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106 lines
3.2 KiB
106 lines
3.2 KiB
# -*- coding: utf-8 -*-
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import numpy as np
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import warnings
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class Image(np.ndarray):
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def __new__(cls, array):
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return np.asarray(array).view(cls)
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def __init__(self, array):
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self._timestamp = None
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self._frame_number = None
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self._field_of_view = None
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self._channel = None
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self._z_level = None
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def add_params(self, timestamp, frame_number, field_of_view, channel, z_level):
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"""
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A wrapper around the raw pixel data of an image.
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:param timestamp: The number of milliseconds after the beginning of the acquisition that this image was taken.
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:type timestamp: float
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:param frame_number: The order in which this image was taken, with images of different channels/z-levels
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at the same field of view treated as being in the same frame.
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:type frame_number: 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: The name of the color of this image
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:type channel: 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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"""
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self._timestamp = timestamp
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self._frame_number = int(frame_number)
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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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@property
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def height(self):
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return self.shape[1]
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@property
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def width(self):
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return self.shape[0]
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@property
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def field_of_view(self):
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"""
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Which of the fixed locations this image was taken at.
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:rtype int:
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"""
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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
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field of view and z-level offset, if you have images of multiple channels, they will all be given the same
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timestamp. No, this doesn't make much sense. But that's how ND2s are structured, so if your experiment depends
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on millisecond accuracy, you need to find an alternative imaging system.
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:rtype float:
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"""
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return self._timestamp / 1000.0
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@property
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def frame_number(self):
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return self._frame_number
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@property
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def channel(self):
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"""
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The name of the filter used to acquire this image. These are user-supplied in NIS Elements.
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:rtype str:
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"""
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return self._channel
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@property
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def z_level(self):
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"""
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The vertical offset of the image. These are simple integers starting from 0, where the 0 is the lowest
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z-level and each subsequent level incremented by 1.
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For example, if you acquired images at -3 µm, 0 µm, and +3 µm, your z-levels would be:
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-3 µm: 0
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0 µm: 1
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+3 µm: 2
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:rtype int:
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"""
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return self._z_level
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@property
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def data(self):
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warnings.warn("Image objects now directly subclass Numpy arrays, so using the data attribute will be removed in the near future.", DeprecationWarning)
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return self
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