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import cv2

import numpy as np

import matplotlib

from matplotlib.pyplot import imshow

from matplotlib import pyplot as plt



#Get image to gray scale and process in GaussianBlur

img = cv2.imread('IMG_0940.png')

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



kernel_size = 5

blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0) #gray



#Process the edges of parking spots with Canny

low_threshold = 50

high_threshold = 150

edges = cv2.Canny(blur_gray, low_threshold, high_threshold)



#To get the lines of the parking spots use HoughLinesP

rho = 1  # distance resolution in pixels of the Hough grid

theta = np.pi / 180  # angular resolution in radians of the Hough grid

threshold = 15  # minimum number of votes (intersections in Hough grid cell)

min_line_length = 50  # minimum number of pixels making up a line

max_line_gap = 20  # maximum gap in pixels between connectable line segments

line_image = np.copy(img) * 0  # creating a blank to draw lines on



# Run Hough on edge detected image

# Output "lines" is an array containing endpoints of detected line segments

lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array(),

            min_line_length, max_line_gap)



for line in lines:

for x1,y1,x2,y2 in line:

  cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),5)



#Draw the lines on the srcImage

lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)

cv2.imwrite('IMG_0940_LINES.png',lines_edges)

result = cv2.imread('IMG_0940_LINES.png')



cv2.namedWindow('Result',cv2.WINDOW_AUTOSIZE)

cv2.imshow('Result',result)

cv2.resizeWindow('Result',1000,1200)

cv2.waitKey(0)

cv2.destroyAllWindows()             #Closes all the windows

import cv2

import numpy as np

import matplotlib

from matplotlib.pyplot import imshow

from matplotlib import pyplot as plt



#Get image to gray scale and process in GaussianBlur

img = cv2.imread('IMG_0940.png')

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



kernel_size = 5

blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0) #gray



#Process the edges of parking spots with Canny

low_threshold = 50

high_threshold = 150

edges = cv2.Canny(blur_gray, low_threshold, high_threshold)



#To get the lines of the parking spots use HoughLinesP

rho = 1  # distance resolution in pixels of the Hough grid

theta = np.pi / 180  # angular resolution in radians of the Hough grid

threshold = 15  # minimum number of votes (intersections in Hough grid cell)

min_line_length = 50  # minimum number of pixels making up a line

max_line_gap = 20  # maximum gap in pixels between connectable line segments

line_image = np.copy(img) * 0  # creating a blank to draw lines on



# Run Hough on edge detected image

# Output "lines" is an array containing endpoints of detected line segments

lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array(),

            min_line_length, max_line_gap)



for line in lines:

for x1,y1,x2,y2 in line:

  cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),5)



#Draw the lines on the srcImage

lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)

cv2.imwrite('IMG_0940_LINES.png',lines_edges)

result = cv2.imread('IMG_0940_LINES.png')



cv2.namedWindow('Result',cv2.WINDOW_AUTOSIZE)

cv2.imshow('Result',result)

cv2.resizeWindow('Result',1000,1200)

cv2.waitKey(0)

cv2.destroyAllWindows()             #Closes all the windows

import cv2

import numpy as np

import matplotlib

from matplotlib.pyplot import imshow

from matplotlib import pyplot as plt



#Get image to gray scale and process in GaussianBlur

img = cv2.imread('IMG_0940.png')

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



kernel_size = 5

blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0) #gray



#Process the edges of parking spots with Canny

low_threshold = 50

high_threshold = 150

edges = cv2.Canny(blur_gray, low_threshold, high_threshold)



#To get the lines of the parking spots use HoughLinesP

rho = 1  # distance resolution in pixels of the Hough grid

theta = np.pi / 180  # angular resolution in radians of the Hough grid

threshold = 15  # minimum number of votes (intersections in Hough grid cell)

min_line_length = 50  # minimum number of pixels making up a line

max_line_gap = 20  # maximum gap in pixels between connectable line segments

line_image = np.copy(img) * 0  # creating a blank to draw lines on



# Run Hough on edge detected image

# Output "lines" is an array containing endpoints of detected line segments

lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array(),

            min_line_length, max_line_gap)



for line in lines:

for x1,y1,x2,y2 in line:

  cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),5)



#Draw the lines on the srcImage

lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)

cv2.imwrite('IMG_0940_LINES.png',lines_edges)

result = cv2.imread('IMG_0940_LINES.png')



cv2.namedWindow('Result',cv2.WINDOW_AUTOSIZE)

cv2.imshow('Result',result)

cv2.resizeWindow('Result',1000,1200)

cv2.waitKey(0)

cv2.destroyAllWindows()             #Closes all the windows

import cv2

import numpy as np

import matplotlib

from matplotlib.pyplot import imshow

from matplotlib import pyplot as plt



#Get image to gray scale and process in GaussianBlur

img = cv2.imread('IMG_0940.png')

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



kernel_size = 5

blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0) #gray



#Process the edges of parking spots with Canny

low_threshold = 50

high_threshold = 150

edges = cv2.Canny(blur_gray, low_threshold, high_threshold)



#To get the lines of the parking spots use HoughLinesP

rho = 1  # distance resolution in pixels of the Hough grid

theta = np.pi / 180  # angular resolution in radians of the Hough grid

threshold = 15  # minimum number of votes (intersections in Hough grid cell)

min_line_length = 50  # minimum number of pixels making up a line

max_line_gap = 20  # maximum gap in pixels between connectable line segments

line_image = np.copy(img) * 0  # creating a blank to draw lines on



# Run Hough on edge detected image

# Output "lines" is an array containing endpoints of detected line segments

lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array(),

            min_line_length, max_line_gap)



for line in lines:

for x1,y1,x2,y2 in line:

  cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),5)



#Draw the lines on the srcImage

lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)

cv2.imwrite('IMG_0940_LINES.png',lines_edges)

result = cv2.imread('IMG_0940_LINES.png')



cv2.namedWindow('Result',cv2.WINDOW_AUTOSIZE)

cv2.imshow('Result',result)

cv2.resizeWindow('Result',1000,1200)

cv2.waitKey(0)

cv2.destroyAllWindows()             #Closes all the windows

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