SIFLIM

Software for image selection and marker labeling for FLIM using feature space projections

Requirements

Create virtual environment with Python 3.8 (ex. anaconda or miniconda)

• pip install -r requirements.txt

Generanting Dataset of Patches from Input Images

SIFLIM can recieve as input an OPFDataset with each sample being an NxNx3 patch extracted from a given image's superpixel segmentation.
To create such dataset you can run
python generate_patches_dataset.py <INPUT> <OUTPUT.zip> <PATCHSIZE> <MAX-SAMPLES-PER-CLASS>
on your virtual environment, where

• <INPUT> is the path to the fileset of original .png images
• <OUTPUT.zip> is the desired output file path/name
• <PATCHSIZE> is the patch size (e.g. = 75 generates 75x75x3 patches)
• <MAX-SAMPLES-PER-CLASS> is the max number of generates sample points per image