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feat: rewrite resize

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add more nodes
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马南
2024-07-04 13:05:46 +08:00
parent 134d8e382b
commit 1db63dd3db
25 changed files with 4655 additions and 498 deletions
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7
.github/workflows/upload-assets.yml vendored
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@@ -58,7 +58,7 @@ jobs:
- name: Checkout Code
uses: actions/checkout@v4
with:
lfs: 'true'
lfs: "true"
- name: Zip Add-on
run: |
zip -r package.zip bioxelnodes
@@ -81,15 +81,14 @@ jobs:
- name: Checkout Code
uses: actions/checkout@v4
with:
lfs: 'true'
lfs: "true"
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: 3.11
- name: Zip Extension
run: |
pip download SimpleITK --dest bioxelnodes/wheels --only-binary=:all: --python-version=3.11 --platform=macosx_11_0_arm64
pip download SimpleITK --dest bioxelnodes/wheels --only-binary=:all: --python-version=3.11 --platform=win_amd64
pip download SimpleITK==2.3.1 --dest bioxelnodes/wheels --only-binary=:all: --python-version=3.11 --platform=win_amd64
rm -r bioxelnodes/externalpackage
cp extension/__init__.py bioxelnodes/__init__.py
cp extension/preferences.py bioxelnodes/preferences.py

18
README.md
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@@ -1,23 +1,23 @@
[中文文档](https://uj6xfhbzp0.feishu.cn/wiki/Qx3VwHuNPimeI8kr6nDcvl1DnHf?from=from_copylink)
# Bioxel Nodes
# 🦖 Bioxel Nodes
![Static Badge](https://img.shields.io/badge/Blender-orange?style=for-the-badge&logo=blender&logoColor=white)
![GitHub License](https://img.shields.io/github/license/OmooLab/BioxelNodes?style=for-the-badge)
![GitHub Release](https://img.shields.io/github/v/release/OmooLab/BioxelNodes?style=for-the-badge)
![GitHub Repo stars](https://img.shields.io/github/stars/OmooLab/BioxelNodes?style=for-the-badge)
Bioxel Nodes is a Blender add-on for scientific volumetric data visualization. It using Blender's powerful Geometry Nodes | Cycles to process and render volumetric data.
Bioxel Nodes is a Blender extension for scientific volumetric data visualization. It using Blender's powerful Geometry Nodes and Cycles to process and render volumetric data. You are free to share your blender file to anyone who does not install this extension, since most processes were done by Blender's native nodes.
## About
Before us, there have been many tutorials and add-ons for importing volumetric data into Blender. However, we found that there were many details that were not addressed in place, some scientific facts were ignored, and the volume rendering was not pretty enough. With Bioxel Nodes, you can easily import the volumetric data into Blender, and more importantly, it can quickly make a beautiful realistic rendering of it.
Before us, there have been many tutorials and extensions for importing volumetric data into Blender. However, we found that there were many scientific issues that were not addressed in place, and the volume render results were not epic. With Bioxel Nodes, you can easily import any format volumetric data into Blender, and more importantly, make a beautiful realistic volume rendering quickly.
Below are some examples with Bioxel Nodes. Thanks to Cycles Render, the volumetric data can be rendered with great detail:
![cover](docs/assets/cover.png)
![cover](https://omoolab.github.io/BioxelNodes/latest/assets/cover.png)
The "Bioxel" in "Bioxel Nodes", is a combination of the words "Bio-" and "Voxel". Bioxel is a voxel that stores biological data. We are developing a toolkit around Bioxel for better biological data visualization. but before its release, we made this Blender version of bioxels toolkit first, in order to let more people to have fun with volumetric data. [Getting Started](https://omoolab.github.io/BioxelNodes/latest/getting-started)
So how to use this extension? please check [Getting Started](https://omoolab.github.io/BioxelNodes/latest/getting-started)
## Supported Format
@@ -44,16 +44,16 @@ The "Bioxel" in "Bioxel Nodes", is a combination of the words "Bio-" and "Voxel"
- Sections cannot be generated (will be supported soon)
- Time sequence volume not supported (will be supported soon)
## To Upgrade Add-on
## Upgrade from 0.1.x to 0.2.x
To upgrade from an older version of the add-on to the latest, you need to do the following:
You need to do the following:
1. Remove the old version of Bioxel Nodes at Preferences > Add-ons
2. Add the new version and restart Blender.
It is not support editing the same blender file across add-on versions. In order to make sure that the previous file works properly. You need to save the staged data before upgrading ( read the last section of [Getting Started](https://omoolab.github.io/BioxelNodes/latest/getting-started/#share-your-file) ).
It is not support editing the same blender file across extension versions. In order to make sure that the previous file works properly. You need to save the staged data before upgrading ( read the last section of [Getting Started](https://omoolab.github.io/BioxelNodes/latest/getting-started/#share-your-file) ).
But even then, there is still no guarantee that the new version of the add-on will work on the old blender file. Therefore, it is highly recommended to open a new blender file to start the creating, not based on the old one.
But even then, there is still no guarantee that the new version of the extension will work on the old blender file. Therefore, it is highly recommended to open a new blender file to start the creating, not based on the old one.
Alternatively, objects from the old file that have nothing to do with Bioxel Nodes could be append to the new blender file.

4
bioxelnodes/__init__.py
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@@ -9,7 +9,7 @@ bl_info = {
"author": "Ma Nan",
"description": "",
"blender": (4, 0, 0),
"version": (0, 2, 0),
"version": (0, 2, 3),
"location": "File -> Import",
"warning": "",
"category": "Node"
@@ -22,7 +22,7 @@ def register():
auto_load.register()
menus.add()
bpy.types.Scene.bioxel_layer_dir = bpy.props.StringProperty(
name="Bioxel Layers Directory",
name="Layer Directory",
subtype='DIR_PATH',
default="//"
)

BIN
bioxelnodes/assets/Nodes/BioxelNodes_4.1.blend LFS
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Binary file not shown.

12
bioxelnodes/customnodes/menus.py
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@@ -6,7 +6,7 @@ from .nodes import AddCustomNode
class SaveAllNodes(bpy.types.Operator):
bl_idname = "customnodes.save_all_nodes"
bl_label = "Save All Custom Nodes"
bl_label = "Save All Nodes"
bl_description = "Save All Custom Nodes to Directory."
bl_options = {'UNDO'}
@@ -21,9 +21,9 @@ class SaveAllNodes(bpy.types.Operator):
for file in files:
file_name = Path(file).name
# "//"
custom_nodes_dir = bpy.path.abspath(context.scene.custom_nodes_dir)
customnodes_node_dir = bpy.path.abspath(context.scene.customnodes_node_dir)
output_path: Path = Path(custom_nodes_dir, file_name).resolve()
output_path: Path = Path(customnodes_node_dir, file_name).resolve()
source_path: Path = Path(file).resolve()
if output_path != source_path:
@@ -51,7 +51,7 @@ class CUSTOMNODES_PT_CustomNodes(bpy.types.Panel):
def draw(self, context):
layout = self.layout
scene = context.scene
layout.prop(scene, 'custom_nodes_dir')
layout.prop(scene, 'customnodes_node_dir')
layout.operator(SaveAllNodes.bl_idname)
@@ -181,8 +181,8 @@ class CustomNodes():
bpy.utils.register_class(cls)
bpy.types.NODE_MT_add.append(self.add_node_menu)
bpy.types.Scene.custom_nodes_dir = bpy.props.StringProperty(
name="Nodes Directory",
bpy.types.Scene.customnodes_node_dir = bpy.props.StringProperty(
name="Node Directory",
subtype='DIR_PATH',
default="//"
)

437
bioxelnodes/io.py
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@@ -1,3 +1,4 @@
import math
import bpy
import shutil
from bpy_extras.io_utils import axis_conversion
@@ -8,17 +9,18 @@ from uuid import uuid4
import mathutils
import random
from . import skimage as ski
from .nodes import custom_nodes
from .props import BIOXELNODES_Series
from .utils import (calc_bbox_verts, get_all_layers, get_container, get_layer, get_text_index_str,
get_node_by_type, hide_in_ray, lock_transform, show_message)
from .utils import (calc_bbox_verts, get_all_layers, get_container_from_selection, get_layer, get_text_index_str,
get_node_by_type, hide_in_ray, lock_transform, save_vdb, show_message)
try:
import SimpleITK as sitk
except:
...
SUPPORT_EXTS = ['.dcm', '.DCM', '.DICOM',
SUPPORT_EXTS = ['', '.dcm', '.DCM', '.DICOM',
'.bmp', '.BMP',
'.PIC', '.pic',
'.gipl', '.gipl.gz',
@@ -40,9 +42,9 @@ SEQUENCE_EXTS = ['.dcm', '.DCM', '.DICOM',
'.tif', '.TIF', '.tiff', '.TIFF',
'.png', '.PNG']
DICOM_EXTS = ['.dcm', '.DCM', '.DICOM']
DICOM_EXTS = ['', '.dcm', '.DCM', '.DICOM']
FH_EXTS = ['.dcm', '.DCM', '.DICOM',
FH_EXTS = ['', '.dcm', '.DCM', '.DICOM',
'.gipl', '.gipl.gz',
'.mnc', '.MNC',
'.mrc', '.rec',
@@ -64,6 +66,14 @@ def get_layer_shape(bioxel_size: float, orig_shape: tuple, orig_spacing: tuple):
shape[2] if shape[2] > 0 else 1)
def get_layer_size(shape: tuple, bioxel_size: float, scale: float = 1.0):
size = (float(shape[0] * bioxel_size * scale),
float(shape[1] * bioxel_size * scale),
float(shape[2] * bioxel_size * scale))
return size
def get_ext(filepath: str) -> str:
file_path = Path(filepath)
if file_path.name.endswith(".nii.gz"):
@@ -90,7 +100,7 @@ def get_sequence_index(filepath: str) -> int:
return int(index) if index else 0
def collect_image_sequence(filepath: str):
def collect_sequence(filepath: str):
file_path = Path(filepath).resolve()
files = list(file_path.parent.iterdir())
@@ -103,7 +113,7 @@ def collect_image_sequence(filepath: str):
return sequence
def read_image(filepath: str, series_id=""):
def parse_volume_data(filepath: str, series_id=""):
ext = get_ext(filepath)
if ext in DICOM_EXTS:
@@ -114,37 +124,42 @@ def read_image(filepath: str, series_id=""):
series_files = reader.GetGDCMSeriesFileNames(
str(dir_path), series_id)
reader.SetFileNames(series_files)
image = reader.Execute()
itk_volume = reader.Execute()
name = dir_path.name
elif ext in SEQUENCE_EXTS:
image = sitk.ReadImage(filepath)
if image.GetDimension() == 2:
sequence = collect_image_sequence(filepath)
image = sitk.ReadImage(sequence)
itk_volume = sitk.ReadImage(filepath)
if itk_volume.GetDimension() == 2:
sequence = collect_sequence(filepath)
itk_volume = sitk.ReadImage(sequence)
name = get_sequence_name(filepath)
else:
image = sitk.ReadImage(filepath)
itk_volume = sitk.ReadImage(filepath)
name = Path(filepath).name.removesuffix(ext)
else:
image = sitk.ReadImage(filepath)
itk_volume = sitk.ReadImage(filepath)
name = Path(filepath).name.removesuffix(ext)
return image, name
itk_volume = sitk.DICOMOrient(itk_volume, 'RAS')
volume = sitk.GetArrayFromImage(itk_volume)
if volume.ndim == 4:
volume = np.transpose(volume, (2, 1, 0, 3))
else:
volume = np.transpose(volume)
def rgb2gray(image):
# Convert sRGB image to gray scale and rescale results to [0,255]
layers = [sitk.VectorIndexSelectionCast(
image, i, sitk.sitkFloat32) for i in range(image.GetNumberOfComponentsPerPixel())]
# linear mapping
I = (0.2126*layers[0] + 0.7152*layers[1] + 0.0722*layers[2])
meta = {
"name": name,
"shape": tuple(itk_volume.GetSize()),
"spacing": tuple(itk_volume.GetSpacing()),
"origin": tuple(itk_volume.GetOrigin()),
"direction": tuple(itk_volume.GetDirection()),
}
return sitk.Cast(I, sitk.sitkFloat32)
for key, value in meta.items():
print(f"{key}: {value}")
def x2gray(image):
return sitk.VectorIndexSelectionCast(image, 0, sitk.sitkUInt16)
return volume, meta
class ImportVolumeDataDialog(bpy.types.Operator):
@@ -167,14 +182,6 @@ class ImportVolumeDataDialog(bpy.types.Operator):
series_id: bpy.props.StringProperty() # type: ignore
resample_method: bpy.props.EnumProperty(
name="Resample Method",
default="linear",
items=[("linear", "Linear", ""),
("nearest_neighbor", "Nearest Neighbor", ""),
("gaussian", "Gaussian", "")]
) # type: ignore
container: bpy.props.StringProperty() # type: ignore
read_as: bpy.props.EnumProperty(
@@ -203,7 +210,7 @@ class ImportVolumeDataDialog(bpy.types.Operator):
scene_scale: bpy.props.FloatProperty(
name="Scene Scale (Bioxel Unit pre Blender Unit)",
soft_min=0.001, soft_max=100.0,
soft_min=0.0001, soft_max=10.0,
min=1e-6, max=1e6,
default=0.01,
) # type: ignore
@@ -213,95 +220,21 @@ class ImportVolumeDataDialog(bpy.types.Operator):
default=True,
) # type: ignore
invert_scalar: bpy.props.BoolProperty(
name="Invert Scalar (Background value maybe higher than object)",
default=False,
) # type: ignore
def execute(self, context):
is_first_import = len(get_all_layers()) == 0
image, name = read_image(self.filepath, self.series_id)
container_name = self.container_name or name
volume, meta = parse_volume_data(self.filepath)
container_name = self.container_name or meta['name'] or "Container"
bioxel_size = self.bioxel_size
orig_spacing = self.orig_spacing
image_spacing = image.GetSpacing()
image_shape = image.GetSize()
layer_spacing = (
image_spacing[0] / orig_spacing[0] * bioxel_size,
image_spacing[1] / orig_spacing[1] * bioxel_size,
image_spacing[2] / orig_spacing[2] * bioxel_size
meta['spacing'][0] / orig_spacing[0] * bioxel_size,
meta['spacing'][1] / orig_spacing[1] * bioxel_size,
meta['spacing'][2] / orig_spacing[2] * bioxel_size
)
layer_shape = get_layer_shape(
bioxel_size, image_shape, orig_spacing)
if self.read_as == "labels":
if "vector" in image.GetPixelIDTypeAsString():
print("Conveting to Grayscale...")
image = x2gray(image)
else:
image = sitk.Cast(image, sitk.sitkUInt16)
default_value = 0
elif self.read_as == "scalar":
if "vector" in image.GetPixelIDTypeAsString():
print("Conveting to Grayscale...")
image = rgb2gray(image)
else:
image = sitk.Cast(image, sitk.sitkFloat32)
stats = sitk.StatisticsImageFilter()
stats.Execute(image)
default_value = stats.GetMaximum() if self.invert_scalar else stats.GetMinimum()
if self.resample_method == "linear":
interpolator = sitk.sitkLinear
elif self.resample_method == "nearest_neighbor":
interpolator = sitk.sitkNearestNeighbor
elif self.resample_method == "gaussian":
interpolator = sitk.sitkGaussian
if self.read_as == "labels":
interpolator = sitk.sitkNearestNeighbor
print(f"Resampling...")
image = sitk.Resample(
image1=image,
size=layer_shape,
transform=sitk.Transform(),
interpolator=interpolator,
outputOrigin=image.GetOrigin(),
outputSpacing=layer_spacing,
outputDirection=image.GetDirection(),
defaultPixelValue=default_value,
outputPixelType=image.GetPixelID(),
)
if self.do_orient:
print("Orienting to RAS...")
image = sitk.DICOMOrient(image, 'RAS')
print("Oriented Origin:", image.GetOrigin())
print("Oriented Direction:", image.GetDirection())
# return {'FINISHED'}
# ITK indices, by convention, are [i,j,k] while NumPy indices are [k,j,i]
# https://www.slicer.org/wiki/Coordinate_systems
# ITK Numpy 3D
# R (ight) i -> k -> x
# A (nterior) j -> j -> y
# S (uperior) k -> i -> z
array = sitk.GetArrayFromImage(image)
orig_dtype = str(array.dtype)
# print(f"Coverting Dtype from {orig_dtype} to float...")
# array = array.astype(float)
if array.ndim == 4:
array = array[:, :, :, 0]
array = np.transpose(array)
bioxel_size, meta['shape'], orig_spacing)
# After sitk.DICOMOrient(), origin and direction will also orient base on LPS
# so we need to convert them into RAS
@@ -312,23 +245,22 @@ class ImportVolumeDataDialog(bpy.types.Operator):
to_up='Y'
).to_4x4()
origin = image.GetOrigin()
direction = image.GetDirection()
mat_location = mathutils.Matrix.Translation(
mathutils.Vector(origin)
mathutils.Vector(meta['origin'])
)
mat_rotation = mathutils.Matrix(
np.array(direction).reshape((3, 3))
np.array(meta['direction']).reshape((3, 3))
).to_4x4()
mat_scale = mathutils.Matrix.Scale(
bioxel_size, 4
)
transfrom = mat_lps2ras @ mat_location @ mat_rotation @ mat_scale \
if self.do_orient else mat_location @ mat_rotation @ mat_scale
# transfrom = mat_lps2ras @ mat_location @ mat_rotation @ mat_scale \
# if self.do_orient else mat_location @ mat_rotation @ mat_scale
transfrom = mat_lps2ras @ mat_location @ mat_rotation @ mat_scale
# Wrapper a Container
if not self.container:
@@ -353,7 +285,7 @@ class ImportVolumeDataDialog(bpy.types.Operator):
)
container = bpy.context.active_object
bbox_verts = calc_bbox_verts((0, 0, 0), array.shape)
bbox_verts = calc_bbox_verts((0, 0, 0), volume.shape)
for index, vert in enumerate(container.data.vertices):
bbox_transform = transfrom
vert.co = bbox_transform @ mathutils.Vector(bbox_verts[index])
@@ -370,49 +302,24 @@ class ImportVolumeDataDialog(bpy.types.Operator):
'NodeGroupInput')[0]
container_node_tree.links.remove(input_node.outputs[0].links[0])
# bpy.ops.mesh.primitive_cube_add(
# enter_editmode=False, align='WORLD', location=(0, 0, 0), scale=(1, 1, 1)
# )
# frame = bpy.context.active_object
# bbox_verts = calc_bbox_verts((0, 0, 0), array.shape)
# for index, vert in enumerate(frame.data.vertices):
# # bbox_transform = mat_ras2blender @ mat_scene_scale @ transfrom
# bbox_transform = transfrom
# vert.co = bbox_transform @ mathutils.Vector(bbox_verts[index])
# frame.name = f"Frame_{container_name}"
# frame.data.name = f"Frame_{container_name}"
# lock_transform(frame)
# hide_in_ray(frame)
# frame.hide_select = True
# frame.hide_render = True
# frame.display_type = 'WIRE'
# frame.parent = container
else:
container = bpy.data.objects[self.container]
container_node_tree = container.modifiers[0].node_group
preferences = context.preferences.addons[__package__].preferences
cache_dir = Path(preferences.cache_dir, 'VDBs')
cache_dir.mkdir(parents=True, exist_ok=True)
loc, rot, sca = transfrom.decompose()
layer_origin = tuple(loc)
layer_rotation = tuple(rot.to_euler())
layer_shape = tuple(array.shape)
def create_layer(array, layer_name, layer_type="scalar"):
def create_layer(volume, layer_name, layer_type="scalar"):
grid = vdb.FloatGrid()
array = array.copy().astype(float)
grid.copyFromArray(array)
volume = volume.copy().astype(np.float32)
grid.copyFromArray(volume)
grid.transform = vdb.createLinearTransform(transfrom.transposed())
grid.name = layer_type
vdb_path = Path(cache_dir, f"{uuid4()}.vdb")
print(f"Storing the cache ({str(vdb_path)})...")
vdb.write(str(vdb_path), grids=[grid])
vdb_path = save_vdb([grid], context)
# Read VDB
print(f"Loading the cache to Blender scene...")
@@ -469,51 +376,66 @@ class ImportVolumeDataDialog(bpy.types.Operator):
return layer
if self.read_as == "labels":
orig_max = int(np.max(array))
orig_min = int(np.min(array))
if volume.ndim == 4:
volume = np.amax(volume, -1)
volume = volume.astype(int)
orig_max = int(np.max(volume))
orig_min = int(np.min(volume))
layer_name = self.layer_name or "Label"
for i in range(orig_max):
label = array == np.full_like(array, i+1)
layer = create_layer(array=label,
label = volume == np.full_like(volume, i+1)
print(f"Resampling...")
label = ski.resize(label.astype(np.float32),
layer_shape,
anti_aliasing=True)
layer = create_layer(volume=label,
layer_name=f"{container_name}_{layer_name}_{i+1}",
layer_type="label")
output_node = get_node_by_type(container_node_tree.nodes,
'NodeGroupOutput')[0]
# Deselect all nodes first
for node in container_node_tree.nodes:
if node.select:
node.select = False
mask_node = custom_nodes.add_node(container_node_tree.nodes,
'BioxelNodes_MaskByLabel')
mask_node.label = f"{layer_name}_{i+1}"
mask_node.inputs[0].default_value = layer
# Connect to output if no output linked
output_node = get_node_by_type(container_node_tree.nodes,
'NodeGroupOutput')[0]
if len(output_node.inputs[0].links) == 0:
container_node_tree.links.new(mask_node.outputs[0],
output_node.inputs[0])
else:
if self.invert_scalar:
array = -array
if volume.ndim == 4:
volume = np.amax(volume, -1)
# volume = skimage.color.rgb2gray(volume)
orig_max = float(np.max(array))
orig_min = float(np.min(array))
orig_median = float(np.median(array))
orig_percentile80 = float(np.percentile(array, 80)) \
if self.invert_scalar else float(np.percentile(array, 80))
if volume.dtype.kind == 'u':
imax_in = np.iinfo(volume.dtype).max
volume = np.multiply(volume, 255.0 / imax_in, dtype=np.float32)
elif volume.dtype.kind == 'i':
volume = volume.astype(np.float32)
stats_table = [("Max", orig_max),
("Min", orig_min),
("Median", orig_median),
("80%", orig_percentile80)]
print(f"Resampling...")
volume = ski.resize(volume,
layer_shape,
anti_aliasing=True)
print("Volume Data Stats:")
for stats in stats_table:
print("| {: >10} | {: >40} |".format(*stats))
orig_max = float(np.max(volume))
orig_min = float(np.min(volume))
scalar_offset = 0
if orig_min < 0:
scalar_offset = -orig_min
array = array + np.full_like(array, scalar_offset)
volume = volume + np.full_like(volume, scalar_offset)
layer_name = self.layer_name or "Scalar"
layer = create_layer(array=array,
layer = create_layer(volume=volume,
layer_name=f"{container_name}_{layer_name}",
layer_type="scalar")
@@ -523,12 +445,18 @@ class ImportVolumeDataDialog(bpy.types.Operator):
to_layer_node.inputs['Scalar Max'].default_value = orig_max
to_layer_node.inputs['Scalar Min'].default_value = orig_min
output_node = get_node_by_type(container_node_tree.nodes,
'NodeGroupOutput')[0]
# Deselect all nodes first
for node in container_node_tree.nodes:
if node.select:
node.select = False
mask_node = custom_nodes.add_node(container_node_tree.nodes,
'BioxelNodes_MaskByThreshold')
mask_node.label = layer_name
mask_node.inputs[0].default_value = layer
# Connect to output if no output linked
output_node = get_node_by_type(container_node_tree.nodes,
'NodeGroupOutput')[0]
if len(output_node.inputs[0].links) == 0:
container_node_tree.links.new(mask_node.outputs[0],
output_node.inputs[0])
@@ -538,50 +466,64 @@ class ImportVolumeDataDialog(bpy.types.Operator):
# Change render setting for better result
if preferences.do_change_render_setting and is_first_import:
bpy.context.scene.render.engine = 'CYCLES'
bpy.context.scene.cycles.volume_bounces = 12
bpy.context.scene.cycles.transparent_max_bounces = 16
bpy.context.scene.cycles.volume_preview_step_rate = 10
try:
bpy.context.scene.cycles.volume_bounces = 12
bpy.context.scene.cycles.transparent_max_bounces = 16
bpy.context.scene.cycles.volume_preview_step_rate = 10
bpy.context.scene.cycles.volume_step_rate = 10
bpy.context.scene.eevee.volumetric_tile_size = '2'
bpy.context.scene.eevee.volumetric_shadow_samples = 128
bpy.context.scene.eevee.volumetric_samples = 256
except:
pass
self.report({"INFO"}, "Successfully Imported")
return {'FINISHED'}
def invoke(self, context, event):
context.window_manager.invoke_props_dialog(self, width=500)
if self.read_as == "labels":
volume_dtype = "Label"
elif self.read_as == "scalar":
volume_dtype = "Scalar"
title = f"Import '{volume_dtype}' Layer (Add to Container: {self.container})" \
if self.container != "" else f"Import '{volume_dtype}' Layer (Init a Container)"
context.window_manager.invoke_props_dialog(self,
width=500,
title=title)
return {'RUNNING_MODAL'}
def draw(self, context):
layer_shape = get_layer_shape(
self.bioxel_size, self.orig_shape, self.orig_spacing)
layer_shape = get_layer_shape(self.bioxel_size,
self.orig_shape,
self.orig_spacing)
layer_size = get_layer_size(layer_shape,
self.bioxel_size,
self.scene_scale)
bioxel_count = layer_shape[0] * layer_shape[1] * layer_shape[2]
text = f"Shape will be: {str(layer_shape)} {bioxel_count:,} "
layer_shape_text = f"Shape will be: {str(layer_shape)} {bioxel_count:,} "
if bioxel_count > 100000000:
text += "**TOO LARGE!**"
layer_shape_text += "**TOO LARGE!**"
layer_size_text = f"Size will be: ({layer_size[0]:.2f}, {layer_size[1]:.2f}, {layer_size[2]:.2f}) m"
layout = self.layout
panel = layout.box()
panel.prop(self, "container_name")
if self.container == "":
panel.prop(self, "container_name")
panel.prop(self, "layer_name")
panel = layout.box()
panel.prop(self, "resample_method")
panel.prop(self, "bioxel_size")
row = panel.row()
row.prop(self, "orig_spacing")
panel.label(text=text)
panel.label(text=layer_shape_text)
panel = layout.box()
panel.prop(self, "do_orient")
panel = layout.box()
panel.prop(self, "read_as")
if self.read_as == "labels":
...
else:
panel.prop(self, "invert_scalar")
panel = layout.box()
panel.prop(self, "scene_scale")
if self.container == "":
panel = layout.box()
panel.prop(self, "scene_scale")
panel.label(text=layer_size_text)
def get_series_ids(self, context):
@@ -607,6 +549,13 @@ class ParseVolumeData(bpy.types.Operator):
directory: bpy.props.StringProperty(subtype='DIR_PATH') # type: ignore
container: bpy.props.StringProperty() # type: ignore
read_as: bpy.props.EnumProperty(
name="Read as",
default="scalar",
items=[("scalar", "Scalar", ""),
("labels", "Labels", "")]
) # type: ignore
series_id: bpy.props.EnumProperty(
name="Select Series",
items=get_series_ids
@@ -622,31 +571,24 @@ class ParseVolumeData(bpy.types.Operator):
return {'CANCELLED'}
print("Collecting Meta Data...")
image, name = read_image(self.filepath, self.series_id)
stats_table = [("Shape", str(image.GetSize())),
("Spacing", str(image.GetSpacing())),
("Origin", str(image.GetOrigin())),
("Direction", str(image.GetDirection())),
("Data Type", image.GetPixelIDTypeAsString())]
for k in image.GetMetaDataKeys():
stats_table.append((k, str(image.GetMetaData(k))))
print("Meta Data:")
for stats in stats_table:
print("| {: >20} | {: >100} |".format(*stats))
volume, meta = parse_volume_data(self.filepath)
do_orient = ext not in SEQUENCE_EXTS or ext in DICOM_EXTS
orig_shape = image.GetSize()
orig_spacing = image.GetSpacing()
orig_shape = meta['shape']
orig_spacing = meta['spacing']
min_size = min(orig_spacing[0], orig_spacing[1], orig_spacing[2])
bioxel_size = max(min_size, 1.0)
layer_size = get_layer_size(orig_shape,
bioxel_size)
log10 = math.floor(math.log10(max(*layer_size)))
scene_scale = math.pow(10, -log10)
if self.container:
container = bpy.data.objects[self.container]
container_name = container.name
else:
container_name = name
container_name = meta['name']
bpy.ops.bioxelnodes.import_volume_data_dialog(
'INVOKE_DEFAULT',
@@ -657,7 +599,9 @@ class ParseVolumeData(bpy.types.Operator):
bioxel_size=bioxel_size,
series_id=self.series_id or "",
do_orient=do_orient,
container=self.container
container=self.container,
read_as=self.read_as,
scene_scale=scene_scale
)
self.report({"INFO"}, "Successfully Readed.")
@@ -705,21 +649,32 @@ class ParseVolumeData(bpy.types.Operator):
text="Please be patient...")
class ImportVolumeData(bpy.types.Operator):
bl_idname = "bioxelnodes.import_volume_data"
bl_label = "Volume Data as Bioxel Layer"
bl_description = "Import Volume Data as Bioxel Layer"
class ImportVolumeData():
bl_options = {'UNDO'}
filepath: bpy.props.StringProperty(subtype="FILE_PATH") # type: ignore
directory: bpy.props.StringProperty(subtype='DIR_PATH') # type: ignore
read_as = "scalar"
def execute(self, context):
bpy.ops.bioxelnodes.parse_volume_data(
'INVOKE_DEFAULT',
filepath=self.filepath,
directory=self.directory
)
containers = get_container_from_selection()
if len(containers) > 0:
bpy.ops.bioxelnodes.parse_volume_data(
'INVOKE_DEFAULT',
filepath=self.filepath,
directory=self.directory,
container=containers[0].name,
read_as=self.read_as
)
else:
bpy.ops.bioxelnodes.parse_volume_data(
'INVOKE_DEFAULT',
filepath=self.filepath,
directory=self.directory
)
return {'FINISHED'}
def invoke(self, context, event):
@@ -727,6 +682,20 @@ class ImportVolumeData(bpy.types.Operator):
return {'RUNNING_MODAL'}
class ImportAsScalarLayer(bpy.types.Operator, ImportVolumeData):
bl_idname = "bioxelnodes.import_as_scalar_layer"
bl_label = "Import as Scale Layer"
bl_description = "Import Volume Data to Container as Scale Layer"
read_as = "scalar"
class ImportAsLabelLayer(bpy.types.Operator, ImportVolumeData):
bl_idname = "bioxelnodes.import_as_label_layer"
bl_label = "Import as Label Layer"
bl_description = "Import Volume Data to Container as Label Layer"
read_as = "labels"
try:
class BIOXELNODES_FH_ImportVolumeData(bpy.types.FileHandler):
bl_idname = "BIOXELNODES_FH_ImportVolumeData"
@@ -743,8 +712,8 @@ except:
class AddVolumeData(bpy.types.Operator):
bl_idname = "bioxelnodes.add_volume_data"
bl_label = "Add Volume Data to Container"
bl_description = "Add Volume Data to Container"
bl_label = "Import as Bioxel Layer"
bl_description = "Import additional Volume Data to Container"
bl_options = {'UNDO'}
filepath: bpy.props.StringProperty(subtype="FILE_PATH") # type: ignore
@@ -752,17 +721,17 @@ class AddVolumeData(bpy.types.Operator):
@classmethod
def poll(cls, context):
container = get_container(bpy.context.active_object)
return True if container else False
containers = get_container_from_selection()
return len(containers) > 0
def execute(self, context):
container = get_container(bpy.context.active_object)
containers = get_container_from_selection()
bpy.ops.bioxelnodes.parse_volume_data(
'INVOKE_DEFAULT',
filepath=self.filepath,
directory=self.directory,
container=container.name
container=containers[0].name
)
return {'FINISHED'}
@@ -771,9 +740,9 @@ class AddVolumeData(bpy.types.Operator):
return {'RUNNING_MODAL'}
class ExportVDB(bpy.types.Operator):
bl_idname = "bioxelnodes.export_vdb"
bl_label = "Bioxel Layer as VDB"
class ExportVolumeData(bpy.types.Operator):
bl_idname = "bioxelnodes.export_volume_data"
bl_label = "Export Layer"
bl_description = "Export Bioxel Layer as VDB"
bl_options = {'UNDO'}

109
bioxelnodes/menus.py
View File

@@ -1,67 +1,128 @@
import bpy
from .operators import ConvertToMesh, JoinLayers
from .io import ExportVDB, ImportVolumeData, AddVolumeData
from .misc import SaveBioxelLayers
from .operators import (AddPlaneCutter, AddCylinderCutter, AddCubeCutter, AddSphereCutter, CombineLabels,
ConvertToMesh, InvertScalar, FillByLabel, FillByThreshold, FillByRange)
from .io import ExportVolumeData, ImportAsLabelLayer, ImportAsScalarLayer, ImportVolumeData, AddVolumeData
from .misc import SaveLayers
class View3DBioxelsMenu(bpy.types.Menu):
class ModifyLayer(bpy.types.Menu):
bl_idname = "BIOXELNODES_MT_MODIFY_LAYERS"
bl_label = "Modify Layer"
def draw(self, context):
layout = self.layout
layout.operator(InvertScalar.bl_idname)
layout.operator(FillByThreshold.bl_idname)
layout.operator(FillByRange.bl_idname)
layout.operator(FillByLabel.bl_idname)
layout.operator(CombineLabels.bl_idname)
class AddCutterMenu(bpy.types.Menu):
bl_idname = "BIOXELNODES_MT_CUTTERS"
bl_label = "Add Cutter"
def draw(self, context):
layout = self.layout
layout.operator(AddPlaneCutter.bl_idname)
layout.operator(AddCylinderCutter.bl_idname)
layout.operator(AddCubeCutter.bl_idname)
layout.operator(AddSphereCutter.bl_idname)
class ImportLayerMenu(bpy.types.Menu):
bl_idname = "BIOXELNODES_MT_LAYERS"
bl_label = "Import Layer"
def draw(self, context):
layout = self.layout
layout.operator(ImportAsScalarLayer.bl_idname)
layout.operator(ImportAsLabelLayer.bl_idname)
class BioxelNodesView3DMenu(bpy.types.Menu):
bl_idname = "BIOXELNODES_MT_VIEW3D"
bl_label = "Bioxel Nodes"
def draw(self, context):
layout = self.layout
layout.menu(ImportLayerMenu.bl_idname)
layout.menu(AddCutterMenu.bl_idname)
layout.operator(ConvertToMesh.bl_idname)
layout.operator(AddVolumeData.bl_idname)
layout.separator()
layout.operator(SaveBioxelLayers.bl_idname)
layout.operator(SaveLayers.bl_idname)
class OutlinerBioxelsMenu(bpy.types.Menu):
class BioxelNodesOutlinerMenu(bpy.types.Menu):
bl_idname = "BIOXELNODES_MT_OUTLINER"
bl_label = "Bioxel Nodes"
def draw(self, context):
layout = self.layout
layout.operator(JoinLayers.bl_idname)
layout.menu(ImportLayerMenu.bl_idname)
layout.menu(AddCutterMenu.bl_idname)
layout.operator(ConvertToMesh.bl_idname)
layout.operator(AddVolumeData.bl_idname)
layout.separator()
layout.operator(SaveBioxelLayers.bl_idname)
layout.menu(ModifyLayer.bl_idname)
layout.separator()
layout.operator(SaveLayers.bl_idname)
def TOPBAR_FILE_IMPORT(self, context):
layout = self.layout
layout.separator()
layout.operator(ImportVolumeData.bl_idname)
# def TOPBAR_FILE_IMPORT(self, context):
# layout = self.layout
# layout.separator()
# layout.operator(ImportVolumeData.bl_idname)
def TOPBAR_FILE_EXPORT(self, context):
layout = self.layout
layout.separator()
layout.operator(ExportVDB.bl_idname)
# def TOPBAR_FILE_EXPORT(self, context):
# layout = self.layout
# layout.separator()
# layout.operator(ExportVolumeData.bl_idname)
def VIEW3D_OBJECT(self, context):
layout = self.layout
layout.menu(View3DBioxelsMenu.bl_idname)
layout.menu(BioxelNodesView3DMenu.bl_idname)
layout.separator()
def OUTLINER_OBJECT(self, context):
layout = self.layout
layout.menu(OutlinerBioxelsMenu.bl_idname)
layout.menu(BioxelNodesOutlinerMenu.bl_idname)
layout.separator()
class BioxelNodesTopbarMenu(bpy.types.Menu):
bl_idname = "BIOXELNODES_MT_TOPBAR"
bl_label = "Bioxel Nodes"
def draw(self, context):
layout = self.layout
layout.menu(ImportLayerMenu.bl_idname)
layout.operator(ExportVolumeData.bl_idname)
layout.separator()
layout.menu(AddCutterMenu.bl_idname)
layout.operator(ConvertToMesh.bl_idname)
layout.separator()
layout.operator(SaveLayers.bl_idname)
def TOPBAR(self, context):
layout = self.layout
layout.menu(BioxelNodesTopbarMenu.bl_idname)
def add():
bpy.types.TOPBAR_MT_file_import.append(TOPBAR_FILE_IMPORT)
bpy.types.TOPBAR_MT_file_export.append(TOPBAR_FILE_EXPORT)
# bpy.types.TOPBAR_MT_file_import.append(TOPBAR_FILE_IMPORT)
# bpy.types.TOPBAR_MT_file_export.append(TOPBAR_FILE_EXPORT)
bpy.types.OUTLINER_MT_object.prepend(OUTLINER_OBJECT)
bpy.types.VIEW3D_MT_object_context_menu.prepend(VIEW3D_OBJECT)
bpy.types.TOPBAR_MT_editor_menus.append(TOPBAR)
def remove():
bpy.types.TOPBAR_MT_file_import.remove(TOPBAR_FILE_IMPORT)
bpy.types.TOPBAR_MT_file_export.remove(TOPBAR_FILE_EXPORT)
# bpy.types.TOPBAR_MT_file_import.remove(TOPBAR_FILE_IMPORT)
# bpy.types.TOPBAR_MT_file_export.remove(TOPBAR_FILE_EXPORT)
bpy.types.OUTLINER_MT_object.remove(OUTLINER_OBJECT)
bpy.types.VIEW3D_MT_object_context_menu.remove(VIEW3D_OBJECT)
bpy.types.TOPBAR_MT_editor_menus.remove(TOPBAR)

14
bioxelnodes/misc.py
View File

@@ -22,9 +22,9 @@ def save_layer(layer, output_dir):
str(output_path), start=str(blend_path))
class SaveBioxelLayers(bpy.types.Operator):
bl_idname = "bioxelnodes.save_bioxel_layers"
bl_label = "Save Bioxel Layers"
class SaveLayers(bpy.types.Operator):
bl_idname = "bioxelnodes.save_layers"
bl_label = "Save Layers"
bl_description = "Save Bioxel Layers to Directory."
bl_options = {'UNDO'}
@@ -52,9 +52,9 @@ class SaveBioxelLayers(bpy.types.Operator):
return {'FINISHED'}
class SaveAllBioxelLayers(bpy.types.Operator):
bl_idname = "bioxelnodes.save_all_bioxel_layers"
bl_label = "Save All Bioxel Layers"
class SaveAllLayers(bpy.types.Operator):
bl_idname = "bioxelnodes.save_all_layers"
bl_label = "Save All Layers"
bl_description = "Save All Bioxel Layers to Directory."
bl_options = {'UNDO'}
@@ -89,4 +89,4 @@ class BIOXELNODES_PT_Bioxels(bpy.types.Panel):
layout = self.layout
scene = context.scene
layout.prop(scene, 'bioxel_layer_dir')
layout.operator(SaveAllBioxelLayers.bl_idname)
layout.operator(SaveAllLayers.bl_idname)

175
bioxelnodes/nodes.py
View File

@@ -3,39 +3,57 @@ from pathlib import Path
from .customnodes import CustomNodes
import bpy
def add_driver_to_node_factory(source_prop, target_prop):
callback_str = f"""
import bpy
from .utils import add_direct_driver, get_bioxels_obj
bioxels_obj = get_bioxels_obj(bpy.context.active_object)
if bioxels_obj:
container_obj = bioxels_obj.parent
add_direct_driver(
target=node,
target_prop='{target_prop}',
source=container_obj,
source_prop='{source_prop}'
)
else:
print('Cannot find any bioxels.')
"""
return callback_str
# def set_object_to_node_factory(object_type:str):
# if object_type == "plane":
# create_object = """
# bpy.ops.mesh.primitive_plane_add(size=2, enter_editmode=False, align='WORLD', location=(0, 0, 0), scale=(1, 1, 1))
# """
# callback_str = f"""
# import bpy
# from ..utils import get_container
# container = get_container(bpy.context.active_object)
# if container:
# {create_object}
# object = bpy.context.active_object
# node.inputs[0].default_value = object
# else:
# print('Cannot find any Bioxel Container.')
# """
# return callback_str
def set_prop_to_node_factory(source_prop, target_prop):
callback_str = f"""
import bpy
from .utils import get_bioxels_obj
bioxels_obj = get_bioxels_obj(bpy.context.active_object)
if bioxels_obj:
container_obj = bioxels_obj.parent
node.inputs.get('{target_prop}').default_value = container_obj.get(
'{source_prop}')
else:
print('Cannot find any bioxels.')
"""
return callback_str
# def add_driver_to_node_factory(source_prop, target_prop):
# callback_str = f"""
# import bpy
# from .utils import add_direct_driver, get_bioxels_obj
# bioxels_obj = get_bioxels_obj(bpy.context.active_object)
# if bioxels_obj:
# container_obj = bioxels_obj.parent
# add_direct_driver(
# target=node,
# target_prop='{target_prop}',
# source=container_obj,
# source_prop='{source_prop}'
# )
# else:
# print('Cannot find any bioxels.')
# """
# return callback_str
# def set_prop_to_node_factory(source_prop, target_prop):
# callback_str = f"""
# import bpy
# from .utils import get_bioxels_obj
# bioxels_obj = get_bioxels_obj(bpy.context.active_object)
# if bioxels_obj:
# container_obj = bioxels_obj.parent
# node.inputs.get('{target_prop}').default_value = container_obj.get(
# '{source_prop}')
# else:
# print('Cannot find any bioxels.')
# """
# return callback_str
if bpy.app.version >= (4, 1, 0):
@@ -70,6 +88,12 @@ if bpy.app.version >= (4, 1, 0):
'label': 'Shaders',
'icon': 'SHADING_RENDERED',
'items': [
{
'label': 'Membrane Shader',
'icon': 'NODE_MATERIAL',
'node_type': 'BioxelNodes_MembraneShader',
'node_description': ''
},
{
'label': 'Solid Shader',
'icon': 'SHADING_SOLID',
@@ -78,19 +102,19 @@ if bpy.app.version >= (4, 1, 0):
},
{
'label': 'Slime Shader',
'icon': 'OUTLINER_OB_META',
'icon': 'OUTLINER_DATA_META',
'node_type': 'BioxelNodes_AssignSlimeShader',
'node_description': ''
},
{
'label': 'Volume Shader',
'icon': 'OUTLINER_OB_VOLUME',
'icon': 'VOLUME_DATA',
'node_type': 'BioxelNodes_AssignVolumeShader',
'node_description': ''
},
{
'label': 'Universal Shader',
'icon': 'SHADING_RENDERED',
'icon': 'MATSHADERBALL',
'node_type': 'BioxelNodes_AssignUniversalShader',
'node_description': ''
}
@@ -106,6 +130,7 @@ if bpy.app.version >= (4, 1, 0):
'node_type': 'BioxelNodes_SetColorPresets',
'node_description': ''
},
"separator",
{
'label': 'Color Ramp 2',
'icon': 'IPO_QUAD',
@@ -145,14 +170,26 @@ if bpy.app.version >= (4, 1, 0):
"separator",
{
'label': 'Plane Cutter',
'icon': 'MOD_LATTICE',
'node_type': 'BioxelNodes_PlaneCutter',
'icon': 'MESH_PLANE',
'node_type': 'BioxelNodes_PlaneObjectCutter',
'node_description': ''
},
{
'label': 'Cylinder Cutter',
'icon': 'MESH_CYLINDER',
'node_type': 'BioxelNodes_CylinderObjectCutter',
'node_description': '',
},
{
'label': 'Plane Object Cutter',
'icon': 'OUTLINER_OB_LATTICE',
'node_type': 'BioxelNodes_PlaneObjectCutter',
'label': 'Cube Cutter',
'icon': 'MESH_CUBE',
'node_type': 'BioxelNodes_CubeObjectCutter',
'node_description': '',
},
{
'label': 'Sphere Cutter',
'icon': 'MESH_UVSPHERE',
'node_type': 'BioxelNodes_SphereObjectCutter',
'node_description': '',
}
]
@@ -166,6 +203,44 @@ if bpy.app.version >= (4, 1, 0):
'icon': 'CONSTRAINT_BONE',
'node_type': 'BioxelNodes_JoinComponent',
'node_description': ''
},
"separator",
{
'label': 'To Mesh',
'icon': 'OUTLINER_OB_MESH',
'node_type': 'BioxelNodes_ToMesh',
'node_description': ''
},
{
'label': 'To Volume',
'icon': 'OUTLINER_OB_VOLUME',
'node_type': 'BioxelNodes_ToVolume',
'node_description': ''
},
{
'label': 'To Bbox Wire',
'icon': 'MESH_CUBE',
'node_type': 'BioxelNodes_ToBboxWire',
'node_description': ''
},
"separator",
{
'label': 'Inflate',
'icon': 'OUTLINER_OB_META',
'node_type': 'META_DATA',
'node_description': ''
},
{
'label': 'Smooth',
'icon': 'MOD_SMOOTH',
'node_type': 'BioxelNodes_M_Smooth',
'node_description': ''
},
{
'label': 'Remove Small Island',
'icon': 'FORCE_LENNARDJONES',
'node_type': 'BioxelNodes_M_RemoveSmallIsland',
'node_description': ''
}
]
}
@@ -271,14 +346,26 @@ else:
"separator",
{
'label': 'Plane Cutter',
'icon': 'MOD_LATTICE',
'node_type': 'BioxelNodes_PlaneCutter',
'icon': 'MESH_PLANE',
'node_type': 'BioxelNodes_PlaneObjectCutter',
'node_description': ''
},
{
'label': 'Cylinder Cutter',
'icon': 'MESH_CYLINDER',
'node_type': 'BioxelNodes_CylinderObjectCutter',
'node_description': '',
},
{
'label': 'Plane Object Cutter',
'icon': 'OUTLINER_OB_LATTICE',
'node_type': 'BioxelNodes_PlaneObjectCutter',
'label': 'Cube Cutter',
'icon': 'MESH_CUBE',
'node_type': 'BioxelNodes_CubeObjectCutter',
'node_description': '',
},
{
'label': 'Sphere Cutter',
'icon': 'MESH_UVSPHERE',
'node_type': 'BioxelNodes_SphereObjectCutter',
'node_description': '',
}
]

750
bioxelnodes/operators.py
View File

@@ -2,16 +2,146 @@ from pathlib import Path
import random
import bpy
import pyopenvdb as vdb
from uuid import uuid4
import numpy as np
from . import skimage as ski
from . import scipy
from .nodes import custom_nodes
from .utils import (get_container, get_container_layers,
get_layer, get_node_by_type, hide_in_ray, lock_transform)
from .utils import (get_container, get_container_from_selection, get_container_layers,
get_layer, get_node_by_type, hide_in_ray, lock_transform, save_vdb)
def get_layer_name(layer):
container = layer.parent
return layer.name.removeprefix(container.name).replace("_", "")
def get_grids(layer):
layer_dir = bpy.path.abspath(layer.data.filepath)
grids, base_metadata = vdb.readAll(layer_dir)
return grids
def set_volume(grids, index, volume):
grids[index].clear()
grids[index].copyFromArray(volume.copy().astype(np.float32))
def get_volume(grids, index, shape):
volume = np.ndarray(shape, np.float32)
volume.fill(index)
grids[0].copyToArray(volume)
return volume
def get_shape(layer):
layer_node = layer.modifiers[0].node_group.nodes['BioxelNodes__ConvertToLayer']
return [int(a)
for a in layer_node.inputs['Shape'].default_value]
def get_layer_meta(layer, key: str):
layer_node = layer.modifiers[0].node_group.nodes['BioxelNodes__ConvertToLayer']
return layer_node.inputs[key].default_value
def set_layer_meta(layer, key: str, value):
layer_node = layer.modifiers[0].node_group.nodes['BioxelNodes__ConvertToLayer']
layer_node.inputs[key].default_value = value
def add_mask_node(container, layer, node_type: str, node_label: str):
modifier = container.modifiers[0]
node_tree = modifier.node_group
# Deselect all nodes first
for node in node_tree.nodes:
if node.select:
node.select = False
mask_node = custom_nodes.add_node(node_tree.nodes, node_type)
mask_node.label = node_label
mask_node.inputs[0].default_value = layer
# Connect to output if no output linked
output_node = get_node_by_type(node_tree.nodes,
'NodeGroupOutput')[0]
if len(output_node.inputs[0].links) == 0:
node_tree.links.new(mask_node.outputs[0],
output_node.inputs[0])
return mask_node
def deep_copy_layer(vdb_path, base_layer, name):
# Read VDB
print(f"Loading the cache to Blender scene...")
bpy.ops.object.volume_import(
filepath=str(vdb_path), align='WORLD', location=(0, 0, 0), scale=(1, 1, 1))
copyed_layer = bpy.context.active_object
# Set props to VDB object
copyed_layer.name = name
copyed_layer.data.name = name
lock_transform(copyed_layer)
hide_in_ray(copyed_layer)
copyed_layer.hide_select = True
copyed_layer.hide_render = True
copyed_layer.hide_viewport = True
copyed_layer.data.display.use_slice = True
copyed_layer.data.display.density = 1e-05
copyed_layer['bioxel_layer'] = True
copyed_layer['bioxel_layer_type'] = base_layer['bioxel_layer_type']
copyed_layer.parent = base_layer.parent
for collection in copyed_layer.users_collection:
collection.objects.unlink(copyed_layer)
for collection in base_layer.users_collection:
collection.objects.link(copyed_layer)
# add convert to layer node
base_layer_node = base_layer.modifiers[0].node_group.nodes['BioxelNodes__ConvertToLayer']
bioxel_size = base_layer_node.inputs['Bioxel Size'].default_value
layer_shape = base_layer_node.inputs['Shape'].default_value
layer_origin = base_layer_node.inputs['Origin'].default_value
layer_rotation = base_layer_node.inputs['Rotation'].default_value
scalar_offset = base_layer_node.inputs['Scalar Offset'].default_value
scalar_min = base_layer_node.inputs['Scalar Min'].default_value
scalar_max = base_layer_node.inputs['Scalar Max'].default_value
bpy.ops.node.new_geometry_nodes_modifier()
node_tree = copyed_layer.modifiers[0].node_group
input_node = get_node_by_type(node_tree.nodes, 'NodeGroupInput')[0]
output_node = get_node_by_type(node_tree.nodes, 'NodeGroupOutput')[0]
copyed_layer_node = custom_nodes.add_node(node_tree.nodes,
"BioxelNodes__ConvertToLayer")
node_tree.links.new(input_node.outputs[0], copyed_layer_node.inputs[0])
node_tree.links.new(copyed_layer_node.outputs[0], output_node.inputs[0])
copyed_layer_node.inputs['Layer ID'].default_value = random.randint(-200000000,
200000000)
copyed_layer_node.inputs['Bioxel Size'].default_value = bioxel_size
copyed_layer_node.inputs['Shape'].default_value = layer_shape
copyed_layer_node.inputs['Origin'].default_value = layer_origin
copyed_layer_node.inputs['Rotation'].default_value = layer_rotation
copyed_layer_node.inputs['Scalar Offset'].default_value = scalar_offset
copyed_layer_node.inputs['Scalar Min'].default_value = scalar_min
copyed_layer_node.inputs['Scalar Max'].default_value = scalar_max
return copyed_layer
def get_scalar_layer_selection(self, context):
items = [("None", "None", "")]
base_layer = bpy.data.objects[self.base_layer]
container = base_layer.parent
container = get_container(bpy.context.active_object)
for layer in get_container_layers(container):
if layer.get("bioxel_layer_type") == "scalar":
items.append((
@@ -25,8 +155,7 @@ def get_scalar_layer_selection(self, context):
def get_label_layer_selection(self, context):
items = [("None", "None", "")]
base_layer = bpy.data.objects[self.base_layer]
container = base_layer.parent
container = get_container(bpy.context.active_object)
for layer in get_container_layers(container):
if layer.get("bioxel_layer_type") == "label":
items.append((
@@ -38,197 +167,556 @@ def get_label_layer_selection(self, context):
return items
class JoinLayers(bpy.types.Operator):
bl_idname = "bioxelnodes.join_layers"
bl_label = "Join Additinal Layers"
bl_description = "Join Layers"
# class JoinLayers(bpy.types.Operator):
# bl_idname = "bioxelnodes.join_layers"
# bl_label = "Join Bioxel Layers"
# bl_description = "Join Additional Bioxel Layers"
# bl_options = {'UNDO'}
# base_layer: bpy.props.StringProperty(
# options={"HIDDEN"}
# ) # type: ignore
# scalar_layer: bpy.props.EnumProperty(
# name="Scaler Layer",
# items=get_scalar_layer_selection
# ) # type: ignore
# label_layer: bpy.props.EnumProperty(
# name="Label Layer",
# items=get_label_layer_selection
# ) # type: ignore
# # color_layer: bpy.props.StringProperty() # type: ignore
# @classmethod
# def poll(cls, context):
# layer = get_layer(bpy.context.active_object)
# return True if layer else False
# def execute(self, context):
# base_layer = bpy.data.objects[self.base_layer]
# if not base_layer:
# self.report({"WARNING"}, "Cannot find any bioxel layer as base.")
# return {'FINISHED'}
# base_layer_dir = bpy.path.abspath(base_layer.data.filepath)
# base_grids, base_metadata = vdb.readAll(base_layer_dir)
# layers = []
# if self.scalar_layer != "None":
# scalar_layer = bpy.data.objects[self.scalar_layer]
# layers.append(scalar_layer)
# if self.label_layer != "None":
# label_layer = bpy.data.objects[self.label_layer]
# layers.append(label_layer)
# # TODO: add color and vector
# if len(layers) == 0:
# self.report({"WARNING"}, "No additinal layers setted.")
# return {'FINISHED'}
# for layer in layers:
# layer_dir = bpy.path.abspath(layer.data.filepath)
# grids, metadata = vdb.readAll(layer_dir)
# base_grids.extend(grids)
# vdb_path = save_vdb(base_grids, context)
# joined_layer = deep_copy_layer(vdb_path,
# base_layer,
# f"{base_layer.name}_Joined")
# return {'FINISHED'}
# def invoke(self, context, event):
# base_layer = get_layer(bpy.context.active_object)
# self.base_layer = base_layer.name
# context.window_manager.invoke_props_dialog(self, width=400)
# return {'RUNNING_MODAL'}
class InvertScalar(bpy.types.Operator):
bl_idname = "bioxelnodes.invert_scalar"
bl_label = "Invert Scalar"
bl_description = "Invert the scalar value"
bl_options = {'UNDO'}
base_layer: bpy.props.StringProperty(
options={"HIDDEN"}
) # type: ignore
scalar_layer: bpy.props.EnumProperty(
name="Scaler Layer",
items=get_scalar_layer_selection
) # type: ignore
label_layer: bpy.props.EnumProperty(
name="Label Layer",
items=get_label_layer_selection
) # type: ignore
# color_layer: bpy.props.StringProperty() # type: ignore
@classmethod
def poll(cls, context):
layer = get_layer(bpy.context.active_object)
return True if layer else False
if layer:
return layer.get("bioxel_layer_type") == "scalar"
else:
return False
def execute(self, context):
base_layer = bpy.data.objects[self.base_layer]
base_layer = get_layer(bpy.context.active_object)
if not base_layer:
self.report({"WARNING"}, "Cannot find any bioxel layer as base.")
return {'FINISHED'}
container = base_layer.parent
inverted_layer_name = f"{get_layer_name(base_layer)}_Inverted"
base_layer_dir = bpy.path.abspath(base_layer.data.filepath)
base_grids, base_metadata = vdb.readAll(base_layer_dir)
base_shape = get_shape(base_layer)
scalar_offset = get_layer_meta(base_layer, "Scalar Offset")
preferences = context.preferences.addons[__package__].preferences
cache_dir = Path(preferences.cache_dir, 'VDBs')
cache_dir.mkdir(parents=True, exist_ok=True)
base_grids = get_grids(base_layer)
base_volume = get_volume(base_grids, 0, base_shape)
layers = []
if self.scalar_layer != "None":
scalar_layer = bpy.data.objects[self.scalar_layer]
layers.append(scalar_layer)
base_volume = -(base_volume - scalar_offset)
if self.label_layer != "None":
label_layer = bpy.data.objects[self.label_layer]
layers.append(label_layer)
base_min = float(np.min(base_volume))
base_max = float(np.max(base_volume))
# TODO: add color and vector
scalar_offset = 0
if base_min < 0:
scalar_offset = -base_min
base_volume = base_volume + scalar_offset
if len(layers) == 0:
self.report({"WARNING"}, "No additinal layers setted.")
return {'FINISHED'}
set_volume(base_grids, 0, base_volume)
vdb_path = save_vdb(base_grids, context)
inverted_layer = deep_copy_layer(vdb_path,
base_layer,
f"{container.name}_{inverted_layer_name}")
set_layer_meta(inverted_layer, 'Scalar Offset', scalar_offset)
set_layer_meta(inverted_layer, 'Scalar Min', base_min)
set_layer_meta(inverted_layer, 'Scalar Max', base_max)
for layer in layers:
layer_dir = bpy.path.abspath(layer.data.filepath)
grids, metadata = vdb.readAll(layer_dir)
base_grids.extend(grids)
add_mask_node(container,
inverted_layer,
"BioxelNodes_MaskByThreshold",
inverted_layer_name)
vdb_path = Path(cache_dir, f"{uuid4()}.vdb")
print(f"Storing the VDB file ({str(vdb_path)})...")
vdb.write(str(vdb_path), grids=base_grids)
bpy.context.view_layer.objects.active = container
# Read VDB
print(f"Loading the cache to Blender scene...")
bpy.ops.object.volume_import(
filepath=str(vdb_path), align='WORLD', location=(0, 0, 0), scale=(1, 1, 1))
return {'FINISHED'}
joined_layer = bpy.context.active_object
# Set props to VDB object
joined_layer.name = f"{base_layer.name}_Joined"
joined_layer.data.name = f"{base_layer.name}_Joined"
class FillByThreshold(bpy.types.Operator):
bl_idname = "bioxelnodes.fill_by_threshold"
bl_label = "Fill by Threshold"
bl_description = "Fill Value by Threshold"
bl_options = {'UNDO'}
lock_transform(joined_layer)
hide_in_ray(joined_layer)
joined_layer.hide_select = True
joined_layer.hide_render = True
joined_layer.hide_viewport = True
joined_layer.data.display.use_slice = True
joined_layer.data.display.density = 1e-05
threshold: bpy.props.FloatProperty(
name="Threshold",
soft_min=0, soft_max=1024,
default=128,
) # type: ignore
joined_layer['bioxel_layer'] = True
joined_layer.parent = base_layer.parent
fill_value: bpy.props.FloatProperty(
name="Fill Value",
soft_min=0, soft_max=1024.0,
default=0,
) # type: ignore
for collection in layer.users_collection:
collection.objects.unlink(layer)
invert: bpy.props.BoolProperty(
name="Invert Area",
default=True,
) # type: ignore
for collection in base_layer.users_collection:
collection.objects.link(layer)
@classmethod
def poll(cls, context):
layer = get_layer(bpy.context.active_object)
if layer:
return layer.get("bioxel_layer_type") == "scalar"
else:
return False
# add convert to layer node
base_layer_node = base_layer.modifiers[0].node_group.nodes['BioxelNodes__ConvertToLayer']
bioxel_size = base_layer_node.inputs['Bioxel Size'].default_value
layer_shape = base_layer_node.inputs['Shape'].default_value
layer_origin = base_layer_node.inputs['Origin'].default_value
layer_rotation = base_layer_node.inputs['Rotation'].default_value
scalar_offset = base_layer_node.inputs['Scalar Offset'].default_value
scalar_min = base_layer_node.inputs['Scalar Min'].default_value
scalar_max = base_layer_node.inputs['Scalar Max'].default_value
def execute(self, context):
base_layer = get_layer(bpy.context.active_object)
if self.scalar_layer != "None":
scalar_layer_node = scalar_layer.modifiers[0].node_group.nodes['BioxelNodes__ConvertToLayer']
scalar_offset = scalar_layer_node.inputs['Scalar Offset'].default_value
scalar_min = scalar_layer_node.inputs['Scalar Min'].default_value
scalar_max = scalar_layer_node.inputs['Scalar Max'].default_value
container = base_layer.parent
filled_layer_name = f"{get_layer_name(base_layer)}_{self.threshold}-Filled"
scalar_offset = get_layer_meta(base_layer, "Scalar Offset")
base_shape = get_shape(base_layer)
bpy.ops.node.new_geometry_nodes_modifier()
node_tree = joined_layer.modifiers[0].node_group
nodes = node_tree.nodes
links = node_tree.links
base_grids = get_grids(base_layer)
base_volume = get_volume(base_grids, 0, base_shape)
mask = base_volume > (self.threshold + scalar_offset)
mask = scipy.median_filter(mask.astype(np.float32), size=2)
if self.invert:
base_volume = mask * base_volume + \
(1-mask) * (self.fill_value + scalar_offset)
else:
base_volume = (1-mask) * base_volume + \
mask * (self.fill_value + scalar_offset)
input_node = get_node_by_type(nodes, 'NodeGroupInput')[0]
output_node = get_node_by_type(nodes, 'NodeGroupOutput')[0]
set_volume(base_grids, 0, base_volume)
vdb_path = save_vdb(base_grids, context)
filled_layer = deep_copy_layer(vdb_path,
base_layer,
f"{container.name}_{filled_layer_name}")
mask_node = add_mask_node(container,
filled_layer,
"BioxelNodes_MaskByThreshold",
filled_layer_name)
joined_layer_node = custom_nodes.add_node(
nodes, "BioxelNodes__ConvertToLayer")
mask_node.inputs[1].default_value = self.threshold
links.new(input_node.outputs[0], joined_layer_node.inputs[0])
links.new(joined_layer_node.outputs[0], output_node.inputs[0])
joined_layer_node.inputs['Layer ID'].default_value = random.randint(-200000000,
200000000)
joined_layer_node.inputs['Bioxel Size'].default_value = bioxel_size
joined_layer_node.inputs['Shape'].default_value = layer_shape
joined_layer_node.inputs['Origin'].default_value = layer_origin
joined_layer_node.inputs['Rotation'].default_value = layer_rotation
joined_layer_node.inputs['Scalar Offset'].default_value = scalar_offset
joined_layer_node.inputs['Scalar Min'].default_value = scalar_min
joined_layer_node.inputs['Scalar Max'].default_value = scalar_max
bpy.context.view_layer.objects.active = container
return {'FINISHED'}
def invoke(self, context, event):
base_layer = get_layer(bpy.context.active_object)
self.base_layer = base_layer.name
scalar_min = get_layer_meta(base_layer, "Scalar Min")
self.fill_value = scalar_min
context.window_manager.invoke_props_dialog(self, width=400)
return {'RUNNING_MODAL'}
class FillByRange(bpy.types.Operator):
bl_idname = "bioxelnodes.fill_by_range"
bl_label = "Fill by Range"
bl_description = "Fill Value by Range"
bl_options = {'UNDO'}
from_min: bpy.props.FloatProperty(
name="From Min",
soft_min=0, soft_max=1024,
default=128,
) # type: ignore
from_max: bpy.props.FloatProperty(
name="From Max",
soft_min=0, soft_max=1024,
default=256,
) # type: ignore
fill_value: bpy.props.FloatProperty(
name="Fill Value",
soft_min=0, soft_max=1024.0,
default=0,
) # type: ignore
invert: bpy.props.BoolProperty(
name="Invert Area",
default=True,
) # type: ignore
@classmethod
def poll(cls, context):
layer = get_layer(bpy.context.active_object)
if layer:
return layer.get("bioxel_layer_type") == "scalar"
else:
return False
def execute(self, context):
base_layer = get_layer(bpy.context.active_object)
container = base_layer.parent
filled_layer_name = f"{get_layer_name(base_layer)}_{self.from_min}-{self.from_max}-Filled"
scalar_offset = get_layer_meta(base_layer, "Scalar Offset")
base_shape = get_shape(base_layer)
base_grids = get_grids(base_layer)
base_volume = get_volume(base_grids, 0, base_shape)
mask = (base_volume > self.from_min + scalar_offset) \
& (base_volume < self.from_max + scalar_offset)
mask = scipy.median_filter(mask.astype(np.float32), size=2)
if self.invert:
base_volume = mask * base_volume + \
(1-mask) * (self.fill_value + scalar_offset)
else:
base_volume = (1-mask) * base_volume + \
mask * (self.fill_value + scalar_offset)
set_volume(base_grids, 0, base_volume)
vdb_path = save_vdb(base_grids, context)
filled_layer = deep_copy_layer(vdb_path,
base_layer,
f"{container.name}_{filled_layer_name}")
mask_node = add_mask_node(container,
filled_layer,
"BioxelNodes_MaskByThreshold",
filled_layer_name)
mask_node.inputs[1].default_value = self.from_min
bpy.context.view_layer.objects.active = container
return {'FINISHED'}
def invoke(self, context, event):
base_layer = get_layer(bpy.context.active_object)
scalar_min = get_layer_meta(base_layer, "Scalar Min")
self.fill_value = scalar_min
context.window_manager.invoke_props_dialog(self, width=400)
return {'RUNNING_MODAL'}
class FillByLabel(bpy.types.Operator):
bl_idname = "bioxelnodes.fill_by_label"
bl_label = "Fill by Label"
bl_description = "Fill Value by Label Area"
bl_options = {'UNDO'}
label_layer: bpy.props.EnumProperty(
name="Label Layer",
items=get_label_layer_selection
) # type: ignore
fill_value: bpy.props.FloatProperty(
name="Fill Value",
soft_min=0, soft_max=1024.0,
default=0,
) # type: ignore
invert: bpy.props.BoolProperty(
name="Invert Label",
default=True,
) # type: ignore
@classmethod
def poll(cls, context):
layer = get_layer(bpy.context.active_object)
if layer:
return layer.get("bioxel_layer_type") == "scalar"
else:
return False
def execute(self, context):
base_layer = get_layer(bpy.context.active_object)
label_layer = bpy.data.objects[self.label_layer]
if not label_layer:
self.report({"WARNING"}, "Cannot find any label layer.")
return {'FINISHED'}
container = base_layer.parent
filled_layer_name = f"{get_layer_name(base_layer)}_{get_layer_name(label_layer)}-Filled"
scalar_offset = get_layer_meta(base_layer, "Scalar Offset")
base_shape = get_shape(base_layer)
label_shape = get_shape(label_layer)
base_grids = get_grids(base_layer)
base_volume = get_volume(base_grids, 0, base_shape)
label_grids = get_grids(label_layer)
mask = get_volume(label_grids, 0, label_shape)
mask = ski.resize(mask,
base_shape,
anti_aliasing=True)
mask = scipy.median_filter(mask.astype(np.float32), size=2)
if self.invert:
base_volume = mask * base_volume + \
(1-mask) * (self.fill_value + scalar_offset)
else:
base_volume = (1-mask) * base_volume + \
mask * (self.fill_value + scalar_offset)
set_volume(base_grids, 0, base_volume)
vdb_path = save_vdb(base_grids, context)
filled_layer = deep_copy_layer(vdb_path,
base_layer,
f"{container.name}_{filled_layer_name}")
add_mask_node(container,
filled_layer,
"BioxelNodes_MaskByThreshold",
filled_layer_name)
bpy.context.view_layer.objects.active = container
return {'FINISHED'}
def invoke(self, context, event):
base_layer = get_layer(bpy.context.active_object)
scalar_min = get_layer_meta(base_layer, "Scalar Min")
self.fill_value = scalar_min
context.window_manager.invoke_props_dialog(self, width=400)
return {'RUNNING_MODAL'}
class CombineLabels(bpy.types.Operator):
bl_idname = "bioxelnodes.combine_labels"
bl_label = "Combine Labels"
bl_description = "Combine all selected labels"
bl_options = {'UNDO'}
@classmethod
def poll(cls, context):
labels = [obj for obj in context.selected_ids
if obj.get("bioxel_layer_type") == "label"]
return True if len(labels) > 1 else False
def execute(self, context):
labels = [obj for obj in context.selected_ids
if obj.get("bioxel_layer_type") == "label"]
base_layer = labels[0]
labels = labels[1:]
container = base_layer.parent
label_names = [get_layer_name(base_layer)]
base_shape = get_shape(base_layer)
base_grids = get_grids(base_layer)
base_volume = get_volume(base_grids, 0, base_shape)
base_volume = base_volume
for label in labels:
label_shape = get_shape(label)
label_grids = get_grids(label)
label_volume = get_volume(label_grids, 0, label_shape)
label_volume = ski.resize(label_volume,
base_shape,
anti_aliasing=True)
base_volume = np.maximum(base_volume, label_volume)
label_names.append(get_layer_name(label))
set_volume(base_grids, 0, base_volume)
combined_layer_name = f"{'-'.join(label_names)}-Combined"
vdb_path = save_vdb(base_grids, context)
combined_layer = deep_copy_layer(vdb_path,
base_layer,
f"{container.name}_{combined_layer_name}")
add_mask_node(container,
combined_layer,
"BioxelNodes_MaskByLabel",
combined_layer_name)
bpy.context.view_layer.objects.active = container
return {'FINISHED'}
class ConvertToMesh(bpy.types.Operator):
bl_idname = "bioxelnodes.convert_to_mesh"
bl_label = "Bioxel Components To Mesh"
bl_label = "Convert To Mesh"
bl_description = "Convert Bioxel Components To Mesh"
bl_options = {'UNDO'}
@classmethod
def poll(cls, context):
container = get_container(bpy.context.active_object)
return True if container else False
containers = get_container_from_selection()
return len(containers) > 0
def execute(self, context):
container = get_container(bpy.context.active_object)
containers = get_container_from_selection()
if not container:
if len(containers) == 0:
self.report({"WARNING"}, "Cannot find any bioxel container.")
return {'FINISHED'}
container = containers[0]
bpy.ops.mesh.primitive_cube_add(
size=2, enter_editmode=False, align='WORLD', location=(0, 0, 0), scale=(1, 1, 1))
mesh = bpy.context.active_object
mesh.name = f"Mesh_{container.name}"
bpy.ops.object.constraint_add(type='COPY_TRANSFORMS')
mesh.constraints[0].target = container
# bpy.ops.object.constraint_add(type='COPY_TRANSFORMS')
# mesh.constraints[0].target = container
bpy.ops.node.new_geometry_nodes_modifier()
modifier = mesh.modifiers[0]
nodes = modifier.node_group.nodes
links = modifier.node_group.links
node_tree = modifier.node_group
output_node = get_node_by_type(nodes, 'NodeGroupOutput')[0]
object_node = nodes.new("GeometryNodeObjectInfo")
realize_nodes = nodes.new("GeometryNodeRealizeInstances")
separate_node = custom_nodes.add_node(nodes,
"BioxelNodes__SeparateComponent")
output_node = get_node_by_type(node_tree.nodes, 'NodeGroupOutput')[0]
to_mesh_node = custom_nodes.add_node(node_tree.nodes,
"BioxelNodes_ToMesh")
object_node.inputs[0].default_value = container
separate_node.inputs[1].default_value = 1
links.new(object_node.outputs['Geometry'], separate_node.inputs[0])
links.new(separate_node.outputs[0], realize_nodes.inputs[0])
links.new(realize_nodes.outputs[0], output_node.inputs[0])
to_mesh_node.inputs[0].default_value = container
node_tree.links.new(to_mesh_node.outputs[0], output_node.inputs[0])
bpy.ops.object.convert(target='MESH')
bpy.ops.constraint.apply(constraint="Copy Transforms", owner='OBJECT')
# bpy.ops.constraint.apply(
# constraint=mesh.constraints[0].name, owner='OBJECT')
bpy.context.object.active_material_index = 1
bpy.ops.object.material_slot_remove()
self.report({"INFO"}, f"Successfully convert to mesh")
return {'FINISHED'}
class AddCutter():
bl_options = {'UNDO'}
@classmethod
def poll(cls, context):
containers = get_container_from_selection()
return len(containers) > 0
def execute(self, context):
containers = get_container_from_selection()
if len(containers) == 0:
self.report({"WARNING"}, "Cannot find any bioxel container.")
return {'FINISHED'}
container = containers[0]
if self.object_type == "plane":
node_type = "BioxelNodes_PlaneObjectCutter"
bpy.ops.mesh.primitive_plane_add(
size=2, enter_editmode=False, align='WORLD', location=(0, 0, 0), scale=(1, 1, 1))
elif self.object_type == "cylinder":
node_type = "BioxelNodes_CylinderObjectCutter"
bpy.ops.mesh.primitive_cylinder_add(
radius=1, depth=2, enter_editmode=False, align='WORLD', location=(0, 0, 0), scale=(1, 1, 1))
bpy.context.object.rotation_euler[0] = container.rotation_euler[0]
elif self.object_type == "cube":
node_type = "BioxelNodes_CubeObjectCutter"
bpy.ops.mesh.primitive_cube_add(
size=2, enter_editmode=False, align='WORLD', location=(0, 0, 0), scale=(1, 1, 1))
elif self.object_type == "sphere":
node_type = "BioxelNodes_SphereObjectCutter"
bpy.ops.mesh.primitive_ico_sphere_add(
radius=1, enter_editmode=False, align='WORLD', location=(0, 0, 0), scale=(1, 1, 1))
cutter = bpy.context.active_object
cutter.visible_camera = False
cutter.visible_diffuse = False
cutter.visible_glossy = False
cutter.visible_transmission = False
cutter.visible_volume_scatter = False
cutter.visible_shadow = False
cutter.hide_render = True
cutter.display_type = 'WIRE'
modifier = container.modifiers[0]
node_tree = modifier.node_group
cutter_node = custom_nodes.add_node(node_tree.nodes, node_type)
cutter_node.inputs[0].default_value = cutter
bpy.context.view_layer.objects.active = container
return {'FINISHED'}
class AddPlaneCutter(bpy.types.Operator, AddCutter):
bl_idname = "bioxelnodes.add_plane_cutter"
bl_label = "Add Plane Cutter"
bl_description = "Add Plane Cutter to Container"
bl_options = {'UNDO'}
object_type = "plane"
class AddCylinderCutter(bpy.types.Operator, AddCutter):
bl_idname = "bioxelnodes.add_cylinder_cutter"
bl_label = "Add Cylinder Cutter"
bl_description = "Add Cylinder Cutter to Container"
bl_options = {'UNDO'}
object_type = "cylinder"
class AddCubeCutter(bpy.types.Operator, AddCutter):
bl_idname = "bioxelnodes.add_cube_cutter"
bl_label = "Add Cube Cutter"
bl_description = "Add Cube Cutter to Container"
bl_options = {'UNDO'}
object_type = "cube"
class AddSphereCutter(bpy.types.Operator, AddCutter):
bl_idname = "bioxelnodes.add_sphere_cutter"
bl_label = "Add Sphere Cutter"
bl_description = "Add Sphere Cutter to Container"
bl_options = {'UNDO'}
object_type = "sphere"

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from ._interpolation import zoom
from ._filters import gaussian_filter, median_filter

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import numpy as np
import warnings
from . import _ni_support
from . import _nd_image
from ._utils import normalize_axis_index
def _prepad_for_spline_filter(input, mode, cval):
if mode in ['nearest', 'grid-constant']:
npad = 12
if mode == 'grid-constant':
padded = np.pad(input, npad, mode='constant',
constant_values=cval)
elif mode == 'nearest':
padded = np.pad(input, npad, mode='edge')
else:
# other modes have exact boundary conditions implemented so
# no prepadding is needed
npad = 0
padded = input
return padded, npad
def spline_filter1d(input, order=3, axis=-1, output=np.float64,
mode='mirror'):
"""
Calculate a 1-D spline filter along the given axis.
The lines of the array along the given axis are filtered by a
spline filter. The order of the spline must be >= 2 and <= 5.
Parameters
----------
%(input)s
order : int, optional
The order of the spline, default is 3.
axis : int, optional
The axis along which the spline filter is applied. Default is the last
axis.
output : ndarray or dtype, optional
The array in which to place the output, or the dtype of the returned
array. Default is ``numpy.float64``.
%(mode_interp_mirror)s
Returns
-------
spline_filter1d : ndarray
The filtered input.
See Also
--------
spline_filter : Multidimensional spline filter.
Notes
-----
All of the interpolation functions in `ndimage` do spline interpolation of
the input image. If using B-splines of `order > 1`, the input image
values have to be converted to B-spline coefficients first, which is
done by applying this 1-D filter sequentially along all
axes of the input. All functions that require B-spline coefficients
will automatically filter their inputs, a behavior controllable with
the `prefilter` keyword argument. For functions that accept a `mode`
parameter, the result will only be correct if it matches the `mode`
used when filtering.
For complex-valued `input`, this function processes the real and imaginary
components independently.
.. versionadded:: 1.6.0
Complex-valued support added.
Examples
--------
We can filter an image using 1-D spline along the given axis:
>>> from scipy.ndimage import spline_filter1d
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> orig_img = np.eye(20) # create an image
>>> orig_img[10, :] = 1.0
>>> sp_filter_axis_0 = spline_filter1d(orig_img, axis=0)
>>> sp_filter_axis_1 = spline_filter1d(orig_img, axis=1)
>>> f, ax = plt.subplots(1, 3, sharex=True)
>>> for ind, data in enumerate([[orig_img, "original image"],
... [sp_filter_axis_0, "spline filter (axis=0)"],
... [sp_filter_axis_1, "spline filter (axis=1)"]]):
... ax[ind].imshow(data[0], cmap='gray_r')
... ax[ind].set_title(data[1])
>>> plt.tight_layout()
>>> plt.show()
"""
if order < 0 or order > 5:
raise RuntimeError('spline order not supported')
input = np.asarray(input)
complex_output = np.iscomplexobj(input)
output = _ni_support._get_output(output, input,
complex_output=complex_output)
if complex_output:
spline_filter1d(input.real, order, axis, output.real, mode)
spline_filter1d(input.imag, order, axis, output.imag, mode)
return output
if order in [0, 1]:
output[...] = np.array(input)
else:
mode = _ni_support._extend_mode_to_code(mode)
axis = normalize_axis_index(axis, input.ndim)
_nd_image.spline_filter1d(input, order, axis, output, mode)
return output
def spline_filter(input, order=3, output=np.float64, mode='mirror'):
"""
Multidimensional spline filter.
Parameters
----------
%(input)s
order : int, optional
The order of the spline, default is 3.
output : ndarray or dtype, optional
The array in which to place the output, or the dtype of the returned
array. Default is ``numpy.float64``.
%(mode_interp_mirror)s
Returns
-------
spline_filter : ndarray
Filtered array. Has the same shape as `input`.
See Also
--------
spline_filter1d : Calculate a 1-D spline filter along the given axis.
Notes
-----
The multidimensional filter is implemented as a sequence of
1-D spline filters. The intermediate arrays are stored
in the same data type as the output. Therefore, for output types
with a limited precision, the results may be imprecise because
intermediate results may be stored with insufficient precision.
For complex-valued `input`, this function processes the real and imaginary
components independently.
.. versionadded:: 1.6.0
Complex-valued support added.
Examples
--------
We can filter an image using multidimentional splines:
>>> from scipy.ndimage import spline_filter
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> orig_img = np.eye(20) # create an image
>>> orig_img[10, :] = 1.0
>>> sp_filter = spline_filter(orig_img, order=3)
>>> f, ax = plt.subplots(1, 2, sharex=True)
>>> for ind, data in enumerate([[orig_img, "original image"],
... [sp_filter, "spline filter"]]):
... ax[ind].imshow(data[0], cmap='gray_r')
... ax[ind].set_title(data[1])
>>> plt.tight_layout()
>>> plt.show()
"""
if order < 2 or order > 5:
raise RuntimeError('spline order not supported')
input = np.asarray(input)
complex_output = np.iscomplexobj(input)
output = _ni_support._get_output(output, input,
complex_output=complex_output)
if complex_output:
spline_filter(input.real, order, output.real, mode)
spline_filter(input.imag, order, output.imag, mode)
return output
if order not in [0, 1] and input.ndim > 0:
for axis in range(input.ndim):
spline_filter1d(input, order, axis, output=output, mode=mode)
input = output
else:
output[...] = input[...]
return output
def zoom(input, zoom, output=None, order=3, mode='constant', cval=0.0,
prefilter=True, *, grid_mode=False):
"""
Zoom an array.
The array is zoomed using spline interpolation of the requested order.
Parameters
----------
%(input)s
zoom : float or sequence
The zoom factor along the axes. If a float, `zoom` is the same for each
axis. If a sequence, `zoom` should contain one value for each axis.
%(output)s
order : int, optional
The order of the spline interpolation, default is 3.
The order has to be in the range 0-5.
%(mode_interp_constant)s
%(cval)s
%(prefilter)s
grid_mode : bool, optional
If False, the distance from the pixel centers is zoomed. Otherwise, the
distance including the full pixel extent is used. For example, a 1d
signal of length 5 is considered to have length 4 when `grid_mode` is
False, but length 5 when `grid_mode` is True. See the following
visual illustration:
.. code-block:: text
| pixel 1 | pixel 2 | pixel 3 | pixel 4 | pixel 5 |
|<-------------------------------------->|
vs.
|<----------------------------------------------->|
The starting point of the arrow in the diagram above corresponds to
coordinate location 0 in each mode.
Returns
-------
zoom : ndarray
The zoomed input.
Notes
-----
For complex-valued `input`, this function zooms the real and imaginary
components independently.
.. versionadded:: 1.6.0
Complex-valued support added.
Examples
--------
>>> from scipy import ndimage, datasets
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = datasets.ascent()
>>> result = ndimage.zoom(ascent, 3.0)
>>> ax1.imshow(ascent, vmin=0, vmax=255)
>>> ax2.imshow(result, vmin=0, vmax=255)
>>> plt.show()
>>> print(ascent.shape)
(512, 512)
>>> print(result.shape)
(1536, 1536)
"""
if order < 0 or order > 5:
raise RuntimeError('spline order not supported')
input = np.asarray(input)
if input.ndim < 1:
raise RuntimeError('input and output rank must be > 0')
zoom = _ni_support._normalize_sequence(zoom, input.ndim)
output_shape = tuple(
[int(round(ii * jj)) for ii, jj in zip(input.shape, zoom)])
complex_output = np.iscomplexobj(input)
output = _ni_support._get_output(output, input, shape=output_shape,
complex_output=complex_output)
if complex_output:
# import under different name to avoid confusion with zoom parameter
from scipy.ndimage._interpolation import zoom as _zoom
kwargs = dict(order=order, mode=mode, prefilter=prefilter)
_zoom(input.real, zoom, output=output.real,
cval=np.real(cval), **kwargs)
_zoom(input.imag, zoom, output=output.imag,
cval=np.imag(cval), **kwargs)
return output
if prefilter and order > 1:
padded, npad = _prepad_for_spline_filter(input, mode, cval)
filtered = spline_filter(padded, order, output=np.float64, mode=mode)
else:
npad = 0
filtered = input
if grid_mode:
# warn about modes that may have surprising behavior
suggest_mode = None
if mode == 'constant':
suggest_mode = 'grid-constant'
elif mode == 'wrap':
suggest_mode = 'grid-wrap'
if suggest_mode is not None:
warnings.warn(
(f"It is recommended to use mode = {suggest_mode} instead of {mode} "
f"when grid_mode is True."),
stacklevel=2
)
mode = _ni_support._extend_mode_to_code(mode)
zoom_div = np.array(output_shape)
zoom_nominator = np.array(input.shape)
if not grid_mode:
zoom_div -= 1
zoom_nominator -= 1
# Zooming to infinite values is unpredictable, so just choose
# zoom factor 1 instead
zoom = np.divide(zoom_nominator, zoom_div,
out=np.ones_like(input.shape, dtype=np.float64),
where=zoom_div != 0)
zoom = np.ascontiguousarray(zoom)
_nd_image.zoom_shift(filtered, zoom, None, output, order, mode, cval, npad,
grid_mode)
return output

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bioxelnodes/scipy/_ni_support.py Normal file
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# Copyright (C) 2003-2005 Peter J. Verveer
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of the author may not be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from collections.abc import Iterable
import operator
import warnings
import numpy as np
def _extend_mode_to_code(mode):
"""Convert an extension mode to the corresponding integer code.
"""
if mode == 'nearest':
return 0
elif mode == 'wrap':
return 1
elif mode in ['reflect', 'grid-mirror']:
return 2
elif mode == 'mirror':
return 3
elif mode == 'constant':
return 4
elif mode == 'grid-wrap':
return 5
elif mode == 'grid-constant':
return 6
else:
raise RuntimeError('boundary mode not supported')
def _normalize_sequence(input, rank):
"""If input is a scalar, create a sequence of length equal to the
rank by duplicating the input. If input is a sequence,
check if its length is equal to the length of array.
"""
is_str = isinstance(input, str)
if not is_str and isinstance(input, Iterable):
normalized = list(input)
if len(normalized) != rank:
err = "sequence argument must have length equal to input rank"
raise RuntimeError(err)
else:
normalized = [input] * rank
return normalized
def _get_output(output, input, shape=None, complex_output=False):
if shape is None:
shape = input.shape
if output is None:
if not complex_output:
output = np.zeros(shape, dtype=input.dtype.name)
else:
complex_type = np.promote_types(input.dtype, np.complex64)
output = np.zeros(shape, dtype=complex_type)
elif isinstance(output, (type, np.dtype)):
# Classes (like `np.float32`) and dtypes are interpreted as dtype
if complex_output and np.dtype(output).kind != 'c':
warnings.warn("promoting specified output dtype to complex", stacklevel=3)
output = np.promote_types(output, np.complex64)
output = np.zeros(shape, dtype=output)
elif isinstance(output, str):
output = np.dtype(output)
if complex_output and output.kind != 'c':
raise RuntimeError("output must have complex dtype")
elif not issubclass(output.type, np.number):
raise RuntimeError("output must have numeric dtype")
output = np.zeros(shape, dtype=output)
elif output.shape != shape:
raise RuntimeError("output shape not correct")
elif complex_output and output.dtype.kind != 'c':
raise RuntimeError("output must have complex dtype")
return output
def _check_axes(axes, ndim):
if axes is None:
return tuple(range(ndim))
elif np.isscalar(axes):
axes = (operator.index(axes),)
elif isinstance(axes, Iterable):
for ax in axes:
axes = tuple(operator.index(ax) for ax in axes)
if ax < -ndim or ax > ndim - 1:
raise ValueError(f"specified axis: {ax} is out of range")
axes = tuple(ax % ndim if ax < 0 else ax for ax in axes)
else:
message = "axes must be an integer, iterable of integers, or None"
raise ValueError(message)
if len(tuple(set(axes))) != len(axes):
raise ValueError("axes must be unique")
return axes

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def normalize_axis_index(axis, ndim):
# Check if `axis` is in the correct range and normalize it
if axis < -ndim or axis >= ndim:
msg = f"axis {axis} is out of bounds for array of dimension {ndim}"
raise Exception(msg)
if axis < 0:
axis = axis + ndim
return axis

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bioxelnodes/skimage/__init__.py Normal file
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from ._warps import resize

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bioxelnodes/skimage/_utils.py Normal file
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import numpy as np
def convert_to_float(image, preserve_range):
"""Convert input image to float image with the appropriate range.
Parameters
----------
image : ndarray
Input image.
preserve_range : bool
Determines if the range of the image should be kept or transformed
using img_as_float. Also see
https://scikit-image.org/docs/dev/user_guide/data_types.html
Notes
-----
* Input images with `float32` data type are not upcast.
Returns
-------
image : ndarray
Transformed version of the input.
"""
if image.dtype == np.float16:
return image.astype(np.float32)
if preserve_range:
# Convert image to double only if it is not single or double
# precision float
if image.dtype.char not in 'df':
image = image.astype(float)
else:
from .dtype import img_as_float
image = img_as_float(image)
return image

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import warnings
import numpy as np
from .. import scipy as ndi
from ._utils import convert_to_float
def _clip_warp_output(input_image, output_image, mode, cval, clip):
"""Clip output image to range of values of input image.
Note that this function modifies the values of `output_image` in-place
and it is only modified if ``clip=True``.
Parameters
----------
input_image : ndarray
Input image.
output_image : ndarray
Output image, which is modified in-place.
Other parameters
----------------
mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}
Points outside the boundaries of the input are filled according
to the given mode. Modes match the behaviour of `numpy.pad`.
cval : float
Used in conjunction with mode 'constant', the value outside
the image boundaries.
clip : bool
Whether to clip the output to the range of values of the input image.
This is enabled by default, since higher order interpolation may
produce values outside the given input range.
"""
if clip:
min_val = np.min(input_image)
if np.isnan(min_val):
# NaNs detected, use NaN-safe min/max
min_func = np.nanmin
max_func = np.nanmax
min_val = min_func(input_image)
else:
min_func = np.min
max_func = np.max
max_val = max_func(input_image)
# Check if cval has been used such that it expands the effective input
# range
preserve_cval = (
mode == 'constant'
and not min_val <= cval <= max_val
and min_func(output_image) <= cval <= max_func(output_image)
)
# expand min/max range to account for cval
if preserve_cval:
# cast cval to the same dtype as the input image
cval = input_image.dtype.type(cval)
min_val = min(min_val, cval)
max_val = max(max_val, cval)
# Convert array-like types to ndarrays (gh-7159)
min_val, max_val = np.asarray(min_val), np.asarray(max_val)
np.clip(output_image, min_val, max_val, out=output_image)
def _validate_interpolation_order(image_dtype, order):
"""Validate and return spline interpolation's order.
Parameters
----------
image_dtype : dtype
Image dtype.
order : int, optional
The order of the spline interpolation. The order has to be in
the range 0-5. See `skimage.transform.warp` for detail.
Returns
-------
order : int
if input order is None, returns 0 if image_dtype is bool and 1
otherwise. Otherwise, image_dtype is checked and input order
is validated accordingly (order > 0 is not supported for bool
image dtype)
"""
if order is None:
return 0 if image_dtype == bool else 1
if order < 0 or order > 5:
raise ValueError(
"Spline interpolation order has to be in the " "range 0-5.")
if image_dtype == bool and order != 0:
raise ValueError(
"Input image dtype is bool. Interpolation is not defined "
"with bool data type. Please set order to 0 or explicitly "
"cast input image to another data type."
)
return order
def _preprocess_resize_output_shape(image, output_shape):
"""Validate resize output shape according to input image.
Parameters
----------
image: ndarray
Image to be resized.
output_shape: iterable
Size of the generated output image `(rows, cols[, ...][, dim])`. If
`dim` is not provided, the number of channels is preserved.
Returns
-------
image: ndarray
The input image, but with additional singleton dimensions appended in
the case where ``len(output_shape) > input.ndim``.
output_shape: tuple
The output image converted to tuple.
Raises
------
ValueError:
If output_shape length is smaller than the image number of
dimensions
Notes
-----
The input image is reshaped if its number of dimensions is not
equal to output_shape_length.
"""
output_shape = tuple(output_shape)
output_ndim = len(output_shape)
input_shape = image.shape
if output_ndim > image.ndim:
# append dimensions to input_shape
input_shape += (1,) * (output_ndim - image.ndim)
image = np.reshape(image, input_shape)
elif output_ndim == image.ndim - 1:
# multichannel case: append shape of last axis
output_shape = output_shape + (image.shape[-1],)
elif output_ndim < image.ndim:
raise ValueError(
"output_shape length cannot be smaller than the "
"image number of dimensions"
)
return image, output_shape
def _to_ndimage_mode(mode):
"""Convert from `numpy.pad` mode name to the corresponding ndimage mode."""
mode_translation_dict = dict(
constant='constant',
edge='nearest',
symmetric='reflect',
reflect='mirror',
wrap='wrap',
)
if mode not in mode_translation_dict:
raise ValueError(
f"Unknown mode: '{mode}', or cannot translate mode. The "
f"mode should be one of 'constant', 'edge', 'symmetric', "
f"'reflect', or 'wrap'. See the documentation of numpy.pad for "
f"more info."
)
return _fix_ndimage_mode(mode_translation_dict[mode])
def _fix_ndimage_mode(mode):
# SciPy 1.6.0 introduced grid variants of constant and wrap which
# have less surprising behavior for images. Use these when available
grid_modes = {'constant': 'grid-constant', 'wrap': 'grid-wrap'}
return grid_modes.get(mode, mode)
def resize(
image,
output_shape,
order=None,
mode='reflect',
cval=0,
clip=True,
preserve_range=False,
anti_aliasing=None,
anti_aliasing_sigma=None,
):
"""Resize image to match a certain size.
Performs interpolation to up-size or down-size N-dimensional images. Note
that anti-aliasing should be enabled when down-sizing images to avoid
aliasing artifacts. For downsampling with an integer factor also see
`skimage.transform.downscale_local_mean`.
Parameters
----------
image : ndarray
Input image.
output_shape : iterable
Size of the generated output image `(rows, cols[, ...][, dim])`. If
`dim` is not provided, the number of channels is preserved. In case the
number of input channels does not equal the number of output channels a
n-dimensional interpolation is applied.
Returns
-------
resized : ndarray
Resized version of the input.
Other parameters
----------------
order : int, optional
The order of the spline interpolation, default is 0 if
image.dtype is bool and 1 otherwise. The order has to be in
the range 0-5. See `skimage.transform.warp` for detail.
mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
Points outside the boundaries of the input are filled according
to the given mode. Modes match the behaviour of `numpy.pad`.
cval : float, optional
Used in conjunction with mode 'constant', the value outside
the image boundaries.
clip : bool, optional
Whether to clip the output to the range of values of the input image.
This is enabled by default, since higher order interpolation may
produce values outside the given input range.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input
image is converted according to the conventions of `img_as_float`.
Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
anti_aliasing : bool, optional
Whether to apply a Gaussian filter to smooth the image prior
to downsampling. It is crucial to filter when downsampling
the image to avoid aliasing artifacts. If not specified, it is set to
True when downsampling an image whose data type is not bool.
It is also set to False when using nearest neighbor interpolation
(``order`` == 0) with integer input data type.
anti_aliasing_sigma : {float, tuple of floats}, optional
Standard deviation for Gaussian filtering used when anti-aliasing.
By default, this value is chosen as (s - 1) / 2 where s is the
downsampling factor, where s > 1. For the up-size case, s < 1, no
anti-aliasing is performed prior to rescaling.
Notes
-----
Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge
pixels are duplicated during the reflection. As an example, if an array
has values [0, 1, 2] and was padded to the right by four values using
symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it
would be [0, 1, 2, 1, 0, 1, 2].
Examples
--------
>>> from skimage import data
>>> from skimage.transform import resize
>>> image = data.camera()
>>> resize(image, (100, 100)).shape
(100, 100)
"""
image, output_shape = _preprocess_resize_output_shape(image, output_shape)
input_shape = image.shape
input_type = image.dtype
if input_type == np.float16:
image = image.astype(np.float32)
if anti_aliasing is None:
anti_aliasing = (
not input_type == bool
and not (np.issubdtype(input_type, np.integer) and order == 0)
and any(x < y for x, y in zip(output_shape, input_shape))
)
if input_type == bool and anti_aliasing:
raise ValueError("anti_aliasing must be False for boolean images")
factors = np.divide(input_shape, output_shape)
order = _validate_interpolation_order(input_type, order)
if order > 0:
image = convert_to_float(image, preserve_range)
# Translate modes used by np.pad to those used by scipy.ndimage
ndi_mode = _to_ndimage_mode(mode)
if anti_aliasing:
if anti_aliasing_sigma is None:
anti_aliasing_sigma = np.maximum(0, (factors - 1) / 2)
else:
anti_aliasing_sigma = np.atleast_1d(anti_aliasing_sigma) * np.ones_like(
factors
)
if np.any(anti_aliasing_sigma < 0):
raise ValueError(
"Anti-aliasing standard deviation must be "
"greater than or equal to zero"
)
elif np.any((anti_aliasing_sigma > 0) & (factors <= 1)):
warnings.warn(
"Anti-aliasing standard deviation greater than zero but "
"not down-sampling along all axes",
stacklevel=2
)
filtered = ndi.gaussian_filter(
image, anti_aliasing_sigma, cval=cval, mode=ndi_mode
)
else:
filtered = image
zoom_factors = [1 / f for f in factors]
out = ndi.zoom(
filtered, zoom_factors, order=order, mode=ndi_mode, cval=cval, grid_mode=True
)
_clip_warp_output(image, out, mode, cval, clip)
return out

600
bioxelnodes/skimage/dtype.py Normal file
View File

@@ -0,0 +1,600 @@
import warnings
from warnings import warn
import numpy as np
__all__ = [
'img_as_float32',
'img_as_float64',
'img_as_float',
'img_as_int',
'img_as_uint',
'img_as_ubyte',
'img_as_bool',
'dtype_limits',
]
# Some of these may or may not be aliases depending on architecture & platform
_integer_types = (
np.int8,
np.byte,
np.int16,
np.short,
np.int32,
np.int64,
np.longlong,
np.int_,
np.intp,
np.intc,
int,
np.uint8,
np.ubyte,
np.uint16,
np.ushort,
np.uint32,
np.uint64,
np.ulonglong,
np.uint,
np.uintp,
np.uintc,
)
_integer_ranges = {t: (np.iinfo(t).min, np.iinfo(t).max) for t in _integer_types}
dtype_range = {
bool: (False, True),
np.bool_: (False, True),
float: (-1, 1),
np.float16: (-1, 1),
np.float32: (-1, 1),
np.float64: (-1, 1),
}
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
# np.bool8 is a deprecated alias of np.bool_
if hasattr(np, 'bool8'):
dtype_range[np.bool8] = (False, True)
dtype_range.update(_integer_ranges)
_supported_types = list(dtype_range.keys())
def dtype_limits(image, clip_negative=False):
"""Return intensity limits, i.e. (min, max) tuple, of the image's dtype.
Parameters
----------
image : ndarray
Input image.
clip_negative : bool, optional
If True, clip the negative range (i.e. return 0 for min intensity)
even if the image dtype allows negative values.
Returns
-------
imin, imax : tuple
Lower and upper intensity limits.
"""
imin, imax = dtype_range[image.dtype.type]
if clip_negative:
imin = 0
return imin, imax
def _dtype_itemsize(itemsize, *dtypes):
"""Return first of `dtypes` with itemsize greater than `itemsize`
Parameters
----------
itemsize: int
The data type object element size.
Other Parameters
----------------
*dtypes:
Any Object accepted by `np.dtype` to be converted to a data
type object
Returns
-------
dtype: data type object
First of `dtypes` with itemsize greater than `itemsize`.
"""
return next(dt for dt in dtypes if np.dtype(dt).itemsize >= itemsize)
def _dtype_bits(kind, bits, itemsize=1):
"""Return dtype of `kind` that can store a `bits` wide unsigned int
Parameters:
kind: str
Data type kind.
bits: int
Desired number of bits.
itemsize: int
The data type object element size.
Returns
-------
dtype: data type object
Data type of `kind` that can store a `bits` wide unsigned int
"""
s = next(
i
for i in (itemsize,) + (2, 4, 8)
if bits < (i * 8) or (bits == (i * 8) and kind == 'u')
)
return np.dtype(kind + str(s))
def _scale(a, n, m, copy=True):
"""Scale an array of unsigned/positive integers from `n` to `m` bits.
Numbers can be represented exactly only if `m` is a multiple of `n`.
Parameters
----------
a : ndarray
Input image array.
n : int
Number of bits currently used to encode the values in `a`.
m : int
Desired number of bits to encode the values in `out`.
copy : bool, optional
If True, allocates and returns new array. Otherwise, modifies
`a` in place.
Returns
-------
out : array
Output image array. Has the same kind as `a`.
"""
kind = a.dtype.kind
if n > m and a.max() < 2**m:
mnew = int(np.ceil(m / 2) * 2)
if mnew > m:
dtype = f'int{mnew}'
else:
dtype = f'uint{mnew}'
n = int(np.ceil(n / 2) * 2)
warn(
f'Downcasting {a.dtype} to {dtype} without scaling because max '
f'value {a.max()} fits in {dtype}',
stacklevel=3,
)
return a.astype(_dtype_bits(kind, m))
elif n == m:
return a.copy() if copy else a
elif n > m:
# downscale with precision loss
if copy:
b = np.empty(a.shape, _dtype_bits(kind, m))
np.floor_divide(a, 2 ** (n - m), out=b, dtype=a.dtype, casting='unsafe')
return b
else:
a //= 2 ** (n - m)
return a
elif m % n == 0:
# exact upscale to a multiple of `n` bits
if copy:
b = np.empty(a.shape, _dtype_bits(kind, m))
np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
return b
else:
a = a.astype(_dtype_bits(kind, m, a.dtype.itemsize), copy=False)
a *= (2**m - 1) // (2**n - 1)
return a
else:
# upscale to a multiple of `n` bits,
# then downscale with precision loss
o = (m // n + 1) * n
if copy:
b = np.empty(a.shape, _dtype_bits(kind, o))
np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
b //= 2 ** (o - m)
return b
else:
a = a.astype(_dtype_bits(kind, o, a.dtype.itemsize), copy=False)
a *= (2**o - 1) // (2**n - 1)
a //= 2 ** (o - m)
return a
def _convert(image, dtype, force_copy=False, uniform=False):
"""
Convert an image to the requested data-type.
Warnings are issued in case of precision loss, or when negative values
are clipped during conversion to unsigned integer types (sign loss).
Floating point values are expected to be normalized and will be clipped
to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
signed integers respectively.
Numbers are not shifted to the negative side when converting from
unsigned to signed integer types. Negative values will be clipped when
converting to unsigned integers.
Parameters
----------
image : ndarray
Input image.
dtype : dtype
Target data-type.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
uniform : bool, optional
Uniformly quantize the floating point range to the integer range.
By default (uniform=False) floating point values are scaled and
rounded to the nearest integers, which minimizes back and forth
conversion errors.
.. versionchanged:: 0.15
``_convert`` no longer warns about possible precision or sign
information loss. See discussions on these warnings at:
https://github.com/scikit-image/scikit-image/issues/2602
https://github.com/scikit-image/scikit-image/issues/543#issuecomment-208202228
https://github.com/scikit-image/scikit-image/pull/3575
References
----------
.. [1] DirectX data conversion rules.
https://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
.. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25",
pp 7-8. Khronos Group, 2010.
.. [3] Proper treatment of pixels as integers. A.W. Paeth.
In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
.. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels",
pp 47-57. Morgan Kaufmann, 1998.
"""
image = np.asarray(image)
dtypeobj_in = image.dtype
if dtype is np.floating:
dtypeobj_out = np.dtype('float64')
else:
dtypeobj_out = np.dtype(dtype)
dtype_in = dtypeobj_in.type
dtype_out = dtypeobj_out.type
kind_in = dtypeobj_in.kind
kind_out = dtypeobj_out.kind
itemsize_in = dtypeobj_in.itemsize
itemsize_out = dtypeobj_out.itemsize
# Below, we do an `issubdtype` check. Its purpose is to find out
# whether we can get away without doing any image conversion. This happens
# when:
#
# - the output and input dtypes are the same or
# - when the output is specified as a type, and the input dtype
# is a subclass of that type (e.g. `np.floating` will allow
# `float32` and `float64` arrays through)
if np.issubdtype(dtype_in, dtype):
if force_copy:
image = image.copy()
return image
if not (dtype_in in _supported_types and dtype_out in _supported_types):
raise ValueError(f'Cannot convert from {dtypeobj_in} to ' f'{dtypeobj_out}.')
if kind_in in 'ui':
imin_in = np.iinfo(dtype_in).min
imax_in = np.iinfo(dtype_in).max
if kind_out in 'ui':
imin_out = np.iinfo(dtype_out).min
imax_out = np.iinfo(dtype_out).max
# any -> binary
if kind_out == 'b':
return image > dtype_in(dtype_range[dtype_in][1] / 2)
# binary -> any
if kind_in == 'b':
result = image.astype(dtype_out)
if kind_out != 'f':
result *= dtype_out(dtype_range[dtype_out][1])
return result
# float -> any
if kind_in == 'f':
if kind_out == 'f':
# float -> float
return image.astype(dtype_out)
if np.min(image) < -1.0 or np.max(image) > 1.0:
raise ValueError("Images of type float must be between -1 and 1.")
# floating point -> integer
# use float type that can represent output integer type
computation_type = _dtype_itemsize(
itemsize_out, dtype_in, np.float32, np.float64
)
if not uniform:
if kind_out == 'u':
image_out = np.multiply(image, imax_out, dtype=computation_type)
else:
image_out = np.multiply(
image, (imax_out - imin_out) / 2, dtype=computation_type
)
image_out -= 1.0 / 2.0
np.rint(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
elif kind_out == 'u':
image_out = np.multiply(image, imax_out + 1, dtype=computation_type)
np.clip(image_out, 0, imax_out, out=image_out)
else:
image_out = np.multiply(
image, (imax_out - imin_out + 1.0) / 2.0, dtype=computation_type
)
np.floor(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
return image_out.astype(dtype_out)
# signed/unsigned int -> float
if kind_out == 'f':
# use float type that can exactly represent input integers
computation_type = _dtype_itemsize(
itemsize_in, dtype_out, np.float32, np.float64
)
if kind_in == 'u':
# using np.divide or np.multiply doesn't copy the data
# until the computation time
image = np.multiply(image, 1.0 / imax_in, dtype=computation_type)
# DirectX uses this conversion also for signed ints
# if imin_in:
# np.maximum(image, -1.0, out=image)
elif kind_in == 'i':
# From DirectX conversions:
# The most negative value maps to -1.0f
# Every other value is converted to a float (call it c)
# and then result = c * (1.0f / (2⁽ⁿ⁻¹⁾-1)).
image = np.multiply(image, 1.0 / imax_in, dtype=computation_type)
np.maximum(image, -1.0, out=image)
else:
image = np.add(image, 0.5, dtype=computation_type)
image *= 2 / (imax_in - imin_in)
return np.asarray(image, dtype_out)
# unsigned int -> signed/unsigned int
if kind_in == 'u':
if kind_out == 'i':
# unsigned int -> signed int
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
return image.view(dtype_out)
else:
# unsigned int -> unsigned int
return _scale(image, 8 * itemsize_in, 8 * itemsize_out)
# signed int -> unsigned int
if kind_out == 'u':
image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
result = np.empty(image.shape, dtype_out)
np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
return result
# signed int -> signed int
if itemsize_in > itemsize_out:
return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)
image = image.astype(_dtype_bits('i', itemsize_out * 8))
image -= imin_in
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
image += imin_out
return image.astype(dtype_out)
def convert(image, dtype, force_copy=False, uniform=False):
warn(
"The use of this function is discouraged as its behavior may change "
"dramatically in scikit-image 1.0. This function will be removed "
"in scikit-image 1.0.",
FutureWarning,
stacklevel=2,
)
return _convert(image=image, dtype=dtype, force_copy=force_copy, uniform=uniform)
if _convert.__doc__ is not None:
convert.__doc__ = (
_convert.__doc__
+ """
Warns
-----
FutureWarning:
.. versionadded:: 0.17
The use of this function is discouraged as its behavior may change
dramatically in scikit-image 1.0. This function will be removed
in scikit-image 1.0.
"""
)
def img_as_float32(image, force_copy=False):
"""Convert an image to single-precision (32-bit) floating point format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of float32
Output image.
Notes
-----
The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
converting from unsigned or signed datatypes, respectively.
If the input image has a float type, intensity values are not modified
and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0].
"""
return _convert(image, np.float32, force_copy)
def img_as_float64(image, force_copy=False):
"""Convert an image to double-precision (64-bit) floating point format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of float64
Output image.
Notes
-----
The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
converting from unsigned or signed datatypes, respectively.
If the input image has a float type, intensity values are not modified
and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0].
"""
return _convert(image, np.float64, force_copy)
def img_as_float(image, force_copy=False):
"""Convert an image to floating point format.
This function is similar to `img_as_float64`, but will not convert
lower-precision floating point arrays to `float64`.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of float
Output image.
Notes
-----
The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
converting from unsigned or signed datatypes, respectively.
If the input image has a float type, intensity values are not modified
and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0].
"""
return _convert(image, np.floating, force_copy)
def img_as_uint(image, force_copy=False):
"""Convert an image to 16-bit unsigned integer format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of uint16
Output image.
Notes
-----
Negative input values will be clipped.
Positive values are scaled between 0 and 65535.
"""
return _convert(image, np.uint16, force_copy)
def img_as_int(image, force_copy=False):
"""Convert an image to 16-bit signed integer format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of int16
Output image.
Notes
-----
The values are scaled between -32768 and 32767.
If the input data-type is positive-only (e.g., uint8), then
the output image will still only have positive values.
"""
return _convert(image, np.int16, force_copy)
def img_as_ubyte(image, force_copy=False):
"""Convert an image to 8-bit unsigned integer format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of ubyte (uint8)
Output image.
Notes
-----
Negative input values will be clipped.
Positive values are scaled between 0 and 255.
"""
return _convert(image, np.uint8, force_copy)
def img_as_bool(image, force_copy=False):
"""Convert an image to boolean format.
Parameters
----------
image : ndarray
Input image.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
Returns
-------
out : ndarray of bool (`bool_`)
Output image.
Notes
-----
The upper half of the input dtype's positive range is True, and the lower
half is False. All negative values (if present) are False.
"""
return _convert(image, bool, force_copy)

30
bioxelnodes/utils.py
View File

@@ -1,5 +1,8 @@
import bpy
import mathutils
from pathlib import Path
import pyopenvdb as vdb
from uuid import uuid4
def get_type(cls):
@@ -89,9 +92,34 @@ def hide_in_ray(obj):
obj.visible_shadow = False
def save_vdb(grids, context):
preferences = context.preferences.addons[__package__].preferences
cache_dir = Path(preferences.cache_dir, 'VDBs')
cache_dir.mkdir(parents=True, exist_ok=True)
vdb_path = Path(cache_dir, f"{uuid4()}.vdb")
print(f"Storing the VDB file ({str(vdb_path)})...")
vdb.write(str(vdb_path), grids=grids)
return vdb_path
def get_container_from_selection():
containers = []
for obj in bpy.context.selected_objects:
if get_container(obj):
containers.append(obj)
return containers
def get_container(current_obj):
if current_obj:
return current_obj if current_obj.get('bioxel_container') else None
if current_obj.get('bioxel_container'):
return current_obj
elif current_obj.get('bioxel_layer'):
parent = current_obj.parent
return parent if parent.get('bioxel_container') else None
return None

2
extension/__init__.py
View File

@@ -11,7 +11,7 @@ def register():
auto_load.register()
menus.add()
bpy.types.Scene.bioxel_layer_dir = bpy.props.StringProperty(
name="Bioxel Layers Directory",
name="Layer Directory",
subtype='DIR_PATH',
default="//"
)

71
extension/blender_manifest.toml
View File

@@ -3,54 +3,67 @@ schema_version = "1.0.0"
# Example of manifest file for a Blender extension
# Change the values according to your extension
id = "bioxelnodes"
version = "0.2.2"
version = "0.2.3"
name = "Bioxel Nodes"
tagline = "For scientific volumetric data visualization in Blender"
maintainer = "Ma Nan <icrdr2010@outlook.com>"
# Supported types: "add-on", "theme"
type = "add-on"
# Optional: add-ons can list which resources they will require:
# * "files" (for access of any filesystem operations)
# * "network" (for internet access)
# * "clipboard" (to read and/or write the system clipboard)
# * "camera" (to capture photos and videos)
# * "microphone" (to capture audio)
permissions = ["files"]
# Optional link to documentation, support, source files, etc
website = "https://omoolab.github.io/BioxelNodes/latest"
# Optional list defined by Blender and server, see:
# https://docs.blender.org/manual/en/dev/extensions/tags.html
tags = ["Geometry Nodes", "Render", "Material"]
# https://docs.blender.org/manual/en/dev/advanced/extensions/tags.html
tags = ["Geometry Nodes", "Render"]
blender_version_min = "4.2.0"
# Optional: maximum supported Blender version
# blender_version_max = "5.1.0"
# License conforming to https://spdx.org/licenses/ (use "SPDX: prefix)
# https://docs.blender.org/manual/en/dev/extensions/licenses.html
license = ["SPDX:MIT"]
# https://docs.blender.org/manual/en/dev/advanced/extensions/licenses.html
license = [
"SPDX:MIT",
]
# Optional: required by some licenses.
copyright = ["2024 OmooLab"]
# Optional list of supported platforms. If omitted, the extension will be available in all operating systems.
platforms = ["windows-amd64", "macos-x86_64"]
# Other supported platforms: "windows-arm64", "macos-x86_64"
# TODO: externalpackage to wheels
# Optional: bundle 3rd party Python modules.
# https://docs.blender.org/manual/en/dev/extensions/python_wheels.html
wheels = [
"./wheels/SimpleITK-2.3.1-cp311-cp311-win_amd64.whl",
"./wheels/SimpleITK-2.3.1-cp311-cp311-macosx_11_0_arm64.whl",
copyright = [
"2024 OmooLab"
]
# Optional: build setting.
# https://docs.blender.org/manual/en/dev/extensions/command_line_arguments.html#command-line-args-extension-build
# Optional list of supported platforms. If omitted, the extension will be available in all operating systems.
platforms = ["windows-x64"]
# Other supported platforms: "windows-arm64", "macos-x64"
# Optional: bundle 3rd party Python modules.
# https://docs.blender.org/manual/en/dev/advanced/extensions/python_wheels.html
wheels = [
"./wheels/SimpleITK-2.3.1-cp311-cp311-win_amd64.whl"
]
## Optional: add-ons can list which resources they will require:
## * files (for access of any filesystem operations)
## * network (for internet access)
## * clipboard (to read and/or write the system clipboard)
## * camera (to capture photos and videos)
## * microphone (to capture audio)
##
## If using network, remember to also check `bpy.app.online_access`
## https://docs.blender.org/manual/en/dev/advanced/extensions/addons.html#internet-access
##
## For each permission it is important to also specify the reason why it is required.
## Keep this a single short sentence without a period (.) at the end.
## For longer explanations use the documentation or detail page.
#
[permissions]
files = "Import/export volume data from/to disk"
# Optional: build settings.
# https://docs.blender.org/manual/en/dev/advanced/extensions/command_line_arguments.html#command-line-args-extension-build
# [build]
# paths_exclude_pattern = [
# "/.git/"
# "__pycache__/"
# "__pycache__/",
# "/.git/",
# "/*.zip",
# ]

264
poetry.lock generated
View File

@@ -539,6 +539,38 @@ files = [
{file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
]
[[package]]
name = "imageio"
version = "2.34.2"
description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
optional = false
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@@ -1744,6 +1989,23 @@ pure-eval = "*"
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@@ -1963,4 +2225,4 @@ cffi = ["cffi (>=1.11)"]
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python-versions = "~=3.10.0"
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content-hash = "e9980867106af8e9e7524ff319d3c0785906b359022595732260fd4b3ca135ca"

3
pyproject.toml
View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "bioxelnodes"
version = "0.2.2"
version = "0.2.3"
description = ""
authors = ["Ma Nan <icrdr2010@outlook.com>"]
license = "MIT"
@@ -10,6 +10,7 @@ readme = "README.md"
python = "~=3.10.0"
bpy = "~=4.0"
simpleitk = "^2.3.1"
scikit-image = "^0.24.0"
[tool.poetry.group.dev.dependencies]
ipykernel = "^6.25.2"