本文介绍了Python OpenCV Hough Circles返回无的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!

问题描述

在尝试将Hough Circles整合到我要编写的跟踪程序的主要代码中之前,我试图弄清楚Hough Circles,但是除了None之外,我似乎一无所获.我将孟加拉语标志用作我的图像,因为它很简单并且很容易检测到.这是我的代码:

I'm trying to figure out Hough Circles before I incorporate it into my main code for a tracking program I'm trying to write, but I can't seem to get anything but None out from the circles. I'm using the Bengali flag as my image, since it's simple and will be easy to detect. Here's my code:

import numpy as np
import cv2


img = cv2.imread('Capture.PNG')

grayput = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

circles = cv2.HoughCircles(grayput, cv2.cv.CV_HOUGH_GRADIENT, 1, 20, param1 =50, param2 =10, minRadius=10, maxRadius=40)
print (circles)

    # need circles 
if circles is not None:
    # convert the coord. 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
        cv2.circle(img, (x, y), r, (0, 0, 0), 4)


cv2.imwrite("image.PNG",img)

推荐答案

以下代码将为您提供非无个圈子:

The following code will give you non-None circles:

import numpy as np
import cv2

img = cv2.imread("../images/opencv_logo.png", 0)
img = cv2.medianBlur(img,5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
cv2.imshow("grayscale", cimg)
cv2.waitKey(0)

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

实际上,输出为:

[[[  45.5         133.5          16.50757408]
  [  97.5          45.5          16.80773544]
  [ 147.5         133.5          16.32482719]]]


注意:该代码段使用以下内容作为其输入图像:


Note: the snippet uses the following as its input image:

这篇关于Python OpenCV Hough Circles返回无的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!

09-18 00:13