Interpolation
!git clone https://github.com/lanshui98/UniST.git
%cd UniST
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/content/UniST
!pip install -r requirements.txt
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Installing collected packages: pytz, namex, mpmath, libclang, flatbuffers, fastjsonschema, addict, wrapt, urllib3, tzdata, tqdm, threadpoolctl, termcolor, tensorboard-data-server, sympy, scooby, rpds-py, retrying, pyyaml, pyparsing, protobuf, pillow, optree, opt_einsum, numpy, ninja, networkx, natsort, narwhals, mdurl, markupsafe, kiwisolver, joblib, itsdangerous, importlib-resources, idna, grpcio, google_pasta, gast, fsspec, fonttools, filelock, einops, cycler, configargparse, click, charset_normalizer, certifi, blinker, attrs, array-api-compat, absl-py, werkzeug, tifffile, scipy, requests, referencing, pyquaternion, plotly, pandas, ml_dtypes, markdown-it-py, markdown, jinja2, imageio, h5py, contourpy, astunparse, torch, tensorboard, scikit-learn, rich, pooch, matplotlib, jsonschema-specifications, Flask, anndata, vtk, keras, jsonschema, dash, tensorflow, pyvista, nbformat, open3d
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Successfully installed Flask-3.1.2 absl-py-2.3.1 addict-2.4.0 anndata-0.10.9 array-api-compat-1.11.2 astunparse-1.6.3 attrs-25.4.0 blinker-1.9.0 certifi-2026.1.4 charset_normalizer-3.4.4 click-8.1.8 configargparse-1.7.1 contourpy-1.3.0 cycler-0.12.1 dash-4.0.0 einops-0.7.0 fastjsonschema-2.21.2 filelock-3.19.1 flatbuffers-25.12.19 fonttools-4.60.2 fsspec-2025.10.0 gast-0.7.0 google_pasta-0.2.0 grpcio-1.78.0 h5py-3.14.0 idna-3.11 imageio-2.37.2 importlib-resources-6.5.2 itsdangerous-2.2.0 jinja2-3.1.6 joblib-1.5.3 jsonschema-4.25.1 jsonschema-specifications-2025.9.1 keras-3.10.0 kiwisolver-1.4.7 libclang-18.1.1 markdown-3.9 markdown-it-py-3.0.0 markupsafe-3.0.3 matplotlib-3.9.4 mdurl-0.1.2 ml_dtypes-0.5.4 mpmath-1.3.0 namex-0.1.0 narwhals-2.16.0 natsort-8.4.0 nbformat-5.10.4 networkx-3.2.1 ninja-1.13.0 numpy-2.0.2 open3d-0.18.0 opt_einsum-3.4.0 optree-0.18.0 pandas-2.3.3 pillow-11.3.0 plotly-6.5.2 pooch-1.9.0 protobuf-6.33.5 pyparsing-3.3.2 pyquaternion-0.9.9 pytz-2025.2 pyvista-0.46.5 pyyaml-6.0.3 referencing-0.36.2 requests-2.32.5 retrying-1.4.2 rich-14.3.2 rpds-py-0.27.1 scikit-learn-1.6.1 scipy-1.13.1 scooby-0.11.0 sympy-1.14.0 tensorboard-2.20.0 tensorboard-data-server-0.7.2 tensorflow-2.20.0 termcolor-3.1.0 threadpoolctl-3.6.0 tifffile-2024.8.30 torch-2.8.0 tqdm-4.67.3 tzdata-2025.3 urllib3-2.6.3 vtk-9.5.2 werkzeug-3.1.5 wrapt-2.1.1
The example data used for this tutorial can be downloaded from:
https://drive.google.com/drive/folders/1rLF8vWLfOw3xlZzFCfi5X39Rs2W1K5yJ?usp=sharing
The pre-trained weight can be downloaded from:
https://drive.google.com/drive/u/1/folders/1zw6kgpnxat_CEFoDWaVIHndxuKqk5vmD
and put under ./external/InterpolAI/interpolation/
!ls external/InterpolAI/interpolation/model/
!ls external/InterpolAI/interpolation/model/variables
keras_metadata.pb saved_model.pb variables
variables.data-00000-of-00001 variables.index
Prepare the images
import matplotlib.pyplot as plt
import imageio.v3 as iio
import os
import numpy as np
slice_dir = "occupancy"
slice_ids = [1, 5]
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
for ax, i in zip(axes, slice_ids):
file = os.path.join(slice_dir, f"slice{i}.tif")
image = iio.imread(file)
ax.imshow(image)
ax.set_title(f"slice {i}")
ax.axis("off")
plt.tight_layout()
plt.show()
# convert into 3 channels
!python -m interpolation.convert_images \
--input_dir occupancy \
--output_dir occupancy_3ch
✅ Converted: slice1.tif → (192, 191, 3)
✅ Converted: slice5.tif → (192, 191, 3)
📊 Summary: 2 converted, 0 skipped
✅ Successfully converted 2 image(s)
##### Auto Mode (Automatically Detect Missing Slices)
!python -m interpolation.main \
--mode auto \
--tile_size 1024 1024 \
--pth occupancy_3ch/
2026-02-10 03:56:47.912073: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2026-02-10 03:56:47.929561: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1770695807.950918 8302 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1770695807.957392 8302 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1770695807.973697 8302 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1770695807.973723 8302 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1770695807.973726 8302 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1770695807.973729 8302 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2026-02-10 03:56:47.978375: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2026-02-10 03:56:54.347444: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:47] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
I0000 00:00:1770695814.348573 8302 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 38546 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:00:04.0, compute capability: 8.0
Interpolating for skip 3:
No stitching needed
Interpolating slice1.tif: 0% 0/3 [00:00<?, ?frame/s]I0000 00:00:1770695818.722157 8408 cuda_dnn.cc:529] Loaded cuDNN version 91002
Interpolating slice1.tif: 100% 3/3 [00:07<00:00, 2.47s/frame]
# convert back to single channel
!python -m interpolation.merge_volume \
--base_dir occupancy \
--interp_dir occupancy_3ch \
--out_binary_dir binary_slices \
--out_stack_path volume.tif \
--threshold 127
Found 2 base slices and 3 interpolated slices
Total unique indices: 5 (min: 1, max: 5)
Saved binary slices to: /content/UniST/binary_slices
Saved volume to: /content/UniST/volume.tif
Volume shape: (5, 192, 191), dtype: uint8
Base indices (sample): [1, 5]
Interp indices (sample): [2, 3, 4]
All indices range: 1 - 5
✅ Successfully created volume with shape (5, 192, 191)
### Visualize results
import os
import imageio.v2 as iio
import matplotlib.pyplot as plt
slice_dir = "binary_slices"
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
for idx, i in enumerate(range(2, 5)):
slice_filename = f"slice_{i:03d}.tif"
file_path = os.path.join(slice_dir, slice_filename)
image = iio.imread(file_path)
axes[idx].imshow(image, cmap="gray")
axes[idx].set_title(f"Interpolated slice {i}")
axes[idx].axis("off")
plt.tight_layout()
plt.show()
### Visualize authentic slices
import os
import imageio.v2 as iio
import matplotlib.pyplot as plt
slice_dir = "true_slices"
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
for idx, i in enumerate(range(2, 5)):
slice_filename = f"slice{i}.tif"
file_path = os.path.join(slice_dir, slice_filename)
image = iio.imread(file_path)
axes[idx].imshow(image, cmap="gray")
axes[idx].set_title(f"Authentic slice {i}")
axes[idx].axis("off")
plt.tight_layout()
plt.show()
Alternative method: ReHo (MATLAB based)
Run in Python:
Install MATLAB Engine API for Python (see https://www.mathworks.com/help/matlab/matlab-engine-for-python.html for details);
Image Processing Toolbox is also required for the MATLAB code to run.
import matlab.engine
! python -m interpolation.reho_main \
--input_dir occupancy \
--output_dir occupancy_reho \
--binarize \
--threshold 127
Starting ReHo interpolation...
Input directory: occupancy
Output directory: occupancy_reho
Parameters: lambda=10, tau=100, max_iter=1000, tol=0.01
iteration 1: error = 0.28402
iteration 2: error = 0.28402
iteration 3: error = 0.28402
iteration 4: error = 0.28402
iteration 5: error = 0.28402
iteration 6: error = 0.28402
iteration 7: error = 0.28402
iteration 8: error = 0.28402
iteration 9: error = 0.28402
iteration 10: error = 0.28402
iteration 11: error = 0.21516
iteration 12: error = 0.18741
iteration 13: error = 0.16397
iteration 14: error = 0.14222
iteration 15: error = 0.11839
iteration 16: error = 0.098233
iteration 17: error = 0.081334
iteration 18: error = 0.068524
iteration 19: error = 0.059122
iteration 20: error = 0.051314
iteration 21: error = 0.046277
iteration 22: error = 0.042317
iteration 23: error = 0.040516
iteration 24: error = 0.039924
iteration 25: error = 0.039924
iteration 26: error = 0.039924
iteration 27: error = 0.039924
iteration 28: error = 0.03946
iteration 29: error = 0.038371
iteration 30: error = 0.037658
iteration 31: error = 0.036987
iteration 32: error = 0.03576
iteration 33: error = 0.034159
iteration 34: error = 0.033139
iteration 35: error = 0.0319
iteration 36: error = 0.03051
iteration 37: error = 0.029677
iteration 38: error = 0.028511
iteration 39: error = 0.026168
iteration 40: error = 0.023869
iteration 41: error = 0.021836
iteration 42: error = 0.020521
iteration 43: error = 0.019712
iteration 44: error = 0.019584
iteration 45: error = 0.019584
iteration 46: error = 0.019584
iteration 47: error = 0.019584
iteration 48: error = 0.019584
iteration 49: error = 0.019565
iteration 50: error = 0.019454
iteration 51: error = 0.018932
iteration 52: error = 0.017655
iteration 53: error = 0.016237
iteration 54: error = 0.014754
iteration 55: error = 0.013485
iteration 56: error = 0.012321
iteration 57: error = 0.011244
iteration 58: error = 0.010294
iteration 59: error = 0.0095653
Tolerance reached. 59 iterations needed.
Completed: Missing slices written to /Users/shuilan/Documents/GitHub/UniST/occupancy_reho
✅ ReHo interpolation completed successfully!
Output saved to: /Users/shuilan/Documents/GitHub/UniST/occupancy_reho
### Visualize results
import os
import imageio.v2 as iio
import matplotlib.pyplot as plt
slice_dir = "occupancy_reho"
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
for idx, i in enumerate(range(2, 5)):
slice_filename = f"slice_{i:03d}.tif"
file_path = os.path.join(slice_dir, slice_filename)
image = iio.imread(file_path)
axes[idx].imshow(image, cmap="gray")
axes[idx].set_title(f"Interpolated slice {i}")
axes[idx].axis("off")
plt.tight_layout()
plt.show()
Run in MATLAB:
Set the path to the MATLAB code: interpolation/ReHo,
and run the following script in MATLAB command window.
inDir = ''; % input path
outDir = ''; % output path
interpolate_missing_slices_spline(inDir, outDir, ...
'CopyOriginal', true, ... % Copy existing slices to output directory
'Binarize', true, ... % Treat input as binary mask
'Thresh', 127, ... % Threshold for binarization (0–255)
'Lambda', 10, ... % Smoothness regularization parameter
'Tau', 100, ... % Inter-slice consistency weight
'TOL', 0.01, ...
'MaxIter', 1000, ...
'BorderSize', 0.1); % Relative border padding size