From f32b87c8e8fb7ae526ac48ab1e370501d455343b Mon Sep 17 00:00:00 2001
From: Christopher Rhodes <christopher.rhodes@embl.de>
Date: Mon, 5 Feb 2024 10:20:06 +0100
Subject: [PATCH] Removed example, workflow, and product-generating method that
exported
---
.../examples/export_patch_focus_metrics.py | 164 ------------------
model_server/extensions/chaeo/products.py | 84 +--------
2 files changed, 2 insertions(+), 246 deletions(-)
delete mode 100644 model_server/extensions/chaeo/examples/export_patch_focus_metrics.py
diff --git a/model_server/extensions/chaeo/examples/export_patch_focus_metrics.py b/model_server/extensions/chaeo/examples/export_patch_focus_metrics.py
deleted file mode 100644
index 6063af53..00000000
--- a/model_server/extensions/chaeo/examples/export_patch_focus_metrics.py
+++ /dev/null
@@ -1,164 +0,0 @@
-from pathlib import Path
-import re
-from time import localtime, strftime
-from typing import Dict
-
-import pandas as pd
-
-from model_server.extensions.ilastik.models import IlastikPixelClassifierModel
-from model_server.extensions.chaeo.products import export_3d_patches_with_focus_metrics, export_patches_from_zstack
-from model_server.extensions.chaeo.zmask import RoiSet
-from model_server.base.accessors import generate_file_accessor, InMemoryDataAccessor, write_accessor_data_to_file
-from model_server.base.workflows import Timer
-
-
-def export_patch_focus_metrics_from_multichannel_zstack(
- input_zstack_path: str,
- ilastik_project_file: str,
- pxmap_threshold: float,
- pixel_class: int,
- zmask_channel: int,
- patches_channel: int,
- where_output: str,
- mask_type: str = 'boxes',
- zmask_filters: Dict = None,
- zmask_expand_box_by: int = None,
- annotate_focus_metric=None,
- **kwargs,
-) -> Dict:
-
- ti = Timer()
- stack = generate_file_accessor(Path(input_zstack_path))
- fstem = Path(input_zstack_path).stem
- ti.click('file_input')
- assert stack.nz > 1, 'Expecting z-stack'
-
- # MIP and classify pixels
- mip = InMemoryDataAccessor(
- stack.get_one_channel_data(channel=0).data.max(axis=-1, keepdims=True)
- )
- px_model = IlastikPixelClassifierModel(
- params={'project_file': Path(ilastik_project_file)}
- )
- pxmap, _ = px_model.infer(mip)
- ti.click('infer_pixel_probability')
-
- obmask = InMemoryDataAccessor(
- pxmap.data > pxmap_threshold
- )
- ti.click('threshold_pixel_mask')
-
- # make zmask
- # zmask, zmask_meta, df, interm = build_zmask_from_object_mask(
- # obmask.get_one_channel_data(pixel_class),
- # stack.get_one_channel_data(zmask_channel),
- # mask_type=mask_type,
- # filters=zmask_filters,
- # expand_box_by=zmask_expand_box_by,
- # )
- obj_table = RoiSet(
- obmask.get_one_channel_data(pixel_class),
- stack.get_one_channel_data(zmask_channel),
- mask_type=mask_type,
- filters=zmask_filters,
- expand_box_by=zmask_expand_box_by,
- )
- zmask_acc = InMemoryDataAccessor(obj_table.zmask)
- ti.click('generate_zmasks')
-
- files = export_3d_patches_with_focus_metrics(
- Path(where_output) / '3d_patches',
- stack.get_one_channel_data(patches_channel),
- obj_table.zmask_meta,
- prefix=fstem,
- rescale_clip=0.0,
- make_3d=True,
- annotate_focus_metric=annotate_focus_metric,
- )
- ti.click('export_3d_patches')
-
- files = export_patches_from_zstack(
- Path(where_output) / '2d_patches',
- stack.get_one_channel_data(patches_channel),
- obj_table.zmask_meta,
- prefix=fstem,
- draw_bounding_box=True,
- rescale_clip=0.0,
- # focus_metric=lambda x: np.max(sobel(x)),
- focus_metric='max_sobel',
- make_3d=False,
- )
- ti.click('export_2d_patches')
-
- return {
- 'pixel_model_id': px_model.model_id,
- 'input_filepath': input_zstack_path,
- 'number_of_objects': len(obj_table.zmask_meta),
- 'success': True,
- 'timer_results': ti.events,
- 'dataframe': df,
- 'interm': obj_table.interm,
- }
-
-if __name__ == '__main__':
- where_czi = Path(
- 'c:/Users/rhodes/projects/proj0004-marine-photoactivation/data/exp0038/AutoMic/20230906-163415/Selection'
- )
-
- where_output_root = Path(
- 'c:/Users/rhodes/projects/proj0011-plankton-seg/exp0009/output'
- )
- yyyymmdd = strftime('%Y%m%d', localtime())
- idx = 0
- while Path(where_output_root / f'batch-output-{yyyymmdd}-{idx:04d}').exists():
- idx += 1
- where_output = Path(
- where_output_root / f'batch-output-{yyyymmdd}-{idx:04d}'
- )
-
- csv_args = {'mode': 'w', 'header': True} # when creating file
- px_ilp = Path.home() / 'model_server' / 'ilastik' / 'AF405-bodies_boundaries.ilp'
-
- for ff in where_czi.iterdir():
- if ff.stem != 'Selection--W0000--P0009-T0001':
- continue
-
- pattern = 'Selection--W([\d]+)--P([\d]+)-T([\d]+)'
- ma = re.match(pattern, ff.stem)
-
- print(ff)
- if not ff.suffix.upper() == '.CZI':
- continue
- if int(ma.groups()[1]) > 10: # skip second half of set
- continue
-
- export_kwargs = {
- 'input_zstack_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.__str__(),
- 'mask_type': 'boxes',
- 'zmask_filters': {'area': (1e3, 1e8)},
- 'zmask_expand_box_by': (128, 3),
- 'annotate_focus_metric': 'max_sobel'
- }
-
- result = export_patch_focus_metrics_from_multichannel_zstack(**export_kwargs)
-
- # parse and record results
- df = result['dataframe']
- df['filename'] = ff.name
- df.to_csv(where_output / 'df_objects.csv', **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
-
- # 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
diff --git a/model_server/extensions/chaeo/products.py b/model_server/extensions/chaeo/products.py
index d001114f..679271ee 100644
--- a/model_server/extensions/chaeo/products.py
+++ b/model_server/extensions/chaeo/products.py
@@ -97,7 +97,7 @@ def get_patches_from_zmask_meta(
patches = []
# for mi in zmask_meta:
- for i, roi in enumerate(roiset.get_df().itertuples()):
+ for i, roi in enumerate(roiset.get_df().itertuples()): # TODO: call RoiSet.iter() when implemented
# sl = roi['slice']
# rbb = mi['relative_bounding_box'] # TODO: call rel_ fields in DF
@@ -204,7 +204,7 @@ def export_patches_from_zstack(
)
exported = []
- for i, roi in enumerate(roiset.get_df().itertuples()):
+ for i, roi in enumerate(roiset.get_df().itertuples()): # just used for label info
# for i in range(0, len(zmask_meta)):
# mi = zmask_meta[i]
patch = patches_acc.iat_yxcz(i)
@@ -224,86 +224,6 @@ def export_patches_from_zstack(
})
return exported
-def export_3d_patches_with_focus_metrics(
- where: Path,
- stack: GenericImageDataAccessor,
- zmask_meta: list,
- rescale_clip: float = 0.0,
- pad_to: int = 256,
- prefix='patch',
- **kwargs
-):
- """
- Export 3D patches as multi-level z-stacks, along with CSV of various focus methods for each z-position
-
- :param kwargs:
- annotate_focus_metric: name focus metric to use when drawing bounding box at optimal focus z-position
- :return:
- list of exported files
- """
- assert stack.chroma == 1, 'Expecting monochromatic image data'
- assert stack.nz > 1, 'Expecting z-stack'
-
- def get_zstack_focus_metrics(zs):
- nz = zs.shape[3]
- me = _focus_metrics()
- dd = {}
- for zi in range(0, nz):
- spf = zs[:, :, :, zi]
- dd[zi] = {k: me[k](spf) for k in me.keys()}
- return dd
-
- exported = []
- patch_meta = []
- for mi in zmask_meta:
- obj = mi['info']
- sl = mi['slice']
- rbb = mi['relative_bounding_box'] # TODO: use rel_ fields in DF
- idx = mi['df_index']
-
- patch = stack.data[sl]
-
- assert len(patch.shape) == 4
- assert patch.shape[2] == stack.chroma
-
- if rescale_clip is not None:
- patch = rescale(patch, rescale_clip)
-
- # unpack relative bounding box and define subset of patch data
- x0 = rbb['x0']
- y0 = rbb['y0']
- x1 = rbb['x1']
- y1 = rbb['y1']
- sp_sl = np.s_[y0: y1, x0: x1, :, :]
- subpatch = patch[sp_sl]
-
- # compute focus metrics for all z-levels
- me_dict = get_zstack_focus_metrics(subpatch)
- patch_meta.append({'label': obj.label, 'zi': obj.zi, 'metrics': me_dict})
- me_df = pd.DataFrame(me_dict).T
-
- # drawing bounding box only on focused slice
- ak = kwargs.get('annotate_focus_metric')
- if ak and ak in me_df.columns:
- zi_foc = me_df.idxmax().to_dict()[ak]
- patch[:, :, 0, zi_foc] = draw_box_on_patch(
- patch[:, :, 0, zi_foc],
- ((x0, y0), (x1, y1)),
- )
-
- if pad_to:
- patch = pad(patch, pad_to)
-
- fstem = f'{prefix}-la{obj.label:04d}-zi{obj.zi:04d}'
- write_patch_to_file(where, fstem + '.tif', resample_to_8bit(patch))
- me_df.to_csv(where / (fstem + '.csv'))
- exported.append({
- 'df_index': idx,
- 'patch_filename': fstem + '.tif',
- 'focus_metrics_filename': fstem + '.csv',
- })
-
- return exported
def export_multichannel_patches_from_zstack(
where: Path,
--
GitLab