如何计算用 cv2.HoughCircles() 检测到的圆的面积？

Question

伙计们，

我编写了一个程序，我用 cv2.HoughCircles() 识别圆圈。该代码也有效。不幸的是，我的项目需要圆圈的区域。但我不知道如何计算，我在互联网上搜索不成功。

谢谢。

Answer 1

https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_houghcircles/py_houghcircles.html

import cv2
import numpy as np

img = cv2.imread('opencv_logo.png',0)
img = cv2.medianBlur(img,5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)

circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,
                            param1=50,param2=30,minRadius=0,maxRadius=0)

circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)

cv2.imshow('detected circles',cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

i[0] 是 x 位置
i[1] 是 y 位置
i[2] 是半径

面积计算公式为pi * r²

所以每个检测到的圆圈的面积是：

for i in circles[0,:]:
  area = 3.14159 * i[2] * i[2]

Answer 2

从 HoughCircles() 可以得到一个包含圆的 x、y、r 的“圆”列表。 x, y 是中心的坐标，r 是半径。 根据半径，您可以计算圆的面积：

A = PI * r^2

Answer 3

谢谢你们，

@Tin Nguyen

如果我使用：

for i in circles[0,:]:
area = 3.14159 * i[2] * i[2]

然后我收到以下错误：“TypeError: 'NoneType' object is not subscriptable” 这是我的代码，我想实时检测圆圈并计算面积。

import pypylon.pylon as py  # wrapper to control Basler camera with python
import cv2  # openCV
import numpy as np

first_device = py.TlFactory.GetInstance().CreateFirstDevice()
icam = py.InstantCamera(first_device)
icam.Open()
# set parameters
icam.PixelFormat = "RGB8"
# if only a part of image sensor is used an offset is required or centering
'''icam.Width = 640
icam.Height = 480
icam.CenterX = False
icam.CenterY = False'''
# Demonstration of setting parameters - properties can be found on Pylon Viewer
# Auto property values are 'Off', 'Once', 'Continuous'
icam.GainAuto = 'Off'
icam.ExposureAuto = 'Continuous'
icam.BalanceWhiteAuto = 'Off'
icam.Gain = 0  # minimum gain value
# icam.ExposureTime       = 50000 # exposure time or use ExposureAuto
icam.StartGrabbing(py.GrabStrategy_LatestImages)
while True:
   res = icam.RetrieveResult(1000)  # 1000 = time constant for time-out
   frame = cv2.cvtColor(res.Array, cv2.COLOR_RGB2BGR)

   output = frame.copy()
   gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

   gray = cv2.GaussianBlur(gray,(5,5),0)
   gray = cv2.medianBlur(gray, 5)

   gray = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
                             cv2.THRESH_BINARY,11,3.5)

   kernel = np.ones((2,3),np.uint8)
   gray = cv2.erode(gray,kernel)

   gray = cv2.dilate(gray, kernel)

   # detect circles in the image
   circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 200, param1=30, param2=45, 
                       minRadius=0, maxRadius=0)
# print circles

# ensure at least some circles were found
if circles is not None:
    # convert the (x, y) coordinates and radius of the circles to integers
    circles = np.round(circles[0, :]).astype("int")

    # loop over the (x, y) coordinates and radius of the circles
    for (x, y, r) in circles:
        # draw the circle in the output image, then draw a rectangle in the image
        # corresponding to the center of the circle
        cv2.circle(output, (x, y), r, (0, 255, 0), 4)
        cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
        # time.sleep(0.5)
        print
        "Column Number: "
        print
        x
        print
        "Row Number: "
        print
        y
        print
        "Radius is: "
        print
        r

        # Display the resulting frame
        cv2.imshow('gray', gray)
cv2.imshow('frame', output)
if cv2.waitKey(1) & 0xFF == ord('q'):
    break

for i in circles[0, :]:
    area = 3.14159 * i[2] * i[2]
    print(area)

icam.StopGrabbing()
icam.Close()
cv2.destroyAllWindows()