我正在尝试运行用于检测齿轮缺陷的代码，无论齿轮中的螺栓是否存在，但面临同样的困难。
我正在运行的代码面临一个问题，即SSIM值对于好齿轮和坏齿轮都不正确。
此外，如果可能的话，请告诉我它如何检测到否。齿轮的齿数。
请找到代码
import cv2
import numpy as np
from skimage.metrics import structural_similarity as compare_ssim
import argparse
import imutils
original = cv2.imread("Pictures2/train/Corrected/60.jpg")
duplicate = cv2.imread("Pictures/train/Defected/3.jpg")
original=cv2.resize(original,(1000,640))
duplicate=cv2.resize(duplicate,(1000,640))
# convert the images to grayscale
grayA = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
grayB = cv2.cvtColor(duplicate, cv2.COLOR_BGR2GRAY)
_, threshold1= cv2.threshold( grayA,80,255,cv2.THRESH_BINARY_INV)
_, threshold2= cv2.threshold( grayB,80,255,cv2.THRESH_BINARY_INV)
# compute the Structural Similarity Index (SSIM) between the two
# images, ensuring that the difference image is returned
(score, diff) = compare_ssim(threshold1, threshold2, full=True)
diff = (diff * 255).astype("uint8")
print("SSIM: {}".format(score))
# threshold the difference image, followed by finding contours to
# obtain the regions of the two input images that differ
thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# loop over the contours
#checking similarities using ORB Algorithm
orb= cv2.ORB_create()
kp_1, desc_1= orb.detectAndCompute(threshold1,None)
kp_2, desc_2= orb.detectAndCompute(threshold2,None)
matcher= cv2.BFMatcher(cv2.NORM_HAMMING)
matches=matcher.knnMatch(desc_1,desc_2,k=2)
good=[]
for m,n in matches:
if m.distance<0.7*n.distance:
good.append([m])
final_image=cv2.drawMatchesKnn(threshold1,kp_1,threshold2,kp_2,good,None)
final_image=cv2.resize(final_image,(1000,640))
print(len(matches))
cv2.imshow("matches",final_image)
if score>0.20:
print("ok")
else:
print("not ok")
#cv2.imshow("Original", original)
#cv2.imshow("Duplicate", duplicate)
#cv2.imshow("Difference", difference)
cv2.waitKey(0)
cv2.destroyAllWindows()
i/p 图片：
已更正
缺陷
输出的ss
我想检测齿轮的缺失螺栓（即圆孔）。并使用以下代码：
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import cv2
import numpy as np
def find_circles(img, min=95, max=100):
equ = cv2.equalizeHist(img)
k_smooth = 9 # 9
gauss = cv2.GaussianBlur(equ, (k_smooth, k_smooth), 0) # equ
ret, otsu = cv2.threshold(gauss, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
blur = cv2.medianBlur(otsu, 21)
circles = cv2.HoughCircles(blur, cv2.HOUGH_GRADIENT, 1, 100,
param1=200, param2=15, minRadius=min, maxRadius=max)
return circles
def draw(img, circles, counter):
res = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if circles is not None:
idx = 1
for i in circles[0, :]:
# draw the outer circle
cv2.circle(res, (i[0], i[1]), i[2], (0, 0, 255), 3)
# draw the center of the circle
cv2.circle(res, (i[0], i[1]), 2, (255, 0, 0), 15)
res = cv2.putText(res, str(idx), (i[0] + 100, i[1] + 100), cv2.FONT_HERSHEY_COMPLEX, 4, (255, 255, 255), 3,
cv2.LINE_AA)
idx += 1
cv2.namedWindow("There are {} holes without bolts.\n".format(counter), cv2.WINDOW_NORMAL)
cv2.imshow("There are {} holes without bolts.\n".format(counter), res)
cv2.waitKey(0)
cv2.destroyAllWindows()
empty = cv2.imread("Pictures/train/Defected/3.jpg", 0)
full = cv2.imread("Pictures2/train/Defected/60.jpg", 0)
img = full
circles = find_circles(img)
if circles is not None:
circles = np.uint16(np.around(circles))
counter = len(circles[0, :])
else:
counter = 0
draw(img, circles, counter)
但对于不同的馈送图像，我没有得到所需的输出。
请找到相同的ss。
请找到不同方向的输入图像
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