Open Intelligence processes any camera motion triggered images and sorts seen objects using Yolo, it provides easy to use front end web interface with rich features so that you can have up to date intel what is the current status on your property. Open Intelligence uses license plate detection (ALPR) to detect vehicle plates and face detection to detect people faces which then can be sorted into person folders and then can be trained so that Open Intelligence can try to identify seen people. All this can be done from front end interface.

Open Intelligence uses super resolution neural network to process super resolution images for improved license plate detection.

Project goal is to be useful information gathering tool to provide data for easy property monitoring without need for expensive camera systems because any existing cameras are suitable.

I developed this to my own use because were tired to use existing monitoring tools to go through recorded video. I wanted to know what has been happening quickly.

Click below to watch promo video

Cameras view	Plate calendar

• It's possible to make cameras view play heard microphone sounds.
• Calendar view can open full source detection image by clicking car plate event.

Face wall	Face wall source dialog

• Face wall is one of the creepiest features.
• You can go trough pile of faces and by clicking them, you can see source image.

Open Intelligence can run with Docker, directly at host or mixed.

Open Intelligence is suitable from private properties to small businesses with medium activity.

Table of contents

• Environment
• Installing with Docker
• Installing manually
  • Api side
  • Build react front end
  • Python side (Windows)
• Process drawing
• Project folder structure
• Python Apps
  • App.py
  • StreamGrab.py
  • SuperResolution.py
  • InsightFace.py
  • SimilarityProcess.py
• Config ini
• Multi node support
• Cuda GPU Support
• Postgresql notes
• Openalpr notes
• Front end development
• Troubleshooting
• Todo
• Authors
• License

Environment

Everything can be installed on one server or to separate servers meaning that database is at server one, python application at server two and api hosting at server three.

Installing with Docker

Follow Installation-using-Docker instruction from Wiki page.

Installing manually

Terminology for words like API side and Python side:

• "API Side" is /api folder containing node api process intelligence.js and web user interface served by same process.
• "Python side" is project root folder containing different python processes.

See Project folder structure for more details about folders.

API side

1. Go to /api folder and run npm install
2. Install PostgreSQL server: https://www.postgresql.org/
   • Accessing postgres you need to find tool like pgAdmin which comes with postgres, command line or some IDE having db tools.
3. Rename .env_tpl to .env and fill details.
4. Run intelligence-tasks.js or with PM2 process manager pm2 start intelligence-tasks.js.
5. Run node intelligence.js or with PM2 process manager pm2 start intelligence.js -i 2.
6. Running these NodeJS scripts will create database and table structures, if you see error run it again.
7. Go to /api/front-end folder and rename .env_tpl to .env.
8. At /api/front-end run npm start so you have both api and front end running.
9. Access localhost:3000 if react app doesn't open browser window automatically.
10. Outdated frontend user manual for old ui version https://docs.google.com/document/d/1BwjXO0tUM9aemt1zNzofSY-DKeno321zeqpcmPI-wEw/edit?usp=sharing

Build react front end

1. Go to /api/front-end
2. Check your .env REACT_APP_API_BASE_URL that it corresponds your machine ip address where node js api is running.
3. Build react front end via running npm run build
4. Copy/replace /build folder contents into /api/html folder so that api can serve build webpage.

Python side

(Windows)

1. Download Python 3.6 ( https://www.python.org/ftp/python/3.6.0/python-3.6.0-amd64.exe )
   • Only tested to work with Python 3.6. Newer ones caused problems with packages when tested.
2. Activate python virtual env.

   .\venv\Scripts\activate.bat
   

3. Install dependencies pip install -r requirements_windows.txt
4. Get models using these instruction https://github.com/norkator/open-intelligence/wiki/Models
5. Download PostgreSQL server ( https://www.postgresql.org/ ) I am using version 11.6 but its also tested with version 12. (if you didn't install at upper api section)
6. Rename config.ini.tpl to config.ini and fill details.
   • Config.ini content settings explained, see Config ini
   • For multiple nodes, see Multi node support)
7. Ensure you have Microsoft Visual C++ 2015 Redistributable (x64) installed.
   • This is needed by openALPR
8. Separate camera and folder names with comma just like at base config template
9. Run wanted python apps, see Python Apps section.

It's critical to setup ini configuration right.

Python side

(Linux)

1. Install required Python version.

   sudo add-apt-repository ppa:deadsnakes/ppa
   sudo apt-get install python3.6
   virtualenv --python=/usr/bin/python3.6 ./
   source ./bin/activate
   

2. Install dependencies pip install -r requirements_linux.txt
3. Get models using these instruction https://github.com/norkator/open-intelligence/wiki/Models
4. Download PostgreSQL server ( https://www.postgresql.org/ ) I am using version 11.6 but its also tested with version 12. (if you didn't install at upper api section)
5. Rename config.ini.tpl to config.ini and fill details.
   • Config.ini content settings explained, see Config ini
   • For multiple nodes, see Multi node support)
6. Separate camera and folder names with comma just like at base config template
7. Run wanted python apps, see Python Apps section.

Process drawing

Overall process among different python processes for Open Intelligence.

Project folder structure

Default folders
.
├── api                      # Front end API which is also serving react js based web page
├── classifiers              # Classifiers for different detectors like faces
├── docs                     # Documents folder containing images and drawings
├── libraries                # Modified third party libraries
├── models                   # Yolo and other detector model files
├── module                   # Python side application logic, source files 
├── objects                  # Base objects for internal logic
├── scripts                  # Scripts to ease things      

Python Apps

This part is explaining in better detail what each of base python app scripts is meant for. Many tasks are separated for each part. App.py is always main process, the first thing that sees images.

App

• File: App.py
• Status: Mandatory
• This is main app which is responsible for processing input images from configured sources.
• Cluster support: Yes.
• One computer, multiple instances: Just open app.py on multiple shell's like python .\App.py
• Multi instance command when run on network computer: \.App.py --bool_slave_node True This slave node option means that script uses config_slave.ini instead of stock config.ini Reason is that in this case master node has database installation. If database and camera images are accessible else where like other ip and camera images on smb share having same mount letter/path then it's possible to run only python .\App.py on multiple individual machines.

StreamGrab

• File: StreamGrab.py
• Status: Optional
• If you don't have cameras which are outputting images, you can configure multiple camera streams using this stream grabber tool to create constant input images.
• Cluster support: No.

SuperResolution

• File: SuperResolution.py
• Status: Optional
• This tool processes super resolution images and run's new detections for these processed sr images. This is no way mandatory for process.
• Cluster support: No.
• Mainly meant for improved license plate detection.
• Testing: use command python SuperResolutionTest.py --testfile="some_file.jpg" which will load image by given name from /images folder.

InsightFace

• File: InsightFace.py
• Status: Optional
• Processes faces page 'face wall' images using InsightFace retina model. This is currently for testing use.
• Cluster support: No.

SimilarityProcess

• File: SimilarityProcess.py
• Status: Optional
• Compares current running day images for close duplicates and deletes images determined as duplicate having no higher value (no detection result). Processes images in one hour chunks.
• Cluster support: No.
• Process is trying to save some space.

Config ini

This section explains config.ini file contents which are used by python processes. Config ini start with contents like below.

[app]
move_to_processed=True
process_sleep_seconds=4
cv2_imshow_enabled=True
...

• move_to_processed => When set to True, processed input image is moved into /processed folder, otherwise file is deleted.
• process_sleep_seconds => After every batch of files, process will sleep this amount of seconds.
• cv2_imshow_enabled => Set to True will show window showing processed images, bounding boxes and more.
• ignored_labels => This will ignore labels, example if you get a lot of false positives from umbrellas and you don't care saving any images of umbrellas anyway then ignore it.
• camera_names => Camera name/location name, up to you. Must of separated with comma ,
• camera_folders => Input image folders, same camera names and folders must be in same order and same count
• postgresql section => fill in database credentials. This should not need explaining.

Other parameters are case specific.

Multi node support

Multi node support requires little bit more work to configure but it's doable. Follow instructions below.

1. Each node needs to have access to source files hosted by one main node via network share.
2. Create configuration file config_slave.ini from template config_slave.ini.tpl
3. Fill in postgres connection details having server running postgres as target location.
4. Fill [camera] section folders, these should be behind same mount letter+path on each node.
5. Point your command prompt into network share folder containing App.py and other files.
6. On each slave node run App.py via giving argument: \.App.py --bool_slave_node True

Cuda GPU Support

Cuda only works with some processes like super resolution and insightface. Requirements are:

1. NVIDIA only; GPU hardware compute capability: The minimum required Cuda capability is 3.5 so old GPU's won't work.
2. CUDA toolkit version. Windows link for right 10.0 is https://developer.nvidia.com/cuda-10.0-download-archive?target_os=Windows&target_arch=x86_64
3. Download cuDNN "Download cuDNN v7.6.3 (August 23, 2019), for CUDA 10.0" https://developer.nvidia.com/rdp/cudnn-archive
4. Place cuDNN files inside proper Cuda toolkit installation folders. cuDNN archive has folder structure.

Postgresql notes

All datetime fields are inserted without timezone so that:

File     : 2020-01-03 08:51:43
Database : 2020-01-03 06:51:43.000000

Database timestamps are shifted on use based on local time offset.

Openalpr notes

These notes are for Windows. Current Docker way makes this installation automatic.

Got it running with following works. Downloaded 2.3.0 release from here https://github.com/openalpr/openalpr/releases

1. Unzipped openalpr-2.3.0-win-64bit.zip to /libraries folder
2. Downloaded and unzipped Source code(zip)
3. Navigated to src/bindings/python
4. Run python setup.py install
5. From appeared build/lib moved contents to project libraries/openalpr_64/openalpr folder.
6. At license plate detection file imported contents with from libraries.openalpr_64.openalpr import Alpr

Now works without any python site-package installation.

Front end development

There is separate Readme for this side so see more at ./api/front-end/README.md

Troubleshooting

Refer to troubleshooting wiki.

Authors

• Norkator - Initial work - norkator

Note that /libraries folder has Python applications made by other people. I have needed to make small changes to them, that's why those are included here.

License

See LICENSE file.