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71 lines
2.0 KiB
71 lines
2.0 KiB
import numpy as np
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import logging
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log = logging.getLogger(__name__)
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class ImageSet(object):
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"""
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A group of images that share the same timestamp. NIS Elements doesn't store a unique timestamp for every
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image, rather, it stores one for each set of images that share the same field of view and z-axis level.
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"""
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def __init__(self):
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self._images = []
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def add(self, image):
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"""
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:type image: nd2reader.model.Image()
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"""
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self._images.append(image)
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def __iter__(self):
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for image in self._images:
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yield image
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class Image(object):
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def __init__(self, timestamp, raw_array, field_of_view, channel, z_level, height, width):
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self._timestamp = timestamp
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self._raw_data = raw_array
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self._field_of_view = field_of_view
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self._channel = channel
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self._z_level = z_level
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self._height = height
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self._width = width
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self._data = None
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@property
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def field_of_view(self):
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return self._field_of_view
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@property
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def timestamp(self):
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"""
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The number of seconds after the beginning of the acquisition that the image was taken. Note that for a given field
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of view and z-level offset, if you have images of multiple channels, they will all be given the same timestamp.
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No, this doesn't make much sense. But that's how ND2s are structured, so if your experiment depends on millisecond
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accuracy, you need to find an alternative imaging system.
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"""
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return self._timestamp / 1000.0
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@property
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def channel(self):
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return self._channel
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@property
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def z_level(self):
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return self._z_level
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@property
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def data(self):
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if self._data is None:
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# The data is just a flat, 1-dimensional array. We convert it to a 2D image here.
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self._data = np.reshape(self._raw_data, (self._height, self._width))
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return self._data
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@property
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def is_valid(self):
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return np.any(self._raw_data)
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