def resampleDataInverse(sink,
source=None,
dataSizeSource=None,
orientation=None,
resolutionSource=(4.0625, 4.0625, 3),
resolutionSink=(25, 25, 25),
processingDirectory=None,
processes=1,
cleanup=True,
verbose=True,
interpolation='linear',
**args):
"""Resample data inversely to :func:`resampleData` routine
Arguments:
sink (str or None): image to be inversly resampled (=sink in :func:`resampleData`)
source (str or array): destination for inversly resmapled image (=source in :func:`resampleData`)
dataSizeSource (tuple or None): target size of the resampled image
orientation (tuple): orientation specified by permuation and change in sign of (1,2,3)
resolutionSource (tuple): resolution of the source image (in length per pixel)
resolutionSink (tuple): resolution of the resampled image (in length per pixel)
processingDirectory (str or None): directory in which to perform resmapling in parallel, None a temporary directry will be created
processes (int): number of processes to use for parallel resampling
cleanup (bool): remove temporary files
verbose (bool): display progress information
interpolation (str): method to use for interpolating to the resmapled image
Returns:
(array or str): data or file name of resampled image
Notes:
* resolutions are assumed to be given for the axes of the intrinsic
orientation of the data and reference as when viewed by matplotlib or ImageJ
* orientation: permuation of 1,2,3 with potential sign, indicating which
axes map onto the reference axes, a negative sign indicates reversal
of that particular axes
* only a minimal set of information to detremine the resampling parameter
has to be given, e.g. dataSizeSource and dataSizeSink
"""
#orientation
orientation = fixOrientation(orientation)
#assume we can read data fully into memory
resampledData = io.readData(sink)
dataSizeSink = resampledData.shape
if isinstance(dataSizeSource, basestring):
dataSizeSource = io.dataSize(dataSizeSource)
dataSizeSource, dataSizeSink, resolutionSource, resolutionSink = resampleDataSize(
dataSizeSource=dataSizeSource,
dataSizeSink=dataSizeSink,
resolutionSource=resolutionSource,
resolutionSink=resolutionSink,
orientation=orientation)
#print (dataSizeSource, dataSizeSink, resolutionSource, resolutionSink )
dataSizeSinkI = orientDataSizeInverse(dataSizeSink, orientation)
#flip axes back and permute inversely
if not orientation is None:
if orientation[0] < 0:
resampledData = resampledData[::-1, :, :]
if orientation[1] < 0:
resampledData = resampledData[:, ::-1, :]
if orientation[2] < 0:
resampledData = resampledData[:, :, ::-1]
#reorient
peri = inverseOrientation(orientation)
peri = orientationToPermuation(peri)
resampledData = resampledData.transpose(peri)
# upscale in z
interpolation = fixInterpolation(interpolation)
resampledDataXY = numpy.zeros(
(dataSizeSinkI[0], dataSizeSinkI[1], dataSizeSource[2]),
dtype=resampledData.dtype)
for i in range(dataSizeSinkI[0]):
if verbose and i % 25 == 0:
print "resampleDataInverse: processing %d/%d" % (i,
dataSizeSinkI[0])
#cv2.resize takes reverse order of sizes !
resampledDataXY[i, :, :] = cv2.resize(
resampledData[i, :, :], (dataSizeSource[2], dataSizeSinkI[1]),
interpolation=interpolation)
# upscale x, y in parallel
if io.isFileExpression(source):
files = source
else:
if processingDirectory == None:
processingDirectory = tempfile.mkdtemp()
files = os.path.join(sink[0], 'resample_\d{4}.tif')
io.writeData(files, resampledDataXY)
nZ = dataSizeSource[2]
pool = multiprocessing.Pool(processes=processes)
argdata = []
for i in range(nZ):
argdata.append((source, fl.fileExpressionToFileName(files, i),
dataSizeSource, interpolation, i, nZ))
pool.map(_resampleXYParallel, argdata)
if io.isFileExpression(source):
return source
else:
data = io.convertData(files, source)
if cleanup:
shutil.rmtree(processingDirectory)
return data
def resampleData(source,
sink=None,
orientation=None,
dataSizeSink=None,
resolutionSource=(4.0625, 4.0625, 3),
resolutionSink=(25, 25, 25),
processingDirectory=None,
processes=1,
cleanup=True,
verbose=True,
interpolation='linear',
**args):
"""Resample data of source in resolution and orientation
Arguments:
source (str or array): image to be resampled
sink (str or None): destination of resampled image
orientation (tuple): orientation specified by permuation and change in sign of (1,2,3)
dataSizeSink (tuple or None): target size of the resampled image
resolutionSource (tuple): resolution of the source image (in length per pixel)
resolutionSink (tuple): resolution of the resampled image (in length per pixel)
processingDirectory (str or None): directory in which to perform resmapling in parallel, None a temporary directry will be created
processes (int): number of processes to use for parallel resampling
cleanup (bool): remove temporary files
verbose (bool): display progress information
interpolation (str): method to use for interpolating to the resmapled image
Returns:
(array or str): data or file name of resampled image
Notes:
* resolutions are assumed to be given for the axes of the intrinsic
orientation of the data and reference as when viewed by matplotlib or ImageJ
* orientation: permuation of 1,2,3 with potential sign, indicating which
axes map onto the reference axes, a negative sign indicates reversal
of that particular axes
* only a minimal set of information to detremine the resampling parameter
has to be given, e.g. dataSizeSource and dataSizeSink
"""
orientation = fixOrientation(orientation)
if isinstance(dataSizeSink, basestring):
dataSizeSink = io.dataSize(dataSizeSink)
#orient actual resolutions onto reference resolution
dataSizeSource = io.dataSize(source)
dataSizeSource, dataSizeSink, resolutionSource, resolutionSink = resampleDataSize(
dataSizeSource=dataSizeSource,
dataSizeSink=dataSizeSink,
resolutionSource=resolutionSource,
resolutionSink=resolutionSink,
orientation=orientation)
dataSizeSinkI = orientDataSizeInverse(dataSizeSink, orientation)
#print dataSizeSource, dataSizeSink, resolutionSource, resolutionSink, dataSizeSinkI
#rescale in x y in parallel
if processingDirectory == None:
processingDirectory = tempfile.mkdtemp()
interpolation = fixInterpolation(interpolation)
nZ = dataSizeSource[2]
pool = multiprocessing.Pool(processes=processes)
argdata = []
for i in range(nZ):
argdata.append(
(source, os.path.join(processingDirectory,
'resample_%04d.tif' % i), dataSizeSinkI,
interpolation, i, nZ, verbose))
#print argdata[i]
pool.map(_resampleXYParallel, argdata)
#rescale in z
fn = os.path.join(processingDirectory, 'resample_%04d.tif' % 0)
data = io.readData(fn)
zImage = numpy.zeros((dataSizeSinkI[0], dataSizeSinkI[1], nZ),
dtype=data.dtype)
for i in range(nZ):
if verbose and i % 10 == 0:
print "resampleData; reading %d/%d" % (i, nZ)
fn = os.path.join(processingDirectory, 'resample_%04d.tif' % i)
zImage[:, :, i] = io.readData(fn)
resampledData = numpy.zeros(dataSizeSinkI, dtype=zImage.dtype)
for i in range(dataSizeSinkI[0]):
if verbose and i % 25 == 0:
print "resampleData: processing %d/%d" % (i, dataSizeSinkI[0])
#resampledImage[:, iImage ,:] = scipy.misc.imresize(zImage[:,iImage,:], [resizedZAxisSize, sagittalImageSize[1]] , interp = 'bilinear');
#cv2.resize takes reverse order of sizes !
resampledData[i, :, :] = cv2.resize(
zImage[i, :, :], (dataSizeSinkI[2], dataSizeSinkI[1]),
interpolation=interpolation)
#resampledData[i ,:, :] = cv2.resize(zImage[i,:, :], (dataSize[1], resizedZSize));
#account for using (z,y,x) array representation -> (y,x,z)
#resampledData = resampledData.transpose([1,2,0]);
#resampledData = resampledData.transpose([2,1,0]);
if cleanup:
shutil.rmtree(processingDirectory)
if not orientation is None:
#reorient
per = orientationToPermuation(orientation)
resampledData = resampledData.transpose(per)
#reverse orientation after permuting e.g. (-2,1) brings axis 2 to first axis and we can reorder there
if orientation[0] < 0:
resampledData = resampledData[::-1, :, :]
if orientation[1] < 0:
resampledData = resampledData[:, ::-1, :]
if orientation[2] < 0:
resampledData = resampledData[:, :, ::-1]
#bring back from y,x,z to z,y,x
#resampledImage = resampledImage.transpose([2,0,1]);
if verbose:
print "resampleData: resampled data size: " + str(resampledData.shape)
if sink == []:
if io.isFileExpression(source):
sink = os.path.split(source)
sink = os.path.join(sink[0], 'resample_\d{4}.tif')
elif isinstance(source, basestring):
sink = source + '_resample.tif'
else:
raise RuntimeError(
'resampleData: automatic sink naming not supported for non string source!'
)
return io.writeData(sink, resampledData)
def resampleDataInverse(sink, source = None, dataSizeSource = None, orientation = None, resolutionSource = (4.0625, 4.0625, 3), resolutionSink = (25, 25, 25),
processingDirectory = None, processes = 1, cleanup = True, verbose = True, interpolation = 'linear', **args):
"""Resample data inversely to :func:`resampleData` routine
Arguments:
sink (str or None): image to be inversly resampled (=sink in :func:`resampleData`)
source (str or array): destination for inversly resmapled image (=source in :func:`resampleData`)
dataSizeSource (tuple or None): target size of the resampled image
orientation (tuple): orientation specified by permuation and change in sign of (1,2,3)
resolutionSource (tuple): resolution of the source image (in length per pixel)
resolutionSink (tuple): resolution of the resampled image (in length per pixel)
processingDirectory (str or None): directory in which to perform resmapling in parallel, None a temporary directry will be created
processes (int): number of processes to use for parallel resampling
cleanup (bool): remove temporary files
verbose (bool): display progress information
interpolation (str): method to use for interpolating to the resmapled image
Returns:
(array or str): data or file name of resampled image
Notes:
* resolutions are assumed to be given for the axes of the intrinsic
orientation of the data and reference as when viewed by matplotlib or ImageJ
* orientation: permuation of 1,2,3 with potential sign, indicating which
axes map onto the reference axes, a negative sign indicates reversal
of that particular axes
* only a minimal set of information to detremine the resampling parameter
has to be given, e.g. dataSizeSource and dataSizeSink
"""
#orientation
orientation = fixOrientation(orientation);
#assume we can read data fully into memory
resampledData = io.readData(sink);
dataSizeSink = resampledData.shape;
if isinstance(dataSizeSource, basestring):
dataSizeSource = io.dataSize(dataSizeSource);
dataSizeSource, dataSizeSink, resolutionSource, resolutionSink = resampleDataSize(dataSizeSource = dataSizeSource, dataSizeSink = dataSizeSink,
resolutionSource = resolutionSource, resolutionSink = resolutionSink, orientation = orientation);
#print (dataSizeSource, dataSizeSink, resolutionSource, resolutionSink )
dataSizeSinkI = orientDataSizeInverse(dataSizeSink, orientation);
#flip axes back and permute inversely
if not orientation is None:
if orientation[0] < 0:
resampledData = resampledData[::-1, :, :];
if orientation[1] < 0:
resampledData = resampledData[:, ::-1, :];
if orientation[2] < 0:
resampledData = resampledData[:, :, ::-1];
#reorient
peri = inverseOrientation(orientation);
peri = orientationToPermuation(peri);
resampledData = resampledData.transpose(peri);
# upscale in z
interpolation = fixInterpolation(interpolation);
resampledDataXY = numpy.zeros((dataSizeSinkI[0], dataSizeSinkI[1], dataSizeSource[2]), dtype = resampledData.dtype);
for i in range(dataSizeSinkI[0]):
if verbose and i % 25 == 0:
print "resampleDataInverse: processing %d/%d" % (i, dataSizeSinkI[0])
#cv2.resize takes reverse order of sizes !
resampledDataXY[i ,:, :] = cv2.resize(resampledData[i,:,:], (dataSizeSource[2], dataSizeSinkI[1]), interpolation = interpolation);
# upscale x, y in parallel
if io.isFileExpression(source):
files = source;
else:
if processingDirectory == None:
processingDirectory = tempfile.mkdtemp();
files = os.path.join(sink[0], 'resample_\d{4}.tif');
io.writeData(files, resampledDataXY);
nZ = dataSizeSource[2];
pool = multiprocessing.Pool(processes=processes);
argdata = [];
for i in range(nZ):
argdata.append( (source, fl.fileExpressionToFileName(files, i), dataSizeSource, interpolation, i, nZ) );
pool.map(_resampleXYParallel, argdata);
if io.isFileExpression(source):
return source;
else:
data = io.convertData(files, source);
if cleanup:
shutil.rmtree(processingDirectory);
return data;
def stitchData(xmlPlacementFile, resultPath, algorithm = None, resolutions = None, form = None, channel = None, subRegion = None, bitDepth = None, blockSize = None, cleanup = True, compress = False):
"""Runs the final stiching step of TeraSticher
Arguments:
xmlPlacementFile (str or None): the xml placement descriptor
resultPath (str): result path, file name or file expression for the stiched data
algorithm (str or None): optional algorithm to use for placement:
'NOBLEND' for no blending
'SINBLEND' for sinusoidal blending
resolutions (tuple or None): the different resolutions to produce
form (str or None): the output form, if None determined automatically
channel (str or None): the channels to use, 'R', 'G', 'B' or 'all'
subRegion (tuple or None): optional sub region in the form ((xmin,xmax),(ymin,ymax), (zmin, zmax))
bitDepth (int or None): the pits per pixel to use, default is 8
blockSize (tuple): the sizes of various blocks to save stiched image into
cleanup (bool): if True delete the TeraSticher file structure
compress (bool): if True compress final tif images
Returns:
str : the result path or file name of the stiched data
See also:
`TeraStitcher project step <https://github.com/abria/TeraStitcher/wiki/Step-6:-Merge>`_.
"""
checkSticherInitialized();
global TeraStitcherBinary;
cmd = TeraStitcherBinary + ' --merge --imout_format="tif" ';
cmd = cmd + '--projin="' + xmlPlacementFile + '" ';
if len(resultPath) > 3 and resultPath[-4:] == '.tif':
if io.isFileExpression(resultPath, check = False):
form = 'TiledXY|2Dseries';
resultPath, filename = os.path.split(resultPath);
else:
form = 'TiledXY|3Dseries';
resultPath, filename = os.path.split(resultPath);
else:
filename = None;
cmd = cmd + '--volout="' + resultPath + '" ';
if algorithm is not None:
cmd = cmd + ' --algorithm="' + algorithm + '" ';
if resolutions is not None:
cmd = cmd + '--resolutions="'
for r in sorted(resolutions):
cmd = cmd + str(r);
cmd = cmd + ' ';
if form is not None:
cmd = cmd + '--volout_plugin="' + form + '" ';
if channel is not None:
cmd = cmd + '--imin_channel="' + channel + '" ';
if subRegion is not None:
sns = (('--R0=', '--R1='), ('--C0=', '--C1='), ('--D0=', '--D1-'));
for d in range(3):
for m in range(2):
if subRegion[d][m] is not None:
cmd = cmd + sns[d][m] + str(subRegion[d][m]) + ' ';
if blockSize is not None:
bs = ('--slicewidth=', '--sliceheight=', '--slicedepth=');
for d in range(3):
if blockSize[d] is not None:
cmd = cmd + bs[d] + str(blockSize[d]) + ' ';
if bitDepth is not None:
cmd = cmd + '--imout_depth=' + str(bitDepth) + ' ';
if not compress:
cmd = cmd + '--libtiff_uncompress ';
#print resultPath
io.createDirectory(resultPath, split = False)
print 'running: ' + cmd;
res = os.system(cmd);
if res != 0:
raise RuntimeError('stitchData: failed executing: ' + cmd);
if filename is not None:
if io.isFileExpression(filename, check = False): # convert list of files in TeraSticher from
#TODO: multiple resolutions
basedir = max(glob.glob(os.path.join(resultPath, '*')), key = os.path.getmtime);
if cleanup:
moveTeraStitcherStackToFileList(basedir, os.path.join(resultPath, filename), deleteDirectory=True);
#shutil.rmtree(basedir);
else:
copyTeraStitcherStackToFileList(basedir, os.path.join(resultPath, filename));
else: # single file in TeraSticher folder
#get most recent created file
#TODO: test if this works
imgfile = max(glob.glob(os.path.join(resultPath, '*/*/*/*')), key = os.path.getmtime);
filename = os.path.join(resultPath, filename);
os.rename(imgfile, filename);
if cleanup:
imgpath = os.path.sep.join(imgfile.split(os.path.sep)[:-3]);
shutil.rmtree(imgpath)
return filename;
else:
return resultPath;
def resampleData(source, sink = None, orientation = None, dataSizeSink = None, resolutionSource = (4.0625, 4.0625, 3), resolutionSink = (25, 25, 25),
processingDirectory = None, processes = 1, cleanup = True, verbose = True, interpolation = 'linear', **args):
"""Resample data of source in resolution and orientation
Arguments:
source (str or array): image to be resampled
sink (str or None): destination of resampled image
orientation (tuple): orientation specified by permuation and change in sign of (1,2,3)
dataSizeSink (tuple or None): target size of the resampled image
resolutionSource (tuple): resolution of the source image (in length per pixel)
resolutionSink (tuple): resolution of the resampled image (in length per pixel)
processingDirectory (str or None): directory in which to perform resmapling in parallel, None a temporary directry will be created
processes (int): number of processes to use for parallel resampling
cleanup (bool): remove temporary files
verbose (bool): display progress information
interpolation (str): method to use for interpolating to the resmapled image
Returns:
(array or str): data or file name of resampled image
Notes:
* resolutions are assumed to be given for the axes of the intrinsic
orientation of the data and reference as when viewed by matplotlib or ImageJ
* orientation: permuation of 1,2,3 with potential sign, indicating which
axes map onto the reference axes, a negative sign indicates reversal
of that particular axes
* only a minimal set of information to detremine the resampling parameter
has to be given, e.g. dataSizeSource and dataSizeSink
"""
orientation = fixOrientation(orientation);
if isinstance(dataSizeSink, basestring):
dataSizeSink = io.dataSize(dataSizeSink);
#orient actual resolutions onto reference resolution
dataSizeSource = io.dataSize(source);
dataSizeSource, dataSizeSink, resolutionSource, resolutionSink = resampleDataSize(dataSizeSource = dataSizeSource, dataSizeSink = dataSizeSink,
resolutionSource = resolutionSource, resolutionSink = resolutionSink, orientation = orientation);
dataSizeSinkI = orientDataSizeInverse(dataSizeSink, orientation);
#print dataSizeSource, dataSizeSink, resolutionSource, resolutionSink, dataSizeSinkI
#rescale in x y in parallel
if processingDirectory == None:
processingDirectory = tempfile.mkdtemp();
interpolation = fixInterpolation(interpolation);
nZ = dataSizeSource[2];
pool = multiprocessing.Pool(processes=processes);
argdata = [];
for i in range(nZ):
argdata.append( (source, os.path.join(processingDirectory, 'resample_%04d.tif' % i), dataSizeSinkI, interpolation, i, nZ, verbose) );
#print argdata[i]
pool.map(_resampleXYParallel, argdata);
#rescale in z
fn = os.path.join(processingDirectory, 'resample_%04d.tif' % 0);
data = io.readData(fn);
zImage = numpy.zeros((dataSizeSinkI[0], dataSizeSinkI[1], nZ), dtype = data.dtype);
for i in range(nZ):
if verbose and i % 10 == 0:
print "resampleData; reading %d/%d" % (i, nZ);
fn = os.path.join(processingDirectory, 'resample_%04d.tif' % i);
zImage[:,:, i] = io.readData(fn);
resampledData = numpy.zeros(dataSizeSinkI, dtype = zImage.dtype);
for i in range(dataSizeSinkI[0]):
if verbose and i % 25 == 0:
print "resampleData: processing %d/%d" % (i, dataSizeSinkI[0])
#resampledImage[:, iImage ,:] = scipy.misc.imresize(zImage[:,iImage,:], [resizedZAxisSize, sagittalImageSize[1]] , interp = 'bilinear');
#cv2.resize takes reverse order of sizes !
resampledData[i ,:, :] = cv2.resize(zImage[i,:,:], (dataSizeSinkI[2], dataSizeSinkI[1]), interpolation = interpolation);
#resampledData[i ,:, :] = cv2.resize(zImage[i,:, :], (dataSize[1], resizedZSize));
#account for using (z,y,x) array representation -> (y,x,z)
#resampledData = resampledData.transpose([1,2,0]);
#resampledData = resampledData.transpose([2,1,0]);
if cleanup:
shutil.rmtree(processingDirectory);
if not orientation is None:
#reorient
per = orientationToPermuation(orientation);
resampledData = resampledData.transpose(per);
#reverse orientation after permuting e.g. (-2,1) brings axis 2 to first axis and we can reorder there
if orientation[0] < 0:
resampledData = resampledData[::-1, :, :];
if orientation[1] < 0:
resampledData = resampledData[:, ::-1, :];
if orientation[2] < 0:
resampledData = resampledData[:, :, ::-1];
#bring back from y,x,z to z,y,x
#resampledImage = resampledImage.transpose([2,0,1]);
if verbose:
print "resampleData: resampled data size: " + str(resampledData.shape)
if sink == []:
if io.isFileExpression(source):
sink = os.path.split(source);
sink = os.path.join(sink[0], 'resample_\d{4}.tif');
elif isinstance(source, basestring):
sink = source + '_resample.tif';
else:
raise RuntimeError('resampleData: automatic sink naming not supported for non string source!');
return io.writeData(sink, resampledData);