# napari labeling GUI

We replaced wxPython with PySide6 + as of version 2.3. Here is how to use the napari-aspects of the new GUI. It is available in napari-hub as a stand alone GUI as well as integrated into our main GUI, [please see docs here](https://deeplabcut.github.io/DeepLabCut/docs/PROJECT_GUI.html).

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A napari plugin for keypoint annotation with DeepLabCut.


## Installation

You can install the full DeepLabCut napari-based GUI via [pip] by running this in your conda env:

`    pip install 'deeplabcut[gui]'`

*please note this is available since v2.3

You can install the stand-alone `napari-deeplabcut` via [pip]:

`     pip install napari-deeplabcut `



To install latest development version:

  `  pip install git+https://github.com/DeepLabCut/napari-deeplabcut.git `


## Usage

To use the full GUI, please run:

`python -m deeplabcut`

To use the stand-alone napari plugin, please launch napari:

`    napari `

Then, activate the plugin in Plugins > napari-deeplabcut: Keypoint controls.

All accepted files (config.yaml, images, h5 data files) can be loaded
either by dropping them directly onto the canvas or via the File menu.

The easiest way to get started is to drop a folder (typically a folder from within a DeepLabCut's `labeled-data` directory), and, if labeling from scratch, drop the corresponding `config.yaml` to automatically add a `Points layer` and populate the dropdown menus.

**Tools & shortcuts are:**

- `2` and `3`, to easily switch between labeling and selection mode
- `4`, to enable pan & zoom (which is achieved using the mouse wheel or finger scrolling on the Trackpad)
- `M`, to cycle through regular (sequential), quick, and cycle annotation mode (see the description [here](https://github.com/DeepLabCut/DeepLabCut-label/blob/ee71b0e15018228c98db3b88769e8a8f4e2c0454/dlclabel/layers.py#L9-L19))
- `E`, to enable edge coloring (by default, if using this in refinement GUI mode, points with a confidence lower than 0.6 are marked
in red)
- `F`, to toggle between animal and body part color scheme.
- `backspace` to delete a point.
- Check the box "display text" to show the label names on the canvas.
- To move to another folder, be sure to save (Ctrl+S), then delete the layers, and re-drag/drop the next folder.


### Save Layers

Annotations and segmentations are saved with `File > Save Selected Layer(s)...` (or its shortcut `Ctrl+S`).
Only when saving segmentation masks does a save file dialog pop up to name the destination folder;
keypoint annotations are otherwise automatically saved in the corresponding folder as `CollectedData_<ScorerName>.h5`.
- As a reminder, DLC will only use the H5 file; so be sure if you open already labeled images you save/overwrite the H5.
- Note, before saving a layer, make sure the points layer is selected. If the user clicked on the image(s) layer first, does `Save As`, then closes the window, any labeling work during that session will be lost!


### Video frame extraction and prediction refinement

Since v0.0.4, videos can be viewed in the GUI.

Since v0.0.5, trailing points can be visualized; e.g., helping in the identification
of swaps or outlier, jittery predictions.

Loading a video (and its corresponding output h5 file) will enable the video actions
at the top of the dock widget: they offer the option to manually extract video
frames from the GUI, or to define cropping coordinates.
Note that keypoints can be displaced and saved, as when annotating individual frames.


## Workflow

Suggested workflows, depending on the image folder contents:

1. **Labeling from scratch** – the image folder does not contain `CollectedData_<ScorerName>.h5` file.

    Open *napari* as described in [Usage](#usage) and open an image folder together with the DeepLabCut project's `config.yaml`.
    The image folder creates an *image layer* with the images to label.
    Supported image formats are: `jpg`, `jpeg`, `png`.
    The `config.yaml` file creates a *Points layer*, which holds metadata (such as keypoints read from the config file) necessary for labeling.
    Select the *Points layer* in the layer list (lower left pane on the GUI) and click on the *+*-symbol in the layer controls menu (upper left pane) to start labeling.
    The current keypoint can be viewed/selected in the keypoints dropdown menu (right pane).
    The slider below the displayed image (or the left/right arrow keys) allows selecting the image to label.

    To save the labeling progress refer to [Save Layers](#save-layers).
    `Data successfully saved` should be shown in the status bar, and the image folder should now contain a `CollectedData_<ScorerName>.h5` file.
    (Note: For convenience, a CSV file with the same name is also saved.)

2. **Resuming labeling** – the image folder contains a `CollectedData_<ScorerName>.h5` file.

    Open *napari* and open an image folder (which needs to contain a `CollectedData_<ScorerName>.h5` file).
    In this case, it is not necessary to open the DLC project's `config.yaml` file, as all necessary metadata is read from the `h5` data file.

    Saving works as described in *1*.

3. **Refining labels** – the image folder contains a `machinelabels-iter<#>.h5` file.

    The process is analog to *2*.

4. **Drawing segmentation masks**

    Drop an image folder as in *1*, manually add a *shapes layer*. Then select the *rectangle* in the layer controls (top left pane),
    and start drawing rectangles over the images. Masks and rectangle vertices are saved as described in [Save Layers](#save-layers).
    Note that masks can be reloaded and edited at a later stage by dropping the `vertices.csv` file onto the canvas.


### Labeling multiple image folders

Labeling multiple image folders has to be done in sequence; i.e., only one image folder can be opened at a time.
After labeling the images of a particular folder is done and the associated *Points layer* has been saved, *all* layers should be removed from the layers list (lower left pane on the GUI) by selecting them and clicking on the trashcan icon.
Now, another image folder can be labeled, following the process described in *1*, *2*, or *3*, depending on the particular image folder.


### Defining cropping coordinates

Prior to defining cropping coordinates, two elements should be loaded in the GUI:
a video and the DLC project's `config.yaml` file (into which the crop dimensions will be stored).
Then it suffices to add a `Shapes layer`, draw a `rectangle` in it with the desired area,
and hit the button `Store crop coordinates`; coordinates are automatically written to the configuration file.


## Contributing

Contributions are very welcome. Tests can be run with [tox], please ensure
the coverage at least stays the same before you submit a pull request.

To locally install the code, please git clone the repo and then run `pip install -e .`

## License

Distributed under the terms of the [BSD-3] license,
"napari-deeplabcut" is free and open source software.

## Issues

If you encounter any problems, please [file an issue] along with a detailed description.

[file an issue]: https://github.com/DeepLabCut/napari-deeplabcut/issues


## Acknowledgements


This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template. We thank the Chan Zuckerberg Initiative (CZI) for funding this work!

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[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
