Fischer Robert
379 lines
16 KiB
Python
379 lines
16 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Spyder Editor
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This is a temporary script file.
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TODO: allow general deselection. does it it need a rerun of "prepare"?
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TODO: allow deselection in derived features (gradients, hessian)
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"""
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import numpy as np
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from scipy import ndimage
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import dask_image.ndfilters
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import dask
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import dask.array
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from itertools import combinations_with_replacement, combinations
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import xarray as xr
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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, 1, 3, 6],
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sigma_for_ref = 2,
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mod_feat_dict = None,
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sigma_t = 40,
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sigma_0_derivatives = False,
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take_means = True,
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num_means = 3,
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ignored_features = None
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):
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if sigma_for_ref not in sigmas:
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sigmas.append(sigma_for_ref)
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self.ignored_features = ignored_features
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self.sigma_for_ref = sigma_for_ref
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self.sigmas = sigmas
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#wheter considering means for first and last time step
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self.take_means = take_means
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self.num_means = num_means
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#wether to use the pixel coordinates as feature (not recommended, therefore no variable, can be set after initialization)
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self.loc_features = False
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# wether to calculate image derivates for raw image (rather useless because of high noise, set as above)
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self.sigma_0_derivatives = False
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# set up dicts and lists to feed dask graph
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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.calculated_features_time_independent = []
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self.feature_names_time_independent = []
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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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self.verbose = False
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## features
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def Gaussian_Blur_4D(self, sigma):
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G = dask_image.ndfilters.gaussian_filter(self.data, mode='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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sigmas = np.ones(self.data.ndim)*sigma
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sigmas[-1] = 0 # potenital option: weak time sigma
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G = dask_image.ndfilters.gaussian_filter(self.data, mode='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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sigmas = np.ones(self.data.ndim)*sigma
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sigmas[:-1] = 0 # potenital option: weak space sigma
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G = dask_image.ndfilters.gaussian_filter(self.data, mode='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 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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G1 = lookup_dict[comb[1]]
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G0 = lookup_dict[comb[0]]
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DG = dask.array.subtract(G1,G0)
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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, take_mean, means):
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# use small sigma Gaussian instead of raw data ?
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DA = self.data
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if take_mean:
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first = DA[...,:means].mean(axis=-1)
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last = DA[...,-means:].mean(axis=-1)
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else:
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first = DA[...,0]
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last = DA[...,-1]
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DF = DA - first[...,None]
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DL = DA - last[...,None]
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self.calculated_features.append(DF)
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self.feature_names.append('diff_to_first_')
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self.calculated_features.append(DL)
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self.feature_names.append('diff_to_last_')
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self.feature_names_time_independent.append('first_')
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self.calculated_features_time_independent.append(first)
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self.feature_names_time_independent.append('last_')
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self.calculated_features_time_independent.append(last)
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def time_stats(self):
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DA = self.data
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mean = DA.mean(axis=-1)
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minimum = DA.min(axis=-1)
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# median sounds good on paper, but is an expensive calculation with little benefit over mean
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diff_min = DA - minimum[...,None]
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# TODO: consider diffs to gaussian filtered mimimum, and diffs of gaussians
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G = self.Gaussian_4D_dict[f'{self.sigma_for_ref:.1f}']
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Gmin = G.min(axis=-1)
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Gmindiff = G - Gmin[...,None]
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self.calculated_features.append(diff_min)
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self.feature_names.append('diff_to_min_')
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self.feature_names_time_independent.append('full_temp_mean_')
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self.calculated_features_time_independent.append(mean)
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self.feature_names_time_independent.append('full_temp_min_')
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self.calculated_features_time_independent.append(minimum)
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self.feature_names_time_independent.append(''.join(['full_temp_min_Gauss_',f'{self.sigma_for_ref:.1f}']))
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self.calculated_features_time_independent.append(Gmin)
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self.feature_names.append(''.join(['diff_temp_min_Gauss_',f'{self.sigma_for_ref:.1f}']))
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self.calculated_features.append(Gmindiff)
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def Gradients(self):
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for key in self.Gaussian_4D_dict:
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if key == '0.0' and not self.sigma_0_derivatives: continue
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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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if key == '0.0' and not self.sigma_0_derivatives: continue
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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 pixel_coordinates(self):
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#create 3 arrays with the pixel coordinates
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da = self.data
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loc_x = dask.array.ones(da.shape[:-1])*dask.array.arange(da.shape[0])[:,None, None]
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self.feature_names_time_independent.append('loc_'+'x')
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self.calculated_features_time_independent.append(loc_x)
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loc_y = dask.array.ones(da.shape[:-1])*dask.array.arange(da.shape[1])[None,:, None]
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self.feature_names_time_independent.append('loc_'+'y')
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self.calculated_features_time_independent.append(loc_y)
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loc_z = dask.array.ones(da.shape[:-1])*dask.array.arange(da.shape[2])[None, None, :]
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self.feature_names_time_independent.append('loc_'+'z')
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self.calculated_features_time_independent.append(loc_z)
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# stack featrues
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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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sig = 0
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self.Gaussian_Blur_4D(sig)
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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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sig = 0
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self.Gaussian_Blur_space(sig)
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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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sig = 0
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self.Gaussian_Blur_time(sig)
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else:
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self.Gaussian_Blur_time(sigma)
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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() #dask_image might provide rank-like filters soon you have to load the entire raw data set for the dynamic part of this filter --> not so good for many time steps
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self.time_stats() #does something similar like the dynamic rank filter, however only one pixel in space
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self.diff_to_first_and_last(self.take_means, self.num_means)
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if self.loc_features:
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self.pixel_coordinates()
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# #this feature is a double-edged sword, use with care!!
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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_names = np.array(self.feature_names)
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self.feature_names_time_independent = np.array(self.feature_names_time_independent)
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self.stack_has_been_reduced = False
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self.feature_stack = dask.array.stack(self.calculated_features, axis = 4)
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self.feature_stack_time_independent = dask.array.stack(self.calculated_features_time_independent, axis=3)
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shp = self.feature_stack_time_independent.shape
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self.feature_stack_time_independent = self.feature_stack_time_independent.reshape(shp[0],shp[1],shp[2],1,shp[3])
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if self.ignored_features is not None:
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ids_time = np.ones(len(self.feature_names), dtype=bool)
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ids_independent = np.ones(len(self.feature_names_time_independent), dtype=bool)
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for i in range(len(ids_time)):
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if self.feature_names[i] in self.ignored_features:
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ids_time[i] = False
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for i in range(len(ids_independent)):
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if self.feature_names_time_independent[i] in self.ignored_features:
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ids_independent[i] = False
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self.reduce_feature_stack(ids_time, ids_independent, verbose=self.verbose)
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def create_feature_ids(self, feature_names_to_use):
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# time dependent features
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ids_time = np.zeros(len(self.feature_names), dtype=bool)
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for i in range(len(ids_time)):
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if self.feature_names[i] in feature_names_to_use:
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ids_time[i] = True
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self.ids_time = ids_time
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# time in-dependent features
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ids_independent = np.zeros(len(self.feature_names_time_independent), dtype=bool)
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for i in range(len(ids_independent)):
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if self.feature_names_time_independent[i] in feature_names_to_use:
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ids_independent[i] = True
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self.ids_independent = ids_independent
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def reduce_feature_stack(self, feature_names_to_use):
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# what about adding features? --> not implemented, better idea to start from the start and use saved label images
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# in jupyter notebook to add a step to reduce feature stack
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# TODO: option selection in GUI would be nice, maybe jupyter widget is a straightforward option
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"""
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Parameters
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----------
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ids_time : boolean array
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same length as self.calculated_features True for features to use
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ids_independent : boolean array
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same length as self.calculated_features True for features to use
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verbose : boolean, optional
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Print overview of used features. The default is False.
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Returns
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-------
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2 lazy dask arrays only using selected features
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2 lists of feature names
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"""
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self.create_feature_ids(feature_names_to_use)
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ids_time = self.ids_time
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ids_independent = self.ids_independent
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self.reduced_stack = self.feature_stack[...,ids_time]
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self.reduced_stack_time_independent = self.feature_stack_time_independent[...,ids_independent]
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self.feature_names_reduced = self.feature_names[ids_time]
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self.feature_names_reduced_time_independent = self.feature_names_time_independent[ids_independent]
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self.feature_selection = ids_time
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self.feature_selection_time_idependent = ids_independent
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self.stack_has_been_reduced = True
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if self.verbose:
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print('Considered dynamic features')
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for name in self.feature_names_reduced:
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print(name)
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print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
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print('Considered static features')
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for name in self.feature_names_reduced_time_independent:
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print(name)
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def make_xarray(self, use_reduced=True):
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if use_reduced and self.stack_has_been_reduced:
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shp = self.reduced_stack.shape
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feature_names = self.feature_names_reduced
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feature_names_time_independent = self.feature_names_reduced_time_independent
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feature_stack = self.reduced_stack
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feature_stack_time_independent = self.reduced_stack_time_independent
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print('using reduced feature stack')
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else:
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shp = self.feature_stack.shape
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feature_names = self.feature_names
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feature_names_time_independent = self.feature_names_time_independent
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feature_stack = self.feature_stack
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feature_stack_time_independent = self.feature_stack_time_independent
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print('using full feature stack because')
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if not use_reduced:
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print('- reduced stack not selected')
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if use_reduced and not self.stack_has_been_reduced:
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print('- reduced stack not calculated')
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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]), 'time_0': [0],
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'feature': feature_names,
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'feature_time_independent': feature_names_time_independent}
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self.feature_xarray = xr.Dataset({'feature_stack': (['x','y','z','time', 'feature'], feature_stack),
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'feature_stack_time_independent': (['x','y','z','time_0', 'feature_time_independent'], feature_stack_time_independent)},
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coords = coords
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)
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