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304 lines
12 KiB
304 lines
12 KiB
# -*- coding: utf-8 -*-
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import struct
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import array
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import six
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import warnings
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from pims.base_frames import Frame
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import numpy as np
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from nd2reader.common import get_version, read_chunk
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from nd2reader.label_map import LabelMap
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from nd2reader.raw_metadata import RawMetadata
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from nd2reader import stitched
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class Parser(object):
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"""Parses ND2 files and creates a Metadata and driver object.
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"""
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CHUNK_HEADER = 0xabeceda
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CHUNK_MAP_START = six.b("ND2 FILEMAP SIGNATURE NAME 0001!")
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CHUNK_MAP_END = six.b("ND2 CHUNK MAP SIGNATURE 0000001!")
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supported_file_versions = {(3, None): True}
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def __init__(self, fh):
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self._fh = fh
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self._label_map = None
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self._raw_metadata = None
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self.metadata = None
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# First check the file version
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self.supported = self._check_version_supported()
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# Parse the metadata
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self._parse_metadata()
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def calculate_image_properties(self, index):
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"""Calculate FOV, channels and z_levels
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Args:
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index(int): the index (which is simply the order in which the image was acquired)
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Returns:
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tuple: tuple of the field of view, the channel and the z level
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"""
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field_of_view = self._calculate_field_of_view(index)
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channel = self._calculate_channel(index)
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z_level = self._calculate_z_level(index)
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return field_of_view, channel, z_level
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def get_image(self, index):
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"""
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Creates an Image object and adds its metadata, based on the index (which is simply the order in which the image
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was acquired). May return None if the ND2 contains multiple channels and not all were taken in each cycle (for
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example, if you take bright field images every minute, and GFP images every five minutes, there will be some
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indexes that do not contain an image. The reason for this is complicated, but suffice it to say that we hope to
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eliminate this possibility in future releases. For now, you'll need to check if your image is None if you're
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doing anything out of the ordinary.
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Args:
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index(int): the index (which is simply the order in which the image was acquired)
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Returns:
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Frame: the image
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"""
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field_of_view, channel, z_level = self.calculate_image_properties(index)
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channel_offset = index % len(self.metadata["channels"])
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image_group_number = int(index / len(self.metadata["channels"]))
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frame_number = self._calculate_frame_number(image_group_number, field_of_view, z_level)
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try:
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timestamp, image = self._get_raw_image_data(image_group_number, channel_offset, self.metadata["height"],
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self.metadata["width"])
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except (TypeError):
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return Frame([], frame_no=frame_number, metadata=self._get_frame_metadata())
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else:
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return Frame(image, frame_no=frame_number, metadata=self._get_frame_metadata())
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def get_image_by_attributes(self, frame_number, field_of_view, channel, z_level, height, width):
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"""Gets an image based on its attributes alone
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Args:
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frame_number: the frame number
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field_of_view: the field of view
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channel_name: the color channel name
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z_level: the z level
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height: the height of the image
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width: the width of the image
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Returns:
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Frame: the requested image
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"""
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frame_number = 0 if frame_number is None else frame_number
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field_of_view = 0 if field_of_view is None else field_of_view
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channel = 0 if channel is None else channel
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z_level = 0 if z_level is None else z_level
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image_group_number = self._calculate_image_group_number(frame_number, field_of_view, z_level)
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try:
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timestamp, raw_image_data = self._get_raw_image_data(image_group_number, channel,
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height, width)
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except (TypeError):
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return Frame([], frame_no=frame_number, metadata=self._get_frame_metadata())
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else:
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return Frame(raw_image_data, frame_no=frame_number, metadata=self._get_frame_metadata())
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@staticmethod
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def get_dtype_from_metadata():
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"""Determine the data type from the metadata.
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For now, always use float64 to prevent unexpected overflow errors when manipulating the data (calculating sums/
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means/etc.)
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"""
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return np.float64
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def _check_version_supported(self):
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"""Checks if the ND2 file version is supported by this reader.
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Returns:
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bool: True on supported
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"""
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major_version, minor_version = get_version(self._fh)
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supported = self.supported_file_versions.get(
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(major_version, minor_version)) or self.supported_file_versions.get((major_version, None))
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if not supported:
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print("Warning: No parser is available for your current ND2 version (%d.%d). " % (
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major_version, minor_version) + "This might lead to unexpected behaviour.")
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return supported
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def _parse_metadata(self):
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"""Reads all metadata and instantiates the Metadata object.
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"""
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# Retrieve raw metadata from the label mapping
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self._label_map = self._build_label_map()
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self._raw_metadata = RawMetadata(self._fh, self._label_map)
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self.metadata = self._raw_metadata.__dict__
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self.acquisition_times = self._raw_metadata.acquisition_times
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def _build_label_map(self):
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"""
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Every label ends with an exclamation point, however, we can't directly search for those to find all the labels
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as some of the bytes contain the value 33, which is the ASCII code for "!". So we iteratively find each label,
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grab the subsequent data (always 16 bytes long), advance to the next label and repeat.
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Returns:
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LabelMap: the computed label map
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"""
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# go 8 bytes back from file end
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self._fh.seek(-8, 2)
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chunk_map_start_location = struct.unpack("Q", self._fh.read(8))[0]
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self._fh.seek(chunk_map_start_location)
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raw_text = self._fh.read(-1)
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return LabelMap(raw_text)
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def _calculate_field_of_view(self, index):
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"""Determines what field of view was being imaged for a given image.
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Args:
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index(int): the index (which is simply the order in which the image was acquired)
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Returns:
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int: the field of view
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"""
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images_per_cycle = len(self.metadata["z_levels"]) * len(self.metadata["channels"])
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return int((index - (index % images_per_cycle)) / images_per_cycle) % len(self.metadata["fields_of_view"])
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def _calculate_channel(self, index):
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"""Determines what channel a particular image is.
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Args:
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index(int): the index (which is simply the order in which the image was acquired)
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Returns:
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string: the name of the color channel
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"""
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return self.metadata["channels"][index % len(self.metadata["channels"])]
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def _calculate_z_level(self, index):
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"""Determines the plane in the z-axis a given image was taken in.
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In the future, this will be replaced with the actual offset in micrometers.
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Args:
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index(int): the index (which is simply the order in which the image was acquired)
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Returns:
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The z level
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"""
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return self.metadata["z_levels"][int(
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((index - (index % len(self.metadata["channels"]))) / len(self.metadata["channels"])) % len(
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self.metadata["z_levels"]))]
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def _calculate_image_group_number(self, frame_number, fov, z_level):
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"""
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Images are grouped together if they share the same time index, field of view, and z-level.
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Args:
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frame_number: the time index
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fov: the field of view number
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z_level: the z level number
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Returns:
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int: the image group number
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"""
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z_length = len(self.metadata['z_levels'])
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z_length = z_length if z_length > 0 else 1
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fields_of_view = len(self.metadata["fields_of_view"])
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fields_of_view = fields_of_view if fields_of_view > 0 else 1
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return frame_number * fields_of_view * z_length + (fov * z_length + z_level)
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def _calculate_frame_number(self, image_group_number, field_of_view, z_level):
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"""
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Images are in the same frame if they share the same group number and field of view and are taken sequentially.
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Args:
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image_group_number: the image group number (see _calculate_image_group_number)
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field_of_view: the field of view number
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z_level: the z level number
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Returns:
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"""
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return (image_group_number - (field_of_view * len(self.metadata["z_levels"]) + z_level)) / (len(self.metadata["fields_of_view"]) * len(self.metadata["z_levels"]))
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@property
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def _channel_offset(self):
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"""
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Image data is interleaved for each image set. That is, if there are four images in a set, the first image
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will consist of pixels 1, 5, 9, etc, the second will be pixels 2, 6, 10, and so forth.
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Returns:
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dict: the channel offset for each channel
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"""
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return {channel: n for n, channel in enumerate(self.metadata["channels"])}
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def _get_raw_image_data(self, image_group_number, channel_offset, height, width):
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"""Reads the raw bytes and the timestamp of an image.
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Args:
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image_group_number: the image group number (see _calculate_image_group_number)
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channel_offset: the number of the color channel
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height: the height of the image
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width: the width of the image
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Returns:
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"""
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chunk = self._label_map.get_image_data_location(image_group_number)
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data = read_chunk(self._fh, chunk)
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# All images in the same image group share the same timestamp! So if you have complicated image data,
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# your timestamps may not be entirely accurate. Practically speaking though, they'll only be off by a few
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# seconds unless you're doing something super weird.
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timestamp = struct.unpack("d", data[:8])[0]
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image_group_data = array.array("H", data)
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image_data_start = 4 + channel_offset
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image_group_data = stitched.remove_parsed_unwanted_bytes(image_group_data, image_data_start, height, width)
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# The images for the various channels are interleaved within the same array. For example, the second image
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# of a four image group will be composed of bytes 2, 6, 10, etc. If you understand why someone would design
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# a data structure that way, please send the author of this library a message.
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number_of_true_channels = int(len(image_group_data[4:]) / (height * width))
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try:
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image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, width))
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except ValueError:
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new_width = len(image_group_data[image_data_start::number_of_true_channels]) // height
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image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, new_width))
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# Skip images that are all zeros! This is important, since NIS Elements creates blank "gap" images if you
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# don't have the same number of images each cycle. We discovered this because we only took GFP images every
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# other cycle to reduce phototoxicity, but NIS Elements still allocated memory as if we were going to take
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# them every cycle.
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if np.any(image_data):
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return timestamp, image_data
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# If a blank "gap" image is encountered, generate an array of corresponding height and width to avoid
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# errors with ND2-files with missing frames. Array is filled with nan to reflect that data is missing.
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else:
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empty_frame = np.full((height, width), np.nan)
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warnings.warn(
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"ND2 file contains gap frames which are represented by np.nan-filled arrays; to convert to zeros use e.g. np.nan_to_num(array)")
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return timestamp, image_data
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def _get_frame_metadata(self):
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"""Get the metadata for one frame
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Returns:
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dict: a dictionary containing the parsed metadata
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"""
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return self.metadata
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