Update macbook conda (#72)

* Update napari_cellseg3d_m1.yml * Update napari_cellseg3d_m1.yml * Rename conda install file to more general name * Update references to M1 chips and conda env file * Update install instructions re:backend * Update install instructions w/ Qt backend * Add links to usage section in docs * Update napari_cellseg3d_ARM64.yml * Rename napari_cellseg3d_ARM64.yml to napari_CellSeg3D_ARM64.yml - keep standard naming * Update napari_CellSeg3D_ARM64.yml * Add conda env update instructions * Update detailed_walkthrough.rst * Add backends as optional reqs * Update README.md * Update napari_CellSeg3D_ARM64.yml --------- Co-authored-by: C-Achard <cyril.achard@epfl.ch>
AdaptiveMotorControlLab · May 6, 2024 · ba0ee67 · ba0ee67
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diff --git a/README.md b/README.md
@@ -59,26 +59,20 @@ Previous additions :
 
 
 
-### Install note for M1/M2 Mac users
+### Install note for ARM64 (Silicon) Mac users
 
 To avoid issues when installing on the ARM64 architecture, please follow these steps.
 
-1) Create a new conda env using the provided conda/napari_cellseg3d_m1.yml file :
+1) Create a new conda env using the provided conda/napari_CellSeg3D_ARM64.yml file :
 
         git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git
         cd CellSeg3d
-        conda env create -f conda/napari_cellseg3d_m1.yml
-        conda activate napari_cellseg3d_m1
+        conda env create -f conda/CellSeg3D_ARM64.yml
+        conda activate napari_CellSeg3D_ARM64
 
-2) Install the plugin.
-   From repository root folder, run :
+2) Install a Qt backend (PySide or PyQt5)
+3) Launch napari, the plugin should be available in the plugins menu.
 
-        pip install -e .
-   OR directly via PyPi :
-
-        pip install napari-cellseg3d
-
-   OR directly via [napari-hub] (see Installation section above)
 
 
 ## Requirements

diff --git a/conda/napari_cellseg3d_m1.yml → conda/napari_CellSeg3D_ARM64.yml b/conda/napari_cellseg3d_m1.yml → conda/napari_CellSeg3D_ARM64.yml
@@ -1,4 +1,4 @@
-name: napari_cellseg3d_m1
+name: napari_CellSeg3D_ARM64
 channels:
   - anaconda
   - conda-forge
@@ -11,18 +11,14 @@ dependencies:
     - pip:
         - numpy
         - napari>=0.4.14
-        # - opencv-python>=4.5.5
         - scikit-image>=0.19.2
         - matplotlib>=3.4.1
         - tifffile>=2022.2.9
-        # - imageio-ffmpeg>=0.4.5
         - torch>=1.11
         - monai[nibabel, einops]>=0.9.0
         - tqdm
-        # - nibabel
         - scikit-image
-        # - pillow
         - pyclesperanto-prototype
         - tqdm
         - matplotlib
-        # - vispy>=0.9.6
+        - napari_cellseg3d
diff --git a/docs/source/guides/detailed_walkthrough.rst b/docs/source/guides/detailed_walkthrough.rst
@@ -32,6 +32,11 @@ For quick model checks, check the "Inference" sections in our docs.
   If you need to start labeling volumes from scratch or correct initial labels, we recommend consulting the sections on :ref:`Review<walkthrough_reviewing>` section right after :ref:`Cropping <walkthrough_cropping>`.
 
 
+Launching the plugin
+************************
+
+See `Usage section <https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html#usage>`_ for instructions on launching the plugin.
+
 Cropping
 *********
 .. _walkthrough_cropping:

diff --git a/docs/source/guides/inference_module_guide.rst b/docs/source/guides/inference_module_guide.rst
@@ -11,6 +11,9 @@ Inference📊
 **Inference** allows you to use pre-trained segmentation algorithms, written in Pytorch,
 to automatically label cells in 3D volumes.
 
+See `Usage section <https://adaptivemotorcontrollab.github.io/CellSeg3d/welcome.html#usage>`_ for instructions on launching the plugin.
+See :ref:`training_module_guide` for instructions on training your own models before inference.
+
 .. important::
     Currently, the module supports inference on **3D volumes**. When running on folders, make sure that your image folder
     only contains a set of **3D image files** saved with the **`.tif`** extension.

diff --git a/docs/source/guides/installation_guide.rst b/docs/source/guides/installation_guide.rst
@@ -2,8 +2,8 @@ Installation guide ⚙
 ======================
 This guide outlines the steps for installing CellSeg3D and its dependencies. The plugin is compatible with Windows, Linux, and MacOS.
 
-**Note for M1/M2 (ARM64) Mac Users:**
-Please refer to the :ref:`section below <source/guides/installation_guide:M1/M2 (ARM64) Mac installation>` for specific instructions.
+**Note for ARM64 Mac Users:**
+Please refer to the :ref:`section below <source/guides/installation_guide:ARM64 Mac installation>` for specific instructions.
 
 .. warning::
     If you encounter any issues during installation, feel free to open an issue on our `GitHub repository`_.
@@ -46,7 +46,7 @@ Installing CellSeg3D
 --------------------------------------------
 
 .. warning::
-    For M1 Mac users, please see the :ref:`section below <source/guides/installation_guide:M1/M2 (ARM64) Mac installation>`
+    For ARM64 Mac users, please see the :ref:`section below <source/guides/installation_guide:ARM64 Mac installation>`
 
 **Via pip**:
 
@@ -69,14 +69,17 @@ Navigate to the cloned CellSeg3D folder and run:
 Successful installation will add the napari-cellseg3D plugin to napari’s Plugins section.
 
 
-M1/M2 (ARM64) Mac installation
+ARM64 Mac installation
 --------------------------------------------
 .. _ARM64_Mac_installation:
 
-For ARM64 Macs, we recommend using our custom CONDA environment. This is particularly important for M1 or M2 MacBooks.
+For ARM64 Macs, we recommend using our custom CONDA environment. This is particularly important for ARM64 (Silicon chips) MacBooks.
 
 Start by installing `miniconda3`_.
 
+Creating the environment
+______________________________
+
 .. _miniconda3: https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html
 
 1. **Clone the repository** (`link <https://github.com/AdaptiveMotorControlLab/CellSeg3d>`_):
@@ -91,25 +94,47 @@ In the terminal, navigate to the CellSeg3D folder:
 .. code-block::
 
     cd CellSeg3D
-    conda env create -f conda/napari_cellseg3d_m1.yml
+    conda env create -f conda/napari_cellseg3d_ARM64.yml
+
+This will also install the necessary dependencies as well as the plugin.
 
 3. **Activate the environment** :
 
 .. code-block::
 
-    conda activate napari_cellseg3d_m1
+    conda activate napari_cellseg3d_ARM64
 
-4. **Install the plugin** :
+4. **Install a Qt backend** :
+Important : you only need to install one of the following backends.
+PyQt5:
 
 .. code-block::
 
-    pip install napari-cellseg3d
+        pip install PyQt5
+
+OR
+PySide2:
+
+.. code-block::
+
+        pip install PySide2
+
+5. **Install PyTorch** :
+Refer to `PyTorch's website`_ for installation instructions.
+
+6. **Launch napari** :
+You should now see the CellSeg3D plugin in the Plugins section of napari.
+See `Usage section <https://adaptivemotorcontrollab.github.io/CellSeg3d/welcome.html#usage>`_ for a guide on how to use the plugin.
+
+Updating the environment
+______________________________
 
-OR to install from source:
+In order to update the environment, navigate to the CellSeg3D folder and run:
 
 .. code-block::
 
-    pip install -e .
+    conda deactivate
+    conda env update -f conda/napari_cellseg3d_ARM64.yml
 
 Optional requirements
 ------------------------------

diff --git a/docs/source/guides/review_module_guide.rst b/docs/source/guides/review_module_guide.rst
@@ -12,6 +12,8 @@ Labeling🔍
 The system will save the updated status of each file in a csv file.
 Additionally, the time taken per slice review is logged, enabling efficient monitoring.
 
+See `Usage section <https://adaptivemotorcontrollab.github.io/CellSeg3d/welcome.html#usage>`_ for instructions on launching the plugin.
+
 Launching the review process
 ---------------------------------
 .. figure:: ../images/Review_Parameters.png

diff --git a/docs/source/guides/training_module_guide.rst b/docs/source/guides/training_module_guide.rst
@@ -12,6 +12,8 @@ Training📉
 **Training** allows you to train models for cell segmentation.
 Whenever necessary, pre-trained weights will be automatically downloaded and integrated.
 
+See `Usage section <https://adaptivemotorcontrollab.github.io/CellSeg3d/welcome.html#usage>`_ for instructions on launching the plugin.
+
 .. important::
     At present, only inference on **3D volumes is supported**. Ensure that both your image and label folders contain a set of
     **3D image files**, in either **`.tif`** or **`.tiff`** format. Loading a folder of 2D images as a stack is supported only if

diff --git a/docs/source/guides/utils_module_guide.rst b/docs/source/guides/utils_module_guide.rst
@@ -4,6 +4,7 @@ Utilities 🛠
 ============
 
 Here you will find a range of tools for image processing and analysis.
+See `Usage section <https://adaptivemotorcontrollab.github.io/CellSeg3d/welcome.html#usage>`_ for instructions on launching the plugin.
 
 .. note::
     The utility selection menu is found at the bottom of the plugin window.

diff --git a/docs/welcome.rst b/docs/welcome.rst
@@ -58,7 +58,7 @@ For detailed installation instructions, including installing pre-requisites,
 please see :ref:`source/guides/installation_guide:Installation guide ⚙`
 
 .. warning::
-    **M1/M2 MacOS users**, please refer to the :ref:`dedicated section <source/guides/installation_guide:M1/M2 (ARM64) Mac installation>`
+    **ARM64 MacOS users**, please refer to the :ref:`dedicated section <source/guides/installation_guide:ARM64 Mac installation>`
 
 You can install ``napari-cellseg3d`` via pip:
 
@@ -74,9 +74,11 @@ For local installation after cloning from GitHub, please run the following in th
 
 If the installation was successful, you will find the napari-cellseg3D plugin in the Plugins section of napari.
 
+
 Usage
 --------------------------------------------
 
+
 To use the plugin, please run:
 
 .. code-block::

diff --git a/pyproject.toml b/pyproject.toml
@@ -126,6 +126,15 @@ line_length = 79
 #crf = [
 #   "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master",
 #]
+pyqt5 = [
+    "pyqt5",
+]
+pyside2 = [
+    "pyside2",
+]
+pyside6 = [
+    "pyside6",
+]
 onnx-cpu = [
     "onnx",
     "onnxruntime"