pretrained and instructions for annotating and training segmentation of bacterial fluorescent images
Create environment:
conda create --name napari-env
conda activate napari-env
conda install -c conda-forge napari
conda install opencv
Install Tensorflow for macOS M1/M2:
pip install tensorflow-macos
pip install tensorflow-metal
Install stardist for cell segmentation:
pip install gputools
pip install stardist
pip install csbdeep
pip install splinedist
python augment_image_data.py
python training.py
Open up tensorboard to follow the results:
tensorboard --logdir=.
python prediction.py './examples/'
Where examples
is the folder that contains .tif
files for analysis.
Format has to be 16-bit monochrome TIFF.
The output will be produced in the active wokring directory as the original file names with prefix output_
.
output_*.tif
labels, 16-bit TIFF each cell has a unique unint16 value.output_*.zip
ImageJ ROI zip file, can be opened in ImageJ/Fiji ROI Manager.