Image Scrapper

Image Scrapper is a Python application designed for efficiently scraping, displaying, and navigating through images stored in a hierarchical folder structure. This tool leverages PyQt5 for its graphical user interface, providing an interactive, zoomable, and scrollable canvas to visualize images from multiple directories.

Key Features:

• Folder Traversal: Automatically scans a root directory and recursively includes all subfolders, displaying images from every level of the hierarchy.
• Interactive Canvas: Displays images on a dynamic, infinite canvas with zoom and pan capabilities, allowing for easy navigation of large datasets.
• Image Organization: Groups images from each folder into vertical columns, with folder names and image names clearly labeled below each image.
• File Type Support: Supports commonly used image file formats, including JPG, PNG, and TIFF.
• Customizable Layout: Dynamically adjusts the canvas size to accommodate all images and folders, ensuring a smooth viewing experience.
• Performance Optimization: Handles large image datasets efficiently while maintaining interactivity and responsiveness.

Usage

This tool is ideal for:

• Researchers or analysts working with image datasets.
• Professionals who need to explore images organized in nested folders.
• Developers requiring an easy-to-use image visualization tool.

Python Branch and Complexity

• Python Branch: This project utilizes PyQt5 for GUI development, with Python’s os module for file handling and QGraphicsView for interactive graphics rendering.
• Complexity: The application handles recursive folder traversal, dynamic canvas resizing, and interactive image rendering, making it moderately complex. The integration of zoom and pan functionalities ensures a professional-grade user experience.

Code Structure

• Main Interface: Built using PyQt5's QGraphicsView and QGraphicsScene to support infinite canvas functionality with zoom and pan capabilities.
• Folder and File Handling: Recursively traverses directories to identify image files and display them in structured columns.
• Dynamic Canvas Layout: Automatically adjusts the canvas size and positioning of images and labels, ensuring clarity and usability.

Demonstration:

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/image-scrapper.git
   cd image-scrapper

2. Install the required packages:

   pip install -r requirements.txt

Usage

Run the main application:

python main.py

Select the root folder containing images, and the application will automatically scrape all subfolders and display the images in an organized layout on the canvas.

Freezing

To package the application as a standalone executable:

pyinstaller --onefile --windowed --icon=icon.png --add-data "icon.png;." --name "Image Scrapper" main.py

Dependencies

• Python 3.x
• PyQt5

Future Enhancements

• Thumbnail Generation: Add an option to load smaller thumbnails for improved performance with extremely large datasets.
• Image Filtering: Allow filtering by file type or other metadata.
• Annotation Support: Add support for basic annotation or markup on images.
• Save Layout: Provide the ability to save the current canvas layout for later use.

License

This project is licensed under the MIT License for non-commercial use.