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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Self-Driving Car Engineer Nanodegree
Project
Step1:  Read in an Image
Step9:  Ideas for Lane Detection Pipeline
Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are
Step10:  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.
Step11:  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.
Step12:  Test on Videos
You know what's cooler than drawing lanes over images? Drawing lanes over video!
We can test our solution on two provided videos
Step13:  Let's try the one with the solid white lane on the right first ...
Step15:  Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice.
Step17:  Improve the draw_lines() function
At this point, if you were successful with making the pipeline and tuning parameters, you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)?  Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform. As mentioned previously, 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".
Go back and modify your draw_lines function accordingly and try re-running your pipeline. The new output should draw a single, solid line over the left lane line and a single, solid line over the right lane line. The lines should start from the bottom of the image and extend out to the top of the region of interest.
Now for the one with the solid yellow lane on the left. This one's more tricky!
Step19:  Writeup and Submission
If you're satisfied with your video outputs, it's time to make the report writeup in a pdf or markdown file. Once you have this Ipython notebook ready along with the writeup, it's time to submit for review! Here is a link to the writeup template file.
Optional Challenge
Try your lane finding pipeline on the video below.  Does it still work?  Can you figure out a way to make it more robust?  If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!
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Python Code:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
Explanation: 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.
<figure>
 <img src="line-segments-example.jpg" width="380" alt="Combined Image" />
 <figcaption>
 <p></p> 
 <p style="text-align: center;"> Your output should look something like this (above) after detecting line segments using the helper functions below </p> 
 </figcaption>
</figure>
<p></p>
<figure>
 <img src="laneLines_thirdPass.jpg" width="380" alt="Combined Image" />
 <figcaption>
 <p></p> 
 <p style="text-align: center;"> Your goal is to connect/average/extrapolate line segments to get output like this</p> 
 </figcaption>
</figure>
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
End of explanation
#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')
Explanation: Read in an Image
End of explanation
import math
from scipy import stats
prev_l_x1 = None
prev_l_x2 = None
prev_r_x1 = None
prev_r_x2 = None
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 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
    else:
        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=10):
    
    1. Separate left and right lanes based on slope
    2. Find best fit line through linear regression
    3. Adjust the best fit line based on line from the previous frame
    4. Draw the lines
    
    global prev_l_x1, prev_l_x2, prev_r_x1, prev_r_x2
    # fix Y1 and Y2 for cv2.line
    Y1 = int(img.shape[0] / 2 + 90)                
    Y2 = int(img.shape[0])
        
    if lines is not None:
        l_x = []
        l_y = []
        r_x = []
        r_y = []
        for line in lines:
            for x1, y1, x2, y2 in line:
                s = get_slope( x1, y1, x2, y2)
                if s < 0 and s > -math.inf:
                    # left lane has negative slope
                    l_x += [x1, x2]
                    l_y += [y1, y2]
                elif s > 0 and s < math.inf:
                    # right lane has positive slope
                    r_x += [x1, x2]
                    r_y += [y1, y2]
        
        # get left lane params
        l_x1, l_x2 = get_bestfit_x1_x2(l_x, l_y, Y1, Y2)
        # get right lane params
        r_x1, r_x2 = get_bestfit_x1_x2(r_x, r_y, Y1, Y2)
    else:
        l_x1, l_x2, r_x1, r_x2 = prev_l_x1, prev_l_x2, prev_r_x1, prev_r_x2
        
    # adjust endpoints based on the previous frame
    if all([prev_l_x1, prev_l_x2, prev_r_x1, prev_r_x2]):
        l_x1 = calculate_new_position(prev_l_x1, l_x1)
        l_x2 = calculate_new_position(prev_l_x2, l_x2)
        r_x1 = calculate_new_position(prev_r_x1, r_x1)
        r_x2 = calculate_new_position(prev_r_x2, r_x2)
    draw_line(img, l_x1, Y1, l_x2, Y2) # draw left lane
    draw_line(img, r_x1, Y1, r_x2, Y2) # draw right lane
    
    # update global variable - in prep for the next frame
    prev_l_x1 = l_x1
    prev_l_x2 = l_x2
    prev_r_x1 = r_x1
    prev_r_x2 = r_x2
        
def get_bestfit_x1_x2(x, y, y1, y2):
    # find best fit line
    line = stats.linregress(x, y)
    m = line.slope
    y0 = line.intercept
    x1 = int((y1-y0)/m)
    x2 = int((y2-y0)/m)
    return (x1, x2)
def calculate_new_position(prev_p, p):
    weighted_p = prev_p
    # avoid unusually large deviations
    if math.fabs(prev_p - p) < 100:
        weighted_p = int(prev_p * 0.75 + p * 0.25)
    return weighted_p
def draw_line(img, x1, y1, x2, y2, color=[255, 0, 0], thickness=10):
    # only draw line if all four endpoints are not None
    if all([x1, y1, x2, y2]):
        cv2.line(img, (x1, y1), (x2, y2), color, thickness)
def get_slope(x1, y1, x2, y2):
    return (y2-y1)/(x2-x1) if x2 != x1 else math.inf
def reset_global_parameters():
    global prev_l_x1, prev_l_x2, prev_r_x1, prev_r_x2
    # reset the global parameters from each image
    prev_l_x1 = None
    prev_l_x2 = None
    prev_r_x1 = None
    prev_r_x2 = None
        
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
    
    `img` should be the output of a Canny transform.
        
    Returns an image with hough lines drawn.
    
    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
    draw_lines(line_img, lines, )
    return line_img
# Python 3 has support for cool math symbols.
def weighted_img(img, initial_img, α=0.8, 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)
Explanation: 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!
End of explanation
import os
os.listdir("test_images/")
Explanation: 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.
End of explanation
# parameters
test_image_dir = 'test_images/'
kernel_size = 3 # kernel size for Gaussian smoothing
low_threshold = 50 # low threshold for Canny
high_threshold = 150 # high threshold for Canny
rho = 1 # distance resolution in pixels of the Hough grid
theta = np.pi/180 # angular resolution in radians of the Hough grid
threshold = 35     # minimum number of votes (intersections in Hough grid cell)
min_line_length = 5 #minimum number of pixels making up a line
max_line_gap = 2    # maximum gap in pixels between connectable line segments
def find_lane(img):
    # grayscale the image
    img_gray = grayscale(img)
    # apply Gaussian smoothing
    img_blur = gaussian_blur(img_gray, kernel_size)
    # apply Canny edge detection 
    img_canny = canny(np.uint8(img_blur), low_threshold, high_threshold)
    # define a trapezoid region of interest  
    h = img.shape[0]
    w = img.shape[1]
    vertices = np.array([[(40, h), (w/2-10, h/2+40), (w/2+10, h/2+40), (w-40, h)]], dtype=np.int32)
    # create masked edges
    masked_edges = region_of_interest(img_canny, vertices)
    # apply Hough transform
    lines = hough_lines(masked_edges, rho, theta, threshold, min_line_length, max_line_gap)
    
    return weighted_img(lines, img)
def run_test():
    for i in os.listdir(test_image_dir):
        reset_global_parameters()
        img = mpimg.imread("%s%s" % (test_image_dir, i))
        plt.imshow(find_lane(img))
        plt.show()
run_test()
Explanation: 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.
End of explanation
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
def process_image(image):
    # NOTE: The output you return should be a color image (3 channel) for processing video below
    # TODO: put your pipeline here,
    # you should return the final output (image where lines are drawn on lanes)
    return find_lane(image)
Explanation: Test on Videos
You know what's cooler than drawing lanes over images? Drawing lanes over video!
We can test our solution on two provided videos:
solidWhiteRight.mp4
solidYellowLeft.mp4
Note: if you get an import error when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel).  Still have problems?  Try relaunching Jupyter Notebook from the terminal prompt. Also, check out this forum post for more troubleshooting tips.
If you get an error that looks like this:
NeedDownloadError: Need ffmpeg exe. 
You can download it by calling: 
imageio.plugins.ffmpeg.download()
Follow the instructions in the error message and check out this forum post for more troubleshooting tips across operating systems.
End of explanation
white_output = 'white.mp4'
reset_global_parameters()
clip1 = VideoFileClip("solidWhiteRight.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
Explanation: Let's try the one with the solid white lane on the right first ...
End of explanation
HTML(
<video width="960" height="540" controls>
  <source src="{0}">
</video>
.format(white_output))
Explanation: Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice.
End of explanation
yellow_output = 'yellow.mp4'
reset_global_parameters()
clip2 = VideoFileClip('solidYellowLeft.mp4')
yellow_clip = clip2.fl_image(process_image)
%time yellow_clip.write_videofile(yellow_output, audio=False)
HTML(
<video width="960" height="540" controls>
  <source src="{0}">
</video>
.format(yellow_output))
Explanation: Improve the draw_lines() function
At this point, if you were successful with making the pipeline and tuning parameters, you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)?  Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform. As mentioned previously, 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".
Go back and modify your draw_lines function accordingly and try re-running your pipeline. The new output should draw a single, solid line over the left lane line and a single, solid line over the right lane line. The lines should start from the bottom of the image and extend out to the top of the region of interest.
Now for the one with the solid yellow lane on the left. This one's more tricky!
End of explanation
challenge_output = 'extra.mp4'
clip2 = VideoFileClip('challenge.mp4')
challenge_clip = clip2.fl_image(process_image)
%time challenge_clip.write_videofile(challenge_output, audio=False)
HTML(
<video width="960" height="540" controls>
  <source src="{0}">
</video>
.format(challenge_output))
Explanation: Writeup and Submission
If you're satisfied with your video outputs, it's time to make the report writeup in a pdf or markdown file. Once you have this Ipython notebook ready along with the writeup, it's time to submit for review! Here is a link to the writeup template file.
Optional Challenge
Try your lane finding pipeline on the video below.  Does it still work?  Can you figure out a way to make it more robust?  If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!
End of explanation
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