AdaptiveMotorControlLab · C-Achard · Jul 12, 2023 · Mar 17, 2023 · Mar 22, 2023 · Mar 22, 2023
diff --git a/.coveragerc b/.coveragerc
@@ -0,0 +1,7 @@
+[report]
+exclude_lines =
+    if __name__ == .__main__.:
+
+[run]
+omit =
+    napari_cellseg3d/setup.py
diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml
@@ -7,15 +7,11 @@ on:
   push:
     branches:
       - main
-      - npe2
-      - cy/voronoi-otsu
     tags:
       - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
   pull_request:
     branches:
       - main
-      - npe2
-      - cy/voronoi-otsu
   workflow_dispatch:
 
 jobs:
@@ -55,6 +51,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           python -m pip install setuptools tox tox-gh-actions
+#         pip install git+https://github.com/lucasb-eyer/pydensecrf.git@master#egg=pydensecrf
 
 # this runs the platform-specific tests declared in tox.ini
       - name: Test with tox

diff --git a/.gitignore b/.gitignore
@@ -104,9 +104,11 @@ notebooks/csv_cell_plot.html
 notebooks/full_plot.html
 *.csv
 *.png
+notebooks/instance_test.ipynb
 *.prof
 
 #include test data
 !napari_cellseg3d/_tests/res/test.tif
 !napari_cellseg3d/_tests/res/test.png
 !napari_cellseg3d/_tests/res/test_labels.tif
+cov.syspath.txt
diff --git a/docs/index.rst b/docs/index.rst
@@ -39,8 +39,8 @@ Welcome to napari-cellseg3d's documentation!
     res/code/plugin_convert
     res/code/plugin_metrics
     res/code/model_framework
-    res/code/model_workers
-    res/code/model_instance_seg
+    res/code/workers
+    res/code/instance_segmentation
     res/code/plugin_model_inference
     res/code/plugin_model_training
     res/code/utils

diff --git a/docs/res/code/instance_segmentation.rst b/docs/res/code/instance_segmentation.rst
@@ -0,0 +1,53 @@
+instance_segmentation.py
+===========================================
+
+Classes
+-------------
+
+InstanceMethod
+**************************************
+.. autoclass:: napari_cellseg3d.code_models.instance_segmentation::InstanceMethod
+    :members: __init__
+
+ConnectedComponents
+**************************************
+.. autoclass:: napari_cellseg3d.code_models.instance_segmentation::ConnectedComponents
+    :members: __init__
+
+Watershed
+**************************************
+.. autoclass:: napari_cellseg3d.code_models.instance_segmentation::Watershed
+    :members: __init__
+
+VoronoiOtsu
+**************************************
+.. autoclass:: napari_cellseg3d.code_models.instance_segmentation::VoronoiOtsu
+    :members: __init__
+
+
+Functions
+-------------
+
+binary_connected
+**************************************
+.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::binary_connected
+
+binary_watershed
+**************************************
+.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::binary_watershed
+
+volume_stats
+**************************************
+.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::volume_stats
+
+clear_small_objects
+**************************************
+.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::clear_small_objects
+
+to_instance
+**************************************
+.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::to_instance
+
+to_semantic
+**************************************
+.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::to_semantic
diff --git a/docs/res/code/model_instance_seg.rst b/docs/res/code/model_instance_seg.rst
diff --git a/docs/res/code/plugin_convert.rst b/docs/res/code/plugin_convert.rst
@@ -28,18 +28,3 @@ ThresholdUtils
 **********************************
 .. autoclass:: napari_cellseg3d.code_plugins.plugin_convert::ThresholdUtils
     :members: __init__
-
-Functions
------------------------------------
-
-save_folder
-*****************************************
-.. autofunction:: napari_cellseg3d.code_plugins.plugin_convert::save_folder
-
-save_layer
-****************************************
-.. autofunction:: napari_cellseg3d.code_plugins.plugin_convert::save_layer
-
-show_result
-****************************************
-.. autofunction:: napari_cellseg3d.code_plugins.plugin_convert::show_result
diff --git a/docs/res/code/plugin_model_training.rst b/docs/res/code/plugin_model_training.rst
@@ -18,6 +18,5 @@ Methods
 
 Attributes
 *********************
-
 .. autoclass:: napari_cellseg3d.code_plugins.plugin_model_training::Trainer
    :members:  _viewer, worker, loss_dict, canvas, train_loss_plot, dice_metric_plot
diff --git a/docs/res/code/utils.rst b/docs/res/code/utils.rst
@@ -62,7 +62,3 @@ denormalize_y
 load_images
 **************************************
 .. autofunction:: napari_cellseg3d.utils::load_images
-
-format_Warning
-**************************************
-.. autofunction:: napari_cellseg3d.utils::format_Warning
diff --git a/docs/res/code/model_workers.rst → docs/res/code/workers.rst b/docs/res/code/model_workers.rst → docs/res/code/workers.rst
@@ -1,4 +1,4 @@
-model_workers.py
+workers.py
 ===========================================
 
 
@@ -10,7 +10,7 @@ Class : LogSignal
 
 Attributes
 ************************
-.. autoclass:: napari_cellseg3d.code_models.model_workers::LogSignal
+.. autoclass:: napari_cellseg3d.code_models.workers::LogSignal
    :members: log_signal
    :noindex:
 
@@ -24,7 +24,7 @@ Class : InferenceWorker
 
 Methods
 ************************
-.. autoclass:: napari_cellseg3d.code_models.model_workers::InferenceWorker
+.. autoclass:: napari_cellseg3d.code_models.workers::InferenceWorker
    :members: __init__, log, create_inference_dict, inference
    :noindex:
 
@@ -39,6 +39,6 @@ Class : TrainingWorker
 
 Methods
 ************************
-.. autoclass:: napari_cellseg3d.code_models.model_workers::TrainingWorker
+.. autoclass:: napari_cellseg3d.code_models.workers::TrainingWorker
    :members: __init__, log, train
    :noindex:
diff --git a/docs/res/guides/custom_model_template.rst b/docs/res/guides/custom_model_template.rst
@@ -3,35 +3,33 @@
 Advanced : Declaring a custom model
 =============================================
 
-To add a custom model, you will need a **.py** file with the following structure to be placed in the *napari_cellseg3d/models* folder:
+.. warning::
+    **WIP** : Adding new models is still a work in progress and will likely not work out of the box, leading to errors.
 
-.. note::
-    **WIP** : Currently you must modify :ref:`model_framework.py` as well : import your model class and add it to the ``model_dict`` attribute
-
-::
-
-    def get_net():
-        return ModelClass # should return the class of the model,
-        # for example SegResNet or UNET
+    Please `file an issue`_ if you would like to add a custom model and we will help you get it working.
 
+To add a custom model, you will need a **.py** file with the following structure to be placed in the *napari_cellseg3d/models* folder::
 
-    def get_weights_file():
-        return "weights_file.pth" # name of the weights file for the model,
-        # which should be in *napari_cellseg3d/models/pretrained*
+    class ModelTemplate_(ABC): # replace ABC with your PyTorch model class name
+        use_default_training = True  # not needed for now, will serve for WNet training if added to the plugin
+        weights_file = (
+            "model_template.pth"  # specify the file name of the weights file only
+        ) # download URL goes in pretrained_models.json
 
+        @abstractmethod
+        def __init__(
+            self, input_image_size, in_channels=1, out_channels=1, **kwargs
+        ):
+            """Reimplement this as needed; only include input_image_size if necessary. For now only in/out channels = 1 is supported."""
+            pass
 
-    def get_output(model, input):
-        out = model(input) # should return the model's output as [C, N, D,H,W]
-        # (C: channel, N, batch size, D,H,W : depth, height, width)
-        return out
+        @abstractmethod
+        def forward(self, x):
+            """Reimplement this as needed. Ensure that output is a torch tensor with dims (batch, channels, z, y, x)."""
+            pass
 
 
-    def get_validation(model, val_inputs):
-        val_outputs = model(val_inputs) # should return the proper type for validation
-        # with sliding_window_inference from MONAI
-        return val_outputs
-
+.. note::
+    **WIP** : Currently you must modify :ref:`model_framework.py` as well : import your model class and add it to the ``model_dict`` attribute
 
-    def ModelClass(x1,x2...):
-        # your Pytorch model here...
-        return results # should return as [C, N, D,H,W]
+.. _file an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
diff --git a/docs/res/guides/detailed_walkthrough.rst b/docs/res/guides/detailed_walkthrough.rst
@@ -1,6 +1,6 @@
 .. _detailed_walkthrough:
 
-Detailed walkthrough
+Detailed walkthrough - Supervised learning
 ===================================
 
 The following guide will show you how to use the plugin's workflow, starting from human-labeled annotation volume, to running inference on novel volumes.
@@ -109,7 +109,7 @@ of two no matter the size you choose. For optimal performance, make sure to use
 a power of two still, such as 64 or 120.
 
 .. important::
-    Using a too large value for the size will cause memory issues. If this happens, restart napari (better handling for these situations might be added in the future).
+    Using a too large value for the size will cause memory issues. If this happens, restart the worker with smaller volumes.
 
 You also have the option to use data augmentation, which can improve performance and generalization.
 In most cases this should left enabled.

diff --git a/docs/res/guides/inference_module_guide.rst b/docs/res/guides/inference_module_guide.rst
@@ -7,8 +7,9 @@ This module allows you to use pre-trained segmentation algorithms (written in Py
 to automatically label cells.
 
 .. important::
-    Currently, only inference on **3D volumes is supported**. Your image and label folders should both contain a set of
-    **3D image files**, currently either **.tif** or **.tiff**.
+    Currently, only inference on **3D volumes is supported**. If using folders, your images and labels folders
+    should both contain a set of **3D image files**, either **.tif** or **.tiff**.
+    Otherwise you may run inference on layers in napari.
 
 Currently, the following pre-trained models are available :
 
@@ -20,13 +21,18 @@ SegResNet        `3D MRI brain tumor segmentation using autoencoder regularizati
 TRAILMAP_MS       A PyTorch implementation of the `TRAILMAP project on GitHub`_ pretrained with mesoSPIM data
 TRAILMAP          An implementation of the `TRAILMAP project on GitHub`_ using a `3DUNet for PyTorch`_
 SwinUNetR         `Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images`_
+WNet             `WNet, A Deep Model for Fully Unsupervised Image Segmentation`_
 ==============   ================================================================================================
 
 .. _Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation: https://arxiv.org/pdf/1606.04797.pdf
 .. _3D MRI brain tumor segmentation using autoencoder regularization: https://arxiv.org/pdf/1810.11654.pdf
 .. _TRAILMAP project on GitHub: https://github.com/AlbertPun/TRAILMAP
 .. _3DUnet for Pytorch: https://github.com/wolny/pytorch-3dunet
 .. _Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images: https://arxiv.org/abs/2201.01266
+.. _WNet, A Deep Model for Fully Unsupervised Image Segmentation: https://arxiv.org/abs/1711.08506
+
+.. note::
+    For WNet-specific instruction please refer to  the appropriate section below.
 
 Interface and functionalities
 --------------------------------
@@ -67,8 +73,7 @@ Interface and functionalities
 
 * **Instance segmentation** :
 
-  | You can convert the semantic segmentation into instance labels by using either the `watershed`_ or `connected components`_ method.
-  | You can set the probability threshold from which a pixel is considered as a valid instance, as well as the minimum size in pixels for objects. All smaller objects will be removed.
+  | You can convert the semantic segmentation into instance labels by using either the Voronoi-Otsu, `Watershed`_ or `Connected Components`_ method, as detailed in :ref:`utils_module_guide`.
   | Instance labels will be saved (and shown if applicable) separately from other results.
 
 
@@ -78,7 +83,7 @@ Interface and functionalities
 
 * **Computing objects statistics** :
 
-    You can choose to compute various stats from the labels and save them to a csv for later use.
+    You can choose to compute various stats from the labels and save them to a .csv for later use.
 
     This includes, for each object :
 
@@ -98,13 +103,6 @@ Interface and functionalities
 
     In the ``notebooks`` folder you can find an example of plotting cell statistics using the result csv.
 
-* **Viewing results** :
-
-  | You can also select whether you'd like to **see the results** in napari afterwards.
-  | By default the first image processed will be displayed, but you can choose to display up to **ten at once**.
-  | You can also request to see the originals.
-
-
 When you are done choosing your parameters, you can press the **Start** button to begin the inference process.
 Once it has finished, results will be saved then displayed in napari; each output will be paired with its original.
 On the left side, a progress bar and a log will keep you informed on the process.
@@ -115,7 +113,7 @@ On the left side, a progress bar and a log will keep you informed on the process
     |    ``{original_name}_{model}_{date & time}_pred{id}.file_ext``
     | For example, using a VNet on the third image of a folder, called "somatomotor.tif" will yield the following name :
     |   *somatomotor_VNet_2022_04_06_15_49_42_pred3.tif*
-    | Instance labels will have the "Instance_seg" prefix appened to the name.
+    | Instance labels will have the "Instance_seg" prefix appended to the name.
 
 
 .. hint::
@@ -128,8 +126,19 @@ On the left side, a progress bar and a log will keep you informed on the process
 .. note::
     You can save the log after the worker is finished to easily remember which parameters you ran inference with.
 
+WNet
+--------------------------------
+
+The WNet model, from the paper `WNet, A Deep Model for Fully Unsupervised Image Segmentation`_, is a fully unsupervised model that can be used to segment images without any labels.
+It clusters pixels based on brightness, and can be used to segment cells in a variety of modalities.
+Its use and available options are similar to the above models, with a few differences :
+.. note::
+    | Our provided, pre-trained model should use an input size of 64x64x64. As such, window inference is always enabled
+    | and set to 64. If you want to use a different size, you will have to train your own model using the provided notebook.
+All it requires are images; for nucleus segmentation, it is recommended to use 2 classes (default).
+
 Source code
 --------------------------------
 * :doc:`../code/plugin_model_inference`
 * :doc:`../code/model_framework`
-* :doc:`../code/model_workers`
+* :doc:`../code/workers`