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