Source code for nighres.microscopy.stack_intensity_regularisation

import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
                    _check_topology_lut_dir, _check_available_memory

[docs]def stack_intensity_regularisation(image, ratio=50, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Stack intensity regularisation Estimates an image-to-image linear intensity scaling for a stack of 2D images Parameters ---------- image: niimg Input 2D images, stacked in the Z dimension ratio: float, optional Ratio of image differences to keep (default is 50%) save_data: bool Save output data to file (default is False) overwrite: bool Overwrite existing results (default is False) output_dir: str, optional Path to desired output directory, will be created if it doesn't exist file_name: str, optional Desired base name for output files with file extension (suffixes will be added) Returns ---------- dict Dictionary collecting outputs under the following keys (suffix of output files in brackets) * result (niimg): The intensity regularised input Notes ---------- Original Java module by Pierre-Louis Bazin. """ print('\nStack Intensity Regularisation') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir, image) regularised_file = os.path.join(output_dir, _fname_4saving(module=__name__,file_name=file_name, rootfile=image, suffix='sir-img')) if overwrite is False \ and os.path.isfile(regularised_file) : print("skip computation (use existing results)") output = {'result': regularised_file} return output # start virtual machine, if not already running try: mem = _check_available_memory() nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max']) except ValueError: pass # create instance sir = nighresjava.StackIntensityRegularisation() # set parameters # load image and use it to set dimensions and resolution img = load_volume(image) data = img.get_data() affine = img.affine header = img.header resolution = [x.item() for x in header.get_zooms()] dimensions = data.shape sir.setDimensions(dimensions[0], dimensions[1], dimensions[2]) sir.setInputImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) # set algorithm parameters sir.setVariationRatio(float(ratio)) # execute the algorithm try: sir.execute() except: # if the Java module fails, reraise the error it throws print("\n The underlying Java code did not execute cleanly: ") print(sys.exc_info()[0]) raise return # reshape output to what nibabel likes regularised_data = np.reshape(np.array(sir.getRegularisedImage(), dtype=np.float32), dimensions, 'F') # adapt header max for each image so that correct max is displayed # and create nifiti objects header['cal_min'] = np.nanmin(regularised_data) header['cal_max'] = np.nanmax(regularised_data) regularised = nb.Nifti1Image(regularised_data, affine, header) if save_data: save_volume(regularised_file, regularised) return {'result': regularised_file} else: return {'result': regularised}