我目前正在检测正确检测图像波纹(预处理)中的圆圈的问题,输出可能是零星的,因为它会正确地显示圆圈(后处理)。图像是通过800 * 600分辨率的网络摄像头进行实时拍摄,然后通过双边过滤器,这有助于摆脱一些假阴性(我尝试过GaussianBlur,但有时会非常慢......)。 p>
之后它会变为灰色,然后通过HoughCircles函数提供所提供的输出。
我已经查看了轮廓函数,但是我没有找到很好的文档来弄清楚每个变量的含义,如果这是有道理的(至少对于python函数)。
我将非常感谢所有帮助以使其更加准确,因为最终目标是获取已知尺寸的孔并转换它以查看圆圈之间的距离是否关闭以进行质量控制测试。 (并检查是否没有移除圆圈,即不存在)。
CODE:
import cv2
import os
import math
import numpy
minRad = 50
maxRad = 75
b1 = 2
b2 = 5
b3 = 5
c1 = 5
c2 = 200
c3 = 50
c4 = 100
bw = 1
vc =cv2.VideoCapture(0)
if vc.isOpened():
vc.set(3,800)
vc.set(4,600)
# vc.set(10,10)
rval, frame = vc.read()
else:
rval = False
while rval:
rval, frame = vc.read()
blur = cv2.bilateralFilter(frame,b1,b2,b3)
# blur = cv2.GaussianBlur(frame,(5,5),1)
gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)#frame
# edges = cv2.Canny(gray, 200, 20, apertureSize=3)#80 120 3
edges = gray
circles = cv2.HoughCircles(edges,cv2.cv.CV_HOUGH_GRADIENT,c1,c2,param1=c3,param2=c4,minRadius=minRad,maxRadius=maxRad)
print "\n\n"
print circles
if circles != None:
circles = numpy.uint16(numpy.around(circles),decimals=1)
for cir in circles[0,:]:
if bw == 1:
cv2.circle(edges,(cir[0],cir[1]),cir[2],(0,255,0),2)#frame
cv2.circle(edges,(cir[0],cir[1]),2,(0,0,255),)#frame
else:
#draw outer circle
cv2.circle(blur,(cir[0],cir[1]),cir[2],(0,255,0),2)#frame
#draw center
cv2.circle(blur,(cir[0],cir[1]),2,(0,0,255),)#frame
if bw == 1:
cv2.imwrite('/home/kasper/test/test.jpg', edges, [int(cv2.IMWRITE_JPEG_QUALITY), 90])
else:
cv2.imwrite('/home/kasper/test/test.jpg', blur, [int(cv2.IMWRITE_JPEG_QUALITY), 90])
ch = cv2.waitKey(10)
if ch != -1:
print "keypressed"
print ch
break
cv2.destroyAllWindows()
圆检测输出:
[[[ 652.5 507.5 62.45398331]
[ 282.5 522.5 57.36288071]
[ 102.5 342.5 52.84410858]
[ 462.5 327.5 67.7089386 ]
[ 697.5 242.5 52.52142334]
[ 82.5 547.5 52.50238037]
[ 307.5 167.5 63.04363632]
[ 92.5 137.5 67.79749298]]]
[[[ 287.5 522.5 52.616539 ]
[ 647.5 507.5 57.50217438]
[ 472.5 337.5 67.7089386 ]
[ 87.5 512.5 67.78273773]
[ 82.5 292.5 67.64983368]
[ 687.5 212.5 52.5594902 ]
[ 302.5 162.5 67.88593292]]]
答案 0 :(得分:0)
对我而言,输出的格式为(x,y,radius),其中(x,y)是每个圆圈的中心。
答案 1 :(得分:0)
您可以使用以下代码检测漏洞:
import numpy as np
import cv2
from matplotlib import pyplot as plt
plt.ion()
filteredContour = []
img = cv2.imread('circle.png')
grayImage = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
binaryImage = np.uint8((grayImage < 100) *1)
binaryForContour = binaryImage*1
contour,hierarchy=cv2.findContours(binaryForContour,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
for iteration in range (0,len(contour)):
areaOfContour = cv2.contourArea(contour[iteration])
if areaOfContour >= 5000:
filteredContour.append(contour[iteration])
cv2.drawContours(img,filteredContour, -1, (0,255,0), 2)
plt.imshow(img)
图像不清晰。如果照明适当,它将起作用。