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.
38 lines
1.7 KiB
38 lines
1.7 KiB
# -*- coding: utf-8 -*-
|
|
import numpy as np # type: ignore
|
|
|
|
|
|
def get_unwanted_bytes_ids(image_group_data, image_data_start, height, width):
|
|
# Check if the byte array size conforms to the image axes size. If not, check
|
|
# that the number of unexpected (unwanted) bytes is a multiple of the number of
|
|
# rows (height), as the same number of unwanted bytes is expected to be
|
|
# appended at the end of each row. Then, returns the indexes of the unwanted
|
|
# bytes.
|
|
# Skip the first 4 elements that correspond to the time stamp
|
|
number_of_true_channels = int(len(image_group_data[4:]) / (height * width))
|
|
n_unwanted_bytes = (len(image_group_data[4:])) % (height * width)
|
|
if not n_unwanted_bytes:
|
|
return np.arange(0)
|
|
assert 0 == n_unwanted_bytes % height, (
|
|
"An unexpected number of extra bytes was encountered based on the expected"
|
|
+ " frame size, therefore the file could not be parsed."
|
|
)
|
|
return np.arange(
|
|
image_data_start + width * number_of_true_channels,
|
|
len(image_group_data) - n_unwanted_bytes + 1,
|
|
width * number_of_true_channels,
|
|
)
|
|
|
|
|
|
def remove_parsed_unwanted_bytes(image_group_data, image_data_start, height, width):
|
|
# Stitched ND2 files have been reported to contain unexpected (according to
|
|
# image shape) zero bytes at the end of each image data row. This hinders
|
|
# proper reshaping of the data. Hence, here the unwanted zero bytes are
|
|
# identified and removed.
|
|
unwanted_byte_ids = get_unwanted_bytes_ids(
|
|
image_group_data, image_data_start, height, width
|
|
)
|
|
if 0 != len(unwanted_byte_ids):
|
|
image_group_data = np.delete(np.array(image_group_data), unwanted_byte_ids)
|
|
|
|
return image_group_data
|