复杂背景的缺陷提取

摘要

本篇用halcon和opencv分别实现对于复杂背景下的缺陷提取实战

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如下图，背景很复杂，周围划痕都是正常区域。要提取中间小块的黑色区域（缺陷区域）。单纯用频域滤波和阈值提取，效果一般。都会把周围的划痕提取出来。

Halcon实现

思路：

通过中值滤波后，对图像进行动态阈值提取细化缺陷部分，结合开运算，闭运算提取缺陷。

read_image (Image, 'D:/opencv练习图片/复杂背景提取缺陷.jpg')
dev_set_line_width (3)
threshold (Image, Region, 30, 255)
reduce_domain (Image, Region, ImageReduced)
mean_image (ImageReduced, ImageMean, 150, 150)
dyn_threshold (ImageReduced, ImageMean, SmallRaw, 37, 'dark')
opening_circle (SmallRaw, RegionOpening,4.5)
closing_circle (RegionOpening, RegionClosing, 7)
connection (RegionClosing, ConnectedRegions)
dev_set_color ('red')
dev_display (Image)
dev_set_draw ('margin')
dev_display (ConnectedRegions)

 Opencv实现

 实现方法与思路:

1. 原图转灰度图后使用核大小201（奇数）做中值滤波；
2. 灰度图与滤波图像做差,阈值处理
3. 形态学进一步提取缺陷
4. 轮廓查找，通过面积筛选缺陷，显示

int main(int argc, char** argv)
{
    Mat src = imread("D:/opencv练习图片/复杂背景提取缺陷.jpg");
    imshow("输入图像", src);
    Mat gray, gray_mean,dst,binary1, binary2, binary;
    cvtColor(src, gray, COLOR_BGR2GRAY);
    medianBlur(gray, gray_mean, 201);
    imshow("中值滤波", gray_mean);
    addWeighted(gray, -1, gray_mean, 1, 0, dst);
    imshow("做差", dst);
    //阈值提取
    threshold(dst, binary1, 10, 255, THRESH_BINARY|THRESH_OTSU);
    imshow("二值化", binary1);
    Mat src_open, src_close;
    //形态学
    Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(7, 7), Point(-1, -1));
    morphologyEx(binary1, src_open, MORPH_OPEN, kernel, Point(-1, -1));
    imshow("开运算", src_open);
    morphologyEx(src_open, src_close, MORPH_CLOSE, kernel, Point(-1, -1));
    imshow("闭运算", src_close);
    vector<vector<Point>>contours;
    findContours(src_close, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE, Point());
    for (int i = 0; i < contours.size(); i++)
    {
        float area = contourArea(contours[i]);
        cout << area << endl;
        if (area > 1000)
        {
            drawContours(src, contours, i, Scalar(0, 0, 255), 2, 8);
        }
    }
    imshow("结果", src);
    waitKey(0);
    return 0;
}

这里巧用了addWeighted函数进行做差，得出图像：

然后二值化，寻找轮廓，筛选得出缺陷轮廓。