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Real-time robust autofocus method enabling sustained intravital scanning light field imaging.

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The authors' code

Python · 112 lines · 4.8 KB · GPL-2.0

  2. # Copyright (C) 2026 Yuedi Wang
  3. #
  4. # This file is part of AFsLF.
  5. #
  6. # AFsLF is free software: you can redistribute it and/or modify it
  7. # under the terms of the GNU General Public License version 2
  8. # as published by the Free Software Foundation.
  9. #
  10. # AFsLF is distributed in the hope that it will be useful,
  11. # but WITHOUT ANY WARRANTY. See the LICENSE file for details.
  12. #
  13. # SPDX-License-Identifier: GPL-2.0-only
  15. import tifffile
  16. import numpy as np
  17. import pickle
  18. import cv2
  20. def distort_model(params, x, y):
  21. fx, fy, cx, cy, k1, k2, k3, p1, p2 = params
  22. matrix = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
  23. objpoints = np.concatenate((x[:, np.newaxis], y[:, np.newaxis], np.ones_like(y[:, np.newaxis])), axis=1)
  24. objpoints_rotated = np.matmul(objpoints, matrix)
  25. objpoints_projected = objpoints_rotated[:, :2] / (objpoints_rotated[:, 2:] + 1e-17)
  26. shift = objpoints_projected - np.array([cx, cy])
  28. x_shifted = shift[:, 0]
  29. y_shifted = shift[:, 1]
  30. r2 = x_shifted**2 + y_shifted**2
  31. x_distorted = x_shifted * (1 + k1*r2 + k2*r2**2 + k3*r2**3) + 2*p1*x_shifted*y_shifted + p2*(r2 + 2*x_shifted**2) + cx
  32. y_distorted = y_shifted * (1 + k1*r2 + k2*r2**2 + k3*r2**3) + p1*(r2 + 2*y_shifted**2) + 2*p2*x_shifted*y_shifted + cy
  33. return x_distorted, y_distorted
  35. def undistort_coor(params):
  36. H, W = (10748, 14304)
  37. gty, gtx = np.mgrid[:H, :W]
  38. gtxy = np.c_[gtx.ravel(), gty.ravel()]
  39. x_undistorted, y_undistorted = distort_model(params['inv_undistort'], (gtxy[:,0]-W//2)/100, (gtxy[:,1]-H//2)/100)
  40. x_undistorted = x_undistorted*100 + W//2
  41. y_undistorted = y_undistorted*100 + H//2
  42. return x_undistorted, y_undistorted
  44. def merge(wigner, group_mode, nshift=3):
  45. '''
  46. input:
  47. wigner: ( 180, ny_2, nx_2), dtype=torch.tensor
  48. output:
  49. merge_wigner: (172(20), ny_2*nshift, nx_2*nshift)
  50. '''
  51. order = [6, 5, 4, 7, 8, 3, 0, 1, 2]
  52. n, h, w = wigner.shape
  53. if group_mode == 1:
  54. merged_wigner = np.zeros([n - nshift**2 + 1, h*nshift, w*nshift])
  55. for i in range(merged_wigner.shape[0]):
  56. wigner_tmp = wigner[i:i + nshift**2]
  57. wigner_tmp = np.roll(wigner_tmp, i%nshift**2, 0)
  58. merged_wigner[i] = wigner_tmp[order].reshape(nshift, nshift, h, w).transpose(2, 0, 3, 1).reshape(h*nshift, w*nshift)
  59. else:
  60. merged_wigner = np.zeros([n//nshift**2, h*nshift, w*nshift])
  61. for i in range(merged_wigner.shape[0]):
  62. wigner_tmp = wigner[i * nshift**2:(i + 1) * nshift**2]
  63. merged_wigner[i] = wigner_tmp[order].reshape(nshift, nshift, h, w).transpose(2, 0, 3, 1).reshape(h*nshift, w*nshift)
  65. return merged_wigner
  67. def register_ecc(img1, img2):
  68. warp_matrix = np.eye(2, 3, dtype=np.float32)
  69. criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 100, 1e-6)
  70. cc, warp_matrix = cv2.findTransformECC(img2, img1, warp_matrix, cv2.MOTION_TRANSLATION, criteria)
  71. xshift = warp_matrix[0, 2]
  72. yshift = warp_matrix[1, 2]
  73. return yshift, xshift
  75. if __name__ == '__main__':
  76. H, W, centerX, centerY = 10748, 14304, 7151, 5373
  77. crop_H, crop_W = 1995, 1995
  78. kk=1#kk is the parameter KK is a pre-corrected parameter associated with system parameters.
  79. grid_x = np.arange(centerX-crop_W//2+7, centerX+crop_W//2+1, 15, dtype=np.int16)
  80. grid_y = np.arange(centerY-crop_H//2+7, centerY+crop_H//2+1, 15, dtype=np.int16)
  81. gtx, gty = np.meshgrid(grid_x, grid_y)
  82. gtxy = np.c_[gtx.ravel(), gty.ravel()]
  84. with open("./undistort_params_dict_points_240620.pkl", 'rb') as file:
  85. params = pickle.load(file)
  87. x_undistorted, y_undistorted = distort_model(params['inv_undistort'], (gtxy[:,0]-W//2)/100, (gtxy[:,1]-H//2)/100)
  88. x_undistorted = np.round(x_undistorted*100 + W//2).astype(np.int16)
  89. y_undistorted = np.round(y_undistorted*100 + H//2).astype(np.int16)
  90. start_x = centerX-2415//2
  91. start_y = centerY-2415//2
  92. x_undistorted -= start_x
  93. y_undistorted -= start_y
  95. with open("output_fft.txt", "w") as f:
  96. for i in range(0, 25):
  97. raw_path = f"Y:/C2/B18_{i}.tiff"
  99. lf = tifffile.imread(raw_path)
  101. wdf0 = lf[:, y_undistorted, x_undistorted-2].reshape(lf.shape[0], 133, 133)
  102. wdf1 = lf[:, y_undistorted, x_undistorted+2].reshape(lf.shape[0], 133, 133)
  103. merged_wdf_0 = merge(wdf0, 0).astype(np.float32)
  104. merged_wdf_1 = merge(wdf1, 0).astype(np.float32)
  107. for frame in range(len(merged_wdf_0)):
  108. yshift, xshift = register_ecc(merged_wdf_0[frame], merged_wdf_1[frame])
  109. fractor_X = kk
  110. defocus_x = xshift / fractor_X
  111. print('frame:', frame+i*20, 'yshift:', yshift, ', xshift:', xshift, ', defocus_x:', defocus_x)
  112. f.write(f"{defocus_x}\n")

autofocus_valid.py at commit 166cfc6, under GPL-2.0 · at the source

Overview

Authors: Yuedi Wang1, Jingyao Wu2,3, Jiamin Wu3,4,5, Yuan Li3, Wenjin Lv6, Fangfei Yu6,7, Jun Yan6, Zhi Lu8, Yi Yang6,9,10,11, Qionghai Dai3,5
ORCID iDs: Jingyao Wu, Jiamin Wu, Zhi Lu, Yi Yang
  1. School of Information and Communication Engineering, Communication University of China, Beijing, China
  2. Artificial Intelligence Institute, China Academy of Information and Communications Technology, Beijing, China
  3. Department of Automation, Tsinghua University, Beijing, China
  4. IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
  5. Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
  6. Zhejiang Hehu Technology, Hangzhou, China
  7. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China
  8. Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China
  9. Hangzhou Innovation Institute, Beihang University, Hangzhou, China
  10. School of Reliability and Systems Engineering, Beihang University, Beijing, China
  11. Tianmushan Laboratory, Hangzhou, China
Institutions: Communication University of China (China); China Academy of Information and Communications Technology (China); Tsinghua University (China); Hangzhou Dianzi University (China); Tianmushan Laboratory (China); Beihang University (China)
Journal: Nature communications, volume 17, issue 1, article 8105
Dates: received 18 November 2025; accepted 19 June 2026; published online 29 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74976-z · PMID 42373656 · PMCID PMC13458141 · OpenAlex W7166510692
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), mouse (organism)
Methods: Evoked potentials, Physiology & signal measures
Keywords: Microscopy, Imaging
MeSH: Imaging, Three-Dimensional*, Intravital Microscopy*, Animals, Brain, Liver, Mice (* major topic)
Topic: Digital Holography and Microscopy (Atomic and Molecular Physics, and Optics, Physics and Astronomy), according to OpenAlex
Funding: Young Scientists Fund; Fundamental Research Funds for the Central Universities
Citations: not cited yet (Europe PMC); 46 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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yuedi-wang/AFsLF

License: GPL-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 166cfc6a61d169ae2a81a882177d05eff95eeca2, 14 May 2026
Languages: Python (1)
Size: 13 files, 1 script
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), OpenCV (1 file), tifffile (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/yuedi-wang/AFsLF

Zenodo 20695290

License: GPL-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), OpenCV (1 file), tifffile (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-74976-z.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-74976-z.

Versions

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Version 2, 28 September 2026

  • Funding: added Young Scientists Fund; Fundamental Research Funds for the Central Universities

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 6 MeSH terms, 35 references.

Cite

This paper

Wang, Y., Wu, J., Wu, J., Li, Y., Lv, W., Yu, F., Yan, J., Lu, Z., Yang, Y., & Dai, Q. (2026). Real-time robust autofocus method enabling sustained intravital scanning light field imaging. Nature communications, 17(1), 8105. https://doi.org/10.1038/s41467-026-74976-z

BibTeX

@article{wang2026real,
author = {Wang, Yuedi and Wu, Jingyao and Wu, Jiamin and Li, Yuan and Lv, Wenjin and Yu, Fangfei and Yan, Jun and Lu, Zhi and Yang, Yi and Dai, Qionghai},
title = {{Real-time robust autofocus method enabling sustained intravital scanning light field imaging}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {8105},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74976-z},
url = {https://doi.org/10.1038/s41467-026-74976-z},
pmid = {42373656},
pmcid = {PMC13458141}
}

RIS

TY - JOUR
AU - Wang, Yuedi
AU - Wu, Jingyao
AU - Wu, Jiamin
AU - Li, Yuan
AU - Lv, Wenjin
AU - Yu, Fangfei
AU - Yan, Jun
AU - Lu, Zhi
AU - Yang, Yi
AU - Dai, Qionghai
TI - Real-time robust autofocus method enabling sustained intravital scanning light field imaging
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/29
VL - 17
IS - 1
SP - 8105
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74976-z
UR - https://doi.org/10.1038/s41467-026-74976-z
LA - en
ER -

CSL-JSON

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"container-title": "Nature communications",
"author": [
{
"family": "Wang",
"given": "Yuedi"
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{
"family": "Wu",
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{
"family": "Wu",
"given": "Jiamin"
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{
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"PMCID": "PMC13458141",
"ISSN": "2041-1723",
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}

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