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CATMAIDImport.py
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"""CATMAID to Blender Import Script.
Connects to CATMAID servers and retrieves skeleton data.
Copyright (C) Philipp Schlegel, 2014.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import bmesh
import bpy
import collections
import colorsys
import json
import math
import re
import requests
import time
import urllib
import numpy as np
from bpy.types import Panel, Operator, AddonPreferences
from bpy.props import (FloatVectorProperty, FloatProperty, StringProperty,
BoolProperty, EnumProperty, IntProperty,
CollectionProperty)
from bpy_extras.io_utils import orientation_helper, axis_conversion
from mathutils import Matrix
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
from requests.exceptions import HTTPError
########################################
# Settings
########################################
bl_info = {
"name": "CATMAID Import",
"author": "Philipp Schlegel",
"version": (7, 0, 2),
"for_catmaid_version": '2020.02.15-684-gcbe37bd',
"blender": (2, 80, 0), # this MUST be 2.80.0 (i.e. not 2.9x)
"location": "View3D > Sidebar (N) > CATMAID",
"description": "Imports Neuron from CATMAID server, Analysis tools, Export to SVG",
"warning": "",
"wiki_url": "",
"tracker_url": "",
"category": "Object"}
client = None
# Will populate this later
catmaid_volumes = [('None', 'None', 'Do not import volume from this list')]
DEFAULTS = {
"connectors": {
0: {'color': (0, 0.8, 0.8, 1), # postsynapses
'name': 'Presynapses'},
1: {'color': (1, 0, 0, 1), # presynapses
'name': 'Postsynapses'},
2: {'color': (0, 1, 0, 1), # gap junctions
'name': 'GapJunctions'},
3: {'color': (0.5, 0, 0.5, 1), # abutting
'name': 'Abutting'},
}
}
########################################
# UI Elements
########################################
class CATMAID_PT_import_panel(Panel):
"""Creates import menu in viewport side menu."""
bl_space_type = 'VIEW_3D'
bl_region_type = 'UI'
bl_label = "Import"
bl_category = "CATMAID"
def draw(self, context):
layout = self.layout
ver_str = '.'.join([str(i) for i in bl_info['version']])
layout.label(text=f'CATMAID plugin v{ver_str}')
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("catmaid.connect", text="Connect to CATMAID", icon='PLUGIN')
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("fetch.neuron", text="Import Neuron(s)", icon='ARMATURE_DATA')
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("fetch.connectors", text="Import Connectors", icon='PMARKER_SEL')
row.operator("display.help", text="", icon='QUESTION').entry = 'retrieve.connectors'
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("import.volume", text='Import Volume', icon='IMPORT')
class CATMAID_PT_export_panel(Panel):
"""Creates export menu in viewport side menu."""
bl_space_type = 'VIEW_3D'
bl_region_type = 'UI'
bl_label = "Export"
bl_category = "CATMAID"
def draw(self, context):
layout = self.layout
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("export.volume", text='Export Volume', icon='EXPORT')
class CATMAID_PT_properties_panel(Panel):
"""Creates properties menu in viewport side menu."""
bl_space_type = 'VIEW_3D'
bl_region_type = 'UI'
bl_label = "Neuron Properties"
bl_category = "CATMAID"
def draw(self, context):
layout = self.layout
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("curve.change", text="Curve properties",
icon='COLOR_BLUE')
row.operator("display.help", text="", icon='QUESTION').entry = 'curve.change'
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("material.change", text="Set colors",
icon='COLOR_BLUE')
row.operator("display.help", text="", icon='QUESTION').entry = 'change.material'
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("material.randomize", text="Randomize colors", icon='COLOR')
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("material.kmeans", text="Color by clusters", icon='LIGHT_SUN')
row.operator("display.help", text="", icon='QUESTION').entry = 'material.kmeans'
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("color.by_annotation", text="Color by Annotation", icon='SORTALPHA')
row = layout.row(align=True)
row.alignment = 'EXPAND'
row.operator("color.by_strahler", text="Color by Strahler Index", icon='MOD_ARRAY')
row.operator("display.help", text="", icon='QUESTION').entry = 'color.by_strahler'
########################################
# Operators
########################################
class CATMAID_OP_connect(Operator):
bl_idname = "catmaid.connect"
bl_label = 'Connect CATMAID'
bl_description = "Connect to given CATMAID server"
local_http_user: StringProperty(name="HTTP User")
local_http_pw: StringProperty(name="HTTP Password",
subtype='PASSWORD')
local_token: StringProperty(name="API token",
description='How to retrieve Token: '
'http://catmaid.github.io/dev/api.html#api-token')
local_server_url: StringProperty(name="Server Url")
local_project_id: IntProperty(name='Project ID', default=0)
def draw(self, context):
layout = self.layout
row = layout.row(align=True)
row.prop(self, "local_server_url")
row = layout.row(align=True)
row.prop(self, "local_token")
row = layout.row(align=True)
row.prop(self, "local_project_id")
row = layout.row(align=True)
row.prop(self, "local_http_user")
row = layout.row(align=True)
row.prop(self, "local_http_pw")
layout.label(text="Use Addon preferences to set peristent server url, credentials, etc.")
def invoke(self, context, event):
self.local_http_user = get_pref('http_user', '')
self.local_token = get_pref('api_token', '')
self.local_http_pw = get_pref('http_pw', '')
self.local_project_id = get_pref('project_id', 0)
self.local_server_url = get_pref('server_url', '')
self.max_threads = get_pref('max_threads', 20)
return context.window_manager.invoke_props_dialog(self)
def execute(self, context):
print('Connecting to CATMAID server')
print('URL: %s' % self.local_server_url)
print('HTTP user: %s' % self.local_http_user)
print('Token: %s' % self.local_token)
global client
client = CatmaidClient(server=self.local_server_url,
api_token=self.local_token,
http_user=self.local_http_user,
http_password=self.local_http_pw,
project_id=self.local_project_id,
max_threads=self.max_threads)
# Retrieve volumes, to test if connection is good:
try:
volumes = client.get_volume_list()
self.report({'INFO'}, 'Connection successful')
print('Test call successful')
except BaseException:
self.report({'ERROR'}, 'Connection failed: see console')
raise
global catmaid_volumes
catmaid_volumes = [('None', 'None', 'Do not import volume from list')]
catmaid_volumes += [(str(e[0]), e[1], str(e[2])) for e in volumes]
return {'FINISHED'}
class CATMAID_OP_fetch_neuron(Operator):
"""Fetch neurons."""
bl_idname = "fetch.neuron"
bl_label = 'Fetch neurons'
bl_description = "Fetch given neurons from global server"
names: StringProperty(name="Name(s)",
description="Search by neuron names. Separate "
"multiple names by commas. For example: "
"'neuronA,neuronB,neuronC'")
partial_match: BoolProperty(name="Allow partial matches?", default=False,
description="Allow partial matches for neuron "
"names and annotations! Will also "
"become case-insensitive")
annotations: StringProperty(name="Annotations(s)",
description="Search by skeleton IDs. Separate "
"multiple annotations by commas. For "
"example: 'annotation 1,annotation2"
",annotation3'")
intersect: BoolProperty(name="Intersect", default=False,
description="If true, all identifiers (e.g. two "
"annotations or name + annotation) "
"have to be true for a neuron to be "
"loaded")
skeleton_ids: StringProperty(name="Skeleton ID(s)",
description="Search by skeleton IDs. Separate "
"multiple IDs by commas. "
"Does not accept more than 400 "
"characters in total")
minimum_nodes: IntProperty(name="Minimum node count", default=1, min=1,
description="Neurons with fewer nodes than this "
" will be ignored")
import_synapses: BoolProperty(name="Synapses", default=True,
description="Import chemical synapses (pre- "
"and postsynapses), similarly to "
"3D Viewer in CATMAID")
import_gap_junctions: BoolProperty(name="Gap Junctions", default=False,
description="Import gap junctions, "
"similarly to 3D Viewer in "
"CATMAID")
import_abutting: BoolProperty(name="Abutting Connectors", default=False,
description="Import abutting connectors")
cn_spheres: BoolProperty(name="Connectors as spheres", default=False,
description="Import connectors as spheres instead "
"of curves")
downsampling: IntProperty(name="Downsampling Factor", default=2, min=1, max=20,
description="Will reduce number of nodes by given "
"factor. Root, ends and forks are "
"preserved")
use_radius: BoolProperty(name="Use node radii", default=False,
description="If true, neuron will use node radii "
"for thickness. If false, radius is "
"assumed to be 70nm (for visibility)")
neuron_mat_for_connectors: BoolProperty(name="Connectors same color as neuron",
default=False,
description="If true, connectors "
"will have the same "
"color as the neuron")
skip_existing: BoolProperty(name="Skip existing", default=True,
description="If True, will not add neurons that "
"are already in the scene")
# ATTENTION:
# using check() in an operator that uses threads, will lead to segmentation faults!
def check(self, context):
return True
@classmethod
def poll(cls, context):
if client:
return True
else:
return False
def draw(self, context):
layout = self.layout
box = layout.box()
row = box.row(align=False)
row.prop(self, "names")
row = box.row(align=False)
row.prop(self, "annotations")
row = box.row(align=False)
row.prop(self, "skeleton_ids")
row = box.row(align=False)
row.prop(self, "partial_match")
row.enabled = True if self.names or self.annotations else False
row.prop(self, "intersect")
row.enabled = True if self.names or self.annotations else False
row = box.row(align=False)
row.prop(self, "minimum_nodes")
layout.label(text="Import Options")
box = layout.box()
row = box.row(align=False)
row.prop(self, "import_synapses")
row.prop(self, "import_gap_junctions")
row.prop(self, "import_abutting")
row = box.row(align=False)
row.prop(self, "cn_spheres")
row.enabled = True if self.import_synapses or self.import_gap_junctions or self.import_abutting else False
row = box.row(align=False)
row.prop(self, "neuron_mat_for_connectors")
row.enabled = True if self.import_synapses or self.import_gap_junctions or self.import_abutting else False
row = box.row(align=False)
row.prop(self, "downsampling")
row = box.row(align=False)
row.prop(self, "use_radius")
row.prop(self, "skip_existing")
def invoke(self, context, event):
return context.window_manager.invoke_props_dialog(self)
def execute(self, context):
retrieve_by_names = []
if self.names:
retrieve_by_names = client.search_names(self.names, self.partial_match)
if not len(retrieve_by_names):
print('ERROR: Search name(s) not found! Import stopped')
self.report({'ERROR'}, 'Search tag(s) not found! Import stopped')
return{'FINISHED'}
retrieve_by_skids = []
if self.skeleton_ids:
retrieve_by_skids = np.array([x.strip() for x in self.skeleton_ids.split(',')])
retrieve_by_annotations = []
if self.annotations:
# In case there are commas in the annotation, we use quotation marks
if self.annotations.startswith('"') and self.annotations.endswith('"'):
annotations = [self.annotations[1:-1]]
else:
annotations = [x.strip() for x in self.annotations.split(',')]
retrieve_by_annotations = client.search_annotations(annotations,
allow_partial=self.partial_match,
intersect=self.intersect)
if not len(retrieve_by_annotations):
print('ERROR: No matching annotation(s) found! Import stopped')
self.report({'ERROR'}, 'No matching annotation(s) found! Import stopped')
return{'FINISHED'}
if self.intersect:
skeletons_to_retrieve = set.intersection(set(retrieve_by_annotations),
set(retrieve_by_names))
if not skeletons_to_retrieve:
print('WARNING: No neurons left after intersection! Import stopped')
self.report({'ERROR'}, 'Intersection empty! Import stopped')
return{'FINISHED'}
else:
skeletons_to_retrieve = set.union(set(retrieve_by_annotations),
set(retrieve_by_names),
set(retrieve_by_skids))
if self.skip_existing:
existing_skids = {ob['skeleton_id'] for ob in bpy.data.objects if 'skeleton_id' in ob}
skeletons_to_retrieve = skeletons_to_retrieve - existing_skids
if self.minimum_nodes > 1 and skeletons_to_retrieve:
print(f'Filtering {{len(skeletons_to_retrieve)}} neurons for size')
counts = client.get_node_counts(skeletons_to_retrieve)
skeletons_to_retrieve = [e for e in skeletons_to_retrieve if counts.get(str(e), 0) >= self.minimum_nodes]
# Extract skeleton IDs from skeleton_id string
print(f'{len(skeletons_to_retrieve)} neurons found - resolving names...')
neuron_names = client.get_names(skeletons_to_retrieve)
print("Collecting skeleton data...")
start = time.time()
skdata = client.get_skeletons(list(skeletons_to_retrieve),
with_history=False,
with_abutting=self.import_abutting)
print(f"Importing for {len(skdata)} skeletons into Blender")
for skid in skdata:
# Create an object name
object_name = f'#{skid} - {neuron_names[str(skid)]}'
import_skeleton(skdata[skid],
skeleton_id=str(skid),
object_name=object_name,
downsampling=self.downsampling,
import_synapses=self.import_synapses,
import_gap_junctions=self.import_gap_junctions,
import_abutting=self.import_abutting,
use_radii=self.use_radius,
cn_as_curves=not self.cn_spheres,
neuron_mat_for_connectors=self.neuron_mat_for_connectors)
print(f'Finished Import in {time.time()-start:.1f}s')
return {'FINISHED'}
def _get_available_volumes(self, context):
"""Simply returns parsed list of available volumes."""
# Must be defined before CATMAID_OP_fetch_volume
return catmaid_volumes
class CATMAID_OP_fetch_volume(Operator):
"""Imports a volume as mesh from CATMAID."""
bl_idname = "import.volume"
bl_label = "Import volumes from CATMAID"
bl_description = "Fetch volume from server"
volume: EnumProperty(name='Import from List',
items=_get_available_volumes,
description='Select volume to be imported. List will '
'refresh on (re-)connecting to CATMAID '
'server.')
by_name: StringProperty(name='Import by Name', default='',
description='Name of volume to import.')
allow_partial: BoolProperty(name='Allow partial match', default=True,
description='If True, name can be a partial match.')
@classmethod
def poll(cls, context):
if client:
return True
return False
def draw(self, context):
layout = self.layout
row = layout.row()
row.alignment = 'CENTER'
row.label(text="Reconnect to CATMAID server to refresh list")
row = layout.row()
row.prop(self, "volume")
row = layout.row()
row.prop(self, "by_name")
row = layout.row()
row.prop(self, "allow_partial")
def invoke(self, context, event):
return context.window_manager.invoke_props_dialog(self)
def execute(self, context):
volumes_to_retrieve = []
if self.volume != 'None':
volumes_to_retrieve.append(self.volume)
if self.by_name:
if self.allow_partial:
volumes_to_retrieve += [v[0] for v in catmaid_volumes if self.by_name.lower() in v[1].lower()]
else:
volumes_to_retrieve += [v[0] for v in catmaid_volumes if self.by_name.lower() == v[1].lower()]
for k, vol in enumerate(volumes_to_retrieve):
vertices, faces, name = client.get_volume(vol)
print(f'Importing volume {k} of {len(volumes_to_retrieve)}: '
f'{name} (ID {vol}) - {len(vertices)} vertices/'
f'{len(faces)} faces after clean-up')
import_mesh(vertices, faces, name=name)
return{'FINISHED'}
class CATMAID_OP_upload_volume(Operator):
"""Export a mesh as volume to CATMAID."""
bl_idname = "export.volume"
bl_label = "Export mesh to CATMAID"
bl_description = "Export mesh to CATMAID as volume"
volume_name: StringProperty(name='Name', default='',
description='If not explicitly provided, will '
'use the Blender object name.')
comment: StringProperty(name='Comment', default='',
description='Add comment to volume.')
@classmethod
def poll(cls, context):
if client:
return True
return False
def draw(self, context):
layout = self.layout
if not len(self.selected):
layout.label(text='Must have one or more meshes selected!')
return
elif len(self.selected) == 1:
layout.label(text='Single mesh selected for export:')
layout.prop(self, "volume_name")
else:
layout.label(text=f"{len(self.selected)} meshes selected: using objects' names for export:")
layout.prop(self, "comment")
layout.label(text="Meshes will show up in CATMAID 3D viewer and volume manager.")
layout.label(text="Requires CATMAID version 2016.04.18 or higher.")
layout.label(text="Polygon faces will be converted into triangles - please save Blender file before clicking OK!")
def invoke(self, context, event):
# Set selected objects at draw time
self.selected = [o for o in bpy.context.selected_objects if o.type == 'MESH']
if len(self.selected) == 1:
self.volume_name = self.selected[0].name
return context.window_manager.invoke_props_dialog(self)
def execute(self, context):
if not self.selected:
print("No meshes selected!")
return{'FINISHED'}
for i, obj in enumerate(self.selected):
if self.volume_name == '':
vol_name = obj.name
else:
vol_name = self.volume_name
# Make this object the active one
bpy.context.view_layer.objects.active = obj
# Check if mesh is trimesh:
is_trimesh = np.all([len(f.vertices) == 3 for f in obj.data.polygons])
if not is_trimesh:
print('Mesh not a trimesh - trying to convert')
# First go out of edit mode and select all vertices while in object mode
if obj.mode != 'OBJECT':
bpy.ops.object.mode_set(mode='OBJECT')
# Select all vertices
for v in obj.data.vertices:
v.select = True
# Now go to edit mode and convert to trimesh
bpy.ops.object.mode_set(mode='EDIT')
bpy.ops.mesh.quads_convert_to_tris()
bpy.ops.object.mode_set(mode='OBJECT')
# Check if again mesh is trimesh:
is_trimesh = np.all([len(f.vertices) == 3 for f in obj.data.polygons])
if all(is_trimesh):
print(f"{i} of {len(self.selected)}: Mesh '{vol_name}"
'successfully converted to trimesh!')
else:
print(f"{i} of {len(self.selected)}: Error during "
f"conversion of '{vol_name}' to trimesh "
"- try manually!")
continue
# Now create postdata
verts = np.empty(len(obj.data.vertices) * 3)
obj.data.vertices.foreach_get('co', verts) # this fills above array
verts = verts.reshape(len(obj.data.vertices), 3)
faces = np.empty(len(obj.data.polygons) * 3)
obj.data.polygons.foreach_get('vertices', faces)
faces = faces.reshape(len(obj.data.polygons), 3)
resp = client.upload_volume(verts, faces,
name=vol_name, comment=self.comment)
if 'success' in resp and resp['success'] is True:
print(f"{i} of {len(self.selected)}: Export of mesh '{vol_name}' "
"successful")
self.report({'INFO'}, 'Success!')
else:
print(f"{i} of {len(self.selected)}: Export of mesh '{vol_name}' "
"failed!")
self.report({'ERROR'}, 'See console for details!')
print(resp)
return{'FINISHED'}
class CATMAID_OP_display_help(Operator):
"""Displays popup with additional help."""
bl_idname = "display.help"
bl_label = "Advanced Tooltip"
bl_description = "Display help tooltip"
entry: StringProperty(name="which entry to show",
default='', options={'HIDDEN'})
def execute (self, context):
return {'FINISHED'}
def draw(self, context):
layout = self.layout
if self.entry == 'color.by_similarity':
row = layout.row()
row.alignment = 'CENTER'
row.label(text='Color Neurons by Similarity - Tooltip')
box = layout.box()
box.label(text='This function colors neurons based on how similar they are with respect to either: morphology, synapse placement, connectivity or paired connectivity.')
box.label(text='It is highly recommended to have SciPy installed - this will increase speed of calculation a lot!')
box.label(text='See https://github.com/schlegelp/CATMAID-to-Blender on how to install SciPy in Blender.')
box.label(text='Use <Settings> button to set parameters, then <Start Calculation>.')
layout.label(text='Morphology:')
box = layout.box()
box.label(text='Neurons that have close-by projections with similar orientation are')
box.label(text='similar. See Kohl et al. (2013, Cell).')
layout.label(text='Synapses:')
box = layout.box()
box.label(text='Neurons that have similar numbers of synapses in the same area')
box.label(text='are similar. See Schlegel et al. (2016, eLife).')
layout.label(text='Connectivity:')
box = layout.box()
box.label(text='Neurons that connects with similar number of synapses to the same')
box.label(text='partners are similar. See Schlegel et al. (2016, eLife).')
layout.label(text='Paired Connectivity:')
box = layout.box()
box.label(text='Neurons that mirror (left/right comparison) each others connectivity')
box.label(text='are similar. This requires synaptic partners to be paired with a')
box.label(text='<paired with #skeleton_id> annotation.')
elif self.entry == 'color.by_pairs':
row = layout.row()
row.alignment = 'CENTER'
row.label(text='Color Neurons by Pairs - Tooltip')
box = layout.box()
box.label(text='Gives paired neurons the same color. Pairing is based on annotations:')
box.label(text='Neuron needs to have a <paired with #skeleton_id> annotation.')
box.label(text='For example <paired with #1874652>.')
elif self.entry == 'material.kmeans':
row = layout.row()
row.alignment = 'CENTER'
row.label(text='Color by Spatial Distribution - Tooltip')
box = layout.box()
box.label(text='This function colors neurons based on spatial clustering of their somas.')
box.label(text='Uses the k-Mean algorithm. You need to set the number of clusters you')
box.label(text='expect and the algorithm finds clusters with smallest variance.')
elif self.entry == 'retrieve.by_pairs':
row = layout.row()
row.alignment = 'CENTER'
row.label(text='Retrieve Pairs of Neurons - Tooltip')
box = layout.box()
box.label(text='Retrieves neurons paired with already the loaded neurons. Pairing is')
box.label(text='based on annotations: neuron needs to have a <paired with #skeleton_id>')
box.label(text='annotation. For example neuron #1234 has annotation')
box.label(text='<paired with #5678> and neuron 5678 has annotation')
box.label(text='<paired with #1234>.')
elif self.entry == 'retrieve.connectors':
row = layout.row()
row.alignment = 'CENTER'
row.label(text='Retrieve Connectors - Tooltip')
box = layout.box()
box.label(text='Retrieves connectors as spheres. Outgoing (presynaptic) connectors can be')
box.label(text='scaled (weighted) based on the number of postsynaptically connected')
box.label(text='neurons. Incoming (postsynaptic) connectors always have base radius.')
elif self.entry == 'change.material':
row = layout.row()
row.alignment = 'CENTER'
row.label(text='Change Materials - Tooltip')
box = layout.box()
box.label(text='By default, all imported neurons have a standard material with random')
box.label(text='color. You can change the material of individual neurons in the ')
box.label(text='material tab or in bulk using this function. For more options')
box.label(text='see material tab.')
elif self.entry == 'color.by_strahler':
row = layout.row()
row.alignment = 'CENTER'
row.label(text='Color by Strahler - Tooltip')
box = layout.box()
box.label(text='Colors neurons by Strahler index. Result may will look odd')
box.label(text='in the viewport unless viewport shading is set to <render> or')
box.label(text='<material>. In any case, if you render it will look awesome!')
elif self.entry == 'animate.history':
row = layout.row()
row.alignment = 'CENTER'
row.label(text='Animate history - Tooltip')
box = layout.box()
box.label(text='This works essentially like the corresponding function of CATMAIDs 3D viewer: nodes and connectors pop into existence ')
box.label(text='as they were traced originally. Attention: using the <Skip idle phases> option will compromise relation between neurons')
box.label(text='because idle phases are currently calculated for each neuron individually. The <Show timer> will also be affected!')
elif self.entry == 'curve.change':
row = layout.row()
row.alignment = 'CENTER'
row.label(text='Change Curve Properties - Tooltip')
box = layout.box()
box.label(text='Skeletons are created using curves. Bevel depth determines the ')
box.label(text='thickness of the curves. Radial (bevel) and curve resolution ')
box.label(text='determine how detailed the curves are - lower to improve ')
box.label(text='render performance.')
def invoke(self, context, event):
return context.window_manager.invoke_popup(self, width=400)
class CATMAID_OP_material_change(Operator):
"""Change colors."""
bl_idname = "material.change"
bl_label = "Change colors"
bl_options = {'UNDO'}
bl_description = "Change color"
which_neurons: EnumProperty(name="Which Objects?",
items=[('Selected', 'Selected', 'Selected'),
('All', 'All', 'All')],
default='Selected',
description="Assign common material to which neurons")
to_neurons: BoolProperty(name='Neurons',
default=True,
description='Include neurons?')
to_outputs: BoolProperty(name='Presynapses',
default=False,
description='Include presynaptic sites (outgoing)?')
to_inputs: BoolProperty(name='Postsynapses',
default=False,
description='Include postsynaptic sites (incoming)?')
change_color: BoolProperty(name='Color', default=True, description='Change color?')
new_color: FloatVectorProperty(name="", description="Set new color",
default=(0.0, 1, 0.0), min=0.0, max=1.0,
subtype='COLOR')
def check(self, context):
return True
def draw(self, context):
layout = self.layout
layout.label(text="Apply to")
box = layout.box()
row = box.row(align=False)
row.prop(self, "which_neurons")
row = box.row(align=False)
row.prop(self, "to_neurons")
row.prop(self, "to_outputs")
row.prop(self, "to_inputs")
layout.label(text="Change")
box = layout.box()
row = box.row(align=False)
col = row.column()
col.prop(self, "change_color")
if self.change_color:
col = row.column()
col.prop(self, "new_color")
def invoke(self, context, event):
return context.window_manager.invoke_props_dialog(self)
def execute(self, context):
if self.which_neurons == 'Selected':
ob_list = bpy.context.selected_objects
elif self.which_neurons == 'All':
ob_list = bpy.data.objects
filtered_ob_list = []
# First find the objects
for ob in ob_list:
if 'subtype' not in ob:
continue
if ob['subtype'] in ('NEURITES', 'SOMA') and not self.to_neurons:
continue
if ob['subtype'] in ('CONNECTORS'):
if ob['cn_type'] == 'Presynapses' and not self.to_outputs:
continue
if ob['cn_type'] == 'Postsynapses' and not self.to_inputs:
continue
filtered_ob_list.append(ob)
# Now apply cahnges
for ob in filtered_ob_list:
if self.change_color:
ob.active_material.diffuse_color = (self.new_color[0],
self.new_color[1],
self.new_color[2],
1)
self.report({'INFO'}, f'{len(filtered_ob_list)} materials changed')
return {'FINISHED'}
class CATMAID_OP_curve_change(Operator):
"""Change curve settings."""
bl_idname = "curve.change"
bl_label = "Change curve properties"
bl_options = {'UNDO'}
bl_description = "Change curve properties"
which_neurons: EnumProperty(name="Which Objects?",
items=[('Selected', 'Selected', 'Selected'),
('All', 'All', 'All')],
default='Selected',
description="Assign common material to which neurons")
to_neurons: BoolProperty(name='Neurons',
default=True,
description='Include neurons?')
to_outputs: BoolProperty(name='Presynapses',
default=False,
description='Include presynaptic sites (outgoing)?')
to_inputs: BoolProperty(name='Postsynapses',
default=False,
description='Include postsynaptic sites (incoming)?')
change_bevel: BoolProperty(name='Thickness', default=False,
description='Change neuron thickness (bevel)?')
new_bevel: FloatProperty(name="", description="Set new thickness.",
default=0.015, min=0)
change_bevel_res: BoolProperty(name='Radial resolution', default=False,
description='Change radial (bevel) resolution?')
new_bevel_res: IntProperty(name="", description="Set new resolution.",
default=5, min=0)
change_curve_res: BoolProperty(name='Curve resolution', default=False,
description='Change curve resolution?')
new_curve_res: IntProperty(name="", description="Set new resolution.",
default=10, min=0)
def check(self, context):
return True
def draw(self, context):
layout = self.layout
layout.label(text="Apply to")
box = layout.box()
row = box.row(align=False)
row.prop(self, "which_neurons")
row = box.row(align=False)
row.prop(self, "to_neurons")
row.prop(self, "to_outputs")
row.prop(self, "to_inputs")
layout.label(text="Change")
box = layout.box()
row = box.row(align=False)
col = row.column()
col.prop(self, "change_bevel")
if self.change_bevel:
col = row.column()
col.prop(self, "new_bevel")
row = box.row(align=False)
col = row.column()
col.prop(self, "change_bevel_res")
if self.change_bevel_res:
col = row.column()
col.prop(self, "new_bevel_res")
row = box.row(align=False)
col = row.column()
col.prop(self, "change_curve_res")
if self.change_curve_res:
col = row.column()
col.prop(self, "new_curve_res")
def invoke(self, context, event):
return context.window_manager.invoke_props_dialog(self)
def execute(self, context):
if self.which_neurons == 'Selected':
ob_list = bpy.context.selected_objects
elif self.which_neurons == 'All':
ob_list = bpy.data.objects
filtered_ob_list = []
# First find the objects
for ob in ob_list:
if 'subtype' not in ob:
continue
if ob['subtype'] in ('NEURITES', 'SOMA') and not self.to_neurons:
continue
if ob['subtype'] in ('CONNECTORS'):
if ob['cn_type'] == 'Presynapses' and not self.to_outputs:
continue
if ob['cn_type'] == 'Postsynapses' and not self.to_inputs:
continue
filtered_ob_list.append(ob)
# Now apply cahnges
for ob in filtered_ob_list:
if self.change_bevel and ob.type == 'CURVE':
ob.data.bevel_depth = self.new_bevel
if self.change_bevel_res and ob.type == 'CURVE':
ob.data.bevel_resolution = self.new_bevel_res
if self.change_curve_res and ob.type == 'CURVE':
ob.data.resolution_u = self.new_curve_res
self.report({'INFO'}, f'{len(filtered_ob_list)} curves changed')
return {'FINISHED'}
class CATMAID_OP_material_randomize(Operator):
"""Assigns new semi-random colors to neurons"""
bl_idname = "material.randomize"
bl_label = "Assign (semi-) random colors"
bl_description = "Assign (semi-) random colors"
bl_options = {'UNDO'}
which_neurons: EnumProperty(name="Which Neurons?",
items=[('Selected', 'Selected', 'Selected'),
('All', 'All', 'All')],
description="Choose which neurons to give random color.",
default='All')
color_range: EnumProperty(name="Range",
items=(('RGB', 'RGB', 'RGB'),
("Grayscale", "Grayscale", "Grayscale"),),
default="RGB",
description="Choose mode of randomizing colors")
start_color: FloatVectorProperty(name="Color range start",
description="Set start of color range (for RGB). Keep start and end the same to use full range.",
default=(1, 0.0, 0.0), min=0.0, max=1.0,
subtype='COLOR')
end_color: FloatVectorProperty(name="Color range end",
description="Set end of color range (for RGB). Keep start and end the same to use full range.",
default=(1, 0.0, 0.0), min=0.0, max=1.0,
subtype='COLOR')
def invoke(self, context, event):
return context.window_manager.invoke_props_dialog(self)
def execute(self, context):
if self.which_neurons == 'All':
to_process = bpy.data.objects
elif self.which_neurons == 'Selected':
to_process = bpy.context.selected_objects
to_process = [o for o in to_process if 'type' in o]
to_process = [o for o in to_process if o['type'] == 'NEURON']
neurons = set([o['id'] for o in to_process])
colors = random_colors(len(neurons),
color_range=self.color_range,
start_rgb=self.start_color,
end_rgb=self.end_color,
alpha=1)
colormap = dict(zip(neurons, colors))
for ob in to_process:
ob.active_material.diffuse_color = colormap[ob['id']]
return {'FINISHED'}
class CATMAID_OP_material_spatial(Operator):
"""Color neurons by spatially clustering their somas."""
bl_idname = "material.kmeans"
bl_label = "Color neurons by spatially clustering of their somas (k-Means algorithm)"
bl_description = "Color neurons by spatially clustering of their somas (k-Means algorithm)"