Halcon轉OpenCV執行個體--複雜背景下缺陷檢測(附源碼)

導讀

本文主要介紹一個複雜背景下缺陷檢測的執行個體，并将Halcon實作轉為OpenCV。

執行個體來源

執行個體來源于51Halcon論壇的讨論貼：

​​https://www.51halcon.com/forum.php?mod=viewthread&tid=1173&extra=page%3D1​​

Halcon實作

參考回帖内容，将代碼精簡如下:

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)      

如上圖所示，可以較好的定位缺陷位置。

OpenCV實作

分析實作方法與思路:

[1] 原圖轉灰階圖後使用核大小201做中值濾波；

[2] 灰階圖與濾波圖像做差，然後門檻值處理

[3] 圓形核做開運算，去除雜訊

[4] 圓形核做閉運算，缺陷連接配接

[5] 輪廓查找繪制

實作代碼(Python-OpenCV)：

import cv2
import 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 - mean
cv2.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_high
cv2.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!')      

逐漸效果示範

濾波效果：mean

做差效果：diff

門檻值效果：thres

開運算效果：opening

閉運算效果：closing

輪廓查找繪制最終結果：

結尾語

[1] 算法隻是針對這一張圖檔，實際應用為驗證算法魯棒性還需大量圖檔做測試方可；

[2] 缺陷檢測如果用傳統方法不易實作，可以考慮使用深度學習分割網絡如：mask-rcnn、U-net

​