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.
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!
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 "Main.ipynb" (lines 8 through 63).
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:
To demonstrate this step, I will describe how I apply the distortion correction to one of the test images like this one:
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 took the image and converted it into the HLS color space in order to gain access to the vary usefull saturation layer (5th code block line 5). Both a sobel x threshold and color threshold are combined to get the result below (5th code block lines 13 - 38):
3. Describe how (and identify where in your code) you performed a perspective transform and provide an example of a transformed image.
Located in the 6th code block I used both src points and dst points which describe the original points and the resulting points respectively.
This resulted in the following source and destination points:
| Source | Destination |
|---|---|
| 180, 720 | 320, 720 |
| 1125, 720 | 960, 720 |
| 585, 450 | 320, 0 |
| 690, 450 | 960, 0 |
I verified that my perspective transform was working as expected looking at the warped image and checking to see if the lines are parallel:
4. Describe how (and identify where in your code) you identified lane-line pixels and fit their positions with a polynomial?
I used a histogram to find all of the maximum pixels in a vertical coloumn in 9 rectagles spanning the hieght of the warped image. You can see this in code block 8 9. Block 8 is the code to find all of the hot pixels were as the block 9 is the code to view and color the left and right hot pixels. A polynomial of degree 2 is fit to each line shown 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.
Code block 12 is where the curvature of the lane is calculated, after the pixels have been taken from pixel space to real life space (meters).
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 in code block 11. 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?
When the road changes color that is when my algorithm breaks and doesn't do so well. Future work would be to test out different thresholds on those images that have different lane colors so that it can be more robust.