This package contains routines to process and analyse data obtained from predicted segmentation of microscopy data in package. See UnetSegmentation.
Used to get metrics from the predicted data
Constructor (filename): initialise with a hdf5 containing the volume data that has been predicted and combined from several different orientations. This file is normally the output from the prediction produced by binary unet segmentation https://github.com/rosalindfranklininstitute/UnetSegmentation
Normally, after setting the data file, user has to choose the weighting method used for calculating consistency score.
Uses pytrorch to do a 3D average pooling, by providing parameters data3d (data volume), w_width ( width in pixels of the cube chunk that is calculated in the GPU), k_width (does the average in the cube with this width), s_stride (step, in pixels for next average calculation).
This operation is normally done with the weighted predicted data to determine regions with higher consistency.
Class to store and analyse score data. Contains routines to generate histograms and suggest volumes of interest based in histogram positions.
Utility that can be used to generate a report of consistency score analysis from a combined prediction file. This script uses lg-genpredreport.ipynb notebook. Information about how to use and parameters to use can be obtained by running
python lg-genpredcsreport.ipynb --help
An average pooling operation using PyTorch CUDA routines are used. The data obtained gives a score about how consistent the data is in the each of smaller volumes.
Data from the average pooling is analysed and consistency score based visualisations are available
