def loadTiffStack(fname,useLibTiff=False):
"""
Read a TIFF stack.
We're using tifflib by default as, right now, only this works when the application is compile on Windows. [17/08/15]
Bugs: known to fail with tiffs produced by Icy [23/07/15]
"""
if not os.path.exists(fname):
print "imageStackLoader.loadTiffStack can not find %s" % fname
return
purePython = True
if useLibTiff:
from libtiff import TIFFfile
import numpy as np
tiff = TIFFfile(fname)
samples, sample_names = tiff.get_samples() #we should have just one
print "Loading:\n" + tiff.get_info() + " with libtiff\n"
im = np.asarray(samples[0])
else:
print "Loading:\n" + fname + " with tifffile\n"
from tifffile import imread
im = imread(fname)
im=im.swapaxes(1,2)
print "read image of size: cols: %d, rows: %d, layers: %d" % (im.shape[1],im.shape[2],im.shape[0])
return im
def load_tiff_stack(fname, use_lib_tiff=False):
"""
Read a TIFF stack.
We're using tifflib by default as, right now, only this works when the application is compile on Windows. [17/08/15]
Bugs: known to fail with tiffs produced by Icy [23/07/15]
"""
if not check_file_exists(fname, "load_tiff_stack"):
return
if use_lib_tiff:
try:
from libtiff import TIFFfile
except ImportError: # Suppresses error in IDE when libtiff not installed
raise
tiff = TIFFfile(fname)
samples, sample_names = tiff.get_samples() # we should have just one
print("Loading: " + tiff.get_info() + " with libtiff\n")
im = np.asarray(samples[0])
else:
print("Loading: " + fname + " with tifffile\n")
from tifffile import imread
im = imread(fname)
im = im.swapaxes(1, 2)
print("read image of size: cols: %d, rows: %d, layers: %d" %
(im.shape[1], im.shape[2], im.shape[0]))
return im
def process(self, options=None, validate=default_validate):
template_xml = list(self.make_xml())
s = None
for (detector, fn, uuid), tif_image in self.tif_images.items ():
xml= ome.OME(ATTR('xsi','schemaLocation',"%s %s/ome.xsd" % ((namespace_map['ome'],)*2)),
UUID = uuid)
for item in template_xml:
if item.tag.endswith('Image') and item.get('ID')!='Image:%s' % (detector):
continue
if item.tag.endswith('Instrument'):
if detector=='Confocal':
instrument = 'Airy'
elif detector in ['Imperx', 'Andor']:
instrument = 'Suga'
else:
instrument = None
if instrument and item.get('ID')!='Instrument:%s' % (instrument):
continue
xml.append(item)
if s is None and validate:
s = etree.tostring(xml, pretty_print = True, xml_declaration=True)
#print s
validate_xml(xml)
else:
s = etree.tostring(xml, pretty_print = True, xml_declaration=True)
tif_image.description = s
if detector=='Confocal':
c = tif_image.write_file(fn, compression='lzw')
if c<1.0:
print 'Resetting compression to none'
tif_image.write_file(fn, compression='none')
else:
tif_image.write_file(fn, compression='none')
if validate and 0:
print 'Validating written data..',
from libtiff import TIFFfile
t = TIFFfile(fn)
samples, sample_names = t.get_samples()
assert len (sample_names)==1,`sample_names`
samples = samples[0]
samples_orig = tif_image.data
if (samples != samples_orig).any():
print 'DATA CORRUPTION DETECTED!!'
print 'original data:', samples_orig.dtype, samples_orig.shape, samples_orig.nbytes
print 'written data:', samples.dtype, samples.shape, samples.nbytes
diff = samples - samples_orig
ia,ja,ka = diff.nonzero()
print len(ia)
print ia[:10]
print ja[:10]
print ka[:10]
print samples[ia[:10],ja[:10], ka[:10]]
print samples_orig[ia[:10],ja[:10], ka[:10]]
else:
print 'SUCCESS!'
#validate = False
return s
def test_rw_rgb():
itype = uint8
dt = dtype(dict(names=list('rgb'), formats=[itype] * 3))
image = zeros((2, 3), dtype=dt)
image['r'][:, 0] = 250
image['g'][:, 1] = 251
image['b'][:, 2] = 252
fn = mktemp('.tif')
tif = TIFFimage(image)
tif.write_file(fn, compression='lzw') #, samples='rgb')
del tif
tif = TIFFfile(fn)
data, names = tif.get_samples()
#os.remove(fn)
atexit.register(os.remove, fn)
print image
print data
assert itype == data[0].dtype, ` itype, data[0].dtype `
assert (image['r'] == data[0]).all()
assert (image['g'] == data[1]).all()
assert (image['b'] == data[2]).all()
def test_write_read():
for compression in [None, 'lzw']:
for itype in [
uint8, uint16, uint32, uint64, int8, int16, int32, int64,
float32, float64, complex64, complex128
]:
image = array([[1, 2, 3], [4, 5, 6]], itype)
fn = mktemp('.tif')
if 0:
tif = TIFF.open(fn, 'w')
tif.write_image(image, compression=compression)
tif.close()
else:
tif = TIFFimage(image)
tif.write_file(fn, compression=compression)
del tif
tif = TIFFfile(fn)
data, names = tif.get_samples()
assert names == ['sample0'], repr(names)
assert len(data) == 1, repr(len(data))
assert image.dtype == data[0].dtype, repr(
(image.dtype, data[0].dtype))
assert (image == data[0]).all()
#os.remove(fn)
atexit.register(os.remove, fn)
def readTiff(fileName):
"""
Read a tiff file into a numpy array
Usage: img = readTiff(fileName)
"""
tiff = TIFFfile(fileName)
samples, sample_names = tiff.get_samples()
outList = []
for sample in samples:
outList.append(np.copy(sample))
out = np.concatenate(outList, axis=-1)
tiff.close()
return out
def read(fileName):
"""
Script to import tif file from imageJ,
usage: zstack = tiff.read(inFileName)
PTW 2015/01/29
"""
tiff = TIFFfile(fileName)
samples, sample_names = tiff.get_samples()
outList = []
for sample in samples:
outList.append(np.copy(sample)[..., np.newaxis])
out = np.concatenate(outList, axis=-1)
out = np.rollaxis(out, 0, 3)
out = np.flipud(out)
tiff.close()
return out
def read(fileName):
"""
Script to import tif file from imageJ,
usage: zstack = tiff.read(inFileName)
PTW 2015/01/29
"""
tiff = TIFFfile(fileName)
samples, sample_names = tiff.get_samples()
outList = []
for sample in samples:
outList.append(np.copy(sample)[...,np.newaxis])
out = np.concatenate(outList,axis=-1)
out = np.rollaxis(out,0,3)
out = np.flipud(out)
tiff.close()
return out
def read_tif(fn, div2=False, oldmethod=False, offset2=False):
"""Reads a 3D TIFF file and returns a 3D numpy matrix
from a path
Inputs :
- fn (string): the path of the 3D TIFF file
- div2 (bool): if `True`, only one every to planes in $z$ will be loaded
Returns :
- a 3D numpy matrix
"""
if offset2:
kp = 1
else:
kp = 0
if not oldmethod:
ti = TIFFfile(fn)
ti = ti.get_samples()[0][0].swapaxes(0, 2).swapaxes(0, 1)
if div2:
I = []
for i in range(ti.shape[2]):
j = ti[:, :, i]
if i % 2 == kp:
I.append(j)
ti = np.zeros((I[0].shape[0], I[0].shape[1], len(I)))
for (i, j) in enumerate(I):
ti[:, :, i] = j
return ti
else: # Kept for compatibility. Fails to load some 16 bits images.
im = TIFF.open(fn)
I = []
for (j, i) in enumerate(im.iter_images()):
if div2 and (j + 1) % 2 == 0:
pass
else:
I.append(i)
ret = np.zeros((I[0].shape[0], I[1].shape[1], len(I)))
for (i, ii) in enumerate(I):
ret[:, :, i] = ii
return ret
def run():
images = TIFFfile(args.image)
labels = TIFFfile(args.label)
samples, _ = images.get_samples()
images = np.array(samples).transpose([1, 2, 3, 0])
images = np.array([cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) for im in images])
samples, _ = labels.get_samples()
labels = np.array(samples).transpose([1, 2, 3, 0])
labels = np.array(
[cv2.cvtColor(label, cv2.COLOR_BGR2GRAY) for label in labels])
m = np.array([int(x / 2) for x in args.shape])
seg = labels.copy()
corner, partitions = compute_partitions(seg[...],
[float(x) for x in args.thr], m,
args.min_size)
print(corner)
totals = defaultdict(int) # partition -> voxel count
indices = defaultdict(list) # partition -> [(vol_id, 1d index)]
vol_shapes = partitions.shape
uniques, counts = np.unique(partitions, return_counts=True)
for val, cnt in zip(uniques, counts):
if val == 255:
continue
totals[val] += cnt
indices[val].extend(
[flat_index for flat_index in np.flatnonzero(partitions == val)])
max_count = max(totals.values())
indices = np.concatenate([
np.resize(np.random.permutation(v), max_count)
for v in indices.values()
],
axis=0)
np.random.shuffle(indices)
coor = []
for coord_idx in indices:
z, y, x = np.unravel_index(coord_idx, vol_shapes)
coor.append([z + m[2], y + m[1], x + m[0]])
with h5py.File(args.save, 'w') as f:
f.create_dataset('image', data=images, compression='gzip')
f.create_dataset('label', data=labels, compression='gzip')
f.create_dataset('coor', data=coor, compression='gzip')
def test_write_read():
for compression in ['none', 'lzw']:
for itype in [
uint8, uint16, uint32, uint64, int8, int16, int32, int64,
float32, float64, complex64, complex128
]:
image = array([[1, 2, 3], [4, 5, 6]], itype)
fn = mktemp('.tif')
tif = TIFFimage(image)
tif.write_file(fn, compression=compression)
del tif
tif = TIFFfile(fn)
data, names = tif.get_samples()
#os.remove(fn)
atexit.register(os.remove, fn)
assert names == ['sample0'], ` names `
assert len(data) == 1, ` len(data) `
assert image.dtype == data[0].dtype, ` image.dtype, data[0].dtype `
assert (image == data[0]).all()
def _get_description(self):
self.is_ok = False
if not os.path.isfile (self.file_name):
if os.path.exists (self.file_name):
if os.path.isdir (self.file_name):
files = []
for ext in ['tif', 'lsm']:
files += glob.glob(self.file_name+'/*.'+ext)
n = len (self.file_name)
files = sorted([f[n+1:] for f in files])
return 'Directory contains:\n%s' % ('\n'.join (files))
return 'not a file'
return 'file does not exists'
if os.path.basename(self.file_name)=='configuration.txt':
return unicode(open(self.file_name).read(), errors='ignore')
raise NotImplementedError('opening configuration.txt data')
try:
tiff = TIFFfile(self.file_name, verbose=True)
except ValueError, msg:
return 'not a TIFF file\n%s' % (msg)
def test_simple_slicing():
for planar_config in [1, 2]:
for compression in [None, 'lzw']:
for itype in [
uint8, uint16, uint32, uint64, int8, int16, int32, int64,
float32, float64, complex64, complex128
]:
image = random.randint(0, 100, size=(10, 6, 7)).astype(itype)
fn = mktemp('.tif')
if 0:
if planar_config == 2:
continue
tif = TIFF.open(fn, 'w')
tif.write_image(image, compression=compression)
tif.close()
else:
tif = TIFFimage(image)
tif.write_file(fn,
compression=compression,
planar_config=planar_config)
del tif
tif = TIFFfile(fn)
arr = tif.get_tiff_array()
data = arr[:]
assert len(data) == len(image), repr(len(data))
assert image.dtype == data.dtype, repr(
(image.dtype, data[0].dtype))
assert (image == data).all()
assert arr.shape == image.shape
_indices = [0, slice(None), slice(0, 2), slice(0, 5, 2)]
for _i0 in _indices[:1]:
for i1 in _indices:
for i2 in _indices:
sl = (_i0, i1, i2)
assert (arr[sl] == image[sl]).all(), repr(sl)
tif.close()
atexit.register(os.remove, fn)
def test_issue19():
size = 1024 * 32 # 1GB
# size = 1024*63 # almost 4GB, test takes about 60 seconds but succeeds
image = ones((size, size), dtype=uint8)
print('image size:', image.nbytes / 1024**2, 'MB')
fn = mktemp('issue19.tif')
tif = TIFFimage(image)
try:
tif.write_file(fn)
except OSError as msg:
if 'Not enough storage is available to process this command'\
in str(msg):
# Happens in Appveyour CI
del tif
atexit.register(os.remove, fn)
return
else:
raise
del tif
tif = TIFFfile(fn)
tif.get_tiff_array()[:] # expected failure
tif.close()
atexit.register(os.remove, fn)
def _file_name_changed(self):
print self.file_name
if not os.path.isfile(self.file_name):
raise ValueError ("File does not exist: %r" % (self.file_name))
self.reset()
tiff_array_info = {}
tables_info = {}
if os.path.basename(self.file_name)=='configuration.txt':
tiff_files = {}
dir_path = os.path.dirname(self.file_name)
csv_files = glob.glob(os.path.join(dir_path, '*.csv'))
default_kind = 'image_timeseries'
for d in ['Imperx', 'Andor', 'Confocal']:
channel_label = '%s' % (d)
d_path = os.path.join(dir_path, '%s_index.txt' % (d))
if not os.path.isfile (d_path): # mari-ism support
d_path = os.path.join(dir_path, d, '%s_index.txt' % (d))
if os.path.isfile (d_path):
d_index = {}
time_map = defaultdict(lambda:[])
file_map = defaultdict(lambda:[])
for index, line in enumerate(open (d_path).readlines ()):
t, fn = line.strip().split()
t = float(t)
fn = os.path.join(os.path.dirname(d_path), fn)
d_index[t, index] = fn
time_map[fn].append(t)
file_map[t].append(fn)
if len (file_map)<=1:
default_kind = 'image_stack'
elif len (file_map[t])>1:
default_kind = 'image_stack_timeseries'
files = [d_index[k] for k in sorted (d_index)]
tiff = TiffFiles(files, time_map = time_map)
tiff_files[channel_label] = tiff
tiff_array_info[channel_label] = dict(channel=channel_label, subfile_type=0, sample_index=0,
assume_one_image_per_file=True)
tables = {}
for csv_path in csv_files:
print 'Reading',csv_path,'..'
name = os.path.basename(csv_path)[:-4]
titles = None
table_data = defaultdict(lambda:[])
for line in open(csv_path).readlines():
line = line.strip()
if not line:
continue
if titles is None:
titles = [title[1:-1] for title in line. split('\t')]
else:
data = line.split ('\t')
for title, value in zip (titles, data):
table_data[title].append (float(value))
tables[name] = table_data
print 'done'
for channel_label in tiff_files:
tables_info[channel_label] = tables
tiff = TiffChannelsAndFiles(tiff_files)
else:
tiff = TIFFfile(self.file_name)
default_kind = 'image_stack'
for subfile_type in tiff.get_subfile_types():
ifd = tiff.get_first_ifd(subfile_type=subfile_type)
depth = tiff.get_depth(subfile_type=subfile_type)
width = ifd.get_value('ImageWidth')
height = ifd.get_value('ImageLength')
if subfile_type!=0:
print '%s._file_name_changed: ignoring subfile_type %r' % (self.__class__.__name__, subfile_type)
continue
for i, (name, dtype) in enumerate(zip (ifd.get_sample_names(), ifd.get_sample_dtypes())):
tname = str(dtype)
channel_label = '%s: %s [%sx%sx%s %s]' % (subfile_type, name, depth, height, width, tname)
tiff_array_info[channel_label] = dict (subfile_type=subfile_type, sample_index=i)
self.kind = default_kind
self.tiff = tiff
try:
info = tiff.get_info()
except Exception, msg:
info = 'failed to get TIFF info: %s' % (msg)
def read_tif(filename,channel=0):
"""Read a tif image
:Parameters:
- `filename` (str) - name of the file to read
"""
# TIF reader
tif = libtiff.TIFF.open(filename)
if tif.GetField('ImageDescription'):
tif = TIFFfile(filename)
arr = tif.get_tiff_array()
_data = arr[:].T
info_str = tif.get_info()
else:
i = 1
while not tif.LastDirectory():
i+=1
tif.ReadDirectory()
tif.SetDirectory(0)
_data = np.zeros((i,)+tif.read_image().shape,dtype=tif.read_image().dtype)
for ii,i in enumerate(tif.iter_images()):
_data[ii] = i
_data = _data.transpose(2, 1, 0)
info_str = tif.info()
nx, ny, nz = _data.shape
# -- prepare metadata dictionnary --
info_dict = dict( filter( lambda x: len(x)==2,
(inf.split(':') for inf in info_str.split("\n"))
) )
info_dict.update(dict( filter( lambda x: len(x)==2,(inf.split('=') for inf in info_str.split("\n"))) ))
for k,v in info_dict.iteritems():
info_dict[k] = v.strip()
info_dict.update({'Filename':filename.split('/')[-1]})
print info_dict
# -- getting the voxelsizes from the tiff image: sometimes
# there is a BoundingBox attribute, sometimes there are
# XResolution, YResolution, ZResolution or spacing.
# the object returned by get_tiff_array has a "get_voxel_sizes()"
# method but it fails, so here we go. --
if "BoundingBox" in info_dict:
bbox = info_dict["BoundingBox"]
xm, xM, ym, yM, zm, zM = map(float,bbox.split())
_vx = (xM-xm)/nx
_vy = (yM-ym)/ny
_vz = (zM-zm)/nz
else:
# -- When we have [XYZ]Resolution fields, it describes the
# number of voxels per real unit. In SpatialImage we want the
# voxelsizes, which is the number of real units per voxels.
# So we must invert the result. --
if "XResolution" in info_dict:
# --resolution is stored in a [(values, precision)] list-of-one-tuple, or
# sometimes as a single number --
xres_str = eval(info_dict["XResolution"])
if isinstance(xres_str, list) and isinstance(xres_str[0], tuple):
xres_str = xres_str[0]
_vx = float(xres_str[0])/xres_str[1]
elif isinstance(xres_str, (int, float)):
_vx = float(xres_str)
else:
_vx = 1.
_vx = 1./_vx if _vx != 0 else 1.
else:
_vx = 1.0 # dumb fallback, maybe we will find something smarter later on
if "YResolution" in info_dict:
# --resolution is stored in a [(values, precision)] list-of-one-tuple, or
# sometimes as a single number --
yres_str = eval(info_dict["YResolution"])
if isinstance(yres_str, list) and isinstance(yres_str[0], tuple):
yres_str = yres_str[0]
_vy = float(yres_str[0])/yres_str[1]
elif isinstance(yres_str, (int, float)):
_vy = float(yres_str)
else:
_vy = 1.
_vy = 1./_vy if _vy != 0 else 1.
else:
_vy = 1.0 # dumb fallback, maybe we will find something smarter later on
if "ZResolution" in info_dict:
# --resolution is stored in a [(values, precision)] list-of-one-tuple, or
# sometimes as a single number --
zres_str = eval(info_dict["ZResolution"])
if isinstance(zres_str, list) and isinstance(zres_str[0], tuple):
zres_str = zres_str[0]
_vz = float(zres_str[0])/zres_str[1]
elif isinstance(zres_str, (int, float)):
_vz = float(zres_str)
else:
_vz = 1.
_vz = 1./_vz if _vz != 0 else 1.
else:
if "spacing" in info_dict:
_vz = eval(info_dict["spacing"])
else:
_vz = 1.0 # dumb fallback, maybe we will find something smarter later on
tif.close()
# -- dtypes are not really stored in a compatible way (">u2" instead of uint16)
# but we can convert those --
dt = np.dtype(_data.dtype.name)
# -- Return a SpatialImage please! --
im = SpatialImage(_data, dtype=dt)
im.resolution = _vx,_vy,_vz
im.info = info_dict
return im
def iter_Image(self, func):
sys.stdout.write('iter_Image: reading image data from TIFF files\n')
for detector in self.data:
sys.stdout.write(' detector: %s\n' % (detector))
d_index = self.data[detector]
# write the content of tiff files to a single raw files
f,fn,dtype = None, None, None
time_set = set()
mn, mx = None, None
mnz = float(self.config['PROTOCOL_Z_STACKER_Minimum'])
mxz = float(self.config['PROTOCOL_Z_STACKER_Maximum'])
nz = int(self.config['PROTOCOL_Z_STACKER_NumberOfFrames'])
if nz > 1:
dz = (mxz-mnz)/(nz-1)
else:
dz = 0
plane_l = []
ti = -1
exptime = '0'
if detector=='Confocal':
exptime = float(self.config['CONFOCAL_PixelAcqusitionTime']) * 1e-6
elif detector=='Andor':
exptime = self.config['CAMERA_ANDOR_ExposureTime']
elif detector=='Imperx':
for line in self.config['CAMERA_IMPERX_HardwareInformation'].split('\n'):
if line.startswith ('Exposure time:'):
v,u = line[14:].lstrip().split()
v = v.strip (); u = u.strip ()
if u=='usec': exptime = float(v)*1e-6
elif u=='msec': exptime = float(v)*1e-3
elif u=='sec': exptime = float(v)
else:
raise NotImplementedError (`v,u,line`)
else:
raise NotImplementedError(`detector`)
for t, index in sorted(d_index):
if t not in time_set:
time_set.add(t)
ti += 1
zi = 0
else:
zi += 1
z = mnz + dz * zi
d = dict(DeltaT=str(t), TheT=str(ti), TheZ = str(zi), PositionZ=str(z), TheC='0', ExposureTime=str(exptime))
plane_l.append(d)
tif = TIFFfile(d_index[t, index])
samples, sample_names = tif.get_samples()
assert len (sample_names)==1,`sample_names`
data = samples[0]
if mn is None:
mn, mx = data.min(), data.max()
else:
mn = min (data.min(), mn)
mx = min (data.max(), mx)
if f is None:
shape = list(data.shape)
dtype = data.dtype
fn = tempfile.mktemp(suffix='.raw', prefix='%s_%s_' % (detector, dtype))
f = open (fn, 'wb')
else:
assert dtype is data.dtype,`dtype,data.dtype`
shape[0] += 1
data.tofile(f)
sys.stdout.write('\r copying TIFF image data to RAW file: %5s%% done' % (int(100.0*(index+1)/len(d_index))))
sys.stdout.flush()
if f is None:
continue
f.close ()
shape = tuple (shape)
xsz = shape[2]
ysz = shape[1]
tsz = len(time_set)
zsz = shape[0] // tsz
order = 'XYZTC'
sys.stdout.write("\n RAW file contains %sx%sx%sx%sx%s [%s] array, dtype=%s, MIN/MAX=%s/%s\n" \
% (xsz, ysz,zsz,tsz,1,order, dtype, mn,mx))
assert zsz*tsz==shape[0],`zsz,tsz,shape`
tif_filename = '%s%s.ome.tif' % (self.file_prefix, detector)
sys.stdout.write(" creating memmap image for OME-TIF file %r..." % (tif_filename))
sys.stdout.flush()
mmap = numpy.memmap(fn, dtype=dtype, mode='r', shape=shape)
tif_image = TIFFimage(mmap)
atexit.register(os.remove, fn)
tif_uuid = self._mk_uuid()
self.tif_images[detector, tif_filename, tif_uuid] = tif_image
sys.stdout.write (' done\n')
sys.stdout.flush()
pixels_d = {}
channel_d = dict(SamplesPerPixel='1')
lpath_l = []
#channel_d todo: ExcitationWavelength, EmissionWavelength, Fluor, NDFilter, PockelCellSetting, Color
if detector in ['Confocal']:
objective = ome.ObjectiveSettings(ID='Objective:%s' % (self.config['olympus_optics_objective']))
instrument_id = 'Instrument:Airy'
pixels_d['PhysicalSizeX'] = str(self.config['CONFOCAL_PixelSizeX'])
pixels_d['PhysicalSizeY'] = str(self.config['CONFOCAL_PixelSizeY'])
pixels_d['TimeIncrement'] = str(self.config['CONFOCAL_TimeBetweenFrames'])
channel_d['Name'] = 'Confocal'
channel_d['IlluminationType'] = 'Epifluorescence'
channel_d['PinholeSize'] = '180'
# todo: FluorescenceCorrelationSpectroscopy
channel_d['AcquisitionMode'] = 'LaserScanningConfocalMicroscopy'
for i in range (1,5):
d1 = 'AOTFLine%s' % i
ft = confocal_filters.get(d1)
if ft is None:
continue
fn = ft['ex'][0]
fn = get_aotf_filter_name (fn, self.config)
if 'OFF' in fn:
continue
lpath_l.append(ome.ExcitationFilterRef(ID='Filter:%s:%s' % (d1,fn)))
fn = confocal_filters['OpticalTableSplitter']['di'][0]
lpath_l.append(ome.DichroicRef(ID='Dichroic:OpticalTableSplitter:%s' % (fn)))
d1 = 'ThorlabsWheelPosition%s' % (self.config['thorlabs_filter_wheel_position'][3])
ft = confocal_filters.get(d1)
if ft is not None:
fn = ft['em'][0]
lpath_l.append(ome.EmissionFilterRef (ID='Filter:%s:%s' % (d1,fn)))
elif detector in ['Andor', 'Imperx']:
objective = ome.ObjectiveSettings(ID='Objective:%s' % (self.config['optics_objective']))
instrument_id = 'Instrument:Suga'
channel_d['Name'] = '%s camera' % (detector)
channel_d['AcquisitionMode'] = 'WideField'
pixels_d['PhysicalSizeX'] = pixels_d['PhysicalSizeY'] = str(self.config['CAMERA_%s_PixelSize' % (detector.upper ())])
tbf = float(self.config['CAMERA_%s_TimeBetweenFrames' % (detector.upper ())])
d1 = 'NikonTurretTopCube%s' % (self.config['top_turret_cube'][3])
d2 = 'NikonTurretBottomCube%s' % (self.config['bottom_turret_cube'][3])
top_cube = nikon_filters[d1]
bottom_cube = nikon_filters[d2]
if detector=='Andor':
channel_d['IlluminationType'] = 'Epifluorescence'
if self.config['CAMERA_ANDOR_FrameTransferMode']=='1':
m = re.search(r'Kinetic cycle time:\s*(?P<time>\d+[.]\d*)\s*sec', self.config['CAMERA_ANDOR_HardwareInformation'], re.M)
pixels_d['TimeIncrement'] = str(m.group('time'))
else:
pixels_d['TimeIncrement'] = str(tbf)
if 'ex' in top_cube:
fn = top_cube['ex'][0]
lpath_l.append(ome.ExcitationFilterRef(ID='Filter:%s:%s' % (d1,fn)))
if 'di' in top_cube:
fn = top_cube['di'][0]
lpath_l.append(ome.DichroicRef(ID='Dichroic:%s:%s' % (d1,fn)))
if 'em' in top_cube:
fn = top_cube['em'][0]
lpath_l.append(ome.EmissionFilterRef(ID='Filter:%s:%s' % (d1,fn)))
if 'ex' in bottom_cube:
fn = bottom_cube['ex'][0]
lpath_l.append(ome.EmissionFilterRef(ID='Filter:%s:%s' % (d2,fn)))
else:
#m = re.search(r'Exposure time:\s*(?P<time>\d+[.]\d*)\s*msec', self.config['CAMERA_IMPERX_HardwareInformation'], re.M)
#exp_time = float (m.group ('time'))
if self.config['main_protocol_mode'].startswith('MyocyteMechanicsFluorescence'):
tbf = float(self.config['PROTOCOL_MYOCYTE_MECHANICS_TimeBetweenSavedFrames'])
pixels_d['TimeIncrement'] = str(tbf)
channel_d['IlluminationType'] = 'Transmitted'
if self.config['optics_transmission_light_filter']!='Empty':
fn = nikon_filters['NikonIllumination']['ex'][0]
lpath_l.append(ome.ExcitationFilterRef(ID='Filter:NikonIllumination:%s' % (fn)))
if 'di' in top_cube:
fn = top_cube['di'][0]
lpath_l.append(ome.EmissionFilterRef(ID='Filter:%s:%s' % (d1, fn)))
if 'em' in top_cube:
fn = top_cube['em'][0]
lpath_l.append(ome.EmissionFilterRef(ID='Filter:%s:%s' % (d1,fn)))
if 'em' in bottom_cube:
fn = bottom_cube['em'][0]
lpath_l.append(ome.EmissionFilterRef(ID='Filter:%s:%s' % (d2,fn)))
if 'di' in bottom_cube:
fn = bottom_cube['di'][0]
lpath_l.append(ome.EmissionFilterRef(ID='Filter:%s:%s' % (d2,fn)))
else:
raise NotImplementedError (`detector`)
if zsz>1:
pixels_d['PhysicalSizeZ'] = str(dz)
channel = ome.Channel(ID='Channel:%s' % (detector),
**channel_d)
lpath = ome.LightPath(*lpath_l)
channel.append (lpath)
#todo attributes:
#todo elements: BIN:BinData, MetadataOnly, SA:AnnotationRef
tiffdata = ome.TiffData(ome.UUID (tif_uuid, FileName=tif_filename))
pixels = ome.Pixels(channel,
tiffdata,
DimensionOrder=order, ID='Pixels:%s' % (detector),
SizeX = str(xsz), SizeY = str(ysz), SizeZ = str(zsz), SizeT=str(tsz), SizeC = str(1),
Type = self.dtype2PixelIType (dtype),
**pixels_d
)
for d in plane_l:
pixels.append(ome.Plane(**d))
#todo attributes: Name
#todo elements: Description, ExperimentRef, DatasetRef ,
# ImagingEnvironment, StageLabel, ROIRef, MicrobeamManipulationRef, AnnotationRef
image = ome.Image (ome.AcquiredDate (self.get_AcquiredDate()),
ome.ExperimenterRef(ID='Experimenter:%s' % (self.current_user)),
ome.GroupRef(ID='Group:SysBio'),
ome.InstrumentRef(ID=instrument_id),
objective,
pixels,
ID='Image:%s' % (detector))
if 0:
image.append(sa.AnnotationRef (ID='Annotation:configuration.txt'))
yield image
return
parser.add_argument('-Q', '--q-max', type=float, help='Maximum momentum transfer to plot')
parser.add_argument('files', type=argparse.FileType('r'), nargs='+', help='Files to process')
args = parser.parse_args()
center = None
if args.center is not None:
x,y = args.center.split(',')
center = (float(x), float(y))
background = None
if args.bg is not None:
background = None # TODO
for f in args.files:
print "Processing %s" % f
tif = TIFFfile(f.name)
samples, sample_names = tif.get_samples()
img = samples[0][0,:,:]
img = blur_image(img, 3)
print "Image size", img.shape
#center = np.array(img.shape) / 2
if center is None:
center = prompt_center(img)
print "Image center", center
pl.figure()
pl.imshow(np.log1p(img))
pl.colorbar()
pl.savefig('proj1.png')
def loadData(outputFilename, frames=None, ycrop=None, xcrop=None, transpose=False, datatype=np.float64):
"""
Reads data from a tiff, hdf5, or npy file and returns a numpy array.
Parameters
----------
outputFilename : string
The absolute or relative location of the particular hdf5 or
tiff file to be read in. Filename must end in one of the following
extensions ['tif', 'tiff', 'hdf5', 'h5', 'npy', 'lsm']. **If the file is
hdf5, loadData assumes the data is in a dataset called \"/raw\"**.
frames : array_like
A tuple, list, or array containing the beginning and ending indices
for the frames to be extracted. i.e. frames=[23,77] will return a
numpy array containing the frames 23-76 within the file, 'filename'.
xcrop : array_like
A tuple, list, or array containing the beginning and ending indices
for the columns to be extracted. This allows you to specify a subset
or roi of the data to be returned. i.e. xcrop=[0,100] will return a
numpy array containing first 100 columns of each frame within the file
'filename'.
ycrop : array_like
A tuple, list, or array containing the beginning and ending indices
for the rows to be extracted. This allows you to specify a subset
or roi of the data to be returned. i.e. ycrop=[0,100] will return a
numpy array containing first 100 rows of each frame within the file
'filename'.
transpose : boolean
This specifies whether or not to transpose the last two dimensions
of each frame. This might happen when reading and writing data between
matlab and python, for example.
dataset : numpy.dtype
Specify the datatype of the returned numpy array. If dataset is of
lower precision than the original data then truncation will take place.
Returns
-------
filename_data : array
The data read from within the file specified in filename
Notes
-----
- If the file type is hdf5, loadData assumes the data to be read in is stored in
a dataset called \"/raw\".
- After the data has been read in, a .npy file is created and saved with a filename
that specifies the parameters of the modified data. If in the future you wish to
read in the same data, the .npy file will be read instead, saving time.
Example
-------
>>> fname = "data/testHDF5.hdf5"
>>> yroi = [175,250]
>>> xroi = [100,150]
>>> hdfData = loadData(fname, frames=[0,32], ycrop=yroi, xcrop=xroi)
>>> hdfData.shape
(32, 75, 50)
>>> hdfData.dtype
dtype('float32')
>>> hdfData = loadData(fname, ycrop=yroi, xcrop=xroi, datatype=np.int16)
>>> hdfData.shape
(544, 75, 50)
>>> hdfData.dtype
dtype('int16')
"""
print "-- Loading Data..."
print "outputFilename = ", outputFilename
filename = outputFilename.rstrip('/')
print "filename = ", filename
basePath, fName = os.path.split(filename)
name, ext = os.path.splitext(fName)
if basePath and not os.path.exists(basePath):
raise IOError, "Directory does not exist: %s" % (basePath)
elif not os.path.exists(filename):
raise IOError, "File does not exist: %s" % (fName)
npFilenameBase = os.path.join(basePath, name)
if not frames is None:
f0 = frames[0]; f1 = frames[1]
npFilenameBase += "_frames" + str(f0) + "-" + str(f1-1)
if not ycrop is None:
y0 = ycrop[0]; y1 = ycrop[1]
npFilenameBase += "_ycrop" + str(y0) + "-" + str(y1-1)
if not xcrop is None:
x0 = xcrop[0]; x1 = xcrop[1]
npFilenameBase += "_xcrop" + str(x0) + "-" + str(x1-1)
if transpose:
npFilenameBase += "_T"
npFilenameBase += "_" + str(np.dtype(datatype))
# File name for the numpy file
np_filename = npFilenameBase + '.npy'
# Check if a numpy file already exists for this data
if os.path.exists(np_filename):
print "\t- Numpy file already exists. Loading %s..." % (np_filename)
# If so, load it and be done
try:
volumeData = np.load(np_filename)
except IOError:
raise IOError, "Error reading filename: \"%s\"" % filename
return volumeData
elif ext.lower() in ['.npy']: # If it is a numpy file
try:
volumeData = np.load(filename)
except IOError:
raise IOError, "Error reading filename: \"%s\"" % filename
# Otherwise check if the data is in hdf5 format
elif ext.lower() in ['.h5', '.hdf5']:
from h5py import File
print "\t- Reading from hdf5 file %s..." % (filename)
try:
h5File = File(filename, 'r')
except IOError:
raise IOError, "Error reading filename: \"%s\"" % (filename)
volumeData = np.array(h5File["/raw"])
# Then check to see if it is an lsm file
elif ext.lower() in ['.lsm']:
print "\n\nIN LSM SECTION\n\n"
from libtiff import TIFFfile
try:
tiff = TIFFfile(filename)
except IOError:
raise IOError, "Error opening lsm file: \"%s\"" % (filename)
samples, sample_names = tiff.get_samples()
outList = []
for sample in samples:
outList.append(np.copy(sample)[...,np.newaxis])
out = np.concatenate(outList,axis=-1)
out = np.rollaxis(out,0,3)
data = np.swapaxes(out[:,:,:,0],0,2)
volumeData = np.swapaxes(data,1,2)
tiff.close()
# Then finally it must a tif file (hopefully)...
elif ext.lower() in ['.tif', '.tiff']:
print "\t- Reading from tiff file %s..." % (filename)
from libtiff import TIFF
try:
tiff = TIFF.open(filename, 'r')
except IOError:
raise IOError, "Error opening tiff file: \"%s\"" % (filename)
for count,image in enumerate(tiff.iter_images()):
image_shape = image.shape
volumeData = np.ndarray((count+1, image_shape[0], image_shape[1]), order='c', dtype=np.float64) # DO NOT HARDCODE
for count,image in enumerate(tiff.iter_images()):
volumeData[count,:,:] = image
else:
assert False, "The filename must have one of the following extensions [\"h5\", \"hdf5\", \"tif\", \"tiff\", \"npy\"]"
if transpose:
# For some reason the hdf5 data is transposed when written from matlab...
volumeData = np.swapaxes(volumeData,1,2)
dims = volumeData.shape
if frames is None:
f0 = 0; f1 = dims[0]
if ycrop is None:
y0 = 0; y1 = dims[1]
if xcrop is None:
x0 = 0; x1 = dims[2]
print "made it to here!"
finalData = np.array(volumeData[f0:f1,y0:y1,x0:x1], dtype=datatype)
print "\t- Saving to numpy data file %s" % (np_filename)
# Save it so we don't have to parse the tiff/hdf5 file every time
np.save(npFilenameBase, finalData)
return finalData
#
# 1.py
# @author bulbasaur
# @description
# @created 2020-01-29T15:06:32.299Z+08:00
# @last-modified 2020-01-31T14:02:20.634Z+08:00
#
from libtiff import TIFFfile
src = TIFFfile("C:\\Users\\sheld\\Desktop\\ms.bsq")
print(src)
def iter_Image(self, func):
sys.stdout.write("iter_Image: reading image data from TIFF files\n")
for detector in self.data:
sys.stdout.write(" detector: %s\n" % (detector))
d_index = self.data[detector]
# write the content of tiff files to a single raw files
f, fn, dtype = None, None, None
time_set = set()
mn, mx = None, None
mnz = float(self.config["PROTOCOL_Z_STACKER_Minimum"])
mxz = float(self.config["PROTOCOL_Z_STACKER_Maximum"])
nz = int(self.config["PROTOCOL_Z_STACKER_NumberOfFrames"])
if nz > 1:
dz = (mxz - mnz) / (nz - 1)
else:
dz = 0
plane_l = []
ti = -1
exptime = "0"
if detector == "Confocal":
exptime = float(self.config["CONFOCAL_PixelAcqusitionTime"]) * 1e-6
elif detector == "Andor":
exptime = self.config["CAMERA_ANDOR_ExposureTime"]
elif detector == "Imperx":
for line in self.config["CAMERA_IMPERX_HardwareInformation"].split("\n"):
if line.startswith("Exposure time:"):
v, u = line[14:].lstrip().split()
v = v.strip()
u = u.strip()
if u == "usec":
exptime = float(v) * 1e-6
elif u == "msec":
exptime = float(v) * 1e-3
elif u == "sec":
exptime = float(v)
else:
raise NotImplementedError(` v, u, line `)
else:
raise NotImplementedError(` detector `)
for t, index in sorted(d_index):
if t not in time_set:
time_set.add(t)
ti += 1
zi = 0
else:
zi += 1
z = mnz + dz * zi
d = dict(
DeltaT=str(t), TheT=str(ti), TheZ=str(zi), PositionZ=str(z), TheC="0", ExposureTime=str(exptime)
)
plane_l.append(d)
tif = TIFFfile(d_index[t, index])
samples, sample_names = tif.get_samples()
assert len(sample_names) == 1, ` sample_names `
data = samples[0]
if mn is None:
mn, mx = data.min(), data.max()
else:
mn = min(data.min(), mn)
mx = min(data.max(), mx)
if f is None:
shape = list(data.shape)
dtype = data.dtype
fn = tempfile.mktemp(suffix=".raw", prefix="%s_%s_" % (detector, dtype))
f = open(fn, "wb")
else:
assert dtype is data.dtype, ` dtype, data.dtype `
shape[0] += 1
data.tofile(f)
sys.stdout.write(
"\r copying TIFF image data to RAW file: %5s%% done" % (int(100.0 * (index + 1) / len(d_index)))
)
sys.stdout.flush()
if f is None:
continue
f.close()
shape = tuple(shape)
xsz = shape[2]
ysz = shape[1]
tsz = len(time_set)
zsz = shape[0] // tsz
order = "XYZTC"
sys.stdout.write(
"\n RAW file contains %sx%sx%sx%sx%s [%s] array, dtype=%s, MIN/MAX=%s/%s\n"
% (xsz, ysz, zsz, tsz, 1, order, dtype, mn, mx)
)
assert zsz * tsz == shape[0], ` zsz, tsz, shape `
tif_filename = "%s%s.ome.tif" % (self.file_prefix, detector)
sys.stdout.write(" creating memmap image for OME-TIF file %r..." % (tif_filename))
sys.stdout.flush()
mmap = numpy.memmap(fn, dtype=dtype, mode="r", shape=shape)
tif_image = TIFFimage(mmap)
atexit.register(os.remove, fn)
tif_uuid = self._mk_uuid()
self.tif_images[detector, tif_filename, tif_uuid] = tif_image
sys.stdout.write(" done\n")
sys.stdout.flush()
pixels_d = {}
channel_d = dict(SamplesPerPixel="1")
lpath_l = []
# channel_d todo: ExcitationWavelength, EmissionWavelength, Fluor, NDFilter, PockelCellSetting, Color
if detector in ["Confocal"]:
objective = ome.ObjectiveSettings(ID="Objective:%s" % (self.config["olympus_optics_objective"]))
instrument_id = "Instrument:Airy"
pixels_d["PhysicalSizeX"] = str(self.config["CONFOCAL_PixelSizeX"])
pixels_d["PhysicalSizeY"] = str(self.config["CONFOCAL_PixelSizeY"])
pixels_d["TimeIncrement"] = str(self.config["CONFOCAL_TimeBetweenFrames"])
channel_d["Name"] = "Confocal"
channel_d["IlluminationType"] = "Epifluorescence"
channel_d["PinholeSize"] = "180"
# todo: FluorescenceCorrelationSpectroscopy
channel_d["AcquisitionMode"] = "LaserScanningConfocalMicroscopy"
for i in range(1, 5):
d1 = "AOTFLine%s" % i
ft = confocal_filters.get(d1)
if ft is None:
continue
fn = ft["ex"][0]
fn = get_aotf_filter_name(fn, self.config)
if "OFF" in fn:
continue
lpath_l.append(ome.ExcitationFilterRef(ID="Filter:%s:%s" % (d1, fn)))
fn = confocal_filters["OpticalTableSplitter"]["di"][0]
lpath_l.append(ome.DichroicRef(ID="Dichroic:OpticalTableSplitter:%s" % (fn)))
d1 = "ThorlabsWheelPosition%s" % (self.config["thorlabs_filter_wheel_position"][3])
ft = confocal_filters.get(d1)
if ft is not None:
fn = ft["em"][0]
lpath_l.append(ome.EmissionFilterRef(ID="Filter:%s:%s" % (d1, fn)))
elif detector in ["Andor", "Imperx"]:
objective = ome.ObjectiveSettings(ID="Objective:%s" % (self.config["optics_objective"]))
instrument_id = "Instrument:Suga"
channel_d["Name"] = "%s camera" % (detector)
channel_d["AcquisitionMode"] = "WideField"
pixels_d["PhysicalSizeX"] = pixels_d["PhysicalSizeY"] = str(
self.config["CAMERA_%s_PixelSize" % (detector.upper())]
)
tbf = float(self.config["CAMERA_%s_TimeBetweenFrames" % (detector.upper())])
d1 = "NikonTurretTopCube%s" % (self.config["top_turret_cube"][3])
d2 = "NikonTurretBottomCube%s" % (self.config["bottom_turret_cube"][3])
top_cube = nikon_filters[d1]
bottom_cube = nikon_filters[d2]
if detector == "Andor":
channel_d["IlluminationType"] = "Epifluorescence"
if self.config["CAMERA_ANDOR_FrameTransferMode"] == "1":
m = re.search(
r"Kinetic cycle time:\s*(?P<time>\d+[.]\d*)\s*sec",
self.config["CAMERA_ANDOR_HardwareInformation"],
re.M,
)
pixels_d["TimeIncrement"] = str(m.group("time"))
else:
pixels_d["TimeIncrement"] = str(tbf)
if "ex" in top_cube:
fn = top_cube["ex"][0]
lpath_l.append(ome.ExcitationFilterRef(ID="Filter:%s:%s" % (d1, fn)))
if "di" in top_cube:
fn = top_cube["di"][0]
lpath_l.append(ome.DichroicRef(ID="Dichroic:%s:%s" % (d1, fn)))
if "em" in top_cube:
fn = top_cube["em"][0]
lpath_l.append(ome.EmissionFilterRef(ID="Filter:%s:%s" % (d1, fn)))
if "ex" in bottom_cube:
fn = bottom_cube["ex"][0]
lpath_l.append(ome.EmissionFilterRef(ID="Filter:%s:%s" % (d2, fn)))
else:
# m = re.search(r'Exposure time:\s*(?P<time>\d+[.]\d*)\s*msec', self.config['CAMERA_IMPERX_HardwareInformation'], re.M)
# exp_time = float (m.group ('time'))
if self.config["main_protocol_mode"].startswith("MyocyteMechanicsFluorescence"):
tbf = float(self.config["PROTOCOL_MYOCYTE_MECHANICS_TimeBetweenSavedFrames"])
pixels_d["TimeIncrement"] = str(tbf)
channel_d["IlluminationType"] = "Transmitted"
if self.config["optics_transmission_light_filter"] != "Empty":
fn = nikon_filters["NikonIllumination"]["ex"][0]
lpath_l.append(ome.ExcitationFilterRef(ID="Filter:NikonIllumination:%s" % (fn)))
if "di" in top_cube:
fn = top_cube["di"][0]
lpath_l.append(ome.EmissionFilterRef(ID="Filter:%s:%s" % (d1, fn)))
if "em" in top_cube:
fn = top_cube["em"][0]
lpath_l.append(ome.EmissionFilterRef(ID="Filter:%s:%s" % (d1, fn)))
if "em" in bottom_cube:
fn = bottom_cube["em"][0]
lpath_l.append(ome.EmissionFilterRef(ID="Filter:%s:%s" % (d2, fn)))
if "di" in bottom_cube:
fn = bottom_cube["di"][0]
lpath_l.append(ome.EmissionFilterRef(ID="Filter:%s:%s" % (d2, fn)))
else:
raise NotImplementedError(` detector `)
if zsz > 1:
pixels_d["PhysicalSizeZ"] = str(dz)
channel = ome.Channel(ID="Channel:%s" % (detector), **channel_d)
lpath = ome.LightPath(*lpath_l)
channel.append(lpath)
# todo attributes:
# todo elements: BIN:BinData, MetadataOnly, SA:AnnotationRef
tiffdata = ome.TiffData(ome.UUID(tif_uuid, FileName=tif_filename))
pixels = ome.Pixels(
channel,
tiffdata,
DimensionOrder=order,
ID="Pixels:%s" % (detector),
SizeX=str(xsz),
SizeY=str(ysz),
SizeZ=str(zsz),
SizeT=str(tsz),
SizeC=str(1),
Type=self.dtype2PixelIType(dtype),
**pixels_d
)
for d in plane_l:
pixels.append(ome.Plane(**d))
# todo attributes: Name
# todo elements: Description, ExperimentRef, DatasetRef ,
# ImagingEnvironment, StageLabel, ROIRef, MicrobeamManipulationRef, AnnotationRef
image = ome.Image(
ome.AcquiredDate(self.get_AcquiredDate()),
ome.ExperimenterRef(ID="Experimenter:%s" % (self.current_user)),
ome.GroupRef(ID="Group:SysBio"),
ome.InstrumentRef(ID=instrument_id),
objective,
pixels,
ID="Image:%s" % (detector),
)
if 0:
image.append(sa.AnnotationRef(ID="Annotation:configuration.txt"))
yield image
return
from libtiff import TIFFfile
import os
import numpy as np
from PIL import Image
input_file = '../data/FIB_segmentaion/grayscale_maps_500.tif'
out_path = '../data/raw'
if not os.path.exists(out_path):
os.makedirs(out_path)
tif = TIFFfile(input_file)
data, _ = tif.get_samples()
data = data[0]
print('the shape of data: ', data.shape)
for k in range(data.shape[0]):
Image.fromarray(data[k]).save(
os.path.join(out_path, 'raw_' + str(k).zfill(4) + '.png'))
print('Done')
def read_tif(filename,channel=0):
"""Read a tif image
:Parameters:
- `filename` (str) - name of the file to read
"""
# TIF reader
tif = libtiff.TIFF.open(filename)
if tif.GetField('ImageDescription'):
tif = TIFFfile(filename)
arr = tif.get_tiff_array()
_data = arr[:].T
info_str = tif.get_info()
else:
i = 1
while not tif.LastDirectory():
i+=1
tif.ReadDirectory()
tif.SetDirectory(0)
_data = np.zeros((i,)+tif.read_image().shape,dtype=tif.read_image().dtype)
for ii,i in enumerate(tif.iter_images()):
_data[ii] = i
_data = _data.transpose(2, 1, 0)
info_str = tif.info()
nx, ny, nz = _data.shape
# -- prepare metadata dictionnary --
info_dict = dict( filter( lambda x: len(x)==2,
(inf.split(':') for inf in info_str.split("\n"))
) )
for k,v in info_dict.iteritems():
info_dict[k] = v.strip()
# -- getting the voxelsizes from the tiff image: sometimes
# there is a BoundingBox attribute, sometimes there are
# XResolution, YResolution, ZResolution or spacing.
# the object returned by get_tiff_array has a "get_voxel_sizes()"
# method but it fails, so here we go. --
if "BoundingBox" in info_dict:
bbox = info_dict["BoundingBox"]
xm, xM, ym, yM, zm, zM = map(float,bbox.split())
_vx = (xM-xm)/nx
_vy = (yM-ym)/ny
_vz = (zM-zm)/nz
else:
# -- When we have [XYZ]Resolution fields, it describes the
# number of voxels per real unit. In SpatialImage we want the
# voxelsizes, which is the number of real units per voxels.
# So we must invert the result. --
if "XResolution" in info_dict:
# --resolution is stored in a [(values, precision)] list-of-one-tuple, or
# sometimes as a single number --
xres_str = eval(info_dict["XResolution"])
if isinstance(xres_str, list) and isinstance(xres_str[0], tuple):
xres_str = xres_str[0]
_vx = float(xres_str[0])/xres_str[1]
elif isinstance(xres_str, (int, float)):
_vx = float(xres_str)
else:
_vx = 1.
_vx = 1./_vx if _vx != 0 else 1.
else:
_vx = 1.0 # dumb fallback, maybe we will find something smarter later on
if "YResolution" in info_dict:
# --resolution is stored in a [(values, precision)] list-of-one-tuple, or
# sometimes as a single number --
yres_str = eval(info_dict["YResolution"])
if isinstance(yres_str, list) and isinstance(yres_str[0], tuple):
yres_str = yres_str[0]
_vy = float(yres_str[0])/yres_str[1]
elif isinstance(yres_str, (int, float)):
_vy = float(yres_str)
else:
_vy = 1.
_vy = 1./_vy if _vy != 0 else 1.
else:
_vy = 1.0 # dumb fallback, maybe we will find something smarter later on
if "ZResolution" in info_dict:
# --resolution is stored in a [(values, precision)] list-of-one-tuple, or
# sometimes as a single number --
zres_str = eval(info_dict["ZResolution"])
if isinstance(zres_str, list) and isinstance(zres_str[0], tuple):
zres_str = zres_str[0]
_vz = float(zres_str[0])/zres_str[1]
elif isinstance(zres_str, (int, float)):
_vz = float(zres_str)
else:
_vz = 1.
_vz = 1./_vz if _vz != 0 else 1.
else:
if "spacing" in info_dict:
_vz = eval(info_dict["spacing"])
else:
_vz = 1.0 # dumb fallback, maybe we will find something smarter later on
tif.close()
# -- dtypes are not really stored in a compatible way (">u2" instead of uint16)
# but we can convert those --
dt = np.dtype(_data.dtype.name)
# -- Return a SpatialImage please! --
im = SpatialImage(_data, dtype=dt)
im.resolution = _vx,_vy,_vz
return im
def process(self, options=None, validate=default_validate):
template_xml = list(self.make_xml())
s = None
for (detector, fn, uuid), tif_image in self.tif_images.items():
xml = ome.OME(ATTR(
'xsi', 'schemaLocation',
"%s %s/ome.xsd" % ((namespace_map['ome'], ) * 2)),
UUID=uuid)
for item in template_xml:
if item.tag.endswith(
'Image') and item.get('ID') != 'Image:%s' % (detector):
continue
if item.tag.endswith('Instrument'):
if detector == 'Confocal':
instrument = 'Airy'
elif detector in ['Imperx', 'Andor']:
instrument = 'Suga'
else:
instrument = None
if instrument and item.get(
'ID') != 'Instrument:%s' % (instrument):
continue
xml.append(item)
if s is None and validate:
s = etree.tostring(xml,
pretty_print=True,
xml_declaration=True)
#print s
validate_xml(xml)
else:
s = etree.tostring(xml,
pretty_print=True,
xml_declaration=True)
tif_image.description = s
if detector == 'Confocal':
c = tif_image.write_file(fn, compression='lzw')
if c < 1.0:
print 'Resetting compression to none'
tif_image.write_file(fn, compression='none')
else:
tif_image.write_file(fn, compression='none')
if validate and 0:
print 'Validating written data..',
from libtiff import TIFFfile
t = TIFFfile(fn)
samples, sample_names = t.get_samples()
assert len(sample_names) == 1, ` sample_names `
samples = samples[0]
samples_orig = tif_image.data
if (samples != samples_orig).any():
print 'DATA CORRUPTION DETECTED!!'
print 'original data:', samples_orig.dtype, samples_orig.shape, samples_orig.nbytes
print 'written data:', samples.dtype, samples.shape, samples.nbytes
diff = samples - samples_orig
ia, ja, ka = diff.nonzero()
print len(ia)
print ia[:10]
print ja[:10]
print ka[:10]
print samples[ia[:10], ja[:10], ka[:10]]
print samples_orig[ia[:10], ja[:10], ka[:10]]
else:
print 'SUCCESS!'
#validate = False
return s