def init_isbi2012_train(): isbi2012_train = DataSet(meta_folder, "isbi2012_train") raw_path = os.path.join(data_path, "raw/train-volume.h5") raw_key = "data" # nasims baseline prob map inp_path = os.path.join( data_path, "probabilities/old_probs/nasims_oldbaseline_train.h5") inp_key = "exported_data" isbi2012_train.add_raw(raw_path, raw_key) isbi2012_train.add_input(inp_path, inp_key) # 2d wsdt on namsis pmap seg_path0 = os.path.join( data_path, "watersheds/old_watersheds/ws_dt_nasims_baseline_train.h5") seg_key = "superpixel" isbi2012_train.add_seg(seg_path0, seg_key) # layerwise gt gt_path = os.path.join(data_path, "groundtruth/gt_cleaned.h5") isbi2012_train.add_gt(gt_path, "data") meta.add_dataset("isbi2012_train", isbi2012_train)
def init_isbi2012_train(): isbi2012_train = DataSet(meta_folder, "isbi2012_train") raw_path = os.path.join(data_path, "raw/train-volume.h5") raw_key = "data" # nasims baseline prob map inp_path = os.path.join(data_path, "probabilities/unet_train.h5") inp_key = "data" isbi2012_train.add_raw(raw_path, raw_key) isbi2012_train.add_input(inp_path, inp_key) # 2d wsdt on namsis pmap seg_path0 = os.path.join(data_path, "watersheds/wsdt_unet_train.h5") seg_path1 = os.path.join(data_path, "watersheds/wssmoothed_unet_train.h5") seg_key = "data" isbi2012_train.add_seg(seg_path0, seg_key) isbi2012_train.add_seg(seg_path1, seg_key) # layerwise gt gt_path = os.path.join(data_path, "groundtruth/gt_cleaned.h5") isbi2012_train.add_gt(gt_path, "data") # cutouts for learning the lifted multicut isbi2012_train.make_cutout([0, 512, 0, 512, 0, 5]) isbi2012_train.make_cutout([0, 512, 0, 512, 5, 25]) isbi2012_train.make_cutout([0, 512, 0, 512, 25, 30]) meta.add_dataset("isbi2012_train", isbi2012_train)
def init_neuroproof_train(): neuroproof_train = DataSet(meta_folder, "neuroproof_train") raw_path = os.path.join(data_path, "raw_train.h5") raw_key = "data" inp_path = os.path.join(data_path, "probabilities_train.h5") inp_key = "data" seg_path = os.path.join(data_path, "overseg_train.h5") seg_key = "data" gt_path = os.path.join(data_path, "gt_train.h5") gt_key = "data" assert os.path.exists(inp_path), inp_path assert os.path.exists(gt_path), gt_path neuroproof_train.add_raw(raw_path, raw_key) neuroproof_train.add_input(inp_path, inp_key) #seg = make_ws(neuroproof_train.inp(1), .3, 2.6) #neuroproof_train.add_seg_from_data(seg) neuroproof_train.add_seg(seg_path, seg_key) neuroproof_train.add_gt(gt_path, gt_key) neuroproof_train.make_cutout([0, 250, 0, 250, 0, 50]) # lower 20 slices neuroproof_train.make_cutout([0, 250, 0, 250, 50, 200]) # upper 60 slices neuroproof_train.make_cutout([0, 250, 0, 250, 200, 250]) # upper 20 slices meta.add_dataset("neuroproof_train", neuroproof_train)
def init_snemi3d_train(): snemi3d_train = DataSet(meta_folder, "snemi3d_train") raw_path = os.path.join(data_path, "raw/train-input.h5") raw_key = "data" # path to the idsia probability maps inp_path = os.path.join(data_path, "probabilities/train-probs-nn.h5") inp_key = "exported_data" snemi3d_train.add_raw(raw_path, raw_key) snemi3d_train.add_input(inp_path, inp_key) # 2d - distance trafo watershed on idsia pmaps seg_path0 = os.path.join(data_path, "watersheds/wsdt_2d_train.h5") # 2d distance trafo watershed on idsia pmaps with myelin segs inserted seg_path1 = os.path.join(data_path, "watersheds/myelin_train.h5") seg_key = "superpixel" snemi3d_train.add_seg(seg_path0, seg_key) snemi3d_train.add_seg(seg_path1, seg_key) gt_path = os.path.join(data_path, "groundtruth/train-gt.h5") gt_key = "gt" snemi3d_train.add_gt(gt_path, gt_key) # cutouts TODO snemi3d_train.make_cutout([0, 1024, 0, 1024, 0, 20]) # lower 20 slices snemi3d_train.make_cutout([0, 1024, 0, 1024, 20, 80]) # upper 60 slices snemi3d_train.make_cutout([0, 1024, 0, 1024, 80, 100]) # upper 20 slices meta.add_dataset("snemi3d_train", snemi3d_train)
def init_testds(): ds = DataSet("/home/consti/Work/data_neuro/cache/testds", "ds_test") raw_path = "/home/consti/Work/data_neuro/test_block/test-raw.h5" seg_path = "/home/consti/Work/data_neuro/test_block/test-seg.h5" prob_path = "/home/consti/Work/data_neuro/test_block/test-probs.h5" gt_path = "/home/consti/Work/data_neuro/test_block/test-gt.h5" ds.add_raw(raw_path, "data") ds.add_input(prob_path, "data") ds.add_seg(seg_path, "data") ds.add_gt(gt_path, "data") meta.add_dataset("ds_test", ds_test)
def init_isbi2012_test(): isbi2012_test = DataSet(meta_folder, "isbi2012_test") raw_path = os.path.join(data_path, "raw/test-volume.h5") raw_key = "data" inp_path = os.path.join( data_path, "probabilities/old_probs/nasims_oldbaseline_test.h5") inp_key = "exported_data" isbi2012_test.add_raw(raw_path, raw_key) isbi2012_test.add_input(inp_path, inp_key) seg_key = "superpixel" seg_path0 = os.path.join( data_path, "watersheds/old_watersheds/ws_dt_nasims_baseline_test.h5") isbi2012_test.add_seg(seg_path0, seg_key) meta.add_dataset("isbi2012_test", isbi2012_test)
def init_isbi2012_test(): isbi2012_test = DataSet(meta_folder, "isbi2012_test") raw_path = os.path.join(data_path, "raw/test-volume.h5") raw_key = "data" inp_path = os.path.join(data_path, "probabilities/unet_test.h5") inp_key = "data" isbi2012_test.add_raw(raw_path, raw_key) isbi2012_test.add_input(inp_path, inp_key) seg_path0 = os.path.join(data_path, "watersheds/wsdt_unet_test.h5") seg_path1 = os.path.join(data_path, "watersheds/wssmoothed_unet_test.h5") seg_key = "data" isbi2012_test.add_seg(seg_path0, seg_key) isbi2012_test.add_seg(seg_path1, seg_key) meta.add_dataset("isbi2012_test", isbi2012_test)
def init_snemi3d_test(): snemi3d_test = DataSet(meta_folder, "snemi3d_test") raw_path = os.path.join(data_path, "raw/test-input.h5") raw_key = "data" # path to the idsia probability maps inp_path0 = os.path.join(data_path, "probabilities/SnemiTheUltimateMapTest.h5") # path to idsia prob maps refined with autocontext inp_key = "data" snemi3d_test.add_raw(raw_path, raw_key) snemi3d_test.add_input(inp_path0, inp_key) # 2d - distance trafo watershed on idsia pmaps seg_path0 = os.path.join(data_path, "watersheds/snemiTheUltimateMapWsdtSpecialTest_myel.h5") seg_key = "data" snemi3d_test.add_seg(seg_path0, seg_key) meta.add_dataset("snemi3d_test", snemi3d_test)
def init_snemi3d_test(): snemi3d_test = DataSet(meta_folder, "snemi3d_test") raw_path = os.path.join(data_path, "raw/test-input.h5") raw_key = "data" # path to the idsia probability maps inp_path = os.path.join(data_path, "probabilities/test-probs-nn.h5") # path to idsia prob maps refined with autocontext inp_key = "exported_data" snemi3d_test.add_raw(raw_path, raw_key) snemi3d_test.add_input(inp_path, inp_key) # 2d - distance trafo watershed on idsia pmaps seg_path0 = os.path.join(data_path, "watersheds/wsdt_2d_test.h5") # 2d distance trafo watershed on idsia pmaps with myelin segs inserted seg_path1 = os.path.join(data_path, "watersheds/myelin_test.h5") seg_key = "superpixel" snemi3d_test.add_seg(seg_path0, seg_key) snemi3d_test.add_seg(seg_path1, seg_key) meta.add_dataset("snemi3d_test", snemi3d_test)
def init_neuroproof_test(): neuroproof_test = DataSet(meta_folder, "neuroproof_test") raw_path = os.path.join(data_path, "raw_test.h5") raw_key = "data" inp_path = os.path.join(data_path, "probabilities_test.h5") inp_key = "data" seg_path = os.path.join(data_path, "overseg_test.h5") seg_key = "data" gt_path = os.path.join(data_path, "gt_test.h5") gt_key = "data" neuroproof_test.add_raw(raw_path, raw_key) neuroproof_test.add_input(inp_path, inp_key) #seg = make_ws(neuroproof_test.inp(1), .3, 2.6) #neuroproof_test.add_seg_from_data(seg) neuroproof_test.add_seg(seg_path, seg_key) meta.add_dataset("neuroproof_test", neuroproof_test)
def init_snemi3d_train(for_validation = False): snemi3d_train = DataSet(meta_folder, "snemi3d_train") raw_path = os.path.join(data_path, "raw/train-input.h5") raw_key = "data" # path to the idsia probability maps inp_path0 = os.path.join(data_path, "probabilities/SnemiTheMapTrain.h5") inp_key = "data" snemi3d_train.add_raw(raw_path, raw_key) snemi3d_train.add_input(inp_path0, inp_key) #snemi3d_train.add_input(inp_path1, inp_key) # 2d - distance trafo watershed on idsia pmaps seg_path0 = os.path.join(data_path, "watersheds/snemiTheUltimateMapWsdtSpecialTrain_myel.h5") seg_key = "data" snemi3d_train.add_seg(seg_path0, seg_key) gt_path = os.path.join(data_path, "groundtruth/train-gt.h5") gt_key = "data" snemi3d_train.add_gt(gt_path, gt_key) if for_validation: # train / validation split snemi3d_train.make_cutout([0,1024,0,1024,0,50]) snemi3d_train.make_cutout([0,1024,0,1024,50,100]) else: # cutouts for LMC snemi3d_train.make_cutout([0,1024,0,1024,0,20]) # lower 20 slices snemi3d_train.make_cutout([0,1024,0,1024,20,80]) # upper 60 slices snemi3d_train.make_cutout([0,1024,0,1024,80,100]) # upper 20 slices meta.add_dataset("snemi3d_train", snemi3d_train)