image-slicer 3.1.0

A high-performance Python library to slice images into tiles.

Image Slicer

A high-performance Python library to slice images into tiles.

image-slicer provides a flexible and intuitive API to slice large images into smaller, manageable tiles. It uses pyvips as its backend, which makes it extremely fast and memory-efficient (up to 5x faster and 10x more memory-efficient than other approaches). It can process images far larger than your available system RAM.

Features

• High Performance: Leverages the speed of libvips.
• Memory Efficient: Low memory usage, ideal for very large images.
• Simple API: Slice images with a single function call or use the ImageSlicer class for more control.
• Flexible: Customise tile sizes and output naming conventions.
• Generator Support: Process tiles in-memory without writing them all to disk.
• Handles Any Image Size: Automatically manages images that are not perfectly divisible by the tile size.

Installation

You can use any modern Python package manager, such as pip or uv.

# Using uv
uv add image-slicer

# Or using pip
pip install image-slicer

CLI Usage

image-slicer comes with a command-line interface (CLI) for easy use in scripts or for quick slicing tasks.

Slice by Number of Tiles

This will slice the image into a specific number of tiles, automatically calculating the grid layout to be as square as possible.

imslice my_large_image.tif output_tiles --number-of-tiles 16

Slice by Grid Dimensions

You can also specify the exact grid dimensions (columns and rows).

imslice my_large_image.tif output_tiles --grid 4 4

Custom Naming Format

You can customise the output filenames using the --format option.

imslice my_large_image.tif output_tiles --grid 4 4 --format "tile_{row}_{col}.webp"

Usage

You can use the simple slice_image function for a quick one-off task or the ImageSlicer class for more advanced use cases.

Basic Example

This is the easiest way to get started. It will slice the image and save the tiles to the specified directory.

from image_slicer import slice_image

# Slice a large image into 256x256 pixel tiles
slice_image(
    source_path="my_large_image.tif",
    output_dir="output_tiles",
    tile_width=256,
    tile_height=256,
)

print("Slicing complete!")

Advanced Usage (Class-based)

For more control, instantiate the ImageSlicer class. This is useful if you want to inspect image properties before slicing or use the tile generator.

from image_slicer import ImageSlicer

# Initialise the slicer
slicer = ImageSlicer("my_large_image.jpg")

print(f"Image dimensions: {slicer.width}x{slicer.height}")

# Slice the image with a custom naming format
slicer.slice(
    output_dir="output_tiles_custom",
    tile_width=512,
    tile_height=512,
    naming_format="slice_row-{row}_col-{col}.webp"
)

In-Memory Tile Generation

If you need to process tiles without saving them to disk, you can use the generate_tiles method. It returns a generator that yields each tile as a pyvips.Image object.

from image_slicer import ImageSlicer

slicer = ImageSlicer("another_image.png")

# Generate 100x100 pixel tiles
tile_generator = slicer.generate_tiles(tile_width=100, tile_height=100)

for tile, row, col in tile_generator:
    # ---
    # Process the 'tile' object here.
    # For example, you could convert it to a NumPy array,
    # run analysis, or stream it somewhere.
    # ---
    print(f"Processing tile ({row}, {col}) with size {tile.width}x{tile.height}")
    
    # Example: save only the first column of tiles
    if col == 0:
        tile.write_to_file(f"tile_col0_{row}.jpg")