midas-nf-process-images

Differentiable PyTorch port of NF_HEDM/src/ProcessImagesCombined.c for CPU, CUDA, and MPS.

Notebooks

Worked-example Jupyter notebooks live in notebooks/. They are not shipped with pip install — get them by cloning the MIDAS repository.

Pipeline

For one layer of a near-field HEDM scan:

  1. Load NrFilesPerLayer raw TIFF frames into a [N, Z, Y] tensor.
  2. Temporal median across frames → per-pixel background [Z, Y].
  3. Per-frame:
    • Subtract median + BlanketSubtraction, clamp at 0.
    • Spatial median (3x3 or 5x5).
    • LoG response at one or more scales.
    • Threshold and label connected components → integer spot labels (detached).
    • Sigmoid surrogate → continuous spot-probability map (autograd path).
  4. Accumulate spot pixels into a bit-packed SpotsInfo.bin mmap (drop-in compatible with FitOrientationOMP / simulateNF).

Phases 1, 2, and 3 (filtered, log_response, soft spot probability) are differentiable end-to-end. Connected components and the binary SpotsInfo.bin output are computed on detached tensors and do not participate in autograd.

Quickstart

from midas_nf_process_images import ProcessParams, ProcessImagesPipeline

params = ProcessParams.from_paramfile("ps.txt")
pipe = ProcessImagesPipeline(params, device="cuda")  # or "mps", "cpu"
spots = pipe.process_layer(layer_nr=1)
spots.write("/path/to/SpotsInfo.bin")

# Or all layers in one call:
spots = pipe.process_all(layers=range(1, params.n_distances + 1))

CLI

midas-nf-process-images <ParameterFile> <LayerNr> [--device cuda] [--dtype fp32] [--n-cpus 8]

Behaves like the C ProcessImagesCombined executable.

Backend selection

Same contract as midas-transforms:

  • Default device: cuda -> mps -> cpu.
  • Default dtype: float64 on CPU, float32 on CUDA/MPS.
  • Override with device= / dtype= kwargs, or env vars MIDAS_NF_PROCESS_IMAGES_DEVICE / MIDAS_NF_PROCESS_IMAGES_DTYPE.

Differentiability

The end-to-end pipeline produces three tensors:

  • filtered: median-subtracted, spatial-median-filtered image (autograd).
  • log_response: LoG convolution output (autograd).
  • spot_prob: soft, continuous spot-probability map via sigmoid(L / temperature) (autograd).

Plus one detached output:

  • labels: integer connected-component IDs from hard-thresholded LoG (no gradient).

Optimization through the discrete spot mask uses the soft spot_prob surrogate.

Project links

Data verified by PyPI on Aug 4, 2026
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Key dates

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  • Released:
Newer release available (0.7.1)

1 maintainer

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Credits

Author: Hemant Sharma

GitHub Statistics

Data verified by PyPI on Aug 4, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.9

Provides Extra

dev parity

Tags

MIDAS NF-HEDM diffraction PyTorch differentiable image processing peak detection near-field preprocessing hex grid tomography

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