pcnaDeep

pcnaDeep.predictor

pcnaDeep.refiner

pcnaDeep.resolver

pcnaDeep.tracker

pcnaDeep.evaluate

class pcnaDeep.evaluate.pcna_ctcEvaluator(root, dt_id, digit_num=3, t_base=0, path_ctc_software=None, init_dir=True)[source]

Bases: object

__init__(root, dt_id, digit_num=3, t_base=0, path_ctc_software=None, init_dir=True)[source]

Evaluation of tracking output

set_evSoft(path_ctc_software)[source]

Set evaluation software path

Parameters:

path_ctc_software (str) – path to CTC evaluation software

generate_raw(stack)[source]

Save raw images by slice

Parameters:

stack (numpy.ndarray) – raw image

generate_ctc(mask, track, mode='RES')[source]

Generate standard format for Cell Tracking Challenge Evaluation, for RES or GT.

Parameters:
  • mask (numpy.ndarray) – mask output, no need to have cell cycle labeled

  • track (pandas.DataFrame) – tracked object table, can have gaped tracks

  • mode (str) – either “RES” or “GT”.

init_ctc_dir()[source]

Initialize Cell Tracking Challenge directory

An example directory

|-- 0001
    |-- 0001_RES
    |-- 0001_GT
        |-- SEG
        |-- TRA
evaluate()[source]

Call CTC evaluation software to run ((Unix) Linux/Mac only)

pcnaDeep.split

pcnaDeep.correct

Subpackages