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94 lines
4.4 KiB
94 lines
4.4 KiB
# -*- coding: utf-8 -*-
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import array
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import numpy as np
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import struct
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import six
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from nd2reader.model.image import Image
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class V3ImageReader(object):
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def __init__(self, metadata):
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self._metadata = metadata
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def _calculate_field_of_view(self, frame_number):
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images_per_cycle = len(self._metadata.z_levels) * len(self._metadata.channels)
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return int((frame_number - (frame_number % images_per_cycle)) / images_per_cycle) % len(self._metadata.fields_of_view)
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def _calculate_channel(self, frame_number):
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return self._metadata.channels[frame_number % len(self._metadata.channels)]
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def _calculate_z_level(self, frame_number):
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return self._metadata.z_levels[int(((frame_number - (frame_number % len(self._metadata.channels))) / len(self._metadata.channels)) % len(self._metadata.z_levels))]
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def _calculate_image_group_number(self, time_index, 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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:type time_index: int
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:type fov: int
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:type z_level: int
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:rtype: int
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"""
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return time_index * len(self._metadata.fields_of_view) * len(self._metadata.z_levels) + (fov * len(self._metadata.z_levels) + z_level)
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def _calculate_frame_number(self, image_group_number, fov, z_level):
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return (image_group_number - (fov * len(self._metadata.z_levels) + z_level)) / (len(self._metadata.fields_of_view) * len(self._metadata.z_levels))
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def get_image(self, index):
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channel_offset = index % len(self._metadata.channels)
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fov = 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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image_group_number = int(index / len(self._metadata.channels))
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frame_number = self._calculate_frame_number(image_group_number, fov, z_level)
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timestamp, image = self._get_raw_image_data(image_group_number, channel_offset, self._metadata.height, self._metadata.width)
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image.add_params(timestamp, frame_number, fov, channel, z_level)
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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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:rtype: dict
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"""
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channel_offset = {}
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for n, channel in enumerate(self._metadata.channels):
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channel_offset[channel] = n
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return channel_offset
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def _get_raw_image_data(self, image_group_number, channel_offset, height, width):
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"""
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Reads the raw bytes and the timestamp of an image.
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:param image_group_number: groups are made of images with the same time index, field of view and z-level.
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:type image_group_number: int
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:param channel_offset: the offset in the array where the bytes for this image are found.
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:type channel_offset: int
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:return: (int, array.array()) or None
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"""
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chunk = self._label_map[six.b("ImageDataSeq|%d!" % image_group_number)]
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data = self._read_chunk(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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# 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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image_data = np.reshape(image_group_data[image_data_start::len(self._metadata.channels)], (height, 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(image_data)
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return None
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