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GitHub - dasaintgray/DeepCamera: DeepCamera is not only an AI Face Recognition/Person Detection NVR. DeepCamera is a computer vision machine learning pipeline. You can use DeepCamera to collect data from Cameras, upload prelabeled images to server, retrieve data and retrain model then deploy model to edge device (Jetson Nano for example).

SharpAI helps companies applying machine learning on the edge computing devices

DeepCamera is a computer vision machine learning pipeline. You can use DeepCamera to collect data from Cameras, upload prelabeled images to server, retrieve data and retrain model then deploy model to edge device such as Jetson Nano for example.

Features

  • Integration with Home Assistant
  • FFMpeg with Nvidia Nano hardware decoder
  • Face Detector with Nvidia Nano GPU TensorRT MTCNN
  • Face Embedding with Nvidia Nano GPU Pytorch InsightFace
  • Person Detection with GPU
  • Integrate with telegram bot API
  • Porting to Jetson Nano
  • High accurate Face Recognition
  • Face Detection
  • Inference on ARM Mali GPU
  • Support Android TF Lite(GPU/CPU/NPU)
  • Support open source embedded linux
  • Control from mobile application
  • Management System for devices
  • Push Notification to Mobile Device
  • Object Detection
  • Distributed System based on celery
  • Plugin to process video by Shinobi CCTV
  • Application on Android to decode video with hw acc
  • Motion Detection with Android GPU
  • Lable and train from Mobile to Edge Device
  • Native raspberry pi camera support
  • Labelling server and application is down, need BYOD document API server repo
  • Image upload to AWS or on premise AWS compatiable server(MINIO)

Commercial Version

  • Provide real time pipeline on edge device
  • E2E pipeline to support model customization
  • Cluster on the edge
  • Port to specific edge device/chipset
  • Voice application (ASR/KWS) end to end pipeline
  • ReID model
  • Behavior analysis model
  • Transformer model
  • Contrastive learning
  • Click to join sharpai slack channel for commercial support

DeepCamera Architecture

architecture

Demo On Youtube

Demo On Youtube

Get Started on Jetson Nano

Install Docker-compose

sudo apt-get install -y libhdf5-dev python3 python3-pip
pip3 install -U pip
sudo pip3 install docker-compose==1.27.4

Get source code

git clone https://github.com/SharpAI/DeepCamera

Create Token for Telegram Bot

  • Create Telegram Bot through @BotFather
  • Set Telegram Token in Configure File
  • Send message to the new bot you created

Start DeepCamera

cd DeepCamera  
./run-on-nano.sh start

Connect To Camera through RTSP URL

On Jetson Nano, Access to 8080 port. http://localhost:8080
Default username and password is:
username: user@sharpaibox.com
password: SharpAI2018

Tested Camera: DaHua / Lorex / AMCREST, URL Path: /cam/realmonitor?channel=1&subtype=0 Port: 554

You can use iSpyConnect to get camera streaming URL

When setup done, you will see live view on web page, when detected person in camera, you will receive video clips on telegram.

Label on Web GUI, train face recognition model on device

cat docker/workaipython/ro_serialno 
82f28703d001

82f28703d001 is device ID.

Access http://165.232.62.29:3000/

how to config on web gui

Detail information

Camera streaming URL format

Use Mobile APP to label and train face recognition model on device

Get device serial number

cat docker/workaipython/ro_serialno 
82f28703d001

82f28703d001 is device ID.
Generate QRCode of device ID

Download and install SharpAI Mobile APP

Configure on Mobile APP

Develop your own Application GUI with DeepCamera API Server

If you don't like the GUI or you want to develop your own application.
You can use following API:

Get device serial number

cat docker/workaipython/ro_serialno 
82f28703d001

82f28703d001 is device ID

Create User on API Server

REST API:

curl -X POST -H "Content-type: application/json" http://localhost:3000/api/v1/sign-up -d '{"username": "test11", "email": "xxxx@xxx.xx", "password": "xxxxxx"}'

Response:

Get Token of created user

REST API:

curl -X POST -H "Content-type: application/json" http://localhost:3000/api/v1/login/ -d '{"username": "test11", "email": "xxxx@xxx.xx", "password": "123456"}'

Response:

{
  "status": "success",
  "data": {
    "authToken": "t6QsPaU3VdbfUQMkNIf6I3MDtox29WLrPJRAKkOCfpc",
    "userId": "tiK8RYG87sGJAErdB"
  }
}

Create Group on API Server

Rest API:

Fill in X-Auth-Token and X-User-Id in previous response.

curl -X POST -H "X-Auth-Token: t6QsPaU3VdbfUQMkNIf6I3MDtox29WLrPJRAKkOCfpc" -H "X-User-Id: tiK8RYG87sGJAErdB" http://localhost:3000/api/v1/groups -d "name=group01"

Response:

{
  "groupId": "e309ff8c7a3a8ceb4011e86e"
}

Add device to Group on API Server

REST API: Replace X-Auth-Token and X-User-Id. Replace group id in requesting URL: http://localhost:3000/api/v1/groups/`e309ff8c7a3a8ceb4011e86e`/devices

curl -X POST -H "X-Auth-Token: t6QsPaU3VdbfUQMkNIf6I3MDtox29WLrPJRAKkOCfpc" -H "X-User-Id: tiK8RYG87sGJAErdB" -H "Content-type: application/json" http://localhost:3000/api/v1/groups/e309ff8c7a3a8ceb4011e86e/devices -d '{"uuid": "82f28703d001", "deviceName": "testDevice", "name":"testdevice","type": "inout"}'

Response:

Then restart DeepCamera service.

API Server document can be found here: SharpAI/ApiServer

You can also develop/debug code on your PC How to run DeepCamera On PC

Deploy your own API_Server on X86/Cloud Server

Now, you got the idea of DeepCamera,
the public testing server is open to the internet.
You can deploy your own API server on your OWN device.

git clone https://github.com/SharpAI/DeepCamera
cd DeepCamera
./start-cloud.sh start

You need ip address of private cloud server on next step (replace ip address to <Server_IP> on next step).
If you don't want to setup your own server for now, a test server can be used for evaluation, the ip address of test server is 165.232.62.29

If your have any question or feature request, please feel free to join slack for commercial support

Slack

Click to join sharpai slack channel

Contributions