import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle


# Read in an image and grayscale it
image = mpimg.imread('signs_vehicles_xygrad.png')

# Define a function that applies Sobel x or y, 
# then takes an absolute value and applies a threshold.
# Note: calling your function with orient='x', thresh_min=5, thresh_max=100
# should produce output like the example image shown above this quiz.
def abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):

    # Apply the following steps to img
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    if orient == 'x':
        sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
    else:
        sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
    abs_sobel = np.absolute(sobel)
    scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
    sobel_binary = np.zeros_like(scaled_sobel)
    sobel_binary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
    return sobel_binary

# Run the function
grad_binary = abs_sobel_thresh(image, orient='x', thresh_min=20, thresh_max=100)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(grad_binary, cmap='gray')
ax2.set_title('Thresholded Gradient', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)