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Find all circles in image

Tags:

python

opencv

I am new to python & image processing. I am working on a hobby project in which I want to find ALL circles in image and then figure out which one have cross ('X') marked inside it. I have put some code together to find circles so far (below). It works on one image but fails to recognize all circles on another one. Please guide me how i can improve performance of find_circles algorithm.

Test Image:

test image

Result Image:

result image

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()
like image 355
user1146904 Avatar asked Mar 11 '14 05:03

user1146904


People also ask

How do I find circles in OpenCV?

Use the OpenCV function HoughCircles() to detect circles in an image.

What algorithm is used to detect circles?

Automatic circle detection is an important element of many image processing algorithms. Traditionally the Hough transform has been used to find circular objects in images but more modern approaches that make use of heuristic optimisation techniques have been developed.

How do I find an image in a circle in Python?

cv2. HoughCircles(image, method, dp, minDist) Where Image is the image file converted to grey scale Method is the algorithm used to detct the circles. Dp is the inverse ratio of the accumulator resolution to the image resolution. minDist is the Minimum distance between the center coordinates of detected circles.


1 Answers

#!/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

The result:

cimg = detect_circles("circles.png")

Detected circles

There are some left-over false detection. If your images are aligned, then you can filter these false positives based on their y-coordinates. I'll leave that to you.

like image 178
lightalchemist Avatar answered Oct 05 '22 11:10

lightalchemist