Halcon转OpenCV实例--复杂背景下缺陷检测(附源码)
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2021-07-04 01:23
实例来源
Halcon实现
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)
OpenCV实现
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!')
逐步效果演示
闭运算效果:closing
轮廓查找绘制最终结果:
结尾语
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