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Realtime Person Re-Identification using Kafka, Spark and Deep Learning

realtime-reid 최신버젼 다운로드

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<h2 style="text-align: center">
    ⚡Real-Time Person Re-ID in Multi-Camera Surveillance Systems⚡<br>
</h2>

<p style="text-align: center">Build with</p>
<p style="text-align: center">
  <img alt="kafka" src="https://img.shields.io/badge/-Apache Kafka-05122A?style=for-the-badge&logo=apachekafka"/>
  <img alt="spark" src="https://img.shields.io/badge/-Apache Spark-05122A?style=for-the-badge&logo=apachespark"/>
  <img alt="torch" src="https://img.shields.io/badge/-Pytorch-05122A?style=for-the-badge&logo=pytorch"/>
  <img alt="flask" src="https://img.shields.io/badge/-Flask-05122A?style=for-the-badge&logo=flask"/>
  <img alt="python" src="https://img.shields.io/badge/-Python-05122A?style=for-the-badge&logo=python"/>
</p>

## Introduction

This project leverages a combination of cutting-edge technologies, including Apache Kafka and Apache Spark, along with straightforward Deep Learning Models like YOLOv5 (for *Human Detection*) and ResNet (for *Person Re-identification*). The primary objective is to tackle the challenge of *Person Re-identification* in real-time within a given scenario.

In the context of a building equipped with multiple cameras, the aim is to detect individuals across these camera feeds and assign unique IDs to each person. Notably, a person may initially appear in one camera and subsequently reappear in another. The overarching objective is to seamlessly identify the same individual across these cameras by associating them with a consistent ID—a task commonly referred to as *Person Re-identification*.

## Project overview

This project presents a *real-time Person Re-identification System* using Deep Learning techniques. The system is designed to (1) process video streams, (2) detect persons in the video and (3) identify them based on their features.

The project is structured around several key components:
-  The `Producer.py` and `Consumer.py` scripts handle the ingestion and consumption of data. 
-  The `realtime_reid` *module* contains the core functionality of the system, including **person detection** (`person_detector.py`), **feature extraction** (`feature_extraction.py`), and **person identification** (`classifier.py`). 
-  The streaming module includes services for **video production** (`kafka_services/video_producer.py`) and **data processing** (`spark_services/spark_processing.py`). 
-  The project is configured via a `settings.json` file (TODO) and dependencies are managed through a `requirements.txt` file.

## Prerequisites

- `Python 3.11`
- `Apache Spark (>= 3.5.0)`
- `Apache Kafka (>= 3.6.0)`

Note: 
- I tested this on `python=3.12` (November 2023) but it didn't work. You can test it on the latest version, if it still doesn't work, I recommend using `python=3.11`.

## Getting Started
- Install Apache Spark and Apache Kafka (Remember to test if it works).

- Clone this repository

- Create a Python environment and install the necessary packages
  
```bash
conda create -n human_reid python=3.11
conda activate human_reid
pip install -r requirements.txt
```

- There are 2 models needed for this project:
  - A **YOLOv5** model (`yolov5s.pt`) for *Human Detection*. The model will be automatically downloaded when you run the code for the first time, or if you want to use your own yolov5 model, you can put it in the `checkpoints/` folder.
  - A **ResNet50** model (`resnet50_model.pt`) for *Person Re-identification* (Feature Extraction specifically). This model is required, put it in the `checkpoints/` folder.

## Usage

- Run `Zookeeper`
```
/path/to/your/kafka/bin/zookeeper-server-start.sh /path/to/your/kafka/config/zookeeper.properties
```
- Run `Kafka`
```
/path/to/your/kafka/bin/kafka-server-start.sh /path/to/your/kafka/config/server.properties
```
- Run the  Consumer. You can run `python Consumer.py --help` to see the arguments.
```bash
python Consumer.py --topic topic_name --topic-2 topic_name_2 --reid y
```

- Run Producers to publish recorded footage from our cameras on these *topics* (run each producer separately).
```bash
python Producer.py --topic topic_name --camera /path/to/your/video.mp4
```
```bash
python Producer.py --topic topic_name_2 --camera /path/to/your/video.mp4
```

- A cv2 window will pop up if the producer is running successfully (2 windows if run 2 topics).

## Contribution
This repo is continuously fixed and updated over time, feel free to fix some issues/bugs, all are welcome here!

## About Us

We are a Group of Students majoring in Data Science at **University of Information Technology (UIT), VNU-HCM** of Vietnam.

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