Advanced Lane Finding Project

The goals / steps of this project are the following:

• Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
• Apply a distortion correction to raw images.
• Use color transforms, gradients, etc., to create a thresholded binary image.
• Apply a perspective transform to rectify binary image ("birds-eye view").
• Detect lane pixels and fit to find the lane boundary.
• Determine the curvature of the lane and vehicle position with respect to center.
• Warp the detected lane boundaries back onto the original image.
• Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

Rubric Points

Here I will consider the rubric points individually and describe how I addressed each point in my implementation.

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Writeup / README

1. Provide a Writeup / README that includes all the rubric points and how you addressed each one. You can submit your writeup as markdown or pdf. Here is a template writeup for this project you can use as a guide and a starting point.

You're reading it!

Camera Calibration

1. Briefly state how you computed the camera matrix and distortion coefficients. Provide an example of a distortion corrected calibration image.

The code for this step is contained in lines 7 through 42 of the file called helpers.py, which included all the functions.

I start by preparing "object points", which will be the (x, y, z) coordinates of the chessboard corners in the world. Here I am assuming the chessboard is fixed on the (x, y) plane at z=0, such that the object points are the same for each calibration image. Thus, objp is just a replicated array of coordinates, and objpoints will be appended with a copy of it every time I successfully detect all chessboard corners in a test image. imgpoints will be appended with the (x, y) pixel position of each of the corners in the image plane with each successful chessboard detection.

I then used the output objpoints and imgpoints to compute the camera calibration and distortion coefficients using the cv2.calibrateCamera() function. I applied this distortion correction to the test image using the cv2.undistort() function and obtained this result:

Pipeline (single images)

1. Provide an example of a distortion-corrected image.

To demonstrate this step, I will describe how I apply the distortion correction to one of the test images like this one:

def cal_undistort(image,cal_file='camera_cal/dist_pickle.p'):
    # Use cv2.calibrateCamera() and cv2.undistort()
    dist_pickle = pickle.load( open( cal_file, "rb" ) )
    mtx = dist_pickle["mtx"]
    dist = dist_pickle["dist"]
    undist = cv2.undistort(image,mtx,dist,None,mtx)  
    return undist

2. Describe how (and identify where in your code) you used color transforms, gradients or other methods to create a thresholded binary image. Provide an example of a binary image result.

I used a combination of color and gradient thresholds to generate a binary image (thresholding functions/steps at lines 54 through 137 in helpers.py). Here's an example of my output for this step.

3. Describe how (and identify where in your code) you performed a perspective transform and provide an example of a transformed image.

The code for my perspective transform includes a function called corners_unwarp(), which appears in lines 139 through 149 in the file helpers.py. The corners_unwarp() function takes as inputs an image (img), as well as source (src) and destination (dst) points. I chose the hardcode the source and destination points in the following manner:

src=np.float32([[200,715],[1150,715],[620,450],[725,450]]) 
dst=np.float32([[280,715], [950,715],[280,0], [950,0]])

This resulted in the following source and destination points:

Source	Destination
200, 715	280, 715
1150, 715	950, 715
620, 450	280, 0
725, 450	950, 0

I verified that my perspective transform onto a test image and its warped counterpart to verify that the lines appear parallel in the warped image, shown as below:

4. Describe how (and identify where in your code) you identified lane-line pixels and fit their positions with a polynomial?

I did this in lines 151 through 225 in my code in helpers.py.First I take a histogram of the bottom half of the image, find the starting point for the left and right lines, then implement Sliding Windows to find pixels for left and right lane, then fit a second order polynomial for left and right lane line. The outcome shown as below:

5. Describe how (and identify where in your code) you calculated the radius of curvature of the lane and the position of the vehicle with respect to center.

I did this in lines 268 through 286 in my code in helpers.py. First converting image x and y values to real world space, then fit new polynomials to x,y in world space, calculate the new radii of curvature.

def find_curvature(binary_warped):
    ploty, left_fitx, right_fitx=find_lanelines(binary_warped)
    # Define conversions in x and y from pixels space to meters
    ym_per_pix = 30/720 # meters per pixel in y dimension
    xm_per_pix = 3.7/700 # meters per pixel in x dimension

    y_eval = np.max(ploty)
    # Fit new polynomials to x,y in world space
    left_fit_cr = np.polyfit(ploty*ym_per_pix, left_fitx*xm_per_pix, 2)
    right_fit_cr = np.polyfit(ploty*ym_per_pix, right_fitx*xm_per_pix, 2)
    # Calculate the new radii of curvature
    left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
    right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
    # Now our radius of curvature is in meters
    img_middle = (left_fitx[-1] + right_fitx[-1])//2
    veh_pos = binary_warped.shape[1]//2
    dx = (veh_pos - img_middle)*xm_per_pix # Positive if on right, Negative on left
    return left_curverad, right_curverad, dx

6. Provide an example image of your result plotted back down onto the road such that the lane area is identified clearly.

I implemented this step in lines 288 through 306 in my code in helpers.py in the function vis(). Here is an example of my result on a test image:

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Pipeline (video)

1. Provide a link to your final video output. Your pipeline should perform reasonably well on the entire project video (wobbly lines are ok but no catastrophic failures that would cause the car to drive off the road!).

This same video can also be found at: /test_videos_output/project_video_out.mp4

Here's a [link to my video result].(./test_videos_output/project_video.mp4)

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Discussion

1. Briefly discuss any problems / issues you faced in your implementation of this project. Where will your pipeline likely fail? What could you do to make it more robust?

Although the pipeline works with the project video, but it's better to check if curvature values make sense, and make sanity check about diffrent video clip to determine if current dectection problematic, and if problematic, we actually may can skip current detection data, and use last frame or next frame, to make the pipeline more roboustic.

The pipeline not works well with chanellge video due to the lane lines color not steady, and the light also changes, so maybe it's still need refine the image pre-processing method.