ImageCropper

Warning

Development version – more features coming soon!

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📷 Introduction

ImageCropper is an interactive image cropping tool built with Python and PyQt5. This tool allows you to open large images, select specific areas to crop, and save them efficiently to your local storage. It's designed to handle large images efficiently, displaying blocks of the image without consuming excessive memory.

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🚀 Features

• Interactive Image Display: Visualize large images and navigate through them seamlessly.
• Mouse & Keyboard Controls: Crop and move around the image using your mouse and keyboard arrows.
• Mini-Map: Preview the entire image and quickly jump to specific areas using the mini-map.
• Configurable Crop Sizes: Set custom crop sizes to capture the perfect image segment.
• Efficient Memory Usage: Loads only parts of the image that are visible, ensuring smooth performance.
• Save Cropped Areas: Save selected areas to your desired folder in real time.
• And more are coming soon!

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🔧 Installation

1. Clone the repository:

   git clone https://github.com/your-repo/ImageCropper.git
   cd ImageCropper

2. Install required dependencies:

   pip install -r requirements.txt

   Note: The main dependencies are PyQt5, OpenCV, and NumPy.

3. Run ImageCropper:

   python ImageCropper.py

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🎨 Usage

1. Click "Open Image" to select an image file.
2. Adjust the crop size using the dialog box (default is 100x100 pixels).
3. Select a destination folder for the cropped images.
4. Move around the image using the arrow keys or mini-map:
   • Arrows to navigate left/right/up/down.
   • Ctrl + Arrows to move in larger steps.
5. Click on the image to crop and save the selected portion.

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📚 Shortcuts & Controls

Action	Shortcut / Control
Open Image	Open Image button
Move Left	Left Arrow
Move Right	Right Arrow
Move Up	Up Arrow
Move Down	Down Arrow
Move Faster	Ctrl + Arrow Keys
Click to Crop	Left Mouse Button
Adjust Crop Size	Crop Size dialog box

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📂 Project Structure

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🔮 Future Enhancements

• Advanced Editing: Include image adjustments like brightness, contrast, and filters.
• Support for More File Formats: Add support for additional image file types.
• Computer Vision Features: Implement object detection and image segmentation.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.