stereo.tools.cell_cut.CellCut.cell_cut#

CellCut.cell_cut(bgef_path=None, gem_path=None, mask_path=None, image_path=None, model_path=None, mask_save=True, model_type='deep-learning', depp_cro_size=20000, overlap=100, gen_mask_on_gpu='-1', tissue_seg_model_path=None, tissue_seg_method=None, post_processing_workers=10)[source]#
Generate CGEF resutl via following combinations:

  • BGEF and mask

  • BGEF and ssDNA image

Parameters:

  • bgef_path (Optional[str]) – the path to BGEF file.

  • gem_path (Optional[str]) – the path to GEM file.

  • mask_path (Optional[str]) – the path to mask file.

  • image_path (Optional[str]) – the path to ssDNA image file.

  • model_path (Optional[str]) – the path to model file.

  • mask_save (bool) – whether to save mask file after correction, generated from ssDNA image.

  • model_type (str) – the type of model to generate mask, whcih only could be set to deep learning model and deep cell model.

  • depp_cro_size (int) – deep crop size.

  • overlap (int) – overlap size.

  • gen_mask_on_gpu (str) – specify gpu id to predict when generate mask, if '-1', use cpu for prediction.

  • tissue_seg_model_path (Optional[str]) – the path of deep-learning model of tissue segmentation, if set it to None, it would use OpenCV to process.

  • tissue_seg_method (Optional[str]) – the method of tissue segmentation, 0 is deep-learning and 1 is OpenCV.

  • post_processing_workers (int) – the number of processes for post-processing.

Returns:

Path to CGEF result.