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导读本文主要介绍一个复杂背景下缺陷检测的实例，并将Halcon实现转为OpenCV。实例来源
实例来源于51Halcon论坛的讨论贴：https://www.51halcon.com/forum.php?mod=viewthread&tid=1173&extra=page%3D1
Halcon实现
参考回帖内容，将代码精简如下:read_image (Image, './1.bmp')dev_set_line_width (3)threshold (Image, Region, 30, 255)reduce_domain (Image, Region, ImageReduced)mean_image (ImageReduced, ImageMean, 200, 200)dyn_threshold (ImageReduced, ImageMean, SmallRaw, 35, 'dark')opening_circle (SmallRaw, RegionOpening, 8)closing_circle (RegionOpening, RegionClosing, 10)connection (RegionClosing, ConnectedRegions)dev_set_color ('red')dev_display (Image)dev_set_draw ('margin')dev_display (ConnectedRegions)
如上图所示，可以较好的定位缺陷位置。
OpenCV实现
分析实现方法与思路:[1] 原图转灰度图后使用核大小201做中值滤波；[2] 灰度图与滤波图像做差，然后阈值处理
[3] 圆形核做开运算，去除杂讯
[4] 圆形核做闭运算，缺陷连接[5] 轮廓查找绘制实现代码(Python-OpenCV)：import cv2import numpy as np
img = cv2.imread('./1.bmp')cv2.imshow('src',img)gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
mean = cv2.medianBlur(gray,201)cv2.imshow('mean',mean)
#diff = cv2.absdiff(gray, mean)diff = gray - meancv2.imshow('diff',diff)cv2.imwrite('diff.jpg',diff)_,thres_low = cv2.threshold(diff,150,255,cv2.THRESH_BINARY)#二值化_,thres_high = cv2.threshold(diff,220,255,cv2.THRESH_BINARY)#二值化thres = thres_low - thres_highcv2.imshow('thres',thres)
k1 = np.zeros((18,18,1), np.uint8)cv2.circle(k1,(8,8),9,(1,1,1),-1, cv2.LINE_AA)k2 = np.zeros((20,20,1), np.uint8)cv2.circle(k2,(10,10),10,(1,1,1),-1, cv2.LINE_AA)opening = cv2.morphologyEx(thres, cv2.MORPH_OPEN, k1)cv2.imshow('opening',opening)closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, k2)cv2.imshow('closing',closing)
contours,hierarchy = cv2.findContours(closing, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for cnt in contours: (x, y, w, h) = cv2.boundingRect(cnt) if w > 5 and h > 5: #cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2) cv2.drawContours(img,contours,-1,(0,0,255),2)
cv2.drawContours(img,cnt,2,(0,0,255),2)cv2.imshow('result',img)
cv2.waitKey(0)cv2.destroyAllWindows()print('Done!')
逐步效果演示
滤波效果：mean
做差效果：diff
阈值效果：thres
开运算效果：opening
闭运算效果：closing
轮廓查找绘制最终结果：
结尾语
[1] 算法只是针对这一张图片，实际应用为验证算法鲁棒性还需大量图片做测试方可；[2] 缺陷检测如果用传统方法不易实现，可以考虑使用深度学习分割网络如：mask-rcnn、U-net等完整代码与素材将发布在知识星球中，有兴趣可加入获取。相关文章连接：Halcon缺陷检测实例转OpenCV实现(一)
Halcon缺陷检测实例转OpenCV实现(二)
Halcon缺陷检测实例转OpenCV实现(三)
Halcon缺陷检测实例转OpenCV实现(四)
Halcon缺陷检测实例转OpenCV实现(五) 混合颜色药片缺陷检测
Halcon缺陷检测实例转OpenCV实现(六) 瓶口缺陷检测—THE END—
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