{"id":29550887,"url":"https://github.com/hamed-gharghi/objshomar","last_synced_at":"2026-05-17T01:42:36.837Z","repository":{"id":304946521,"uuid":"1020636130","full_name":"Hamed-Gharghi/ObjShomar","owner":"Hamed-Gharghi","description":"ObjShomar is an easy-to-use desktop application for real-time object detection and counting using YOLOv8. Designed for non-technical users, it features a modern English interface and supports live camera feeds, video files, and network streams. 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ObjShomar 🎥📦🔢\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE)\n![Python](https://img.shields.io/badge/Python-3.10%2B-blue?logo=python)\n![PySide6](https://img.shields.io/badge/PySide6-Qt%20for%20Python-green?logo=qt)\n![Platform](https://img.shields.io/badge/Platform-Windows%20%7C%20Linux-lightgrey?logo=windows\u0026logoColor=white)\n\n---\n\n## 📑 Navigation | ناوبری\n- [🇬🇧 English](#english)\n- [🇮🇷 فارسی](#persian)\n\n---\n\n\u003ca name=\"english\"\u003e\u003c/a\u003e\n# 🇬🇧 English\n\n\u003e **ObjShomar** — An application for object counting and detection in videos and camera streams using YOLOv8 and a modern, user-friendly desktop interface.\n\u003e\n\u003e **Name origin:** \"ObjShomar\" is a combination of \"Object\" (English) and \"Shomar\" (Persian for \"counting\").\n\n---\n\n## 🚀 Quick Start\n\n1. **Clone the repository:**\n   ```bash\n   git clone https://github.com/YourUsername/ObjShomar.git\n   cd ObjShomar\n   ```\n2. **Install Python 3.10+** (Recommended: 3.10, 3.11, or 3.12)\n3. **Install dependencies:**\n   ```bash\n   pip install -r requirements.txt\n   ```\n4. **Run the app:**\n   ```bash\n   python main.py\n   ```\n\n---\n\n## 🖼️ Screenshots\n\n| Main Window (Initial) | Model Selection | Class Selection |\n|-----------------------|----------------|-----------------|\n| ![Empty Main Window](assets/Empty-Main-Window.png) | ![YOLO Engine Select](assets/Yolo-Engine-Select.png) | ![Classes Selection](assets/Classes-Selection.png) |\n\n| Camera Link Input | Detection on Camera Stream | Detection on Video File |\n|------------------|---------------------------|------------------------|\n| ![Camera Link Input](assets/Camera-Link-Input.png) | ![Camera Link Car Detection](assets/Camera-Link-Car-Detection.png) | ![Video Person \u0026 Backpack Detection](assets/Video-Person\u0026Backpack-Detection.png) |\n\n---\n\n## ✨ Features\n\n- **YOLOv8 Object Detection:**  \n  Detect and count objects in real-time using the latest YOLOv8 models.\n- **Flexible Model Selection:**  \n  Choose from multiple YOLOv8 variants (nano, small, medium, large, x-large). Download missing models automatically.\n- **Class Filtering:**  \n  Select which object classes to count and display.\n- **Multiple Video Sources:**  \n  - Open local video files (MP4, AVI, etc.)\n  - Enter camera/network stream URLs (RTSP, HTTP, etc.)\n- **Live Visualization:**  \n  Bounding boxes, labels, and object counts overlaid on video.\n- **Zoom \u0026 Pan:**  \n  Inspect video frames in detail with interactive zoom and pan.\n- **Screenshot:**  \n  Save annotated frames as images.\n- **Modern GUI:**  \n  Built with PySide6 (Qt for Python) for a responsive, cross-platform experience.\n- **Easy Setup:**  \n  Automatic dependency checks and guided installation.\n\n---\n\n## ❓ What is ObjShomar?\n\nObjShomar is a desktop tool for object counting and detection in videos or live streams using the powerful YOLOv8 deep learning models. It’s designed for ease of use, flexibility, and real-time performance—ideal for research, surveillance, traffic analysis, and more.\n\n---\n\n## 🤖 Powered by YOLO\n\nThis project uses the amazing [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) object detection models.  \nA huge thank you to the Ultralytics team and the open-source community for making state-of-the-art vision accessible!\n\n---\n\n## 📝 License\n\nMIT\n\n---\n\n## 🤝 Contributing\n\nContributions, issues, and feature requests are welcome!  \nFeel free to open an issue or submit a pull request.\n\n---\n\n## 📬 Contact\n\nFor questions, suggestions, or collaboration, feel free to reach out:\n- 💼 [GitHub](https://github.com/YourUsername)\n- 📧 Email: your.email@example.com\n\n---\n\n## 🏷️ Keywords\n\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eYOLO\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eObject Detection\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003ePySide6\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eQt for Python\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eVideo Analysis\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eReal-time\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eDeep Learning\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eComputer Vision\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eObject Counting\u003c/span\u003e\n\n---\n\n## 🙏 Thanks\n\nSpecial thanks to [Ultralytics YOLO](https://github.com/ultralytics/ultralytics) and the open-source community!\n\n---\n\n\u003ca name=\"persian\"\u003e\u003c/a\u003e\n# 🇮🇷 فارسی\n\n\u003e **اُبجِ شمار** — برنامه‌ای برای شمارش و تشخیص اشیا در ویدیوها و استریم‌های دوربین با استفاده از YOLOv8 و رابط کاربری مدرن و ساده.\n\u003e\n\u003e **درباره نام:** اُبجِ شمار ترکیبی از واژه انگلیسی \"Object\" (شیء) و واژه فارسی \"شمار\" (شمارش) است.\n\n---\n\n## 🚀 شروع سریع\n\n۱. **کلون کردن مخزن:**\n   ```bash\n   git clone https://github.com/YourUsername/ObjShomar.git\n   cd ObjShomar\n   ```\n۲. **نصب پایتون ۳.۱۰ یا بالاتر (پیشنهادی: ۳.۱۰، ۳.۱۱ یا ۳.۱۲)**\n۳. **نصب وابستگی‌ها:**\n   ```bash\n   pip install -r requirements.txt\n   ```\n۴. **اجرای برنامه:**\n   ```bash\n   python main.py\n   ```\n\n---\n\n## 🖼️ اسکرین‌شات\n\n| پنجره اصلی | انتخاب مدل YOLO | انتخاب کلاس‌ها |\n|------------|-----------------|----------------|\n| ![Empty Main Window](assets/Empty-Main-Window.png) | ![YOLO Engine Select](assets/Yolo-Engine-Select.png) | ![Classes Selection](assets/Classes-Selection.png) |\n\n| ورودی لینک دوربین | تشخیص خودرو در استریم | تشخیص افراد و کوله‌پشتی در ویدیو |\n|-------------------|----------------------|-------------------------------|\n| ![Camera Link Input](assets/Camera-Link-Input.png) | ![Camera Link Car Detection](assets/Camera-Link-Car-Detection.png) | ![Video Person \u0026 Backpack Detection](assets/Video-Person\u0026Backpack-Detection.png) |\n\n---\n\n## ✨ ویژگی‌ها\n\n- **تشخیص و شمارش اشیا با YOLOv8:**  \n  شمارش و تشخیص اشیا به صورت بلادرنگ با مدل‌های YOLOv8\n- **انتخاب مدل دلخواه:**  \n  انتخاب از بین مدل‌های مختلف YOLOv8 (nano, small, medium, large, x-large) و دانلود خودکار مدل‌های مورد نیاز\n- **فیلتر کلاس‌ها:**  \n  انتخاب کلاس‌های مورد نظر برای شمارش و نمایش\n- **پشتیبانی از منابع ویدیویی مختلف:**  \n  - باز کردن فایل‌های ویدیویی (MP4, AVI و ...)\n  - وارد کردن لینک استریم دوربین (RTSP, HTTP و ...)\n- **نمایش زنده:**  \n  نمایش جعبه و برچسب و شمارش اشیا روی ویدیو\n- **بزرگ‌نمایی و جابجایی تصویر:**  \n  امکان بزرگ‌نمایی و جابجایی برای بررسی دقیق‌تر فریم‌ها\n- **گرفتن اسکرین‌شات:**  \n  ذخیره فریم‌های حاشیه‌نویسی شده به عنوان تصویر\n- **رابط کاربری مدرن:**  \n  ساخته شده با PySide6 (Qt for Python) برای تجربه کاربری سریع و مدرن\n- **نصب آسان:**  \n  بررسی خودکار وابستگی‌ها و نصب راهنما\n\n---\n\n## ❓ اُبجِ شمار چیست؟\n\nاُبجِ شمار یک ابزار دسکتاپ برای شمارش و تشخیص اشیا در ویدیوها یا استریم‌های زنده با استفاده از مدل‌های قدرتمند YOLOv8 است. این برنامه برای سهولت استفاده، انعطاف‌پذیری و عملکرد بلادرنگ طراحی شده است—مناسب برای پژوهش، نظارت، تحلیل ترافیک و موارد دیگر.\n\n---\n\n## 🤖 قدرت‌گرفته از YOLO\n\nاین پروژه از مدل‌های قدرتمند [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) استفاده می‌کند.  \nاز تیم Ultralytics و جامعه متن‌باز بابت در دسترس قرار دادن فناوری بینایی ماشین پیشرفته سپاسگزاریم!\n\n---\n\n## 📝 مجوز\n\nMIT\n\n---\n\n## 🤝 مشارکت\n\nپیشنهادات، گزارش باگ و درخواست ویژگی جدید خوش‌آمد است!  \nمی‌توانید issue باز کنید یا pull request ارسال نمایید.\n\n---\n\n## 📬 ارتباط\n\nبرای سوال، پیشنهاد یا همکاری:\n- 💼 [GitHub](https://github.com/YourUsername)\n- 📧 ایمیل: your.email@example.com\n\n---\n\n## 🏷️ کلیدواژه‌ها\n\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eYOLO\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eتشخیص اشیا\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003ePySide6\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eQt for Python\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eتحلیل ویدیو\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eبلادرنگ\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eیادگیری عمیق\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eبینایی ماشین\u003c/span\u003e\n\u003cspan style=\"display:inline-block;background:#f3f3f3;border-radius:6px;padding:3px 10px;margin:2px 2px;font-size:90%;\"\u003eشمارش اشیا\u003c/span\u003e\n\n---\n\n## 🙏 تشکر\n\nتشکر ویژه از [Ultralytics YOLO](https://github.com/ultralytics/ultralytics) و جامعه متن‌باز!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhamed-gharghi%2Fobjshomar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhamed-gharghi%2Fobjshomar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhamed-gharghi%2Fobjshomar/lists"}