def importImage(img, ppm=None):
if ppm:
with tiff.TiffFile(img) as tif:
images = tif.asarray().astype(float)
return images, ppm
else:
with tiff.TiffFile(img) as tif:
images = tif.asarray().astype(float)
metadata = tif[0].tags
pixel_per_micron = metadata['x_resolution'].value[0] / metadata[
'x_resolution'].value[1]
return images, pixel_per_micron
def load_image(path, type=None, mode=None):
"""Read image from the passed path and return it in numpy array form.
Automatically performs channel mode conversions of the loaded image.
Args:
path (str): Path to image file.
type (str): Value type to return the image as. See image_convtype()
for documentation of accepted values.
mode (str): Channel mode to return the image as. See image_convmode()
for documentation of accepted values.
Returns:
numpy.ndarray: Image data.
"""
if not os.path.isfile(path):
return None
_, ext = os.path.splitext(path.lower())
# Image read
if ext == '.tif' or ext == '.tiff':
with tifffile.TiffFile(path) as tif:
out = tif.asarray()
elif ext == '.png':
out = imageio.imread(path)
else:
return None
if out is None:
raise RuntimeError('File could not be loaded: {0}'.format(path))
out = image_convmode(out, mode)
out = image_convtype(out, type)
return out
def flat_field(self, ff_image, channel, affix='_FF'):
"""Flat-Field correction.
Parameters
----------
ff_image : str, NumPy array
Flat-field image file or NumPy array.
channel : str
Channel to apply flat-field correction.
affix : str
Affix for channel name to be appended.
Defaults to '_FF'
"""
if isinstance(ff_image, string_types):
flat_field = tff.TiffFile(ff_image).asarray()
else:
flat_field = ff_image
flat_field = flat_field / flat_field.max() # Normalize Flat-Field
if isinstance(self._dataframe, dict):
ff_dict_df = {}
for key in self._dataframe.keys():
ff_df = self._dataframe[key].loc[:, [channel]]
ff_dict_df[key] = ff_df / flat_field
ff_dict_df[key] = self._rename_coord(ff_dict_df[key], 'c',
channel,
"%s%s" % (channel, affix))
self.combine(ff_dict_df)
def preprocess(input_dir, output_dir, bfchannel, meta_export, register, png_export):
"""Run preprocessing on OME-TIFF images in the given directory."""
print("Running on {}".format(input_dir))
# bfchannel = int(bfchannel)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
for file in os.listdir(input_dir):
if file.endswith(".ome.tif"):
print("Opening: ", os.path.join(input_dir, file))
# Open ome-tiff file with tifffile.TiffFile
with tfile.TiffFile(os.path.join(input_dir, file), is_ome=False) as tif:
print(tif.series[0].shape)
imagej_hyperstack = tif.asarray()
#tfile.imshow(imagej_hyperstack[:,bfchannel,:,:])
#plt.show()
print(imagej_hyperstack.shape)
#imagej_metadata = tif.imagej_metadata
#micromanager_metadata = tif.micromanager_metadata
#ome_metadata = tif.ome_metadata
nbchannels = 1
if register:
registration(imagej_hyperstack, bfchannel, nbchannels, file, output_dir)
if png_export:
io.imsave(os.path.join(output_dir,file)+'.png',imagej_hyperstack[bfchannel,:,:])
def readOmeTiff(input_path):
with tifffile.TiffFile(input_path) as tif:
array = tif.asarray()
omexmlString = tif[0].image_description.decode("utf-8")
# Turn Ome XML String to an Bioformats object for parsing
metadata = omexmlClass.OMEXML(omexmlString)
# Parse pixel sizes
pixels = metadata.image(0).Pixels
SizeC = pixels.SizeC
SizeT = pixels.SizeT
SizeZ = pixels.SizeZ
SizeX = pixels.SizeX
SizeY = pixels.SizeY
pixels.DimensionOrder
# Expand image array to 5D of order (T, Z, C, X, Y)
if SizeC == 1:
array = np.expand_dims(array, axis=-3)
if SizeZ == 1:
array = np.expand_dims(array, axis=-4)
if SizeT == 1:
array = np.expand_dims(array, axis=-5)
return (array, omexmlString)
def load_tiff(path):
_, img_name = os.path.split(path)
with tf.TiffFile(path) as tif:
if tif.is_imagej is not None:
metadata = tif.pages[0].imagej_tags
dt = metadata['finterval'] if 'finterval' in metadata else 1
# asuming square pixels
xr = tif.pages[0].tags['x_resolution'].value
res = float(xr[0]) / float(xr[1]) # pixels per um
if metadata['unit'] == 'centimeter':
res = res / 1e4
images = None
if len(tif.pages) == 1:
if ('slices' in metadata and metadata['slices'] > 1) or (
'frames' in metadata and metadata['frames'] > 1):
images = tif.pages[0].asarray()
else:
images = [tif.pages[0].asarray()]
elif len(tif.pages) > 1:
images = list()
for i, page in enumerate(tif.pages):
images.append(page.asarray())
return images, res, dt, metadata['frames'], metadata[
'channels'] if 'channels' in metadata else 1
def iter_mask():
for key in param_dict.keys():
if key.startswith('mask_'):
with tifffile.TiffFile(param_dict[key]['filepath']) as infile:
mask = infile.series[0].pages[0].asarray(memmap=True)
outline = segmentation.find_boundaries(mask > 0, mode='inner')
yield outline
def register(tiff_path, trans_mat, out_tiff_path, highres = True, compress = 1, pad = True):
'''
tiff_path: tiff file name
trans_mat: translation matrix, can be obtained by roi2mat()
highres: optional, if set to True, will multiply the trans_mat by compress (which is set to 3 by default)
compress: as above
pad: whehther or not pad the periphery to zeros. If false, will crop the tiff
This function returns a dict of metadata, and writes the tiff to current working directory
'''
with tifffile.TiffFile(tiff_path) as tif:
# read tiff
im_in = tif.asarray()
# read metadata as tif_tags (dict)
tif_tags = tif.pages[0].tags.values()
# register using trans_mat
im_out = translate(im_in,trans_mat,hi_res=highres,compression=compress,padzeros=pad)
im_out = im_out.astype('uint16')
# save registered tiff, no compression
with tifffile.TiffWriter(out_tiff_path, bigtiff = highres) as tif:
for i in range(im_out.shape[0]):
tif.save(im_out[i])
return tif_tags
def _load_image(path, obj):
"""Load an image from disk into a data object.
Args:
path (str): Path to image.
obj (io.DisplotData): Data object to populate with the data.
Returns:
io.DisplotData: Displot data object.
"""
if not os.path.exists(path):
raise FileNotFoundError(path)
if os.path.isdir(path):
raise IsADirectoryError(path)
if not os.access(path, os.R_OK):
raise PermissionError(path)
ext = os.path.splitext(path)[1].lower()
obj.image_path = path
if ext == '.tiff' or ext == '.tif':
with tifffile.TiffFile(path) as tif:
obj.image = tif.asarray()
obj.image_meta = {}
for f in tif.flags:
obj.image_meta[f] = getattr(tif, f + '_metadata')
else:
raise RuntimeError(path)
return obj
def dna_corrcoef(image_filepath, marker_list, output_filepath):
# parse markers
dna_pattern = [
'DNA\d+', # 'DNA' followed by one or more digits
'DNA_\d+', # 'DNA' followed by one or more digits
]
def is_dna(n):
for p in dna_pattern:
if re.fullmatch(pattern=p, string=n) is not None:
return True
return False
dna_index = [i for i, name in enumerate(marker_list) if is_dna(name)]
# calculate
record = []
ref_channel = 0
with tifffile.TiffFile(image_filepath) as tif:
ref_array = tif.series[0].pages[ref_channel]\
.asarray(memmap=True)\
.flatten()
for cycle, channel in enumerate(dna_index):
test_array = tif.series[0].pages[channel]\
.asarray(memmap=True)\
.flatten()
cc = np.corrcoef(ref_array, test_array)[0, 1]
record.append([cycle + 1, channel + 1, ref_channel + 1, cc])
# write output file
df = pd.DataFrame.from_records(
record, columns=['cycle', 'channel', 'reference_channel', 'corrcoef'])
df.to_csv(output_filepath, index=False, na_rep='nan')
def load(self, file_path):
self.file_path = file_path
with tifffile.TiffFile(file_path) as tif:
self.data = tif.asarray()
self.metadata = tif.info()
self.features.set_image_data(self.data)
def getarray(idx_buffer_filename):
idx, buf, fname = idx_buffer_filename
fbuf = BytesIO(buf)
tfh = tifffile.TiffFile(fbuf)
ary = tfh.asarray()
pageCount = ary.shape[0]
if nplanes is not None:
extra = pageCount % nplanes
if extra:
if discard_extra:
pageCount = pageCount - extra
logging.getLogger('thunder').warn(
'Ignored %d pages in file %s' % (extra, fname))
else:
raise ValueError(
"nplanes '%d' does not evenly divide '%d'" %
(nplanes, pageCount))
values = [
ary[i:(i + nplanes)] for i in range(0, pageCount, nplanes)
]
else:
values = [ary]
tfh.close()
if ary.ndim == 3:
values = [val.squeeze() for val in values]
nvals = len(values)
keys = [(idx * nvals + timepoint, ) for timepoint in range(nvals)]
return zip(keys, values)
def read_ometiff(input_path):
with tifffile.TiffFile(input_path) as tif:
array = tif.asarray()
omexml_string = tif[0].image_description.decode("utf-8")
# Turn Ome XML String to an Bioformats object for parsing
metadata = omexmlClass.OMEXML(omexml_string)
# Parse pixel sizes
pixels = metadata.image(0).Pixels
size_c = pixels.SizeC
size_t = pixels.SizeT
size_z = pixels.SizeZ
size_x = pixels.SizeX
size_y = pixels.SizeY
# Expand image array to 5D of order (T, Z, C, X, Y)
if size_c == 1:
array = np.expand_dims(array, axis=-3)
if size_z == 1:
array = np.expand_dims(array, axis=-4)
if size_t == 1:
array = np.expand_dims(array, axis=-5)
return array, omexml_string
def execute(image_path):
image_name = os.path.basename(image_path)
print(image_name)
with tifffile.TiffFile(image_path) as tif:
array6D = []
# Turn Ome XML String to a Bioformats object for parsing
omexml = tif[0].image_description.decode("utf-8")
metadata = omexmlClass.OMEXML(omexml)
# Parse pixel sizes
size_c = metadata.image(0).Pixels.SizeC
size_z = metadata.image(0).Pixels.SizeZ
size_t = metadata.image(0).Pixels.SizeT
# Read each series, get the 5d array -> process it -> append to array6D
for i in range(len(tif.series)):
# Returned value is a 5D array of order (T, Z, C, X, Y)
array5D = read_serie(tif.series[i], size_c, size_z, size_t)
# Apply inversion operation
result5d = apply_2d_transfo_to_serie(_invert, array5D, False)
array6D.append(result5d)
# Save the inverted image
write_ometiff(image_name, array6D, omexml)
print("INVERTION DONE")
return {'output_image': image_name}
def __init__(self, t_file_names, b_verbose=False):
super().__init__(t_file_names)
# to be turned into tuples at the end of this constructor
l_vid_files = []
l_vid_streams = []
for idx in range(len(self.t_file_names)):
self.df_info.loc[idx, 'file_name'] = self.t_file_names[idx]
oc_tiff_record = tifffile.TiffFile(self.t_file_names[idx])
if hasattr(oc_tiff_record, 'pages'):
oc_tiff = oc_tiff_record.pages
else:
oc_tiff = oc_tiff_record['pages']
oc_tmp_frame = oc_tiff[0]
# TODO we can use this later: print(oc_tmp_frame.tags)
self.df_info.loc[idx, 'duration'] = len(oc_tiff)
self.df_info.loc[idx, 'frames'] = len(oc_tiff)
# frame rate is not available in TIFFs: self.df_info.loc[idx, 'frame_rate'] =
self.df_info.loc[idx, 'width'] = int(
oc_tmp_frame.shape[1]) # this is correct
self.df_info.loc[idx, 'height'] = int(oc_tmp_frame.shape[0])
self.df_info.loc[idx, 'format'] = oc_tmp_frame.dtype
l_vid_files.append(oc_tiff)
l_vid_streams.append(oc_tiff)
# check if all video files have the same frame width and height
if self.df_info['width'].sum() != \
self.df_info['width'][0] * len(self.df_info['width']):
raise ValueError(
"Frame width is not consistent across input video files")
if self.df_info['height'].sum() != \
self.df_info['height'][0] * len(self.df_info['height']):
raise ValueError(
"Frame height is not consistent across input video files")
self.df_info['start'] = self.df_info['duration'].cumsum(
) - self.df_info['duration']
self.df_info['end'] = self.df_info['duration'].cumsum()
# self.df_info = self.df_info.set_index(['file_name'], append = True)
# freeze lists into tuples
self.t_vid_files = tuple(l_vid_files)
self.t_vid_streams = tuple(l_vid_streams)
# [1000, 2000, 3000, 4000, 4963], read only!
self.na_ends = np.array(self.df_info['end'], dtype=np.int32)
self.shape = (int(self.df_info['height'][0]),
int(self.df_info['width'][0]),
int(self.df_info['duration'].cumsum().sum()))
self.t_frame_hw = (int(self.df_info['height'][0]),
int(self.df_info['width'][0]))
self.i_nframes = int(self.df_info['duration'].cumsum().sum())
if b_verbose:
print(self.df_info)
def work(self):
# get the parameters from gui
p = self.p
# check if GPU
if p['useGPU']:
device = mx.gpu()
else:
device = mx.cpu()
# load files
self.status_signal.emit('Loading images...')
files = self.filepaths
nimgs = len(files)
imgs = [tifffile.TiffFile(f, multifile=True).asarray() for f in files]
# imgs = [plt.imread(f) for f in files]
# adjust the dimensions of the images if needed
# if z-stack, reorder the stack to be x,y,z:
if nimgs==1:
if len(imgs[0].shape) > 2 and imgs[0].shape[0] < imgs[0].shape[1] and imgs[0].shape[0] < imgs[0].shape[2]:
imgs_new = []
for z in range(imgs[0].shape[0]):
imgs_new.append(imgs[0][z,:,:].squeeze())
imgs = imgs_new
nimgs = len(imgs)
# assuming single channel grayscale image, reshape into 3d array:
for f in range(nimgs):
if len(imgs[f].shape) < 3:
imgs[f] = imgs[f].reshape(512,512,1) # assuming 512x512!
# run cellpose
self.status_signal.emit('Running segmentation...')
cell_diam_px = p['cellSize']
rescale = 30 / cell_diam_px
model = models.Cellpose(device, model_type=p['modelType'])
masks, flows, styles, diams = model.eval(imgs, rescale=rescale, channels=[0,0], threshold=p['threshold'])
# save the results to MAT file
self.status_signal.emit('Saving to MAT file...')
base = os.path.splitext(files[0])[0]
sio.savemat(base + '_CELLPOSE.mat',
{'software': 'cellpose',
'files': files,
'model': p['modelType'],
'cellsize': p['cellSize'],
'threshold': p['threshold'],
'styles': styles,
'masks': masks,
'img': imgs,
'filename': base,
'flows': flows,
'diams': diams})
self.status_signal.emit('Done')
self.finished_signal.emit()
def load_image(path):
"""Read image from the passed path and return it in numpy array form.
Args:
path (str): Path to image file.
Returns:
numpy.ndarray: Image data.
"""
with tifffile.TiffFile(path) as tif:
return tif.asarray()
def _getStamps(myData):
from skimage.external import tifffile
filename = myData[0]
nPlanes = myData[1]
tfh = tifffile.TiffFile(filename)
nPages = len(tfh.pages)
if nPages % nPlanes != 0:
print('error: incomplete volume')
# return None
customHeaders = np.zeros((nPlanes, 4))
out = []
for page in range(0, nPages):
k = (page + 1) % nPlanes
if k == 0:
k = nPlanes
desStr = tfh.pages[page].tags[
'image_description'].value.decode('utf-8')
desStr = " ".join(desStr.split()).replace('\\', ' ')
desStr = desStr.replace(')', '')
desStr = desStr.replace('(', '')
try:
customHeaders[k - 1, 0] = re.search(
'(?<=Frame\sNumber\s=\s)[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?',
desStr).group(0)
customHeaders[k - 1, 1] = re.search(
'(?<=Frame\sTimestamps\s=\s)[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?',
desStr).group(0)
customHeaders[k - 1, 2] = re.search(
'(?<=Acq\sTrigger\sTimestamps\s=\s)[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?',
desStr).group(0)
customHeaders[k - 1, 3] = re.search(
'(?<=Next\sFile\sMarker\sTimestamps\s=\s)[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?',
desStr).group(0)
except AttributeError:
#SI2016
customHeaders[k - 1, 0] = re.search(
'(?<=frameNumbers\s=\s)[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?',
desStr).group(0)
customHeaders[k - 1, 1] = re.search(
'(?<=frameTimestamps_sec\s=\s)[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?',
desStr).group(0)
customHeaders[k - 1, 2] = re.search(
'(?<=acqTriggerTimestamps_sec\s=\s)[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?',
desStr).group(0)
customHeaders[k - 1, 3] = re.search(
'(?<=nextFileMarkerTimestamps_sec\s=\s)[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?',
desStr).group(0)
if k == nPlanes:
out.append(copy.deepcopy(customHeaders))
return out
def get_background_vector(cellmask_filepath, img_filepath):
cellmask = io.imread(cellmask_filepath)
background_index = np.argwhere(cellmask == 0)
with tifffile.TiffFile(img_filepath) as tif:
img = tif.series[0].asarray() # axis order: CXY
img = img[:-1, ...] # last channel is ROI mask
background = img[:, background_index[:, 0], background_index[:, 1]]
background = background.T # axis order: NC
background = background.astype(
float) + 1e-12 # for numerical stability of log
background = np.log10(
background) # usually more gaussian-like in log space
return np.median(background, axis=0)
def open(self, file_name=None):
if file_name is not None:
self.file_name = file_name
if os.path.isdir(self.file_name):
self.glob_mode = True
flist = []
flist += glob.glob(os.path.join(self.file_name, '*.tif*'))
flist += glob.glob(os.path.join(self.file_name, '*.TIF*'))
flist = sorted(flist)
fname = flist[0]
self.flist = flist
else:
self.glob_mode = False
fname = self.file_name
self.tfile = tiff.TiffFile(fname)
setattr(self, 'close', getattr(self.tfile, 'close'))
def s3_to_array(f, cci):
"""
Read a tif file straight from an S3 bucket (provided as a cottoncandy
interface) into a numpy array
Parameters
----------
f : str
The name of the file in the bucket
cci : cottoncandy interface
"""
o = cci.download_object(f)
b = BytesIO(o)
t = tif.TiffFile(b)
a = t.asarray()
return a
def find_tiff_dims(src):
'''Find dimensions of a tifffile without having to load
Inputs
--------
src: file path
Returns
--------
pages: number of pages in tiff, for LVBT this is the number of horizontal foci
y,x: pixel dims
'''
from skimage.external import tifffile
with tifffile.TiffFile(os.path.join(src)) as tif:
pages = len(tif.pages) #number of horizontal foci
y, x = tif.pages[0].shape
tif.close()
return pages, y, x
def load(cls, file_path, series=0):
"""Load image files into data structures.
Class method. Can be used without instantiating.
Parameters
----------
file_path : string/list [string, string, ...]
File path as string, e.g. 'C:/folder/file.tif', or as list of file
paths, e.g. ['C:/folder/file.tif', 'C:/folder/file.tif'].
series : int, optional
Sets the series number if file has multiple series (or positions).
Use series='all' for loading all series.
Defaults to 0.
Examples
--------
>>> ImageSetRead.load('C:/folder/file.tif')
>>> image_files = ['C:/folder/file.tif', 'C:/folder/file.tif']
>>> ImageSetRead.load(image_files)
"""
if isinstance(file_path, str):
file_path = [file_path]
with tff.TiffSequence(file_path, pattern='XYCZT') as ts, \
tff.TiffFile(file_path[0]) as tf:
files = ts.files
image_metadata = cls._get_metadata(tf)
if len(tf.series) > 1 and series == 'all':
data = []
for idx, _ in enumerate(tf.series):
data.append(ts.asarray(series=idx))
panel_data = np.array(data)
else:
panel_data = ts.asarray(series=series)
if len(file_path) == 1 and series != 'all':
panel_data = np.vstack(panel_data)
image_data = cls._convert_to_xd(
panel_data, image_metadata, file_path, series)
return image_data, image_metadata, files
def _load_dpfile(path, obj):
"""Load a displot data file from disk into a data object.
Args:
path (str): Path to image.
obj (io.DisplotData): Data object to populate with the data.
Returns:
io.DisplotData: Displot data object.
"""
a = tarfile.open(path, 'r')
objjson_f = a.extractfile('dp.json')
objjson = json.loads(str(objjson_f.read(), 'ascii'))
obj.fromDict(objjson)
image_f = a.extractfile(os.path.basename(objjson['image_path']))
with tifffile.TiffFile(image_f) as tif:
obj.image = tif.asarray()
return obj
def generate_cell(image_filepath,
mask_filepath,
channel_list,
tile_shape,
outline=False):
mask = io.imread(mask_filepath)
with tifffile.TiffFile(image_filepath) as infile:
img_shape = infile.series[0].pages[0].shape
for region in measure.regionprops(mask):
# calculate tile coordinate
c = region.centroids
txl = int(np.round(c[0] - tile_shape[0] / 2))
tyl = int(np.round(c[1] - tile_shape[1] / 2))
txu, tyu = txl + tile_shape[0], tyl + tile_shape[1]
# skip cells too close to image edge
checklist = [
txl >= 0, txu < img_shape[0], tyl >= 0, tyu < img_shape[1]
]
if not all(checklist):
continue
# compose
cell = np.zeros(tile_shape + (len(channel_list), ))
for channel in channel_list:
img = infile.series[0].pages[channel].asarray(memmap=True)
cell_img = img[txl:txu, tyl:tyu, channel]
cell[..., channel] = img_as_float(cell_img)
# add outline
if outline:
cm = mask[txl:txu, tyl:tyu].copy()
co = segmentation.find_boundaries(cm, mode='inner')\
.astype(float)
for ch in range(cell.shape[2]):
cell[..., ch] = np.maximum(cell[..., ch], co)
yield cell
def getarray(idxAndBuf):
idx, buf = idxAndBuf
fbuf = BytesIO(buf)
tfh = tifffile.TiffFile(fbuf)
ary = tfh.asarray()
pageCount = ary.shape[0]
if nplanes is not None:
values = [
ary[i:(i + nplanes)] for i in range(0, ary.shape[0], nplanes)
]
else:
values = [ary]
tfh.close()
if ary.ndim == 3:
values = [val.squeeze() for val in values]
if nplanes and (pageCount % nplanes):
raise ValueError("nplanes '%d' does not evenly divide '%d'" %
(nplanes, pageCount))
nvals = len(values)
keys = [(idx * nvals + timepoint, ) for timepoint in range(nvals)]
return zip(keys, values)
def load(self):
if self.ext == "tif":
#self.data = skimage_io.imread(self.file_name,plugin="tifffile")
with tff.TiffFile(self.path) as tiff:
for tiff_page in tiff:
pass
#print(tiff_page.page_name)
self.data = tiff.asarray()
self.metadata = tiff[0].image_description
self.info = tiff[0].info()
#logging.debug(self.info)
self.axes = self.data.shape
logging.debug("---Image data: Shape - " + str(self.data.shape))
return
def get_lsm_ch(image_dir, color):
"""
Getting the channel based on the color
"""
full_image = tf.TiffFile(image_dir)
is_lsm = full_image.is_lsm
assert (is_lsm), "File is not lsm"
all_pages = full_image.pages
page_1 = all_pages[0]
lsm_info = page_1.cz_lsm_scan_info
channels = lsm_info.tracks
channels_dict = {}
for n in range(0, len(channels)):
channels_dict[n] = channels[n].data_channels[0]
table = df(channels_dict)
colors = {}
colors['blue'] = 16711680
colors['red'] = 255
colors['green'] = 65280
colors['dark_green'] = 48896
channel = table.ix['acquire', :][table.ix['color', :] == colors[color]]
if len(channel) == 0:
channel = table.ix['acquire', :][table.ix['color', :] ==
colors['dark_green']]
if len(channel) == 0:
print "Error: No Color\n"
return None
else:
return channel.index[0]
else:
return channel.index[0]
import os
import numpy as np
from scipy import io
from jaratoolbox import loadbehavior
from matplotlib import pyplot as plt
from skimage.external import tifffile
dataDir = '/home/nick/data/imag002_20181201/'
session = '000_002'
Fn = 'imag002_20181201_{}.tif'.format(session)
#Read in data file (nFrames, m, n)
fullFile = os.path.join(dataDir, Fn)
im0 = tifffile.TiffFile(fullFile, pages=[0])
# im = tifffile.imread(fullFile)
#Read the events file
rtMat = os.path.join(dataDir,
'imag002_20181201_{}_realtime.mat'.format(session))
rtData = io.loadmat(rtMat)
#Get onset frame numbers
ttl = rtData['ttl_log']
ttl = ttl.ravel().astype(int) #uint8 making things strange for diff
onsets = np.flatnonzero(
np.diff(ttl.astype(int)) == 2) + 1 #Should catch the leading edges
#How many samples before and after onset to average
samplesBefore = 0
samplesAfter = 20
traceLen = samplesBefore + samplesAfter
import numpy as np
import skimage.external.tifffile as tiff
from skimage.filters import threshold_otsu, gaussian, sobel
from skimage.morphology import watershed
from skimage import measure, draw
from scipy import ndimage as ndi
from scipy import optimize
import matplotlib.pyplot as plt
from intersection import calcIntersections
testfile = "img/test_3balls.tif"
with tiff.TiffFile(testfile) as tif:
img = tif.asarray()
markers = np.zeros_like(img)
markers[img < 100] = 1
markers[img > 100] = 2
elevation_map = sobel(img)
segmentation = watershed(elevation_map, markers)
segmentation = ndi.binary_fill_holes(segmentation - 1)
labeled_nuclei, _ = ndi.label(segmentation)
regions = measure.regionprops(labeled_nuclei)
r = 30
fig, ax = plt.subplots()
perim = np.zeros((labeled_nuclei.shape[0], labeled_nuclei.shape[1]))
processed_regions = []
for props in regions:
y0, x0 = props.centroid
processed_regions.append(props.centroid)
