sem-meta 0.1.1

Unified interface for SEM image processing: metadata extraction, OCR-based pixel size estimation, and unit conversion

sem-meta

A unified Python package for SEM (Scanning Electron Microscopy) image processing, providing metadata extraction, OCR-based pixel size estimation, and unit conversion utilities.

Features

• SEMMetaData: Extract and format metadata from SEM images
• SEMOCR: OCR-based pixel size estimation from scale bars
• ConvertPS: Unit conversion and error analysis for pixel size data
• FullSEMKeys: Standardized metadata key list for SEM images

Installation

Install from PyPI:

pip install sem-meta

Quick Start

from sem_meta import SEMMeta, OCRPS, ConvertScale, FullSEMKeys

# Extract metadata from SEM images
metadata = SEMMeta.extract_metadata("path/to/sem/image.tif")

# Perform OCR on scale bars
pixel_size = OCRPS.extract_pixel_size("path/to/image/with/scalebar.tif")

# Convert units
converted_size = ConvertScale.convert_units(pixel_size, "μm", "nm")

# Access standardized SEM metadata keys
sem_keys = FullSEMKeys

Main Components

SEMMetaData

Extracts and processes metadata from SEM image files, particularly TIFF files with EXIF data.

SEMOCR

Uses OCR (Optical Character Recognition) to extract pixel size information from scale bars in SEM images. Includes noise filtering and error correction for common OCR mistakes.

ConvertPS

Handles unit conversion and normalization for pixel size measurements, supporting various scientific units commonly used in microscopy.

FullSEMKeys

Provides a standardized set of metadata keys for consistent SEM image annotation and data extraction.

Dependencies

• numpy: Numerical computations
• PIL (Pillow): Image processing
• pymysql: SQL safety utilities
• termcolor: Terminal output styling
• matplotlib: Visualization
• opencv-python: Advanced image preprocessing
• pytesseract: OCR functionality

Requirements

• Python 3.6+
• Tesseract OCR engine (for OCR functionality)

Installing Tesseract

Ubuntu/Debian:

sudo apt install tesseract-ocr

macOS:

brew install tesseract

Windows: Download and install from: https://github.com/UB-Mannheim/tesseract/wiki

Usage Examples

Extracting SEM Metadata

from sem_meta import SEMMeta

# Initialize and extract metadata
sem_processor = SEMMeta
metadata = sem_processor.extract_metadata("sample.tif")
print(metadata)

OCR-based Scale Bar Reading

from sem_meta import OCRPS

# Extract pixel size from scale bar
ocr_processor = OCRPS
pixel_size = ocr_processor.extract_pixel_size("sem_image.tif")
print(f"Pixel size: {pixel_size}")

Unit Conversion

from sem_meta import ConvertScale

# Convert between units
converter = ConvertScale
result = converter.convert_units("0.5 μm", "nm")
print(f"Converted: {result}")

File Structure

sem-meta/
├── src/
│   └── sem_meta/
│       ├── __init__.py
│       ├── metadata_Module.py    # SEMMetaData class
│       ├── ocr_Module.py         # SEMOCR class
│       ├── convert_Module.py     # ConvertPS class
│       ├── SEMKEYS.py           # FullSEMKeys definitions
│       └── OCR_NOISE_DB.py      # OCR noise patterns database
├── README.md
├── LICENSE
└── pyproject.toml

Contributing

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

License

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

Authors

• Ahmed Khalil - Initial work

Acknowledgments

• Built for the SEM imaging community
• Supports various SEM manufacturers' metadata formats
• Includes extensive OCR noise pattern recognition

Support

If you encounter any problems or have questions, please open an issue on the GitHub repository.