Image Resizer Tool 🖼️

Developed by Abhranil Singha Roy

An advanced, high-performance Python utility designed for professional batch processing and resizing of images. Built with Pillow and concurrent.futures, this tool automates large-scale image scaling operations using multi-core processing, making it incredibly fast and efficient.

Ideal for web developers preparing thumbnails, data scientists managing massive image datasets, or anyone needing quick, reliable bulk image processing.

✨ Advanced Features

• Blazing Fast Multiprocessing: Utilizes all available CPU cores (ProcessPoolExecutor) to process images concurrently, drastically reducing processing time for large batches.
• Progress Tracking: Features a beautiful, real-time progress bar powered by tqdm.
• Smart Aspect Ratio & EXIF Handling: Automatically reads EXIF data to correct image orientation before resizing. Preserves original proportions by default.
• Format Conversion: Convert your entire batch to modern formats like WEBP, JPEG, or PNG on the fly.
• Comprehensive Logging: Generates a detailed resizer.log file, recording successes, warnings, and skipped files without cluttering your terminal.
• Robust Error Recovery: Skips corrupt files and incompatible formats cleanly. Automatically handles RGBA to RGB color space conversions.
• CLI Configuration: Fully controllable via command-line arguments.

🛠️ Requirements

• Python 3.8+
• Pillow >= 10.0.0 (Core image manipulation)
• tqdm >= 4.66.0 (Progress bar)

🚀 Installation

1. Clone the repository:

   git clone https://github.com/abhranilsingharoy-cloud/Image-Resizer-Tool.git
   cd Image-Resizer-Tool

2. Install the dependencies:

   pip install -r requirements.txt

💻 Usage

1. Directory Structure

By default, the script looks for an input_images directory. You can use the provided script to generate some test images if needed.

.
├── image_resizer.py
├── generate_test_images.py  <-- Run this to create dummy images for testing
├── requirements.txt
├── input_images/            <-- Place your original images here
└── resized_images/          <-- The tool will save output here

2. Basic Run

Run the script with default settings (Input: input_images, Output: resized_images, Size: 800x600, maintaining aspect ratio):

python image_resizer.py

3. Advanced Configuration (CLI Arguments)

The tool acts as a powerful CLI. You can specify exact constraints, change output formats, and control image quality.

python image_resizer.py --source "raw_photos" --output "web_thumbnails" --width 1024 --height 768 --format webp --quality 80

Argument	Description	Default
--source	Source directory containing the original images.	input_images
--output	Target directory to save the resized images.	resized_images
--width	Target maximum width in pixels.	800
--height	Target maximum height in pixels.	600
--force	Force the image to exact dimensions (ignores aspect ratio).	False
--format	Convert all output images to a specific format (jpg, png, webp).	Original format
--quality	Set output compression quality for JPEG/WEBP (1-100).	85
--workers	Number of concurrent CPU processes to use.	Optimal CPU Count

🧪 Testing

To test the script's multiprocessing and progress bar:

python generate_test_images.py
python image_resizer.py --format webp

Check resizer.log for execution details.

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page or submit a pull request.