tadqeeq 1.11.7

An interactive PyQt5 image annotation tool for segmentation masks and bounding boxes.

Tadqeeq – Image Annotator Tool

An interactive image annotation tool built with PyQt5, designed for efficient labeling of segmentation masks and bounding boxes.

Developed by Mohamed Behery @ RTR Software Development - An "Orbits" Subsidiary 📅 April 30, 2025 🪪 Licensed under the MIT License

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🚀 Widget Features

• ✅ Minimalist Interactive Design
• 🖌️ Scroll through label classes / Adjust pen size with the mouse wheel
• 🎨 Supports segmentation masks (.png) and bounding boxes (.txt)
• 🧠 Dynamic label color generation (HSV-based)
• 💬 Floating labels showing hovered and selected classes
• 💾 Auto-save and manual save (Ctrl+S)
• 🧽 Flood-fill segmentation with a postprocessing stage of binary hole filling
• 🚫 Right-click erase mode and double-click to clear all

🚀 CLI Features

• ✅ Minimalist Design
• 🎨 Navigate through images using A and D.

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📦 Installation

Option 1: Install via pip

pip install tadqeeq

Option 2: Run from source

git clone https://github.com/orbits-it/tadqeeq.git
cd tadqeeq
pip install -r requirements.txt

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🛠️ Usage

Import in your code:

from tadqeeq.widgets import ImageAnnotator

from tadqeeq.implementations import ImageAnnotatorWindow

Run CLI tool from command line (if installed via pip):

tadqeeq [--void_background] [--verbose] [--autosave] [--use_bounding_boxes] --images <images_directory_path> --classes <class_names_filepath> [--bounding-boxes <bounding_boxes_directory_path>] [--semantic-segments <semantic_segments_directory_path>]

Notes:

1. Use A and D to navigate through images.
   
2. At least one of --bounding-boxes or --semantic-segments must be provided.
3. The annotation files could either be:
   

a) PNG for semantic segmentation masks with class-labeled pixels on a white background.
b) txt for YOLO-style bounding boxes formatted as: label_index x_offset y_offset width height.

4. <class_names_filepath> is a txt file containing a list of a class names used in annotating.
   
5. Tool Behavior in Segmentation:
   

• If void_background is False:
  

• Increments all label values by 1, turning background (255) into 0 and shifting segment labels to start from 1.
  

• If void_background is True:
  

• Leaves label values unchanged (255 remains as void).
  

• Detects boundaries between segments and sets those boundary pixels to 255 (void label).

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🧭 Controls

Action	Mouse / Key
Toggle erase mode	Right-click
Draw / Erase	Left-click / Drag
Mark Bounding Box / Semantic Segment	Double left-click (drawing mode)
Clear all	Double right-click
Toggle slider mode	Wheel-click
Slide through label classes / Brush widths	Scroll wheel
Save manually	Ctrl + S
Reveal annotated segment's label name	Move cursor over annotation
Navigate between images	A / D (keyboard)

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📁 Project Structure

root/
├── tadqeeq/
|   ├── __init__.py         # Entry point for importing
|   ├── widgets.py          # Contains ImageAnnotator class
|   ├── utils.py            # Helper methods (flood fill, bounding box logic)
|   |
|   ├── implementations.py  # Provides a complete, minimal working example of how to integrate the ImageAnnotator 
│   │                       # within an annotation pipeline. These implementation objects are intended to be 
│   │                       # used as-is and should not be modified directly in other applications.
|   |
|   └── cli.py              # Entry point for a full annotation solution utilizing the code in `implementations.py`
├── README.md
├── LICENSE
├── setup.py
├── pyproject.toml
└── requirements.txt

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🧑‍💻 Contributing

Repository

Pull requests are welcome!
If you add features (e.g. COCO export, brush tools, batch processing), please document them in the README.

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📄 License

This project is licensed under the MIT License.
See LICENSE for the full license text.

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💡 Acknowledgements

🎉 Built for computer vision practitioners needing fast, mouse-based labeling with clean overlays and autosave logic.

🌟 Special thanks to PyQt5 for providing the powerful and flexible GUI toolkit that made the development of this interactive image annotator possible.

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🔗 Related Resources

• PyQt5 Docs
• NumPy
• scikit-image
• SciPy