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# Do all the relevant imports
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2

# Read in the image and convert to grayscale
# Note: in the previous example we were reading a .jpg 
# Here we read a .png and convert to 0,255 bytescale
image = (mpimg.imread('exit_ramp.png')*255).astype('uint8')
# mpimg.imsave("EXIT_RAMP_CHECK",image)
gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
cv2.imshow('img',gray)
mpimg.imsave("EXIT_RAMP_CHECK",gray)
# Define a kernel size for Gaussian smoothing / blurring
kernel_size = 5 # Must be an odd number (3, 5, 7...)
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)

# Define our parameters for Canny and run it
low_threshold = 50
high_threshold = 150
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

# Display the image
plt.imshow(edges, cmap='Greys_r')

mpimg.imsave("exit_ramp_edge", edges)


    

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