scratch.txt

# 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
    # 1
    gray_image = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    #
    x, y = (1, 0) if orient == 'x' else (0, 1)
    # 2
    sobel = cv2.Sobel(gray_image, cv2.CV_64F, x, y)
    # 3
    abs_sobel = np.absolute(sobel)
    # 4
    scaled_sobel = np.uint8(255 * abs_sobel / np.max(abs_sobel))
    # 5
    min_threshold, max_threshold = 5, 100
    binary_output = np.zeros_like(scaled_sobel)
    binary_output[(scaled_sobel >= min_threshold) & (scaled_sobel <= max_threshold)] = 1

    return binary_output




Now your model should work, however, not correctly  you should add the number of the total images and their augmentationsfor example if you want your `fit_generator()` to take only the center camera images you