Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/nazanin1369/behavioral-cloning
https://github.com/nazanin1369/behavioral-cloning
behavioral-cloning deep-learning keras self-driving-car
Last synced: 23 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/nazanin1369/behavioral-cloning
- Owner: Nazanin1369
- Created: 2017-06-12T04:26:08.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-06-17T20:22:42.000Z (over 7 years ago)
- Last Synced: 2024-11-10T14:51:34.417Z (3 months ago)
- Topics: behavioral-cloning, deep-learning, keras, self-driving-car
- Language: Python
- Size: 15 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Behaviorial Cloning Project
Overview
---
This repository contains starting files for the Behavioral Cloning Project.
In this project, you will use what you've learned about deep neural networks and convolutional neural networks to clone driving behavior. You will train, validate and test a model using Keras. The model will output a steering angle to an autonomous vehicle.
We have provided a simulator where you can steer a car around a track for data collection. You'll use image data and steering angles to train a neural network and then use this model to drive the car autonomously around the track.
We also want you to create a detailed writeup of the project. Check out the [writeup template](https://github.com/udacity/CarND-Behavioral-Cloning-P3/blob/master/writeup_template.md) for this project and use it as a starting point for creating your own writeup. The writeup can be either a markdown file or a pdf document.
To meet specifications, the project will require submitting five files:
* model.py (script used to create and train the model)
* drive.py (script to drive the car - feel free to modify this file)
* model.h5 (a trained Keras model)
* a report writeup file (either markdown or pdf)
* video.mp4 (a video recording of your vehicle driving autonomously around the track for at least one full lap)
## Details About Files In This Directory
### `drive.py`
Usage of `drive.py` requires you have saved the trained model as an h5 file, i.e. `model.h5`. See the [Keras documentation](https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model) for how to create this file using the following command:
```sh
model.save(filepath)
```
Once the model has been saved, it can be used with drive.py using this command:
```sh
python drive.py model.h5
```
The above command will load the trained model and use the model to make predictions on individual images in real-time and send the predicted angle back to the server via a websocket connection.
Note: There is known local system's setting issue with replacing "," with "." when using drive.py. When this happens it can make predicted steering values clipped to max/min values. If this occurs, a known fix for this is to add "export LANG=en_US.utf8" to the bashrc file.
#### Saving a video of the autonomous agent
```sh
python drive.py model.h5 run1
```
The fourth argument, `run1`, is the directory in which to save the images seen by the agent. If the directory already exists, it'll be overwritten.
```sh
ls run1
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_424.jpg
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_451.jpg
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_477.jpg
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_528.jpg
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_573.jpg
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_618.jpg
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_697.jpg
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_723.jpg
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_749.jpg
[2017-01-09 16:10:23 EST] 12KiB 2017_01_09_21_10_23_817.jpg
...
```
The image file name is a timestamp of when the image was seen. This information is used by `video.py` to create a chronological video of the agent driving.
### `video.py`
```sh
python video.py run1
```
Creates a video based on images found in the `run1` directory. The name of the video will be the name of the directory followed by `'.mp4'`, so, in this case the video will be `run1.mp4`.
Optionally, one can specify the FPS (frames per second) of the video:
```sh
python video.py run1 --fps 48
```
Will run the video at 48 FPS. The default FPS is 60.
#### Why create a video
1. It's been noted the simulator might perform differently based on the hardware. So if your model drives succesfully on your machine it might not on another machine (your reviewer). Saving a video is a solid backup in case this happens.
2. You could slightly alter the code in `drive.py` and/or `video.py` to create a video of what your model sees after the image is processed (may be helpful for debugging).