pdf-image-extract-annotate 1.0.0

Extract images from PDFs and create annotated versions with watermarks

PDFImageExtractAnnotate

A Python package for extracting images from PDF documents and creating annotated versions with watermarks showing the extracted image filenames.

Features

• Image Extraction: Extract all images from PDF documents with configurable filters
• Page-based Organization: Images are organized by page number for easy reference
• Watermark Annotation: Add watermarks to the original PDF showing extracted image filenames
• Flexible Filtering: Filter images by dimensions, file size, or relative compression
• Azure Blob Storage Support: Optional support for storing images in Azure Blob Storage
• Customizable Watermarks: Configure font size, color, background, and text format

Installation

From PyPI (when published)

pip install pdf-image-extract-annotate

From Source

git clone https://github.com/thijshakkenbergecolab/pdf-image-extract-annotate
cd pdf-image-extract-annotate
pip install -e .

With Azure Support

pip install pdf-image-extract-annotate[azure]

Quick Start

Basic Image Extraction

from pathlib import Path
from pdf_image_extract_annotate import PDFImageExtractor, ExtractionConfig

# Configure extraction
config = ExtractionConfig(
    output_dir="extracted_images",
    dim_limit=50,  # Minimum dimension in pixels
    abs_size=1000  # Minimum file size in bytes
)

# Extract images
extractor = PDFImageExtractor(config)
result = extractor.extract_all_images(Path("document.pdf"))

print(f"Extracted {result['images_extracted']} images")
print(f"Saved to: {result['output_directory']}")

Extract and Watermark PDF

from pathlib import Path
from pdf_image_extract_annotate import PDFImageWatermarker, WatermarkConfig

# Configure watermark appearance
watermark_config = WatermarkConfig(
    font_size=10,
    font_color=(1.0, 0.0, 0.0),  # Red text
    background_color=(1.0, 1.0, 1.0, 0.7),  # Semi-transparent white
    text_format="filename"  # Show just the filename
)

# Process PDF
watermarker = PDFImageWatermarker(
    pdf_path=Path("document.pdf"),
    watermark_config=watermark_config
)

result = watermarker.process_pdf_with_watermarks()

# Save the annotated PDF
result.output_pdf.save("annotated_document.pdf")
result.output_pdf.close()

print(f"Extracted {result.images_extracted} images")
print(f"Watermarked {result.images_watermarked} images")

Configuration Options

ExtractionConfig

• output_dir (str): Directory to save extracted images
• dim_limit (int): Minimum dimension filter (0 = no limit)
• rel_size (float): Relative size filter (0.0-1.0, 0 = no limit)
• abs_size (int): Absolute size filter in bytes (0 = no limit)
• blob_connection_string (str, optional): Azure Blob Storage connection string

WatermarkConfig

• font_size (int): Font size for watermark text
• font_color (tuple): RGB color values (0.0-1.0)
• background_color (tuple): RGBA background color
• text_format (str): Format for watermark text ("filename", "filepath", or "custom")
• padding (int): Padding around text in pixels

Output Structure

Images are organized using a page-based structure:

output_dir/
├── images/
│   ├── page_1/
│   │   ├── img00001.png
│   │   └── img00002.jpg
│   ├── page_2/
│   │   └── img00003.png
│   └── page_N/
│       └── imgXXXXX.ext
└── annotated_pdf.pdf  (if using watermarker)

Advanced Usage

Using with Azure Blob Storage

from pdf_image_extract_annotate import PDFImageExtractor, ExtractionConfig

config = ExtractionConfig(
    output_dir="my-container",
    blob_connection_string="DefaultEndpointsProtocol=https;..."
)

extractor = PDFImageExtractor(config)
result = extractor.extract_all_images(Path("document.pdf"))

Custom Image Filtering

from pdf_image_extract_annotate import PDFImageExtractor, ExtractionConfig

# Only extract large, high-quality images
config = ExtractionConfig(
    output_dir="high_quality_images",
    dim_limit=200,      # At least 200px in smallest dimension
    rel_size=0.5,       # At least 50% of uncompressed size
    abs_size=50000      # At least 50KB
)

Dependency Injection in Larger Projects

from pathlib import Path
from pdf_image_extract_annotate import PDFImageWatermarker, ExtractionConfig, WatermarkConfig

class DocumentProcessor:
    def __init__(self, extraction_config: ExtractionConfig, watermark_config: WatermarkConfig):
        self.extraction_config = extraction_config
        self.watermark_config = watermark_config

    def process_document(self, pdf_path: Path):
        watermarker = PDFImageWatermarker(
            pdf_path=pdf_path,
            extraction_config=self.extraction_config,
            watermark_config=self.watermark_config
        )
        return watermarker.process_pdf_with_watermarks()

API Reference

Classes

• PDFImageExtractor: Core image extraction functionality
• PDFImageWatermarker: Extended extractor with watermarking capabilities
• ExtractionConfig: Configuration for image extraction
• WatermarkConfig: Configuration for watermark appearance
• ImageMetadata: Metadata for extracted images
• ImageWatermarkEntry: Entry for images with watermark information
• WatermarkResult: Result of the PDF watermarking process

Requirements

• Python 3.11+
• PyMuPDF >= 1.23.0
• pydantic >= 2.0.0
• azure-storage-blob >= 12.0.0 (optional, for Azure support)

Development

Setting up development environment

# Clone the repository
git clone https://github.com/thijshakkenbergecolab/pdf-image-extract-annotate
cd pdf-image-extract-annotate

# Install in development mode with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black pdf_image_extract_annotate tests

# Type checking
mypy pdf_image_extract_annotate

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=pdf_image_extract_annotate

# Run specific test file
pytest tests/test_extractor.py

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Support

For issues, questions, or suggestions, please open an issue on GitHub.