Plantcv

[](https://doi.org/10.5281/zenodo.3561216)

* Dropped Python v2.7 support (https://python3statement.org/).
* Added a Hyperspectral tool sub-package:
* Read in ENVI hyperspectral data with new option for the existing `plantcv.readimage` function. While reading in hyperspectral data a pseudo-rgb image is also created.
* Calibrate raw hyperspectral image data with white and dark reference images with `plantcv.hyperspectral.calibrate`.
* Calculate indices (e.g. NDVI) from a hyperspectral datacube with `plantcv.hyperspectral.extract_index`.
* Extract bands from a hyperspectral datacube that are the closest to user defined wavelengths with `plantcv.hyperspectral.extract_wavelength`.
* Add functionality to the existing function `plantcv.apply_mask` that allows users to mask hyperspectral images.
* Add documentation pages and edit existing documentation pages to reflect all additions.
* Add a hyperspectral workflow tutorial.
* Add documentation
* Underlying functions used in `plantcv-workflow`.
* Information about updating PlantCV.
* Minor update to `plantcv.morphology.segment_sort` to ensure the function is robust.
* Enhance various region of interest `plantcv.roi.*` functions to draw the debug image before hitting the fatal error when an ROI extends beyond the image boundaries and start printing a warning if a user defined grid causes ROI's to overlap

* Documentation updates.
* Coerce n to be an integer in `plantcv.util.sample_images` function.
* Add regex metadata parsing option to PlantCV workflow.
* Add ISO standard for timestamp separator.
* Minor bug fixes.

[](https://doi.org/10.5281/zenodo.3483049)

* Add a new template for opening an issue for discussions, requesting a new feature, and for reporting an issue/requesting help.
* Give more detail in documentation (how to change version of documentation, types of images compatible with workflow parallelization, example batch script for windows).
* Updates to `plantcv.analyze_color`. Users are given the option to create histograms from data about all available colorspaces or just a subset but data was getting stored out for all channels regardless of the selected colorspace. Update so that only data requested gets saved out (since color frequency data is verbose).
* Update method for `plantcv.apply_mask` to make it more robust to the types of data that can get masked.
* Bugfix with `plantcv.cluster_contours` to allow number of row and/or columns to exceed 9.
* Update method for `plantcv.roi_objects` since the old algorithm fails when a ROI is small enough to be fully enclosed by a contour.
* Bugfix for the sorting algorithm within `plantcv.morphology.segment_insertion_angle` since it was overly sensitive.
* Update docker.

[](https://doi.org/10.5281/zenodo.3375003)

PlantCV v3.6.0 adds new functionality and fixes several bugs.

Summary of changes
* Updated `pcv.visualize.pseudocolor` to stop scaling the background values
* Add image dataset random sampling tool `plantcv-utils.py sample_images`
* Added custom ROI polygon tool `pcv.roi.custom`
* Added grayscale image value rescaling function `pcv.transform.rescale`
* Fixed bug in `pcv.roi_objects` for evaluating the largest contour
* Fixed bugs in documentation
* Added function for correcting for non-uniform illumination `pcv.nonuniform_illumination`
* Restricted scikit-image dependency to v0.14.2 to bypass an issue with v0.14.3 in Windows
* Added function to generate kernel structuring elements
* Added pip to conda environment dependencies in `environment.yml`
* Added improvements to `pcv.morphology` functions
* Make it optional for the first segment to be classified as stem regardless in the segment_sort function
* Update method for sorting through segments in the segment_insertion_angle function, makes it quicker and more robust to measuring every leaf.
* Update plotting method for segment_id since the previous method can give a weird artifact when plotting segments that have been combined.
* Update pruning function
* Removing old warnings that aren't really relevant anymore

[](https://doi.org/10.5281/zenodo.3338666)

PlantCV v3.5.0 adds new functionality and fixes several bugs and usability issues.

Summary of changes:

- Added `pcv.analyze_thermal_values` to handle thermal data analysis.
- Update Dockerfile
- Added a thermal tutorial
- Added functionality to `pcv.readimage` to allow it to handle .csv format files for thermal imaging.
- Add ` pcv.visualize.clustered_contours` which creates an image that assists with debugging parameters upstream of using `pcv.cluster_contours`
- Bug fix regarding listing observations while running PlantCV parallel workflows.
- Removed legacy format where `pcv.analyze_*` functions returned lists. When a function returns a single image it will no longer store that image inside a list object.
- Various documentation updates and improvements
- The function `pcv.within_frame` now stores an observation in addition to returning a boolean to the user

[](https://doi.org/10.5281/zenodo.3249350)

PlantCV v3.4.1 is an intermediate release to address a few issues, particularly with the new JSON output data format.

**Summary of changes**:

* Updated format of JSON output files
* Added `plantcv-utils.py` script with a `json2csv` conversion tool for exporting CSV files from the JSON output data
* `plantcv-workflow.py`, `plantcv-train.py`, and `plantcv-utils.py` are now installed in the environment `bin` directory
* `pcv.visualize.pseudocolor ` now has the ability to apply custom padding when cropping
* Updated skeleton pruning algorithm
* Combined pruning and skeleton segmentation
* Put the iterative pruning method into an internal function
* set roi_type='partial' default for the roi_objects function
* Add fill_holes function that does a flood fill on black holes inside a binary mask
* Various documentation updates and improvements
* Updated the `analyze_nir_intensity` function to use `cv2.calcHist` instead of `np.histogram`