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.
The code for this step is contained in the 1 ~ 5 code cell of the IPython notebook located in "find_lane_line.ipynb"
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:
I used a combination of color and gradient thresholds to generate a binary image (thresholding steps in cells 8 ~ 9 in find_lane_line.ipynb). Here's an example of my output for this step.
The code for my perspective transform includes a function called perspective_transform(), which appears in cell 15 ~ 16 in the file find_lane_line.ipynb. The perspective_transform() function takes as inputs an image (img), as well as the perspective transform matrix (M). I chose the hardcode the source and destination points in the following manner:
# Outer points of the trapezoid
src_outer_points = np.float32([[140, image.shape[0]], [1200, image.shape[0]], [800, 480], [520, 480]])
# inner points of the trapezoid
src_inner_points = np.float32([[360, image.shape[0]], [980, image.shape[0]], [660, 480], [660, 480]])
# Perspective transformed points of trapezoid
dst_points = np.float32([[500, image.shape[0]], [900, image.shape[0]], [900, 0], [500, 0]])There are two kinds of source points, src_outer_points and src_inner_points, I use them to get the rigion of interest like this:
This resulted in the following source and destination points:
| Outer Source | Inner Source | Destination |
|---|---|---|
| 140, 720 | 360, 720 | 500, 720 |
| 1200, 720 | 980, 720 | 900, 720 |
| 800, 480 | 660, 480 | 900, 0 |
| 520, 480 | 660, 480 | 500, 0 |
I verified that my perspective transform was working as expected by looking at the source binary image and transformed binary image. The lines appear parallel in the transformed image.
In cell 19 ~ 21 I did some other stuff and fit my lane lines with a 2nd order polynomial kinda like this:
I try to find the lane line with last frame first, if it is not found, then I go back to search it from scratch. I also try to cache with recent frames, so the lane line would appear smooth. I did this in cell 26 ~ 27
I did this in cell 18 with function calculate_r_curve() in find_lane_line.ipynb
I implemented this step in cell 22 ~ 25 in my code in find_lane_line.ipynb in the function color_image() and the function text_image(). Here is an example of my result on a test image:
