diff --git a/extensions/chaeo/params.py b/extensions/chaeo/params.py
index e516f49bf86c55ffc6603feb42048ca356976c0a..796469e9d7cd52b9af9c3d852b0af1dd1f10a0bd 100644
--- a/extensions/chaeo/params.py
+++ b/extensions/chaeo/params.py
@@ -26,11 +26,6 @@ class RoiSetExportParams(BaseModel):
annotated_z_stack: Union[AnnotatedZStackParams, None] = None
-class RoiClassifierValue(BaseModel):
- name: str
- value: int
-
-
class RoiFilterRange(BaseModel):
min: float
max: float
@@ -39,5 +34,3 @@ class RoiFilterRange(BaseModel):
class RoiFilter(BaseModel):
area: Union[RoiFilterRange, None] = None
solidity: Union[RoiFilterRange, None] = None
- classifiers: List[RoiClassifierValue] = []
-
diff --git a/extensions/chaeo/workflows.py b/extensions/chaeo/workflows.py
index 6e9732f6d5c0f074c1cdaceb7076b738457b8fd1..50428f09b0c7f9a6f51e89ef586598bc016f9284 100644
--- a/extensions/chaeo/workflows.py
+++ b/extensions/chaeo/workflows.py
@@ -12,7 +12,7 @@ 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 ZMaskExportParams
+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
@@ -240,7 +240,7 @@ def infer_object_map_from_zstack(
zmask_type: str = 'boxes',
zmask_filters: Dict = None,
# zmask_expand_box_by: int = None,
- exports: ZMaskExportParams = None,
+ exports: RoiSetExportParams = None,
**kwargs,
) -> Dict:
assert len(models) == 2
@@ -281,7 +281,7 @@ def infer_object_map_from_zstack(
ti.click('threshold_pixel_mask')
# make zmask
- obj_table = RoiSet(
+ rois = RoiSet(
obmask.get_one_channel_data(pxmap_foreground_channel),
stack.get_one_channel_data(segmentation_channel),
mask_type=zmask_type,
@@ -291,7 +291,7 @@ def infer_object_map_from_zstack(
ti.click('generate_zmasks')
# record pixel scale
- obj_table.df['pixel_scale_in_micrometers'] = float(stack.pixel_scale_in_micrometers.get('X'))
+ rois.df['pixel_scale_in_micrometers'] = float(stack.pixel_scale_in_micrometers.get('X'))
# ti, stack, fstem, obmask, pxmap, obj_table = get_zmask_meta(
# input_file_path,
@@ -307,79 +307,79 @@ def infer_object_map_from_zstack(
# **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
- )
-
- # TODO: make this a method of ZMaskObjectTable class
- # extract masks
- patch_masks_acc = get_patch_masks_from_zmask_meta(
- stack,
- obj_table.zmask_meta,
- **kwargs
- )
-
- # TODO: add ZMaskObjectTable method to apply object classification results as new DataFrame column
- # 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
+ # # 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
+ # )
- # 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})
+ # # 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)
# TODO: add ZMaskObjectTable method to export object map
output_path = Path(output_folder_path) / ('obj_classes_' + (fstem + '.tif'))
write_accessor_data_to_file(
output_path,
- InMemoryDataAccessor(output_map)
+ InMemoryDataAccessor(object_class_map)
)
ti.click('export_object_classes')
if exports.patches_3d:
- obj_table.export_3d_patches(
- Path(output_folder_path) / '3d_patches',
- fstem,
- patches_channel,
- exports.patches_3d
- )
- ti.click('export_3d_patches')
+ rois.export_3d_patches(
+ Path(output_folder_path) / '3d_patches',
+ fstem,
+ patches_channel,
+ exports.patches_3d
+ )
+ ti.click('export_3d_patches')
if exports.patches_2d_for_annotation:
- obj_table.export_2d_patches_for_annotation(
+ rois.export_2d_patches_for_annotation(
Path(output_folder_path) / '2d_patches_annotation',
fstem,
patches_channel,
exports.patches_2d_for_annotation
)
- ti.click('export_2d_patches_for_annotation')
+ ti.click('export_2d_patches_for_annotation')
if exports.patches_2d_for_training:
- obj_table.export_2d_patches_for_training(
+ rois.export_2d_patches_for_training(
Path(output_folder_path) / '2d_patches_training',
fstem,
patches_channel,
exports.patches_2d_for_training
)
- ti.click('export_2d_patches_for_training')
+ ti.click('export_2d_patches_for_training')
if exports.patch_masks:
- obj_table.export_patch_masks(
+ rois.export_patch_masks(
Path(output_folder_path) / 'patch_masks',
fstem,
patches_channel,
@@ -387,18 +387,17 @@ def infer_object_map_from_zstack(
)
if exports.annotated_z_stack:
- obj_table.export_annotated_zstack(
+ rois.export_annotated_zstack(
Path(output_folder_path) / 'patch_masks',
fstem,
patches_channel,
exports.annotated_z_stack
)
- ti.click('export_annotated_zstack')
-
+ ti.click('export_annotated_zstack')
return {
'timer_results': ti.events,
- 'dataframe': pd.DataFrame(meta),
+ 'dataframe': rois.df,
'interm': {},
'output_path': output_path.__str__(),
}
diff --git a/extensions/chaeo/zmask.py b/extensions/chaeo/zmask.py
index 67a78334a7cbe754f9fa7d1cf0d3be82ca80ca8c..69e128ef871fcd47ed9e385a6da9e128c38f2a76 100644
--- a/extensions/chaeo/zmask.py
+++ b/extensions/chaeo/zmask.py
@@ -8,10 +8,11 @@ from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from extensions.chaeo.annotators import draw_boxes_on_3d_image
-from extensions.chaeo.products import export_patches_from_zstack, export_multichannel_patches_from_zstack, export_patch_masks_from_zstack
+from extensions.chaeo.products import export_patches_from_zstack, export_multichannel_patches_from_zstack, export_patch_masks_from_zstack, get_patches_from_zmask_meta, get_patch_masks_from_zmask_meta
from extensions.chaeo.params import RoiFilter, RoiSetExportParams
+from extensions.chaeo.process import mask_largest_object
from model_server.accessors import GenericImageDataAccessor, InMemoryDataAccessor, write_accessor_data_to_file
-
+from model_server.models import InstanceSegmentationModel
class RoiSet(object):
@@ -30,13 +31,11 @@ class RoiSet(object):
filters=filters,
mask_type=mask_type,
expand_box_by=expand_box_by
- ) # currently, some methods can add columns to self.df
+ )
self.acc_raw = acc_raw
self.count = len(self.zmask_meta)
-
- def get_label_map(self):
- return self.interm.lamap
+ self.object_id_labels = self.interm['label_map']
def get_argmax(self):
return self.interm.argmax
@@ -152,19 +151,48 @@ class RoiSet(object):
projected = self.acc_raw.data.max(axis=-1)
return projected
- def classify_by(self, column_name, object_classification_model, patch_basis=True):
- # add one column to df where each value is an integer
- pass
+ def get_raw_patches(self, channel):
+ return get_patches_from_zmask_meta(self.acc_raw(channel), self.zmask_meta)
- def get_raw_patches(self, filters: RoiFilter):
- pass
+ def get_patch_masks(self):
+ return get_patch_masks_from_zmask_meta(self.acc_raw, self.zmask_meta)
+
+ def classify_by(self, channel, object_classification_model: InstanceSegmentationModel):
+ # do this on a patch basis, i.e. only one object per frame
+ obmap_patches = object_classification_model.label_instance_class(
+ self.get_raw_patches(channel),
+ self.get_patch_masks()
+ )
- def get_patch_masks(self, filters: RoiFilter):
+ lamap = self.object_id_labels
+ output_map = np.zeros(lamap.shape, dtype=lamap.dtype)
+ self.df['instance_class'] = np.nan
+
+ # assign labels to object map:
+ for ii in range(0, self.count):
+ object_id = self.zmask_meta[ii]['info'].label
+ result_patch = mask_largest_object(obmap_patches.iat(ii))
+ object_class = np.unique(result_patch)[1]
+ output_map[self.object_id_labels == object_id] = object_class
+ self.df[object_id, 'instance_class'] = object_class
+
+ return InMemoryDataAccessor(output_map)
+
+ # TODO: test
+ def get_object_mask_by_id(self, obj_id):
+ return self.object_id_labels == obj_id
+
+ def get_object_mask_by_class(self, class_id):
+ return self.object_id_labels == class_id
+
+ # TODO: implement
+ def get_object_patch_by_id(self, obj_id):
pass
def get_object_map(self, filters: RoiFilter):
pass
+
def build_zmask_from_object_mask(
obmask: GenericImageDataAccessor,
zstack: GenericImageDataAccessor,