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Christopher Randolph Rhodes
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from pathlib import Path
import h5py
import numpy as np
import pandas as pd
from base.accessors import generate_file_accessor, write_accessor_data_to_file, InMemoryDataAccessor
from extensions.ilastik.models import IlastikPixelClassifierModel, IlastikObjectClassifierFromPixelPredictionsModel
Christopher Randolph Rhodes
committed
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def get_input_files(where_ilp: Path) -> list:
files = []
with h5py.File(where_ilp, 'r') as h5:
infos = h5['Input Data/infos']
for lane in infos.keys():
lane_dict = {}
for role in infos[lane].keys():
if len(infos[lane][role]) == 0:
continue
rel_path = Path(infos[lane][role]['filePath'][()].decode())
lane_dict[role] = where_ilp.parent / rel_path
files.append(lane_dict)
return files
if __name__ == '__main__':
where_out = Path('c:/Users/rhodes/projects/proj0015-model-server/issues/0032_multiple_input_channels/output')
root = Path('w:/03_analysis/Trial3_LSM900')
max_files = 1
ilps = [
'01_ilastik_files/relpath_240301_LSM900_DNA_PC.ilp',
'01_ilastik_files/relpath_240320_LSM900_DNA_OC_new.ilp',
'01_ilastik_files/relpath_240301_LSM900_TM_PC.ilp',
'01_ilastik_files/relpath_240320_LSM900_TM_OC_new.ilp'
]
records = []
for f in ilps:
ilp = root / f
assert ilp.exists()
outdir = where_out / ilp.stem
outdir.mkdir(parents=True, exist_ok=True)
if ilp.stem.upper().endswith('_PC'):
mod = IlastikPixelClassifierModel(
params={'project_file': str(ilp)},
enforce_embedded=False
)
infiles = get_input_files(ilp)
for ln in infiles[0:max_files]:
acc_raw = generate_file_accessor(root / ln['Raw Data'])
pxmap = mod.infer(acc_raw)[0]
pxmap_fn = 'pxmap_' + ln['Raw Data'].stem + '.tif'
write_accessor_data_to_file(outdir / pxmap_fn, pxmap)
record = {
'classifier': str(ilp.relative_to(root)),
'input_raw_data': str(ln['Raw Data'].relative_to(root)),
'input_raw_data_chroma': acc_raw.chroma,
'input_raw_data_dtype': acc_raw.dtype,
'input_raw_data_shape_dict': acc_raw.shape_dict,
'output_file': pxmap_fn,
'output_dtype': pxmap.dtype,
'output_chroma': pxmap.chroma,
'output_shape_dict': pxmap.shape_dict,
}
records.append(record)
elif ilp.stem.upper().endswith('_OC_NEW'):
mod = IlastikObjectClassifierFromPixelPredictionsModel(
params={'project_file': str(ilp)},
enforce_embedded=False
)
infiles = get_input_files(ilp)
for ln in infiles[0:max_files]:
acc_raw = generate_file_accessor(root / ln['Raw Data'])
pa_pxmap = root / ln['Prediction Maps']
if pa_pxmap.parts[-2].upper().endswith('.H5'):
pa_h5f = root / Path(*pa_pxmap.parts[0:-1])
h5_key = pa_pxmap.parts[-1]
pxmap_data = h5py.File(pa_h5f)[h5_key][()] # C x Y x X ?
pxmap_yxc = np.moveaxis(
pxmap_data,
[1, 2, 0],
[0, 1, 2]
)
acc_pxmap = InMemoryDataAccessor(np.expand_dims(pxmap_yxc, -1))
else:
acc_pxmap = generate_file_accessor(pa_pxmap)
obmap = mod.infer(acc_raw, acc_pxmap)[0]
obmap_fn = 'obmap_' + ln['Raw Data'].stem + '.tif'
write_accessor_data_to_file(outdir / obmap_fn, obmap)
record = {
'classifier': str(ilp.relative_to(root)),
'input_raw_data': str(ln['Raw Data'].relative_to(root)),
'input_raw_data_chroma': acc_raw.chroma,
'input_raw_data_dtype': acc_raw.dtype,
'input_raw_data_shape_dict': acc_raw.shape_dict,
'input_pxmap': str(ln['Prediction Maps'].relative_to(root)),
'input_pxmap_chroma': acc_pxmap.chroma,
'input_pxmap_dtype': acc_pxmap.dtype,
'input_pxmap_shape_dict': acc_pxmap.shape_dict,
'output_file': obmap_fn,
'output_dtype': obmap.dtype,
'output_chroma': obmap.chroma,
'output_shape_dict': obmap.shape_dict,
}
records.append(record)
else:
raise Exception(f'unidentified project file {ilp}')
pd.DataFrame(records).to_csv(where_out / 'record.csv', index=False)
print('Finished')