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 "./advanced_lane_finding.ipynb" (or in called advanced_lane_finding.py).
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:
By acquiring the camera matrix and destination points return from the cv2.calibrateCamera() function in the previous step, and feeding those to the cv2.undistort() function we can remove the distortion caused by various lens aberrations and ultimately produce an image free of any distortion.
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 steps in advanced_lane_finding.ipynb (or in advanced_lane_finder.py). Here's an example of my output for this step. (note: this is not actually from one of the test images)
After converting the original image into the HLS colorspace I separated the S channel and applied a Sobel threshold to it to identify strong pixels for the the x and y direction separately. I then combined those thresholds with others using methods such as taking the direction of the gradient as well as the magintude. Overall this produced a nicely thresholded image on which I could start to detect where the lane lines fell in each image.
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(), which appears in the file advanced_lane_finding.ipynb (output_images/warped.py) (or in advanced_lane_finding.py). The warp() function takes as inputs an image (img). The (src) and (dst) were found and are accessible as global variables.
bottom_y = 720
top_y = 455
L1 = (190, bottom_y)
L2 = (585, top_y)
L1_x, L1_y = L1
L2_x, L2_y = L2
R1 = (705, top_y)
R2 = (1130, bottom_y)
R1_x, R1_y = R1
R2_x, R2_y = R2
gray = cv2.cvtColor(undist, cv2.COLOR_BGR2GRAY)
ny, nx = gray.shape
img_size = (nx, ny)
offset = 200
src = np.float32([
[L2_x, L2_y],
[R1_x, R1_y],
[R2_x, R2_y],
[L1_x, L1_y]
])
dst = np.float32([
[offset, 0],
[nx - offset, 0],
[nx - offset, ny],
[offset, ny]
])This resulted in the following source and destination points:
| Source | Destination |
|---|---|
| 585, 455 | 200, 0 |
| 705, 455 | 1080, 0 |
| 1130, 720 | 1080, 720 |
| 190, 720 | 200, 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?
Then I executed a function that uses a sliding window technique to find the path of the pixels likely to be lane lines, using a lane finding class and it's method called fit_poly() I 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.
By converting the pixels in the images I tested the pipeline on I was able to determine a radius of curvature for each line as it relates to the actual real world dimensions of the road.
6. Provide an example image of your result plotted back down onto the road such that the lane area is identified clearly.
I implemented in my code in advanced_lane_finding.ipynb in the function lane_finder.draw_lanes_on_road(). Here is an example of my result on a test image along with the output of the cuvrvature info:
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?
I took the approach of useing a OpenCV's computer vision functionality to analyze an image of the road and using things like calibrateCamera() and undistort() was able to create a calibration for the vehicle mounted camera which I could then use to remove any distortion from the resulting image.
This in turn allowed me to use a technique called thresholding whereby I take an absolute soble threshold and apply it to the image. With this technique we are able to clearly and accurately detect which pixels in the image are worth paying attention to. In other words since we know the lane lines are likely to be "vertical" in the image we can adjust our thresholding to pull out and magnify those pixels for further analysis.
After using a sliding window technique on the image we receive from our previous thresholding we apply a second order polynomial to the pixels we've identified as likely to be lane pixels.
Having done this we are now able to draw the area we've identified onto the image of the road and using the inverse matrix we receive from our getPerspectiveTransform() function (also a utility provided by OpenCV) we can transform our image of the road with the lane clearly marked back into it's original perspective.
With all of the above in place we have a fully functioning image analysis pipeline for identifying the lanes enabling a self driving car to stay in the correct lane.
Some limitations of the current pipeline are that I do not do any sort of averaging or detecting of the lanes after the inital check. So if we get a bad lane we just keep right on analyzing. Another limitation is that because we don't to any of those things the lane gets a little wobbly from time to time, nothing catastrophic but it would be better to smooth the lane out.
The limitations above will also affect the pipelines ability to deal with more challenging terrain and lighting conditions.