diff --git a/extensions/chaeo/accessors.py b/extensions/chaeo/accessors.py
index 82f726ba86fddf817583e349027a83b134d483e2..60cc2c70e285d173021ea7e81b4b900862dd874c 100644
--- a/extensions/chaeo/accessors.py
+++ b/extensions/chaeo/accessors.py
@@ -82,7 +82,10 @@ class Multichannel3dPatchStack(InMemoryDataAccessor):
"""
if isinstance(data, list): # list of YXCZ patches
- nda = np.array(data)
+ nda = np.zeros((len(data), *np.array([e.shape for e in data]).max(axis=0)), dtype=data[0].dtype)
+ for i in range(0, len(data)):
+ nzi = data[i].shape[-1]
+ nda[i, :, :, :, 0:nzi] = data[i]
assert nda.ndim == 5
# self._data = np.moveaxis( # pos-YXCZ
# nda,
diff --git a/extensions/chaeo/tests/test_zstack.py b/extensions/chaeo/tests/test_zstack.py
index a78fbea4e9b4721fb19d10c60c86688cbdc15edb..f472c7734bff93eedb880d1581e5bc84cf503c84 100644
--- a/extensions/chaeo/tests/test_zstack.py
+++ b/extensions/chaeo/tests/test_zstack.py
@@ -209,6 +209,7 @@ class TestZStackDerivedDataProducts(unittest.TestCase):
DummyInstanceSegmentationModel(),
]
export_params = RoiSetExportParams(**{
+ 'expand_box_by': [128, 2],
'pixel_probabilities': True,
'patches_3d': {},
'patches_2d_for_annotation': {
diff --git a/extensions/chaeo/workflows.py b/extensions/chaeo/workflows.py
index 91b09c9d45178659a740efa85551b9caa6486776..8dd7554fe6657e19e21a6d3f0852927cfb160151 100644
--- a/extensions/chaeo/workflows.py
+++ b/extensions/chaeo/workflows.py
@@ -9,223 +9,15 @@ from skimage.measure import label, regionprops_table
from skimage.morphology import dilation
from sklearn.model_selection import train_test_split
-from extensions.chaeo.accessors import MonoPatchStack
-from extensions.chaeo.annotators import draw_boxes_on_3d_image
-from extensions.chaeo.models import PatchStackObjectClassifier
from extensions.chaeo.params import RoiSetExportParams
from extensions.chaeo.process import mask_largest_object
-from extensions.chaeo.products import export_patches_from_zstack, export_patch_masks_from_zstack, export_multichannel_patches_from_zstack, get_patches_from_zmask_meta, get_patch_masks_from_zmask_meta
-from extensions.chaeo.zmask import project_stack_from_focal_points, RoiSet
-from extensions.ilastik.models import IlastikPixelClassifierModel
+from extensions.chaeo.zmask import RoiSet
from model_server.accessors import generate_file_accessor, InMemoryDataAccessor, write_accessor_data_to_file
from model_server.models import Model, InstanceSegmentationModel, SemanticSegmentationModel
from model_server.process import rescale
from model_server.workflows import Timer
-# def get_zmask_meta(
-# input_file_path: str,
-# ilastik_pixel_classifier: IlastikPixelClassifierModel,
-# segmentation_channel: int,
-# pxmap_threshold: float,
-# pxmap_foreground_channel: int = 0,
-# zmask_zindex: int = None,
-# zmask_clip: int = None,
-# zmask_filters: Dict = None,
-# zmask_type: str = 'boxes',
-# **kwargs,
-# ) -> tuple:
-# ti = Timer()
-# stack = generate_file_accessor(Path(input_file_path))
-# fstem = Path(input_file_path).stem
-# ti.click('file_input')
-#
-# # MIP if no zmask z-index is given, then classify pixels
-# if isinstance(zmask_zindex, int):
-# assert 0 < zmask_zindex < stack.nz
-# zmask_data = stack.get_one_channel_data(channel=segmentation_channel).data[:, :, :, zmask_zindex]
-# else:
-# zmask_data = stack.get_one_channel_data(channel=segmentation_channel).data.max(axis=-1, keepdims=True)
-# if zmask_clip:
-# zmask_data = rescale(zmask_data, zmask_clip)
-# mip = InMemoryDataAccessor(
-# zmask_data,
-# )
-# pxmap, _ = ilastik_pixel_classifier.infer(mip)
-# ti.click('infer_pixel_probability')
-#
-# obmask = InMemoryDataAccessor(
-# pxmap.data > pxmap_threshold
-# )
-# ti.click('threshold_pixel_mask')
-#
-# # make zmask
-# obj_table = ZMaskObjectTable(
-# obmask.get_one_channel_data(pxmap_foreground_channel),
-# stack.get_one_channel_data(segmentation_channel),
-# mask_type=zmask_type,
-# filters=zmask_filters,
-# expand_box_by=kwargs['zmask_expand_box_by'],
-# )
-# ti.click('generate_zmasks')
-#
-# # record pixel scale
-# obj_table.df['pixel_scale_in_micrometers'] = float(stack.pixel_scale_in_micrometers.get('X'))
-#
-# return ti, stack, fstem, obmask, pxmap, obj_table
-
-
-# # called by batch runners
-# def export_patches_from_multichannel_zstack(
-# input_file_path: str,
-# output_folder_path: str,
-# models: List[Model],
-# pxmap_threshold: float,
-# pxmap_foreground_channel: int,
-# segmentation_channel: int,
-# patches_channel: int,
-# zmask_zindex: int = None, # None for MIP,
-# zmask_clip: int = None,
-# zmask_type: str = 'boxes',
-# zmask_filters: Dict = None,
-# zmask_expand_box_by: int = None,
-# export_pixel_probabilities=True,
-# export_2d_patches_for_training=True,
-# export_2d_patches_for_annotation=True,
-# draw_bounding_box_on_2d_patch=True,
-# draw_contour_on_2d_patch=False,
-# draw_mask_on_2d_patch=False,
-# export_3d_patches=True,
-# export_annotated_zstack=True,
-# draw_label_on_zstack=False,
-# export_patch_masks=True,
-# rgb_overlay_channels=(None, None, None),
-# rgb_overlay_weights=(1.0, 1.0, 1.0),
-# ) -> Dict:
-# pixel_classifier = models[0]
-#
-# # ti, stack, fstem, obmask, pxmap, obj_table = get_zmask_meta(
-# # input_file_path,
-# # pixel_classifier,
-# # segmentation_channel,
-# # pxmap_threshold,
-# # pxmap_foreground_channel=pxmap_foreground_channel,
-# # zmask_zindex=zmask_zindex,
-# # zmask_clip=zmask_clip,
-# # zmask_expand_box_by=zmask_expand_box_by,
-# # zmask_filters=zmask_filters,
-# # zmask_type=zmask_type,
-# # )
-#
-# # obj_table = ZMaskObjectTable(
-# # obmask.get_one_channel_data(pxmap_foreground_channel),
-# # stack.get_one_channel_data(segmentation_channel),
-# # mask_type=zmask_type,
-# # filters=zmask_filters,
-# # expand_box_by=kwargs['zmask_expand_box_by'],
-# # )
-#
-# if export_pixel_probabilities:
-# write_accessor_data_to_file(
-# Path(output_folder_path) / 'pixel_probabilities' / (fstem + '.tif'),
-# pxmap
-# )
-# ti.click('export_pixel_probability')
-#
-# if export_3d_patches and len(zmask_meta) > 0:
-# files = export_patches_from_zstack(
-# Path(output_folder_path) / '3d_patches',
-# stack.get_one_channel_data(patches_channel),
-# zmask_meta,
-# prefix=fstem,
-# draw_bounding_box=False,
-# rescale_clip=0.001,
-# make_3d=True,
-# )
-# ti.click('export_3d_patches')
-#
-# if export_2d_patches_for_annotation and len(zmask_meta) > 0:
-# files = export_multichannel_patches_from_zstack(
-# Path(output_folder_path) / '2d_patches_annotation',
-# stack,
-# zmask_meta,
-# prefix=fstem,
-# rescale_clip=0.001,
-# make_3d=False,
-# focus_metric='max_sobel',
-# ch_white=patches_channel,
-# ch_rgb_overlay=rgb_overlay_channels,
-# draw_bounding_box=draw_bounding_box_on_2d_patch,
-# bounding_box_channel=1,
-# bounding_box_linewidth=2,
-# draw_contour=draw_contour_on_2d_patch,
-# draw_mask=draw_mask_on_2d_patch,
-# overlay_gain=rgb_overlay_weights,
-# )
-# 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_2d_patches_for_training and len(zmask_meta) > 0:
-# files = export_multichannel_patches_from_zstack(
-# Path(output_folder_path) / '2d_patches_training',
-# stack.get_one_channel_data(patches_channel),
-# zmask_meta,
-# prefix=fstem,
-# rescale_clip=0.001,
-# make_3d=False,
-# focus_metric='max_sobel',
-# )
-# ti.click('export_2d_patches')
-#
-# if export_patch_masks and len(zmask_meta) > 0:
-# files = export_patch_masks_from_zstack(
-# Path(output_folder_path) / 'patch_masks',
-# stack.get_one_channel_data(patches_channel),
-# zmask_meta,
-# prefix=fstem,
-# )
-#
-# if export_annotated_zstack:
-# annotated = InMemoryDataAccessor(
-# draw_boxes_on_3d_image(
-# stack.get_one_channel_data(patches_channel).data,
-# zmask_meta,
-# add_label=draw_label_on_zstack,
-# )
-# )
-# write_accessor_data_to_file(
-# Path(output_folder_path) / 'annotated_zstacks' / (fstem + '.tif'),
-# annotated
-# )
-# ti.click('export_annotated_zstack')
-#
-# # generate multichannel projection from label centroids
-# dff = df[df['keeper']]
-# if len(zmask_meta) > 0:
-# interm['projected'] = project_stack_from_focal_points(
-# dff['centroid-0'].to_numpy(),
-# dff['centroid-1'].to_numpy(),
-# dff['zi'].to_numpy(),
-# stack,
-# degree=4,
-# )
-# else: # else just return MIP
-# interm['projected'] = stack.data.max(axis=-1)
-#
-# return {
-# 'pixel_model_id': pixel_classifier.model_id,
-# 'input_filepath': input_file_path,
-# 'number_of_objects': len(zmask_meta),
-# 'pixeL_scale_in_micrometers': stack.pixel_scale_in_micrometers,
-# 'success': True,
-# 'timer_results': ti.events,
-# 'dataframe': df[df['keeper'] == True],
-# 'interm': interm,
-# }
def infer_object_map_from_zstack(
input_file_path: str,
@@ -239,9 +31,7 @@ def infer_object_map_from_zstack(
zmask_clip: int = None,
zmask_type: str = 'boxes',
zmask_filters: Dict = None,
- # zmask_expand_box_by: int = None,
exports: RoiSetExportParams = RoiSetExportParams(),
- **kwargs,
) -> Dict:
assert len(models) == 2
pixel_classifier = models[0]
@@ -290,54 +80,6 @@ def infer_object_map_from_zstack(
)
ti.click('generate_zmasks')
- # ti, stack, fstem, obmask, pxmap, obj_table = get_zmask_meta(
- # input_file_path,
- # pixel_classifier,
- # segmentation_channel,
- # pxmap_threshold,
- # pxmap_foreground_channel=pxmap_foreground_channel,
- # zmask_zindex=zmask_zindex,
- # zmask_clip=zmask_clip,
- # # zmask_expand_box_by=zmask_expand_box_by,
- # zmask_filters=zmask_filters,
- # zmask_type=zmask_type,
- # **kwargs
- # )
-
- # # extract patches to accessor
- # patches_acc = get_patches_from_zmask_meta(
- # stack.get_one_channel_data(patches_channel),
- # obj_table.zmask_meta,
- # rescale_clip=zmask_clip,
- # make_3d=False,
- # focus_metric='max_sobel',
- # **kwargs
- # )
- #
- # # extract masks
- # patch_masks_acc = get_patch_masks_from_zmask_meta(
- # stack,
- # obj_table.zmask_meta,
- # **kwargs
- # )
-
- # # send patches and mask stacks to object classifier
- # result_acc, _ = object_classifier.infer(patches_acc, patch_masks_acc)
-
- # labels_map = obj_table.interm['label_map']
- # output_map = np.zeros(labels_map.shape, dtype=labels_map.dtype)
- # assert labels_map.shape == obj_table.get_label_map().shape
- # assert labels_map.dtype == obj_table.get_label_map().dtype
- #
- # # assign labels to object map:
- # meta = []
- # for ii in range(0, len(obj_table.zmask_meta)):
- # object_id = obj_table.zmask_meta[ii]['info'].label
- # result_patch = mask_largest_object(result_acc.iat(ii))
- # object_class = np.unique(result_patch)[1]
- # output_map[labels_map == object_id] = object_class
- # meta.append({'object_id': ii, 'object_class': object_id})
-
object_class_map = rois.classify_by(patches_channel, object_classifier)
# TODO: add ZMaskObjectTable method to export object map
diff --git a/extensions/chaeo/zmask.py b/extensions/chaeo/zmask.py
index 31bf51702d539affc825cea8e0e437ca28b00e8d..76e96bf8bb91b45b7031fbef2a2ac510393e7870 100644
--- a/extensions/chaeo/zmask.py
+++ b/extensions/chaeo/zmask.py
@@ -173,6 +173,16 @@ class RoiSet(object):
def get_object_map(self, filters: RoiFilter):
pass
+ def run_exports(self, where, channel, params):
+ names = [
+ 'pixel_probabilities',
+ 'patches_3d',
+ 'patches_2d_for_annotation',
+ 'patches_2d_for_training',
+ 'patch_masks',
+ 'annotated_z_stack',
+ ]
+
def build_zmask_from_object_mask(
obmask: GenericImageDataAccessor,