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# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2015-2018 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ###########################################################################*/
"""This module provides marching cubes implementation.
It provides a :class:`MarchingCubes` class allowing to build an isosurface
from data provided as a 3D data set or slice by slice.
"""
__authors__ = ["T. Vincent"]
__license__ = "MIT"
__date__ = "16/08/2017"
import numpy
cimport numpy as cnumpy
cimport cython
cimport silx.math.mc as mc
# From numpy_common.pxi to avoid warnings while compiling C code
# See this thread:
# https://mail.python.org/pipermail//cython-devel/2012-March/002137.html
cdef extern from *:
bint FALSE "0"
void import_array()
void import_umath()
if FALSE:
import_array()
import_umath()
cdef class MarchingCubes:
"""Compute isosurface using marching cubes algorithm.
It builds a surface from a 3D scalar dataset as a 3D contour at a
given value.
The resulting surface is not topologically correct.
See: http://paulbourke.net/geometry/polygonise/
Lorensen, W. E. and Cline, H. E. Marching cubes: A high resolution 3D
surface construction algorithm. Computer Graphics, 21, 4 (July 1987).
ACM, 163-169.
Generated vertex and normal coordinates are in the same order
as input array, i.e., (dim 0, dim 1, dim 2).
Expected indices in memory of a (2, 2, 2) dataset:
dim 0 (depth)
|
|
4 +------+ 5
/| /|
/ | / |
6 +------+ 7|
| | | |
|0 +---|--+ 1 --- dim 2 (width)
| / | /
|/ |/
2 +------+ 3
/
/
dim 1 (height)
Example with a 3D data set:
>>> vertices, normals, indices = MarchingCubes(data, isolevel=1.)
Example of code for processing a list of images:
>>> mc = MarchingCubes(isolevel=1.) # Create object with iso-level=1
>>> previous_image = images[0]
>>> for image in images[1:]:
... mc.process_image(previous_image, image) # Process one slice
... previous_image = image
>>> vertices = mc.get_vertices() # Array of vertex positions
>>> normals = mc.get_normals() # Array of normals
>>> triangle_indices = mc.get_indices() # Array of indices of vertices
:param data: 3D dataset of float32 or None
:type data: numpy.ndarray of float32 of dimension 3
:param float isolevel: The value for which to generate the isosurface
:param bool invert_normals:
True (default) for normals oriented in direction of gradient descent
:param sampling: Sampling along each dimension (depth, height, width)
"""
cdef mc.MarchingCubes[float, float] * c_mc # Pointer to the C++ instance
def __cinit__(self, data=None, isolevel=None,
invert_normals=True, sampling=(1, 1, 1)):
self.c_mc = new mc.MarchingCubes[float, float](isolevel)
self.c_mc.invert_normals = bool(invert_normals)
self.c_mc.sampling[0] = sampling[0]
self.c_mc.sampling[1] = sampling[1]
self.c_mc.sampling[2] = sampling[2]
if data is not None:
self.process(data)
def __dealloc__(self):
del self.c_mc
def __getitem__(self, key):
"""Allows one to unpack object as a single liner:
vertices, normals, indices = MarchingCubes(...)
"""
if key == 0:
return self.get_vertices()
elif key == 1:
return self.get_normals()
elif key == 2:
return self.get_indices()
else:
raise IndexError("Index out of range")
def process(self, data):
"""Compute an isosurface from a 3D scalar field.
This builds vertices, normals and indices arrays.
Vertices and normals coordinates are in the same order as input array,
i.e., (dim 0, dim 1, dim 2).
:param numpy.ndarray data: 3D scalar field
"""
# Make sure data is a 3D contiguous array of native endian float32
data = numpy.ascontiguousarray(data, dtype='=f4')
assert data.ndim == 3
cdef float[:] c_data = numpy.ravel(data)
cdef unsigned int depth, height, width
depth = data.shape[0]
height = data.shape[1]
width = data.shape[2]
self.c_mc.process(&c_data[0], depth, height, width)
def process_slice(self, slice0, slice1):
"""Process a new slice to build the isosurface.
:param numpy.ndarray slice0: Slice previously provided as slice1.
:param numpy.ndarray slice1: Slice to process.
"""
# Make sure slices are 2D contiguous arrays of native endian float32
slice0 = numpy.ascontiguousarray(slice0, dtype='=f4')
assert slice0.ndim == 2
slice1 = numpy.ascontiguousarray(slice1, dtype='=f4')
assert slice1.ndim == 2
assert slice0.shape[0] == slice1.shape[0]
assert slice0.shape[1] == slice1.shape[1]
cdef float[:] c_slice0 = numpy.ravel(slice0)
cdef float[:] c_slice1 = numpy.ravel(slice1)
if self.c_mc.depth == 0:
# Starts a new isosurface, bootstrap with slice size
self.c_mc.set_slice_size(slice1.shape[0], slice1.shape[1])
assert slice1.shape[0] == self.c_mc.height
assert slice1.shape[1] == self.c_mc.width
self.c_mc.process_slice(&c_slice0[0], &c_slice1[0])
def finish_process(self):
"""Clear internal cache after processing slice by slice."""
self.c_mc.finish_process()
def reset(self):
"""Reset internal resources including computed isosurface info."""
self.c_mc.reset()
@cython.embedsignature(False)
@property
def shape(self):
"""The shape of the processed scalar field (depth, height, width)."""
return self.c_mc.depth, self.c_mc.height, self.c_mc.width
@cython.embedsignature(False)
@property
def sampling(self):
"""The sampling over each dimension (depth, height, width).
Default: 1, 1, 1
"""
return (self.c_mc.sampling[0],
self.c_mc.sampling[1],
self.c_mc.sampling[2])
@cython.embedsignature(False)
@property
def isolevel(self):
"""The iso-level at which to generate the isosurface"""
return self.c_mc.isolevel
@cython.embedsignature(False)
@property
def invert_normals(self):
"""True to use gradient descent as normals."""
return self.c_mc.invert_normals
def get_vertices(self):
"""Vertices currently computed (ndarray of dim NbVertices x 3)
Order is dim0, dim1, dim2 (i.e., z, y, x if dim0 is depth).
"""
return numpy.array(self.c_mc.vertices).reshape(-1, 3)
def get_normals(self):
"""Normals currently computed (ndarray of dim NbVertices x 3)
Order is dim0, dim1, dim2 (i.e., z, y, x if dim0 is depth).
"""
return numpy.array(self.c_mc.normals).reshape(-1, 3)
def get_indices(self):
"""Triangle indices currently computed (ndarray of dim NbTriangles x 3)
"""
return numpy.array(self.c_mc.indices,
dtype=numpy.uint32).reshape(-1, 3)
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