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AdvancedLaneLines

★ 71 Open GitHub ↗

Lane identification system for camera based systems.

// repository documentation

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## Getting Around 
* The Repository has all the processing done in the file [Advanced Lane Lines Final](Advanced_Lane_lines_Final.ipynb)
A brief description of what was done has been described step by step.
* The outputs are [Project Video](ProjectVideoOutput.mp4) , [Challenge Video](ChallengeVideoOutput.mp4), [Harder Challenge Video](HarderChallengeVideoOutput.mp4)

## Video Pipeline
### Complete Process Involves
<img src="Entire_Process.png">

*  **Calibrate** - Use Camera Calibrate Function to get Distortion Matrix
*   **Undistort** -  Use Computed Distortion Matrix to correct for Distortion
<img src="CameraCalibrationProcess.png">

*   **Warp** - Use Warping Function to get Bird's eye view 
<img src="Image_Warp_Process.png">

*   **Threshold** - Use a combination of static , dynamic thresholding with morph transformations & colorspaces to get thresholded image
<img src="Threshold_Process.png">

*   **Extract Pixel Information** - Use Quantity based thresholding to and extract non-zero pixels
  * **Blind Search** - Look for pixel information throughout the image
  
    <img src= "BlindSearch.png">
    
    | Left Mask Computation | Right Mask Computation |
    | --------------------- | ---------------------- |
    | <img src="Mask_Computation_Example_Left.png"> | <img src="Mask_Computation_Example_Right.png"> |
    
  * **Polynomial Search** 
    <img src="PolynomialSearch.png">

*   **Validate and Fit** - Use Curvature and LaneWidth Sanity mechanisms to check the validity of the two fits obtained for a given frame and decide on whether to use the current fit or use an extrapolated average of the previous fits 
<img src="Validation_DecisionTree.png">
*   **Project Fit on to Image ** - Convert the x, y plane points and fit to image plane points and draw polygon on the image.
*   **Unwarp** - Use Inverse Perspective Transform to unwarp the image. 
<img src="Unwarp_process.png">

*   **Output** - Return the processed frame.

### Example Images from Each of the video 
#### Harder Challenge
<img src="Harder_Challenge_Video_Pipeline.png">
#### Challenge Video 
<img src="Challenge_ Video_pipeline.png">
#### Project Video
<img src="Project_Video_Pipeline.png">

## Camera Calibration 
* Get Chessboard Corners from Standard Images
* Calibrate Camera using differences in distances between expected and actual results
* Use Calibration data to Undistort Camera Images
<img src ="Camera_Calibration.png">
* Example of how Chessboard Corners are obtained before Calibration to Undstort and use it to have a Bird's Eye Perspective
<img src="Camera_calibration_Example.png">

## Warping & Birds Eye View Generation
* Use Interactive Python to plot and understand what is the best set of source and destination points for each/ all the three videos of interest. After several days of experimentation we have finally arived at these numbers
* Use Get Perspective Transform for Matrix calculation that can be used to Warp The Image to Bird's eye view and Unwarp them back to the front view
<img src="Warp_Image_Final.png">

## Image Thresholding 
<img src= "Image_Thresholding_Final.png">
### Morphological Channel :
* Use HLS S- Channel and Gray Channel to create Morphological Channel
* Threshold the Morph Channel to get the most dominant features 
* Determine Levels of Thresholds using Mean and Standard Deviations

### Environment Mask :
* Use B-Channel from the LAB Colorspace to choose Yellow and Shades of Yellow 
* Use HLS Channel to filter out everything not in range of threshold. "
<img src= "Detailed_ThreshProcess.png">

## Queue Helper Functions
<img src="Queues.png">
____________________________________
* Queues are extremely useful tools for First In First Out queues. 
* The size of the queues when they are really large can help stabilize values very well in case of noisy conditions
* The size of the queues can also slow down the speed at which the lanes

### Fit Functions:
____________________
* **Mean Fit** : Get average fit over n-samples specified
* **Predicted Fit**: Extrapolate fit values if next sample is valid
* **Last Fit**: Last valid fit in the queue

### Curvature Functions:
___________________________
* **Mean Curvature** : Get average curvature over n-samples specified
* **Predicted Curvature**: Extrapolate curvature values if next sample is valid
* **Last Curvature**: Last valid curvature in the queue

### Other Functions:
____
* **Clear Queues**: Clear the queues completely
* **Pop Left**: Pop the left most value in the queues

## Gif Outputs of all three processed videos
_____
### Project Video
![](https://raw.githubusercontent.com/vamsiramakrishnan/AdvancedLaneLines/master/ProjectVideo.gif)

### Challenge Video
![](https://raw.githubusercontent.com/vamsiramakrishnan/AdvancedLaneLines/master/Challenge.gif)

### Harder Challenge Video
![](https://raw.githubusercontent.com/vamsiramakrishnan/AdvancedLaneLines/master/Harder_Challenge.gif)

### Key Highlights
____
* Thresholding uses a combination of Morphological Transformations and Mean , Median , STDEV based dynamic thresholding
* Lane Width Rationalization and Dynamic Lane Width Updation to deal with optical illusion.
* State Machine to Handle decision on Found Lanes , and non-found lanes using queues and flags.

### Challenges
____
* Absence of a great road model, absence of a great camera model - The road model would help understand what sort of a curve is sane , what isnt . What colors amount to false positives etc. Similarly the camera model should be designed to dynamically shift field of view and calculate the x and y distances keeping optical illusions in mind. 
* The result is extremely sensitive to Calibration ( Gains, Thresholds, Queue Lengths , Number of Segments etc.) Find the right combination of all these thresholds needed a plugin like Interactive Python .
* What works for one scenario may not work very well with the other. Using multiple scenarios for just the three videos would just amount to cheating, so developing a generic concept to handle most scenarios is the toughest part. 
* These challenges made me appreciate the concepts of deep learning augmenting classic computer vision techniques to process the image mathematically. 
* The entire project was built using global variables , queues and functions without the use of objects to make sure anyone who has just begun coding can read and understand the process easily.

### References
____
The outputs of these Udacity students were taken as references to better my existing algorithms. Although I referred these algorithms I could manage to deliver my own touch to it and also better the results significantly when compared to the authors whose code I referred.

* Vivek Yadav  - https://github.com/vxy10 ( Blind Search & Masking )
* Parilo - https://github.com/parilo ( Exploration of RANSAC Regressor Fit ) 
* Nikolas Ent - https://github.com/NikolasEnt ( Lane Width rationalization )
* Ajsmilutin - https://github.com/ajsmilutin ( Image Thresholding )

AdvancedLaneLines

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