Dingocar...Donkeycar, down-under

Install on Comuter/Laptop (Not the Pi)

Donkeycar has great documentation but we have a few convienace functions that need slightly different setup. Soooo I have litterally copied the docs from donkeycar and added the bits we need.

1. ONLY If you dont have Conda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash ./Miniconda3-latest-Linux-x86_64.sh

2. cd to where you want to clone the dingocar repo, and clone.

cd my_repos
git clone https://github.com/tall-josh/dingocar.git
cd dingocar
git checkout master

3. Create a dingocar environment

For tensorflow-cpu

conda env create -f install/envs/ubuntu-cpu.yml 

For tensorflow-gpu

conda env create -f install/envs/ubuntu-gup.yml 

4. Install

conda activate dingo
pip install -e .[pc]

5. Create your working dir

donkey createcar --path mycar

---

How to Train Responsibly

These are the steps to follow from gather data (manually drive) to training to driving with an Neural Network.

On your local machine

Onetime setup

1. Make sure your running an ssh server on your local machine. If not intall it.

sudo apt-get update
sudo apt-get install openssh-server

2. Add your own public key to authorized keys

cat ~/.ssh/id_rsa.pub >> authorized_keys

Shh to pi, with the right args.

1. ssh to the pi with -AR. A will forward with you're local credentials. The R 2050 will forward a port from the Pi back to your local machine. This will allow our scrips on the Pi scp (secure copy) files back to your loacl machine once you are done driving.

ssh -AR 2050:localhost:22 [email protected]

# Now you're on the Pi

Gather driving data

On the Pi

0. cd play/roba1/mycar

1. Fill in the info.json: The info.json is used to help keep track of when a dataset was gathers and what type of data is in the tub that is gathered. I recomend you fill this is at the start of a labelling session (or whenever your track or track environment changes). A sample info.json is below:

{
  "count"       : SET AUTOMATICALLY,
  "date"        : SET AUTOMATICALLY,
  "tape"        : "thick yellow",
  "floor"       : "timber",
  "location"    : "bill gates' house",
  "centre line" : true,


# donkeycar: a python self driving library

[![Build Status](https://travis-ci.org/autorope/donkeycar.svg?branch=dev)](https://travis-ci.org/autorope/donkeycar)
[![CodeCov](https://codecov.io/gh/autoropoe/donkeycar/branch/dev/graph/badge.svg)](https://codecov.io/gh/autorope/donkeycar/branch/dev)
[![PyPI version](https://badge.fury.io/py/donkeycar.svg)](https://badge.fury.io/py/donkeycar)
[![Py versions](https://img.shields.io/pypi/pyversions/donkeycar.svg)](https://img.shields.io/pypi/pyversions/donkeycar.svg)

Donkeycar is minimalist and modular self driving library for Python. It is
developed for hobbyists and students with a focus on allowing fast experimentation and easy
community contributions.

#### Quick Links
* [Donkeycar Updates & Examples](http://donkeycar.com)
* [Build instructions and Software documentation](http://docs.donkeycar.com)
* [Slack / Chat](https://donkey-slackin.herokuapp.com/)

![donkeycar](./docs/assets/build_hardware/donkey2.PNG)

#### Use Donkey if you want to:
* Make an RC car drive its self.
* Compete in self driving races like [DIY Robocars](http://diyrobocars.com)
* Experiment with autopilots, mapping computer vision and neural networks.
* Log sensor data. (images, user inputs, sensor readings)
* Drive your car via a web or game controller.
* Leverage community contributed driving data.
* Use existing CAD models for design upgrades.

### Get driving.
After building a Donkey2 you can turn on your car and go to http://localhost:8887 to drive.

### Modify your cars behavior.
The donkey car is controlled by running a sequence of events

```python
#Define a vehicle to take and record pictures 10 times per second.

import time
from donkeycar import Vehicle
from donkeycar.parts.cv import CvCam
from donkeycar.parts.datastore import TubWriter
V = Vehicle()

IMAGE_W = 160
IMAGE_H = 120
IMAGE_DEPTH = 3

#Add a camera part
cam = CvCam(image_w=IMAGE_W, image_h=IMAGE_H, image_d=IMAGE_DEPTH)
V.add(cam, outputs=['image'], threaded=True)

#warmup camera
while cam.run() is None:
    time.sleep(1)

#add tub part to record images
tub = TubWriter(path='./dat',
          inputs=['image'],
          types=['image_array'])
V.add(tub, inputs=['image'], outputs=['num_records'])

#start the drive loop at 10 Hz
V.start(rate_hz=10)

See home page, docs or join the Slack channel to learn more.