找到图像中的所有圆圈

Question

我是python＆amp;的新手图像处理。我正在做一个爱好项目，我想在其中找到图像中的所有圆圈，然后找出哪一个在其中标记了交叉（'X'）。到目前为止，我已经将一些代码放在一起找到了圆圈（下图）。它适用于一个图像，但无法识别另一个图像上的所有圆圈。请指导我如何提高find_circles算法的性能。

测试图片：

结果图片：

import cv2
import cv
import numpy as np
import operator
from PIL import Image

def find_circles(img):
    im_gray = cv2.imread(img, cv2.CV_LOAD_IMAGE_GRAYSCALE)
    (thresh, im_bw) = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    img_bw = cv2.threshold(im_gray, thresh, 255, cv2.THRESH_BINARY)[1]
    cv2.imwrite('img_bw.png',img_bw)
    rows, cols =img_bw.shape
    circles = cv2.HoughCircles(img_bw,cv.CV_HOUGH_GRADIENT,1,rows/32, param1=100,param2=40,minRadius=0,maxRadius=100)
    circles = np.uint16(np.around(circles))
    return circles

def draw_circles(img, circles):
    img = cv2.imread(img,0)
    cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
    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.putText(cimg,str(i[0])+str(',')+str(i[1]), (i[0],i[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.4, 255)
    return cimg 

def main():
    img = "query_circle9.png"
    circles = find_circles(img)
    img_circle = draw_circles(img,circles)
    cv2.imwrite('cricle.png',img_circle) 

if __name__=='__main__':
    main()

Answer 1

#!/usr/bin/env python

import cv2

def draw_circles(img, circles):
    # img = cv2.imread(img,0)
    cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
    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.putText(cimg,str(i[0])+str(',')+str(i[1]), (i[0],i[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.4, 255)
    return cimg

def detect_circles(image_path):
    gray = cv2.imread(image_path, cv2.CV_LOAD_IMAGE_GRAYSCALE)
    gray_blur = cv2.medianBlur(gray, 13)  # Remove noise before laplacian
    gray_lap = cv2.Laplacian(gray_blur, cv2.CV_8UC1, ksize=5)
    dilate_lap = cv2.dilate(gray_lap, (3, 3))  # Fill in gaps from blurring. This helps to detect circles with broken edges.
    # Furture remove noise introduced by laplacian. This removes false pos in space between the two groups of circles.
    lap_blur = cv2.bilateralFilter(dilate_lap, 5, 9, 9)
    # Fix the resolution to 16. This helps it find more circles. Also, set distance between circles to 55 by measuring dist in image.
    # Minimum radius and max radius are also set by examining the image.
    circles = cv2.HoughCircles(lap_blur, cv2.cv.CV_HOUGH_GRADIENT, 16, 55, param2=450, minRadius=20, maxRadius=40)
    cimg = draw_circles(gray, circles)
    print("{} circles detected.".format(circles[0].shape[0]))
    # There are some false positives left in the regions containing the numbers.
    # They can be filtered out based on their y-coordinates if your images are aligned to a canonical axis.
    # I'll leave that to you.
    return cimg

结果：

cimg = detect_circles("circles.png")

有一些遗留的错误检测。如果图像对齐，则可以根据y坐标过滤这些误报。我会留给你的。