You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 

533 lines
17 KiB

import re
from nd2reader.common import read_chunk, read_array, read_metadata, parse_date, get_from_dict_if_exists
import xmltodict
import six
import numpy as np
class RawMetadata(object):
"""RawMetadata class parses and stores the raw metadata that is read from the binary file in dict format.
"""
def __init__(self, fh, label_map):
self._fh = fh
self._label_map = label_map
self._metadata_parsed = None
@property
def __dict__(self):
"""Returns the parsed metadata in dictionary form.
Returns:
dict: the parsed metadata
"""
return self.get_parsed_metadata()
def get_parsed_metadata(self):
"""Returns the parsed metadata in dictionary form.
Returns:
dict: the parsed metadata
"""
if self._metadata_parsed is not None:
return self._metadata_parsed
frames_per_channel = self._parse_total_images_per_channel()
self._metadata_parsed = {
"height": self._parse_if_not_none(self.image_attributes, self._parse_height),
"width": self._parse_if_not_none(self.image_attributes, self._parse_width),
"date": self._parse_if_not_none(self.image_text_info, self._parse_date),
"fields_of_view": self._parse_fields_of_view(),
"frames": np.arange(0, frames_per_channel, 1),
"z_levels": self._parse_z_levels(),
"total_images_per_channel": frames_per_channel,
"channels": self._parse_channels(),
"pixel_microns": self._parse_if_not_none(self.image_calibration, self._parse_calibration),
}
self._metadata_parsed['num_frames'] = len(self._metadata_parsed['frames'])
self._parse_roi_metadata()
self._parse_experiment_metadata()
return self._metadata_parsed
@staticmethod
def _parse_if_not_none(to_check, callback):
if to_check is not None:
return callback()
return None
def _parse_width_or_height(self, key):
return self.image_attributes[six.b('SLxImageAttributes')][six.b(key)]
def _parse_height(self):
return self._parse_width_or_height('uiHeight')
def _parse_width(self):
return self._parse_width_or_height('uiWidth')
def _parse_date(self):
return parse_date(self.image_text_info[six.b('SLxImageTextInfo')])
def _parse_calibration(self):
return self.image_calibration.get(six.b('SLxCalibration'), {}).get(six.b('dCalibration'))
def _parse_channels(self):
"""These are labels created by the NIS Elements user. Typically they may a short description of the filter cube
used (e.g. 'bright field', 'GFP', etc.)
Returns:
list: the color channels
"""
if self.image_metadata_sequence is None:
return []
metadata = self.image_metadata_sequence[six.b('SLxPictureMetadata')][six.b('sPicturePlanes')]
channels = self._process_channels_metadata(metadata)
return channels
def _process_channels_metadata(self, metadata):
try:
validity = self.image_metadata[six.b('SLxExperiment')][six.b('ppNextLevelEx')][six.b('')][0][
six.b('ppNextLevelEx')][six.b('')][0][six.b('pItemValid')]
except (KeyError, TypeError):
# If none of the channels have been deleted, there is no validity list, so we just make one
validity = [True for _ in metadata]
# Channel information is contained in dictionaries with the keys a0, a1...an where the number
# indicates the order in which the channel is stored. So by sorting the dicts alphabetically
# we get the correct order.
channels = []
for (label, chan), valid in zip(sorted(metadata[six.b('sPlaneNew')].items()), validity):
if not valid:
continue
channels.append(chan[six.b('sDescription')].decode("utf8"))
return channels
def _parse_fields_of_view(self):
"""The metadata contains information about fields of view, but it contains it even if some fields
of view were cropped. We can't find anything that states which fields of view are actually
in the image data, so we have to calculate it. There probably is something somewhere, since
NIS Elements can figure it out, but we haven't found it yet.
"""
return self._parse_dimension(r""".*?XY\((\d+)\).*?""")
def _parse_z_levels(self):
"""The different levels in the Z-plane.
Returns:
list: the z levels, just a sequence from 0 to n.
"""
return self._parse_dimension(r""".*?Z\((\d+)\).*?""")
def _parse_dimension_text(self):
"""While there are metadata values that represent a lot of what we want to capture, they seem to be unreliable.
Sometimes certain elements don't exist, or change their data type randomly. However, the human-readable text
is always there and in the same exact format, so we just parse that instead.
"""
dimension_text = six.b("")
if self.image_text_info is None:
return dimension_text
textinfo = self.image_text_info[six.b('SLxImageTextInfo')].values()
for line in textinfo:
entry = self._parse_dimension_text_line(line)
if entry is not None:
return entry
return dimension_text
@staticmethod
def _parse_dimension_text_line(line):
if six.b("Dimensions:") in line:
entries = line.split(six.b("\r\n"))
for entry in entries:
if entry.startswith(six.b("Dimensions:")):
return entry
return None
def _parse_dimension(self, pattern):
dimension_text = self._parse_dimension_text()
if dimension_text is None:
return [0]
if six.PY3:
dimension_text = dimension_text.decode("utf8")
match = re.match(pattern, dimension_text)
if not match:
return [0]
count = int(match.group(1))
return list(range(count))
def _parse_total_images_per_channel(self):
"""The total number of images per channel.
Warning: this may be inaccurate as it includes 'gap' images.
"""
if self.image_attributes is None:
return None
return self.image_attributes[six.b('SLxImageAttributes')][six.b('uiSequenceCount')]
def _parse_roi_metadata(self):
"""Parse the raw ROI metadata.
"""
if self.roi_metadata is None or not six.b('RoiMetadata_v1') in self.roi_metadata:
return
raw_roi_data = self.roi_metadata[six.b('RoiMetadata_v1')]
if not six.b('m_vectGlobal_Size') in raw_roi_data:
return
number_of_rois = raw_roi_data[six.b('m_vectGlobal_Size')]
roi_objects = []
for i in range(number_of_rois):
current_roi = raw_roi_data[six.b('m_vectGlobal_%d' % i)]
roi_objects.append(self._parse_roi(current_roi))
self._metadata_parsed['rois'] = roi_objects
def _parse_roi(self, raw_roi_dict):
"""Extract the vector animation parameters from the ROI.
This includes the position and size at the given timepoints.
Args:
raw_roi_dict: dictionary of raw roi metadata
Returns:
dict: the parsed ROI metadata
"""
number_of_timepoints = raw_roi_dict[six.b('m_vectAnimParams_Size')]
roi_dict = {
"timepoints": [],
"positions": [],
"sizes": [],
"shape": self._parse_roi_shape(raw_roi_dict[six.b('m_sInfo')][six.b('m_uiShapeType')]),
"type": self._parse_roi_type(raw_roi_dict[six.b('m_sInfo')][six.b('m_uiInterpType')])
}
for i in range(number_of_timepoints):
roi_dict = self._parse_vect_anim(roi_dict, raw_roi_dict[six.b('m_vectAnimParams_%d' % i)])
# convert to NumPy arrays
roi_dict["timepoints"] = np.array(roi_dict["timepoints"], dtype=np.float)
roi_dict["positions"] = np.array(roi_dict["positions"], dtype=np.float)
roi_dict["sizes"] = np.array(roi_dict["sizes"], dtype=np.float)
return roi_dict
@staticmethod
def _parse_roi_shape(shape):
if shape == 3:
return 'rectangle'
elif shape == 9:
return 'circle'
return None
@staticmethod
def _parse_roi_type(type_no):
if type_no == 4:
return 'stimulation'
elif type_no == 3:
return 'reference'
elif type_no == 2:
return 'background'
return None
def _parse_vect_anim(self, roi_dict, animation_dict):
"""
Parses a ROI vector animation object and adds it to the global list of timepoints and positions.
Args:
roi_dict: the raw roi dictionary
animation_dict: the raw animation dictionary
Returns:
dict: the parsed metadata
"""
roi_dict["timepoints"].append(animation_dict[six.b('m_dTimeMs')])
image_width = self._metadata_parsed["width"] * self._metadata_parsed["pixel_microns"]
image_height = self._metadata_parsed["height"] * self._metadata_parsed["pixel_microns"]
# positions are taken from the center of the image as a fraction of the half width/height of the image
position = np.array((0.5 * image_width * (1 + animation_dict[six.b('m_dCenterX')]),
0.5 * image_height * (1 + animation_dict[six.b('m_dCenterY')]),
animation_dict[six.b('m_dCenterZ')]))
roi_dict["positions"].append(position)
size_dict = animation_dict[six.b('m_sBoxShape')]
# sizes are fractions of the half width/height of the image
roi_dict["sizes"].append((size_dict[six.b('m_dSizeX')] * 0.25 * image_width,
size_dict[six.b('m_dSizeY')] * 0.25 * image_height,
size_dict[six.b('m_dSizeZ')]))
return roi_dict
def _parse_experiment_metadata(self):
"""Parse the metadata of the ND experiment
"""
if self.image_metadata is None or six.b('SLxExperiment') not in self.image_metadata:
return
raw_data = self.image_metadata[six.b('SLxExperiment')]
experimental_data = {
'description': 'unknown',
'loops': []
}
if six.b('wsApplicationDesc') in raw_data:
experimental_data['description'] = raw_data[six.b('wsApplicationDesc')].decode('utf8')
if six.b('uLoopPars') in raw_data:
experimental_data['loops'] = self._parse_loop_data(raw_data[six.b('uLoopPars')])
self._metadata_parsed['experiment'] = experimental_data
@staticmethod
def _get_loops_from_data(loop_data):
loops = [loop_data]
if six.b('uiPeriodCount') in loop_data and loop_data[six.b('uiPeriodCount')] > 0:
# special ND experiment
if six.b('pPeriod') not in loop_data:
return []
# take the first dictionary element, it contains all loop data
loops = loop_data[six.b('pPeriod')][list(loop_data[six.b('pPeriod')].keys())[0]]
return loops
def _parse_loop_data(self, loop_data):
"""Parse the experimental loop data
Args:
loop_data: dictionary of experiment loops
Returns:
list: list of the parsed loops
"""
loops = self._get_loops_from_data(loop_data)
# take into account the absolute time in ms
time_offset = 0
parsed_loops = []
for loop in loops:
# duration of this loop
duration = get_from_dict_if_exists('dDuration', loop) or 0
# uiLoopType == 6 is a stimulation loop
is_stimulation = get_from_dict_if_exists('uiLoopType', loop) == 6
# sampling interval in ms
interval = get_from_dict_if_exists('dAvgPeriodDiff', loop)
parsed_loop = {
'start': time_offset,
'duration': duration,
'stimulation': is_stimulation,
'sampling_interval': interval
}
parsed_loops.append(parsed_loop)
# increase the time offset
time_offset += duration
return parsed_loops
@property
def image_text_info(self):
"""Textual image information
Returns:
dict: containing the textual image info
"""
return read_metadata(read_chunk(self._fh, self._label_map.image_text_info), 1)
@property
def image_metadata_sequence(self):
"""Image metadata of the sequence
Returns:
dict: containing the metadata
"""
return read_metadata(read_chunk(self._fh, self._label_map.image_metadata_sequence), 1)
@property
def image_calibration(self):
"""The amount of pixels per micron.
Returns:
dict: pixels per micron
"""
return read_metadata(read_chunk(self._fh, self._label_map.image_calibration), 1)
@property
def image_attributes(self):
"""Image attributes
Returns:
dict: containing the image attributes
"""
return read_metadata(read_chunk(self._fh, self._label_map.image_attributes), 1)
@property
def x_data(self):
"""X data
Returns:
dict: x_data
"""
return read_array(self._fh, 'double', self._label_map.x_data)
@property
def y_data(self):
"""Y data
Returns:
dict: y_data
"""
return read_array(self._fh, 'double', self._label_map.y_data)
@property
def z_data(self):
"""Z data
Returns:
dict: z_data
"""
return read_array(self._fh, 'double', self._label_map.z_data)
@property
def roi_metadata(self):
"""Contains information about the defined ROIs: shape, position and type (reference/background/stimulation).
Returns:
dict: ROI metadata dictionary
"""
return read_metadata(read_chunk(self._fh, self._label_map.roi_metadata), 1)
@property
def pfs_status(self):
"""Perfect focus system (PFS) status
Returns:
dict: Perfect focus system (PFS) status
"""
return read_array(self._fh, 'int', self._label_map.pfs_status)
@property
def pfs_offset(self):
"""Perfect focus system (PFS) offset
Returns:
dict: Perfect focus system (PFS) offset
"""
return read_array(self._fh, 'int', self._label_map.pfs_offset)
@property
def camera_exposure_time(self):
"""Exposure time information
Returns:
dict: Camera exposure time
"""
return read_array(self._fh, 'double', self._label_map.camera_exposure_time)
@property
def lut_data(self):
"""LUT information
Returns:
dict: LUT information
"""
return xmltodict.parse(read_chunk(self._fh, self._label_map.lut_data))
@property
def grabber_settings(self):
"""Grabber settings
Returns:
dict: Acquisition settings
"""
return xmltodict.parse(read_chunk(self._fh, self._label_map.grabber_settings))
@property
def custom_data(self):
"""Custom user data
Returns:
dict: custom user data
"""
return xmltodict.parse(read_chunk(self._fh, self._label_map.custom_data))
@property
def app_info(self):
"""NIS elements application info
Returns:
dict: (Version) information of the NIS Elements application
"""
return xmltodict.parse(read_chunk(self._fh, self._label_map.app_info))
@property
def camera_temp(self):
"""Camera temperature
Yields:
float: the temperature
"""
camera_temp = read_array(self._fh, 'double', self._label_map.camera_temp)
if camera_temp:
for temp in map(lambda x: round(x * 100.0, 2), camera_temp):
yield temp
@property
def acquisition_times(self):
"""Acquisition times
Yields:
float: the acquisition time
"""
acquisition_times = read_array(self._fh, 'double', self._label_map.acquisition_times)
if acquisition_times:
for acquisition_time in map(lambda x: x / 1000.0, acquisition_times):
yield acquisition_time
@property
def image_metadata(self):
"""Image metadata
Returns:
dict: Extra image metadata
"""
if self._label_map.image_metadata:
return read_metadata(read_chunk(self._fh, self._label_map.image_metadata), 1)