In [9]:

    

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

img = mpimg.imread('stopsign.jpg')
plt.imshow(img)


    

    
Out[9]:





<matplotlib.image.AxesImage at 0x10cb13ac8>






    

In [11]:

    

gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
plt.imshow(gray, cmap='gray')


    

    
Out[11]:





<matplotlib.image.AxesImage at 0x10cbafb00>






    

In [14]:

    

sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
abs_sobelx = np.absolute(sobelx)
scaled_sobel = np.uint8(255 * abs_sobelx / np.max(abs_sobelx))


    

In [25]:

    

thresh_min = 20
thresh_max = 100
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
plt.imshow(sxbinary, cmap='gray')


    

    
Out[25]:





<matplotlib.image.AxesImage at 0x11f7f29e8>






    

In [31]:

    

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
    # 1) Convert to grayscale
    # 2) Take the derivative in x or y given orient = 'x' or 'y'
    # 3) Take the absolute value of the derivative or gradient
    # 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
    # 5) Create a mask of 1's where the scaled gradient magnitude 
            # is > thresh_min and < thresh_max
    # 6) Return this mask as your binary_output image
    grayscale = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    dx, dy = (1, 0) if orient == 'x' else (0, 1)
    sobel = cv2.Sobel(grayscale, cv2.CV_64F, dx, dy)
    abs_sobel = np.absolute(sobel)
   ` scaled_sobel = np.uint8(255 * abs_sobel / np.max(abs_sobel))
    sxbinary = np.zeros_like(sobel)
    sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
    return sxbinary
    
# 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.)


    

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