灰度图像导致 HoughCircles() 方法不准确
grayscale image causing inaccuracies in HoughCircles() method
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，但那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删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,0,255,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, 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,y1,w,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), (0,0,255),3)
cv.putText(output, "rectangle "+str(x1)+" , " +str(y1-5), (x1, y1-5), cv.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 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,y,r)
cv.circle(output, (x, y), r, (0, 0, 0), 3)
cv.circle(output, (x, y), 2, (0, 255, 255), 3)
print(n)
cv.imshow("th",th)
cv.imshow("gray",gray)
cv.imshow('output',output)
cv.waitKey(0)
cv.destroyAllWindows()
最终结果是这样的：
现在矩形检测成功了，但是圆形检测不正确。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，尽管未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）：
如何使用此图像的 HoughCircles() 方法？
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，并且它适用于灰度图像足够好的图像：
这是在 Python/OpenCV 中提取圆的一种方法。转换为LAB，分离B通道，然后在黑圈上做阈值。
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, (255,255,255), 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：
加法
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, 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 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:', center, 'radius:', radius)
x = int(x)
y = int(y)
cv2.circle(result, center, radius, (255, 255, 255), 2)
# save output
cv2.imwrite('4_sign_circle2.jpg', result)
# display results
cv2.imshow('thresh',thresh)
cv2.imshow('morph',morph)
cv2.imshow('result',result)
cv2.waitKey(0)
结果：
文字信息：
center: (410.5, 686.5) radius: 54.4
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，但那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删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,0,255,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, 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,y1,w,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), (0,0,255),3)
cv.putText(output, "rectangle "+str(x1)+" , " +str(y1-5), (x1, y1-5), cv.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 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,y,r)
cv.circle(output, (x, y), r, (0, 0, 0), 3)
cv.circle(output, (x, y), 2, (0, 255, 255), 3)
print(n)
cv.imshow("th",th)
cv.imshow("gray",gray)
cv.imshow('output',output)
cv.waitKey(0)
cv.destroyAllWindows()
最终结果是这样的：
现在矩形检测成功了，但是圆形检测不正确。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，尽管未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）：
如何使用此图像的 HoughCircles() 方法？
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，并且它适用于灰度图像足够好的图像：
这是在 Python/OpenCV 中提取圆的一种方法。转换为LAB，分离B通道，然后在黑圈上做阈值。
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, (255,255,255), 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：
加法
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, 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 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:', center, 'radius:', radius)
x = int(x)
y = int(y)
cv2.circle(result, center, radius, (255, 255, 255), 2)
# save output
cv2.imwrite('4_sign_circle2.jpg', result)
# display results
cv2.imshow('thresh',thresh)
cv2.imshow('morph',morph)
cv2.imshow('result',result)
cv2.waitKey(0)
结果：
文字信息：
center: (410.5, 686.5) radius: 54.4