Argolid

Build Requirements

Argolid uses Tensorstore for reading and writing pixel data. So Tensorstore build requirements are needed to be satisfied. For Linux, these are the requirements:

• GCC 10 or later
• Clang 8 or later
• Python 3.8 or later
• CMake 3.24 or later
• Perl, for building libaom from source (default). Must be in PATH. Not required if -DTENSORSTORE_USE_SYSTEM_LIBAOM=ON is specified.
• NASM, for building libjpeg-turbo, libaom, and dav1d from source (default). Must be in PATH.Not required if -DTENSORSTORE_USE_SYSTEM_{JPEG,LIBAOM,DAV1D}=ON is specified.
• GNU Patch or equivalent. Must be in PATH.

Building and Installing

Here is an example of building and installing Argolid in a Python virtual environment.

python -m virtualenv venv
source venv/bin/activate
pip install cmake
git clone --recurse-submodules https://github.com/sameeul/argolid.git 
cd argolid
python setup.py install

Usage

PyramidGenerator

Argolid can generate 2D Pyramids from a single image or an image collection with a stitching vector provided. It can generate three different kind of pyramids:

• Neuroglancer compatible Zarr (NG_Zarr)
• Precomputed Neuroglancer (PCNG)
• Viv compatible Zarr (Viv)

Currently, three downsampling methods (mean, mode_max and mode_min) are supported. A dictionary with channel id (integer) as key and downsampling method as value can be passed to specify downsampling method for specific channel. If a channel does not exist as a key in the dictionary, mean will be used as the default downsampling method

Here is an example of generating a pyramid from a single image.

from argolid import PyramidGenerartor
input_file = "/home/samee/axle/data/test_image.ome.tif"
output_dir = "/home/samee/axle/data/test_image_ome_zarr"
min_dim = 1024
pyr_gen = PyramidGenerartor()
pyr_gen.generate_from_single_image(input_file, output_dir, min_dim, "NG_Zarr", {0:"mode_max"})

Here is an example of generating a pyramid from a collection of images and a stitching vector.

from argolid import PyramidGenerartor input_dir = "/home/samee/axle/data/intensity1" file_pattern = "x{x:d}_y{y:d}_c{c:d}.ome.tiff" output_dir = "/home/samee/axle/data/test_assembly_out" image_name = "test_image" min_dim = 1024 pyr_gen = PyramidGenerartor() pyr_gen.generate_from_image_collection(input_dir, file_pattern, image_name, output_dir, min_dim, "Viv", {1:"mean"})

Argolid provides two main classes for working with volumetric data and generating multi-resolution pyramids:

VolumeGenerator

The VolumeGenerator class is used to create Zarr arrays from image stacks. It handles reading image files, grouping them based on specified criteria, and writing the data into a Zarr array.

Here's an example of how to use VolumeGenerator:

from argolid import VolumeGenerator

source_dir = "/path/to/image/files"
group_by = "z"  # Group images by z-axis
file_pattern = "image_{z:d}.tif"
out_dir = "/path/to/output"
image_name = "my_volume"

volume_gen = VolumeGenerator(source_dir, group_by, file_pattern, out_dir, image_name)
volume_gen.generate_volume()

PyramidGenerator3D

Here is an example of generating a 3D pyramid from a Zarr array:

from argolid import PyramidGenerator3D

zarr_loc_dir = "/path/to/zarr/array"
base_scale_key = 0
num_levels = 5

pyramid_gen = PyramidGenerator3D(zarr_loc_dir, base_scale_key)
pyramid_gen.generate_pyramid(num_levels)