Opencv二进制项检测

Question

我正在研究一个程序，以找到我制造的零件中卡住的碎片。到目前为止，我已经能够取一个干净的部分和一个有芯片的部分，然后减去这两个图像，留下两者之间的任何差异作为二值图像。我不明白的是如何在二值图像中检测这个项目。到目前为止，我使用的是SimpleBlobDetector函数，但是为了使它正常工作，我不得不对图像进行模糊处理，我担心它不能处理较小的碎片。我希望能够在没有大量模糊的情况下检测到原始图像。任何帮助都将不胜感激。代码和图片如下。在

import cv2
import numpy as np

#Load Images
tempImg = cv2.imread('images/partchip.jpg')
lineImg = cv2.imread('images/partnochip.jpg')

#Crop Images
cropTemp = tempImg[460:589, 647:875]
cropLine = lineImg[460:589, 647:875]

#Gray Scale
grayTemp = cv2.cvtColor(cropTemp,cv2.COLOR_BGR2GRAY)
grayLine = cv2.cvtColor(cropLine,cv2.COLOR_BGR2GRAY)

#Subtract Images
holder = cv2.absdiff(grayTemp,grayLine)

#THreshold Subtracted Image
th, imgDiff = cv2.threshold(holder, 160, 255, cv2.THRESH_BINARY_INV)

#Blur Image
#blur = imgDiff
blur = cv2.blur(imgDiff,(20,20))

#Detect Blobs
detector = cv2.SimpleBlobDetector_create()
blob = detector.detect(blur)


imgkeypoints = cv2.drawKeypoints(blur, blob, np.array([]), (0,255,0),  cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
originalWithPoints=cv2.drawKeypoints(cropTemp, blob, np.array([]), (0,255,0),  cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

cv2.namedWindow("Template", cv2.WINDOW_NORMAL)
cv2.namedWindow("Line", cv2.WINDOW_NORMAL)
cv2.namedWindow("Difference", cv2.WINDOW_NORMAL)

cv2.resizeWindow("Template", 500, 300)
cv2.resizeWindow("Line", 500, 300)
cv2.resizeWindow("Difference", 500, 300)


cv2.imshow('Template',originalWithPoints)
cv2.imshow('Line',cropLine)
cv2.imshow('Difference',imgkeypoints)


cv2.waitKey(0)
cv2.destroyAllWindows()

Part with chipPart with No Chip

Answer 1

我用你的代码找到了异常。我得到了imgDiff二值图像上面积最大的轮廓。用它我可以用一个长方形把它绑起来。在

我希望这就是你想要的。。。。在

编辑：

我已经解释了程序以及以下代码：

注意：使用cv2.bitwise_not(imgDiff)反转你的imgDiff，因为如果对象是白色的，就会发现轮廓。在

# -Finding the contours present in 'imgDiff' -
_, contours,hierarchy = cv2.findContours(imgDiff,2,1)

ff = 0   #  to determine which contour to select -
area = 0   #  to determine the maximum area -
for i in range(len(contours)):
    if(cv2.contourArea(contours[i]) > area):
        area = cv2.contourArea(contours[i])
        ff = i

# -Bounding the contour having largest area -
x,y,w,h = cv2.boundingRect(contours[ff])
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cv2.imshow('fin.jpg',img)