Skip to content

README.md

Latest commit

 

History

History
83 lines (66 loc) · 2.59 KB

README.md

File metadata and controls

83 lines (66 loc) · 2.59 KB
Raw

Streamlit Video Analyzer

This repository hosts a real-time video streaming and analysis app using Streamlit and YOLO. The app provides options for live video streams from various sources, and integrates YOLO for real-time object detection, pose estimation, and segmentation.

Table of Contents

Installation

  1. Clone the repository:

    

    2. 



git clone https://github.com/username/Streamlit-Video-Analyzer.git
cd Streamlit-Video-Analyzer




2. Install the required Python packages:

```bash
pip install -r requirements.txt



Usage


To start the app, ensure that you have the correct model files and video sources. You can run the app as follows:


Running the Pipeline


Execute the following command to start the pipeline:


streamlit run main.py


Arguments


    

  • source: Select a video source from webcam, video files, or RTSP stream.
    • 

  • model_type : Choose the model type for video analysis, including object detection, pose estimation, or segmentation.
    • 



Directory Structure


The project’s directory structure is as follows:


Streamlit-Video-Analyzer/
│
├── camera/
│   ├── capture_video.py
│   ├── capture_video_d.py
│   └── ...
├── models/
│   ├── yolov8.pt
│   ├── pose_estimationv8.pt
│   ├── Segmentationv8.pt
│
├── class/
│   ├── labels.txt
│   └── ...
├── main.py
├── read_json.py
├── prompt.json
├── requirements.txt
└── README.md



Scripts Overview


    

  • **main.py: The main script that starts the Streamlit app and loads the interface.
    • 

  • **capture_video.py: Contains the function to capture video from standard sources.
    • 

  • **capture_video_d.py: Contains the function to capture video from a specific camera type (e.g., Depth Camera).
    • 

  • **draw.py: draws the inferencing.
    • 

  • **read_json.py: reads in json and over writes two locations
    • 

  • **prompt.json: is the prompt
    • 

  • **`models/': Directory for storing pre-trained YOLO models.
    • 

  • **`classes/': Directory for storing text files of classes.
    • 



Scripts Overview


    

  • **Video Source: Choose between webcam, RTSP stream, and video files.
    • 

  • **image: saves image to a direstor
    • 

  • **Model Type: Select the type of analysis model (Object Detection, Pose Estimation, or Segmentation).
    • 

  • **Resolution: Customize resolution for the video stream.
    • 

  • **Confidence Threshold: Set a confidence threshold for the model predictions.
    • 

  • **Classes: Select the class text file
    •