diff --git a/documentation/CP-plugins-documentation/supported_plugins.md b/documentation/CP-plugins-documentation/supported_plugins.md index 3cdc6bc6..ad8f7cba 100644 --- a/documentation/CP-plugins-documentation/supported_plugins.md +++ b/documentation/CP-plugins-documentation/supported_plugins.md @@ -9,24 +9,24 @@ See [using plugins](using_plugins.md) for how to set up CellProfiler for plugin Most plugin documentation can be found within the plugin itself and can be accessed through CellProfiler help. Those plugins that do have extra documentation contain links below. -| Plugin | Description | Requires installation of dependencies? | Install flag | Docker version currently available? | -|--------|-------------|----------------------------------------|--------------|-------------------------------------| -| AddNoise | AddNoise adds Gaussian, Poisson, or Salt and Pepper noise to images. Of particular use for data augmentation in deep learning. | No | | N/A | -| CalculateMoments | CalculateMoments extracts moments statistics from a given distribution of pixel values. | No | | N/A | -| CallBarcodes | CallBarcodes is used for assigning a barcode to an object based on the channel with the strongest intensity for a given number of cycles. It is used for optical sequencing by synthesis (SBS). | No | | N/A | -| CompensateColors | CompensateColors determines how much signal in any given channel is because of bleed-through from another channel and removes the bleed-through. It can be performed across an image or masked to objects and provides a number of preprocessing and rescaling options to allow for troubleshooting if input image intensities are not well matched. | No | | N/A | -| ConvertObjectsToOutlines | ConvertObjectsToOutlines takes in black images with white object outlines and uses them to predict objects. See [Ask Erin Dear Beth video podcast](https://youtu.be/qWYyp_a0IHM) for a walkthrough of using the module. | No | | N/A | -| DistanceTransform | DistanceTransform computes the distance transform of a binary image. The distance of each foreground pixel is computed to the nearest background pixel and the resulting image is then scaled so that the largest distance is 1. | No | | N/A | -| EnforceObjectsOneToOne| EnforceObjectsOneToOne generates Primary and Secondary object relationships for any pair of objects in a similar manner to the relationships established by IdentifyPrimaryObjects and IdentifySecondaryObjects. It is particularly useful for relating objects identified using Deep Learning. | No | | N/A | -| EnhancedMeasureTexture| EnhancedMeasureTexture measures the degree and nature of textures within an image or objects in a more comprehensive/tuneable manner than the MeasureTexture module native to CellProfiler. | No | | N/A | -| FilterObjects_StringMatch| FilterObjects_StringMatch allows filtering of objects using exact or partial string matching in a manner similar to FilterObjects. | No | | N/A | -| 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 | -| [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. It currently supports Cellpose 1-4. | 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. Omnipose usage is also supported from within the RunCellpose module. | 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 | -| RunVista2D | This module uses a pre-trained VISTA2D model to detect cell objects in an image. | No | | N/A | -| VarianceTransform | This module allows you to calculate the variance of an image, using a determined window size. It also has the option to find the optimal window size from a predetermined range to obtain the maximum variance of an image. | No | | N/A | +| Plugin | Description | Requires installation of dependencies? | Install flag | Docker version currently available? | Pixi environment currently available? | +| ------ | ----------- | -------------------------------------- | ------------ | ----------------------------------- | ------------------------------------- | +| AddNoise | AddNoise adds Gaussian, Poisson, or Salt and Pepper noise to images. Of particular use for data augmentation in deep learning. | No | | N/A | N/A | +| CalculateMoments | CalculateMoments extracts moments statistics from a given distribution of pixel values. | No | | N/A | N/A | +| CallBarcodes | CallBarcodes is used for assigning a barcode to an object based on the channel with the strongest intensity for a given number of cycles. It is used for optical sequencing by synthesis (SBS). | No | | N/A | N/A | +| CompensateColors | CompensateColors determines how much signal in any given channel is because of bleed-through from another channel and removes the bleed-through. It can be performed across an image or masked to objects and provides a number of preprocessing and rescaling options to allow for troubleshooting if input image intensities are not well matched. | No | | N/A | N/A | +| ConvertObjectsToOutlines | ConvertObjectsToOutlines takes in black images with white object outlines and uses them to predict objects. See [Ask Erin Dear Beth video podcast](https://youtu.be/qWYyp_a0IHM) for a walkthrough of using the module. | No | | N/A | N/A | +| DistanceTransform | DistanceTransform computes the distance transform of a binary image. The distance of each foreground pixel is computed to the nearest background pixel and the resulting image is then scaled so that the largest distance is 1. | No | | N/A | N/A | +| EnforceObjectsOneToOne | EnforceObjectsOneToOne generates Primary and Secondary object relationships for any pair of objects in a similar manner to the relationships established by IdentifyPrimaryObjects and IdentifySecondaryObjects. It is particularly useful for relating objects identified using Deep Learning. | No | | N/A | N/A | +| EnhancedMeasureTexture | EnhancedMeasureTexture measures the degree and nature of textures within an image or objects in a more comprehensive/tuneable manner than the MeasureTexture module native to CellProfiler. | No | | N/A | N/A | +| FilterObjects_StringMatch | FilterObjects_StringMatch allows filtering of objects using exact or partial string matching in a manner similar to FilterObjects. | No | | N/A | N/A | +| 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 | 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 | N/A | +| PixelShuffle | PixelShuffle takes the intensity of each pixel in an image and randomly shuffles its position. | No | | N/A | 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. It currently supports Cellpose 1-4. | Yes | `cellpose` | Yes | v3 | +| 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 | No | +| 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 | No | +| RunOmnipose | RunOmnipose allows you to run Omnipose within CellProfiler. Omnipose is a general image segmentation tool that builds on Cellpose. Omnipose usage is also supported from within the RunCellpose module. | Yes | `omnipose` | No | 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 | No | +| RunVista2D | This module uses a pre-trained VISTA2D model to detect cell objects in an image. | No | | N/A | N/A | +| VarianceTransform | This module allows you to calculate the variance of an image, using a determined window size. It also has the option to find the optimal window size from a predetermined range to obtain the maximum variance of an image. | No | | N/A | N/A | diff --git a/documentation/CP-plugins-documentation/using_plugins.md b/documentation/CP-plugins-documentation/using_plugins.md index 662319cd..e6fbc786 100644 --- a/documentation/CP-plugins-documentation/using_plugins.md +++ b/documentation/CP-plugins-documentation/using_plugins.md @@ -6,51 +6,55 @@ Typically, if a plugin's dependencies are not installed, it will not be visible If you cannot find any plugins in the "Add Modules" panel then you have not properly set the plugins path (see below). If you can find some but not all plugins in the "Add Modules" panel then the not-visible plugins have unmet dependencies. -Please note that, as CellProfiler-plugins are considered experimental, they may not be as well documented as standard modules and they may not have a window that shows on run. +Please note that, as CellProfiler-plugins are considered experimental, they may not be as well documented as standard modules and they may not have a window that shows on run. Please report any installation issues or bugs related to plugins in the [CellProfiler-plugins repository](https://github.com/CellProfiler/CellProfiler-plugins) and not in the main CellProfiler repository. ## Installation -If the plugin you would like to use does not have any additional dependencies outside of those required for running CellProfiler (this is most plugins), using plugins is very simple. +If the plugin you would like to use does not have any additional dependencies outside of those required for running CellProfiler (this is most plugins), using plugins is very simple. See [Installing plugins without dependencies](#installing-plugins-without-dependencies). -If the plugin you would like to use has dependencies, you have three separate options for installation. +If the plugin you would like to use has dependencies, you have four separate options for installation. + - The first option requires building CellProfiler from source, but plugin installation is simpler. See [Installing plugins with dependencies, using CellProfiler from source](#installing-plugins-with-dependencies-using-cellprofiler-from-source). - The second option allows you to use pre-built CellProfiler, but plugin installation is more complex. 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. +- 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 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. +It is not supported for all plugins with external dependencies. Please have a look at this [table](https://github.com/CellProfiler/CellProfiler-plugins/blob/master/documentation/CP-plugins-documentation/supported_plugins.md) to see the availability of docker versions for plugins. See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-installation-requirements). +- The fourth option uses a pixi environment to simplify installation from source. +It is not supported for all plugins with external dependencies. Please have a look at this [table](https://github.com/CellProfiler/CellProfiler-plugins/blob/master/documentation/CP-plugins-documentation/supported_plugins.md) to see the availability of pixi environments +See [Using a pixi environment](#using-a-pixi-environment) for use. ### Installing plugins without dependencies 1. **Install CellProfiler.** Download a binary (pre-built) version of CellProfiler from the [CellProfiler website](https://cellprofiler.org/releases). - + Or, you can install CellProfiler from source (See instructions for: [Windows](https://github.com/CellProfiler/CellProfiler/wiki/Source-installation-%28Windows%29); [Mac Intel](https://github.com/CellProfiler/CellProfiler/wiki/Source-installation-%28OS-X-and-macOS%29); [Mac Apple Silicon](https://github.com/CellProfiler/CellProfiler/wiki/Installation-of-CellProfiler-4-from-source-on-MacOS-M1); [Linux](https://github.com/CellProfiler/CellProfiler/wiki/Source-installation-%28Linux%29)) 2. **Clone the CellProfiler-plugins repository.** This will download all of the plugins in the CellProfiler-plugins repository. - + In your terminal, type ```bash git clone https://github.com/CellProfiler/CellProfiler-plugins.git ``` - + Alternatively, if you have code for plugins that are not in the CellProfiler-plugins repository, you can place them in any folder that you'll be able to find again. 3. **Set the plugins path in CellProfiler.** - - Open CellProfiler. + - Open CellProfiler. - Go to `CellProfiler` => `Preferences` and set the path in the `CellProfiler plugins directory` to the `active_plugins` folder in the GitHub repository that you just cloned (or, if you didn't clone the whole repository, whatever location you have saved your plugins into). - Select `Save` at the bottom of the Preferences window - Close CellProfiler and re-open it - + You are now ready to use any CellProfiler plugin that does not have additional dependencies. This is most CellProfiler plugins. @@ -70,7 +74,7 @@ See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-i 3. **Set the plugins path in CellProfiler.** - - Open CellProfiler. + - Open CellProfiler. - Go to `CellProfiler` => `Preferences` and set the path in the `CellProfiler plugins directory` to the `active_plugins` folder in the GitHub repository that you just cloned. - Select `Save` at the bottom of the Preferences window - Close CellProfiler @@ -85,13 +89,13 @@ See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-i ``` e.g. To install Cellpose the pip install command would be `pip install -e .[cellpose]` - + If using Mac and getting an error saying `zsh: no matches found: .[somepackage]`, put the dot and square brackets in single quotes, ie `pip install -e '.[cellpose]'` 5. **Open and use CellProfiler.** - Please note that plugins that have separate install flags may have conflicting dependencies so we recommend making a separate python environment in which to run separate installations. - (e.g. while having CellPose and StarDist in the same python environment is technically possible, it has been reported to be quite troublesome to install, so we recommend choosing either CellPose or StarDist.) + Please note that plugins that have separate install flags may have conflicting dependencies so we recommend making a separate Python environment in which to run separate installations. + (e.g. while having CellPose and StarDist in the same Python environment is technically possible, it has been reported to be quite troublesome to install, so we recommend choosing either Cellpose or StarDist.) ### Installing plugins with dependencies, using pre-built CellProfiler @@ -99,7 +103,6 @@ See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-i Download a binary (pre-built) version of CellProfiler from the [CellProfiler website](https://cellprofiler.org/releases). - 2. **Clone the CellProfiler-plugins repository.** In your terminal, type @@ -110,7 +113,7 @@ See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-i 3. **Set the plugins path in CellProfiler.** - - Open CellProfiler. + - Open CellProfiler. - Go to `CellProfiler` => `Preferences` and set the path in the `CellProfiler plugins directory` to the `active_plugins` folder in the GitHub repository that you just cloned. - Select `Save` at the bottom of the Preferences window - Close CellProfiler @@ -118,12 +121,13 @@ See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-i 4. **Identify the Python dependencies of the plugins you want to use.** You can find this information for most plugins by looking in `setup.py`. + ```{admonition} e.g. using RunCellpose plugin If you would like to install the RunCellpose plugin, in `setup.py` you can see that `cellpose_deps = ["cellpose>=1.0.2"]` so the only dependency is `cellpose`. ``` Alternatively, you can find this information directly in the plugin code itself by looking at what is imported at the beginning of the plugin. - + Note that you will want to compare any imports to what is already required by CellProfiler with that caveat that dependencies often have dependencies. ```{admonition} e.g. using RunImageJScript plugin @@ -134,15 +138,15 @@ See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-i What remains is `pyimagej` ``` -5. **Initialize the proper version of python** +5. **Initialize the proper version of Python** - For the subsequent steps, make sure to use the same version of python that CellProfiler uses. For instance, CellProfiler 4 uses python 3.8, (which you can see in CellProfiler's [setup.py](https://github.com/CellProfiler/CellProfiler/blob/4.2.x/setup.py)). You can check what version of python you're using in the terminal: + For the subsequent steps, make sure to use the same version of Python that CellProfiler uses. For instance, CellProfiler 4 uses Python 3.8, (which you can see in CellProfiler's [setup.py](https://github.com/CellProfiler/CellProfiler/blob/4.2.x/setup.py)). You can check what version of Python you're using in the terminal: ```bash python --version ``` - If necessary, you can create a conda environment with a specific version of python. + If necessary, you can create a conda environment with a specific version of Python. ```bash conda create --name py38 python=3.8 @@ -150,12 +154,12 @@ See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-i ``` You may replace the value after `--name` with whatever name you'd like to give to the environment. - If you don't have `conda`/`miniconda`/`mamba` or similar, you can either [manually install python directly](https://www.python.org/downloads/), or use a tool like `pyenv` to manage different versions of python: + If you don't have `conda`/`miniconda`/`mamba` or similar, you can either [manually install Python directly](https://www.python.org/downloads/), or use a tool like `pyenv` to manage different versions of Python: - [pyenv Mac](https://github.com/pyenv/pyenv) - [pyenv Windows](https://github.com/pyenv-win/pyenv-win) ```{note} - If you have multiple versions of python (e.g. you have python 3.8 and python 3.9 both installed with homebrew on Mac), it is sometimes the case that you may need to specify an exact `pip` version in the below steps, e.g. `pip3.8` instead of just `pip`. You can always double check you're using the correct pip with `pip --version`. + If you have multiple versions of Python (e.g. you have Python 3.8 and Python 3.9 both installed with homebrew on Mac), it is sometimes the case that you may need to specify an exact `pip` version in the below steps, e.g. `pip3.8` instead of just `pip`. You can always double check you're using the correct pip with `pip --version`. ``` 6. **Install or copy requirements into CellProfiler installation.** @@ -178,7 +182,7 @@ See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-i ``` ```{note} - Do not run the `pip install` from *within* the CellProfiler directory. If you run `pip` with your terminal's current working directory inside the CellProfiler directory, you will confuse `pip`, since there is a version of python bundled with CellProfiler and your `PATH` and/or `PYTHONPATH` may include your current working directory. + Do not run the `pip install` from *within* the CellProfiler directory. If you run `pip` with your terminal's current working directory inside the CellProfiler directory, you will confuse `pip`, since there is a version of Python bundled with CellProfiler and your `PATH` and/or `PYTHONPATH` may include your current working directory. ``` ```{note} @@ -235,11 +239,21 @@ See [Using Docker to Bypass Installation Requirements](#using-docker-to-bypass-i 2. **Run Docker Desktop** Open Docker Desktop. -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. +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"** +1. **Select "Run with Docker"** - In your plugin, select `Docker` for "Run module in docker or local python environment" setting. + In your plugin, select `Docker` 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 once. +### Using a pixi environment + +[Pixi](https://pixi.prefix.dev/latest/) is a modern, reproducible package manager. + +First, install Pixi using [Pixi's installation instructions](https://pixi.prefix.dev/latest/installation/). + +Then, open your terminal and navigate into the folder where you have cloned the CellProfiler-plugins GitHub repository. +Navigate into the subfolder `pixi_envs` and then into the subfolder that has the environment for the plugin you want to use (e.g. `pixi_envs/cellprofiler428_cellpose3`). +Run `pixi shell --frozen` to initialize the environment (this may take a couple of minutes the first time you run it, since things will need to download). +Run `python -m cellprofiler` to start CellProfiler (again, this may take a minute on the first time). diff --git a/pixi_envs/cellprofiler428_cellpose3/pixi.lock b/pixi_envs/cellprofiler428_cellpose3/pixi.lock new file mode 100644 index 00000000..63152e2f --- /dev/null +++ b/pixi_envs/cellprofiler428_cellpose3/pixi.lock @@ -0,0 +1,10819 @@ +version: 6 +environments: + default: + channels: + - url: https://conda.anaconda.org/conda-forge/ + indexes: + - https://pypi.org/simple + packages: + linux-64: + - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.11-hd590300_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.5-hc2324a3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/boto3-1.16.62-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/botocore-1.19.63-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb9d3cd8_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_3.conda + - 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