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_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_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_B(path_to_nn):
path_to_raw = ""
key_to_raw = ""
key_to_nn = ""
path_to_gt = ""
key_to_gt = ""
if os.path.exists( os.path.join(cache_folder, "sampleB") ):
meta.load()
rmtree(os.path.join(cache_folder, "sampleB"))
sampleB = DataSet(cache_folder, "sampleB")
sampleB.add_raw(path_to_raw, key_to_raw)
sampleB.add_input(path_to_nn, key_to_nn)
sampleB.add_seg_from_data(watersheds(sampleB.inp(1)))
sampleB.add_gt(path_to_gt, key_to_gt)
sampleB.make_cutout([0,1250,0,1250,0,35])
sampleB.make_cutout([0,1250,0,1250,35,75])
meta.update_dataset("sampleB", sampleB)
meta.save()
else:
sampleB = DataSet(cache_folder, "sampleB")
sampleB.add_raw(path_to_raw, key_to_raw)
sampleB.add_input(path_to_nn, key_to_nn)
sampleB.add_seg_from_data(watersheds(sampleB.inp(1)))
sampleB.add_gt(path_to_gt, key_to_gt)
sampleB.make_cutout([0,1250,0,1250,0,35])
sampleB.make_cutout([0,1250,0,1250,35,75])
meta.add_dataset("sampleB", sampleB)
meta.save()
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)
