Detect ellipses, ovals in images with OpenCV python[SOLVED]

asked 2019-11-27 23:05:05 -0600

updated 2019-12-03 13:15:47 -0600

943 ●9 ●6

Hello everyone, I am using opencv to detect shapes that look like circles, my script reads a png image (center_cut.png) and perfectly detects the circles with their centroids, however when I pass another image where the circles are no longer perfect does not recognize them (left_cut). I would like to know if there is any way to detect shapes similar to circles, could someone please guide me? My code is as follows:

import cv2
import numpy as np

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

circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1, 50,
                             param1=80,param2=20,minRadius=3,maxRadius=25)


circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    # Dibuja la circusnferencia del círculo
    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
    # dibuja el centro del círculo
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)

cv2.imshow('círculos detectados',cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

image description

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answered 2019-11-28 00:05:54 -0600

Tetragramm

7376 ●13 ●37

updated 2019-12-03 13:16:16 -0600

supra56

943 ●9 ●6

Well, if these are representative images, you are probably better off doing something besides HoughCircles.

I suggest doing an adaptive threshold to segment the image into white and black, inverting the image, then performing connected components. If you use connectedComponentWithStats, it will return the centroid of every component (which would be the circles)

If you're worried about the edges, you'll have to remove any component that touches the edge of the image, but that shouldn't be hard.

t2 = cv2.imread('Test2.png')
thresh = cv2.cvtColor(t2, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(thresh, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 101, 0)
count, labels, stats, centroids = cv2.connectedComponentsWithStats(thresh)
for i in range(1,count):
    t2 = cv2.circle(t2, (int(centroids[i,0]), int(centroids[i,1])), 5, (0, 255, 0, 0), 5)

cv2.imshow('circles', thresh)
cv2.imshow('centers', t2)
cv2.waitKey()

image description