Python - T.Hough rectas

import cv2

import numpy as np

import matplotlib.pyplot as plt

import skimage

from skimage import exposure, data, img_as_float, io

from skimage.transform import hough_line,hough_line_peaks

from skimage.feature import canny

from skimage import data

from skimage.draw import line

from matplotlib import cm

from skimage.transform import probabilistic_hough_line

imagen = cv2.imread ('Puerta_Alcala.jpg')

img = np.copy(imagen)

img2 = np.copy(imagen)

img3 = np.copy(imagen)

img4 = np.copy(imagen)

img5 = np.copy (imagen)

img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

gauss = cv2.GaussianBlur(img_gray,(11,11),0)

bordes =cv2.Canny(gauss,50,220,apertureSize = 3)

plt.imshow (bordes)

lines = cv2.HoughLines(bordes,1,np.pi/180,200)

print(lines[0])

img2 = img.copy()

for row in lines:

    for rho,theta in row:

        a = np.cos(theta)

        b = np.sin (theta)

        x0 =a*rho

        y0=b*rho

        x1 = int(x0 + 5000*(-b))

        y1 = int(y0 + 5000*(a))

        x2 = int(x0 - 5000*(-b))

        y2 = int(y0 - 5000*(a))

        cv2.line(img2,(x1,y1),(x2,y2),(0,0,255),2)

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

#cv2.imwrite('houghlines.jpg',img)

#houghl=io.imread('houghlines.jpg')

plt.imshow(img2[:,:,::-1]);

plt.title('Rectas');

#Con skimage

tested_angles = np.linspace(-np.pi / 2, np.pi / 2, 360)

h, theta2, d = hough_line(bordes, theta=tested_angles)

# Busqueda de líenas mas votadas

hspace, angles, dists = hough_line_peaks(h, theta2, d, threshold=0, num_peaks=20)

# REpresento las líneas

lines_ski = np.column_stack((dists,angles))

# Añadimos una dimension para que coicida esta matriz con la que empleamos al respresentar líeas en OpenCV

lines_ski = np.expand_dims(lines_ski, axis=1)

for row in lines_ski:

    for d,theta3 in row:

        a = np.cos(theta3)

        b = np.sin (theta3)

        x0 =a*d

        y0=b*d

        x1 = int(x0 + 5000*(-b))

        y1 = int(y0 + 5000*(a))

        x2 = int(x0 - 5000*(-b))

        y2 = int(y0 - 5000*(a))

        cv2.line(img3,(x1,y1),(x2,y2),(0,0,255),2)

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

plt.imshow(img3[:,:,::-1]);

#Probabilística OpenCv

min_acumulator_votes=20

minLineLength =20

maxLineGap = 5

linesP = cv2.HoughLinesP(bordes, 1, np.pi / 180, min_acumulator_votes, minLineLength, maxLineGap)

B,G,R = cv2.split(img4)

img4= cv2.merge((R,G,B))

if linesP is not None:

        for i in range(0, len(linesP)):

            l = linesP[i][0]

            cv2.line(img4, (l[0], l[1]), (l[2], l[3]), (255,0,0), 1)

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

plt.imshow(img4);

#Probabilística Skimage

lines = probabilistic_hough_line(bordes, threshold=10, line_length=5,line_gap=3)

if lines is not None:

        for i in range(0, len(lines)):

            st = lines[i][0]

            en = lines[i][1]

            cv2.line(img5, (st[0], st[1]), (en[0], en[1]), (255,0,0), 1)

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

plt.imshow(img5[:,:,::-1]);