| """The 'deepinterpolation' engine: real Allen Institute DeepInterpolation. |
| |
| This is the wired slot for the actual TensorFlow/Keras `deepinterpolation` |
| package (https://github.com/AllenInstitute/deepinterpolation). It is NOT part of |
| the light default image — install it via Dockerfile.deepinterp and point |
| DEEPINTERP_MODEL at a pretrained inference model (HDF5). |
| |
| The learned network predicts frame t from a stack of its temporal neighbors |
| (center excluded) just like the fast engine, but with a deep U-Net that has been |
| trained on real two-photon / Ophys data, so it removes far more noise. |
| """ |
| from __future__ import annotations |
|
|
| import os |
|
|
| import numpy as np |
|
|
|
|
| def available() -> bool: |
| try: |
| import deepinterpolation |
| return True |
| except Exception: |
| return False |
|
|
|
|
| def interpolate(movie: np.ndarray, pre: int = 30, post: int = 30, |
| omit: int = 0) -> np.ndarray: |
| """Run real DeepInterpolation inference over a (T, H, W) movie. |
| |
| Requires the `deepinterpolation` package (TensorFlow) and a pretrained model |
| given by env DEEPINTERP_MODEL. Raises a clear error if either is missing so |
| the UI/API can fall back or report it. |
| """ |
| model_path = os.environ.get("DEEPINTERP_MODEL", "") |
| if not available(): |
| raise RuntimeError( |
| "The 'deepinterpolation' package is not installed. Build the image " |
| "with Dockerfile.deepinterp to use this engine." |
| ) |
| if not model_path or not os.path.exists(model_path): |
| raise RuntimeError( |
| "Set DEEPINTERP_MODEL to a pretrained DeepInterpolation inference " |
| "model (HDF5). See https://github.com/AllenInstitute/deepinterpolation." |
| ) |
|
|
| |
| import tempfile |
|
|
| import tifffile |
| from deepinterpolation.generic import ClassLoader |
|
|
| movie = np.asarray(movie, dtype=np.float32) |
| with tempfile.TemporaryDirectory() as td: |
| in_tif = os.path.join(td, "input.tif") |
| out_base = os.path.join(td, "di_out") |
| tifffile.imwrite(in_tif, movie) |
|
|
| generator_param = { |
| "type": "generator", |
| "name": "SingleTifGenerator", |
| "pre_frame": int(pre), |
| "post_frame": int(post), |
| "pre_post_omission": int(omit), |
| "train_path": in_tif, |
| "batch_size": 1, |
| "start_frame": int(pre), |
| "end_frame": int(movie.shape[0] - post - 1), |
| } |
| inference_param = { |
| "type": "inferrence", |
| "name": "core_inferrence", |
| "model_path": model_path, |
| "output_file": out_base + ".h5", |
| } |
|
|
| data_generator = ClassLoader(generator_param).find_and_build()(generator_param) |
| inferrence_class = ClassLoader(inference_param).find_and_build()( |
| inference_param, data_generator |
| ) |
| inferrence_class.run() |
|
|
| import h5py |
| with h5py.File(out_base + ".h5", "r") as f: |
| pred = np.asarray(f["data"][:]).astype(np.float32) |
| pred = np.squeeze(pred) |
|
|
| |
| |
| out = movie.copy() |
| out[int(pre):int(pre) + pred.shape[0]] = pred |
| return out |
|
|
