Docstring and documentation file updates

Author: constantinpape

doc/annotation_tools.md

@@ -105,7 +105,7 @@ You can activate tiling by passing the parameters `tile_shape`, which determines
     - If you're using the command line functions you can pass them via the options `--tile_shape 1024 1024 --halo 128 128`
     - Note that prediction with tiling only works when the embeddings are cached to file, so you must specify an `embedding_path` (`-e` in the CLI).
     - You should choose the `halo` such that it is larger than half of the maximal radius of the objects your segmenting.
-- The applications pre-compute the image embeddings produced by SegmentAnything and (optionally) store them on disc. If you are using a CPU this step can take a while for 3d data or timeseries (you will see a progress bar with a time estimate). If you have access to a GPU without graphical interface (e.g. via a local computer cluster or a cloud provider), you can also pre-compute the embeddings there and then copy them to your laptop / local machine to speed this up. You can use the command `micro_sam.precompute_embeddings` for this (it is installed with the rest of the applications). You can specify the location of the precomputed embeddings via the `embedding_path` argument.
+- The applications pre-compute the image embeddings produced by SegmentAnything and (optionally) store them on disc. If you are using a CPU this step can take a while for 3d data or timeseries (you will see a progress bar with a time estimate). If you have access to a GPU without graphical interface (e.g. via a local computer cluster or a cloud provider), you can also pre-compute the embeddings there and then copy them to your laptop / local machine to speed this up. You can use the command `micro_sam.precompute_state` for this (it is installed with the rest of the applications). You can specify the location of the precomputed embeddings via the `embedding_path` argument.
 - Most other processing steps are very fast even on a CPU, so interactive annotation is possible. An exception is the automatic segmentation step (2d segmentation), which takes several minutes without a GPU (depending on the image size). For large volumes and timeseries segmenting an object in 3d / tracking across time can take a couple settings with a CPU (it is very fast with a GPU).
 - You can also try using a smaller version of the SegmentAnything model to speed up the computations. For this you can pass the `model_type` argument and either set it to `vit_b` or to `vit_l` (default is `vit_h`). However, this may lead to worse results.
 - You can save and load the results from the `committed_objects` / `committed_tracks` layer to correct segmentations you obtained from another tool (e.g. CellPose) or to save intermediate annotation results. The results can be saved via `File -> Save Selected Layer(s) ...` in the napari menu (see the tutorial videos for details). They can be loaded again by specifying the corresponding location via the `segmentation_result` (2d and 3d segmentation) or `tracking_result` (tracking) argument.

doc/installation.md

@@ -30,9 +30,9 @@ Once you have it installed you can simply replace the `conda` commands with `mam
 ## From installer
 
 We also provide installers for Linux, Mac and Windows:
-- [Linux](https://owncloud.gwdg.de/index.php/s/HRp948SDkaWzCuV)
-- [Mac](https://owncloud.gwdg.de/index.php/s/HpGzlXrgJ8VDgnI)
-- [Windows](https://owncloud.gwdg.de/index.php/s/BVipOmDPR2TXmxk)
+- [Linux](https://owncloud.gwdg.de/index.php/s/Cw9RmA3BlyqKJeU)
+- [Mac](https://owncloud.gwdg.de/index.php/s/7YupGgACw9SHy2P)
+- [Windows](https://owncloud.gwdg.de/index.php/s/1iD1eIcMZvEyE6d)
 
 Note that these installers are stil experimental and not yet fully tested.
 If you encounter problems with them then please consider installing `micro_sam` via [conda](#from-conda) instead.

micro_sam/evaluation/__init__.py

@@ -1,3 +1,6 @@
+"""Functionality for evaluating Segment Anything models on microscopy data.
+"""
+
 from .automatic_mask_generation import (
     run_amg_inference,
     run_amg_grid_search,

micro_sam/evaluation/automatic_mask_generation.py

@@ -1,3 +1,6 @@
+"""Inference and evaluation for the automatic instance segmentation functionality.
+"""
+
 import os
 from glob import glob
 from pathlib import Path
@@ -190,9 +193,9 @@ def evaluate_amg_grid_search(result_dir: Union[str, os.PathLike], criterion: str
         criterion: The metric to use for determining the best parameters.
 
     Returns:
-        The best value for `pred_iou_thresh`.
-        The best value for ``stability_score_thresh.
-        The evaluation score for the best setting.
+        - The best value for `pred_iou_thresh`.
+        - The best value for `stability_score_thresh`.
+        - The evaluation score for the best setting.
     """
 
     # load all the grid search results

micro_sam/evaluation/evaluation.py

@@ -1,3 +1,7 @@
+"""Evaluation functionality for segmentation predictions from `micro_sam.evaluation.automatic_mask_generation`
+and `micro_sam.evaluation.inference`.
+"""
+
 import os
 from pathlib import Path
 from typing import List, Optional, Union

micro_sam/evaluation/experiments.py

@@ -1,7 +1,11 @@
+"""Predefined experiment settings for experiments with different prompt strategies.
+"""
+
 from typing import Dict, List, Optional
 
 # TODO fully define the dict type
-ExperimentSettings = List[Dict]
+ExperimentSetting = Dict
+ExperimentSettings = List[ExperimentSetting]
 """@private"""
 
 
@@ -63,7 +67,7 @@ def default_experiment_settings() -> ExperimentSettings:
     return experiment_settings
 
 
-def get_experiment_setting_name(setting: ExperimentSettings) -> str:
+def get_experiment_setting_name(setting: ExperimentSetting) -> str:
     """Get the name for the given experiment setting.
 
     Args:

micro_sam/evaluation/inference.py

@@ -1,3 +1,6 @@
+"""Inference with Segment Anything models and different prompt strategies.
+"""
+
 import os
 import pickle
 import warnings

micro_sam/evaluation/livecell.py

@@ -1,3 +1,7 @@
+"""Inference and evaluation for the [LiveCELL dataset](https://www.nature.com/articles/s41592-021-01249-6) and
+the different cell lines contained in it.
+"""
+
 import argparse
 import json
 import os
@@ -147,6 +151,7 @@ def run_livecell_amg(
 ) -> None:
     """Run automatic mask generation grid-search and inference for livecell.
 
+    Args:
         checkpoint: The segment anything model checkpoint.
         input_folder: The folder with the livecell data.
         model_type: The type of the segmenta anything model.

micro_sam/evaluation/model_comparison.py

@@ -1,3 +1,6 @@
+"""Functionality for qualitative comparison of Segment Anything models on microscopy data.
+"""
+
 import os
 from functools import partial
 from glob import glob