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Commit 401c6ee7 authored by Christopher Randolph Rhodes's avatar Christopher Randolph Rhodes
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Fixed bug where error was raised generating 3D patches where expanded 3D stack... 

Fixed bug where error was raised generating 3D patches where expanded 3D stack encountered min/max of range
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extensions/chaeo/accessors.py
+ 4
− 1
View file @ 401c6ee7
...@@ -82,7 +82,10 @@ class Multichannel3dPatchStack(InMemoryDataAccessor): ...@@ -82,7 +82,10 @@ class Multichannel3dPatchStack(InMemoryDataAccessor):
""" """
if isinstance(data, list): # list of YXCZ patches 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 assert nda.ndim == 5
# self._data = np.moveaxis( # pos-YXCZ # self._data = np.moveaxis( # pos-YXCZ
# nda, # nda,
......
extensions/chaeo/tests/test_zstack.py
+ 1
− 0
View file @ 401c6ee7
...@@ -209,6 +209,7 @@ class TestZStackDerivedDataProducts(unittest.TestCase): ...@@ -209,6 +209,7 @@ class TestZStackDerivedDataProducts(unittest.TestCase):
DummyInstanceSegmentationModel(), DummyInstanceSegmentationModel(),
] ]
export_params = RoiSetExportParams(**{ export_params = RoiSetExportParams(**{
'expand_box_by': [128, 2],
'pixel_probabilities': True, 'pixel_probabilities': True,
'patches_3d': {}, 'patches_3d': {},
'patches_2d_for_annotation': { 'patches_2d_for_annotation': {
......
extensions/chaeo/workflows.py
+ 1
− 259
View file @ 401c6ee7
...@@ -9,223 +9,15 @@ from skimage.measure import label, regionprops_table ...@@ -9,223 +9,15 @@ from skimage.measure import label, regionprops_table
from skimage.morphology import dilation from skimage.morphology import dilation
from sklearn.model_selection import train_test_split 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.params import RoiSetExportParams
from extensions.chaeo.process import mask_largest_object 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 RoiSet
from extensions.chaeo.zmask import project_stack_from_focal_points, RoiSet
from extensions.ilastik.models import IlastikPixelClassifierModel
from model_server.accessors import generate_file_accessor, InMemoryDataAccessor, write_accessor_data_to_file 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.models import Model, InstanceSegmentationModel, SemanticSegmentationModel
from model_server.process import rescale from model_server.process import rescale
from model_server.workflows import Timer 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( def infer_object_map_from_zstack(
input_file_path: str, input_file_path: str,
...@@ -239,9 +31,7 @@ def infer_object_map_from_zstack( ...@@ -239,9 +31,7 @@ def infer_object_map_from_zstack(
zmask_clip: int = None, zmask_clip: int = None,
zmask_type: str = 'boxes', zmask_type: str = 'boxes',
zmask_filters: Dict = None, zmask_filters: Dict = None,
# zmask_expand_box_by: int = None,
exports: RoiSetExportParams = RoiSetExportParams(), exports: RoiSetExportParams = RoiSetExportParams(),
**kwargs,
) -> Dict: ) -> Dict:
assert len(models) == 2 assert len(models) == 2
pixel_classifier = models[0] pixel_classifier = models[0]
...@@ -290,54 +80,6 @@ def infer_object_map_from_zstack( ...@@ -290,54 +80,6 @@ def infer_object_map_from_zstack(
) )
ti.click('generate_zmasks') 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) object_class_map = rois.classify_by(patches_channel, object_classifier)
# TODO: add ZMaskObjectTable method to export object map # TODO: add ZMaskObjectTable method to export object map
......
extensions/chaeo/zmask.py
+ 10
− 0
View file @ 401c6ee7
...@@ -173,6 +173,16 @@ class RoiSet(object): ...@@ -173,6 +173,16 @@ class RoiSet(object):
def get_object_map(self, filters: RoiFilter): def get_object_map(self, filters: RoiFilter):
pass 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( def build_zmask_from_object_mask(
obmask: GenericImageDataAccessor, obmask: GenericImageDataAccessor,
......
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