PCB印刷缺陷检测（自定义结构元素）

Posted 2021-06-06 唯有自己强大

摘要

本篇来用OpenCV实现Halcon中一个简单的PCB印刷缺陷检测实例。 Halcon中对应的例子为pcb_inspection.hdev。并自定义一个正八边形结构元素进行开运算，闭运算，然后做差将缺陷标记显示。

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原图如下：

Halcon代码比较简单，这里也贴出来，短短13行：

read_image (Image, \'pcb\')
dev_close_window ()
get_image_size (Image, Width, Height)
dev_open_window (0, 0, Width, Height, \'black\', WindowHandle)
dev_display (Image)
* detect defects ...
gray_opening_shape (Image, ImageOpening, 7, 7, \'octagon\')
gray_closing_shape (Image, ImageClosing, 7, 7, \'octagon\')
dyn_threshold (ImageOpening, ImageClosing, RegionDynThresh, 75, \'not_equal\')
dev_display (Image)
dev_set_color (\'red\')
dev_set_draw (\'margin\')
dev_display (RegionDynThresh)

opencv实现：

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 （一）自定义正八边形结构元素

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    Mat gray,src_open,src_close,dst;
    Mat src = imread("D:/opencv练习图片/pcb缺陷检测.png");
    imshow("原图", src);
    cvtColor(src, gray, COLOR_RGB2GRAY);
    Mat kernel = Mat::ones(Size(7, 7), CV_8UC1);
    kernel.at<uchar>(0, 0) = 0;
    kernel.at<uchar>(0, 1) = 0;
    kernel.at<uchar>(0, 5) = 0;
    kernel.at<uchar>(0, 6) = 0;
    kernel.at<uchar>(1, 0) = 0;
    kernel.at<uchar>(1, 6) = 0;
    kernel.at<uchar>(5, 0) = 0;
    kernel.at<uchar>(5, 6) = 0;
    kernel.at<uchar>(6, 0) = 0;
    kernel.at<uchar>(6, 1) = 0;
    kernel.at<uchar>(6, 5) = 0;
    kernel.at<uchar>(6, 6) = 0;
    cout << kernel << endl;

 这里对矩阵的分别赋值，其实有一个填充函数fillPloy（）（只需输入顶点坐标即可）

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 （二）对图像开运算，闭运算，做差

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    morphologyEx(gray, src_open, MORPH_OPEN, kernel);
    imshow("开运算", src_open);
    morphologyEx(gray, src_close, MORPH_CLOSE, kernel);
    imshow("闭运算", src_close);
    absdiff(src_open, src_close, dst);
    imshow("做差", dst);

 开运算：                                                                                                                                   

 闭运算：

 

 二者做差：

 可以看到，白色的点就是缺陷的位置。

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 （三）二值化，寻找轮廓，显示

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    threshold(dst, dst, 80, 255, THRESH_BINARY);
    vector<vector<Point>>contours;
    findContours(dst, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE, Point());
    drawContours(src, contours, -1, Scalar(0, 0, 255), 2, 8);
    imshow("显示缺陷", src);

 

 参考于：OpenCV与AI深度学习