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.
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 the first code cell of the IPython notebook located in ./pipeline.py line 82-100.
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:
The objpoints and imgpoints are then saved for calibrating others images for the project.
The image below is an example of undistorted image
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 thresholds and gradient thresholds to generate a binary image (thresholding steps at lines 258-284 in pipeline.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 warp_image(), which appears in lines 75 through 80 in the file pipeline.py. The warper() 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:
img_size = np.shape(undist_img)
ht_window = np.uint(img_size[0]/1.5)
hb_window = np.uint(img_size[0])
c_window = np.uint(img_size[1]/2)
ctl_window = c_window - .2*np.uint(img_size[1]/2)
ctr_window = c_window + .2*np.uint(img_size[1]/2)
cbl_window = c_window - 1*np.uint(img_size[1]/2)
cbr_window = c_window + 1*np.uint(img_size[1]/2)
src = np.float32([[cbl_window,hb_window],[cbr_window,hb_window],[ctr_window,ht_window],[ctl_window,ht_window]])
dst = np.float32([[0,img_size[0]],[img_size[1],img_size[0]],
[img_size[1],0],[0,0]])This resulted in the following source and destination points:
| Source | Destination |
|---|---|
| 0, 720 | 0, 720 |
| 1280, 720 | 1280, 720 |
| 768, 480 | 1280, 0 |
| 512, 480 | 0, 0 |
I verified that my perspective transform was working as expected by drawing the src and dst points onto a test image and its warped counterpart to verify that the lines appear parallel in the warped image.
4. Describe how (and identify where in your code) you identified lane-line pixels and fit their positions with a polynomial?
Then I divided the image to 9 equal height horizontal slips and applied sliding window for each slip. Find the coordinates with the highes mean value in histogram. Then fit my lane lines with a 2nd order polynomial kinda like this:
Caculate the coefficients A, B and C
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 124 through 138 in my code in pipeline.py using the function below:
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 229 through 249 in my code in pipeline.py in the function overlay_lane_detection(). Here is an example of my result on a test image:
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!).
Here's a link to my video result
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?
Initially it was very difficult for me to come up with thresholds effectively isolates lane line from other parts of the image. After many attempts and seeking advices from other people, I applied filters individually and observe the changes in output. The process involves many trial and error. There are cases where the system fails to extract lane lines, using moving average made the system more robust.
Tuning different thresholds and combine them is a tedious work, there is no ganrantee that parameters work well on samples will also work well on testing videos, optimizing code efficency and reducing turn around time seems to be the only method to speed up work flow.
With better parameters and better methods to combine the filters, the system would lightly to be more robust.
Computer vision based methods in general are very sensitive to lighting change and the surroundings, besides, it is hard coded and unable to handle exceptions. I assume a learning based system would be more flexible and robust.