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 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 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 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 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 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 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 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 openData(dataSource, x = all, y = all, z = all, inverse = False, 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);
temp= True
dSize = dataSource.shape;
else:
filename = dataSource;
temp = False;
dSize = io.dataSize(dataSource);
colorImage = len(dSize) == 4;
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");';
else:
macro = ('open("%s");' % filename);
cmd = ImageJBinary + " -eval '%s'" % macro;
print 'running: %s' % cmd
res = os.system(cmd);
if res != 0:
raise RuntimeError('openData: failed executing: ' + cmd);
if cleanUp and temp:
os.remove(filename);
filepath, imagename = os.path.split(filename);
imagename = imagename[:-4] + '.raw';
os.remove(os.path.join(filepath, imagename));
return macro;
def testCompletedCumulativesInSpheres(points1, intensities1, points2, intensities2, dataSize = lbl.DefaultLabeledImageFile, radius = 100, method = 'AndresonDarling'):
"""Performs completed cumulative distribution tests for each pixel using points in a ball centered at that cooridnates, returns 4 arrays p value, statistic value, number in each group"""
#TODO: sinple implementation -> slow -> speed up
dataSize = io.dataSize(dataSize);
if len(dataSize) != 3:
raise RuntimeError('dataSize expected to be 3d');
# distances^2 to origin
x1= points1[:,0]; y1 = points1[:,1]; z1 = points1[:,2]; i1 = intensities1;
d1 = x1 * x1 + y1 * y1 + z1 * z1;
x2 = points2[:,0]; y2 = points2[:,1]; z2 = points2[:,2]; i2 = intensities2;
d2 = x2 * x2 + y2 * y2 + z2 * z2;
r2 = radius * radius; # TODO: inhomogenous in 3d !
p = numpy.zeros(dataSize);
s = numpy.zeros(dataSize);
n1 = numpy.zeros(dataSize, dtype = 'int');
n2 = numpy.zeros(dataSize, dtype = 'int');
for x in range(dataSize[0]):
#print x
for y in range(dataSize[1]):
#print y
for z in range(dataSize[2]):
#print z
d11 = d1 - 2 * (x * x1 + y * y1 + z * z1) + (x*x + y*y + z*z);
d22 = d2 - 2 * (x * x2 + y * y2 + z * z2) + (x*x + y*y + z*z);
ii1 = d11 < r2;
ii2 = d22 < r2;
n1[x,y,z] = ii1.sum();
n2[x,y,z] = ii2.sum();
if n1[x,y,z] > 0 and n2[x,y,z] > 0:
(pp, ss) = self.testCompletedCumulatives((i1[ii1], i2[ii2]), method = method);
else:
pp = 0; ss = 0;
p[x,y,z] = pp;
s[x,y,z] = ss;
return (p,s,n1,n2);
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 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 dataSize(filename, **args):
"""Returns size of data stored as a file list
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
tuple: data size
"""
fp, fl = readFileList(filename)
nz = len(fl)
d2 = io.dataSize(os.path.join(fp, fl[0]))
if not len(d2) == 2:
raise RuntimeError("FileList: importing multiple files of dim %d not supported!" % len(d2))
dims = d2 + (nz,)
return io.dataSizeFromDataRange(dims, **args)
def dataSize(filename, **args):
"""Returns size of data stored as a file list
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
tuple: data size
"""
fp, fl = readFileList(filename)
nz = len(fl)
d2 = io.dataSize(os.path.join(fp, fl[0]))
if not len(d2) == 2:
raise RuntimeError(
"FileList: importing multiple files of dim %d not supported!" %
len(d2))
dims = d2 + (nz, )
return io.dataSizeFromDataRange(dims, **args)
datadir ='/home/mtllab/Documents/th/';
fn = os.path.join(datadir, r'160412_mosaic_15-20-19/15-20-19_mosaic_UltraII\[(?P<row>\d{2}) x (?P<col>\d{2})\]_C00_xyz-Table Z(?P<z>\d{4}).ome.tif')
_, gr = st.findFileList(fn , sort = True, groups = ['row','col'], absolute = True)
groups = [];
for i in range(gr.shape[1]):
groups.append(np.unique(gr[:,i]));
print groups
for i in groups[0]:
for j in groups[1]:
fileExpression = os.path.join(datadir, r'160412_mosaic_15-20-19/15-20-19_mosaic_UltraII\[%s x %s]_C00_xyz-Table Z\d{4}.ome.tif' % (i,j))
io.dataSize(fileExpression)
io.readMetaData(fileExpression, info = ['size', 'overlap', 'resolution'])
import ClearMap.IO.FileList as fl;
reload(fl)
fncrop = os.path.join(datadir, r'cropped/15-20-19_mosaic_UltraII_%s_x_%s_C00_xyz-Table Z\d{4}.ome.tif' % (i,j))
fc = fl.cropData(fileExpression, fncrop, x = (400, -400), y = (550, -550), adjustOverlap = True, processes = all)
#fc1 = fl.firstFile(fc)
#io.readMetaData(fc1, info = ['overlap', 'resolution', 'size']);
def findFileInfo(filename):
"""Tries to infer relevant information from filename for tiling / stitching
Arguments:
filename (str): filename to infer information from
Returns:
dict: dictionary with relavant information: resolution (microns per pixel), overlap (microns), size (pixel)
Note:
resolution is in microns per pixel, overlap is in microns and size is in pixel
"""
# TODO: move this to the individual file readers
# this is for ome tif images
from PIL import Image
from PIL.ExifTags import TAGS
def ome_info(fn):
ret = {}
i = Image.open(fn)
info = i.tag.as_dict()
for tag, value in info.iteritems():
decoded = TAGS.get(tag)
ret[decoded] = value
return ret
imginfo = ome_info(filename)
keys = imginfo.keys()
finfo = {"resolution": None, "overlap": None, "size": None}
# get image sizes
if ("ImageHeight" in keys) and ("ImageWidth" in keys):
finfo["size"] = (imginfo["ImageWidth"], imginfo["ImageHeight"])
else:
finfo["size"] = io.dataSize(filename)
if "ImageDescription" in keys:
imgxml = imginfo["ImageDescription"]
if isinstance(imgxml, tuple):
imgxml = imgxml[0]
imgxml = etree.fromstring(str(imgxml))
# get resolution
pix = [x for x in imgxml.iter("{*}Pixels")]
if len(pix) > 0:
pix = pix[0].attrib
keys = pix.keys()
if "PhysicalSizeX" in keys and "PhysicalSizeY" in keys and "PhysicalSizeZ" in keys:
finfo["resolution"] = (
float(pix["PhysicalSizeX"]),
float(pix["PhysicalSizeY"]),
float(pix["PhysicalSizeZ"]),
)
# get overlap
e1 = [x for x in imgxml.iter("{*}xyz-Table_X_Overlap")]
e2 = [x for x in imgxml.iter("{*}xyz-Table_Y_Overlap")]
if len(e1) > 0 and len(e2) > 0:
finfo["overlap"] = (float(e1[0].attrib["Value"]), float(e2[0].attrib["Value"]))
return finfo
def correctIllumination(img, correctIlluminationParameter = None, flatfield = None, background = None, scaling = None, save = None, verbose = False,
subStack = None, out = sys.stdout, **parameter):
"""Correct illumination variations
The intensity image :math:`I(x)` given a flat field :math:`F(x)` and
a background :math:`B(x)` the image is corrected to :math:`C(x)` as:
.. math:
C(x) = \\frac{I(x) - B(x)}{F(x) - B(x)}
If the background is not given :math:`B(x) = 0`.
The correction is done slice by slice assuming the data was collected with
a light sheet microscope.
The image is finally optionally scaled.
Arguments:
img (array): image data
findCenterOfMaximaParameter (dict):
============ ==================== ===========================================================
Name Type Descritption
============ ==================== ===========================================================
*flatfield* (str, None or array) flat field intensities, if None d onot correct image for
illumination, if True the
*background* (str, None or array) background image as file name or array
if None background is assumed to be zero
*scaling* (str or None) scale the corrected result by this factor
if 'max'/'mean' scale to keep max/mean invariant
*save* (str or None) save the corrected image 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: illumination corrected image
References:
Fundamentals of Light Microscopy and Electronic Imaging, p 421
See Also:
:const:`DefaultFlatFieldLineFile`
"""
flatfield = getParameter(correctIlluminationParameter, "flatfield", flatfield);
background = getParameter(correctIlluminationParameter, "background", background);
scaling = getParameter(correctIlluminationParameter, "scaling", scaling);
save = getParameter(correctIlluminationParameter, "save", save);
verbose = getParameter(correctIlluminationParameter, "verbose", verbose);
if verbose:
if flatfield is None or isinstance(flatfield, str) or flatfield is True:
fld = flatfield;
else:
fld = "image of size %s" % str(flatfield.shape);
if background is None or isinstance(background, str):
bkg = background;
else:
bkg = "image of size %s" % str(background.shape);
writeParameter(out = out, head = 'Illumination correction:', flatfield = fld, background = bkg, scaling = scaling, save = save);
print subStack;
if not subStack is None:
x = subStack["x"];
y = subStack["y"];
else:
x = all;
y = all;
#print "sizes", x, y, img.shape
#read data
timer = Timer();
if flatfield is None:
return img;
elif flatfield is True:
# default flatfield correction
if subStack is None:
flatfield = flatfieldFromLine(DefaultFlatFieldLineFile, img.shape[0]);
else:
dataSize = io.dataSize(subStack["source"]);
flatfield = flatfieldFromLine(DefaultFlatFieldLineFile, dataSize[0]);
elif isinstance(flatfield, str):
# point or image file
if io.isPointFile(flatfield):
if subStack is None:
flatfield = flatfieldFromLine(flatfield, img.shape[0]);
else:
dataSize = io.dataSize(subStack["source"]);
flatfield = flatfieldFromLine(flatfield, dataSize[0]);
else:
flatfield = io.readData(flatfield);
ffmean = flatfield.mean();
ffmax = flatfield.max();
#correct for subset
flatfield = io.readData(flatfield, x = x, y = y);
background = io.readData(background, x = x, y = y);
if flatfield.shape != img[:,:,0].shape:
raise RuntimeError("correctIllumination: flatfield does not match image size: %s vs %s" % (flatfield.shape, img[:,:,0].shape));
#convert to float for scaling
dtype = img.dtype;
img = img.astype('float32');
flatfield = flatfield.astype('float32');
# illumination correction in each slice
if background is None:
for z in range(img.shape[2]):
img[:,:,z] = img[:,:,z] / flatfield;
else:
if background.shape != flatfield.shape:
raise RuntimeError("correctIllumination: background does not match image size: %s vs %s" % (background.shape, img[:,:,0].shape));
background = background.astype('float32');
flatfield = (flatfield - background);
for z in range(img.shape[2]):
img[:,:,z] = (img[:,:,z] - background) / flatfield;
# rescale
if scaling is True:
scaling = "mean";
if isinstance(scaling, str):
if scaling.lower() == "mean":
# scale back by average flat field correction:
sf = ffmean;
elif scaling.lower() == "max":
sf = ffmax;
else:
raise RuntimeError('Scaling not "Max" or "Mean" but %s' % scaling);
else:
sf = scaling;
if verbose:
writeParameter(out = out, head = 'Illumination correction:', scaling = sf);
if not sf is None:
img = img * sf;
img = img.astype(dtype);
#write result for inspection
if not save is None:
writeSubStack(save, img, subStack = subStack);
#plot result for inspection
if verbose > 1:
plotTiling(img);
if verbose:
out.write(timer.elapsedTime(head = 'Illumination correction') + '\n');
return img
def correctIllumination(img,
correctIlluminationParameter=None,
flatfield=None,
background=None,
scaling=None,
save=None,
verbose=False,
subStack=None,
out=sys.stdout,
**parameter):
"""Correct illumination variations
The intensity image :math:`I(x)` given a flat field :math:`F(x)` and
a background :math:`B(x)` the image is corrected to :math:`C(x)` as:
.. math:
C(x) = \\frac{I(x) - B(x)}{F(x) - B(x)}
If the background is not given :math:`B(x) = 0`.
The correction is done slice by slice assuming the data was collected with
a light sheet microscope.
The image is finally optionally scaled.
Arguments:
img (array): image data
findCenterOfMaximaParameter (dict):
============ ==================== ===========================================================
Name Type Descritption
============ ==================== ===========================================================
*flatfield* (str, None or array) flat field intensities, if None d onot correct image for
illumination, if True the
*background* (str, None or array) background image as file name or array
if None background is assumed to be zero
*scaling* (str or None) scale the corrected result by this factor
if 'max'/'mean' scale to keep max/mean invariant
*save* (str or None) save the corrected image 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: illumination corrected image
References:
Fundamentals of Light Microscopy and Electronic Imaging, p 421
See Also:
:const:`DefaultFlatFieldLineFile`
"""
flatfield = getParameter(correctIlluminationParameter, "flatfield",
flatfield)
background = getParameter(correctIlluminationParameter, "background",
background)
scaling = getParameter(correctIlluminationParameter, "scaling", scaling)
save = getParameter(correctIlluminationParameter, "save", save)
verbose = getParameter(correctIlluminationParameter, "verbose", verbose)
if verbose:
if flatfield is None or isinstance(flatfield,
str) or flatfield is True:
fld = flatfield
else:
fld = "image of size %s" % str(flatfield.shape)
if background is None or isinstance(background, str):
bkg = background
else:
bkg = "image of size %s" % str(background.shape)
writeParameter(out=out,
head='Illumination correction:',
flatfield=fld,
background=bkg,
scaling=scaling,
save=save)
print subStack
if not subStack is None:
x = subStack["x"]
y = subStack["y"]
else:
x = all
y = all
#print "sizes", x, y, img.shape
#read data
timer = Timer()
if flatfield is None:
return img
elif flatfield is True:
# default flatfield correction
if subStack is None:
flatfield = flatfieldFromLine(DefaultFlatFieldLineFile,
img.shape[0])
else:
dataSize = io.dataSize(subStack["source"])
flatfield = flatfieldFromLine(DefaultFlatFieldLineFile,
dataSize[0])
elif isinstance(flatfield, str):
# point or image file
if io.isPointFile(flatfield):
if subStack is None:
flatfield = flatfieldFromLine(flatfield, img.shape[0])
else:
dataSize = io.dataSize(subStack["source"])
flatfield = flatfieldFromLine(flatfield, dataSize[0])
else:
flatfield = io.readData(flatfield)
ffmean = flatfield.mean()
ffmax = flatfield.max()
#correct for subset
flatfield = io.readData(flatfield, x=x, y=y)
background = io.readData(background, x=x, y=y)
if flatfield.shape != img[:, :, 0].shape:
raise RuntimeError(
"correctIllumination: flatfield does not match image size: %s vs %s"
% (flatfield.shape, img[:, :, 0].shape))
#convert to float for scaling
dtype = img.dtype
img = img.astype('float32')
flatfield = flatfield.astype('float32')
# illumination correction in each slice
if background is None:
for z in range(img.shape[2]):
img[:, :, z] = img[:, :, z] / flatfield
else:
if background.shape != flatfield.shape:
raise RuntimeError(
"correctIllumination: background does not match image size: %s vs %s"
% (background.shape, img[:, :, 0].shape))
background = background.astype('float32')
flatfield = (flatfield - background)
for z in range(img.shape[2]):
img[:, :, z] = (img[:, :, z] - background) / flatfield
# rescale
if scaling is True:
scaling = "mean"
if isinstance(scaling, str):
if scaling.lower() == "mean":
# scale back by average flat field correction:
sf = ffmean
elif scaling.lower() == "max":
sf = ffmax
else:
raise RuntimeError('Scaling not "Max" or "Mean" but %s' % scaling)
else:
sf = scaling
if verbose:
writeParameter(out=out, head='Illumination correction:', scaling=sf)
if not sf is None:
img = img * sf
img = img.astype(dtype)
#write result for inspection
if not save is None:
writeSubStack(save, img, subStack=subStack)
#plot result for inspection
if verbose > 1:
plotTiling(img)
if verbose:
out.write(timer.elapsedTime(head='Illumination correction') + '\n')
return img
def cropData(source, sink=None, x=all, y=all, z=all, adjustOverlap=False, verbose=True, processes=all):
"""Crop source from start to stop point
Arguments:
source (str or array): filename or data array of source
sink (str or None): filename or sink
x,y,z (tuple or all): the range to crop the data to
adjustOverlap (bool): correct overlap meta data if exists
Return:
str or array: array or filename with cropped data
"""
if sink is None:
return readDataFiles(source, x=x, y=y, z=z)
else: # sink assumed to be file expression
if not io.isFileExpression(sink):
raise RuntimeError("cropping data to different format not supported!")
fileheader, fileext, digitfrmt = splitFileExpression(sink)
# read first image to get data size and type
fp, fl = readFileList(source)
nz = len(fl)
rz = io.toDataRange(nz, r=z)
if adjustOverlap: # change overlap in first file
try:
fn = os.path.join(fp, fl[0])
info = io.readMetaData(fn, info=["description", "overlap", "resolution"])
description = str(info["description"])
overlap = numpy.array(info["overlap"], dtype=float)
resolution = numpy.array(info["resolution"], dtype=float)
except:
raise RuntimeWarning("could not modify overlap!")
fullsize = io.dataSize(fn)
data = io.readData(fn, x=x, y=y)
# overlap in pixels
poverlap = overlap[:2] / resolution[:2]
print poverlap
# cropped pixel
xr = io.toDataRange(fullsize[0], r=x)
yr = io.toDataRange(fullsize[1], r=y)
print xr
print yr
print fullsize
poverlap[0] = poverlap[0] - xr[0] - (fullsize[0] - xr[1])
poverlap[1] = poverlap[1] - yr[0] - (fullsize[1] - yr[1])
print poverlap
# new overlap in microns
overlap = poverlap * resolution[:2]
# check for consistency
if numpy.abs(fullsize[0] - xr[1] - xr[0]) > 1 or numpy.abs(fullsize[1] - yr[1] - yr[0]) > 1:
raise RuntimeWarning("cropping is inconsistent with overlap )modification!")
# change image description
import ClearMap.IO.TIF as CMTIF
description = CMTIF.changeOMEMetaDataString(description, {"overlap": overlap})
print len(description)
# write first file
fnout = fileheader + (digitfrmt % 0) + fileext
io.writeData(fnout, data, info=description)
zr = range(rz[0] + 1, rz[1])
else:
zr = range(rz[0], rz[1])
print zr
nZ = len(zr)
if processes is None:
processes = 1
if processes is all:
processes = multiprocessing.cpu_count()
if processes > 1: # parallel processing
pool = multiprocessing.Pool(processes=processes)
argdata = []
for i, z in enumerate(zr):
if verbose:
argdata.append(
(os.path.join(fp, fl[z]), fileheader + (digitfrmt % (i + 1)) + fileext, x, y, (i + 1), (nZ + 1))
)
else:
argdata.append(
(os.path.join(fp, fl[z]), fileheader + (digitfrmt % (i + 1)) + fileext, x, y, None, None)
)
pool.map(_cropParallel, argdata)
else: # sequential processing
for i, z in enumerate(zr):
if verbose:
print "cropData: corpping image %d / %d" % (i + 1, nZ + 1)
fileSource = os.path.join(fp, fl[z])
data = io.readData(fileSource, x=x, y=y)
fileSink = fileheader + (digitfrmt % (i + 1)) + fileext
io.writeData(fileSink, data)
return sink
