def readData(filename, x = all, y = all, z = all, resolution = 0, channel = 0, timepoint = 0, **args):
"""Read data from imaris file
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
resolution (int): resolution level
channel (int): color channel
timepoint (int): time point
Returns:
array: image data
"""
f = h5py.File(filename, "r");
dataset = readDataSet(f, resolution = resolution, channel = channel, timepoint = timepoint);
dsize = dataset.shape;
rz = io.toDataRange(dsize[0], r = z);
ry = io.toDataRange(dsize[1], r = y);
rx = io.toDataRange(dsize[2], r = x);
data = dataset[rz[0]:rz[1],ry[0]:ry[1],rx[0]:rx[1]];
data = data.transpose((2,1,0)); # imaris stores files in reverse x,y,z ordering
#data = dataset[x[0]:x[1],y[0]:y[1],z[0]:z[1]];
f.close();
return data;
def readDataFiles(filename, x=all, y=all, z=all, **args):
"""Read data from individual images assuming they are the z slices
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
array: image data
"""
fpath, fl = readFileList(filename)
nz = len(fl)
#read first image to get data size and type
rz = io.toDataRange(nz, r=z)
sz = io.toDataSize(nz, r=z)
fn = os.path.join(fpath, fl[rz[0]])
img = io.readData(fn, x=x, y=y)
nxy = img.shape
data = numpy.zeros(nxy + (sz, ), dtype=img.dtype)
data[:, :, 0] = img
for i in range(rz[0] + 1, rz[1]):
fn = os.path.join(fpath, fl[i])
data[:, :, i - rz[0]] = io.readData(fn, x=x, y=y)
return data
def joinPoints(results, subStacks=None, shiftPoints=True, **args):
"""Joins a list of points obtained from processing a stack in chunks
Arguments:
results (list): list of point results from the individual sub-processes
subStacks (list or None): list of all sub-stack information, see :ref:`SubStack`
shiftPoints (bool): if True shift points to refer to origin of the image stack considered
when range specification is given. If False, absolute
position in entire image stack.
Returns:
tuple: joined points, joined intensities
"""
nchunks = len(results)
pointlist = [results[i][0] for i in range(nchunks)]
intensities = [results[i][1] for i in range(nchunks)]
results = []
resultsi = []
for i in range(nchunks):
cts = pointlist[i]
cti = intensities[i]
if cts.size > 0:
cts[:, 2] += subStacks[i]["z"][0]
iid = numpy.logical_and(subStacks[i]["zCenters"][0] <= cts[:, 2],
cts[:, 2] < subStacks[i]["zCenters"][1])
cts = cts[iid, :]
results.append(cts)
if not cti is None:
cti = cti[iid]
resultsi.append(cti)
if results == []:
if not intensities is None:
return (numpy.zeros((0, 3)), numpy.zeros((0)))
else:
return numpy.zeros((0, 3))
else:
points = numpy.concatenate(results)
if shiftPoints:
points = points + io.pointShiftFromRange(io.dataSize(
subStacks[0]["source"]),
x=subStacks[0]["x"],
y=subStacks[0]["y"],
z=0)
else:
points = points - io.pointShiftFromRange(io.dataSize(
subStacks[0]["source"]),
x=0,
y=0,
z=subStacks[0]["z"])
#absolute offset is added initially via zranges !
if intensities is None:
return points
else:
return (points, numpy.concatenate(resultsi))
def moveTeraStitcherStackToFileList(source, sink, deleteDirectory=True, verbose=True):
"""Moves image files from TeraSticher file structure to a list of files
Arguments:
source (str): base directory of the TeraStitcher files
sink (str): regular expression of the files to copy to
verbose (bool): show progress
Returns:
str: sink regular expression
"""
fns = glob.glob(os.path.join(source, "*/*/*"))
fns = natsort.natsorted(fns)
io.createDirectory(sink)
for i, f in enumerate(fns):
fn = filelist.fileExpressionToFileName(sink, i)
if verbose:
print "%s -> %s" % (f, fn)
shutil.move(f, fn)
if deleteDirectory:
p, _ = os.path.split(fns[0])
p = p.split(os.path.sep)
p = p[:-2]
p = os.path.sep.join(p)
shutil.rmtree(p)
return sink
def readDataFiles(filename, x=all, y=all, z=all, **args):
"""Read data from individual images assuming they are the z slices
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
array: image data
"""
fpath, fl = readFileList(filename)
nz = len(fl)
# read first image to get data size and type
rz = io.toDataRange(nz, r=z)
sz = io.toDataSize(nz, r=z)
fn = os.path.join(fpath, fl[rz[0]])
img = io.readData(fn, x=x, y=y)
nxy = img.shape
data = numpy.zeros(nxy + (sz,), dtype=img.dtype)
data[:, :, 0] = img
for i in range(rz[0] + 1, rz[1]):
fn = os.path.join(fpath, fl[i])
data[:, :, i - rz[0]] = io.readData(fn, x=x, y=y)
return data
def fileNameToIlastikOuput(filename):
"""Converts *ClearMap* file name to an argument string for use with Ilastik headless mode
Arguments:
filename (str): image file name or expression
Returns:
str: Ilastik headless ouput specifications
Note:
The output is formated accroding to the Ilastik pixel calssification output specifications
"""
if not isValidOutputFileName(filename):
raise RuntimeError('Ilastik: file format not compatibel with Ilastik output');
if io.isFileExpression(filename):
o = '--output_format="' + io.fileExtension(filename) + ' sequence" '+ \
'--output_filename_format="' + filelist.fileExpressionToFileName(filename, '{slice_index}') + '"';
return o;
else: # single file
extensionToOuput = {'bmp' : 'bmp', 'gif' : 'gif', 'hdr' : 'hrd',
'jpg' : 'jpg', 'jpeg': 'jpeg','pbm' : 'pbm',
'pgm' : 'pgm', 'png' : 'png', 'pnm' : 'pnm', 'ppm' : 'ppm',
'ras' : 'ras', 'tif' : 'tif', 'tiff': 'tiff','xv' : 'xv',
'h5' : 'hdf5' , 'npy' : 'numpy'};
ext = extensionToOuput[io.fileExtension(filename)];
o = '--output_format="' + ext +'" ' + \
'--output_filename_format="' + filename + '"';
return o;
def readData(filename, x=all, y=all, z=all, **args):
"""Read data from a single tif image or stack
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
array: image data
"""
dsize = dataSize(filename)
#print("dsize %s" % str(dsize);
if len(dsize) == 2:
data = tiff.imread(filename, key=0)
#print("data.shape %s" % str(data.shape);
return io.dataToRange(data.transpose([1, 0]), x=x, y=y)
#return io.dataToRange(data, x = x, y = y);
else:
if z is all:
data = tiff.imread(filename)
if data.ndim == 2:
# data = data
data = data.transpose([1, 0])
elif data.ndim == 3:
#data = data.transpose([1,2,0]);
data = data.transpose([2, 1, 0])
elif data.ndim == 4: # multi channel image
#data = data.transpose([1,2,0,3]);
data = data.transpose([2, 1, 0, 3])
else:
raise RuntimeError('readData: dimension %d not supproted!' %
data.ndim)
return io.dataToRange(data, x=x, y=y, z=all)
else: #optimize for z ranges
ds = io.dataSizeFromDataRange(dsize, x=x, y=y, z=z)
t = tiff.TiffFile(filename)
p = t.pages[0]
data = numpy.zeros(ds, dtype=p.dtype)
rz = io.toDataRange(dsize[2], r=z)
#print("test"
#print(rz;
#print(dsize
for i in range(rz[0], rz[1]):
xydata = t.pages[i].asarray()
#data[:,:,i-rz[0]] = io.dataToRange(xydata, x = x, y = y);
data[:, :, i - rz[0]] = io.dataToRange(xydata.transpose([1,
0]),
x=x,
y=y)
return data
def readData(filename, x = all, y = all, z = all, **args):
"""Read data from a single tif image or stack
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
array: image data
"""
dsize = dataSize(filename);
#print "dsize %s" % str(dsize);
if len(dsize) == 2:
data = tiff.imread(filename, key = 0);
#print "data.shape %s" % str(data.shape);
return io.dataToRange(data.transpose([1,0]), x = x, y = y);
#return io.dataToRange(data, x = x, y = y);
else:
if z is all:
data = tiff.imread(filename);
if data.ndim == 2:
# data = data
data = data.transpose([1,0]);
elif data.ndim == 3:
#data = data.transpose([1,2,0]);
data = data.transpose([2,1,0]);
elif data.ndim == 4: # multi channel image
#data = data.transpose([1,2,0,3]);
data = data.transpose([2,1,0,3]);
else:
raise RuntimeError('readData: dimension %d not supproted!' % data.ndim)
return io.dataToRange(data, x = x, y = y, z = all);
else: #optimize for z ranges
ds = io.dataSizeFromDataRange(dsize, x = x, y = y, z = z);
t = tiff.TiffFile(filename);
p = t.pages[0];
data = numpy.zeros(ds, dtype = p.dtype);
rz = io.toDataRange(dsize[2], r = z);
#print "test"
#print rz;
#print dsize
for i in range(rz[0], rz[1]):
xydata = t.pages[i].asarray();
#data[:,:,i-rz[0]] = io.dataToRange(xydata, x = x, y = y);
data[:,:,i-rz[0]] = io.dataToRange(xydata.transpose([1,0]), x = x, y = y);
return data
def copyData(source, sink):
"""Copy a imaris file from source to sink
Arguments:
source (str): file name pattern of source
sink (str): file name pattern of sink
Returns:
str: file name patttern of the copy
"""
io.copyFile(source, sink);
def calculateSubStacks(source, z = all, x = all, y = all, **args):
"""Calculates the chunksize and other info for parallel processing and returns a list of sub-stack objects
The sub-stack information is described in :ref:`SubStack`
Arguments:
source (str): image source
x,y,z (tuple or all): range specifications
processes (int): number of parallel processes
chunkSizeMax (int): maximal size of a sub-stack
chunkSizeMin (int): minial size of a sub-stack
chunkOverlap (int): minimal sub-stack overlap
chunkOptimization (bool): optimize chunck sizes to best fit number of processes
chunkOptimizationSize (bool or all): if True only decrease the chunk size when optimizing
verbose (bool): print information on sub-stack generation
Returns:
list: list of sub-stack objects
"""
#determine z ranges
fs = io.dataSize(source);
zs = fs[2];
zr = io.toDataRange(zs, r = z);
nz = zr[1] - zr[0];
#calculate optimal chunk sizes
nchunks, zranges, zcenters = calculateChunkSize(nz, **args);
#adjust for the zrange
zcenters = [c + zr[0] for c in zcenters];
zranges = [(zc[0] + zr[0], zc[1] + zr[0]) for zc in zranges];
#create substacks
subStacks = [];
indexlo = zr[0];
for i in range(nchunks):
indexhi = int(round(zcenters[i+1]));
if indexhi > zr[1] or i == nchunks - 1:
indexhi = zr[1];
zs = zranges[i][1] - zranges[i][0];
subStacks.append({"stackId" : i, "nStacks" : nchunks,
"source" : source, "x" : x, "y" : y, "z" : zranges[i],
"zCenters" : (zcenters[i], zcenters[i+1]),
"zCenterIndices" : (indexlo, indexhi),
"zSubStackCenterIndices" : (indexlo - zranges[i][0], zs - (zranges[i][1] - indexhi))});
indexlo = indexhi; # + 1;
return subStacks;
def openData3D(dataSource, x = all, y = all, z = all, cleanUp = True):
"""Open image in ImageJ
Arguments:
dataSouce (str or array): volumetric image data
x, y, z (all or tuple): sub-range specification
inverse (bool):invert image
Returns:
(object): figure handle
"""
checkImageJInitialized();
if isinstance(dataSource, numpy.ndarray):
filename = tempfile.mktemp(suffix = '.mhd', prefix = 'CM_ImageJ');
io.writeData(filename, dataSource, x = x, y = y, z = z);
filepath, imagename = os.path.split(filename);
imagename = imagename[:-4] + '.raw';
temp = True;
dSize = dataSource.shape;
else:
filename = dataSource;
filepath, imagename = os.path.split(filename);
temp = False;
if len(dSize) == 4:
colorImage = True;
else:
colorImage = False;
if colorImage:
macro = ('open("%s"); ' % filename) + \
'run("Stack to Hyperstack...", "order=xyzct channels=%d slices=%d frames=1 display=Color"); ' % (dSize[3], dSize[2]) + \
'Stack.setDisplayMode("composite"); ' + \
'run("3D Viewer"); call("ij3d.ImageJ3DViewer.setCoordinateSystem", "false"); ' + \
'call("ij3d.ImageJ3DViewer.add", "%s", "None", "%s", "0", "true", "true", "true", "1", "0");' % (imagename, imagename);
else:
macro = ('open("%s");' % filename) + ' run("RGB Color"); run("3D Viewer"); call("ij3d.ImageJ3DViewer.setCoordinateSystem", "false"); ' + \
'call("ij3d.ImageJ3DViewer.add", "%s", "None", "%s", "0", "true", "true", "true", "1", "0");' % (imagename, imagename);
cmd = ImageJBinary + " -eval '%s'" % macro;
print 'running: %s' % cmd
res = os.system(cmd);
if res != 0:
raise RuntimeError('openData3D: failed executing: ' + cmd);
if cleanUp and temp:
os.remove(filename);
os.remove(os.path.join(filepath, imagename));
return macro;
def overlayLabel(dataSource, labelSource, sink = None, alpha = False, labelColorMap = 'jet', x = all, y = all, z = all):
"""Overlay a gray scale image with colored labeled image
Arguments:
dataSouce (str or array): volumetric image data
labelSource (str or array): labeled image to be overlayed on the image data
sink (str or None): destination for the overlayed image
alpha (float or False): transparency
labelColorMap (str or object): color map for the labels
x, y, z (all or tuple): sub-range specification
Returns:
(array or str): figure handle
See Also:
:func:`overlayPoints`
"""
label = io.readData(labelSource, x= x, y = y, z = z);
image = io.readData(dataSource, x= x, y = y, z = z);
lmax = label.max();
if lmax <= 1:
carray = numpy.array([[1,0,0,1]]);
else:
cm = mpl.cm.get_cmap(labelColorMap);
cNorm = mpl.colors.Normalize(vmin=1, vmax = int(lmax));
carray = mpl.cm.ScalarMappable(norm=cNorm, cmap=cm);
carray = carray.to_rgba(numpy.arange(1, int(lmax + 1)));
if alpha == False:
carray = numpy.concatenate(([[0,0,0,1]], carray), axis = 0);
else:
carray = numpy.concatenate(([[1,1,1,1]], carray), axis = 0);
cm = mpl.colors.ListedColormap(carray);
carray = cm(label);
carray = carray.take([0,1,2], axis = -1);
if alpha == False:
cimage = (label == 0) * image;
cimage = numpy.repeat(cimage, 3);
cimage = cimage.reshape(image.shape + (3,));
cimage = cimage.astype(carray.dtype);
cimage += carray;
else:
cimage = numpy.repeat(image, 3);
cimage = cimage.reshape(image.shape + (3,));
cimage = cimage.astype(carray.dtype);
cimage *= carray;
return io.writeData(sink, cimage);
def countPointsInRegions(points, labeledImage = DefaultLabeledImageFile, intensities = None, intensityRow = 0, level= None, allIds = False, sort = True, returnIds = True, returnCounts = False, collapse = None):
global Label;
points = io.readPoints(points);
intensities = io.readPoints(intensities);
pointLabels = labelPoints(points, labeledImage, level = level, collapse = collapse);
if intensities is None:
ll, cc = numpy.unique(pointLabels, return_counts = True);
cci = None;
else:
if intensities.ndim > 1:
intensities = intensities[:,intensityRow];
ll, ii, cc = numpy.unique(pointLabels, return_counts = True, return_inverse = True);
cci = numpy.zeros(ll.shape);
for i in range(ii.shape[0]):
cci[ii[i]] += intensities[i];
if allIds:
lla = numpy.setdiff1d(Label.ids, ll);
ll = numpy.hstack((ll, lla));
cc = numpy.hstack((cc, numpy.zeros(lla.shape, dtype = cc.dtype)));
if not cci is None:
cci = numpy.hstack((cci, numpy.zeros(lla.shape, dtype = cc.dtype)));
#cc = numpy.vstack((ll,cc)).T;
if sort:
ii = numpy.argsort(ll);
cc = cc[ii];
ll = ll[ii];
if not cci is None:
cci = cci[ii];
if returnIds:
if cci is None:
return ll, cc
else:
if returnCounts:
return ll, cc, cci;
else:
return ll, cci
else:
if cci is None:
return cc;
else:
if returnCounts:
return cc, cci;
else:
return cci;
def joinPoints(results, subStacks = None, shiftPoints = True, **args):
"""Joins a list of points obtained from processing a stack in chunks
Arguments:
results (list): list of point results from the individual sub-processes
subStacks (list or None): list of all sub-stack information, see :ref:`SubStack`
shiftPoints (bool): if True shift points to refer to origin of the image stack considered
when range specification is given. If False, absolute
position in entire image stack.
Returns:
tuple: joined points, joined intensities
"""
nchunks = len(results);
pointlist = [results[i][0] for i in range(nchunks)];
intensities = [results[i][1] for i in range(nchunks)];
results = [];
resultsi = [];
for i in range(nchunks):
cts = pointlist[i];
cti = intensities[i];
if cts.size > 0:
cts[:,2] += subStacks[i]["z"][0];
iid = numpy.logical_and(subStacks[i]["zCenters"][0] <= cts[:,2] , cts[:,2] < subStacks[i]["zCenters"][1]);
cts = cts[iid,:];
results.append(cts);
if not cti is None:
cti = cti[iid];
resultsi.append(cti);
if results == []:
if not intensities is None:
return (numpy.zeros((0,3)), numpy.zeros((0)));
else:
return numpy.zeros((0,3))
else:
points = numpy.concatenate(results);
if shiftPoints:
points = points + io.pointShiftFromRange(io.dataSize(subStacks[0]["source"]), x = subStacks[0]["x"], y = subStacks[0]["y"], z = 0);
else:
points = points - io.pointShiftFromRange(io.dataSize(subStacks[0]["source"]), x = 0, y = 0, z = subStacks[0]["z"]); #absolute offset is added initially via zranges !
if intensities is None:
return points;
else:
return (points, numpy.concatenate(resultsi));
def copyData(source, sink):
"""Copy an nrrd file from source to sink
Arguments:
source (str): file name pattern of source
sink (str): file name pattern of sink
Returns:
str: file name of the copy
Notes:
Todo: dealt with nrdh header files!
"""
io.copyFile(source, sink)
def copyData(source, sink):
"""Copy an nrrd file from source to sink
Arguments:
source (str): file name pattern of source
sink (str): file name pattern of sink
Returns:
str: file name of the copy
Notes:
Todo: dealt with nrdh header files!
"""
io.copyFile(source, sink);
def fileNameToIlastikOuput(filename):
"""Converts *ClearMap* file name to an argument string for use with Ilastik headless mode
Arguments:
filename (str): image file name or expression
Returns:
str: Ilastik headless ouput specifications
Note:
The output is formated accroding to the Ilastik pixel calssification output specifications
"""
if not isValidOutputFileName(filename):
raise RuntimeError("Ilastik: file format not compatibel with Ilastik output")
if io.isFileExpression(filename):
o = (
'--output_format="'
+ io.fileExtension(filename)
+ ' sequence" '
+ '--output_filename_format="'
+ filelist.fileExpressionToFileName(filename, "{slice_index}")
+ '"'
)
return o
else: # single file
extensionToOuput = {
"bmp": "bmp",
"gif": "gif",
"hdr": "hrd",
"jpg": "jpg",
"jpeg": "jpeg",
"pbm": "pbm",
"pgm": "pgm",
"png": "png",
"pnm": "pnm",
"ppm": "ppm",
"ras": "ras",
"tif": "tif",
"tiff": "tiff",
"xv": "xv",
"h5": "hdf5",
"npy": "numpy",
}
ext = extensionToOuput[io.fileExtension(filename)]
o = '--output_format="' + ext + '" ' + '--output_filename_format="' + filename + '"'
return o
def isValidOutputFileName(filename):
"""Checks if the file is a valid format for use with Ilastik ouput
Arguments:
filename (str): image file name or expression
Returns:
bool: True if the image file can be written by Ilastik
"""
if io.isFileExpression(filename):
validExtensions = [
"bmp",
"gif",
"hdr",
"jpg",
"jpeg",
"pbm",
"pgm",
"png",
"pnm",
"ppm",
"ras",
"tif",
"tiff",
"xv",
]
return io.fileExtension(filename) in validExtensions
else:
validExtensions = [
"bmp",
"gif",
"hdr",
"jpg",
"jpeg",
"pbm",
"pgm",
"png",
"pnm",
"ppm",
"ras",
"tif",
"tiff",
"xv",
"h5",
"npy",
]
return io.fileExtension(filename) in validExtensions
def labelPoints(points, labeledImage = DefaultLabeledImageFile, level = None, collapse = None):
#points are (y,x,z) -> which is also the way the labeled image is read in
#x = points[:,1];
#y = points[:,0];
#z = points[:,2];
x = points[:,0];
y = points[:,1];
z = points[:,2];
nPoint = x.size;
pointLabels = numpy.zeros(nPoint, 'int32');
labelImage = io.readData(labeledImage);
dsize = labelImage.shape;
for i in range(nPoint):
#if y[i] >= 0 and y[i] < dsize[0] and x[i] >= 0 and x[i] < dsize[1] and z[i] >= 0 and z[i] < dsize[2]:
# pointLabels[i] = labelImage[y[i], x[i], z[i]];
if x[i] >= 0 and x[i] < dsize[0] and y[i] >= 0 and y[i] < dsize[1] and z[i] >= 0 and z[i] < dsize[2]:
pointLabels[i] = labelImage[int(x[i]), int(y[i]), int(z[i])];
if collapse is None:
pointLabels = labelAtLevel(pointLabels, level);
else:
pointLabels = labelAtCollapse(pointLabels);
return pointLabels;
def test():
"""Test Spot Detection Module"""
import os
import ClearMap.ImageProcessing.SpotDetection as self
reload(self)
import ClearMap.IO as io
import ClearMap.Settings as settings
from ClearMap.ImageProcessing.CellDetection import detectCells
basedir = settings.ClearMapPath
#Default tifs do not load for some reason (tifffile is not happy) but
#simply resaving them seems to do the trick.
fn = os.path.join(basedir, 'Test/Data/synthetic2/test_iDISCO_\d{3}.tif')
#fn = os.path.join(basedir, 'Test/Data/OME/16-17-27_0_8X-s3-20HF_UltraII_C00_xyz-Table Z\d{4}.ome.tif');
img = io.readData(fn)
#img = dataset[0:500,0:500,1000:1008];
#img = dataset[600:1000,1600:1800,800:830];
#img = dataset[500:1500,500:1500,800:809];
img = img.astype('int16')
#m = sys.modules['iDISCO.ImageProcessing.SpotDetection']
#c = self.detectCells(img);
c = self.detectSpots(img, hMax=10, threshold=100, verbose=True)
print 'done, found %d cells !' % c[0].shape[0]
#test intensities:
import numpy
x = numpy.random.rand(30, 30, 10)
centers = numpy.array([[0, 0, 0], [29, 29, 9]])
i = self.findIntensity(x, centers, boxSize=(1, 1, 1))
print i
def _processSubStack(dsr):
"""Helper to process stack in parallel"""
sf = dsr[0];
pp = dsr[1];
sub = dsr[2];
verbose = dsr[3];
timer = Timer();
pw = ProcessWriter(sub["stackId"]);
if verbose:
pw.write("processing substack " + str(sub["stackId"]) + "/" + str(sub["nStacks"]));
pw.write("file = " + sub["source"]);
pw.write("segmentation = " + str(sf));
pw.write("ranges: x,y,z = " + str(sub["x"]) + "," + str(sub["y"]) + "," + str(sub["z"]));
img = io.readData(sub["source"], x = sub["x"], y = sub["y"], z = sub["z"]);
if verbose:
pw.write(timer.elapsedTime(head = 'Reading data of size ' + str(img.shape)));
timer.reset();
seg = sf(img, subStack = sub, out = pw, **pp);
if verbose:
pw.write(timer.elapsedTime(head = 'Processing substack of size ' + str(img.shape)));
return seg;
def writeSubStack(filename, img, subStack = None):
"""Write the non-redundant part of a sub-stack to disk
The routine is used to write out images when porcessed in parallel.
It assumes that the filename is a patterned file name.
Arguments:
filename (str or None): file name pattern as described in
:mod:`~ClearMap.Io.FileList`, if None return as array
img (array): image data of sub-stack
subStack (dict or None): sub-stack information, if None write entire image
see :ref:`SubStack`
Returns:
str or array: the file name pattern or image
"""
if not subStack is None:
ii = subStack["zSubStackCenterIndices"][0];
ee = subStack["zSubStackCenterIndices"][1];
si = subStack["zCenterIndices"][0];
else:
si = 0;
ii = 0;
ee = -1;
return io.writeData(filename, img[:,:,ii:ee], startIndex = si );
def isValidInputFileName(filename):
"""Checks if the file is a valid format for use with Ilastik input
Arguments:
filename (str): image file name or expression
Returns:
bool: True if the image file can be read by Ilastik
"""
validExtensions = [
"bmp",
"exr",
"gif",
"jpg",
"jpeg",
"tif",
"tiff",
"ras",
"png",
"pbm",
"pgm",
"ppm",
"pnm",
"hdr",
"xv",
"npy",
]
return io.fileExtension(filename) in validExtensions
def dataSize(filename, **args):
"""Returns size of data in tif file
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
tuple: data size
"""
t = tiff.TiffFile(filename);
d3 = len(t.pages);
d2 = t.pages[0].shape;
#d2 = (d2[0], d2[1]);
if len(d2) == 3:
d2 = (d2[2], d2[1], d2[0]);
else:
d2 = (d2[1], d2[0]);
if d3 > 1:
dims = d2 + (d3,);
else:
dims = d2;
return io.dataSizeFromDataRange(dims, **args);
def labelPoints(points, labeledImage=DefaultLabeledImageFile, level=None, collapse=None):
# points are (y,x,z) -> which is also the way the labeled image is read in
# x = points[:,1];
# y = points[:,0];
# z = points[:,2];
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
nPoint = x.size
pointLabels = numpy.zeros(nPoint, "int32")
labelImage = io.readData(labeledImage)
dsize = labelImage.shape
for i in range(nPoint):
# if y[i] >= 0 and y[i] < dsize[0] and x[i] >= 0 and x[i] < dsize[1] and z[i] >= 0 and z[i] < dsize[2]:
# pointLabels[i] = labelImage[y[i], x[i], z[i]];
if x[i] >= 0 and x[i] < dsize[0] and y[i] >= 0 and y[i] < dsize[1] and z[i] >= 0 and z[i] < dsize[2]:
pointLabels[i] = labelImage[int(x[i]), int(y[i]), int(z[i])]
if collapse is None:
pointLabels = labelAtLevel(pointLabels, level)
else:
pointLabels = labelAtCollapse(pointLabels)
return pointLabels
def thresholdPoints(points, intensities, threshold = 0, row = 0):
"""Threshold points by intensities"""
points, intensities = io.readPoints((points, intensities));
if not isinstance(threshold, tuple):
threshold = (threshold, all);
if not isinstance(row, tuple):
row = (row, row);
if intensities.ndim > 1:
i = intensities[:,row[0]];
else:
i = intensities;
iids = numpy.ones(i.shape, dtype = 'bool');
if not threshold[0] is all:
iids = numpy.logical_and(iids, i >= threshold[0]);
if intensities.ndim > 1:
i = intensities[:,row[1]];
if not threshold[1] is all:
iids = numpy.logical_and(iids, i <= threshold[1]);
return (points[iids, ...], intensities[iids, ...]);
def dataSize(filename, **args):
"""Returns size of data in tif file
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
tuple: data size
"""
t = tiff.TiffFile(filename);
d3 = len(t.pages);
d2 = t.pages[0].shape;
#d2 = (d2[0], d2[1]);
if len(d2) == 3:
d2 = (d2[2], d2[1], d2[0]);
else:
d2 = (d2[1], d2[0]);
if d3 > 1:
dims = d2 + (d3,);
else:
dims = d2;
return io.dataSizeFromDataRange(dims, **args);
def isValidOutputFileName(filename):
"""Checks if the file is a valid format for use with Ilastik ouput
Arguments:
filename (str): image file name or expression
Returns:
bool: True if the image file can be written by Ilastik
"""
if io.isFileExpression(filename):
validExtensions = ['bmp', 'gif', 'hdr', 'jpg', 'jpeg', 'pbm', 'pgm', 'png', 'pnm', 'ppm', 'ras', 'tif', 'tiff', 'xv'];
return io.fileExtension(filename) in validExtensions;
else:
validExtensions = ['bmp', 'gif', 'hdr', 'jpg', 'jpeg', 'pbm', 'pgm', 'png', 'pnm', 'ppm', 'ras', 'tif', 'tiff', 'xv', 'h5', 'npy'];
return io.fileExtension(filename) in validExtensions;
def writePoints(filename, points, indices = True):
"""Write points as elastix/transformix point file
Arguments:
filename (str): file name of the elastix point file.
points (array or str): source of the points.
indices (bool): write as pixel indices or physical coordiantes
Returns:
str : file name of the elastix point file
"""
points = io.readPoints(points);
#points = points[:,[1,0,2]]; # points in ClearMap (y,x,z) -> permute to (x,y,z)
with open(filename, 'w') as pointfile:
if indices:
pointfile.write('index\n')
else:
pointfile.write('point\n')
pointfile.write(str(points.shape[0]) + '\n');
numpy.savetxt(pointfile, points, delimiter = ' ', newline = '\n', fmt = '%.5e')
pointfile.close();
return filename;
def voxelizePixel(points, dataSize = None, weights = None):
"""Mark pixels/voxels of each point in an image array
Arguments:
points (array): point data array
dataSize (tuple or None): size of the final output data, if None size is determined by maximal point coordinates
weights (array or None): weights for each points, if None weights are all 1s.
Returns:
(array): volumetric data with with points marked in voxels
"""
if dataSize is None:
dataSize = tuple(int(math.ceil(points[:,i].max())) for i in range(points.shape[1]));
elif isinstance(dataSize, basestring):
dataSize = io.dataSize(dataSize);
if weights is None:
vox = numpy.zeros(dataSize, dtype=numpy.int16);
for i in range(points.shape[0]):
if points[i,0] > 0 and points[i,0] < dataSize[0] and points[i,1] > 0 and points[i,1] < dataSize[1] and points[i,2] > 0 and points[i,2] < dataSize[2]:
vox[points[i,0], points[i,1], points[i,2]] += 1;
else:
vox = numpy.zeros(dataSize, dtype=weights.dtype);
for i in range(points.shape[0]):
if points[i,0] > 0 and points[i,0] < dataSize[0] and points[i,1] > 0 and points[i,1] < dataSize[1] and points[i,2] > 0 and points[i,2] < dataSize[2]:
vox[points[i,0], points[i,1], points[i,2]] += weights[i];
return vox;
def copyData(source, sink):
"""Copy a raw/mhd file pair from source to sink
Arguments:
source (str): file name of source
sink (str): file name of sink
Returns:
str: file name of the copy
"""
sourceExt = io.fileExtension(source)
sinkExt = io.fileExtension(sink)
sources = [source]
sinks = []
if sourceExt == 'raw':
sources.append(source[:-3] + 'mhd')
if sinkExt == 'raw':
sinks.append(sink)
sinks.append(sink[:-3] + 'mhd')
elif sinkExt == 'mhd':
sinks.append(sink[:-3] + 'raw')
sinks.append(sink)
else:
raise RuntimeError('copyData: sink extension %s not raw or mhd' %
sinkExt)
elif sourceExt == 'mhd':
sources.append(source[:-3] + 'raw')
if sinkExt == 'raw':
sinks.append(sink[:-3] + 'mhd')
sinks.append(sink)
elif sinkExt == 'mhd':
sinks.append(sink)
sinks.append(sink[:-3] + 'raw')
else:
raise RuntimeError('copyData: sink extension %s not raw or mhd' %
sinkExt)
for i in range(2):
io.copyData(sources[i], sinks[i])
return sink
def combineSections(animalPath, section, outTableName):
alph = ['a','b','c','d','e','f'];
for z in range(len(alph)):
fName = section+alph[z];
resultDir = os.path.join(animalPath,'Results',fName);
tableFN = os.path.join(resultDir,'ResultsTable.csv');
if z == 0:
table= pd.read_csv(tableFN,names = ['id','counts','name','subname']);
else:
temp_table = pd.read_csv(tableFN,names = ['id','counts','name','subname']);
table = pd.DataFrame.append(table,temp_table);
#Table generation
totTable = table.groupby(['id','name'],as_index=False).sum();
totTableFN = os.path.join(animalPath,outTableName);
io.writeTable(totTableFN,totTable.values);
print('success!')
def _cropParallel(arg):
"""Cropping helper function to use for parallel cropping of image slices"""
fileSource = arg[0]
fileSink = arg[1]
x = arg[2]
y = arg[3]
ii = arg[4]
nn = arg[5]
if ii is not None:
pw = ProcessWriter(ii)
pw.write("cropData: corpping image %d / %d" % (ii, nn))
# pw.write('%s -> %s' % (fileSource, fileSink));
data = io.readData(fileSource, x=x, y=y)
io.writeData(fileSink, data)
def classifyPixel(project, source, sink = None, processingDirectory = None, cleanup = True):
"""Run pixel classification in headless moded using a trained project file
Arguments:
project (str): ilastik project .ilp file
source (str or array): image source
sink (str or array or None): image sink
Returns:
str or array: classified image sink
"""
#generate source image file if source is array
if isinstance(source, str):
inpfile = source;
else:
#generate temporary file
if processingDirectory is None:
processingDirectory = tempfile.mkdtemp();
inpfile = os.path.join(processingDirectory, 'ilastik.npy')
io.writePoints(inpfile, source.transpose((2,1,0)));
if isinstance(sink, str):
outfile = sink;
else:
#outdir = tempfile.mkdtemp();
#outfile = os.path.join(outdir, 'result\d*.tif');
outfile = tempfile.mktemp('.h5');
ilinp = fileNameToIlastikInput(inpfile);
ilout = fileNameToIlastikOuput(outfile);
args = '--project="' + project + '" ' + ilout + ' ' + ilinp;
run(args);
#clean up
if cleanup and processingDirectory is not None:
shutil.rmtree(processingDirectory);
if not isinstance(sink, str):
sink = readResultH5(outfile);
if cleanup:
os.remove(outfile);
return sink;
def test():
import os;
import ClearMap.IO as io
import ClearMap.Settings as settings
import ClearMap.ImageProcessing.Ilastik as il;
reload(il);
ilp = os.path.join(settings.ClearMapPath, 'Test/Ilastik/Test.ilp')
src = os.path.join(settings.ClearMapPath, 'Test/Data/ImageAnalysis/cfos-substack.tif');
#out = os.path.join(settings.ClearMapPath, 'Test/Data/Ilastik/image.npy');
out = None;
#out = os.path.join(settings.ClearMapPath, 'Test/Data/Ilastik/result\d*.tif');
cls = il.classifyPixel(ilp, src, out);
print io.dataSize(src)
print cls.shape
io.writeData('/home/ckirst/result.raw', cls);
def writeData(filename, data, **args):
""" Write data into to raw/mhd file pair
Arguments:
filename (str): file name as regular expression
data (array): data to write to raw file
Returns:
str: file name of mhd file
"""
fext = io.fileExtension(filename)
if fext == "raw":
fname = filename[:-3] + 'mhd'
else:
fname = filename
assert (fname[-4:] == '.mhd')
meta_dict = {}
meta_dict['ObjectType'] = 'Image'
meta_dict['BinaryData'] = 'True'
meta_dict['BinaryDataByteOrderMSB'] = 'False'
numpy_to_datatype = {
numpy.dtype('int8'): "MET_CHAR",
numpy.dtype('uint8'): "MET_UCHAR",
numpy.dtype('int16'): "MET_SHORT",
numpy.dtype('uint16'): "MET_USHORT",
numpy.dtype('int32'): "MET_INT",
numpy.dtype('uint32'): "MET_UINT",
numpy.dtype('int64'): "MET_LONG",
numpy.dtype('uint64'): "MET_ULONG",
numpy.dtype('float32'): "MET_FLOAT",
numpy.dtype('float64'): "MET_DOUBLE",
}
dtype = data.dtype
meta_dict['ElementType'] = numpy_to_datatype[dtype]
dsize = list(data.shape)
#dsize[0:2] = [dsize[1],dsize[0]]; #fix arrays represented as (y,x,z)
meta_dict['NDims'] = str(len(dsize))
meta_dict['DimSize'] = ' '.join([str(i) for i in dsize])
meta_dict['ElementDataFile'] = os.path.split(fname)[1].replace(
'.mhd', '.raw')
writeHeader(fname, meta_dict)
pwd = os.path.split(fname)[0]
if pwd:
data_file = pwd + '/' + meta_dict['ElementDataFile']
else:
data_file = meta_dict['ElementDataFile']
writeRawData(data_file, data)
return fname
def voxelize(points, dataSize = None, sink = None, voxelizeParameter = None, method = 'Spherical', size = (5,5,5), weights = None):
"""Converts a list of points into an volumetric image array
Arguments:
points (array): point data array
dataSize (tuple): size of final image
sink (str, array or None): the location to write or return the resulting voxelization image, if None return array
voxelizeParameter (dict):
========== ==================== ===========================================================
Name Type Descritption
========== ==================== ===========================================================
*method* (str or None) method for voxelization: 'Spherical', 'Rectangular' or 'Pixel'
*size* (tuple) size parameter for the voxelization
*weights* (array or None) weights for each point, None is uniform weights
========== ==================== ===========================================================
Returns:
(array): volumetric data of smeared out points
"""
if dataSize is None:
dataSize = tuple(int(math.ceil(points[:,i].max())) for i in range(points.shape[1]));
elif isinstance(dataSize, basestring):
dataSize = io.dataSize(dataSize);
points = io.readPoints(points);
if method.lower() == 'spherical':
if weights is None:
data = vox.voxelizeSphere(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2]);
else:
data = vox.voxelizeSphereWithWeights(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2], weights);
elif method.lower() == 'rectangular':
if weights is None:
data = vox.voxelizeRectangle(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2]);
else:
data = vox.voxelizeRectangleWithWeights(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2], weights);
elif method.lower() == 'pixel':
data = voxelizePixel(points, dataSize, weights);
else:
raise RuntimeError('voxelize: mode: %s not supported!' % method);
return io.writeData(sink, data);
def makeColorAnnotations(filename, labeledImage=None):
if labeledImage is None:
labeledImage = DefaultLabeledImageFile
li = io.readData(labeledImage)
dsize = li.shape
lr = numpy.zeros(dsize, dtype=numpy.uint8)
lg = lr.copy()
lb = lr.copy()
global Label
maxlabel = max(Label.ids)
colarray = numpy.zeros((maxlabel, 3))
for i in Label.ids:
colarray[i - 1, :] = Label.color(i)
for i in Label.ids:
ll = li == i
lr[ll] = colarray[i - 1, 0]
lg[ll] = colarray[i - 1, 1]
lb[ll] = colarray[i - 1, 2]
io.writeData(filename + "_r.tif", lr)
io.writeData(filename + "_g.tif", lg)
io.writeData(filename + "_b.tif", lb)
return (lr, lg, lb)
def test():
"""Test Elastix module"""
import ClearMap.Alignment.Elastix as self
reload(self)
from ClearMap.Settings import ClearMapPath;
import os, numpy
p = ClearMapPath;
resultdir = os.path.join(p, 'Test/Elastix/Output');
print 'Searching for transformation parameter file in ' + resultdir;
pf = self.getTransformParameterFile(resultdir)
print 'Found: ' + pf;
#replace path in trasform parameter files:
self.setPathTransformParameterFiles(resultdir)
#initialize
self.initializeElastix('/home/ckirst/programs/elastix')
self.printSettings()
#transform points
pts = numpy.random.rand(5,3);
print 'Transforming points: '
tpts = self.transformPoints(pts, transformParameterFile = pf, indices = False);
print pts
print 'Transformed points: '
print tpts
#deformation and distance fields
df = self.deformationField(transformParameterFile = pf, resultDirectory = None);
#df = '/tmp/elastix_output/deformationField.mhd';
import ClearMap.IO as io
data = io.readData('/tmp/elastix_output/deformationField.mhd');
ds = self.deformationDistance(data);
io.writeData(os.path.join(p, 'Test/Elastix/Output/distances.raw'), ds);
def test():
"""Test Elastix module"""
import ClearMap.Alignment.Elastix as self
reload(self)
from ClearMap.Settings import ClearMapPath;
import os, numpy
p = ClearMapPath;
resultdir = os.path.join(p, 'Test/Elastix/Output');
print 'Searching for transformation parameter file in ' + resultdir;
pf = self.getTransformParameterFile(resultdir)
print 'Found: ' + pf;
#replace path in trasform parameter files:
self.setPathTransformParameterFiles(resultdir)
#initialize
self.initializeElastix('/home/ckirst/programs/elastix')
self.printSettings()
#transform points
pts = numpy.random.rand(5,3);
print 'Transforming points: '
tpts = self.transformPoints(pts, transformParameterFile = pf, indices = False);
print pts
print 'Transformed points: '
print tpts
#deformation and distance fields
df = self.deformationField(transformParameterFile = pf, resultDirectory = None);
#df = '/tmp/elastix_output/deformationField.mhd';
import ClearMap.IO as io
data = io.readData('/tmp/elastix_output/deformationField.mhd');
ds = self.deformationDistance(data);
io.writeData(os.path.join(p, 'Test/Elastix/Output/distances.raw'), ds);
def makeColorAnnotations(filename, labeledImage = None):
if labeledImage is None:
labeledImage = DefaultLabeledImageFile;
li = io.readData(labeledImage);
dsize = li.shape;
lr = numpy.zeros(dsize, dtype = numpy.uint8);
lg = lr.copy();
lb = lr.copy();
global Label;
maxlabel = max(Label.ids);
colarray = numpy.zeros((maxlabel, 3));
for i in Label.ids:
colarray[i-1,:] = Label.color(i);
for i in Label.ids:
ll = li == i;
lr[ll] = colarray[i-1,0];
lg[ll] = colarray[i-1,1];
lb[ll] = colarray[i-1,2];
io.writeData(filename + "_r.tif", lr);
io.writeData(filename + "_g.tif", lg);
io.writeData(filename + "_b.tif", lb);
return (lr,lg,lb);
def copyData(source, sink):
"""Copy a raw/mhd file pair from source to sink
Arguments:
source (str): file name of source
sink (str): file name of sink
Returns:
str: file name of the copy
"""
sourceExt = io.fileExtension(source);
sinkExt = io.fileExtension(sink);
sources = [source];
sinks = [];
if sourceExt == 'raw':
sources.append(source[:-3] + 'mhd');
if sinkExt == 'raw':
sinks.append(sink);
sinks.append(sink[:-3] + 'mhd');
elif sinkExt == 'mhd':
sinks.append(sink[:-3] + 'raw');
sinks.append(sink);
else:
raise RuntimeError('copyData: sink extension %s not raw or mhd' % sinkExt);
elif sourceExt == 'mhd':
sources.append(source[:-3] + 'raw');
if sinkExt == 'raw':
sinks.append(sink[:-3] + 'mhd');
sinks.append(sink);
elif sinkExt == 'mhd':
sinks.append(sink);
sinks.append(sink[:-3] + 'raw');
else:
raise RuntimeError('copyData: sink extension %s not raw or mhd' % sinkExt);
for i in range(2):
io.copyData(sources[i], sinks[i]);
return sink;
def makeColorAnnotations(filename, labeledImage=None):
if labeledImage is None:
labeledImage = DefaultLabeledImageFile
li = io.readData(labeledImage)
dsize = li.shape
lr = numpy.zeros(dsize, dtype=numpy.uint8)
lg = lr.copy()
lb = lr.copy()
global Label
maxlabel = max(Label.ids)
colarray = numpy.zeros((maxlabel, 3))
for i in Label.ids:
colarray[i - 1, :] = Label.color(i)
for i in Label.ids:
ll = li == i
lr[ll] = colarray[i - 1, 0]
lg[ll] = colarray[i - 1, 1]
lb[ll] = colarray[i - 1, 2]
io.writeData(filename + "_r.tif", lr)
io.writeData(filename + "_g.tif", lg)
io.writeData(filename + "_b.tif", lb)
return (lr, lg, lb)
def voxelize(points, dataSize = None, sink = None, voxelizeParameter = None, method = 'Spherical', size = (5,5,5), weights = None):
"""Converts a list of points into an volumetric image array
Arguments:
points (array): point data array
dataSize (tuple): size of final image
sink (str, array or None): the location to write or return the resulting voxelization image, if None return array
voxelizeParameter (dict):
========== ==================== ===========================================================
Name Type Descritption
========== ==================== ===========================================================
*method* (str or None) method for voxelization: 'Spherical', 'Rectangular' or 'Pixel'
*size* (tuple) size parameter for the voxelization
*weights* (array or None) weights for each point, None is uniform weights
========== ==================== ===========================================================
Returns:
(array): volumetric data of smeared out points
"""
if dataSize is None:
dataSize = tuple(int(math.ceil(points[:,i].max())) for i in range(points.shape[1]));
elif isinstance(dataSize, basestring):
dataSize = io.dataSize(dataSize);
points = io.readPoints(points);
if method.lower() == 'spherical':
if weights is None:
data = vox.voxelizeSphere(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2]);
else:
data = vox.voxelizeSphereWithWeights(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2], weights);
elif method.lower() == 'rectangular':
if weights is None:
data = vox.voxelizeRectangle(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2]);
else:
data = vox.voxelizeRectangleWithWeights(points.astype('float'), dataSize[0], dataSize[1], dataSize[2], size[0], size[1], size[2], weights);
elif method.lower() == 'pixel':
data = voxelizePixel(points, dataSize, weights);
else:
raise RuntimeError('voxelize: mode: %s not supported!' % method);
return io.writeData(sink, data);
def overlayPoints(dataSource, pointSource, sink = None, pointColor = [1,0,0], x = all, y = all, z = all):
"""Overlay points on 3D data and return as color image
Arguments:
dataSouce (str or array): volumetric image data
pointSource (str or array): point data to be overlayed on the image data
pointColor (array): RGB color for the overlayed points
x, y, z (all or tuple): sub-range specification
Returns:
(str or array): image overlayed with points
See Also:
:func:`overlayLabel`
"""
data = io.readData(dataSource, x = x, y = y, z = z);
points = io.readPoints(pointSource, x = x, y = y, z = z, shift = True);
#print data.shape
if not pointColor is None:
dmax = data.max(); dmin = data.min();
if dmin == dmax:
dmax = dmin + 1;
cimage = numpy.repeat( (data - dmin) / (dmax - dmin), 3);
cimage = cimage.reshape(data.shape + (3,));
if data.ndim == 2:
for p in points: # faster version using voxelize ?
cimage[p[0], p[1], :] = pointColor;
elif data.ndim == 3:
for p in points: # faster version using voxelize ?
cimage[p[0], p[1], p[2], :] = pointColor;
else:
raise RuntimeError('overlayPoints: data dimension %d not suported' % data.ndim);
else:
cimage = vox.voxelize(points, data.shape, method = 'Pixel');
cimage = cimage.astype(data.dtype) * data.max();
data.shape = data.shape + (1,);
cimage.shape = cimage.shape + (1,);
cimage = numpy.concatenate((data, cimage), axis = 3);
#print cimage.shape
return io.writeData(sink, cimage);
def removeBackground(img, removeBackgroundParameter = None, size = None, save = None, verbose = False,
subStack = None, out = sys.stdout, **parameter):
"""Remove background via subtracting a morphological opening from the original image
Background removal is done z-slice by z-slice.
Arguments:
img (array): image data
removeBackGroundParameter (dict):
========= ==================== ===========================================================
Name Type Descritption
========= ==================== ===========================================================
*size* (tuple or None) size for the structure element of the morphological opening
if None, do not correct for any background
*save* (str or None) file name to save result of this operation
if None dont save to file
*verbose* (bool or int) print / plot information about this step
========= ==================== ===========================================================
subStack (dict or None): sub-stack information
verbose (bool): print progress info
out (object): object to write progress info to
Returns:
array: background corrected image
"""
size = getParameter(removeBackgroundParameter, "size", size);
save = getParameter(removeBackgroundParameter, "save", save);
verbose = getParameter(removeBackgroundParameter, "verbose", verbose);
if verbose:
writeParameter(out = out, head = 'Background Removal:', size = size, save = save);
if size is None:
return img;
img = io.readData(img);
timer = Timer();
# background subtraction in each slice
se = structureElement('Disk', size).astype('uint8');
for z in range(img.shape[2]):
#img[:,:,z] = img[:,:,z] - grey_opening(img[:,:,z], structure = structureElement('Disk', (30,30)));
#img[:,:,z] = img[:,:,z] - morph.grey_opening(img[:,:,z], structure = self.structureELement('Disk', (150,150)));
img[:,:,z] = img[:,:,z] - cv2.morphologyEx(img[:,:,z], cv2.MORPH_OPEN, se)
if not save is None:
writeSubStack(save, img, subStack = subStack)
if verbose > 1:
plotTiling(10*img);
if verbose:
out.write(timer.elapsedTime(head = 'Background') + '\n');
return img