise_not(thresh, inv_thresh);

我的阈值图像是

原文

I need to detect the defects which are more grayish in color.
I have tried removing noise from the image, thresholding the image but due to gray color of the defect, it becomes invisible or only the boundary remains. I don't know how to detect the defects , if I apply dilation it gets mixed with the surrounding circles.
Images with defects:

Images without defect:

My original image with the red pen marked defect is :

I have tried the below code:

//convert to grayscale
    Mat gray_img;
    cv::cvtColor(r_img, gray_img, COLOR_BGR2GRAY);

    
    Mat dst1, dst2, dst;
    cv::blur(gray_img, dst1, Size(3, 3));
    cv::blur(gray_img, dst2, Size(7, 7)); 
    cv::subtract(dst1, dst2, dst);

    //thresholding
    Mat thresh;
    adaptiveThreshold(dst, thresh, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 5, 5);

    Mat inv_thresh;
    bitwise_not(thresh, inv_thresh);

My thresholded image is

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