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.
https://www.youtube.com/playlist?list=PL97OXOB9UA-jytOspNpHNipo-2lyjppeW
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 Here
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, object_points_list is just a replicated array of coordinates, and object_points will be appended with a copy of it every time I successfully detect all chessboard corners in a test image. image_points_list 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 object_points_list and image_points_list 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 used a combination of color and gradient thresholds to generate a binary image Here. Explained in notebook: from In [7] step by step. 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 transform_perspective(), which appears Here In [5]. This 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:
middle = img_size[0] / 2
trapezoid_top_width = 135
src = np.float32([[middle - trapezoid_top_width / 2, 450], [middle + trapezoid_top_width / 2, 450],
[1250, 720], [40, 720]])
dst = np.float32([[0, 0], [1280, 0],
[1250, 720], [40, 720]])
This resulted in the following source and destination points:
| Source | Destination |
|---|---|
| 492, 485 | 0, 0 |
| 805, 485 | 1280, 720 |
| 1245, 720 | 1250, 720 |
| 42, 720 | 40, 720 |
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?
Here in In [9] I did some other stuff and fit my lane lines with a 2nd order polynomial kinda like this:
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 Here in In [9] in last lines. First I got the curvature in pixels from the fit polynomial. Then I selected a pixels to meters scale. Then fit the polynomials in world space. For the offset from middle, I used the distance between center of image and middle point of base lines we have found in previous histogram. Converted that one to meters as well.
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 In [26] to In [29] in my code in Here. 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?
The sanity checks were pretty challenging to implement. I had to look for intersections, get averages of fits throughout the video. But finally it worked. Pipeline would fail where there are more yellow lines or non line marked roads. Definitely adaptive binary treshold parameter modification would be a cool thing to implement here but would be very effortful. Also the code could be revised a bit too, I reused some sets of operations that could be extracted as methods.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent