Merge branch 'master' of https://github.com/computational-cell-analytics/micro-sam

Author: constantinpape

doc/annotation_tools.md

@@ -123,7 +123,7 @@ 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_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.
+- 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.
 - 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

@@ -54,7 +54,7 @@ $ git clone https://github.com/computational-cell-analytics/micro-sam
 ```
 2. Enter it:
 ```
-$ cd micro_sam
+$ cd micro-sam
 ```
 3. Create the GPU or CPU environment:
 

doc/python_library.md

@@ -11,6 +11,13 @@ The library
 - implements training functionality that can be used for finetuning on your own data in `micro_sam.training`.
 - provides functionality for quantitative and qualitative evaluation of Segment Anything models in `micro_sam.evaluation`.
 
+You can import these sub-modules via
+```python
+import micro_sam.prompt_based_segmentation
+import micro_sam.instance_segmentation
+# etc.
+```
+
 This functionality is used to implement the interactive annotation tools and can also be used as a standalone python library.
 Some preliminary examples for how to use the python library can be found [here](https://github.com/computational-cell-analytics/micro-sam/tree/master/examples/use_as_library). Check out the `Submodules` documentation for more details.