diff --git a/analysis/correct_intensities.py b/analysis/correct_intensities.py
index 477ac7e7eccc1ae18e81c94fef8779f3b649779b..3fbe83959120051eb92710d0206381aaa337e40e 100644
--- a/analysis/correct_intensities.py
+++ b/analysis/correct_intensities.py
@@ -1,8 +1,11 @@
#! /g/arendt/EM_6dpf_segmentation/platy-browser-data/software/conda/miniconda3/envs/platybrowser/bin/python
import os
+import json
+from concurrent import futures
+
import numpy as np
-import h5py
import z5py
+import h5py
import vigra
from scipy.ndimage.morphology import binary_dilation
@@ -75,6 +78,41 @@ def correct_intensities(target='slurm', max_jobs=250):
tmp_path=tmp_path)
+def check_chunks():
+ import nifty.tools as nt
+ path = '/g/kreshuk/pape/Work/platy_tmp.n5'
+ key = 'data'
+ f = z5py.File(path, 'r')
+ ds = f[key]
+
+ shape = ds.shape
+ chunks = ds.chunks
+ blocking = nt.blocking([0, 0, 0], shape, chunks)
+
+ def check_chunk(block_id):
+ print("Check", block_id, "/", blocking.numberOfBlocks)
+ block = blocking.getBlock(block_id)
+ chunk_id = tuple(beg // ch for beg, ch in zip(block.begin, chunks))
+ try:
+ ds.read_chunk(chunk_id)
+ except RuntimeError:
+ print("Failed:", chunk_id)
+ return chunk_id
+
+ print("Start checking", blocking.numberOfBlocks, "blocks")
+ with futures.ThreadPoolExecutor(32) as tp:
+ tasks = [tp.submit(check_chunk, block_id) for block_id in range(blocking.numberOfBlocks)]
+ results = [t.result() for t in tasks]
+
+ results = [res for res in results if res is not None]
+ print()
+ print(results)
+ print()
+ with open('./failed_chunks.json', 'w') as f:
+ json.dump(results, f)
+
+
if __name__ == '__main__':
# combine_mask()
- correct_intensities('local', 64)
+ correct_intensities('slurm', 125)
+ # check_chunks()
diff --git a/analysis/extent.py b/analysis/extent.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a8c7fc6fb6c03d1e5a08c2a34e0b1ea128006a4
--- /dev/null
+++ b/analysis/extent.py
@@ -0,0 +1,38 @@
+import h5py
+import numpy as np
+
+
+def number_of_voxels():
+ p = '../data/rawdata/sbem-6dpf-1-whole-raw.h5'
+ with h5py.File(p, 'r') as f:
+ ds = f['t00000/s00/0/cells']
+ shape = ds.shape
+ n_vox = np.prod(list(shape))
+ print("Number of voxel:")
+ print(n_vox)
+ print("corresponds to")
+ print(float(n_vox) / 1e12, "TVoxel")
+
+
+def animal():
+ p = '../data/rawdata/sbem-6dpf-1-whole-mask-inside.h5'
+ with h5py.File(p, 'r') as f:
+ mask = f['t00000/s00/0/cells'][:]
+
+ bb = np.where(mask > 0)
+ mins = [b.min() for b in bb]
+ maxs = [b.max() for b in bb]
+ size = [ma - mi for mi, ma in zip(mins, maxs)]
+
+ print("Animal size in pixel:")
+ print(size)
+ res = [.4, .32, .32]
+
+ size = [si * re for si, re in zip(size, res)]
+ print("Animal size in micron:")
+ print(size)
+
+
+if __name__ == '__main__':
+ number_of_voxels()
+ animal()
diff --git a/scripts/transformation/intensity_correction.py b/scripts/transformation/intensity_correction.py
index 8ab1666606740c0b76a396a3b22f2aece7e9a6fc..be30d54618ce4dc52577afa77661917c77a692fe 100644
--- a/scripts/transformation/intensity_correction.py
+++ b/scripts/transformation/intensity_correction.py
@@ -50,7 +50,7 @@ def downsample(ref_path, in_path, in_key, out_path, resolution,
config_dir = os.path.join(tmp_folder, 'configs')
task = DownscalingWorkflow
config = task.get_config()['downscaling']
- config.update({'library': 'skimage', 'time_limit': 240, 'mem_limit': 4})
+ config.update({'library': 'skimage', 'time_limit': 360, 'mem_limit': 8})
with open(os.path.join(config_dir, 'downscaling.config'), 'w') as f:
json.dump(config, f)
@@ -104,7 +104,7 @@ def intensity_correction(in_path, out_path, mask_path, mask_key,
task = LinearTransformationWorkflow
conf = task.get_config()['linear']
- conf.update({'time_limit': 600, 'mem_limit': 4})
+ conf.update({'time_limit': 360, 'mem_limit': 8})
with open(os.path.join(config_dir, 'linear.config'), 'w') as f:
json.dump(conf, f)
diff --git a/scripts/util.py b/scripts/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..9114d76d6f1fb248d7b749195a1d927362b51a90
--- /dev/null
+++ b/scripts/util.py
@@ -0,0 +1,9 @@
+import h5py # TODO exchange with elf.io.open_file
+
+
+def add_max_id(path, key):
+ with h5py.File(path) as f:
+ ds = f[key]
+ data = ds[:]
+ max_id = int(data.max())
+ ds.attrs['maxId'] = max_id
diff --git a/update_registration.py b/update_registration.py
index 56ca9c81c61d0b0d4bef32b72cfc42c40faed1de..4ecc8d8059b5d6d9ce22c546d912dbd2c0ca9a86 100755
--- a/update_registration.py
+++ b/update_registration.py
@@ -17,7 +17,9 @@ from scripts.files.xml_utils import get_h5_path_from_xml, write_simple_xml
from scripts.release_helper import add_version
from scripts.extension.registration import ApplyRegistrationLocal, ApplyRegistrationSlurm
from scripts.default_config import get_default_shebang
+from scripts.attributes.base_attributes import base_attributes
from scripts.attributes.genes import create_auxiliary_gene_file, write_genes_table
+from scripts.util import add_max_id
REGION_NAMES = ('AllGlands',
@@ -74,29 +76,20 @@ def copy_to_h5(inputs, output_folder):
[t.result() for t in tasks]
-def apply_registration(input_folder, new_folder,
- transformation_file, source_prefix,
- target, max_jobs, name_parser):
+def registration_impl(inputs, outputs, transformation_file, output_folder,
+ tmp_folder, target, max_jobs, interpolation, dtype='unsigned char'):
task = ApplyRegistrationSlurm if target == 'slurm' else ApplyRegistrationLocal
- tmp_folder = './tmp_registration'
- os.makedirs(tmp_folder, exist_ok=True)
-
- # find all input files
- names = os.listdir(input_folder)
- inputs = [os.path.join(input_folder, name) for name in names]
-
- if len(inputs) == 0:
- raise RuntimeError("Did not find any files with prefix %s in %s" % (source_prefix,
- input_folder))
- # writing multiple hdf5 files in parallel with the elastix plugin is broken,
- # so we write temporary files to tif instead and copy them to hdf5 with python
+ # write path name files to json
+ input_file = os.path.join(tmp_folder, 'input_files.json')
+ inputs = [os.path.abspath(inpath) for inpath in inputs]
+ with open(input_file, 'w') as f:
+ json.dump(inputs, f)
- # output_folder = os.path.join(new_folder, 'images')
- output_folder = os.path.join(tmp_folder, 'outputs')
- os.makedirs(output_folder, exist_ok=True)
- output_names = [name_parser(source_prefix, name) for name in inputs]
- outputs = [os.path.join(output_folder, name) for name in output_names]
+ output_file = os.path.join(tmp_folder, 'output_files.json')
+ outputs = [os.path.abspath(outpath) for outpath in outputs]
+ with open(output_file, 'w') as f:
+ json.dump(outputs, f)
# update the task config
config_dir = os.path.join(tmp_folder, 'configs')
@@ -109,24 +102,11 @@ def apply_registration(input_folder, new_folder,
json.dump(global_config, f)
task_config = task.default_task_config()
- task_config.update({'mem_limit': 16, 'time_limit': 240, 'threads_per_job': 4})
+ task_config.update({'mem_limit': 16, 'time_limit': 240, 'threads_per_job': 4,
+ 'ResultImagePixelType': dtype})
with open(os.path.join(config_dir, 'apply_registration.config'), 'w') as f:
json.dump(task_config, f)
- # write path name files to json
- input_file = os.path.join(tmp_folder, 'input_files.json')
- inputs = [os.path.abspath(inpath) for inpath in inputs]
- with open(input_file, 'w') as f:
- json.dump(inputs, f)
-
- output_file = os.path.join(tmp_folder, 'output_files.json')
- outputs = [os.path.abspath(outpath) for outpath in outputs]
- with open(output_file, 'w') as f:
- json.dump(outputs, f)
-
- # once we have other sources that are registered to the em space,
- # we should expose the interpolation mode
- interpolation = 'nearest'
t = task(tmp_folder=tmp_folder, config_dir=config_dir, max_jobs=max_jobs,
input_path_file=input_file, output_path_file=output_file,
transformation_file=transformation_file, output_format='tif',
@@ -135,13 +115,42 @@ def apply_registration(input_folder, new_folder,
if not ret:
raise RuntimeError("Registration failed")
- output_folder = os.path.join(new_folder, 'images')
copy_to_h5(outputs, output_folder)
-def update_prospr(new_folder, target):
+# TODO expose interpolation mode
+def apply_registration(input_folder, new_folder,
+ transformation_file, source_prefix,
+ target, max_jobs, name_parser):
+ tmp_folder = './tmp_registration'
+ os.makedirs(tmp_folder, exist_ok=True)
+
+ # find all input files
+ names = os.listdir(input_folder)
+ inputs = [os.path.join(input_folder, name) for name in names]
+ # we ignore subfolders, because we might have some special volumes there
+ # (e.g. virtual cells for prospr, which are treated as segmentation)
+ inputs = [inp for inp in inputs if not os.path.isdir(inp)]
+
+ if len(inputs) == 0:
+ raise RuntimeError("Did not find any files with prefix %s in %s" % (source_prefix,
+ input_folder))
+
+ # writing multiple hdf5 files in parallel with the elastix plugin is broken,
+ # so we write temporary files to tif instead and copy them to hdf5 with python
+ output_folder = os.path.join(tmp_folder, 'outputs')
+ os.makedirs(output_folder, exist_ok=True)
+ output_names = [name_parser(source_prefix, name) for name in inputs]
+ outputs = [os.path.join(output_folder, name) for name in output_names]
+
+ output_folder = os.path.join(new_folder, 'images')
+ registration_impl(inputs, outputs, transformation_file, output_folder,
+ tmp_folder, target, max_jobs, interpolation='nearest')
+
+
+def update_prospr(new_folder, input_folder, transformation_file, target, max_jobs):
- # update the auxiliaty gene volume
+ # # update the auxiliaty gene volume
image_folder = os.path.join(new_folder, 'images')
aux_out_path = os.path.join(new_folder, 'misc', 'prospr-6dpf-1-whole_meds_all_genes.h5')
create_auxiliary_gene_file(image_folder, aux_out_path)
@@ -170,7 +179,33 @@ def update_prospr(new_folder, target):
write_genes_table(seg_path, aux_out_path, out_path,
labels, tmp_folder, target)
- # TODO we should register the virtual cells here as well
+ # register virtual cells
+ vc_name = 'prospr-6dpf-1-whole-virtual-cells-labels'
+ vc_path = os.path.join(input_folder, 'virtual_cells', 'virtual_cells--prosprspaceMEDs.tif')
+ inputs = [vc_path]
+ outputs = [os.path.join(tmp_folder, 'outputs', vc_name)]
+ output_folder = os.path.join(new_folder, 'segmentations')
+ registration_impl(inputs, outputs, transformation_file, output_folder,
+ tmp_folder, target, max_jobs, interpolation='nearest',
+ dtype='unsigned short')
+
+ # compute the table for the virtual cells
+ vc_table_folder = os.path.join(new_folder, 'tables', vc_name)
+ os.makedirs(vc_table_folder, exist_ok=True)
+ vc_table = os.path.join(vc_table_folder, 'default.csv')
+ if os.path.exists(vc_table):
+ assert os.path.islink(vc_table), vc_table
+ print("Remove link to previous gene table:", vc_table)
+ os.unlink(vc_table)
+ vc_path = os.path.join(new_folder, 'segmentations', vc_name + '.h5')
+ key = 't00000/s00/0/cells'
+ add_max_id(vc_path, key)
+
+ assert os.path.exists(vc_path), vc_path
+ resolution = [.55, .55, .55]
+ base_attributes(vc_path, key, vc_table, resolution,
+ tmp_folder, target, max_jobs,
+ correct_anchors=False)
# we should encode the source prefix and the transformation file to be used
@@ -215,7 +250,7 @@ def update_registration(transformation_file, input_folder, source_prefix, target
target, max_jobs, name_parser)
if source_prefix == "prospr-6dpf-1-whole":
- update_prospr(new_folder, target)
+ update_prospr(new_folder, input_folder, transformation_file, target, max_jobs)
else:
raise NotImplementedError