Newer
Older
# Contributor(s): Eric Debreuve (since 2019), Morgane Nadal (2020)
#
# eric.debreuve@cnrs.fr
#
# This software is governed by the CeCILL license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
# Time profiling:
# python -m cProfile -o runtime/profiling.log -s name main.py
# Memory profiling:
# python -m memory_profiler main.py
# or
# mprof run main.py
# mprof plot
NADAL Morgane
committed
import brick.component.connection as cn_
import brick.component.extension as xt_
import brick.component.soma as sm_
import brick.general.feedback as fb_
import brick.processing.map_labeling as ml_
# import brick.processing.image_verification as iv_
from sklgraph.skl_fgraph import skl_graph_t
from sklgraph.skl_map import skl_map_t
import brick.processing.graph_feat_extraction as ge_
NADAL Morgane
committed
import imageio as io_
from skimage.segmentation import relabel_sequential
import skimage.morphology as mp_
import skimage.measure as ms_
NADAL Morgane
committed
import pandas as pd_
if sy_.argv.__len__() < 2:
print("Missing parameter file argument")
sy_.exit(0)
if not (os_.path.isfile(sy_.argv[1]) and os_.access(sy_.argv[1], os_.R_OK)):
print("Wrong parameter file path or parameter file unreadable")
sy_.exit(0)
data_path = None
channel = None
size_voxel_in_micron = None
soma_low_c = None
soma_high_c = None
soma_selem_micron_c = None
soma_min_area_c = None
ext_low_c = None
ext_high_c = None
ext_selem_micron_c = None
ext_min_area_c = None
max_straight_sq_dist_c = None
max_weighted_length_c = None
scale_range = None
scale_step = None
alpha = None
beta = None
frangi_c = None
diff_mode = None
bright_on_dark = None
method = None
hist_step_length = None
number_of_bins_length = None
hist_bins_borders_length = None
hist_min_curvature = None
hist_step_curvature = None
number_of_bins_curvature = None
hist_bins_borders_curvature = None
with_plot = None
in_parallel = None
exec(open(sy_.argv[1]).read()) # Only with search_parameters.py (stable version)
###### --------------- FOR DVPT -------------------##########
# soma_t = sm_.soma_t
# extension_t = xt_.extension_t
# print(f"STARTED: {tm_.strftime('%a, %b %d %Y @ %H:%M:%S')}")
# start_time = tm_.time()
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
# # TODO add proper warning with warn module
# # --- Images
#
# # Find the name of the file, eq. to the biological tested condition number x
# name_file = os_.path.basename(data_path)
# print("FILE: ", name_file)
# name_file = name_file.replace(".tif", "")
#
# name_dir = os_.path.dirname(data_path)
# print("DIR: ", name_dir)
#
# # Find the dimension of the image voxels in micron
# # TODO do something more general, here too specific to one microscope metadata
# size_voxel_in_micron = in_.FindVoxelDimensionInMicron(data_path, size_voxel_in_micron=size_voxel_in_micron)
#
# # Read the image
# image = io_.volread(data_path)
#
# # Image size verification - simple version without user interface
# image = in_.ImageVerification(image, channel)
#
# # iv_.image_verification(image, channel) # -> PySide2 user interface # TODO: must return the modified image!
# # /!\ conflicts between some versions of PySide2 and Python3
#
# image = image[:, 800:, :300]
# img_shape = image.shape
#
# #
# print(f"IMAGE: s.{img_shape} t.{image.dtype} m.{image.min()} M.{image.max()}")
#
# # Intensity relative normalization (between 0 and 1).
# # Image for soma normalized for hysteresis
# image_for_soma = in_.IntensityNormalizedImage(image)
# # Image for extensions not normalized for frangi enhancement
# image_for_ext = image.copy()
#
# print(f"NRM-IMG: t.{image_for_soma.dtype} m.{image_for_soma.min():.2f} M.{image_for_soma.max():.2f}")
#
# # --- Initialization
# som_nfo = {}
# ext_nfo = {}
# axes = {}
#
# #
# # --- Somas
# print("\n--- Soma Detection")
#
# # Change the soma parameters from micron to pixel dimensions, using the previously determined voxel size
# soma_min_area_c = in_.ToPixel(soma_min_area_c, size_voxel_in_micron, dimension=(0, 1))
# soma_max_area_c = in_.ToPixel(soma_max_area_c, size_voxel_in_micron, dimension=(0, 1))
# soma_selem_c = mp_.disk(in_.ToPixel(soma_selem_micron_c, size_voxel_in_micron))
#
# # - Create the maps for enhancing and detecting the soma
# # Hysteresis thresholding and closing/opening
# print("Hysteresis thresholding: ", end="")
# som_nfo["map_small"] = soma_t.Map(image_for_soma, soma_low_c, soma_high_c, soma_selem_c)
# print("Done")
# # Deletion of too small elements
# print("Morphological cleaning: ", end="")
# som_nfo["map_small"], som_lmp_small = soma_t.DeleteSmallSomas(som_nfo["map_small"], soma_min_area_c)
# # Deletion of too big elements (probably aggregation of cells)
# # Separated from the deletion of small elements to avoid extensions to be detected where too big somas were just deleted
# som_nfo["map"], som_lmp = soma_t.DeleteBigSomas(som_nfo["map_small"], som_lmp_small, soma_max_area_c)
# print("Done")
#
# # Labelling of somas, find the number of somas
# print("Somas labelling: ", end="")
# som_nfo["lmp"], n_somas = ms_.label(som_nfo["map"], return_num=True)
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
# print("Done")
#
# # Use relabel instead of label to optimize the algorithm. /!\ But BUG.
# # som_nfo["lmp"] = relabel_sequential(som_lmp)[0]
# # n_somas = som_nfo["lmp"].max()
#
# # id_soma = 5
# # print(f"/!\ Selected soma {id_soma} for dvpt")
# # for id in range(n_somas + 1):
# # if id != id_soma:
# # som_nfo["lmp"][som_nfo["lmp"] == id] = 0
# # som_nfo["lmp"], n_somas = ms_.label(som_nfo["lmp"], return_num=True)
#
# # po_.MaximumIntensityProjectionZ(som_nfo["lmp"])
#
# # Create the distance and influence maps used next for connexions between somas-extensions and extensions-extensions
# print("Distance and Influence Map creation: ", end="")
# som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(som_nfo["lmp"])
# print("Done")
#
# # po_.MaximumIntensityProjectionZ(som_nfo["influence_map"])
#
# # Create the tuple somas, containing all the objects 'soma'
# print("Somas creation: ", end="")
# somas = tuple(soma_t().FromMap(som_nfo["lmp"], uid) for uid in range(1, n_somas + 1))
# print("Done")
#
# print(f" n = {n_somas}")
#
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
#
# if with_plot:
# fb_.PlotSomas(somas, som_nfo, axes)
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
# # -- Extentions
# print("\n--- Extension Detection")
#
# # Set to zeros the pixels belonging to the somas.
# # Take into account the aggregation of cells detected as one big soma to avoid extension creation there
# del_soma = (som_nfo["map_small"] > 0).nonzero()
# image_for_ext[del_soma] = 0
#
# # Change the extensions parameters from micron to pixel dimensions
# ext_min_area_c = in_.ToPixel(ext_min_area_c, size_voxel_in_micron, dimension=(0, 1))
# ext_max_length_c = in_.ToPixel(ext_max_length_c, size_voxel_in_micron, dimension=(0,))
#
# if ext_selem_micron_c == 0:
# ext_selem_pixel_c = None
# else:
# ext_selem_pixel_c = mp_.disk(in_.ToPixel(ext_selem_micron_c, size_voxel_in_micron))
#
# # scale_range_pixel = []
# # #
# # for value in scale_range:
# # value_in_pixel = in_.ToPixel(value, size_voxel_in_micron, decimals=1)
# # scale_range_pixel.append(value_in_pixel)
# # scale_range = tuple(scale_range_pixel)
# #
# # scale_step = in_.ToPixel(scale_step, size_voxel_in_micron, decimals=1)
#
# # - Perform frangi feature enhancement (via python or c - faster - implementation)
# enhanced_ext, ext_scales = extension_t.EnhancedForDetection(
# image_for_ext,
# scale_range,
# scale_step,
# alpha,
# beta,
# frangi_c,
# bright_on_dark,
# method,
# diff_mode=diff_mode,
# in_parallel=in_parallel)
#
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"Elapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}\n")
#
# # po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
#
# # - Creation of the enhanced maps
# # enhanced_ext = ex_.adjust_log(enhanced_ext, 1) # Not necessary, log contrast adjustment
#
# # Enhanced the contrast in frangi output
# print('Enhancing Contrast: ', end='')
# if adapt_hist_equalization:
# # necessary but not sufficient, histogram equalisation (adaptative)
# enhanced_ext = ex_.equalize_adapthist(enhanced_ext)
# else:
# # necessary but not sufficient, histogram equalisation (global)
# enhanced_ext = ex_.equalize_hist(enhanced_ext)
#
# # po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
#
# # Rescale the image by stretching the intensities between 0 and 255, necessary but not sufficient
# enhanced_ext = ex_.rescale_intensity(enhanced_ext, out_range=(0, 255))
# print('Done')
# # Hysteresis thersholding
# print('Hysteresis Thresholding: ', end='')
# ext_nfo["coarse_map"] = extension_t.CoarseMap(enhanced_ext, ext_low_c, ext_high_c, ext_selem_pixel_c)
# print('Done')
# # po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
# # Deletion of too small elements
# print('Morphological cleaning: ', end='')
# ext_nfo["coarse_map"], ext_lmp = extension_t.FilteredCoarseMap(ext_nfo["coarse_map"], ext_min_area_c)
# print('Done')
# # po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
# # Creation of the extensions skeleton
# print('Skeletonization and Thinning: ', end='')
# ext_nfo["map"] = extension_t.FineMapFromCoarseMap(ext_nfo["coarse_map"])
# print('Done')
# # ext_nfo["map"] = extension_t.FilteredSkeletonMap(ext_nfo["coarse_map"], ext_max_length_c)
# # po_.MaximumIntensityProjectionZ(ext_nfo["map"])
# # Deletion of extensions found within the somas
# print('Map Labelling: ', end='')
# ext_nfo["map"][som_nfo["map"] > 0] = 0
# # Labelling of extensions and number of extensions determined
# ext_nfo["lmp"], n_extensions = ms_.label(ext_nfo["map"], return_num=True)
# print('Done')
#
# # Use relabel instead of label to optimize the algorithm. BUT PROBLEM WITH THE NUMBER OF EXTENSIONS DETECTED !
# # ext_nfo["lmp"] = relabel_sequential(ext_lmp)[0]
# # n_extensions = ext_nfo["lmp"].max()
#
# # Create the tuple extensions, containing all the objects 'extension'
# print('Extensions creation: ', end='')
# extensions = tuple(
# extension_t().FromMap(ext_nfo["lmp"], ext_scales, uid)
# for uid in range(1, n_extensions + 1))
# print('Done')
# print(f" n = {n_extensions}")
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
# #
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
#
# if with_plot:
# fb_.PlotExtensions(extensions, ext_nfo, img_shape)
#
# # -- Preparation for Connections
# # Calculate the COSTS of each pixel on the image and DILATE the existing extensions to
# # avoid tangency of connexions to extensions
# dijkstra_costs = in_.DijkstraCosts(image, som_nfo["map"], ext_nfo["map"], extensions, dilate=True)
#
# # -- Soma-Extention
# print("\n--- Soma <-> Extension")
#
# # Change the extensions parameters from micron to pixel dimensions
# max_straight_sq_dist_c = in_.ToPixel(max_straight_sq_dist_c, size_voxel_in_micron)
# max_weighted_length_c = in_.ToPixel(max_weighted_length_c, size_voxel_in_micron)
#
# # Find the candidate extensions, with their endpoints, for connexion to the somas
# candidate_conn_nfo = cn_.CandidateConnections(
# somas,
# som_nfo["influence_map"],
# som_nfo["dist_to_closest"],
# extensions,
# max_straight_sq_dist_c,
# )
#
# # For each candidate, verify that it is valid based on the shortest path and the maximum allowed straight distance
# for ep_idx, soma, extension, end_point in candidate_conn_nfo:
# # Only try to connect if the extension is not already connected
# if extension.is_unconnected:
# print(f" Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end="")
# # Erode the end_point of the extension in the costs map
# dijkstra_costs = in_.ErodeEndPoint(end_point, dijkstra_costs, extension, image)
# # Use of Dijkstra shortest weighted path
# path, length = cn_.ShortestPathFromToN(
# image,
# end_point,
# extension,
# dijkstra_costs,
# soma.ContourPointsCloseTo,
# max_straight_sq_dist=max_straight_sq_dist_c,
# erode_path=False,
# )
# # Keep the connexion only if inferior to the allowed max weighted distance
# if length is not None:
# length = in_.ToPixel(length, size_voxel_in_micron)
# if length <= max_weighted_length_c:
# # Validate and update all the fields + dilate again the whole extension
# dijkstra_costs = cn_.ValidateConnection(soma, extension, end_point, path, dijkstra_costs)
# print(": Made")
# else:
# dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
# print("")
# else:
# dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
# print("")
# # for soma in somas:
# # soma.Extend(extensions, som_nfo["dist_to_closest"], dijkstra_costs)
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
# fb_.PrintConnectedExtensions(extensions)
#
# #
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
#
# if with_plot:
# fb_.PlotSomasWithExtensions(somas, som_nfo, "all")
#
# # -- Extention-Extention
# print("\n--- Extension <-> Extension")
#
# should_look_for_connections = True
#
# # Update the soma maps with next Ext-Ext connexions
# while should_look_for_connections:
# # Update the costs by dilating everything again #is it necessary given all that is done above?
# for soma in somas:
# for extension in soma.extensions:
# dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
# # Update the final somas + ext map by adding the new extensions and their connexion paths
# som_nfo["soma_w_ext_lmp"] = soma_t.SomasLMap(somas)
# # Update the influence map
# som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(som_nfo["soma_w_ext_lmp"])
#
# # Find the candidate extensions for connexion to the primary extensions
# candidate_conn_nfo = cn_.CandidateConnections(
# somas,
# som_nfo["influence_map"],
# som_nfo["dist_to_closest"],
# extensions,
# max_straight_sq_dist_c,
# )
#
# should_look_for_connections = False
#
# # For each candidate, verify that it is valid based on the shortest path and the maximum allowed straight distance
# for ep_idx, soma, extension, end_point in candidate_conn_nfo:
# # Only try to connect if the extension is not already connected
# if extension.is_unconnected:
# print(f" Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end="")
# # Erode the end_point of the extension in the costs map
# dijkstra_costs = in_.ErodeEndPoint(end_point, dijkstra_costs, extension, image)
# # Use of Dijkstra shortest weighted path
# path, length = cn_.ShortestPathFromToN(
# image,
# extension,
# soma.ExtensionPointsCloseTo,
# max_straight_sq_dist=max_straight_sq_dist_c,
# erode_path=True,
# )
#
# # Keep the connexion only if inferior to the allowed max weighted distance
# if length is not None:
# if length <= max_weighted_length_c:
# # Verify that the last element of the connexion path is contained in the extension to be connected
# # If so, returns the extensions containing the path last site.
# tgt_extenstion = extension_t.ExtensionContainingSite(extensions, path[-1])
# # Validate connexion: update the glial component objects and store more info in their fields
# cn_.ValidateConnection(tgt_extenstion, extension, end_point, path, dijkstra_costs)
#
# should_look_for_connections = True
#
# print(": Made")
# else:
# dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
# else:
# dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
# print("")
# # -- Create an extension map containing all the ext + connexions, without the somas.
# # Used for the graphs extraction
# ext_nfo["lmp_soma"] = som_nfo['soma_w_ext_lmp']
# ext_nfo["lmp_soma"][som_nfo["map"] > 0] = 0
# # po_.MaximumIntensityProjectionZ(ext_nfo["lmp_soma"])
# fb_.PrintConnectedExtensions(extensions)
#
# #
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
# if with_plot:
# fb_.PlotSomasWithExtensions(somas, som_nfo, "with_ext_of_ext")
# # -- Summary
# print("\n")
# for soma in somas:
# print(soma)
# # snapshot = tr_.take_snapshot()
# # top_file_stats = snapshot.statistics('lineno')
# # print("Memory Profiling: Top 10 FILES")
# # for stat in top_file_stats[:10]:
# # print(stat)
# # top_block_stats = snapshot.statistics('traceback')
# # top_block_stats = top_block_stats[0]
# # print(f"Memory Profiling: {top_block_stats.count} memory blocks: {top_block_stats.size / 1024:.1f} KiB")
# # for line in top_block_stats.traceback.format():
# # print(line)
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
# if with_plot:
# pl_.show()
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
# po_.MaximumIntensityProjectionZ(som_nfo['soma_w_ext_lmp'])
# # , output_image_file_name=f"D:\\MorganeNadal\\Results\\Images\\{name_file}.png")
#
# # --- Extract all the extensions of all somas as a graph
# print('\n--- Graph extraction')
#
# # Create the graphs with SKLGraph module (available on Eric Debreuve Gitlab)
# for soma in somas:
# print(f" Soma {soma.uid}", end="")
# # Create SKLGraph skeletonized map
# ext_map = skl_map_t.FromShapeMap(ext_nfo['lmp_soma'] == soma.uid,
# store_widths=True,
# skeletonize=False,
# do_post_thinning=False)
#
# # Create the graph from the SKLGaph skeletonized map
# soma.skl_graph = skl_graph_t.FromSkeleton(ext_map, end_point, size_voxel=size_voxel_in_micron)
#
# # --- Find the root of the {ext+conn} graphs.
# # Roots are the nodes of degree 1 that are to be linked to the soma
# soma.graph_roots = ge_.FindGraphsRootWithNodes(soma)
#
# # Add a node "soma" and link it to the root nodes
# soma_node = f"S-{int(soma.centroid[0])}-{int(soma.centroid[1])}-{int(soma.centroid[2])}"
# soma.skl_graph.add_node(soma_node, soma=True, soma_nfo=soma)
#
# for node in soma.graph_roots.values():
# soma.skl_graph.add_edge(node, soma_node, root=True)
#
# nx_.draw_networkx(soma.skl_graph)
# pl_.show(block=True)
# pl_.close()
#
# print(": Done")
#
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
#
# # --- Extract features
# print('\n--- Features Extraction\n')
#
# # Parameters
# if hist_bins_borders_length is None:
# number_of_bins_length = int(number_of_bins_length)
# bins_length = np_.linspace(hist_min_length, hist_min_length + hist_step_length * number_of_bins_length,
# num=number_of_bins_length)
# bins_length[-1] = np_.inf
# else:
# bins_length = np_.array(hist_bins_borders_length)
# bins_length[-1] = np_.inf
#
# if hist_bins_borders_curvature is None:
# number_of_bins_curvature = int(number_of_bins_curvature)
# bins_curvature = np_.linspace(hist_min_curvature,
# hist_min_curvature + hist_step_curvature * number_of_bins_curvature,
# num=number_of_bins_curvature)
# bins_curvature[-1] = np_.inf
# else:
# bins_curvature = np_.array(hist_bins_borders_curvature)
# bins_curvature[-1] = np_.inf
#
# # Pandas dataframe creation with all the measured features
# features_df = ge_.ExtractFeaturesInDF(name_file, somas, size_voxel_in_micron, bins_length, bins_curvature,
# ext_scales)
#
# # Save the pandas df into .csv
# features_df.to_csv(f"{name_dir}\\{name_file}.csv")
#
# #
# elapsed_time = tm_.gmtime(tm_.time() - start_time)
# print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
# print(f"DONE: {tm_.strftime('%a, %b %d %Y @ %H:%M:%S')}")
# test if connexion paths are directly in contact with the extensions
# (connexion to end points are normal (number = nb of primary and secondary extensions) but not the others)
def Test_Tangency_ext_conn(soma):
pb=[]
for extension in soma.extensions:
for site in list(zip(*extension.sites)):
close_end_pt = tuple(
(site[0] + i, site[1] + j, site[2] + k)
for i in (-1, 0, 1)
for j in (-1, 0, 1)
for k in (-1, 0, 1)
if i != 0 or j != 0 or k != 0)
for ext in tuple(zip(soma.connection_path.values())):
if ext[0] is not None:
for conn in list(zip(*np_.asarray(ext[0]))):
if (conn in close_end_pt) and (conn not in list(zip(*extension.sites))):
for extens in soma.extensions:
if conn not in zip(*extens.end_points):
if conn not in pb:
pb.append(conn)
print("\nconn: ", conn, "\next_site: ", site, "\next_uid: ", extension.uid)
#########----------- Executable version with files AND repositories ------------###########
def NutriMorph(data_path: str,
channel=channel,
size_voxel_in_micron=size_voxel_in_micron,
soma_low_c=soma_low_c,
soma_high_c=soma_high_c,
soma_selem_micron_c=soma_selem_micron_c,
soma_min_area_c=soma_min_area_c,
soma_max_area_c=soma_max_area_c,
adapt_hist_equalization=adapt_hist_equalization,
ext_low_c=ext_low_c,
ext_high_c=ext_high_c,
ext_selem_micron_c=ext_selem_micron_c,
ext_min_area_c=ext_min_area_c,
ext_max_length_c=ext_max_length_c,
max_straight_sq_dist_c=max_straight_sq_dist_c,
max_weighted_length_c=max_weighted_length_c,
scale_range=scale_range,
scale_step=scale_step,
alpha=alpha,
beta=beta,
frangi_c=frangi_c,
diff_mode=diff_mode,
bright_on_dark=bright_on_dark,
method=method,
hist_min_length=hist_min_length,
hist_step_length=hist_step_length,
number_of_bins_length=number_of_bins_length,
hist_bins_borders_length=hist_bins_borders_length,
hist_min_curvature=hist_min_curvature,
hist_step_curvature=hist_step_curvature,
number_of_bins_curvature=number_of_bins_curvature,
hist_bins_borders_curvature=hist_bins_borders_curvature,
with_plot=with_plot,
in_parallel=in_parallel) -> pd_.DataFrame():
soma_t = sm_.soma_t
extension_t = xt_.extension_t
print(f"STARTED: {tm_.strftime('%a, %b %d %Y @ %H:%M:%S')}")
start_time = tm_.time()
# TODO add proper warning with warn module
# --- Images
# Find the name of the file, eq. to the biological tested condition number x
name_file = os_.path.basename(data_path)
print("FILE: ", name_file)
name_file = name_file.replace(".tif", "")
name_dir = os_.path.dirname(data_path)
print("DIR: ", name_dir)
# Find the dimension of the image voxels in micron
# TODO do something more general, here too specific to one microscope metadata
size_voxel_in_micron = in_.FindVoxelDimensionInMicron(data_path, size_voxel_in_micron=size_voxel_in_micron)
# Read the image
image = io_.volread(data_path)
# Image size verification - simple version without user interface
image = in_.ImageVerification(image, channel)
# iv_.image_verification(image, channel) # -> PySide2 user interface # TODO: must return the modified image!
# /!\ conflicts between some versions of PySide2 and Python3
# image = image[:, 800:, :300]
img_shape = image.shape
#
print(f"IMAGE: s.{img_shape} t.{image.dtype} m.{image.min()} M.{image.max()}")
# Intensity relative normalization (between 0 and 1).
# Image for soma normalized for hysteresis
image_for_soma = in_.IntensityNormalizedImage(image)
# Image for extensions not normalized for frangi enhancement
image_for_ext = image.copy()
print(f"NRM-IMG: t.{image_for_soma.dtype} m.{image_for_soma.min():.2f} M.{image_for_soma.max():.2f}")
# --- Initialization
som_nfo = {}
ext_nfo = {}
axes = {}
#
# --- Somas
print("\n--- Soma Detection")
# Change the soma parameters from micron to pixel dimensions, using the previously determined voxel size
soma_min_area_c = in_.ToPixel(soma_min_area_c, size_voxel_in_micron, dimension=(0, 1))
soma_max_area_c = in_.ToPixel(soma_max_area_c, size_voxel_in_micron, dimension=(0, 1))
soma_selem_c = mp_.disk(in_.ToPixel(soma_selem_micron_c, size_voxel_in_micron))
# - Create the maps for enhancing and detecting the soma
# Hysteresis thresholding and closing/opening
print("Hysteresis thresholding: ", end="")
som_nfo["map_small"] = soma_t.Map(image_for_soma, soma_low_c, soma_high_c, soma_selem_c)
print("Done")
# Deletion of too small elements
print("Morphological cleaning: ", end="")
som_nfo["map_small"], som_lmp_small = soma_t.DeleteSmallSomas(som_nfo["map_small"], soma_min_area_c)
# Deletion of too big elements (probably aggregation of cells)
# Separated from the deletion of small elements to avoid extensions to be detected where too big somas were just deleted
som_nfo["map"], som_lmp = soma_t.DeleteBigSomas(som_nfo["map_small"], som_lmp_small, soma_max_area_c)
print("Done")
# Labelling of somas, find the number of somas
print("Somas labelling: ", end="")
som_nfo["lmp"], n_somas = ms_.label(som_nfo["map"], return_num=True)
print("Done")
# Use relabel instead of label to optimize the algorithm. /!\ But BUG.
# som_nfo["lmp"] = relabel_sequential(som_lmp)[0]
# n_somas = som_nfo["lmp"].max()
# Create the distance and influence maps used next for connexions between somas-extensions and extensions-extensions
print("Distance and Influence Map creation: ", end="")
som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(som_nfo["lmp"])
print("Done")
# po_.MaximumIntensityProjectionZ(som_nfo["influence_map"])
# Create the tuple somas, containing all the objects 'soma'
print("Somas creation: ", end="")
somas = tuple(soma_t().FromMap(som_nfo["lmp"], uid) for uid in range(1, n_somas + 1))
print("Done")
print(f" n = {n_somas}")
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
fb_.PlotSomas(somas, som_nfo, axes)
#
# -- Extentions
print("\n--- Extension Detection")
# Set to zeros the pixels belonging to the somas.
# Take into account the aggregation of cells detected as one big soma to avoid extension creation there
del_soma = (som_nfo["map_small"] > 0).nonzero()
image_for_ext[del_soma] = 0
# Change the extensions parameters from micron to pixel dimensions
ext_min_area_c = in_.ToPixel(ext_min_area_c, size_voxel_in_micron, dimension=(0, 1))
if ext_selem_micron_c == 0:
ext_selem_pixel_c = None
else:
ext_selem_pixel_c = mp_.disk(in_.ToPixel(ext_selem_micron_c, size_voxel_in_micron))
# - Perform frangi feature enhancement (via python or c - faster - implementation)
enhanced_ext, ext_scales = extension_t.EnhancedForDetection(
image_for_ext,
scale_range,
scale_step,
alpha,
beta,
frangi_c,
bright_on_dark,
method,
diff_mode=diff_mode,
in_parallel=in_parallel)
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"Elapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}\n")
# po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
# - Creation of the enhanced maps
# enhanced_ext = ex_.adjust_log(enhanced_ext, 1) # Not necessary, log contrast adjustment
# Enhanced the contrast in frangi output
print('Enhancing Contrast: ', end='')
if adapt_hist_equalization:
# necessary but not sufficient, histogram equalisation (adaptative)
enhanced_ext = ex_.equalize_adapthist(enhanced_ext)
else:
# necessary but not sufficient, histogram equalisation (global)
enhanced_ext = ex_.equalize_hist(enhanced_ext)
# po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
# Rescale the image by stretching the intensities between 0 and 255, necessary but not sufficient
enhanced_ext = ex_.rescale_intensity(enhanced_ext, out_range=(0, 255))
print('Done')
# Hysteresis thersholding
print('Hysteresis Thresholding: ', end='')
ext_nfo["coarse_map"] = extension_t.CoarseMap(enhanced_ext, ext_low_c, ext_high_c, ext_selem_pixel_c)
print('Done')
# po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
# Deletion of too small elements
print('Morphological cleaning: ', end='')
ext_nfo["coarse_map"], ext_lmp = extension_t.FilteredCoarseMap(ext_nfo["coarse_map"], ext_min_area_c)
print('Done')
# po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
# Creation of the extensions skeleton
print('Skeletonization and Thinning: ', end='')
ext_nfo["map"] = extension_t.FineMapFromCoarseMap(ext_nfo["coarse_map"])
print('Done')
# Deletion of extensions found within the somas
print('Map Labelling: ', end='')
ext_nfo["map"][som_nfo["map"] > 0] = 0
# Labelling of extensions and number of extensions determined
ext_nfo["lmp"], n_extensions = ms_.label(ext_nfo["map"], return_num=True)
print('Done')
# Use relabel instead of label to optimize the algorithm. BUT PROBLEM WITH THE NUMBER OF EXTENSIONS DETECTED !
# ext_nfo["lmp"] = relabel_sequential(ext_lmp)[0]
# n_extensions = ext_nfo["lmp"].max()
# Create the tuple extensions, containing all the objects 'extension'
print('Extensions creation: ', end='')
extensions = tuple(
extension_t().FromMap(ext_nfo["lmp"], ext_scales, uid)
for uid in range(1, n_extensions + 1))
print('Done')
print(f" n = {n_extensions}")
#
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
fb_.PlotExtensions(extensions, ext_nfo, img_shape)
# -- Preparation for Connections
# Calculate the COSTS of each pixel on the image and DILATE the existing extensions to
# avoid tangency of connexions to extensions
dijkstra_costs = in_.DijkstraCosts(image, som_nfo["map"], ext_nfo["map"], extensions, dilate=True)
# -- Soma-Extention
print("\n--- Soma <-> Extension")
# Change the extensions parameters from micron to pixel dimensions
max_straight_sq_dist_c = in_.ToPixel(max_straight_sq_dist_c, size_voxel_in_micron)
max_weighted_length_c = in_.ToPixel(max_weighted_length_c, size_voxel_in_micron)
# Find the candidate extensions, with their endpoints, for connexion to the somas
candidate_conn_nfo = cn_.CandidateConnections(
somas,
som_nfo["influence_map"],
som_nfo["dist_to_closest"],
extensions,
max_straight_sq_dist_c,
)
# For each candidate, verify that it is valid based on the shortest path and the maximum allowed straight distance
for ep_idx, soma, extension, end_point in candidate_conn_nfo:
# Only try to connect if the extension is not already connected
if extension.is_unconnected:
print(f" Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end="")
# Erode the end_point of the extension in the costs map
dijkstra_costs = in_.ErodeEndPoint(end_point, dijkstra_costs, extension, image)
# Use of Dijkstra shortest weighted path
path, length = cn_.ShortestPathFromToN(
image,
end_point,
extension,
dijkstra_costs,
soma.ContourPointsCloseTo,
max_straight_sq_dist=max_straight_sq_dist_c,
erode_path=False,
)
# Keep the connexion only if inferior to the allowed max weighted distance
if length is not None:
length = in_.ToPixel(length, size_voxel_in_micron)
if length <= max_weighted_length_c:
# Validate and update all the fields + dilate again the whole extension
dijkstra_costs = cn_.ValidateConnection(soma, extension, end_point, path, dijkstra_costs)
print(": Made")
else:
dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
print("")
else:
dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
print("")
fb_.PrintConnectedExtensions(extensions)
#
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
fb_.PlotSomasWithExtensions(somas, som_nfo, "all")
# -- Extention-Extention
print("\n--- Extension <-> Extension")
should_look_for_connections = True
# Update the soma maps with next Ext-Ext connexions
while should_look_for_connections:
# Update the costs by dilating everything again #is it necessary given all that is done above?
for soma in somas:
for extension in soma.extensions:
dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
# Update the final somas + ext map by adding the new extensions and their connexion paths
som_nfo["soma_w_ext_lmp"] = soma_t.SomasLMap(somas)
# Update the influence map
som_nfo["dist_to_closest"], som_nfo["influence_map"] = soma_t.InfluenceMaps(som_nfo["soma_w_ext_lmp"])
# Find the candidate extensions for connexion to the primary extensions
candidate_conn_nfo = cn_.CandidateConnections(
somas,
som_nfo["influence_map"],
som_nfo["dist_to_closest"],
extensions,
max_straight_sq_dist_c,
)
should_look_for_connections = False
# For each candidate, verify that it is valid based on the shortest path and the maximum allowed straight distance
for ep_idx, soma, extension, end_point in candidate_conn_nfo:
# Only try to connect if the extension is not already connected
if extension.is_unconnected:
print(f" Soma.{soma.uid} <-?-> Ext.{extension.uid}({ep_idx})", end="")
# Erode the end_point of the extension in the costs map
dijkstra_costs = in_.ErodeEndPoint(end_point, dijkstra_costs, extension, image)
# Use of Dijkstra shortest weighted path
path, length = cn_.ShortestPathFromToN(
image,
end_point,
extension,
dijkstra_costs,
soma.ExtensionPointsCloseTo,
max_straight_sq_dist=max_straight_sq_dist_c,
erode_path=True,
)
# Keep the connexion only if inferior to the allowed max weighted distance
if length is not None:
if length <= max_weighted_length_c:
# Verify that the last element of the connexion path is contained in the extension to be connected
# If so, returns the extensions containing the path last site.
tgt_extenstion = extension_t.ExtensionContainingSite(extensions, path[-1])
# Validate connexion: update the glial component objects and store more info in their fields
cn_.ValidateConnection(tgt_extenstion, extension, end_point, path, dijkstra_costs)
should_look_for_connections = True
print(": Made")
else:
dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
print("")
else:
dijkstra_costs = in_.Dilate(extension.sites, dijkstra_costs)
print("")
# -- Create an extension map containing all the ext + connexions, without the somas.
# Used for the graphs extraction
ext_nfo["lmp_soma"] = som_nfo['soma_w_ext_lmp'].copy()
ext_nfo["lmp_soma"][som_nfo["map"] > 0] = 0
#
fb_.PrintConnectedExtensions(extensions)
#
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
fb_.PlotSomasWithExtensions(somas, som_nfo, "with_ext_of_ext")
# -- Summary
print("\n")
for soma in somas:
print(soma)
elapsed_time = tm_.gmtime(tm_.time() - start_time)
print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
if with_plot:
pl_.show()
if save_images is not None:
po_.MaximumIntensityProjectionZ(som_nfo['soma_w_ext_lmp'],
block=False,
output_image_file_name=f"{save_images}\\{name_file}.png")
# --- Extract all the extensions of all somas as a graph
print('\n--- Graph extraction')
# Create the graphs with SKLGraph module (available on Eric Debreuve Gitlab)
for soma in somas:
print(f" Soma {soma.uid}", end="")
# Create SKLGraph skeletonized map
ext_map = skl_map_t.FromShapeMap(ext_nfo['lmp_soma'] == soma.uid,
store_widths=True,
skeletonize=False,
do_post_thinning=False)
# Create the graph from the SKLGaph skeletonized map
soma.skl_graph = skl_graph_t.FromSkeleton(ext_map, end_point, size_voxel=size_voxel_in_micron)
# --- Find the root of the {ext+conn} graphs.
# Roots are the nodes of degree 1 that are to be linked to the soma
soma.graph_roots = ge_.FindGraphsRootWithNodes(soma)