bm_dataset.split_images_two_tissues module¶

Splitting image containing two samples

Note, that using these scripts for 1+GB images take several tens of GB RAM

Sample usage:

python split_images_two_tissues.py         -i "/datagrid/Medical/dataset_ANHIR/images/COAD_*/scale-100pc/*_*.png"         --nb_workers 3

bm_dataset.split_images_two_tissues.arg_parse_params()[source]¶: parse the input parameters :return dict: parameters

bm_dataset.split_images_two_tissues.main(path_images, dimension, overwrite, nb_workers)[source]¶

main entry point

Parameters:	path_images – path to images dimension (int) – for 2D inages it is 0 or 1 overwrite (bool) – whether overwrite existing image on output nb_workers (int) – nb jobs running in parallel

bm_dataset.split_images_two_tissues.split_image(img_path, overwrite=False, cut_dim=0)[source]¶

split two images in single dimension

the input images assume to contain two names in the image name separated by “_”

Parameters:	img_path (str) – path to the input / output image overwrite (bool) – allow overwrite exiting output images cut_dim (int) – define splitting dimension

bm_dataset.split_images_two_tissues.CUT_DIMENSION = 0[source]¶: cut image in one dimension/axis

bm_dataset.split_images_two_tissues.SCALE_SIZE = 512[source]¶: use following image size for estimating cutting line