Self-Driving Car Engineer Nanodegree

Project: Finding Lane Lines on the Road

In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really just a series of images). Check out the video clip "raw-lines-example.mp4" (also contained in this repository) to see what the output should look like after using the helper functions below.

Once you have a result that looks roughly like "raw-lines-example.mp4", you'll need to get creative and try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video "P1_example.mp4". Ultimately, you would like to draw just one line for the left side of the lane, and one for the right.

In addition to implementing code, there is a brief writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing both the code in the Ipython notebook and the writeup template will cover all of the rubric points for this project.

Let's have a look at our first image called 'test_images/solidWhiteRight.jpg'. Run the 2 cells below (hit Shift-Enter or the "play" button above) to display the image.

Note: If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the "Kernel" menu above and selecting "Restart & Clear Output".

The tools you have are color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection and Hough Tranform line detection. You are also free to explore and try other techniques that were not presented in the lesson. Your goal is piece together a pipeline to detect the line segments in the image, then average/extrapolate them and draw them onto the image for display (as below). Once you have a working pipeline, try it out on the video stream below.

Your output should look something like this (above) after detecting line segments using the helper functions below

Your goal is to connect/average/extrapolate line segments to get output like this

Run the cell below to import some packages. If you get an import error for a package you've already installed, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, see this forum post for more troubleshooting tips.

Import Packages

In [1]:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline

Read in an Image

In [2]:
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')

#printing out some stats and plotting
print('This image is:', type(image), 'with dimesions:', image.shape)
plt.imshow(image)  # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')

This image is: <class 'numpy.ndarray'> with dimesions: (540, 960, 3)
<matplotlib.image.AxesImage at 0x7f9747c6d668>

Ideas for Lane Detection Pipeline

Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:

cv2.inRange() for color selection
cv2.fillPoly() for regions selection
cv2.line() to draw lines on an image given endpoints
cv2.addWeighted() to coadd / overlay two images cv2.cvtColor() to grayscale or change color cv2.imwrite() to output images to file
cv2.bitwise_and() to apply a mask to an image

Check out the OpenCV documentation to learn about these and discover even more awesome functionality!

Helper Functions

Below are some helper functions to help get you started. They should look familiar from the lesson!

In [90]:
import math

def grayscale(img):
    """Applies the Grayscale transform
    This will return an image with only one color channel
    but NOTE: to see the returned image as grayscale
    (assuming your grayscaled image is called 'gray')
    you should call plt.imshow(gray, cmap='gray')"""
    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Or use BGR2GRAY if you read an image with cv2.imread()
    # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
def intensity_threshold(img, gray_threshold):
    threshold = img[:,:] < gray_threshold
    img[threshold] = 0
    return img

def mask_color_hsv(image_hsv, image, lower, upper): 
    mask = cv2.inRange(image_hsv, lower, upper)
    image_masked = cv2.bitwise_and(image, image, mask = mask)
    return image_masked
def canny(img, low_threshold, high_threshold):
    """Applies the Canny transform"""
    return cv2.Canny(img, low_threshold, high_threshold)

def gaussian_blur(img, kernel_size):
    """Applies a Gaussian Noise kernel"""
    return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)

def region_of_interest(img, vertices):
    Applies an image mask.
    Only keeps the region of the image defined by the polygon
    formed from `vertices`. The rest of the image is set to black.
    #defining a blank mask to start with
    mask = np.zeros_like(img)   
    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image
    if len(img.shape) > 2:
        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image
        ignore_mask_color = (255,) * channel_count
        ignore_mask_color = 255
    #filling pixels inside the polygon defined by "vertices" with the fill color    
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    #returning the image only where mask pixels are nonzero
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

def draw_lines(img, lines, color=[255, 0, 0], thickness=4):
    NOTE: this is the function you might want to use as a starting point once you want to 
    average/extrapolate the line segments you detect to map out the full
    extent of the lane (going from the result shown in raw-lines-example.mp4
    to that shown in P1_example.mp4).  
    Think about things like separating line segments by their 
    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
    line vs. the right line.  Then, you can average the position of each of 
    the lines and extrapolate to the top and bottom of the lane.
    This function draws `lines` with `color` and `thickness`.    
    Lines are drawn on the image inplace (mutates the image).
    If you want to make the lines semi-transparent, think about combining
    this function with the weighted_img() function below
    for line in lines:
        for x1,y1,x2,y2 in line:
            cv2.line(img, (x1, y1), (x2, y2), color, thickness)
def draw_lines_fit(img, lines, color=[255, 0, 0], thickness=12):
    xl = []
    yl = []
    xr = []
    yr = []
    img_width = img.shape[1]
    img_mid = img.shape[1]/2
    for line in lines:
        for x1, y1, x2, y2 in line:
            if (x1 < img_mid):
                xl.extend([x1, x2])
                yl.extend([y1, y2])
                xr.extend([x1, x2])
                yr.extend([y1, y2])
    if (xl and yl):
        f_l = np.poly1d(np.polyfit(xl, yl, 1))
        cv2.line(img, (0, int(f_l(0))), (int(img_mid-64), int(f_l(img_mid-64))), color, thickness)
    if (xr and yr):
        f_r = np.poly1d(np.polyfit(xr, yr, 1))
        cv2.line(img, (int(img_mid+64), int(f_r(img_mid+64))), (int(img_width), int(f_r(img_width))), color, thickness)
def filter_lines(lines):
    bad_lines = []
    filtered_lines = []

    for line in lines:
        for x1,y1,x2,y2 in line:
            slope = float((y1-y2)/(x2-x1))
            if ((slope >= np.tan(np.pi/4)) or (slope <= np.tan(3*np.pi/4))):
    return filtered_lines

def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
    `img` should be the output of a Canny transform.
    Returns an array of hough lines.
    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
    return lines

# Python 3 has support for cool math symbols.

def weighted_img(img, initial_img, α=0.7, β=1., λ=0.):
    `img` is the output of the hough_lines(), An image with lines drawn on it.
    Should be a blank image (all black) with lines drawn on it.
    `initial_img` should be the image before any processing.
    The result image is computed as follows:
    initial_img * α + img * β + λ
    NOTE: initial_img and img must be the same shape!
    return cv2.addWeighted(initial_img, α, img, β, λ)

Test Images

Build your pipeline to work on the images in the directory "test_images"
You should make sure your pipeline works well on these images before you try the videos.

In [4]:
import os
image_names = os.listdir("test_images/")

Build a Lane Finding Pipeline

Build the pipeline and run your solution on all test_images. Make copies into the test_images directory, and you can use the images in your writeup report.

Try tuning the various parameters, especially the low and high Canny thresholds as well as the Hough lines parameters.

In [94]:
# TODO: Build your pipeline that will draw lane lines on the test_images
# then save them to the test_images directory.

def process_image (image, show_output = True,
                   mask_lower_white = np.array([0,0,223]),
                   mask_upper_white = np.array([255, 32, 255]),
                   mask_lower_yellow = np.array([20, 100, 100]),
                   mask_upper_yellow = np.array([30, 255, 255]),
                   gaussian_kernel_size = 5,
                   gaussian_iterations = 5,
                   canny_low_threshold = 50,
                   canny_high_threshold = 150,
                   hough_rho = 4,
                   hough_theta = np.pi/180,
                   hough_threshold = 10,
                   hough_min_line_length = 8,
                   hough_max_line_gap = 16):
    if (show_output):
    # Convert to HSV for white/yellow masking
    image_hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
    # Mask white
    image_white = mask_color_hsv(image_hsv, image, mask_lower_white, mask_upper_white)
    if (show_output):
    # Mask yellow
    image_yellow = mask_color_hsv(image_hsv, image, mask_lower_yellow, mask_upper_yellow)
    if (show_output):
    # Combine both masks
    gray = grayscale(image_white + image_yellow)
    if (show_output):
        plt.imshow(gray, cmap='gray')
    # Histogram Equalization
    gray = cv2.equalizeHist(gray)
    if (show_output):
        plt.imshow(gray, cmap='gray')

    # Apply Gaussian smoothing
    blur_gray = gaussian_blur(gray, gaussian_kernel_size)
    if (show_output):
        plt.imshow(blur_gray,  cmap='gray')

    # Apply Canny edge dectector
    edges = canny(blur_gray, canny_low_threshold, canny_high_threshold)
    if (show_output):
        plt.imshow(edges, cmap='gray')

    # Mask Canny edges using a polygonal ROI
    imshape = image.shape
    vertices = np.array([[(0,imshape[0]),(imshape[1]/2 - 64, imshape[0]/2 + 64), (imshape[1]/2 + 64, imshape[0]/2 +64), (imshape[1],imshape[0])]], dtype=np.int32)
    masked_edges = region_of_interest(edges, vertices)
    if (show_output):
        masked_edges_roi = image
        cv2.line(masked_edges_roi, tuple(vertices[0][0]), tuple(vertices[0][1]), [0, 0, 255], 8)
        cv2.line(masked_edges_roi, tuple(vertices[0][1]), tuple(vertices[0][2]), [0, 0, 255], 8)
        cv2.line(masked_edges_roi, tuple(vertices[0][2]), tuple(vertices[0][3]), [0, 0, 255], 8)
        cv2.line(masked_edges_roi, tuple(vertices[0][3]), tuple(vertices[0][0]), [0, 0, 255], 8)
        plt.imshow(masked_edges, cmap='gray')

    # Apply Hough transform to detect lines
    lines = hough_lines(masked_edges, hough_rho, hough_theta, hough_threshold, hough_min_line_length, hough_max_line_gap)
    # Prune outliers
    filtered_lines = filter_lines(lines)
    line_img = np.zeros((image.shape[0], image.shape[1], 3), dtype=np.uint8)
    draw_lines(line_img, lines, [0, 255, 0])
    draw_lines(line_img, filtered_lines)
    if (show_output):
    # Fit lines
    fitted_lines_img = np.zeros((image.shape[0], image.shape[1], 3), dtype=np.uint8)
    draw_lines_fit(fitted_lines_img, filtered_lines)

    # Combine input with fitted lines for annotation
    result = weighted_img(image, fitted_lines_img)
    if (show_output):
    return result

for image_name in image_names:
    image = mpimg.imread('test_images/' + image_name)
    res_img = process_image(image)