🖼️ ImageConvert

ImageConvert is a robust Python library designed for seamless conversion between modern image formats and PDFs. Unlike basic converters, it prioritizes data integrity—preserving EXIF metadata, GPS coordinates, and original file timestamps during the process.

✨ Key Features

• 🔄 Universal Conversion: Support for JPEG, PNG, WebP, HEIC/AVIF, TIFF, BMP, and more.
• 📄 PDF Power Tools:
  • PDF to Image: Render PDF pages as high-quality images.
  • Image to PDF: Compile multiple images into a single document.
• 💾 Metadata Safe: Preserves EXIF data (Camera model, ISO, GPS) during conversion.
• ⏱️ Time Travel: Retains original file creation, access, and modification timestamps (Windows/Linux/macOS supported).
• 📂 Batch Processing: Recursively convert entire folder structures with a single command (stream results, optional multi-core).

📃 Full Documentation

For a complete guide on all classes, methods, and parameters, please see the API Reference.

Explore the complete documentation and examples here:
👉 https://ricardos-projects.gitbook.io/imageconvert-docs

📦 Installation

pip install imageconvert

Note: This automatically installs necessary dependencies like pillow-heif (for AVIF/HEIC) and pymupdf (for PDF).

🚀 Quick Start

1. Basic Conversion

Convert single images easily. Metadata is preserved by default.

from imageconvert import ImageConvert

ImageConvert.convert("vacation.heic", "vacation.jpg")

ImageConvert.convert("input.png", "output.webp", quality=85)

2. PDF Operations

Handle PDF documents without external tools.

# Extract all pages from a PDF as High-Res JPEGs
ImageConvert.pdf_to_images("contract.pdf", "output_folder", dpi=300)

# Create a PDF from a list of images (A4 size, contained fit)
ImageConvert.images_to_pdf(
    ["scan1.jpg", "scan2.jpg"], 
    "combined_doc.pdf",
    fit_method="contain"
)

3. Batch Processing

Convert an entire directory tree. Useful for optimizing libraries or web assets. Stream results as they finish and optionally use multiple processes.

results = ImageConvert.batch_convert(
    input_dir="./raw_photos", 
    output_dir="./web_ready", 
    output_format=".webp", 
    recursive=True,
    skip_existing=True,
    workers=4,           # optional parallelism
    stream=True          # yields as soon as a file is done
)

for out_path in results:
    print("converted:", out_path)

skip_existing now compares modification times, so edited sources are re-converted even if an older output file exists.

### 4. Extracting Metadata

Get technical details, including GPS coordinates and PDF info.

```python
info = ImageConvert.get_image_info("photo.jpg")

print(f"Camera: {info.get('camera')}")
# Output: {'make': 'Apple', 'model': 'iPhone 13 Pro', 'exposure': {'iso': 120...}}

if 'gps' in info:
    print(f"Location: {info['gps']}")
    # Output: {'latitude': 40.7128, 'longitude': -74.0060}

🩰 Supported Formats

🤝 Contributing

Contributions are welcome! Please visit the GitHub Repository to report bugs or submit pull requests.

📄 License

This project is licensed under the MIT License.