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115 lines
4.5 KiB
115 lines
4.5 KiB
# -*- coding: utf-8 -*-
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from nd2reader.model import Image, ImageSet
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from nd2reader.parser import Nd2Parser
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import six
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class Nd2(Nd2Parser):
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"""
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Allows easy access to NIS Elements .nd2 image files.
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"""
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def __init__(self, filename):
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super(Nd2, self).__init__(filename)
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self._filename = filename
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def __repr__(self):
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return "\n".join(["<ND2 %s>" % self._filename,
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"Created: %s" % self._absolute_start.strftime("%Y-%m-%d %H:%M:%S"),
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"Image size: %sx%s (HxW)" % (self.height, self.width),
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"Image cycles: %s" % self._time_index_count,
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"Channels: %s" % ", ".join(["'%s'" % str(channel) for channel in self._channels]),
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"Fields of View: %s" % self._field_of_view_count,
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"Z-Levels: %s" % self._z_level_count
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])
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def __len__(self):
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"""
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This should be the total number of images in the ND2, but it may be inaccurate. If the ND2 contains a
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different number of images in a cycle (i.e. there are "gap" images) it will be higher than reality.
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:rtype: int
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"""
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return self._image_count * self._channel_count
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@property
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def height(self):
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"""
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:return: height of each image, in pixels
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:rtype: int
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"""
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return self.metadata[six.b('ImageAttributes')][six.b('SLxImageAttributes')][six.b('uiHeight')]
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@property
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def width(self):
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"""
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:return: width of each image, in pixels
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:rtype: int
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"""
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return self.metadata[six.b('ImageAttributes')][six.b('SLxImageAttributes')][six.b('uiWidth')]
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def __iter__(self):
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"""
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Iterates over every image, in the order they were taken.
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:return: model.Image()
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"""
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for i in range(self._image_count):
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for fov in range(self._field_of_view_count):
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for z_level in range(self._z_level_count):
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for channel_name in self._channels:
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image = self.get_image(i, fov, channel_name, z_level)
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if image is not None:
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yield image
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@property
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def image_sets(self):
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"""
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Iterates over groups of related images. This is useful if your ND2 contains multiple fields of view.
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A typical use case might be that you have, say, four areas of interest that you're monitoring, and every
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minute you take a bright field and GFP image of each one. For each cycle, this method would produce four
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ImageSet objects, each containing one bright field and one GFP image.
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:return: model.ImageSet()
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"""
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for time_index in range(self._time_index_count):
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image_set = ImageSet()
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for fov in range(self._field_of_view_count):
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for channel_name in self._channels:
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for z_level in range(self._z_level_count):
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image = self.get_image(time_index, fov, channel_name, z_level)
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if image is not None:
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image_set.add(image)
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yield image_set
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def get_image(self, time_index, field_of_view, channel_name, z_level):
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"""
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Returns an Image if data exists for the given parameters, otherwise returns None. In general, you should avoid
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using this method unless you're very familiar with the structure of ND2 files. If you have a use case that
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cannot be met by the `__iter__` or `image_sets` methods above, please create an issue on Github.
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:param time_index: the frame number
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:type time_index: int
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:param field_of_view: the label for the place in the XY-plane where this image was taken.
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:type field_of_view: int
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:param channel_name: the name of the color of this image
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:type channel_name: str
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:param z_level: the label for the location in the Z-plane where this image was taken.
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:type z_level: int
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:rtype: nd2reader.model.Image() or None
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"""
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image_set_number = self._calculate_image_group_number(time_index, field_of_view, z_level)
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try:
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timestamp, raw_image_data = self._get_raw_image_data(image_set_number, self._channel_offset[channel_name])
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image = Image(timestamp, raw_image_data, field_of_view, channel_name, z_level, self.height, self.width)
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except TypeError:
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return None
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else:
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return image
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