updating docs and readme

README.md

@@ -24,9 +24,13 @@ Please see install instructions [below](README.md/#Installation), and also check
 
 ### :star2: v3 (Feb 2024) :star2:
 
-Cellpose3 enables image restoration in the GUI and the API (CLI support and example notebooks coming soon!) To learn more...
-* Check out the paper [thread](https://neuromatch.social/@computingnature/111932247922392030).
+Cellpose3 enables image restoration in the GUI, API and CLI (saved to `_seg.npy`). To learn more...
 * Check out the [paper](https://www.biorxiv.org/content/10.1101/2024.02.10.579780v1).
+* Check out the paper [thread](https://neuromatch.social/@computingnature/111932247922392030).
+* API documentation [here](https://cellpose.readthedocs.io/en/latest/restore.html)
+* Example google colab notebook for image restoration: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MouseLand/cellpose/blob/main/notebooks/run_cellpose3.ipynb).
+* Example google colab notebook with new super-generalist "cyto3" model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MouseLand/cellpose/blob/main/notebooks/run_cyto3.ipynb).
+
 
 Try out the new `cyto3` super-generalist Cellpose model with `model_type="cyto3"`. There are some Cellpose API changes from v2.0 which will be documented soon.
 

docs/restore.rst

@@ -17,6 +17,7 @@ DenoiseModel
 Initialize a DenoiseModel with the model_type:
 
 :: 
+
     from cellpose import denoise
     dn = denoise.DenoiseModel(model_type="denoise_cyto3", gpu=True)
 
@@ -26,12 +27,14 @@ using the Cellpose channel format (e.g. ``channels=[1,2]``), or leave
 the size of the objects in your image.
 
 ::
+
     imgs_dn = dn.eval(imgs, channels=None, diameter=50.)
 
 If you have two channels, and the second is a nuclear channel, you can specify to use 
 the nuclei restoration models on the second channel, with ``chan2=True``:
 
 :: 
+
     from cellpose import denoise
     dn = denoise.DenoiseModel(model_type="denoise_cyto3", gpu=True, chan2=True)
     imgs_dn = dn.eval(imgs, channels=[1,2], diameter=50.)
@@ -42,6 +45,7 @@ images, for example, in which the objects are of diameter 10, specify that in th
 function call, and then the model will upsample the image to 30 or 17:
 
 :: 
+
     from cellpose import denoise
     dn = denoise.DenoiseModel(model_type="upsample_cyto3", gpu=True, chan2=True)
     imgs_up = dn.eval(imgs, channels=[1,2], diameter=10.)
@@ -55,6 +59,7 @@ The ``CellposeDenoiseModel`` wraps the CellposeModel and DenoiseModel into one c
 ensure the channels and diameters are handled properly. See example:
 
 ::
+    
     from cellpose import denoise
     model = denoise.CellposeDenoiseModel(gpu=True, model_type="cyto3",
                  restore_type="denoise_cyto3", chan2_restore=True)