argolid 0.0.5

Pyramid Generator For OMETiff

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

Argolid can generate 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"})