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https://github.com/houdinisparks/pose_recognition_tello

Control a dji tello drone with body postures processed by tf-openpose using a camera and aws ec2 g2.2xlarge gpu server.
https://github.com/houdinisparks/pose_recognition_tello

action-recognition openpose pose-recognition tello

Last synced: 29 days ago
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Control a dji tello drone with body postures processed by tf-openpose using a camera and aws ec2 g2.2xlarge gpu server.

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README 

        
# Controlling Drone with Body Postures

A makerfaire 2018 project. 



## Overview

![Architecture](./imgs/architecture.png)



**Prerequisites**

- Docker

- AWS EC2 GPU (G2/P2/P3)

- Additional wireless network adaptor to connect to tello 

- Camera ( pc webcam will do just fine )

- DJI Tello 



I initially developed this with a cpu as a target server  to process the images, but it 

unsurprisingly turned out to be very slow. Also, midway developement I was thinking about 

using AWS Kinesis Data Streams to stream my video frames to the server, instead of using low level 

sockets. However, it turned out to be quite of a hassle, as I had to think about how to reorder 

the frames as they arrive after being processed in the server. As such, you will see many different configuration 

and environment files lying around in this repo, you don't have to pay attention to them. Just 

follow the steps below and you should be on your way to moving the drones with your hands! :)



## 1) Setup the environment

```powershell

# create your venv folder

python -m venv venv

call venv/Scripts/activate.bat



# install your requirements

pip install -r requirements_gpu.txt



# install the tf-openpose lib

# ref: https://github.com/ildoonet/tf-pose-estimation#install-1

git clone https://github.com/ildoonet/tf-pose-estimation.git

cd tf-openpose

python setup.py install

cd tf_pose/pafprocess

swig -python -c++ pafprocess.i && python3 setup.py build_ext --inplace



```



## 2) Setup the AWS GPU EC2

I used docker-machine to help me setup the aws gpu machine. I recommend using it, it is a very convenient tool for

launching and deploying your docker containers to AWS.



**Open powershell**

```powershell

# Create and launch the aws ec2

docker-machine create 

--driver amazonec2 

--amazonec2-region  

--amazonec2-instance-type g2.2xlarge



```



AWS machine will be created with with these default values if extra settings is not set during creation:

 - amazonec2-root-size <16gbs>

 - amazonec2-ami 



**Setup latest cuda driver and nvidia-docker 2.0**

```powershell

# Restart the instance first, to be sure we are running the latest installed kernel

docker-machine restart 



# SSH into the machine

docker-machine ssh 



# Install official NVIDIA driver package

sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub

sudo sh -c 'echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/cuda.list'

sudo apt-get update && sudo apt-get install -y --no-install-recommends linux-headers-generic dkms cuda-drivers



# Create the docker group and add your user

sudo groupadd docker

sudo usermod -aG docker $USER



# Log out and log back in so that your group membership is re-evaluated.

exit

docker-machine ssh 



# Add the nvidia-docker 2.0 package repositories

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \

  sudo apt-key add -

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)

curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \

  sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update



# Install nvidia-docker2 and reload the Docker daemon configuration

sudo apt-get install -y nvidia-docker2

sudo pkill -SIGHUP dockerd



# Test nvidia-smi with the latest official CUDA image

docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi



# Exit the machine

exit

```



## 3) Run the image processing server on AWS

Open up powershell and enter the following command



```powershell

cd 

docker-machine start 

docker-machine env  | Invoke-Expression



# stop all running containers just in case one of them is already using pot 8089

docker stop $(docker ps -aq)

docker run --runtime=nvidia -it --rm -p 8089:8089 houdinisparks/pose_recogniser:mf-gpu-30stimeout

```



You should see the following output:



```powershell

('2018-08-01 04:29:12,415 - __main__ - MainThread - INFO - Socket successfuly created and binded to 0.0.0.0:8089

('2018-08-01 04:29:12,416 - __main__ - MainThread - INFO - Listening for connections...

```



## 4)  Run the web application

Open up another console on your pc.



```powershell

cd 

python -m src.webapp.app

```



```powershell

# Output on console

('2018-08-01 12:33:33,305 - werkzeug - Thread-1 - INFO -  * Running on http://127.0.0.1:5001/ (Press CTRL+C to quit)

```



#### Webpage   

![Web Page](./imgs/step_1.png)



#### Demo of the poses recognized 

![Poses](imgs/poses1.gif)



6 poses: takeoff, land, left, right, flip_forward, flip_backward.  

**EDIT**

2 more poses added: back and front. |_ (left shoulder) for back, and _| (right shoulder) for front



## 5) Setup the tello connection

Turn on the tello and connect to the tello's wifi (You need to attach another wifi adapter to connect 

to a 2nd wifi network. It's cheap, I bought mine for SGD 6 from Lazada.) 

Click on the "Connect to Tello" button on the webpage.



![Web Page](./imgs/step_2.png)



**EDIT**

You might need to click the connect button a few times before its connected.



And you are done! :D



## 6) Libraries Use

tf-openpose  

opencv