You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
117 lines
5.4 KiB
117 lines
5.4 KiB
# -*- coding: utf-8 -*-
|
|
|
|
import array
|
|
import numpy as np
|
|
import struct
|
|
import six
|
|
from nd2reader.model.image import Image
|
|
|
|
|
|
class V3Driver(object):
|
|
CHUNK_HEADER = 0xabeceda
|
|
|
|
def __init__(self, metadata, label_map, file_handle):
|
|
self._metadata = metadata
|
|
self._label_map = label_map
|
|
self._file_handle = file_handle
|
|
|
|
def _calculate_field_of_view(self, frame_number):
|
|
images_per_cycle = len(self._metadata.z_levels) * len(self._metadata.channels)
|
|
return int((frame_number - (frame_number % images_per_cycle)) / images_per_cycle) % len(self._metadata.fields_of_view)
|
|
|
|
def _calculate_channel(self, frame_number):
|
|
return self._metadata.channels[frame_number % len(self._metadata.channels)]
|
|
|
|
def _calculate_z_level(self, frame_number):
|
|
return self._metadata.z_levels[int(((frame_number - (frame_number % len(self._metadata.channels))) / len(self._metadata.channels)) % len(self._metadata.z_levels))]
|
|
|
|
def _calculate_image_group_number(self, time_index, fov, z_level):
|
|
"""
|
|
Images are grouped together if they share the same time index, field of view, and z-level.
|
|
|
|
:type time_index: int
|
|
:type fov: int
|
|
:type z_level: int
|
|
|
|
:rtype: int
|
|
|
|
"""
|
|
return time_index * len(self._metadata.fields_of_view) * len(self._metadata.z_levels) + (fov * len(self._metadata.z_levels) + z_level)
|
|
|
|
def _calculate_frame_number(self, image_group_number, fov, z_level):
|
|
return (image_group_number - (fov * len(self._metadata.z_levels) + z_level)) / (len(self._metadata.fields_of_view) * len(self._metadata.z_levels))
|
|
|
|
def get_image(self, index):
|
|
channel_offset = index % len(self._metadata.channels)
|
|
fov = self._calculate_field_of_view(index)
|
|
channel = self._calculate_channel(index)
|
|
z_level = self._calculate_z_level(index)
|
|
image_group_number = int(index / len(self._metadata.channels))
|
|
frame_number = self._calculate_frame_number(image_group_number, fov, z_level)
|
|
timestamp, image = self._get_raw_image_data(image_group_number, channel_offset, self._metadata.height, self._metadata.width)
|
|
image.add_params(timestamp, frame_number, fov, channel, z_level)
|
|
return image
|
|
|
|
@property
|
|
def _channel_offset(self):
|
|
"""
|
|
Image data is interleaved for each image set. That is, if there are four images in a set, the first image
|
|
will consist of pixels 1, 5, 9, etc, the second will be pixels 2, 6, 10, and so forth.
|
|
|
|
:rtype: dict
|
|
|
|
"""
|
|
channel_offset = {}
|
|
for n, channel in enumerate(self._metadata.channels):
|
|
channel_offset[channel] = n
|
|
return channel_offset
|
|
|
|
def _get_raw_image_data(self, image_group_number, channel_offset, height, width):
|
|
"""
|
|
Reads the raw bytes and the timestamp of an image.
|
|
|
|
:param image_group_number: groups are made of images with the same time index, field of view and z-level.
|
|
:type image_group_number: int
|
|
:param channel_offset: the offset in the array where the bytes for this image are found.
|
|
:type channel_offset: int
|
|
|
|
:return: (int, array.array()) or None
|
|
|
|
"""
|
|
chunk = self._label_map[six.b("ImageDataSeq|%d!" % image_group_number)]
|
|
data = self._read_chunk(chunk)
|
|
# All images in the same image group share the same timestamp! So if you have complicated image data,
|
|
# your timestamps may not be entirely accurate. Practically speaking though, they'll only be off by a few
|
|
# seconds unless you're doing something super weird.
|
|
timestamp = struct.unpack("d", data[:8])[0]
|
|
image_group_data = array.array("H", data)
|
|
image_data_start = 4 + channel_offset
|
|
# The images for the various channels are interleaved within the same array. For example, the second image
|
|
# of a four image group will be composed of bytes 2, 6, 10, etc. If you understand why someone would design
|
|
# a data structure that way, please send the author of this library a message.
|
|
image_data = np.reshape(image_group_data[image_data_start::len(self._metadata.channels)], (height, width))
|
|
# Skip images that are all zeros! This is important, since NIS Elements creates blank "gap" images if you
|
|
# don't have the same number of images each cycle. We discovered this because we only took GFP images every
|
|
# other cycle to reduce phototoxicity, but NIS Elements still allocated memory as if we were going to take
|
|
# them every cycle.
|
|
if np.any(image_data):
|
|
return timestamp, Image(image_data)
|
|
return None
|
|
|
|
def _read_chunk(self, chunk_location):
|
|
"""
|
|
Gets the data for a given chunk pointer
|
|
|
|
:rtype: bytes
|
|
|
|
"""
|
|
self._file_handle.seek(chunk_location)
|
|
# The chunk metadata is always 16 bytes long
|
|
chunk_metadata = self._file_handle.read(16)
|
|
header, relative_offset, data_length = struct.unpack("IIQ", chunk_metadata)
|
|
if header != V3Driver.CHUNK_HEADER:
|
|
raise ValueError("The ND2 file seems to be corrupted.")
|
|
# We start at the location of the chunk metadata, skip over the metadata, and then proceed to the
|
|
# start of the actual data field, which is at some arbitrary place after the metadata.
|
|
self._file_handle.seek(chunk_location + 16 + relative_offset)
|
|
return self._file_handle.read(data_length)
|