Seok Lee blog

Advanced Lane Finding

advanced_lane

Summary:

Final result-Please click the thumbnail to view the video:

video result

Braingstorming:

Here is the Rubric points for this project.

Work flow

0. Compute the camera calibration matrix and distortion coefficients given a set of chessboard images

import a function that takes an image, object points, and image points performs the camera calibration, image distortion correction and returns the undistorted image

Camera_Calibration

1. Apply a distortion correction to raw images.

The code for this step is contained in the first code cell of the IPython notebook located in “./cam_cal.py.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 (The file for this work is ‘image_gen-undistort.py.ipynb’):

Camera_Calibration

2. Use color transforms, gradients, etc., to create a thresholded binary image.

To demonstrate this step, I will describe how I apply the distortion correction to one of the test images like this one: original image

3. 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 lines # through # in image_gen-color_gradient.py.ipynb). Here’s an example of my output for this step.

color_gradient

4. 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 calls ‘getPerspectiveTransform’ function from ‘cv2. The 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.shape[1]*(.5-mid_width/2),img.shape[0]*height_pct],[img.shape[1]*(.5+mid_width/2),img.shape[0]*height_pct],[img.shape[1]*(.5+bot_width/2),img.shape[0]*bottom_trim],[img.shape[1]*(.5-bot_width/2),img.shape[0]*bottom_trim]])
    dst = np.float32([[offset, 0], [img_size[0]-offset, 0],[img_size[0]-offset, img_size[1]],[offset, img_size[1]]])

5. Apply a perspective transform to rectify binary image (“birds-eye view”).

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.The associated file is ‘image_gen-perspective_transform.py.ipynb’

perspective_transform

6. Detect lane pixels and fit to find the lane boundary.

Then found the lane line(‘image_gen-identify_lane_finding.py.ipynb) with a 2nd order polynomial (‘image_gen-identify_lane_finding_polynominal.py.ipynb’) this:

lanefinding lanefinding_poly

7. Determine the curvature of the lane and vehicle position with respect to center.

I did this @ my code ( image_gen-Camera_Center_Cal_Curvature.py.ipynb)

Cal_Curvature

8. Provide an example image of your result plotted back down onto the road such that the lane area is identified clearly.

Here is an example of my result on a test image:

final

9. Result

This video shows the result of detecting the lane boundaries and numerical estimation of lane curvature and vehicle position. Please click the thumbnail:

video result

10. Discussion