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.
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 lane_finding_pipeline/camera_calibration.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:
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 at _getThresholdImage in advanced_lane_finding.ipynb). the actual gradient and color transform code is in lane_finding_pipeline/ColorFilter.py and lane_finding_pipeline/GradientFilter.py. I tried several masking criteria to reach the following result
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 is in lane_finding_pipeline/perspective_transformation.py, The process() 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:
src = np.float32(
[[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
[((img_size[0] / 6) - 10), img_size[1]],
[(img_size[0] * 5 / 6) + 60, img_size[1]],
[(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])
dst = np.float32(
[[(img_size[0] / 4), 0],
[(img_size[0] / 4), img_size[1]],
[(img_size[0] * 3 / 4), img_size[1]],
[(img_size[0] * 3 / 4), 0]])This resulted in the following source and destination points:
| Source | Destination |
|---|---|
| 585, 460 | 320, 0 |
| 203, 720 | 320, 720 |
| 1127, 720 | 960, 720 |
| 695, 460 | 960, 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?
In lane_finding_pipeline/sliding_window.py i calculate the left and right lane equation by first transforming the binary image to historgram and find out the peak two x axis as start point, i then use sliding window technique to count number of pixels that fall in a certain window and update the pixel center by averaging the pixel position in the window
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.
In lane_finding_pipeline/sliding_window.py function def calculateMetaData i calulate the curvature and position of deviation. i calculate the curvature by transforming the pixel coordinate to real world coordinate and fit them to a polynominal equation and calculate the curvature using the equation introduced in https://www.intmath.com/applications-differentiation/8-radius-curvature.php
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 advanced_lane_finding.ipynb in the class class FullPipe. 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?
One thing i failed to handle is the challenge video where the dark background cause the lane finding to fail. If given time, i will probably tune further on the color masking part to account for that behavior. Also i will test the segmentation using deep learning if i had more time.