复杂背景的缺陷提取 |
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复杂背景的缺陷提取
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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 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。
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)
实现方法与思路: 原图转灰度图后使用核大小201（奇数）做中值滤波； 灰度图与滤波图像做差,阈值处理 形态学进一步提取缺陷 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); vectorcontours; findContours(src_close, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE, Point()); for (int i = 0; i < contours.size(); i++) { float area = contourArea(contours[i]); cout |
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