imagetensors 0.2.1

imagetensors provides a standardized, unified interface for reading multiple proprietary microscopy image formats, converting them into standardized 5D NumPy arrays (T, Z, C, Y, X) for seamless downstream analysis and processing.

🔬 imagetensors: Unified Microscopy Image Reading

imagetensors provides a standardized, unified interface for reading multiple proprietary microscopy image formats, converting them into standardized 5D NumPy arrays (T, Z, C, Y, X) for seamless downstream analysis and processing.

It aims to abstract away the complexity of handling diverse file formats, allowing researchers and developers to focus on image data analysis.

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✨ Features

• Unified Interface: A single ImageReader class handles all supported formats automatically based on the file extension.
• Standardized Output: All images are read into a consistent 5D array format (T-ime, Z-slice, C-hannel, Y-row, X-column).
• Comprehensive Metadata: Extracts essential physical and dimensional metadata, including pixel/voxel size, time resolution, and display ranges.
• ImageJ Compatibility: Built-in utilities to save images as ImageJ-compatible TIFF files with embedded metadata.
• Extensible Architecture: Based on an Abstract Base Class (BaseImageReader) for easy addition of new formats.

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💾 Supported Formats

Extension	Format Description	Reader Class
.lif	Leica Image File (supports multi-series)	LifImageReader
.czi	Zeiss CZI (supports phase separation)	CziImageReader
.oib	Olympus OIB	OibImageReader
.nd	Nikon ND (supports multi-file assemblies)	NdImageReader
.tif, .tiff	Standard TIFF Stacks	TifImageReader

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⬇️ Installation

You can install imagetensors using various Python package managers or by cloning the repository.

Using Pip or uv

The most common way to install is via PyPI using pip or the faster alternative, uv:

# Using pip
pip install imagetensors

# Using uv
uv add imagetensors

From Source

If you need the latest development version, you can clone the repository and install it locally:

git clone https://github.com/bryanbarcelona/imagetensors.git
cd imagetensors
pip install .

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Reading Images

The core public API is the ImageReader factory class. It automatically selects the correct underlying reader based on the file extension.

from imagetensors import ImageReader
from pathlib import Path

# Initialize the reader for any supported file
file_path = "path/to/my_multi_series_image.lif"
reader = ImageReader(file_path)

# ImageReader is iterable, allowing you to process one series at a time
print(f"Reading file: {reader.path.name}")

for idx, image_data in enumerate(reader):
    # image_data is an ImageData object

    # Access the 5D NumPy array (T, Z, C, Y, X)
    array = image_data.array

    # Access the standardized metadata
    metadata = image_data.metadata

    # Print basic info
    print(f"\nSeries {idx + 1}:")
    print(f"  Shape: {array.shape}")
    print(f"  Dtype: {array.dtype}")
    print(f"  Z-Spacing: {metadata.spacing:.4f} µm")

    # Example usage: process the image data
    # processed_array = my_analysis_function(array)

The ImageData Object

All readers yield an ImageData dataclass containing the processed 5D array and its corresponding Metadata object.

@dataclass
class ImageData:
    array: np.ndarray  # Shape: (T, Z, C, Y, X)
    metadata: Metadata

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🖼️ Image Conversion Utilities

The package provides utilities to save the standardized ImageData objects as compliant ImageJ-compatible TIFF files, embedding critical metadata.

Saving a Single Image Series

Use save_as_tif to save a single ImageData object.

from imagetensors import ImageReader, save_as_tif
from pathlib import Path

file_path = "path/to/my_image.czi"
reader = ImageReader(file_path)

# 1. Get the first image series
first_series = next(iter(reader))

output_path = Path("output_data") / f"{Path(file_path).stem}_series_1.tif"
saved_path = save_as_tif(first_series, output_path, compression='lzw')

print(f"Single image series saved to: {saved_path}")

Saving All Series in a Batch

Use save_all_as_tif to process a multi-series file and save every series into a specified output directory.

from imagetensors import ImageReader, save_all_as_tif
from pathlib import Path

multi_series_file = "path/to/multi_series_image.lif"
reader = ImageReader(multi_series_file)

output_directory = "batch_tiff_output"

# Save all series using a template name
saved_paths = save_all_as_tif(
    reader=reader,
    output_dir=output_directory,
    # Template uses {stem} of the original file and {series} index
    name_template="{stem}_S{series}.tif",
    compression='zlib'
)

print(f"Batch conversion complete. {len(saved_paths)} files saved in: {output_directory}")
# Example output filename: multi_series_image_S1.tif, multi_series_image_S2.tif, etc.

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⚙️ Advanced Configuration

Overriding Physical Pixel Size

You can optionally override the pixel size read from the file's metadata during reader initialization. This can be useful for files with missing or incorrect metadata.

# Force all images from this file to use a 0.1 µm pixel size
reader = ImageReader(
    file_path="path/to/image.oib",
    override_pixel_size_um=0.1
)

for image_data in reader:
    # image_data.metadata.x_resolution and y_resolution will be 0.1
    ...

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🤖 AI Usage Declaration

Look, I’m not proud of it, but yeah...an AI helped scribble parts of this README. I’m basically a human who can’t type fast enough. I went through every word afterward, fixed the weird bits, and made sure it still sounded like the mess I call “me.” It’s less “collaboration” and more “I needed a ghostwriter who doesn’t charge by the hour.”