for quantile in [50, 75]:
thresholds = []
if quantile == 25:
thresholds = [0.8, 0.825, 0.85, 0.875, 0.9]
elif quantile == 50:
thresholds = [0.75, 0.8, 0.85, 0.9]
elif quantile == 75:
thresholds = [0.65, 0.7, 0.75, 0.8]
for threshold in thresholds: #[0.8, 0.85, 0.9, 0.95, 0.975, 0.99]:#[0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
array_in = daisy.open_ds(input_file, dataset)
voxel_size = array_in.voxel_size
context_nm = (50*voxel_size[0],50*voxel_size[1],50*voxel_size[2]) #50 pixel overlap
chunks = array_in.data.chunks
block_size_nm = [chunks[0]*voxel_size[0], chunks[1]*voxel_size[1], chunks[2]*voxel_size[2]]
array_out = daisy.prepare_ds(output_file,
f'{quantile}_{threshold}_smoothed',
array_in.roi,
voxel_size = voxel_size,
write_size= block_size_nm,
dtype = np.uint64)
fsa.segment_blockwise(array_in,
array_out,
block_size = block_size_nm,
context = context_nm,
num_workers = num_processors,
segment_function = lambda array_in,roi: blockwise_segmentation_function(array_in, roi, threshold, quantile))
from blockwise_segmentation_function import *
import funlib.segment.arrays as fsa
import argparse
input_file = '/nrs/cosem/cosem/training/v0003.2/setup35/Jurkat_Cell1_4x4x4nm/Jurkat_Cell1_FS96-Area1_4x4x4nm_it600000.n5'
output_file = '/groups/cosem/cosem/ackermand/Jurkat_Cell1_4x4x4nm_setup35_it600000_results.n5'
dataset = 'nucleus'
if __name__ == '__main__':
array_in = daisy.open_ds(input_file, dataset)
array_out = daisy.prepare_ds(output_file,
f'{dataset}_smoothed_fragments',
array_in.roi,
voxel_size=array_in.voxel_size,
write_size=[180 * 4, 180 * 4, 180 * 4],
dtype=np.uint64)
fsa.segment_blockwise(
array_in,
array_out,
block_size=[180 * 4, 180 * 4, 180 * 4],
context=(200, 200, 200),
num_workers=48,
segment_function=lambda array_in, roi:
blockwise_save_watershed_fragments_function(array_in, roi))