MACHINA

CCTV viewer with realtime object tagger [WIP]

Uses

• LLAVA
• YOLO 11
• OpenCV
• FAISS

How it works

Simply it connects to a high-resolution RTSP stream in a separate thread, queues the frames into memory as it is and resamples it for processing.

YOLO takes this frame, application gives a specific id based on it's coordinates, size and timestamp then tries to match the same object on every iteration.

Another thread runs in background, iterates that object array continuously and makes LLM requests to Ollama server for object tagging

Object matching

It calculates the center of every detection box, pinpoint on screen and gives 16px tolerance on all directions. Script tries to find closest object as fallback and creates a new object in memory in last resort. You can observe persistent objects in /elements folder

Test Environment

Every input frame resampled to 640x480 for processing, got avg 20ms interference time with yolo 11 small model (yolo11s.pt) on Geforce GTX 1060 which is almost 7 years old graphics card. Other models available in "models" directory

Stream delays by 1-2 seconds on every 10~ minutes due to network conditions, script also have a frame skip mechanism on 3 seconds of detection idle.

Prerequisites

Make sure you have all Visual C++ redistributables if you're running on windows https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170

Installation

• Install Python 3.12.x
• Clone the repository
• Install ollama server
• Pull the LLAVA model by running ollama run llava
• Install the dependencies by running pip install -r requirements.txt
• Remove pytorch cpu version and install the cuda version
• Open app.py and set your rtmp stream address at line 18
• Run the script py app.py

git clone https://github.com/PsyChip/machina
cd machina
pip install -r requirements.txt
pip uninstall torch torchvision torchaudio
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
py app.py

Notes

• It's okay to stick with cpu version of FAISS if you're using yolo nano/small/medium model
• Change vsize value depending on your chosen yolo model, you need to delete the index when changing vector size.
• CUDA enabled torch is an absolute necessity for real time interference
• Pretrained models of yolo is not so accurate on low-res streams, it's highly recommended to train your own model by using object images from your /elements folder

Usage

• S : snapshot, actual image from input stream
• R : start/stop recording. it records what you see.
• Q : quit app
• left mouse: select
• middle mouse: zoom
• right mouse: pan

Project direction

This is a living project, trying to create a complete headless security system by taking advantage of open source object detection models on my spare time.

TODO

• Additional UI Layer
• RTS style object selection box and detailed information about selected object(s)
• People crowd, car crash, police, ambulance, running human detection [request]
• Webhook callbacks on new object/disappeared object/movement after long stay

Feel free to contribute with code, ideas or even maybe a little bit support via ko-fi or bitcoin. I'll prioritize the feature requests for every $10 donation

• https://ko-fi.com/psychip
• BTC: bc1qlq067vldngs37l5a4yjc4wvhyt89wv3u68dsuv

Created by PsyChip root@psychip.net

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