# 🚀 VIVA: Versatile Intelligent Visual Annotation Tool ⚙️
<p align="center">
<img src="https://img.shields.io/badge/🚀%20Boost%20Your-Speed%20of%20Annotation-brightgreen?style=for-the-badge" />
<img src="https://img.shields.io/badge/🧠%20Focus%20On-Accuracy%2C%20Not%20Repetition-blue?style=for-the-badge" />
<img src="https://img.shields.io/badge/💾%20Perfect%20For-Custom%20Dataset%20Creation-orange?style=for-the-badge" />
</p>
---
## 🧠 Overview
**VIVA** is a modern, **smart visual annotation tool** built with **PyQt5**, designed to streamline dataset preparation for computer vision tasks such as **object detection** and **image classification**.
Whether annotating a **custom dataset** or working with a **well-organized dataset**, **VIVA** significantly reduces annotation time for users through its:
- ⚡ **Intuitive Interface** — Simple, clean, and user-friendly design
- 🧠 **Smart Automation Techniques** — Efficient features that simplify repetitive tasks
- 🧩 **Optimized Workflow Design** — Smooth navigation and quick annotation management
✨ These capabilities allow annotators to focus more on **accuracy** and less on **manual repetitive work**, making **VIVA** a powerful tool for **AI developers**, **researchers**, and **dataset creators**.
---
## 🌟 Key Features
- 🪶 User-friendly **PyQt5 GUI**
- 🎯 Object detection and image classification annotation modules
- ⌨️ Efficient keyboard shortcuts and Smart Options for fast labeling
- 🔍 Zoom, pan, and interactive label visualization
- 🧾 YOLO-compatible output formats
- 🧩 Modular design for future expansion (augmentation, segmentation)
---
## 🧰 Object Detection Annotation
VIVA’s **Object Detection Module** is packed with innovative features:
- 🎯 **Bounding Box Limits:** Set a maximum number of boxes per image; auto-switch to the next image when the limit is reached
- 🧠 **Smart Undo:** Press `A` to navigate to the previous image and delete the last bounding box drawn
- 🔺 **Flexible Shapes:** Draw rectangles, squares, circles, or polygons
- ⌨️ **Keyboard Shortcuts:**
- `Del` → Remove boxes
- `A` → Smart Previous image with Delete Function
- `D` → Next image
- `Enter` → Save
- 🖱️ One-click mode for drawing boxes
- 🪟 **Interactive GUI:** Zoom, pan, color-coded boxes, and label reflector for multi-class clarity
- 📂 **File Navigation:** Browse images with progress tracking (e.g., “3/10”)
- ⚠️ **Error Handling:** Clear `QMessageBox` alerts ensure smooth operation
💡 These features make **VIVA uniquely efficient**, saving annotation time compared to other tools.
---
## 🖼️ Image Classification Annotation
VIVA’s **Image Classification Module** streamlines labeling with:
- 🔘 **Single & Multi-Label Modes:** Toggle between single-label and multi-label modes via radio buttons
- 💡 **Auto-Suggestion for Labels:** Completer suggests previously used labels while typing
- 🪄 **Default Label Application:** Apply a default label to images using the `Space` key; single-mode overwrites existing labels with confirmation
- ⌨️ **Time-Saving Shortcuts:**
- `D` → Next image
- `A` → Previous image (clears labels for quick corrections)
- `Del` → Remove selected/all labels
- 📁 **Efficient File Management:** Copies images to label-specific directories with progress tracking
- 🔍 **Interactive GUI:** Zoom in/out, reset zoom, and pan with clean layout
- 🪞 **Label Reflector:** Displays current labels with visual feedback
- ⚠️ **Robust Error Handling:** User-friendly alerts for missing directories or invalid images
⚡ These features minimize repetitive tasks, making annotation faster and more intuitive than competing tools.
---
## 💻 Tech Stack
- 🐍 **Python** – Core logic and processing
- 🖥️ **PyQt5** – GUI interface
- 🎯 **YOLO Integration** – Optimized for object detection datasets
- 🧩 **Modular Design** – Scalable for future modules like augmentation or segmentation
---
## 🖼 Background Image Attribution
🖌️ The background image used in VIVA:
- **Title:** *"4K Marvel and DC Vector Art"*
- **Author / Source:** Wallpapers.com
- **URL:** [https://wallpapers.com/wallpapers/4k-marvel-and-dc-vector-art-pmkg7yqt3zz8bmcc.html](https://wallpapers.com/wallpapers/4k-marvel-and-dc-vector-art-pmkg7yqt3zz8bmcc.html)
- **License:** Free, Attribution required
- **Modifications:** Color filters applied for VIVA’s UI
✅ This attribution satisfies the license requirement and ensures legal usage.
---
## 🔮 Future Works
🚧 The following modules are planned for future development:
- 🧬 **Data Augmentation Tool** – To enhance datasets with transformations and increase model robustness
- 🎨 **Instance Segmentation & Key Point Detection Tools** – For detailed labeling in complex vision tasks
- 👨💻 All future updates will be developed solely by the project author
---
## 🪪 License
This project is licensed under the MIT License
. © 2025 Vishnu Vardhan Reddy Biyyapu
---
## 📦 Installation
Follow these steps to install and run **VIVA**:
### 1. Install Python
Make sure you have **Python 3.8 or higher** installed:
```bash
python --version
```
---
## 2. Create a Virtual Environment (Recommended)
It’s recommended to use a virtual environment to avoid conflicts with other Python packages:
```bash
python -m venv viva_env
```
Activate the environment:
- Windows:
```bash
viva_env\Scripts\activate
```
- Linux / macOS:
```bash
source viva_env/bin/activate
```
---
## 3. Install Required Packages
- Before running VIVA, install all required packages listed in requirements.txt:
```bash
pip install -r requirements.txt
```
This ensures all Python dependencies needed for VIVA are installed correctly.
⚠️ Note: Installing from PyPI (pip install viva) will also install these dependencies automatically.
---
## 4. Install and Run VIVA
After dependencies are installed:
From terminal/command prompt:
```bash
vivaa
```
From Python script:
```bash
from vivaa.main import main
main()
```
---
## 🛠 Workflow & Help Section
After successfully installing and running **VIVA** (`viva` command), you can access the **Help Section** directly from the application to learn about its workflow, shortcuts, and smart options designed for faster annotation.
### How to Access Help:
1. Launch VIVA using the terminal or command prompt:
```bash
vivaa
```
2. In the main VIVA window, hover your mouse over the title bar “VIVA □ ▭ ○”.
3. Click on the title — this will open the Help Section.
--
## 🎥 Demo Video
See **VIVA in action**! Watch a short demo showing the object detection and image classification modules, shortcuts, and smart options in real-time.
🔗 [Watch the demo of Object Detection Annotation Tool on LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7354882949181292544/)
🔗 [Watch the demo of Image Classification Tool on LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7357883048501108736/)
> Note: The video showcases VIVA’s workflow, time-saving features, and user-friendly interface for dataset annotation.
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"description": "# \ud83d\ude80 VIVA: Versatile Intelligent Visual Annotation Tool \u2699\ufe0f\r\n\r\n<p align=\"center\">\r\n <img src=\"https://img.shields.io/badge/\ud83d\ude80%20Boost%20Your-Speed%20of%20Annotation-brightgreen?style=for-the-badge\" />\r\n <img src=\"https://img.shields.io/badge/\ud83e\udde0%20Focus%20On-Accuracy%2C%20Not%20Repetition-blue?style=for-the-badge\" />\r\n <img src=\"https://img.shields.io/badge/\ud83d\udcbe%20Perfect%20For-Custom%20Dataset%20Creation-orange?style=for-the-badge\" />\r\n</p>\r\n\r\n---\r\n\r\n## \ud83e\udde0 Overview\r\n\r\n**VIVA** is a modern, **smart visual annotation tool** built with **PyQt5**, designed to streamline dataset preparation for computer vision tasks such as **object detection** and **image classification**.\r\n\r\nWhether annotating a **custom dataset** or working with a **well-organized dataset**, **VIVA** significantly reduces annotation time for users through its:\r\n\r\n- \u26a1 **Intuitive Interface** \u2014 Simple, clean, and user-friendly design \r\n- \ud83e\udde0 **Smart Automation Techniques** \u2014 Efficient features that simplify repetitive tasks \r\n- \ud83e\udde9 **Optimized Workflow Design** \u2014 Smooth navigation and quick annotation management \r\n\r\n\u2728 These capabilities allow annotators to focus more on **accuracy** and less on **manual repetitive work**, making **VIVA** a powerful tool for **AI developers**, **researchers**, and **dataset creators**.\r\n\r\n---\r\n\r\n## \ud83c\udf1f Key Features\r\n\r\n- \ud83e\udeb6 User-friendly **PyQt5 GUI** \r\n- \ud83c\udfaf Object detection and image classification annotation modules \r\n- \u2328\ufe0f Efficient keyboard shortcuts and Smart Options for fast labeling \r\n- \ud83d\udd0d Zoom, pan, and interactive label visualization \r\n- \ud83e\uddfe YOLO-compatible output formats \r\n- \ud83e\udde9 Modular design for future expansion (augmentation, segmentation) \r\n\r\n---\r\n\r\n## \ud83e\uddf0 Object Detection Annotation\r\n\r\nVIVA\u2019s **Object Detection Module** is packed with innovative features: \r\n\r\n- \ud83c\udfaf **Bounding Box Limits:** Set a maximum number of boxes per image; auto-switch to the next image when the limit is reached \r\n- \ud83e\udde0 **Smart Undo:** Press `A` to navigate to the previous image and delete the last bounding box drawn \r\n- \ud83d\udd3a **Flexible Shapes:** Draw rectangles, squares, circles, or polygons \r\n- \u2328\ufe0f **Keyboard Shortcuts:** \r\n - `Del` \u2192 Remove boxes \r\n - `A` \u2192 Smart Previous image with Delete Function \r\n - `D` \u2192 Next image \r\n - `Enter` \u2192 Save \r\n - \ud83d\uddb1\ufe0f One-click mode for drawing boxes \r\n- \ud83e\ude9f **Interactive GUI:** Zoom, pan, color-coded boxes, and label reflector for multi-class clarity \r\n- \ud83d\udcc2 **File Navigation:** Browse images with progress tracking (e.g., \u201c3/10\u201d) \r\n- \u26a0\ufe0f **Error Handling:** Clear `QMessageBox` alerts ensure smooth operation \r\n\r\n\ud83d\udca1 These features make **VIVA uniquely efficient**, saving annotation time compared to other tools.\r\n\r\n---\r\n\r\n## \ud83d\uddbc\ufe0f Image Classification Annotation\r\n\r\nVIVA\u2019s **Image Classification Module** streamlines labeling with: \r\n\r\n- \ud83d\udd18 **Single & Multi-Label Modes:** Toggle between single-label and multi-label modes via radio buttons \r\n- \ud83d\udca1 **Auto-Suggestion for Labels:** Completer suggests previously used labels while typing \r\n- \ud83e\ude84 **Default Label Application:** Apply a default label to images using the `Space` key; single-mode overwrites existing labels with confirmation \r\n- \u2328\ufe0f **Time-Saving Shortcuts:** \r\n - `D` \u2192 Next image \r\n - `A` \u2192 Previous image (clears labels for quick corrections) \r\n - `Del` \u2192 Remove selected/all labels \r\n- \ud83d\udcc1 **Efficient File Management:** Copies images to label-specific directories with progress tracking \r\n- \ud83d\udd0d **Interactive GUI:** Zoom in/out, reset zoom, and pan with clean layout \r\n- \ud83e\ude9e **Label Reflector:** Displays current labels with visual feedback \r\n- \u26a0\ufe0f **Robust Error Handling:** User-friendly alerts for missing directories or invalid images \r\n\r\n\u26a1 These features minimize repetitive tasks, making annotation faster and more intuitive than competing tools.\r\n\r\n---\r\n\r\n## \ud83d\udcbb Tech Stack\r\n\r\n- \ud83d\udc0d **Python** \u2013 Core logic and processing \r\n- \ud83d\udda5\ufe0f **PyQt5** \u2013 GUI interface \r\n- \ud83c\udfaf **YOLO Integration** \u2013 Optimized for object detection datasets \r\n- \ud83e\udde9 **Modular Design** \u2013 Scalable for future modules like augmentation or segmentation \r\n\r\n---\r\n\r\n## \ud83d\uddbc Background Image Attribution\r\n\r\n\ud83d\udd8c\ufe0f The background image used in VIVA:\r\n\r\n- **Title:** *\"4K Marvel and DC Vector Art\"* \r\n- **Author / Source:** Wallpapers.com \r\n- **URL:** [https://wallpapers.com/wallpapers/4k-marvel-and-dc-vector-art-pmkg7yqt3zz8bmcc.html](https://wallpapers.com/wallpapers/4k-marvel-and-dc-vector-art-pmkg7yqt3zz8bmcc.html) \r\n- **License:** Free, Attribution required \r\n- **Modifications:** Color filters applied for VIVA\u2019s UI \r\n\r\n\u2705 This attribution satisfies the license requirement and ensures legal usage.\r\n\r\n---\r\n\r\n## \ud83d\udd2e Future Works\r\n\r\n\ud83d\udea7 The following modules are planned for future development:\r\n\r\n- \ud83e\uddec **Data Augmentation Tool** \u2013 To enhance datasets with transformations and increase model robustness \r\n- \ud83c\udfa8 **Instance Segmentation & Key Point Detection Tools** \u2013 For detailed labeling in complex vision tasks \r\n- \ud83d\udc68\u200d\ud83d\udcbb All future updates will be developed solely by the project author \r\n\r\n---\r\n\r\n## \ud83e\udeaa License\r\n\r\nThis project is licensed under the MIT License\r\n. \u00a9 2025 Vishnu Vardhan Reddy Biyyapu\r\n\r\n---\r\n\r\n## \ud83d\udce6 Installation\r\n\r\nFollow these steps to install and run **VIVA**:\r\n\r\n\r\n\r\n### 1. Install Python\r\n\r\nMake sure you have **Python 3.8 or higher** installed:\r\n\r\n```bash\r\npython --version\r\n```\r\n---\r\n## 2. Create a Virtual Environment (Recommended)\r\n\r\nIt\u2019s recommended to use a virtual environment to avoid conflicts with other Python packages:\r\n\r\n```bash\r\npython -m venv viva_env\r\n```\r\n\r\nActivate the environment:\r\n\r\n- Windows:\r\n```bash\r\nviva_env\\Scripts\\activate\r\n```\r\n- Linux / macOS:\r\n```bash\r\nsource viva_env/bin/activate\r\n```\r\n---\r\n## 3. Install Required Packages\r\n\r\n- Before running VIVA, install all required packages listed in requirements.txt:\r\n```bash\r\npip install -r requirements.txt\r\n```\r\n\r\nThis ensures all Python dependencies needed for VIVA are installed correctly.\r\n\u26a0\ufe0f Note: Installing from PyPI (pip install viva) will also install these dependencies automatically.\r\n---\r\n## 4. 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