CODEWITHSUNDEEP
pythonopencvhough-transform
BigFoot
BigFoot

Reputation: 23

How to find circle faster than by Hough Transform in python opencv

I’m trying to make Hough Transform find a circle faster or find another function that can do it faster.
(i do not need to stick to open cv, but it needs to be opensource)
I need to get centerpoint and radius.

My use case:
I have a square picture in grayscale, 4 aruco markers in the corners(detected earlier),
and a black circle approximately in the middle.
Rest of the picture is quite uniformly white-isch/gray.
Picture size is 1600x1600.
I know the approximate center position and radius of the circle

I use:

cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 1, 100, 100, 30, 200,250)

This takes about 35-40ms. I would love to get it down to about 15ms

I tried reducing resolution by half, but id does not give me very big benefit and makes result a bit "jittery".

Image i try to recognize: Image i try to recognize

Upvotes: 2

Views: 1546

Answers (1)

Mark Setchell
Mark Setchell

Reputation: 208003

I didn't have much success with the method I suggested in the comments so I tried a different approach:

#!/usr/bin/env python3

import cv2

# Load image in greyscale
im = cv2.imread('J65Xt.jpg', cv2.IMREAD_GRAYSCALE)

# Define region of interest to exclude corner markers and reduce processing time
ROI = im[400:-400,400:-400]

# Threshold and invert
_, thr = cv2.threshold(ROI, 80,255,type=cv2.THRESH_BINARY_INV)

# Find contours
contours, _ = cv2.findContours(thr, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Get centroid from moments - may not really need this - could get from bounding box
M = cv2.moments(contours[0])
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
print(f'Centroid: cX={cX}, cY={cY} do not forget 400 offset of ROI')
# Mark centre in black
thr[cY-3:cY+3, cX-3:cX+3] = 0

# Get bounding box and draw it on
x, y, w, h = cv2.boundingRect(contours[0])
print(f'x={x}, y={y}, w={w}, h={h}')
print(f'cX={x+w/2}, cY={y+h/2}')
cv2.rectangle(thr, (x, y), (x + w, y + h), 255, 1)
cv2.imwrite('result.png', thr)

enter image description here

Output

Centroid: cX=432, cY=394 do not forget 400 offset of ROI
cX=432.5, cY=395.0

It takes 127 microseconds on my Mac if you exclude loading the input image and saving the output image.

Upvotes: 1

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