问题描述
这是 openCV polygon detection 的后续。对于第二张图片，我没有检测到任何矩形，但这是因为我的阈值对于该图片不正确。
我使用了 otsu 阈值，并添加了一个约束来移除检测到的小矩形和其他不相关的矩形。
import numpy as np
import cv2 as cv
import math
img = cv.imread("t1.jpeg")
n=0
#rectangle parameters
width=0
height=0
start_x=0
start_y=0
end_x=0
end_y=0
#houghcircles parameters
minr=0
maxr=0
mind=0
maxarea=0
area=0
output = img.copy()
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
ret2,th = cv.threshold(gray,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
#rectangle detection
contours,_ = cv.findContours(th,cv.RETR_TREE,cv.CHAIN_APPROX_NONE)
for contour in contours:
approx = cv.approxpolyDP(contour,0.01* cv.arcLength(contour,True),True)
cv.drawContours(img,[approx],(0,0),5)
x = approx.ravel()[0]
y = approx.ravel()[1]
x1,y1,w,h = cv.boundingRect(approx)
a=w*h
if len(approx) == 4 and x>15 :
aspectRatio = float(w)/h
if aspectRatio >= 2.5 and a>50:
print(x1,h)
width=w
height=h
start_x=x1
start_y=y1
end_x=start_x+width
end_y=start_y+height
cv.rectangle(output,(start_x,start_y),(end_x,end_y),255),3)
cv.putText(output,"rectangle "+str(x1)+"," +str(y1-5),(x1,y1-5),cv.FONT_HERShey_COMPLEX,0.5,0))
minr=int(17*width/192)
maxr=int(7*width/64)
mind=int(width//5)
print("start",start_x,start_y)
print("width",width)
print("height",height)
print("minr",minr)
print("maxr",maxr)
print("mind",mind)
cv.imshow("op1",output)
#circle detection,converting binary to decimal.
circles = cv.HoughCircles(gray,cv.HOUGH_GRADIENT,1,mind,param1=50,param2=20,minRadius=minr,maxRadius=maxr)
detected_circles = np.uint16(np.around(circles))
for (x,y,r) in detected_circles[0,:]:
if(y>start_y and x>start_x and y<start_y+height and x<start_x+width):
cf= ((x-start_x)*8)/width
fp= cf-math.floor(cf)
if(fp>0.50):
idx=math.ceil(cf)
else:
idx=math.floor(cf)
exp=int(4- (0.5* (idx+1)))
n+= 2**exp
print("circle",x,r)
cv.circle(output,(x,y),r,3)
cv.circle(output,2,3)
print(n)
cv.imshow("th",th)
cv.imshow("gray",gray)
cv.imshow('output',output)
cv.waitKey(0)
cv.destroyAllWindows()
最后的结果是这样的：
现在已成功检测到矩形，但未正确检测到圆。检测到的圆与目标圆的大小大致相同，这意味着 HoughCircles() 的参数是正确的，尽管没有在正确的位置检测到圆。
这可能是因为眩光导致圆圈在灰度图像中几乎消失（我在 HoughCircles() 方法中使用）：
如何使用 HoughCircles() 方法处理此图像？
编辑：修复了代码中的一个小错误。问题仍然存在，但是我用其他图像进行了测试，并且它与灰度图像足够好的图像一起使用：
解决方法
这是在 Python/OpenCV 中提取圆的一种方法。转换为LAB，分离B通道，然后在黑眼圈上阈值。
输入：
import cv2
import numpy as np
# load images
img = cv2.imread('4_sign.jpg')
# convert to LAB
lab = cv2.cvtColor(img,cv2.COLOR_BGR2LAB)
# separate B channel
b = lab[:,:,2]
# threshold and invert
thresh = cv2.threshold(b,105,255,cv2.THRESH_BINARY)[1]
thresh = 255 - thresh
# apply morphology to clean it up
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,7))
morph = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11))
morph = cv2.morphologyEx(morph,cv2.MORPH_CLOSE,kernel)
# get min enclosing circle
# numpy points are (y,x),so need to transpose
points = np.argwhere(morph.transpose()>0)
center,radius = cv2.minEnclosingCircle(points)
print('center:',center,'radius:',radius)
# draw circle on copy of input
result = img.copy()
cx = int(round(center[0]))
cy = int(round(center[1]))
rr = int(round(radius))
cv2.circle(result,(cx,cy),rr,(255,255),2)
# save output
cv2.imwrite('4_sign_circle.jpg',result)
# display results
cv2.imshow('thresh',thresh)
cv2.imshow('morph',morph)
cv2.imshow('result',result)
cv2.waitKey(0)
cv2.destroyAllWindows()
结果：
添加
这是一个使用 HoughCircles 的版本。
import cv2
import numpy as np
# load images
img = cv2.imread('4_sign.jpg')
ht,wd = img.shape[:2]
minhw = min(ht,wd)
# convert to LAB
lab = cv2.cvtColor(img,kernel)
# get circle from HoughCircles
min_dist = int(minhw/20)
circles = cv2.HoughCircles(morph,cv2.HOUGH_GRADIENT,1,minDist=min_dist,param1=100,param2=10,minRadius=30,maxRadius=60)
#print(circles)
result = img.copy()
for circle in circles[0]:
# draw the circle on copy of input
(x,y,r) = circle
center = (x,y)
radius = r
print('center:',radius)
x = int(x)
y = int(y)
cv2.circle(result,radius,2)
# save output
cv2.imwrite('4_sign_circle2.jpg',result)
cv2.waitKey(0)
结果：
文本信息：
center: (410.5,686.5) radius: 54.4