Plantcv

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3.13.0

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5146502.svg)](https://doi.org/10.5281/zenodo.5146502)

**PlantCV Version 3.13 Updates**

* Update imports to discontinue the deprecation warnings in `pcv.watershed`
* Update `scikit-image` requirement to `scikit-image>=0.13`
* Reorganizes our tutorials in several ways:
* There is now a main tutorials page that is organized as a gallery of tutorial "cards" that can be filtered by keyword tags. Each card has a launch Binder button to access the interactive tutorial and a link to the static tutorial.
* The tutorial card images and links to notebooks are remote and can be hosted from any GitHub (or other) repository.
* The static tutorial pages are now grouped in a directory called "tutorials."
* The static tutorial pages now only have a launch Binder button and render the complete Jupyter notebooks using nbviewer, rather than having a page that recreates the workflow and has a script version of the workflow.
* Added `pcv.transform.gamma_correct` which performs gamma correction on the input image (wrapper of the skimage gamma correction function).
* Updated the `debug` method in the backend within more miscellaneous functions.
* Expand the functionality of the metadata matcher portion of `plantcv-workflow` to support the matching of multiple metadata values.
* Syntax at the command line `(--match id:1,id:2,id:3)`
* Also supports lists in configuration file based parallelization
* Updates plantcv.hyperspectral.read_data to support Band Sequential (BSQ) in addition to Band Interleaved by Line (BIL) raw data formats for ENVI type multi/hyperspectral datasets.
* Adds `pcv.visualize.obj_sizes` function for annotating the sizes of separate objects onto a visualization.
* Add `pcv.visualize. obj_size_ecdf` for a new way to visualize: empirical cumulative distribution function (eCDF).
* Converted to base python classes `int` and `bool` since numpy is deprecating `np.int` and `np.bool` datatypes.
* Update the fill_segments function in the morphology sub-package
* The added observations are corrected.
* Also return the `filled_mask` (which is a label image as an output) along with the `filled_image` as outputs.
* The `filled_img` is generate by calling the added `colorize_label_img` visualization function.

3.12.1

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4770879.svg)](https://doi.org/10.5281/zenodo.4770879)

**PlantCV Version 3.12.1 Updates**

- Bugfix (from 770 ) for an update since `OpenCV` function `pointPolygonTest` was impacting 3 analysis functions from PCV.

- Numpy is deprecating `np.int` and `np.bool`; converted these to base python classes int and bool.

3.12.0

- `pcv.apply_mask()`, changed the `rgb_img` parameter to `img`
- `pcv.cluster_contours_splitimg`, changed the `rgb_img` parameter to `img`

3.11.1

This release is an update of v3.11.0 to correctly trigger a deployment to PyPI and conda-forge.

3.11.0

* A new `sample` input was added to the `Outputs.add_observations` class method.
* Observations stored in `Outputs` class instances have an extra hierarchical layer to account for sample names. To access an observation, the sample name needs to be included, for example:
* `pcv.resize` deprecated and replaced with `pcv.transform.resize` and `pcv.transform.resize_factor`

python
pcv.outputs.observations["sample_name"]["variable_name"]

3.10.1

* Updates `plantcv.plot_image` and various other plotting methods to create a new figure for each plot. PlantCV now works better with the `%matplotlib notebook` plotting method in Jupyter.
* Fixes an issue where `plantcv.visualize.pseudocolor` plotted the image even when debug mode was disabled.
* Various documentation updates, corrections, and reformatting.

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