deploy: 11b46ee

_sources/supported_plugins.md

@@ -20,8 +20,8 @@ Those plugins that do have extra documentation contain links below.
 | HistogramEqualization | HistogramEqualization increases the global contrast of a low-contrast image or volume. Histogram equalization redistributes intensities to utilize the full range of intensities, such that the most common frequencies are more distinct. This module can perform either global or local histogram equalization. | No | | N/A |
 | HistogramMatching     | HistogramMatching manipulates the pixel intensity values an input image and matches them to the histogram of a reference image. It can be used as a way to normalize intensities across different 2D or 3D images or different frames of the same 3D image. It allows you to choose which frame to use as the reference. | No | | N/A |
 | PixelShuffle          | PixelShuffle takes the intensity of each pixel in an image and randomly shuffles its position. | No | | N/A |
-| Predict               | Predict allows you to use an ilastik pixel classifier to generate a probability image. CellProfiler supports two types of ilastik projects: Pixel Classification and Autocontext (2-stage). | No |  | N/A |
 | [RunCellpose](RunCellPose.md) | RunCellpose allows you to run Cellpose within CellProfiler. Cellpose is a generalist machine-learning algorithm for cellular segmentation and is a great starting point for segmenting non-round cells. You can use pre-trained Cellpose models or your custom model with this plugin. You can use a GPU with this module to dramatically increase your speed/efficiency. | Yes | `cellpose` | Yes |
+| Runilastik            | Runilasitk allows to run ilastik within CellProfiler. You can use pre-trained ilastik projects/models to predict the probability of your input images. The plugin supports two types of ilastik projects: Pixel Classification and Autocontext (2-stage).| Yes |  | Yes | 
 | RunImageJScript       | RunImageJScript allows you to run any supported ImageJ script directly within CellProfiler. It is significantly more performant than RunImageJMacro, and is also less likely to leave behind temporary files. | Yes | `imagejscript` , though note that conda installation may be preferred, see [this link](https://py.imagej.net/en/latest/Install.html#installing-via-pip) for more information | No |
 | RunOmnipose           | RunOmnipose allows you to run Omnipose within CellProfiler. Omnipose is a general image segmentation tool that builds on Cellpose. | Yes | `omnipose` | No |
 | RunStarDist           | RunStarDist allows you to run StarDist within CellProfiler. StarDist is a machine-learning algorithm for object detection with star-convex shapes making it best suited for nuclei or round-ish cells. You can use pre-trained StarDist models or your custom model with this plugin. You can use a GPU with this module to dramatically increase your speed/efficiency. RunStarDist is generally faster than RunCellpose. | Yes | `stardist` | No |

_sources/unsupported_plugins.md

@@ -21,3 +21,4 @@ Information about select plugins is as follows:
 
 **ClassifyPixelsUNET**: ClassifyPixelsUNET is a pixel classifier for background/object edge/object body. As far as we are aware, other deep learning  based plugins that we do currently support (such as RunCellpose) work better.
 **DeclumpObjects**: DeclumpObjects will split objects based on a seeded watershed method. Functionality from this module was [added into CellProfiler](https://github.com/CellProfiler/CellProfiler/pull/4397) in the Watershed module as of CellProfiler 4.2.0.
+**Predict**: Predict module is not supported anymore and one can use **Runilastik** module to run ilastik pixel classifier in Cellprofiler. 

_sources/using_plugins.md

@@ -21,7 +21,7 @@ See [Installing plugins with dependencies, using CellProfiler from source](#inst
 See [Installing plugins with dependencies, using pre-built CellProfiler](#installing-plugins-with-dependencies-using-pre-built-cellprofiler).
 - The third option uses Docker to bypass installation requirements. 
 It is the simplest option that only requires download of Docker Desktop; the module that has dependencies will automatically download a Docker that has all of the dependencies upon run and access that Docker while running the plugin.
-It is currently only supported for the RunCellpose plugin but will be available in other plugins soon.
+It is currently supported for the RunCellpose and Runilastik plugins. Please have a look at this [table](https://github.com/CellProfiler/CellProfiler-plugins/blob/master/documentation/CP-plugins-documentation/supported_plugins.md) to know about the availability of docker versions for plugins.  
 See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-installation-requirements).
 
 ### Installing plugins without dependencies
@@ -173,7 +173,7 @@ Download Docker Desktop from [Docker.com](https://www.docker.com/products/docker
 
 2. **Run Docker Desktop**
 Open Docker Desktop.
-Docker Desktop will need to be open every time you use a plugin with Docker.
+Docker Desktop will need to be open every time you use a plugin with Docker. Please have a look at this [table](https://github.com/CellProfiler/CellProfiler-plugins/blob/master/documentation/CP-plugins-documentation/supported_plugins.md) to know if a docker version is available for a plugin. 
 
 3. **Select "Run with Docker"**
 In your plugin, select `Docker` for "Run module in docker or local python environment" setting.

supported_plugins.html

@@ -458,18 +458,18 @@ <h1>Supported Plugins<a class="headerlink" href="#supported-plugins" title="Perm
 <td><p></p></td>
 <td><p>N/A</p></td>
 </tr>
-<tr class="row-even"><td><p>Predict</p></td>
-<td><p>Predict allows you to use an ilastik pixel classifier to generate a probability image. CellProfiler supports two types of ilastik projects: Pixel Classification and Autocontext (2-stage).</p></td>
-<td><p>No</p></td>
-<td><p></p></td>
-<td><p>N/A</p></td>
-</tr>
-<tr class="row-odd"><td><p><span class="xref myst">RunCellpose</span></p></td>
+<tr class="row-even"><td><p><span class="xref myst">RunCellpose</span></p></td>
 <td><p>RunCellpose allows you to run Cellpose within CellProfiler. Cellpose is a generalist machine-learning algorithm for cellular segmentation and is a great starting point for segmenting non-round cells. You can use pre-trained Cellpose models or your custom model with this plugin. You can use a GPU with this module to dramatically increase your speed/efficiency.</p></td>
 <td><p>Yes</p></td>
 <td><p><code class="docutils literal notranslate"><span class="pre">cellpose</span></code></p></td>
 <td><p>Yes</p></td>
 </tr>
+<tr class="row-odd"><td><p>Runilastik</p></td>
+<td><p>Runilasitk allows to run ilastik within CellProfiler. You can use pre-trained ilastik projects/models to predict the probability of your input images. The plugin supports two types of ilastik projects: Pixel Classification and Autocontext (2-stage).</p></td>
+<td><p>Yes</p></td>
+<td><p></p></td>
+<td><p>Yes</p></td>
+</tr>
 <tr class="row-even"><td><p>RunImageJScript</p></td>
 <td><p>RunImageJScript allows you to run any supported ImageJ script directly within CellProfiler. It is significantly more performant than RunImageJMacro, and is also less likely to leave behind temporary files.</p></td>
 <td><p>Yes</p></td>

unsupported_plugins.html

@@ -424,7 +424,8 @@ <h2>What plugins are unsupported?<a class="headerlink" href="#what-plugins-are-u
 <p>We cannot provide comprehensive information about why we are not supporting a given plugin.
 Information about select plugins is as follows:</p>
 <p><strong>ClassifyPixelsUNET</strong>: ClassifyPixelsUNET is a pixel classifier for background/object edge/object body. As far as we are aware, other deep learning  based plugins that we do currently support (such as RunCellpose) work better.
-<strong>DeclumpObjects</strong>: DeclumpObjects will split objects based on a seeded watershed method. Functionality from this module was <a class="reference external" href="https://github.com/CellProfiler/CellProfiler/pull/4397">added into CellProfiler</a> in the Watershed module as of CellProfiler 4.2.0.</p>
+<strong>DeclumpObjects</strong>: DeclumpObjects will split objects based on a seeded watershed method. Functionality from this module was <a class="reference external" href="https://github.com/CellProfiler/CellProfiler/pull/4397">added into CellProfiler</a> in the Watershed module as of CellProfiler 4.2.0.
+<strong>Predict</strong>: Predict module is not supported anymore and one can use <strong>Runilastik</strong> module to run ilastik pixel classifier in Cellprofiler.</p>
 </section>
 </section>
 

using_plugins.html

@@ -429,7 +429,7 @@ <h2>Installation<a class="headerlink" href="#installation" title="Permalink to t
 See <span class="xref myst">Installing plugins with dependencies, using pre-built CellProfiler</span>.</p></li>
 <li><p>The third option uses Docker to bypass installation requirements.
 It is the simplest option that only requires download of Docker Desktop; the module that has dependencies will automatically download a Docker that has all of the dependencies upon run and access that Docker while running the plugin.
-It is currently only supported for the RunCellpose plugin but will be available in other plugins soon.
+It is currently supported for the RunCellpose and Runilastik plugins. Please have a look at this <a class="reference external" href="https://github.com/CellProfiler/CellProfiler-plugins/blob/master/documentation/CP-plugins-documentation/supported_plugins.md">table</a> to know about the availability of docker versions for plugins.<br />
 See <span class="xref myst">Using Docker to Bypass Installation Requirements</span>.</p></li>
 </ul>
 <section id="installing-plugins-without-dependencies">
@@ -593,7 +593,7 @@ <h3>Using Docker to bypass installation requirements<a class="headerlink" href="
 Download Docker Desktop from <a class="reference external" href="https://www.docker.com/products/docker-desktop/">Docker.com</a>.</p></li>
 <li><p><strong>Run Docker Desktop</strong>
 Open Docker Desktop.
-Docker Desktop will need to be open every time you use a plugin with Docker.</p></li>
+Docker Desktop will need to be open every time you use a plugin with Docker. Please have a look at this <a class="reference external" href="https://github.com/CellProfiler/CellProfiler-plugins/blob/master/documentation/CP-plugins-documentation/supported_plugins.md">table</a> to know if a docker version is available for a plugin.</p></li>
 <li><p><strong>Select “Run with Docker”</strong>
 In your plugin, select <code class="docutils literal notranslate"><span class="pre">Docker</span></code> for “Run module in docker or local python environment” setting.
 On the first run of the plugin, the Docker container will be downloaded, however, this slow downloading process will only have to happen