def make(self,key):
"""
Purpose: To decimate a mesh by a perscribed
decimation ratio and algorithm
Pseudocode:
1) Get the current mesh
2) Decimationt he current mesh with the corresponding decimate method
3) Get n_verts,n_faces and save as h5py file
"""
segment_id = key["segment_id"]
decimation_method = key["decimation_method"]
mesh_fragment_method_set = key["mesh_fragment_method_set"]
ver =key["ver"]
if verbose:
print(f"\n\n--Working on {segment_id}: (decimation_method = {decimation_method})")
param_tables = MeshFragmentMethodSet.restrict_one_part_with_hash(mesh_fragment_method_set)
soma_kwargs = MeshFragmentMethod.restrict_one_part_with_hash(
(param_tables & "table_type = 'Soma'").fetch1("mesh_fragment_method")).fetch1()
glia_kwargs = MeshFragmentMethod.restrict_one_part_with_hash(
(param_tables & "table_type = 'Glia'").fetch1("mesh_fragment_method")).fetch1()
nuclei_kwargs = MeshFragmentMethod.restrict_one_part_with_hash(
(param_tables & "table_type = 'Nuclei'").fetch1("mesh_fragment_method")).fetch1()
all_kwargs = gu.merge_dicts([glia_kwargs,nuclei_kwargs,soma_kwargs])
mesh_fragment_method_soma = (param_tables & dict(table_type='Soma')).fetch1("mesh_fragment_method")
mesh_fragment_method_nuclei = (param_tables & dict(table_type='Nuclei')).fetch1("mesh_fragment_method")
mesh_fragment_method_glia = (param_tables & dict(table_type='Glia')).fetch1("mesh_fragment_method")
st = time.time()
#1) Get the current mesh
new_mesh = (h01mor.Decimation.Object() & key).fetch1("mesh")
if verbose:
print(f"Fetching Decimation time: {time.time()-st}")
current_mesh_verts,current_mesh_faces = new_mesh.vertices,new_mesh.faces
segment_id = key["segment_id"]
(total_soma_list,
run_time,
total_soma_list_sdf,
glia_pieces,
nuclei_pieces) = sm.extract_soma_center(
segment_id,
current_mesh_verts,
current_mesh_faces,
return_glia_nuclei_pieces=True,
verbose = True,
**all_kwargs
)
# -------- 1/9 Addition: Going to save off the glia and nuclei pieces ----------- #
"""
Psuedocode:
For both glia and nuclie pieces
1) If the length of array is greater than 0 --> combine the mesh and map the indices to original mesh
2) If not then just put None
"""
orig_mesh = new_mesh
if len(glia_pieces)>0:
glia_faces = tu.original_mesh_faces_map(orig_mesh,tu.combine_meshes(glia_pieces))
n_glia_faces = len(glia_faces)
else:
glia_faces = None
n_glia_faces = 0
if len(nuclei_pieces)>0:
nuclei_faces = tu.original_mesh_faces_map(orig_mesh,tu.combine_meshes(nuclei_pieces))
n_nuclei_faces = len(nuclei_faces)
else:
nuclei_faces = None
n_nuclei_faces = 0
# --------- saving the nuclei and glia saves
glia_path,nuclei_path = hdju.save_glia_nuclei_files(
glia_faces=glia_faces,
nuclei_faces=nuclei_faces,
segment_id=segment_id,
filename_append = filename_append)
print(f" glia_path = {glia_path} \n nuclei_path = {nuclei_path}")
glia_key = dict(key,
mesh_fragment_method = mesh_fragment_method_glia,
n_faces = n_glia_faces,
faces=glia_path)
nuclei_key = dict(key,
mesh_fragment_method = mesh_fragment_method_nuclei,
n_faces = n_nuclei_faces,
faces=nuclei_path)
# ---------------- End of 1/9 Addition --------------------------------- #
self.insert1(key,
skip_duplicates=True,
ignore_extra_fields=True,
allow_direct_insert=True)
print(f"Run time was {run_time} \n total_soma_list = {total_soma_list}"
f"\n with sdf values = {total_soma_list_sdf}")
#check if soma list is empty and did not find soma
if len(total_soma_list) <= 0:
print("There were no somas found for this mesh so just writing empty data")
returned_file_path = tu.write_h5_file(
vertices=np.array([]),
faces=np.array([]),
segment_id=segment_id,
filename = f'{segment_id}_soma_0.h5',
filepath=str(hdju.external_path_mesh)
)
insert_dict = dict(key,
soma_index=0,
centroid_x=None,
centroid_y=None,
centroid_z=None,
centroid_x_nm=None,
centroid_y_nm=None,
centroid_z_nm=None,
#distance_from_prediction=None,
#prediction_matching_index = None,
n_vertices=0,
n_faces=0,
mesh=returned_file_path,
multiplicity=0,
sdf = None,
volume = None,
surface_area = None,
max_side_ratio = None,
bbox_volume_ratio = None,
max_hole_length=None,
run_time=np.round(run_time,4),
mesh_fragment_method = mesh_fragment_method_soma,
)
#raise Exception("to prevent writing because none were found")
self.Soma.insert1(insert_dict,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.SomaObject.insert1(insert_dict,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
return
#if there is one or more soma found, get the volume and side length checks
max_side_ratio = [np.max(sm.side_length_ratios(m)) for m in total_soma_list]
bbox_volume_ratio = [sm.soma_volume_ratio(m) for m in total_soma_list]
dicts_to_insert = []
for i,(current_soma,soma_sdf,sz_ratio,vol_ratio) in enumerate(zip(total_soma_list,total_soma_list_sdf,max_side_ratio,bbox_volume_ratio)):
print("Trying to write off file")
""" Currently don't need to export the meshes
current_soma.export(f"{key['segment_id']}/{key['segment_id']}_soma_{i}.off")
"""
auto_prediction_center_nm = np.mean(current_soma.vertices,axis=0)
auto_prediction_center = auto_prediction_center_nm / hu.voxel_to_nm_scaling
auto_prediction_center = auto_prediction_center.astype("int")
print(f"Predicted Coordinates are {auto_prediction_center}")
max_hole_length = tu.largest_hole_length(current_soma)
if max_hole_length is not None:
max_hole_length = np.round(max_hole_length,3)
returned_file_path = tu.write_h5_file(
vertices=current_soma.vertices,
faces=current_soma.faces,
segment_id=segment_id,
filename = f'{segment_id}_soma_{i+1}.h5',
filepath=str(hdju.external_path_mesh)
)
divisor=1000000
insert_dict = dict(key,
soma_index=i+1,
centroid_x=auto_prediction_center[0],
centroid_y=auto_prediction_center[1],
centroid_z=auto_prediction_center[2],
centroid_x_nm=auto_prediction_center_nm[0],
centroid_y_nm=auto_prediction_center_nm[1],
centroid_z_nm=auto_prediction_center_nm[2],
n_vertices = len(current_soma.vertices),
n_faces = len(current_soma.faces),
mesh=returned_file_path,
multiplicity=len(total_soma_list),
sdf = np.round(soma_sdf,3),
volume = np.round(current_soma.convex_hull.volume/1000000000,3),
surface_area = np.round(current_soma.area/divisor,3),
max_side_ratio = np.round(sz_ratio,3),
bbox_volume_ratio = np.round(vol_ratio,3),
max_hole_length = max_hole_length,
run_time=np.round(run_time,4),
mesh_fragment_method = mesh_fragment_method_soma,
)
dicts_to_insert.append(insert_dict)
self.Glia.insert1(glia_key,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.GliaObject.insert1(glia_key,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.Nuclei.insert1(nuclei_key,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.NucleiObject.insert1(nuclei_key,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.Soma.insert(dicts_to_insert,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
self.SomaObject.insert(dicts_to_insert,
allow_direct_insert = True,
ignore_extra_fields = True,
skip_duplicates=True)
#error_poisson_somas = du.get_seg_extracted_somas(seg_id)
error_mesh = du.get_decimated_mesh(seg_id)
# error_backtrack_somas = sm.original_mesh_soma(
# mesh = error_mesh,
# soma_meshes=error_poisson_somas,
# sig_th_initial_split=15)
# In[ ]:
import trimesh_utils as tu
sm = reload(sm)
tu = reload(tu)
soma_data = sm.extract_soma_center(seg_id, error_mesh.vertices,
error_mesh.faces)
# # -- getting neurons to help test on --
# In[ ]:
# sm = reload(sm)
# In[ ]:
# import neuron_utils as nru
# filepath = "/notebooks/test_neurons/meshafterparty_processed/12345_double_soma_meshafterparty_fixed_connectors_and_spines"
# double_neuron = nru.decompress_neuron(
# filepath=filepath,
# original_mesh=filepath,
# )
def make(self,key):
"""
Pseudocode:
1) Compute all of the
2) Save the mesh as an h5 py file
3) Store the saved path as the decomposition part of the dictionary and erase the vertices and faces
4) Insert
"""
#get the mesh data
print(f"\n\n\n---- Working on Neuron {key['segment_id']} ----")
print(key)
new_mesh = (minnie.Decimation() & key).fetch1("mesh")
current_mesh_verts,current_mesh_faces = new_mesh.vertices,new_mesh.faces
segment_id = key["segment_id"]
(total_soma_list,
run_time,
total_soma_list_sdf,
glia_pieces,
nuclei_pieces) = sm.extract_soma_center(
segment_id,
current_mesh_verts,
current_mesh_faces,
return_glia_nuclei_pieces=True,
)
# -------- 1/9 Addition: Going to save off the glia and nuclei pieces ----------- #
"""
Psuedocode:
For both glia and nuclie pieces
1) If the length of array is greater than 0 --> combine the mesh and map the indices to original mesh
2) If not then just put None
"""
orig_mesh = trimesh.Trimesh(vertices=current_mesh_verts,
faces=current_mesh_faces)
if len(glia_pieces)>0:
glia_faces = tu.original_mesh_faces_map(orig_mesh,tu.combine_meshes(glia_pieces))
n_glia_faces = len(glia_faces)
else:
glia_faces = None
n_glia_faces = 0
if len(nuclei_pieces)>0:
nuclei_faces = tu.original_mesh_faces_map(orig_mesh,tu.combine_meshes(nuclei_pieces))
n_nuclei_faces = len(nuclei_faces)
else:
nuclei_faces = None
n_nuclei_faces = 0
# --------- saving the nuclei and glia saves
glia_path,nuclei_path = du.save_glia_nuclei_files(glia_faces=glia_faces,
nuclei_faces=nuclei_faces,
segment_id=segment_id)
print(f" glia_path = {glia_path} \n nuclei_path = {nuclei_path}")
glia_nuclei_key = dict(key,
ver=current_version,
n_glia_faces=n_glia_faces,
#glia_faces = glia_faces,
glia_faces = glia_path,
n_nuclei_faces = n_nuclei_faces,
#nuclei_faces = nuclei_faces
nuclei_faces = nuclei_path,
)
NeuronGliaNuclei.insert1(glia_nuclei_key,replace=True)
print(f"Finished saving off glia and nuclei information : {glia_nuclei_key}")
# ---------------- End of 1/9 Addition --------------------------------- #
print(f"Run time was {run_time} \n total_soma_list = {total_soma_list}"
f"\n with sdf values = {total_soma_list_sdf}")
#check if soma list is empty and did not find soma
if len(total_soma_list) <= 0:
print("There were no somas found for this mesh so just writing empty data")
returned_file_path = tu.write_h5_file(
vertices=np.array([]),
faces=np.array([]),
segment_id=segment_id,
filename = f'{segment_id}_0.h5',
filepath=str(du.get_somas_path())
)
insert_dict = dict(key,
soma_index=0,
ver=current_version,
centroid_x=None,
centroid_y=None,
centroid_z=None,
#distance_from_prediction=None,
#prediction_matching_index = None,
n_vertices=0,
n_faces=0,
mesh=returned_file_path,
multiplicity=0,
sdf = None,
volume = None,
max_side_ratio = None,
bbox_volume_ratio = None,
max_hole_length=None,
run_time=run_time
)
#raise Exception("to prevent writing because none were found")
self.insert1(insert_dict,skip_duplicates=True)
return
#if there is one or more soma found, get the volume and side length checks
max_side_ratio = [np.max(sm.side_length_ratios(m)) for m in total_soma_list]
bbox_volume_ratio = [sm.soma_volume_ratio(m) for m in total_soma_list]
dicts_to_insert = []
for i,(current_soma,soma_sdf,sz_ratio,vol_ratio) in enumerate(zip(total_soma_list,total_soma_list_sdf,max_side_ratio,bbox_volume_ratio)):
print("Trying to write off file")
""" Currently don't need to export the meshes
current_soma.export(f"{key['segment_id']}/{key['segment_id']}_soma_{i}.off")
"""
auto_prediction_center = np.mean(current_soma.vertices,axis=0) / np.array([4,4,40])
auto_prediction_center = auto_prediction_center.astype("int")
print(f"Predicted Coordinates are {auto_prediction_center}")
max_hole_length = tu.largest_hole_length(current_soma)
returned_file_path = tu.write_h5_file(
vertices=current_soma.vertices,
faces=current_soma.faces,
segment_id=segment_id,
filename = f'{segment_id}_{i}.h5',
filepath=str(du.get_somas_path())
)
insert_dict = dict(key,
soma_index=i+1,
ver=current_version,
centroid_x=auto_prediction_center[0],
centroid_y=auto_prediction_center[1],
centroid_z=auto_prediction_center[2],
n_vertices = len(current_soma.vertices),
n_faces = len(current_soma.faces),
mesh=returned_file_path,
multiplicity=len(total_soma_list),
sdf = np.round(soma_sdf,3),
volume = current_soma.convex_hull.volume/1000000000,
max_side_ratio = np.round(sz_ratio,3),
bbox_volume_ratio = np.round(vol_ratio,3),
max_hole_length = np.round(max_hole_length,3),
run_time=np.round(run_time,4)
)
dicts_to_insert.append(insert_dict)
self.insert(dicts_to_insert,skip_duplicates=True)