diff --git a/extensions/chaeo/examples/batch_run_patches.py b/extensions/chaeo/examples/batch_run_patches.py
index f0edacd718c00a6bcc3b67354f5ec8139ec5296b..15ef8c994fd876579b690eb16a30a8c6e6b04db8 100644
--- a/extensions/chaeo/examples/batch_run_patches.py
+++ b/extensions/chaeo/examples/batch_run_patches.py
@@ -1,14 +1,8 @@
from pathlib import Path
-import re
-# from time import localtime, strftime
-import pandas as pd
-
-from extensions.chaeo.util import autonumber_new_directory, get_matching_files
+from extensions.chaeo.util import autonumber_new_directory, get_matching_files, loop_workflow
from extensions.chaeo.workflows import export_patches_from_multichannel_zstack
-from model_server.accessors import InMemoryDataAccessor, write_accessor_data_to_file
-
if __name__ == '__main__':
where_czi = 'z:/rhodes/projects/proj0004-marine-photoactivation/data/exp0038/AutoMic/20230906-163415/Selection'
@@ -17,48 +11,33 @@ if __name__ == '__main__':
'batch-output'
)
- csv_args = {'mode': 'w', 'header': True} # when creating file
px_ilp = Path.home() / 'model-server' / 'ilastik' / 'AF405-bodies_boundaries.ilp'
- #TODO: try/catch blocks and error handling around workflow calls
- #TODO: pack JSON-serializable workflow inputs
-
- input_files = get_matching_files(where_czi, 'czi', coord_filter={'P': (0, 10)})
- for ff in input_files:
-
- export_kwargs = {
- 'input_zstack_path': Path(where_czi) / ff.__str__(),
- 'ilastik_project_file': px_ilp.__str__(),
- 'pxmap_threshold': 0.25,
- 'pixel_class': 0,
- 'zmask_channel': 0,
- 'patches_channel': 4,
- 'where_output': where_output,
- 'mask_type': 'boxes',
- 'zmask_filters': {'area': (1e3, 1e8)},
- 'zmask_expand_box_by': (128, 3),
- 'export_pixel_probabilities': False,
- 'export_2d_patches_for_training': True,
- 'export_2d_patches_for_annotation': False,
- 'export_3d_patches': False,
- 'export_annotated_zstack': False,
- 'export_patch_masks': False,
- 'export_patch_label_maps': True,
- }
-
- result = export_patches_from_multichannel_zstack(**export_kwargs)
-
- # parse and record results
- df = result['dataframe']
- df['source_path'] = ff
- df.to_csv(where_output / 'df_objects.csv', index=False, **csv_args)
- pd.DataFrame(result['timer_results'], index=[0]).to_csv(where_output / 'timer_results.csv', **csv_args)
- pd.json_normalize(export_kwargs).to_csv(where_output / 'workflow_params.csv', **csv_args)
- csv_args = {'mode': 'a', 'header': False} # append to CSV from here on
+ params = {
+ 'ilastik_project_file': px_ilp.__str__(),
+ 'pxmap_threshold': 0.25,
+ 'pixel_class': 0,
+ 'zmask_channel': 0,
+ 'patches_channel': 4,
+ 'mask_type': 'boxes',
+ 'zmask_filters': {'area': (1e3, 1e8)},
+ 'zmask_expand_box_by': (128, 3),
+ 'export_pixel_probabilities': False,
+ 'export_2d_patches_for_training': True,
+ 'export_2d_patches_for_annotation': False,
+ 'export_3d_patches': False,
+ 'export_annotated_zstack': False,
+ 'export_patch_masks': False,
+ 'export_patch_label_maps': True,
+ }
+
+ input_files = get_matching_files(where_czi, 'czi', coord_filter={'P': (0, 10)}, )
+
+ loop_workflow(
+ input_files,
+ where_output,
+ export_patches_from_multichannel_zstack,
+ params,
+ )
- # export intermediate data if flagged
- for k in result['interm'].keys():
- write_accessor_data_to_file(
- where_output / k / (ff.stem + '.tif'),
- InMemoryDataAccessor(result['interm'][k])
- )
\ No newline at end of file
+ print('Finished')
\ No newline at end of file
diff --git a/extensions/chaeo/util.py b/extensions/chaeo/util.py
index 402d05ddff618197ea3a70be016f265de4611560..7a8406dbe3cb98a43db20709e9f5afdb0eee7a61 100644
--- a/extensions/chaeo/util.py
+++ b/extensions/chaeo/util.py
@@ -2,6 +2,10 @@ from pathlib import Path
import re
from time import localtime, strftime
+import pandas as pd
+
+from model_server.accessors import InMemoryDataAccessor, write_accessor_data_to_file
+
def autonumber_new_directory(where: str, prefix: str) -> str:
yyyymmdd = strftime('%Y%m%d', localtime())
@@ -10,8 +14,9 @@ def autonumber_new_directory(where: str, prefix: str) -> str:
ma = re.match(f'{prefix}-{yyyymmdd}-([\d]+)', ff.name)
if ma:
idx = max(idx, int(ma.groups()[0]) + 1)
-
- return (Path(where) / f'batch-output-{yyyymmdd}-{idx:04d}').__str__()
+ new_path = (Path(where) / f'batch-output-{yyyymmdd}-{idx:04d}')
+ new_path.mkdir(parents=True, exist_ok=False)
+ return new_path.__str__()
def get_matching_files(where: str, ext: str, coord_filter: dict={}) -> str:
files = []
@@ -33,4 +38,52 @@ def get_matching_files(where: str, ext: str, coord_filter: dict={}) -> str:
if is_filtered_out(ff):
continue
files.append(ff.__str__())
- return files
\ No newline at end of file
+ return files
+
+
+def loop_workflow(files, where_output, workflow_func, params,
+ write_intermediate_products=True):
+ failures = []
+ for ii, ff in enumerate(files):
+ export_kwargs = {
+ 'input_zstack_path': ff,
+ 'where_output': where_output,
+ **params,
+ }
+
+ # record failure information
+ try:
+ result = workflow_func(**export_kwargs)
+ except Exception as e:
+ failures.append({
+ 'input_file': ff,
+ 'error_message': e.__str__(),
+ })
+ print(f'Caught failure on {ff}:\n{e.__str__()}')
+ continue
+
+ # record dataframes associated with workflow results
+ batch_csv = {
+ 'workflow_data': result['dataframe'],
+ 'timer_results': pd.DataFrame(result['timer_results'], index=[0]),
+ 'workflow_parameters': pd.json_normalize(export_kwargs),
+ }
+ for k in batch_csv.keys():
+ df = batch_csv[k]
+ df['input_file'] = ff
+ if ii == 0:
+ csv_args = {'mode': 'w', 'header': True}
+ else: # append to existing file
+ csv_args = {'mode': 'a', 'header': False}
+ csv_path = Path(where_output) / f'{k}.csv'
+ df.to_csv(csv_path, index=False, **csv_args)
+
+ # export intermediate data if flagged
+ if write_intermediate_products:
+ for k in result['interm'].keys():
+ write_accessor_data_to_file(
+ Path(where_output) / k / (Path(ff).stem + '.tif'),
+ InMemoryDataAccessor(result['interm'][k])
+ )
+
+ pd.DataFrame(failures).to_csv(Path(where_output) / 'failures.csv')
\ No newline at end of file
diff --git a/extensions/chaeo/workflows.py b/extensions/chaeo/workflows.py
index 9a290ec5b004a91355d2f008afb6a194955fbcf5..332f8f9f423adc1668ad33ace802cf24b581709d 100644
--- a/extensions/chaeo/workflows.py
+++ b/extensions/chaeo/workflows.py
@@ -12,6 +12,7 @@ from model_server.accessors import generate_file_accessor, InMemoryDataAccessor,
from model_server.workflows import Timer
# TODO: unpack and validate inputs
+# TODO: expose channel indices and color balance vectors to caller
def export_patches_from_multichannel_zstack(
input_zstack_path: str,
ilastik_project_file: str,
@@ -106,6 +107,23 @@ def export_patches_from_multichannel_zstack(
# prepopulate patch UUID
df['patch_id'] = df.apply(lambda _: uuid4(), axis=1)
+ if export_2d_patches_for_training:
+ files = export_multichannel_patches_from_zstack(
+ Path(where_output) / '2d_patches',
+ stack.get_one_channel_data(4),
+ zmask_meta,
+ prefix=fstem,
+ rescale_clip=0.001,
+ make_3d=False,
+ focus_metric='max_sobel',
+ )
+ df_patches = pd.DataFrame(files)
+ ti.click('export_2d_patches')
+ # associate 2d patches, dropping labeled objects that were not exported as patches
+ df = pd.merge(df, df_patches, left_index=True, right_on='df_index').drop(columns='df_index')
+ # prepopulate patch UUID
+ df['patch_id'] = df.apply(lambda _: uuid4(), axis=1)
+
if export_patch_masks:
files = export_patch_masks_from_zstack(
Path(where_output) / 'patch_masks',
@@ -143,4 +161,24 @@ def export_patches_from_multichannel_zstack(
'timer_results': ti.events,
'dataframe': df,
'interm': interm,
- }
\ No newline at end of file
+ }
+
+def transfer_ecotaxa_labels_to_patch_object_maps(
+ path_to_patches: str,
+ path_to_ecotaxa_tsv: str,
+ path_output: str,
+) -> Dict:
+ where_patches = Path(path_to_patches)
+ df_meta = pd.read_csv(
+ path_to_ecotaxa_tsv,
+ sep='\t',
+ header=[0, 1],
+ dtype={
+ ('object_annotation_date', '[t]'): str,
+ ('object_annotation_time', '[t]'): str,
+ ('object_annotation_category_id', '[t]'): str,
+ }
+ )
+ for pp in where_patches.iterdir():
+ patch = generate_file_accessor(pp)
+