Self Driving Car Algorithm

My attempt to intuitively explain how this self driving car algorithm works

Lane line Detection

In particular, I’ve copied the primary function, which is called “draw_lane_lines”. Basically, a function is a block of code that takes some input (in this case a photo), manipulates the input in some way, and then outputs the manipulation (in this case the lane lines).

This primary function uses some other helper functions defined elsewhere in the code, but these helper functions are mostly just slightly cleaner ways of consuming the pre-made functions from the libraries like openCV

Draw lane line function code goes here:

def draw_lane_lines(image):
imshape = image.shape

# Greyscale image
greyscaled_image = grayscale(image)

# Gaussian Blur
blurred_grey_image = gaussian_blur(greyscaled_image, 5)

# Canny edge detection
edges_image = canny(blurred_grey_image, 50, 150)

# Mask edges image
border = 0
vertices = np.array([[(0,imshape[0]),(465, 320), (475, 320), 
(imshape[1],imshape[0])]], dtype=np.int32)
edges_image_with_mask = region_of_interest(edges_image, 
vertices)

# Hough lines
rho = 2 
theta = np.pi/180 
threshold = 45    
min_line_len = 40
max_line_gap = 100 
lines_image = hough_lines(edges_image_with_mask, rho, theta,  
threshold, min_line_len, max_line_gap)

Convert Hough from single channel to RGB to prep for weighted

hough_rgb_image = cv2.cvtColor(lines_image, cv2.COLOR_GRAY2BGR)

# Combine lines image with original image
final_image = weighted_img(hough_rgb_image, image)

return final_image