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use-floline

https://github.com/6438188321yossaphatk/use-floline

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
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    Academic email domains
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    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: 6438188321yossaphatk
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 253 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 12
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Citation

README.md

USE-FLOLINE PeopleCouting IP-cam

A python program to use with an ip camera to count numbers of people entering and leaving the building using opencv and yolov8 for object detection and tracking with a Modbus TCP communication.

Screenshots

Alt Text

Features

  • Real-time object detection and tracking using YOLOv8.
  • Counting objects that enter and leave a defined region.
  • Modbus TCP integration to send the count of objects to a PLC.
  • HTTP frame upload to a specified server endpoint.
  • Video recording of the processed feed.
  • Graceful shutdown handling to ensure resources are released properly.

Requirements

  • Python 3.8 or higher
  • Required Python libraries:
    • opencv-python
    • ultralytics
    • requests
    • pymodbus

Installation

  1. Clone repository in github to VS Code

```bash git clone https://github.com/6438188321yossaphatk/USE-FLOLINE.git cd real-time-object-detection

```

  1. Install required packages:

bash pip install -r requirements.txt

Usage

  1. Run the program: bash python usefloline_cam.py

  2. Stream URL :

  3. The code in usefloline_cam.py currently uses a predefined RTSP stream URL. Replace the ip address with your own source URL. bash source = "rtsp://admin:sec0mmth@192.168.254.3:554/Streaming/Channels/101"

  4. Interact with program:

  5. The program will start processing frames from the video source.

  6. It counts objects crossing defined regions and updates these counts to a Modbus server.

  7. To exit the program, Press Ctrl+C in the terminal until it stop and exit the program. Otherwise, the programm will loop.

  8. The saved videos will be in the directory called "recordings" in which the video is ordered by the datetime it is saved.

Owner

  • Login: 6438188321yossaphatk
  • Kind: user

Citation (CITATION.cff) 

# This CITATION.cff file was generated with https://bit.ly/cffinit

cff-version: 1.2.0
title: Ultralytics YOLO
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Glenn
    family-names: Jocher
    affiliation: Ultralytics
    orcid: 'https://orcid.org/0000-0001-5950-6979'
  - given-names: Ayush
    family-names: Chaurasia
    affiliation: Ultralytics
    orcid: 'https://orcid.org/0000-0002-7603-6750'
  - family-names: Qiu
    given-names: Jing
    affiliation: Ultralytics
    orcid: 'https://orcid.org/0000-0003-3783-7069'
repository-code: 'https://github.com/ultralytics/ultralytics'
url: 'https://ultralytics.com'
license: AGPL-3.0
version: 8.0.0
date-released: '2023-01-10'

GitHub Events

Total
Last Year

Dependencies

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.github/workflows/format.yml actions
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docker/Dockerfile docker
  • pytorch/pytorch 2.2.2-cuda12.1-cudnn8-runtime build
pyproject.toml pypi
  • matplotlib >=3.3.0
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics-thop >=0.2.5
requirements.txt pypi
  • Flask ==1.1.2
  • Pillow ==8.3.1
  • Pillow ==10.3.0
  • PyYAML ==6.0
  • PyYAML ==6.0.1
  • Requests ==2.32.3
  • Shapely ==2.0.4
  • albumentations ==1.4.8
  • beautifulsoup4 ==4.12.3
  • clip ==0.2.0
  • comet_ml ==3.43.1
  • coremltools ==7.2
  • dill ==0.3.8
  • duckdb ==1.0.0
  • dvclive ==3.46.1
  • flatbuffers ==24.3.25
  • hub_sdk ==0.0.8
  • ipython ==8.12.3
  • lancedb ==0.8.2
  • lapx ==0.5.9
  • lvis ==0.5.3
  • matplotlib ==3.4.2
  • mss ==9.0.1
  • ncnn ==1.0.20240410
  • nncf ==2.10.0
  • numpy ==1.23.0
  • numpy ==1.20.2
  • onnx ==1.13.0
  • onnx2tf ==1.22.4
  • onnxruntime ==1.13.1
  • onnxslim ==0.1.29
  • openai ==1.34.0
  • opencv_python ==4.9.0.80
  • openvino ==2024.1.0
  • paddle ==1.0.2
  • pafy ==0.5.5
  • pandas ==1.5.3
  • protobuf ==3.20.3
  • psutil ==5.9.8
  • py_cpuinfo ==9.0.0
  • pycocotools ==2.0.6
  • pymodbus ==3.6.8
  • pytest ==8.2.2
  • pytube ==15.0.0
  • ray ==2.24.0
  • roboflow ==1.1.32
  • scipy ==1.13.1
  • seaborn ==0.11.0
  • sentry_sdk ==2.5.1
  • streamlit ==1.35.0
  • streamlit_select ==0.3
  • super_gradients ==3.2.1
  • tensorflow ==2.16.1
  • tensorflowjs ==4.20.0
  • tensorrt ==10.0.1
  • tflite_runtime ==2.14.0
  • tflite_support ==0.4.4
  • thop ==0.1.1.post2209072238
  • torch ==2.3.0
  • torchvision ==0.18.0
  • tqdm ==4.66.4
  • tritonclient ==2.46.0
  • ultralytics_thop ==0.2.7
  • wandb ==0.17.1
  • x2paddle ==1.4.1
  • yt_dlp ==2024.5.27