Fischer Robert
389 lines
15 KiB
Python
389 lines
15 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Mon Aug 15 16:35:22 2022
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@author: fische_r
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class to create feature stack on 4D tomo data
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transient dimension, e.g. time, should be 4th dimension of input data
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TODO: add GPU support (CUDA, cupy, cucim)
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"""
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# necessary packages for feature stack
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import numpy as np
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from scipy import ndimage
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from skimage import filters
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from skimage.morphology import ball
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import dask
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import dask.array
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# from dask.distributed import Client, LocalCluster
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from itertools import combinations_with_replacement, combinations
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import xarray as xr
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# functions take chunked dask-array as input
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# start-up cluster, TODO: option to connect to exisitng cluster
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# TODO: class/function to boot up cluster with custom options, e.g. workers/threads
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# esp. use SSD for memory spilling
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# cluster = LocalCluster()
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# client = Client(cluster)
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# print('Dashboard at '+cluster.dashboard_link)
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default_feature_dict = {'Gaussian': True,
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# 'Sobel': True,
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'Hessian': True,
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'Diff of Gaussians': True,
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'maximum': True,
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'minimum': True,
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'median': True,
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'extra_time_ranks': True,
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}
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def ball_4d(sig):
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bnd = np.zeros((sig*2+1,sig*2+1,sig*2+1,sig*2+1), dtype = bool)
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bnd[sig,sig,sig,sig] = True
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ecd = ndimage.distance_transform_edt(~bnd)
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bnd = (ecd<sig+0.01).astype(int)
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return bnd
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class image_filter:
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def __init__(self,
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data_path = None,
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outpath = None,
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sigmas = [0,2,4],
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feature_dict = default_feature_dict,
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mod_feat_dict = None,
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chunksize = '20 MiB', #try to align chunks to extend far in time --> should be useful for most filters, esp. the dynamic rank filters
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outchunks = '300 MiB',
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ranks = ['maximum', 'minimum', 'median'], #, 'mean'
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sigma_t = 40
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):
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if mod_feat_dict is not None:
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for key in mod_feat_dict:
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feature_dict[key] = mod_feat_dict[key]
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self.data_path = data_path
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self.outpath = outpath
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self.sigmas = sigmas
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self.feature_dict = feature_dict
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# TODO: allow option of custom shaped chunks
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self.chunks = chunksize
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self.outchunks = outchunks
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# not sure if this is clever, does dask understand that this data is reused?
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self.Gaussian_4D_dict = {}
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self.Gaussian_space_dict = {}
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self.Gaussian_time_dict = {}
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self.Gradient_dict = {}
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self.calculated_features = []
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self.feature_names = []
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self.considered_ranks = ranks
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self.sigma_t = sigma_t
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self.prepared = False
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self.computed = False
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# TODO: currently loads full dataset into memory, consider aligning desired chunk already for original dataset to avoid rechunking
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# .rechunk() causes problems downstream: "Assertion error" , WTF?!
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# if original data soe not fit in RAM, rechunk, store to disk and load again?
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def open_raw_data(self):
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data = xr.open_dataset(self.data_path)
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da = dask.array.from_array(data.tomo.data, chunks = self.chunks)
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self.original_dataset = data
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self.data = da
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def open_lazy_data(self):
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data = xr.open_dataset(self.data_path, chunks = 'auto')
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da = dask.array.from_array(data.tomo)
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print('maybe re-introducing rechunking, but for large datasets auto might be ok')
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print('currently provided chunks are ignored')
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self.original_dataset = data#.rechunk(self.chunks)
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self.data = da
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def load_raw_data(self):
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data = xr.load_dataset(self.data_path)
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# da = dask.array.from_array(data.tomo).rechunk(chunks = self.chunks)
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da = dask.array.from_array(data.tomo.data, chunks = self.chunks)
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self.original_dataset = data
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self.data = da
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def Gaussian_Blur_4D(self, sigma):
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# TODO: check on boundary mode
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deptharray = np.ones(self.data.ndim)+4*sigma
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deptharray = tuple(np.min([deptharray, self.data.shape], axis=0))
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G = self.data.map_overlap(filters.gaussian, depth=deptharray, boundary='nearest', sigma = sigma)
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self.feature_names.append('Gaussian_4D_Blur_'+f'{sigma:.1f}')
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self.calculated_features.append(G)
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self.Gaussian_4D_dict[f'{sigma:.1f}'] = G
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def Gaussian_Blur_space(self, sigma):
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deptharray = np.ones(self.data.ndim)+4*sigma
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deptharray[-1] = 0
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sigmas = np.ones(deptharray.shape)*sigma
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deptharray = tuple(np.min([deptharray, self.data.shape], axis=0))
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sigmas[-1] = 0
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G = self.data.map_overlap(filters.gaussian, depth=deptharray, boundary='nearest', sigma = sigmas)
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self.feature_names.append('Gaussian_space_'+f'{sigma:.1f}')
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self.calculated_features.append(G)
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self.Gaussian_space_dict[f'{sigma:.1f}'] = G
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def Gaussian_Blur_time(self, sigma):
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deptharray = np.ones(self.data.ndim)+4*sigma
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deptharray[:-1] = 0
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sigmas = np.ones(deptharray.shape)*sigma
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deptharray = tuple(np.min([deptharray, self.data.shape], axis=0))
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sigmas[:-1] = 0
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G = self.data.map_overlap(filters.gaussian, depth=deptharray, boundary='nearest', sigma = sigmas)
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self.feature_names.append('Gaussian_time_'+f'{sigma:.1f}')
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self.calculated_features.append(G)
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self.Gaussian_time_dict[f'{sigma:.1f}'] = G
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def Gaussian_4D_stack(self):
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flag = True
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for sigma in self.sigmas:
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if np.abs(sigma-0)<0.1:
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if flag:
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flag = False
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self.Gaussian_4D_dict['original'] = self.data
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self.calculated_features.append(self.data)
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self.feature_names.append('original')
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else:
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self.Gaussian_Blur_4D(sigma)
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def Gaussian_space_stack(self):
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flag = True
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for sigma in self.sigmas:
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if np.abs(sigma-0)<0.1:
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if flag:
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flag = False
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self.Gaussian_space_dict['original'] = self.data
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else:
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self.Gaussian_Blur_space(sigma)
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def Gaussian_time_stack(self):
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flag = True
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for sigma in self.sigmas:
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if np.abs(sigma-0)<0.1:
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if flag:
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flag = False
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self.Gaussian_time_dict['original'] = self.data
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else:
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self.Gaussian_Blur_time(sigma)
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def diff_Gaussian(self, mode):
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if mode == '4D':
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lookup_dict = self.Gaussian_4D_dict
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elif mode == 'space':
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lookup_dict = self.Gaussian_space_dict
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elif mode == 'time':
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lookup_dict = self.Gaussian_time_dict
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for comb in combinations(lookup_dict.keys(),2):
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DG = lookup_dict[comb[1]] - lookup_dict[comb[0]]
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name = ''.join(['diff_of_gauss_',mode,'_',comb[1],'_',comb[0]])
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self.calculated_features.append(DG)
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self.feature_names.append(name)
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def diff_to_first_and_last(self):
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options = self.Gaussian_space_dict.keys()
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for opt in options:
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DA = self.Gaussian_space_dict[opt]
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first = DA[...,0]
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last = DA[...,-1]
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DF = DA.copy()
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DL = DA.copy()
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for i in range(DA.shape[-1]):
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curr = DA[...,i]
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DF[...,i] = curr-first
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DL[...,i] = curr-last
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name1 = 'diff_to_first_'+opt
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name2 = 'diff_to_last_'+opt
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self.calculated_features.append(DF)
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self.calculated_features.append(DL)
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self.feature_names.append(name1)
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self.feature_names.append(name2)
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def Gradients(self):
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for key in self.Gaussian_4D_dict:
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G = self.Gaussian_4D_dict[key]
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gradients = dask.array.gradient(G)
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self.Gradient_dict[key] = gradients
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def Hessian(self):
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# TODO: add max of all dimensions
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for key in self.Gradient_dict.keys():
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axes = range(self.data.ndim)
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gradients = self.Gradient_dict[key]
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H_elems = [dask.array.gradient(gradients[ax0], axis=ax1) for ax0, ax1 in combinations_with_replacement(axes, 2)]
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gradnames = ['Gradient_sigma_'+key+'_'+str(ax0) for ax0 in axes]
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elems = [(ax0,ax1) for ax0, ax1 in combinations_with_replacement(axes, 2)]
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hessnames = [''.join(['hessian_sigma_',key,'_',str(elm[0]),str(elm[1])]) for elm in elems ]
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self.feature_names = self.feature_names + gradnames + hessnames
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self.calculated_features = self.calculated_features+gradients+H_elems
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# def rank_filter(self, option, sigma):
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# # note: rank filters not yet available in CUCIM and don't work with dask --> figure out why
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# da = self.data
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# if not np.abs(sigma-0)<1:
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# if option == 'minimum':
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# fun = filters.rank.minimum
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# elif option == 'maximum':
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# fun = filters.rank.maximum
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# elif option == 'median':
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# fun = filters.rank.median
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# elif option == 'mean':
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# fun = filters.rank.mean
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# fp = ball_4d(sigma)
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# deptharray = np.ones(da.ndim)+sigma
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# deptharray = tuple(np.min([deptharray, da.shape], axis=0))
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# R = da.map_overlap(fun, depth=deptharray, footprint=fp)
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# name = ''.join([option,'_',f'{sigma:.1f}'])
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# self.calculated_features.append(R)
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# self.feature_names.append(name)
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# def dynamic_rank_filter(self, option, sigma):
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# # TODO: add custom dynamic model, eg. sigmoid
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# da = self.data
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# if option == 'minimum':
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# fun = filters.rank.minimum
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# elif option == 'maximum':
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# fun = filters.rank.maximum
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# elif option == 'median':
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# fun = filters.rank.median
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# elif option == 'mean':
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# fun = filters.rank.mean
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# fp_3D = ball(sigma)
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# fp_4D = np.zeros(list(fp_3D.shape)+[2*self.sigma_t], dtype=int)
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# fp_4D[fp_3D>0,:] = 1
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# deptharray = np.ones(da.ndim)+sigma
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# deptharray[-1] = self.sigma_t
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# deptharray = tuple(np.min([deptharray, da.shape], axis=0))
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# R = da.map_overlap(fun, depth=deptharray, footprint=fp_4D)
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# name = ''.join([option,'_dynamic_',f'{sigma:.1f}'])
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# self.calculated_features.append(R)
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# self.feature_names.append(name)
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def rank_like_filter(self, option, sigma):
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# note: rank filters not yet available in CUCIM
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da = self.data
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if not np.abs(sigma-0)<1:
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if option == 'minimum':
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fun = ndimage.minimum_filter
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elif option == 'maximum':
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fun = ndimage.maximum_filter
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elif option == 'median':
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fun = ndimage.median_filter
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# elif option == 'mean':
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# fun = filters.rank.mean
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fp = ball_4d(sigma)
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deptharray = np.ones(da.ndim)+sigma
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deptharray = tuple(np.min([deptharray, da.shape], axis=0))
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R = da.map_overlap(fun, depth=deptharray, footprint=fp)
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name = ''.join([option,'_',f'{sigma:.1f}'])
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self.calculated_features.append(R)
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self.feature_names.append(name)
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def dynamic_rank_like_filter(self, option, sigma):
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# TODO: add custom dynamic model, eg. sigmoid
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da = self.data
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if option == 'minimum':
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fun = ndimage.minimum_filter
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elif option == 'maximum':
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fun = ndimage.maximum_filter
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elif option == 'median':
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fun = ndimage.median_filter
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# elif option == 'mean':
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# fun = filters.rank.mean
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fp_3D = ball(sigma)
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fp_4D = np.zeros(list(fp_3D.shape)+[2*self.sigma_t], dtype=int)
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fp_4D[fp_3D>0,:] = 1
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deptharray = np.ones(da.ndim)+sigma
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deptharray[-1] = self.sigma_t
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deptharray = tuple(np.min([deptharray, da.shape], axis=0))
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R = da.map_overlap(fun, depth=deptharray, footprint=fp_4D)
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name = ''.join([option,'_dynamic_',f'{sigma:.1f}'])
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self.calculated_features.append(R)
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self.feature_names.append(name)
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def rank_filter_stack(self):
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for option in self.considered_ranks:
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for sigma in self.sigmas:
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self.rank_like_filter(option, sigma)
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self.dynamic_rank_like_filter(option, sigma)
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# TODO: include feature selection either in compute (better) or save
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# TODO: maybe add purge function
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def prepare(self):
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self.Gaussian_4D_stack()
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self.diff_Gaussian('4D')
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self.Gradients()
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self.Hessian()
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self.Gaussian_time_stack()
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self.diff_Gaussian('time')
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self.Gaussian_space_stack()
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self.diff_Gaussian('space')
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self.rank_filter_stack()
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self.prepared = True
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def stack_features(self):
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if not self.prepared:
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print('prepare first')
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else:
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self.feature_stack = dask.array.stack(self.calculated_features, axis = 4)
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def compute(self):
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# self.feature_stack = self.feature_stack.compute()
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self.feature_stack = self.feature_stack.persist() #not sure, but persist should be preferred
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self.computed = True
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def make_xarray_nc(self, outpath = None, store=False):
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if outpath is None:
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outpath = self.outpath
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shp = self.feature_stack.shape
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coords = {'x': np.arange(shp[0]), 'y': np.arange(shp[1]), 'z': np.arange(shp[2]), 'time': np.arange(shp[3]), 'feature': self.feature_names}
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if store:
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if self.computed:
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if not type(self.feature_stack) is np.ndarray:
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self.feature_stack.rechunk(self.outchunks)
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#TODO avoid this explcit conversion. however seems necessary ?...
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# if type(self.feature_stack) is not np.ndarray:
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# self.feature_stack = self.feature_stack.compute()
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self.result = xr.Dataset({'feature_stack': (['x','y','z','time', 'feature'], self.feature_stack)},
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coords = coords
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)
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self.result.to_netcdf(outpath)
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else:
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print('maybe you have to compute the stack first ... ?!')
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else:
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self.result = xr.Dataset({'feature_stack': (['x','y','z','time', 'feature'], self.feature_stack)},
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coords = coords
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)
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