def run(args):
try:
import nibabel
nii = nibabel.load(args.inp)
# Nifti data is supposed to be in RAS orientation.
# For Nifti files that violate the standard, the reorient string can be used to correct the orientation.
if isinstance(args.reorient, str):
nii = nit.reorient(nii, args.reorient)
nii = nibabel.as_closest_canonical(nii)
hdr = nii.get_header()
q = hdr.get_best_affine()
img = numpy.squeeze(nii.get_data())
dims = img.shape
f = int(args.factor)
outFile = args.out
if op.isfile(outFile):
print 'Skipping downsample by factor {}. Output file {} exists.'.format(
f, outFile)
else:
(img, q) = nit.downsample3d(img, q, f)
if img.shape[-1] == 3:
img3 = img
img = numpy.zeros(img3.shape[:-1],
dtype=[('R', 'u1'), ('G', 'u1'),
('B', 'u1')])
img['R'] = img3[:, :, :, 0]
img['G'] = img3[:, :, :, 1]
img['B'] = img3[:, :, :, 2]
nii = nibabel.Nifti1Image(img, q)
# nii.set_sform(None,0) # set the sform code to 0 'unknown', to make it viewable in MRIcron
nibabel.nifti1.save(nii, outFile)
print 'Saved downsampled image to "{}" '.format(outFile)
result = {
'shape': (numpy.array(dims) / f).tolist(),
'filesize': op.getsize(outFile)
}
report.success(result)
except:
report.fail(__file__)
def main(self,input_nii,slicedir,outFolder,pctile,origin,colormap,reorient,replace,boundingbox_bgcolor,count_pixels):
nii = nibabel.load(input_nii)
dir2dim = {'x':0,'y':1,'z':2,'0':0,'1':1,'2':2,'coronal':0,'saggital':1,'horizontal':2,'axial':2}
sliceDim = dir2dim[slicedir.lower()]
# Nifti data is supposed to be in RAS orientation.
# For Nifti files that violate the standard, the reorient string can be used to correct the orientation.
if reorient:
nii = nit.reorient(nii,reorient)
hdr = nii.get_header()
q = hdr.get_best_affine();
ornt = nibabel.io_orientation(q)
print('The orientation is: {}'.format(ornt))
dims0 = [int(d) for d in nii.shape if d>1]
dims = list(dims0)
for i,d in enumerate(ornt):
dims[i] = dims0[int(d[0])]
print('The dimensions are: {}'.format(dims))
numSlices = dims[sliceDim];
baseName = op.basename(input_nii)
baseName = re.sub('.gz$', '',baseName)
baseName = re.sub('.nii$', '',baseName)
if not op.exists(outFolder):
os.makedirs(outFolder)
print('Created output folder "{}".'.format(outFolder))
rgbMode = False
img_dtype = nii.get_data_dtype();
if len(dims)==4 and dims[3]==3:
rgbMode = True
elif img_dtype.names:
if len(img_dtype.names)==3:
rgbMode = 'record'
rescale = pctile is not None
fmt = 'png'
if rescale or rgbMode:
fmt = 'jpg'
filePattern = baseName+'_%04d.{}'.format(fmt)
filePattern_py = filePattern.replace('_%04d','_{:04d}')
if origin:
try:
if origin.startswith('['):
ijk0 = json.loads(origin)
elif origin == 'center':
dims = hdr.get_data_shape()
ijk0 = [dims[0]/2,dims[1]/2,dims[2]/2]
q = hdr.get_best_affine()
q[0:3,3] = -q[0:3,0:3].dot(ijk0)
hdr.set_sform(q)
except:
raise Exception('Invalid origin "{}".'.format(origin))
# save coordinate system (Right Anterior Superior) information
rasLimits = nit.rasLimits(hdr)
# Some nii files have no origin set (other than i=0, j=0, k=0)
# Use the origin parameter to overrule this.
##if origin == 'center':
## def ctr(x1,x2):
## w=x2-x1
## return -w/2,w/2
## rasLimits = [ctr(xx[0],xx[1]) for xx in rasLimits]
with open(op.join(outFolder,'raslimits.json'), 'w') as fp:
json.dump(rasLimits,fp)
slicePos = (rasLimits[sliceDim][0] + (numpy.arange(0.0,dims[sliceDim])+0.5)*(rasLimits[sliceDim][1]-rasLimits[sliceDim][0])/dims[sliceDim]).tolist()
with open(op.join(outFolder,'slicepos.json'), 'w') as fp:
json.dump(slicePos,fp)
# quit if ALL slices already exist
if not replace:
done = True
for i in range(0,numSlices):
outFile = filePattern_py.format(i)
fullFile = op.join(outFolder,outFile)
if not op.exists(fullFile):
done = False
break
if done:
return FancyDict(
filePattern = op.join(outFolder,filePattern),
rasLimits = rasLimits
)
# load image, it is needed
img = nii.get_data()
img = nibabel.apply_orientation(img,ornt)
img = numpy.squeeze(img)
if rgbMode == 'record': img = nit.record2rgb(img)
print('Nifti image loaded, shape "{}",data type "{}"'.format(dims,img.dtype))
maxSlices = 2048;
if numSlices>maxSlices:
raise Exception('Too many slices (more than '+str(maxSlices)+')');
if not rgbMode:
minmax = nit.get_limits(img)
if rescale:
minmax = nit.get_limits(img,pctile)
print('minmax {}, rescale {}'.format(minmax,rescale))
index2rgb = nit.parse_colormap(colormap,minmax)
if isinstance(index2rgb,dict):
rgbLen = len(index2rgb[index2rgb.keys()[0]])
else:
rgbLen = len(index2rgb[0])
# save index2rgb
if not rescale:
if isinstance(index2rgb,dict):
index2rgb_hex = {index:'{:02X}{:02X}{:02X}'.format(rgb[0],rgb[1],rgb[2]) for (index,rgb) in index2rgb.iteritems()}
else:
index2rgb_hex = ['{:02X}{:02X}{:02X}'.format(rgb[0],rgb[1],rgb[2]) for rgb in index2rgb]
with open(op.join(outFolder,'index2rgb.json'), 'w') as fp:
json.dump(index2rgb_hex,fp)
elif rgbMode:
grayscale = img.dot(numpy.array([0.2989,0.5870,0.1140]))
minmax = nit.get_limits(grayscale)
if rescale:
minmax = nit.get_limits(grayscale,pctile)
rescale = True
rgbLen = 3
index2rgb = False
else:
rescale = False
rgbLen = 3
index2rgb = False
bbg = boundingbox_bgcolor
if bbg is '': bbg = False
if bbg:
boundingBox = {}
boundingBoxFile = op.join(outFolder,'boundingbox.json')
if op.exists(boundingBoxFile):
with open(boundingBoxFile, 'r') as fp:
boundingBox = json.load(fp)
pxc = count_pixels
if pxc:
pixCount = {};
pixCountFile = op.join(outFolder,'pixcount.json')
if op.exists(pixCountFile):
with open(pixCountFile, 'r') as fp:
pixCount = json.load(fp)
for i in range(0,numSlices):
outFile = filePattern_py.format(i)
fullFile = op.join(outFolder,outFile)
if op.exists(fullFile):
if i==0:
print('image {}{} already exists as {}-file "{}".'.format(sliceDim,i,fmt,fullFile))
if not replace:
continue
slc = nit.get_slice(img,sliceDim,i)
if pxc:
labels = numpy.unique(slc)
cnt = {}
for b in labels:
cnt[str(b)] = numpy.count_nonzero(slc == b)
pixCount[i] = cnt
if index2rgb:
slc = nit.slice2rgb(slc,index2rgb,rescale,minmax[0],minmax[1])
elif rescale:
slc = nit.rgbslice_rescale(slc,minmax[0],minmax[1])
# Save image
ans = scipy.misc.toimage(slc).save(op.join(outFolder,outFile))
if bbg:
if(bbg == "auto"):
# do this only once
bgColor = nit.autoBackgroundColor(slc)
print('boundingbox auto bgColor is {}'.format(bgColor))
else:
bgColor = nit.hex2rgb(bgg)
mask = nit.imageMask(slc,[bgColor])
print 'mask shape {} {}'.format(bgColor,mask.shape)
## bounding box
nonzero = numpy.argwhere(mask)
#print 'nonzero {}'.format(nonzero)
if nonzero.size>0:
lefttop = nonzero.min(0)[::-1] # because y=rows and x=cols
rightbottom = nonzero.max(0)[::-1]
bb = lefttop.tolist()
bb.extend(rightbottom-lefttop+(1,1))
boundingBox[i] = bb
if i==0:
print('image {}{} saved to {}-file "{}".'.format(sliceDim,i,fmt,fullFile))
if bbg:
if len(boundingBox)>0:
bb0 = boundingBox.itervalues().next()
xyMin = [bb0[0],bb0[1]]
xyMax = [bb0[0]+bb0[2],bb0[1]+bb0[3]]
for bb in boundingBox.itervalues():
if bb[0]<xyMin[0]: xyMin[0] = bb[0]
if bb[1]<xyMin[1]: xyMin[1] = bb[1]
if bb[0]+bb[2]>xyMax[0]: xyMax[0] = bb[0]+bb[2]
if bb[1]+bb[3]>xyMax[1]: xyMax[1] = bb[1]+bb[3]
boundingBox['combined'] = [xyMin[0],xyMin[1],xyMax[0]-xyMin[0],xyMax[1]-xyMin[1]]
with open(boundingBoxFile, 'w') as fp:
json.dump(boundingBox,fp)
if pxc:
with open(pixCountFile, 'w') as fp:
json.dump(pixCount,fp)
return FancyDict(
filePattern=op.join(outFolder,filePattern),
rasLimits=rasLimits
)
def run(args):
try:
# inspired by /my/github/3dbar/lib/pymodules/python2.6/bar/rec/default_pipeline.xml
if op.exists(args.out):
if not args.replace:
print('Skipping creation of image "{}", it already exists.'.
format(args.out))
result = {'Status': 'Skipped'}
report.success(result)
return
nii = nibabel.load(args.inp)
# Nifti data is supposed to be in RAS orientation.
# For Nifti files that violate the standard, the reorient string can be used to correct the orientation.
if isinstance(args.reorient, str):
nii = nit.reorient(nii, args.reorient)
nii = nibabel.as_closest_canonical(nii)
# numpy.array() will copy image to 'clean' data buffer
img = numpy.squeeze(nii.get_data())
if (args.bg_color == "auto"):
bgColor = img[0, 0, 0]
else:
bgColor = int(args.bg_color)
mask = nit.imageMask(img, [bgColor])
img[mask] = 8
if img.dtype != numpy.uint8:
img = img.astype(numpy.uint8)
#if numpy.prod(img.shape) > 512*512*512:
hdr = nii.get_header()
q = hdr.get_best_affine()
scalef = [1 + v / 512 for v in img.shape]
if any(numpy.array(scalef) > 1):
print('Downsampling by a factor {} to reduce memory load'.format(
scalef))
print 'Best affine before downsampling: {}'.format(q)
(img, q) = nit.downsample3d(img, q.tolist(), scalef)
print 'Best affine after downsampling: {}'.format(q)
# image zero-padding
bb = nit.get_boundingbox(img, 2)
w = 2
padded = numpy.zeros((bb[3] + 2 * w, bb[4] + 2 * w, bb[5] + 2 * w),
numpy.uint8,
order='F')
padded[w:-w, w:-w,
w:-w] = img[bb[0]:bb[0] + bb[3], bb[1]:bb[1] + bb[4],
bb[2]:bb[2] + bb[5]]
img = padded
dims = img.shape
spacing = q.diagonal()[0:3]
print('Origin before {}'.format(q[0:3, 3]))
# origin adjusted for zero-padding
q[0:3, 3] = apply_affine(q, [bb[0] - w, bb[1] - w, bb[2] - w])
origin = q[0:3, 3]
print('Origin after {}'.format(origin))
doRender = args.render
# create a rendering window and renderer
ren = vtk.vtkRenderer()
if doRender:
renWin = vtk.vtkRenderWindow()
iren = vtk.vtkRenderWindowInteractor()
iren.SetRenderWindow(renWin)
else:
renWin = vtk.vtkRenderWindow()
renWin.SetOffScreenRendering(1)
renWin.AddRenderer(ren)
WIDTH = 800
HEIGHT = 600
renWin.SetSize(WIDTH, HEIGHT)
# import data
dataImporter = vtk.vtkImageImport()
dataImporter.SetImportVoidPointer(img)
dataImporter.SetDataScalarTypeToUnsignedChar()
dataImporter.SetNumberOfScalarComponents(1)
dataImporter.SetDataExtent(0, dims[0] - 1, 0, dims[1] - 1, 0,
dims[2] - 1)
dataImporter.SetWholeExtent(0, dims[0] - 1, 0, dims[1] - 1, 0,
dims[2] - 1)
# new since October 2015
dataImporter.SetDataSpacing(spacing)
dataImporter.SetDataOrigin(origin)
print 'ORIGIN: {}'.format(dataImporter.GetDataOrigin())
print 'Dimensions {}'.format(dims)
# create iso surface
iso = vtk.vtkMarchingCubes()
iso.SetInputConnection(dataImporter.GetOutputPort())
iso.ComputeNormalsOff()
iso.SetValue(0, 3)
iso.Update()
#DEBUG
#from vtk.util.numpy_support import vtk_to_numpy
#iso.Update()
#output = iso.GetOutput()
#A = vtk_to_numpy(output.GetPoints().GetData())
#print 'iso Output {}'.format(A)
tf = vtk.vtkTriangleFilter()
tf.SetInput(iso.GetOutput())
# apply smoothing
smth = vtk.vtkSmoothPolyDataFilter()
smth.SetRelaxationFactor(0.5)
smth.SetInput(tf.GetOutput())
# reduce triangles
qc = vtk.vtkQuadricClustering()
qc.SetNumberOfXDivisions(90)
qc.SetNumberOfYDivisions(90)
qc.SetNumberOfZDivisions(90)
qc.SetInput(smth.GetOutput())
# map data
volumeMapper = vtk.vtkPolyDataMapper()
volumeMapper.SetInput(qc.GetOutput())
volumeMapper.ScalarVisibilityOff()
# actor
act = vtk.vtkActor()
act.SetMapper(volumeMapper)
# assign actor to the renderer
ren.AddActor(act)
bgColor = [0, 0, 0]
ren.SetBackground(bgColor)
camera = ren.GetActiveCamera()
camera.SetViewUp(0, 0, 1)
camera.SetFocalPoint(dims[0] / 2, dims[1] / 2, dims[2] / 2)
camera.SetPosition(dims[0], dims[1] / 2, dims[2] / 2)
camera.ParallelProjectionOn()
camera.Yaw(180)
ren.SetActiveCamera(camera)
ren.ResetCamera()
ren.ResetCameraClippingRange()
ren.GetActiveCamera().Zoom(1.5)
if args.out:
take_screenshot(args.out, ren)
im = Image.open(args.out)
A = numpy.array(im)
mask = nit.imageMask(A, [bgColor])
bgColor = [238, 221, 170]
A[numpy.invert(mask)] = bgColor
nonzero = numpy.argwhere(mask)
if nonzero.size > 0:
lefttop = [v if v > 0 else 0 for v in (nonzero.min(0) - 4)]
rightbottom = [
v if v < A.shape[i] else A.shape[i]
for i, v in enumerate(nonzero.max(0) + 4)
]
A = A[lefttop[0]:rightbottom[0] + 1,
lefttop[1]:rightbottom[1] + 1]
im = Image.fromarray(A)
sz = numpy.array(im.size, 'double')
sz = numpy.ceil((128.0 / sz[1]) * sz)
print 'sz {}'.format(sz)
im.thumbnail(sz.astype(int), Image.ANTIALIAS)
im = im.convert('P', palette=Image.ADAPTIVE, colors=256)
palette = numpy.array(im.getpalette()).reshape(256, 3)
match = numpy.where(numpy.all(palette == bgColor, axis=1))
im.save(args.out, transparency=match[0])
print('Created image "{}"'.format(args.out))
if args.x3d:
vtkX3DExporter = vtk.vtkX3DExporter()
vtkX3DExporter.SetInput(renWin)
vtkX3DExporter.SetFileName(args.x3d)
vtkX3DExporter.Write()
if args.stl:
stlWriter = vtk.vtkSTLWriter()
stlWriter.SetInputConnection(qc.GetOutputPort())
stlWriter.SetFileTypeToBinary()
stlWriter.SetFileName(args.stl)
stlWriter.Write()
# enable user interface interactor
if doRender:
iren.Initialize()
renWin.Render()
pos = camera.GetPosition()
ornt = camera.GetOrientation()
print 'Camera position; orientation {};{}'.format(pos, ornt)
iren.Start()
result = {'Status': 'Done'}
report.success(result)
except:
report.fail(__file__)