angelolab / tmi / 198

import math

import numpy as np
import xarray as xr


def stitch_images(data_xr, num_cols):
    """Stitch together a stack of different channels from different FOVs into a single 2D image
    for each channel

    Args:
        data_xr (xarray.DataArray):
            xarray containing image data from multiple fovs and channels
        num_cols (int):
            number of images stitched together horizontally

    Returns:
        xarray.DataArray:
            the stitched image data
    """

    num_imgs = data_xr.shape[0]
    num_rows = math.ceil(num_imgs / num_cols)
    row_len = data_xr.shape[1]
    col_len = data_xr.shape[2]

    total_row_len = num_rows * row_len
    total_col_len = num_cols * col_len

    stitched_data = np.zeros(
        (1, total_row_len, total_col_len, data_xr.shape[3]), dtype=data_xr.dtype
    )

    img_idx = 0
    for row in range(num_rows):
        for col in range(num_cols):
            stitched_data[
                0, row * row_len : (row + 1) * row_len, col * col_len : (col + 1) * col_len, :
            ] = data_xr[img_idx, ...]
            img_idx += 1
            if img_idx == num_imgs:
                break

    stitched_xr = xr.DataArray(
        stitched_data,
        coords=[["stitched_image"], range(total_row_len), range(total_col_len), data_xr.channels],
        dims=["fovs", "rows", "cols", "channels"],
    )
    return stitched_xr

1	import math	1✔
2
3	import numpy as np	1✔
4	import xarray as xr	1✔
5
6
7	def stitch_images(data_xr, num_cols):	1✔
8	"""Stitch together a stack of different channels from different FOVs into a single 2D image
9	for each channel
10
11	Args:
12	data_xr (xarray.DataArray):
13	xarray containing image data from multiple fovs and channels
14	num_cols (int):
15	number of images stitched together horizontally
16
17	Returns:
18	xarray.DataArray:
19	the stitched image data
20	"""
21
22	num_imgs = data_xr.shape[0]	1✔
23	num_rows = math.ceil(num_imgs / num_cols)	1✔
24	row_len = data_xr.shape[1]	1✔
25	col_len = data_xr.shape[2]	1✔
26
27	total_row_len = num_rows * row_len	1✔
28	total_col_len = num_cols * col_len	1✔
29
30	stitched_data = np.zeros(	1✔
31	(1, total_row_len, total_col_len, data_xr.shape[3]), dtype=data_xr.dtype
32	)
33
34	img_idx = 0	1✔
35	for row in range(num_rows):	1✔
36	for col in range(num_cols):	1✔
37	stitched_data[	1✔
38	0, row * row_len : (row + 1) * row_len, col * col_len : (col + 1) * col_len, :
39	] = data_xr[img_idx, ...]
40	img_idx += 1	1✔
41	if img_idx == num_imgs:	1✔
42	break	1✔
43
44	stitched_xr = xr.DataArray(	1✔
45	stitched_data,
46	coords=[["stitched_image"], range(total_row_len), range(total_col_len), data_xr.channels],
47	dims=["fovs", "rows", "cols", "channels"],
48	)
49	return stitched_xr	1✔

angelolab / tmi / 198

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