def show_ctvolume(vol,
graph=None,
axis=None,
showVol='MIP',
clim=(0, 2500),
isoTh=250,
removeStent=True,
climEditor=False,
stripSize=6,
stripSizeZ=None):
""" Different ways to visualize the CT volume as reference
For '2D' clim (-550,500) often good range
"""
import visvis as vv
colormap = {
'r': [(0.0, 0.0), (0.17727272, 1.0)],
'g': [(0.0, 0.0), (0.27272728, 1.0)],
'b': [(0.0, 0.0), (0.34545454, 1.0)],
'a': [(0.0, 1.0), (1.0, 1.0)]
}
if axis is None:
axis = vv.gca()
axis.MakeCurrent()
if showVol == 'MIP':
t = vv.volshow(vol, clim=clim, renderStyle='mip')
elif showVol == 'ISO':
if removeStent == True:
vol = remove_stent_from_volume(
vol, graph, stripSize=stripSize,
stripSizeZ=stripSizeZ) # rings are removed for vis.
t = vv.volshow(vol, clim=clim, renderStyle='iso')
t.isoThreshold = isoTh
t.colormap = colormap
elif showVol == '2D':
t = vv.volshow2(vol)
t.clim = clim
# bind ClimEditor to figure
if climEditor:
if showVol == 'ISO':
c = _utils_GUI.IsoThEditor(axis)
else:
c = vv.ClimEditor(axis)
c.position = (10, 50)
# bind for show hide
fig = vv.gcf()
fig.eventKeyDown.Bind(
lambda event: _utils_GUI.ShowHideSlider(event, c))
print('****')
print('Use "s" to show/hide slider')
print('****')
return t
def psf_volume(stack, xyz_ratio, filepath):
app = vv.use()
# Init a figure with two axes
a1 = vv.subplot(121)
vv.title('PSF Volume')
a2 = vv.subplot(122)
vv.title('PSF XYZ Cross Sections')
# show
t1 = vv.volshow(stack, axes=a1) # volume
t2 = vv.volshow2(stack, axes=a2) # cross-section interactive
# set labels for both axes
vv.xlabel('Pixel X', axes=a1)
vv.ylabel('Pixel Y', axes=a1)
vv.zlabel('Z-Slice', axes=a1)
vv.xlabel('Pixel X', axes=a2)
vv.ylabel('Pixel Y', axes=a2)
vv.zlabel('Z-Slice', axes=a2)
# set colormaps
t1.colormap = vv.CM_JET
t2.colormap = vv.CM_JET
# set correct aspect ration corresponding to voxel size
a1.daspect = 1, 1, xyz_ratio
a2.daspect = 1, 1, xyz_ratio
# show grid
a1.axis.showGrid = 1
a2.axis.showGrid = 1
# run visvis and show results
app.Run()
# save screenshot
if filepath != 'nosave':
print 'Saving PSF volume.'
savename = filepath[:-4] + '_PSF_3D.png'
# sf: scale factor
vv.screenshot(savename, sf=1, bg='w')
def volshow(*args, **kwargs):
""" volshow(vol, clim=None, cm=CM_GRAY, axesAdjust=True, axes=None)
Display a 3D image (a volume).
This is a convenience function that calls either volshow3() or
volshow2(). If the current system supports it (OpenGL version >= 2.0),
displays a 3D rendering (volshow3). Otherwise shows three slices
that can be moved interactively (volshow2).
Parameters
----------
vol : numpy array
The 3D image to visualize. Can be grayscale, RGB, or RGBA.
If the volume is an anisotropic array (vv.Aaray), the appropriate
scale and translate transformations are applied.
clim : 2-element tuple
The color limits to scale the intensities of the image. If not given,
the im.min() and im.max() are used (neglecting nan and inf).
cm : Colormap
Set the colormap to apply in case the volume is grayscale.
axesAdjust : bool
If axesAdjust==True, this function will call axes.SetLimits(), and set
the camera type to 3D. If daspectAuto has not been set yet, it is
set to False.
axes : Axes instance
Display the image in this axes, or the current axes if not given.
Any other keyword arguments are passed to either volshow2() or volshow3().
"""
# Make sure that a figure exists
vv.gcf()
# Test and run
if vv.settings.volshowPreference == 3 and vv.misc.getOpenGlCapable(2.0):
return vv.volshow3(*args, **kwargs)
else:
return vv.volshow2(*args, **kwargs)
def volshow(*args, **kwargs):
""" volshow(vol, clim=None, cm=CM_GRAY, axesAdjust=True, axes=None)
Display a 3D image (a volume).
This is a convenience function that calls either volshow3() or
volshow2(). If the current system supports it (OpenGL version >= 2.0),
displays a 3D rendering (volshow3). Otherwise shows three slices
that can be moved interactively (volshow2).
Parameters
----------
vol : numpy array
The 3D image to visualize. Can be grayscale, RGB, or RGBA.
If the volume is an anisotropic array (vv.Aaray), the appropriate
scale and translate transformations are applied.
clim : 2-element tuple
The color limits to scale the intensities of the image. If not given,
the im.min() and im.max() are used (neglecting nan and inf).
cm : Colormap
Set the colormap to apply in case the volume is grayscale.
axesAdjust : bool
If axesAdjust==True, this function will call axes.SetLimits(), and set
the camera type to 3D. If daspectAuto has not been set yet, it is
set to False.
axes : Axes instance
Display the image in this axes, or the current axes if not given.
Any other keyword arguments are passed to either volshow2() or volshow3().
"""
# Make sure that a figure exists
vv.gcf()
# Test and run
if vv.settings.volshowPreference==3 and vv.misc.getOpenGlCapable(2.0):
return vv.volshow3(*args, **kwargs)
else:
return vv.volshow2(*args, **kwargs)
#!/usr/bin/env python """ This example illustrates how you can visualize a volume using 2D slices. We explicitly use volshow2() here. Note that volshow() calls volshow3() by default, but volshow2() when the system does not support 3D rendering (OpenGL version <2.0). """ import visvis as vv vol = vv.aVolume() t = vv.volshow2(vol) t.colormap = vv.CM_HOT t.edgeColor = (0.8, 0.8, 0.8) app = vv.use() app.Run()
colormap = {
'r': [(0.0, 0.0), (0.17727272, 1.0)],
'g': [(0.0, 0.0), (0.27272728, 1.0)],
'b': [(0.0, 0.0), (0.34545454, 1.0)],
'a': [(0.0, 1.0), (1.0, 1.0)]
}
import visvis as vv
fig = vv.figure(1)
vv.clf()
fig.position = 0, 22, 1366, 706
a1 = vv.subplot(121)
t1 = vv.volshow(vol1, clim=(0, 3000), renderStyle='iso') # iso or mip
t1.isoThreshold = 400
t1.colormap = colormap
a1b = vv.volshow2(vol1, clim=(-550, 500))
vv.xlabel('x'), vv.ylabel('y'), vv.zlabel('z')
# vv.title('One volume at %i procent of cardiac cycle' % phase )
vv.title('Vol40')
a2 = vv.subplot(122)
a2.daspect = 1, 1, -1
t2 = vv.volshow(vol2, clim=(0, 3000), renderStyle='iso') # iso or mip
t2.isoThreshold = 400
t2.colormap = colormap
a2b = vv.volshow2(vol2, clim=(-550, 500))
vv.xlabel('x'), vv.ylabel('y'), vv.zlabel('z')
# vv.title('One volume at %i procent of cardiac cycle' % phase )
vv.title('Vol78')
a1.camera = a2.camera
## Start vis
f = vv.figure(1)
vv.clf()
f.position = 8.00, 31.00, 944.00, 1001.00
a = vv.gca()
a.axis.axisColor = 1, 1, 1
a.axis.visible = False
a.bgcolor = 0, 0, 0
a.daspect = 1, 1, -1
vv.title('Model for LSPEAS %s - %s' % (ptcode[7:], ctcode))
vv.xlabel('x (mm)')
vv.ylabel('y (mm)')
vv.zlabel('z (mm)')
t = vv.volshow2(volavg, clim=(-550, 500)) # -750, 1000
# m = vv.mesh(mesh)
# m.faceColor = 'g'
# Create deformable mesh
dm = DeformableMesh(a, mesh)
dm.SetDeforms(*[list(reversed(deform)) for deform in deforms_f])
dm.clim = 0, 4
dm.colormap = vv.CM_JET
vv.colorbar()
# Run mesh
a.SetLimits()
# a.SetView(viewringcrop)
dm.MotionPlay(5, 1) # (10, 0.2) = each 10 ms do a step of 20%
#!/usr/bin/env python
""" This example illustrates how to create an isosurface from a volume
and display it. This code relies on scikit-image.
"""
import visvis as vv
vol = vv.volread('stent') # a standard visvis volume
mesh = vv.isosurface(vol) # returns a visvis BaseMesh object
vv.figure(1)
vv.clf()
vv.volshow2(vol)
m = vv.mesh(mesh)
m.faceColor = (0, 1, 1)
vv.title('Isosurface')
app = vv.use()
app.Run()
def showVolPhases(basedir, vols=None, ptcode=None, ctcode=None, cropname=None,
showVol='iso', mipIsocolor=False, isoTh=310,
slider=False, clim=(0,3000), clim2D=(-550, 500), fname=None):
""" Show vol phases in motion container
showVol= mip or iso or 2D; Provide either vols or location
"""
if vols is None:
# Load volumes
s = loadvol(basedir, ptcode, ctcode, cropname, 'phases', fname=fname)
vols = []
for key in dir(s):
if key.startswith('vol'):
vols.append(s[key])
# Start vis
f = vv.figure(1); vv.clf()
f.position = 9.00, 38.00, 992.00, 944.00
a = vv.gca()
a.daspect = 1, 1, -1
a.axis.axisColor = 1,1,1
a.axis.visible = False
a.bgcolor = 0,0,0
if showVol=='mip':
if not ptcode is None and not ctcode is None:
vv.title('Maximum intensity projection cine-loop of the original ECG-gated CT volumes of patient %s at %s ' % (ptcode[8:], ctcode))
else:
vv.title('Maximum intensity projection cine-loop of the original ECG-gated CT volumes ')
else:
if not ptcode is None and not ctcode is None:
vv.title('ECG-gated CT scan cine-loop of the original ECG-gated CT volumes of patient %s at %s ' % (ptcode[8:], ctcode))
else:
vv.title('ECG-gated CT scan cine-loop of the original ECG-gated CT volumes ')
# Setup data container
container = vv.MotionDataContainer(a)
for vol in vols:
if showVol == '2D':
t = vv.volshow2(vol, clim=clim2D) # -750, 1000
t.parent = container
else:
t = vv.volshow(vol, clim=clim, renderStyle = showVol)
t.parent = container
if showVol == 'iso':
t.isoThreshold = isoTh # iso or mip work well
t.colormap = {'r': [(0.0, 0.0), (0.17727272, 1.0)],
'g': [(0.0, 0.0), (0.27272728, 1.0)],
'b': [(0.0, 0.0), (0.34545454, 1.0)],
'a': [(0.0, 1.0), (1.0, 1.0)]}
if mipIsocolor:
t.colormap = {'r': [(0.0, 0.0), (0.17727272, 1.0)],
'g': [(0.0, 0.0), (0.27272728, 1.0)],
'b': [(0.0, 0.0), (0.34545454, 1.0)],
'a': [(0.0, 1.0), (1.0, 1.0)]}
# bind ClimEditor to figure
if slider:
if showVol=='mip':
c = vv.ClimEditor(vv.gcf())
c.position = (10, 50)
f.eventKeyDown.Bind(lambda event: _utils_GUI.ShowHideSlider(event, c) )
if showVol=='iso':
c = IsoThEditor(vv.gcf())
c.position = (10, 50)
f.eventKeyDown.Bind(lambda event: _utils_GUI.ShowHideSlider(event, c) )
f.eventKeyDown.Bind(lambda event: _utils_GUI.ViewPresets(event, [a]) )
print('------------------------')
print('Use keys 1, 2, 3, 4 and 5 for preset anatomic views')
print('Use v for a default zoomed view')
print('Use x to show and hide axis')
print('------------------------')
return t
# clipping is not automatic!
Babsgrid = np.clip(Babsgrid, 0, 0.005)
Babsgrid = vv.Aarray(Babsgrid,
sampling=(resolution, resolution, resolution),
origin=(volume_corner1[2], volume_corner1[1],
volume_corner1[0]))
# set labels
vv.xlabel('x axis')
vv.ylabel('y axis')
vv.zlabel('z axis')
bs.vv_PlotWires()
t = vv.volshow2(Babsgrid, renderStyle='mip', cm=vv.CM_JET)
vv.colorbar()
app = vv.use()
app.Run()
# matplotlib plot
fig = plt.figure()
# 3d quiver
#ax = fig.gca(projection='3d')
#ax.quiver(points[:, 0], points[:, 1], points[:, 2], B[:, 0], B[:, 1], B[:, 2], length=0.005)
#2d quiver
ax = fig.gca()
def dicom2ssdf(dicom_basedir,
ptcode,
ctcode,
basedir,
cropnames=['stent'],
savedistolicavg=False,
visvol=True,
visdynamic=False):
""" read dicom volumes and store as ssdf format
"""
#Step A
vols2 = [vol2 for vol2 in imageio.get_reader(dicom_basedir, 'DICOM', 'V')]
try:
for i, vol in enumerate(vols2):
print(vol.meta.ImagePositionPatient)
for i, vol in enumerate(vols2):
print(vol.shape)
for i, vol in enumerate(vols2):
print(vol.meta.AcquisitionTime)
print(vol.meta.sampling)
assert vol.shape == vols2[0].shape
assert vol.meta.SeriesTime == vols2[0].meta.SeriesTime
except AttributeError:
print('Some meta information is not available')
pass
# check order of phases
vols = vols2.copy()
try:
for i, vol in enumerate(vols):
print(vol.meta.SeriesDescription)
assert str(i * 10) in vol.meta.SeriesDescription # 0% , 10% etc.
except AttributeError: # meta info is missing
print('vol.meta.SeriesDescription meta information is not available')
pass
except AssertionError: # not correct order, fix
vols = [None] * len(vols2)
for i, vol in enumerate(vols2):
print(vol.meta.SeriesDescription)
phase = int(vol.meta.SeriesDescription[:1])
# use phase to fix order of phases
vols[phase] = vol
# Step B: Crop and Save SSDF
# Load and show first volume: crop with a margin of at least ~25 mm
print()
print('Crop with margins ~25 mm around ROI for the registration algorithm')
stenttype = None # deprecate, not needed
for cropname in cropnames:
savecropvols(vols, basedir, ptcode, ctcode, cropname, stenttype)
# Step C: average diastolic phases
if savedistolicavg:
phases = 50, 10 # use 7 phases from 50% to 10%
saveaveraged(basedir, ptcode, ctcode, cropname, phases)
# Visualize 1 phase
import visvis as vv
if visvol:
vol1 = vols[1]
colormap = {
'r': [(0.0, 0.0), (0.17727272, 1.0)],
'g': [(0.0, 0.0), (0.27272728, 1.0)],
'b': [(0.0, 0.0), (0.34545454, 1.0)],
'a': [(0.0, 1.0), (1.0, 1.0)]
}
fig = vv.figure(2)
vv.clf()
fig.position = 0, 22, 1366, 706
a1 = vv.subplot(111)
a1.daspect = 1, 1, -1
renderStyle = 'mip'
t1 = vv.volshow(vol1, clim=(0, 3000),
renderStyle=renderStyle) # iso or mip
if renderStyle == 'iso':
t1.isoThreshold = 300
t1.colormap = colormap
a1 = vv.volshow2(vol1, clim=(-500, 500), renderStyle=renderStyle)
vv.xlabel('x'), vv.ylabel('y'), vv.zlabel('z')
vv.title('One volume at 10\% procent of cardiac cycle')
if visdynamic:
from lspeas.utils.vis import showVolPhases
showVol = 'mip'
t = showVolPhases(basedir,
vols2,
showVol=showVol,
mipIsocolor=True,
isoTh=310,
clim=(60, 3000),
slider=True)
return vols
def __init__(self, dicom_basedir, ptcode, ctcode, basedir):
import imageio
#import easygui
from stentseg.utils.datahandling import loadvol
from stentseg.utils.datahandling import savecropvols, saveaveraged
## Select base directory for LOADING DICOM data
#dicom_basedir = easygui.diropenbox()
print('DICOM Path = ', dicom_basedir)
#ctcode = '12months' # 'pre', 'post_x', '12months'
stenttype = 'nellix'
## Select base directory to SAVE SSDF
#basedir = easygui.diropenbox()
print('Base Path = ', basedir)
# Set which crops to save
cropnames = ['prox'] #,'stent'] # ['ring'] or ['ring','stent'] or ..
#===============================================================================
## Step A: read single volumes to get vols:
# folder1 = '10%'
# folder2 = '60%'
# vol1 = imageio.volread(os.path.join(dicom_basedir, folder1), 'dicom')
# vol2 = imageio.volread(os.path.join(dicom_basedir, folder2), 'dicom')
# print( )
#
# if vol1.meta.SeriesDescription[:2] < vol2.meta.SeriesDescription[:2]:
# vols4078 = [vol1,vol2]
# else:
# vols4078 = [vol2,vol1]
#
# vols = vols4078.copy()
#
# for vol in vols:
# vol.meta.PatientName = ptcode # anonimyze
# vol.meta.PatientID = 'anonymous'
# print(vol.meta.SeriesDescription,'-', vol.meta.sampling)
#===============================================================================
##Orginele code
#===============================================================================
#
# folder1 = '40% iDose'
# folder2 = '78 iDose'
# vol1 = imageio.volread(os.path.join(dicom_basedir, folder1), 'dicom')
# vol2 = imageio.volread(os.path.join(dicom_basedir, folder2), 'dicom')
# print( )
#
# if vol1.meta.SeriesDescription[:2] < vol2.meta.SeriesDescription[:2]:
# vols4078 = [vol1,vol2]
# else:
# vols4078 = [vol2,vol1]
#
# vols = vols4078.copy()
#
# for vol in vols:
# vol.meta.PatientName = ptcode # anonimyze
# vol.meta.PatientID = 'anonymous'
# print(vol.meta.SeriesDescription,'-', vol.meta.sampling)
#===============================================================================
## Step A: read 10 volumes to get vols
# Deze zoekt alle mappen en dat zijn er dus 10 maar niet in de goede volgorde
vols2 = [
vol2 for vol2 in imageio.get_reader(dicom_basedir, 'DICOM', 'V')
]
vols = [None] * len(vols2)
for i, vol in enumerate(vols2):
# print(vol.meta.sampling)
print(vol.meta.SeriesDescription)
phase = int(vol.meta.SeriesDescription[:1])
# use phase to fix order of phases
vols[phase] = vol
#vols[phase].meta.ImagePositionPatient = (0.0,0.0,0.0)
for i, vol in enumerate(
vols): #wat ik heb veranderd is i, en enumerate()
print(vol.meta.SeriesDescription)
assert vol.shape == vols[0].shape
assert str(i * 10) in vol.meta.SeriesDescription # 0% , 10% etc.
## Step B: Crop and Save SSDF
# 1 of 2 cropnames opgeven voor opslaan 1 of 2 crpos.
# Het eerste volume wordt geladen in MIP, crop met marges van minimaal 30 mm
for cropname in cropnames:
savecropvols(vols, basedir, ptcode, ctcode, cropname, stenttype)
# saveaveraged(basedir, ptcode, ctcode, cropname, range(0,100,10))
## Visualize result
#s1 = loadvol(basedir, ptcode, ctcode, cropnames[0], what ='10avgreg')
#s2 = loadvol(basedir, ptcode, ctcode, cropnames[0], what ='10phases')
s1 = loadvol(basedir, ptcode, ctcode, cropnames[0], what='phases')
#s2 = loadvol(basedir, ptcode, ctcode, cropnames[0], what = 'avg010')
#vol1 = s1.vol
vol1 = s1.vol40
# Visualize and compare
colormap = {
'r': [(0.0, 0.0), (0.17727272, 1.0)],
'g': [(0.0, 0.0), (0.27272728, 1.0)],
'b': [(0.0, 0.0), (0.34545454, 1.0)],
'a': [(0.0, 1.0), (1.0, 1.0)]
}
import visvis as vv
fig = vv.figure(1)
vv.clf()
fig.position = 0, 22, 1366, 706
a1 = vv.subplot(111)
a1.daspect = 1, 1, -1
# t1 = vv.volshow(vol1, clim=(0, 3000), renderStyle='iso') # iso or mip
# t1.isoThreshold = 600 # stond op 400 maar je moet hoger zetten als je alleen stent wil
# t1.colormap = colormap
a1 = vv.volshow2(vol1, clim=(-500, 1500), renderStyle='mip')
vv.xlabel('x'), vv.ylabel('y'), vv.zlabel('z')
# vv.title('One volume at %i procent of cardiac cycle' % phase )
vv.title('Vol40')
# Step B: Crop and Save SSDF
vols = [vol]
for cropname in cropnames:
savecropvols(vols, ssdf_basedir, ptcode, ctcode, cropname, stenttype)
## Visualize result
s1 = loadvol(ssdf_basedir, ptcode, ctcode, cropnames[0], what ='phase')
vol = s1.vol
# Visualize and compare
colormap = {'r': [(0.0, 0.0), (0.17727272, 1.0)],
'g': [(0.0, 0.0), (0.27272728, 1.0)],
'b': [(0.0, 0.0), (0.34545454, 1.0)],
'a': [(0.0, 1.0), (1.0, 1.0)]}
import visvis as vv
fig = vv.figure(1); vv.clf()
fig.position = 0, 22, 1366, 706
a = vv.gca()
a.daspect = 1,1,-1
t1 = vv.volshow(vol, clim=(0, 2500), renderStyle='iso') # iso or mip
t1.isoThreshold = 400
t1.colormap = colormap
t2 = vv.volshow2(vol, clim=(-550, 500))
vv.xlabel('x'), vv.ylabel('y'), vv.zlabel('z')
# vv.title('One volume at %i procent of cardiac cycle' % phase )
vv.title('Static CT Volume' )
def showModelsStatic(ptcode,codes, vols, ss, mm, vs, showVol, clim, isoTh, clim2,
clim2D, drawMesh=True, meshDisplacement=True, drawModelLines=True,
showvol2D=False, showAxis=False, drawVessel=False, vesselType=1,
meshColor=None, **kwargs):
""" show one to four models in multipanel figure.
Input: arrays of codes, vols, ssdfs; params from show_models_static
Output: axes, colorbars
"""
# init fig
f = vv.figure(1); vv.clf()
# f.position = 0.00, 22.00, 1920.00, 1018.00
mw = 5
if drawMesh == True:
lc = 'w'
meshColor = meshColor
else:
lc = 'g'
# create subplots
if isinstance(codes, str): # if 1 ctcode, otherwise tuple of strings
a1 = vv.subplot(111)
axes = [a1]
elif codes == (codes[0],codes[1]):
a1 = vv.subplot(121)
a2 = vv.subplot(122)
axes = [a1,a2]
elif codes == (codes[0],codes[1], codes[2]):
a1 = vv.subplot(131)
a2 = vv.subplot(132)
a3 = vv.subplot(133)
axes = [a1,a2,a3]
elif codes == (codes[0],codes[1], codes[2], codes[3]):
a1 = vv.subplot(141)
a2 = vv.subplot(142)
a3 = vv.subplot(143)
a4 = vv.subplot(144)
axes = [a1,a2,a3,a4]
elif codes == (codes[0],codes[1], codes[2], codes[3], codes[4]):
a1 = vv.subplot(151)
a2 = vv.subplot(152)
a3 = vv.subplot(153)
a4 = vv.subplot(154)
a5 = vv.subplot(155)
axes = [a1,a2,a3,a4,a5]
else:
a1 = vv.subplot(111)
axes = [a1]
for i, ax in enumerate(axes):
ax.MakeCurrent()
vv.xlabel('x (mm)');vv.ylabel('y (mm)');vv.zlabel('z (mm)')
vv.title('Model for LSPEAS %s - %s' % (ptcode[7:], codes[i]))
t = show_ctvolume(vols[i], ss[i].model, axis=ax, showVol=showVol, clim=clim, isoTh=isoTh, **kwargs)
label = pick3d(ax, vols[i])
if drawModelLines == True:
ss[i].model.Draw(mc='b', mw = mw, lc=lc)
if showvol2D:
for i, ax in enumerate(axes):
t2 = vv.volshow2(vols[i], clim=clim2D, axes=ax)
cbars = [] # colorbars
if drawMesh:
for i, ax in enumerate(axes):
m = vv.mesh(mm[i], axes=ax)
if meshDisplacement:
m.clim = clim2
m.colormap = vv.CM_JET #todo: use colormap Viridis or Magma as JET is not linear (https://bids.github.io/colormap/)
cb = vv.colorbar(ax)
cbars.append(cb)
elif meshColor is not None:
if len(meshColor) == 1:
m.faceColor = meshColor[0] # (0,1,0,1)
else:
m.faceColor = meshColor[i]
else:
m.faceColor = 'g'
if drawVessel:
for i, ax in enumerate(axes):
v = showVesselMesh(vs[i], ax, type=vesselType)
for ax in axes:
ax.axis.axisColor = 1,1,1
ax.bgcolor = 25/255,25/255,112/255 # midnightblue
# http://cloford.com/resources/colours/500col.htm
ax.daspect = 1, 1, -1 # z-axis flipped
ax.axis.visible = showAxis
# set colorbar position
for cbar in cbars:
p1 = cbar.position
cbar.position = (p1[0], 20, p1[2], 0.98) # x,y,w,h
# bind rotate view and view presets [1,2,3,4,5]
f = vv.gcf()
f.eventKeyDown.Bind(lambda event: _utils_GUI.RotateView(event,axes,axishandling=False) )
f.eventKeyDown.Bind(lambda event: _utils_GUI.ViewPresets(event,axes) )
return axes, cbars
# calculate B field at given points
B = sol.CalculateB(points=points)
Babs = np.linalg.norm(B, axis=1)
# draw results
# prepare axes
a = vv.gca()
a.cameraType = '3d'
a.daspectAuto = False
vol = Babs.reshape(grid.shape[1:]).T
vol = np.clip(vol, 0.002, 0.01)
vol = vv.Aarray(vol,
sampling=(resolution, resolution, resolution),
origin=(volume_corner1[2], volume_corner1[1],
volume_corner1[0]))
# set labels
vv.xlabel('x axis')
vv.ylabel('y axis')
vv.zlabel('z axis')
sol.vv_PlotWires()
t = vv.volshow2(vol, renderStyle='mip', cm=vv.CM_JET)
vv.colorbar()
app = vv.use()
app.Run()
s3 = loadvol(basedir, ptcode, ctcode, cropname, 'phases')
vol0ori = s3.vol0
# todo: backward/forward based on how deforms were obtained??
# deforms was obtained as backward, from original phases to mean volume avgreg
deform = pirt.DeformationFieldBackward(deforms[0])
# vol2 = pirt.interp.deform_backward(vol, deforms[0]) # te low level, gebruikt awarp niet
vol2 = deform.inverse().as_backward().apply_deformation(
vol0ori) # gebruikt pirt deformation.py
vv.figure(1)
vv.clf()
a1 = vv.subplot(131)
t1 = vv.volshow(vol)
a1.daspect = (1, 1, -1)
vv.title('vol average of cardiac cycle')
# vv.figure(2); vv.clf()
a2 = vv.subplot(132)
t2 = vv.volshow(vol2)
a2.daspect = (1, 1, -1)
vv.title('vol 0 deformed to avg volume')
# vv.figure(3); vv.clf()
a3 = vv.subplot(133)
t3 = vv.volshow2((vol2 - vol), clim=(-500, 500))
a3.daspect = (1, 1, -1)
vv.title('difference')
a1.camera = a2.camera = a3.camera
t1.clim = t2.clim = 0, 2000
t3.clim = -500, 500
import numpy as np
import argparse
import visvis as vv
app = vv.use()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Plot the disparity space image (DSI) using 3D slices')
parser.add_argument(
'-i',
'--input',
default='dsi.npy',
type=str,
help='path to the NPY file containing the DSI (default: dsi.npy)')
args = parser.parse_args()
a = vv.gca()
a.daspect = 1, -1, 1
a.daspectAuto = False
vol = np.load(args.input)
# Reorder axis so that the Z axis points forward instead of up
vol = np.swapaxes(vol, 0, 1)
vol = np.flip(vol, axis=0)
t = vv.volshow2(vol)
t.colormap = vv.CM_HOT
app.Run()
