Python - T.Hough círculos

import cv2

import matplotlib.pyplot as plt

import matplotlib.image as mpimg

import numpy as np

from skimage import data,color

from skimage.transform import hough_circle, hough_circle_peaks

from skimage.feature import canny

from skimage.draw import circle_perimeter

from skimage.util import img_as_ubyte

image = mpimg.imread('Bodega.jpg')

img = np.copy(image)

img2 = np.copy(image)

gray     = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

blurred  = cv2.GaussianBlur(gray, (5,5), 20)

circles  = cv2.HoughCircles(blurred, cv2.HOUGH_GRADIENT, 2, 200, param1=100, param2=90, minRadius=20, maxRadius=150)

for i in circles[0,:]:

    cv2.circle(img, (i[0], i[1]), i[2], (0,255,0), 2)

    cv2.circle(img, (i[0],i[1]), 3, (255,0,0), 5);

plt.figure(figsize=(12,12))

plt.imshow(img);

plt.title('CV2');

#con skimage

bordes = canny(blurred,sigma=3,low_threshold=50,high_threshold=90)

hough_radii =np.arange(50,150,1)

hough_res = hough_circle(bordes,hough_radii)

accums,cx,cy,radii = hough_circle_peaks(hough_res,hough_radii,total_num_peaks=8)

for center_y, center_x, radius in zip(cy, cx, radii):

    circy, circx = circle_perimeter(center_y, center_x, radius,

                                    shape=img2.shape)

    img2[circy, circx] = (0, 255, 0)

plt.figure(figsize=(12,12))

plt.imshow(img2);

plt.title('SKIMAGE');