HoughCircles cercle de détection à l'aide d'opencv et python-

Si vous n'obtenez pas HoughCircles pour vous apporter des pixels des solutions parfaites pour des raisons évidentes de cercles alors vous n'êtes pas à l'utiliser tout

De votre faute, que vous essayez de main-d'accorder votre hyperparameters par vous-même. Qui ne va pas au travail. L'ordinateur de l'auto-régler les paramètres pour vous:

import numpy as np
import argparse
import cv2
import signal
from functools import wraps
import errno
import os
import copy
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
orig_image = np.copy(image)
output = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("gray", gray)
cv2.waitKey(0)
circles = None
minimum_circle_size = 100      #this is the range of possible circle in pixels you want to find
maximum_circle_size = 150     #maximum possible circle size you're willing to find in pixels
guess_dp = 1.0
number_of_circles_expected = 1          #we expect to find just one circle
breakout = False
max_guess_accumulator_array_threshold = 100     #minimum of 1, no maximum, (max 300?) the quantity of votes 
#needed to qualify for a circle to be found.
circleLog = []
guess_accumulator_array_threshold = max_guess_accumulator_array_threshold
while guess_accumulator_array_threshold > 1 and breakout == False:
#start out with smallest resolution possible, to find the most precise circle, then creep bigger if none found
guess_dp = 1.0
print("resetting guess_dp:" + str(guess_dp))
while guess_dp < 9 and breakout == False:
guess_radius = maximum_circle_size
print("setting guess_radius: " + str(guess_radius))
print(circles is None)
while True:
#HoughCircles algorithm isn't strong enough to stand on its own if you don't
#know EXACTLY what radius the circle in the image is, (accurate to within 3 pixels) 
#If you don't know radius, you need lots of guess and check and lots of post-processing 
#verification.  Luckily HoughCircles is pretty quick so we can brute force.
print("guessing radius: " + str(guess_radius) + 
" and dp: " + str(guess_dp) + " vote threshold: " + 
str(guess_accumulator_array_threshold))
circles = cv2.HoughCircles(gray, 
cv2.cv.CV_HOUGH_GRADIENT, 
dp=guess_dp,               #resolution of accumulator array.
minDist=100,                #number of pixels center of circles should be from each other, hardcode
param1=50,
param2=guess_accumulator_array_threshold,
minRadius=(guess_radius-3),    #HoughCircles will look for circles at minimum this size
maxRadius=(guess_radius+3)     #HoughCircles will look for circles at maximum this size
)
if circles is not None:
if len(circles[0]) == number_of_circles_expected:
print("len of circles: " + str(len(circles)))
circleLog.append(copy.copy(circles))
print("k1")
break
circles = None
guess_radius -= 5 
if guess_radius < 40:
break;
guess_dp += 1.5
guess_accumulator_array_threshold -= 2
#Return the circleLog with the highest accumulator threshold
# ensure at least some circles were found
for cir in circleLog:
# convert the (x, y) coordinates and radius of the circles to integers
output = np.copy(orig_image)
if (len(cir) > 1):
print("FAIL before")
exit()
print(cir[0, :])
cir = np.round(cir[0, :]).astype("int")
for (x, y, r) in cir:
cv2.circle(output, (x, y), r, (0, 0, 255), 2)
cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
cv2.imshow("output", np.hstack([orig_image, output]))
cv2.waitKey(0)

Le code ci-dessus convertit ce:

:

Pour plus d'informations sur ce que c'est fait, voir: https://stackoverflow.com/a/46500223/445131

OriginalL'auteur Eric Leschinski