Skip to main content

No project description provided

Project description

Parameterized large image pre-processing with pychunklbl.toolkit

The package module pychunklbl.toolkit provides eight parameterized functions designed to work with large image files and provide pre-processing for machine learning.

The package module is intended developers to create machine learning datasets leveraging the openslide library for usage with tensorflow 2.0.


Installation

pip install digipath_mltk

# requires python 3.5 or later
pip3 install -r requirements.txt

Development install from clone in /DigiPath_MLTK/ directory:

pip3 install -r requirements.txt
pip3 install --upgrade ../DigiPath_MLTK

Command line examples

Images used in the examples below was downloaded from openslide data, the other data files are in the repository data/ directory.

Find the patches in a wsi file and write to an image file for preview.

python3 -m digipath_mltk.cli -m write_mask_preview_set -i yourpath/images/CMU-1-Small-Region.svs -o results

Find the patches in a wsi file and write to a directory.

python3 -m digipath_mltk.cli -m wsi_to_patches_dir -i CMU-1-Small-Region.svs -o results

Find the patches in a wsi file and write to a .tfrecords file.

python3 -m digipath_mltk.cli -m wsi_to_tfrecord -i CMU-1-Small-Region.svs -o results

View the patch locations in a .tfrecoreds file.

python3 -m digipath_mltk.cli -m tfrecord_to_masked_thumb -i CMU-1-Small-Region.svs -r CMU-1-Small-Region.tfrecords -o results

( test data not currently available in DigiPath_MLTK repository for the following examples )

Find pairs of patches with registration offset in two wsi files and write to a directory.

python3 -m digipath_mltk.cli -m registration_to_dir -i wsi_fixed.tiff -f wsi_float.tiff -D wsi_pair_sample_offset.csv -o results

Find pairs of patches with registration offset in two wsi files and write to a tfrecords file.

python3 -m digipath_mltk.cli -m registration_to_tfrecord -i  wsi_fixed.tiff -f wsi_float.tiff -D wsi_pair_sample_offset.csv -o results

Find the patches in a wsi file defined in an annotations file with a priority file and write to a directory.

python3 -m digipath_mltk.cli -m annotations_to_dir -i wsi_float.tiff -p wsi_float_annotation.csv -a wsi_float_annotation.xml -o results

Find the patches in a wsi file defined in an annotations file with a priority file and write to a tfrecords file.

python3 -m digipath_mltk.cli -m annotations_to_tfrecord -i wsi_float.tiff -p wsi_float_annotation.csv -a wsi_float_annotation.xml -o results

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for digipath-mltk, version 0.0.0
Filename, size File type Python version Upload date Hashes
Filename, size digipath_mltk-0.0.0-py3-none-any.whl (21.8 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size digipath_mltk-0.0.0.tar.gz (21.1 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page