PlantCV v3.13.2

What's Changed

• Scale color correction matrices to 0-255 in quick color check by @JorgeGtz in #831
• Fix bug in channel order in color correction by @JorgeGtz in #832
• Trigger GitHub Actions deployment workflow when a release is published by @nfahlgren in #833

Full Changelog: v3.13.1...v3.13.2

PlantCV v3.13.1

What's Changed

• Fix line endings when switching between macOS/Linux and Windows by @nfahlgren in #811
• Increase flexibility for processing hyperspectral ENVI header files by @typelogic in #813
• Configure most functions to use the _debug module by @dschneiderch in #807
• Cast contour tuple as a list in find_objects by @JorgeGtz in #830
• Fix bug in color correction by @JorgeGtz in #829

New Contributors

• @typelogic made their first contribution in #813

Full Changelog: v3.13.0...v3.13.1

PlantCV v3.13.0

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.

PlantCV v3.12.1

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.

PlantCV v3.12.0

PlantCV Version 3.12 Updates

• Updated the pcv.apply_mask(), changed the rgb_img parameter to img since the function is now able to handle grayscale, NIR, and hyperspectral data.
• Add json datatype check in the add_observation method.
• Started to move some internal code within functions across the package to start using a debug module rather than having repetitive code for plotting/printing debugging images.
• Extend the pcv.cluster_contours_splitimg functionality to grayscale images.
• Added a bounding parameter to pcv.clustered_contours() function so circles can be turned on or off (default behavior as on).
• Added a pcv.deprecation_warning function for warning statements internal use within functions.
  • Add a "verbose" variable to pcv.params such that users have the option to turn off (deprecation) warnings by setting it to "False".
• Update and resolve a bug within the random_sample utility function so that it works as intended on datasets with subdirectories.
• Added a new method (save_results) to the Outputs class. outputs.save_results supports two output formats, JSON and CSV. The code from plantcv.print_results was moved to the save_results method and a deprecation warning is now printed via print_results. When run with outformat = "json", outputs.save_results has the same functionality as plantcv.print_results. When run with outformat = "csv", a single CSV table in "long" format is produced that is compatible with use in R. CSV format is useful for a user who runs workflows only in Jupyter or wants to utilize data for testing from Jupyter. The output format JSON must be used for parallel workflows as before.
  • Users will need to migrate workflows from pcv.print_results(args.result) to pcv.outputs.save_results(args.result) before pcv.print_results is removed.
• Addition of RGB2CMYK function to PlantCV to allow the conversion of images from the RGB colour space to CMYK colour space and split the channels.
• Updated the way PlantCV handles subdaily timestamps for parallelization. Now will work with months and days even where there are no years.

Version 3.12.0 breaking changes

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

PlantCV v3.11.1

Release v3.11.1

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

PlantCV v3.11.0

3.11.0 updates

• Added a label parameter to all PlantCV functions that save observations. The label parameter can be used to set a sample name when saving observations. Multiple samples can be saved per image now, allowing for repeated measurements, which simplifies workflows that measure multiple objects or regions of interest
• Added a sample parameter to Outputs.add_observation. Outputs class observations are now organized under sample names so that multiple samples can be associated with a single workflow.
• Add function pcv.transform.warp to fuse images that have different size/scale/proportions, e.g. from two (different) cameras in different locations. The function accepts 2 images and 4 pairs of points in order to compute a perspective transformation matrix so the images overlay nicely.
• Update the function pcv.hyperspectral.analyze_index to be more robust to NaN values and to also only consider finite values when calculating index statistics even when values range to positive or negative infinity.
• Documentation updates
• Prioritize the conda-forge channel in environment.yml
• Minor updates/bug fixes
• Add conda-forge badge to README
• Set .gitattributes to use Linux line endings on all platforms
• A handful of minor changes to functions inside the morphology sub-package.
  • Segment insertion angle was failing on skeleton structures that should have been successful, so a tweak to the part of the algorithm that combines the stem segments into a single object helps.
  • Branch and tip points should be integers.
• Bugfix within pcv.canny_edge_detect function where the debug parameter had no effect when the line thickness is set.
• Replaced MAINTAINER command in Dockerfile with LABEL because MAINTAINER is deprecated
• The workflow parallelization was duplicating the name of the image for the metadata "image". Removed the concatenation of the path (including name) and the name.
• Moved pcv.resize to the transform sub-packed. Also extended the functionality into the new pcv.transform.resize and pcv.transform.resize_factor.

Version 3.11.0 breaking changes

• 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

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

PlantCV v3.10.1

3.10.1 updates:

• 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.

PlantCV v3.10.0

3.10 updates:

• Integration with GitHub actions.
  • More specifically, makes the deploy packaging Actions job dependent on the proceeding build job. I.e. we should only upload packages to PyPI if tests pass.
• Update plantcv.auto_crop to allow users to separately parameterize the padding.
  • This edit allows users to add custom padding to the top, bottom, left, and right of a cropped image.
    ‘padding_x’ and ‘padding_y’ can both accept tuples (optionally)
• Modified the Dockerfile to install dependencies (other than OpenCV) from the requirements.txt file (Dockerfile had previously hardcoded dependencies for PlantCV). The Docker container is now built with the correct dependencies each time without manually updating the Dockerfile.
• Python multiprocessing was local parallelization (i.e. computing took place where the program plantcv-workflow.py was run). This functionality is maintained by using the dask.distributed LocalCluster option. However, using dask-jobqueue, we now support remote, distributed computing using cluster options available through dask-jobqueue, currently: HTCondorCluster, LSFCluster, MoabCluster, OARCluster, PBSCluster, SGECluster, and SLURMCluster.
• Add and revise testing to bring code coverage to 100%!
• Added plantcv.morphology.fill_segments
  • This function propogates the labels of a segmented skeleton to fill a mask, using a watershed algorithm. Data about the area of each filled segments automatically gets store to Outputs.
• Added a photosynthesis sub-package
  • Refactored plantcv.fluor_fvfm which is now plantcv.photosynthesis.analyze_fvfm to be consistent with the naming of other analysis functions. Also fixed a bug within this function. Previously, the function returned the Fm image to users. Now corrected so that Fv/Fm image is getting returned.
  • Added support for CropReported fluorescence image data. This was achieved by adding a new function, called plantcv.photosynthesis.read_cropreporter, that reads in, reshapes, and pulls out fdark, fmin, and fmax from a .DAT file which also utilizing the metadata recorded about image data in the corresponding .INF file.

Version 3.10 breaking changes:

• Refactored plantcv.fluor_fvfm which is now plantcv.photosynthesis.analyze_fvfm to be consistent with the naming of other analysis functions. Input parameters are unchanged.

PlantCV v3.9.0

Version 3.9 updates:

• plantcv.morphology.find_tips and plantcv.morphology.find_branch_pts now saves observations (a list of coordinates of the relevant points identified from a skeleton image) to the Outputs class.
• Reworked the plantcv.color_palette function to use matplotlib colormaps instead, so users now have a wide range of coloring options.
  • Added three new parameters to pcv.params to control color_palette (and functions that use it). The parameters are color_scale: the name of the colormap, color_sequence: "sequential" or "random", and saved_color_scale: stores the last color scale. The latter is important for the morphology functions to plot results correctly as the colors of the segments need to get reused to match up from function to function.
• Add plantcv.morphology.analyze_stem
  • This function measures stem height, length, and angle from sorted segments of a skeletonized plant.
  • Plots debug image that shows detected height and angle
• Internal updates to the plantcv.transform.find_color_card function
  • Updates to the contour sorting algorithm to compensate for changes in the outputs of the OpenCV function cv2.minAreaRect in version 3.4.10. Their updates were having implications in the sensitivity of the PlantCV algorithm and causing colorcards to stop being identified in example images.
  • This function now also stores observations to the Outputs class for the mean/median (user defined) color chip area, height, and width. This functionality should remove the need to to include a size marker for scaling measurements in images that already have a color card.
• Updates the documentation and README file to fix a few typos/formatting issues and update some content for relevance to the current version of PlantCV.
• Update/bugfix to plantcv.hyperspectral.calibrate
  • There was a mistake in the calibration code that resulted in the actual algorithm differing slightly from the proposed (raw-dark)/(white-dark).
  • Now also truncate any negative values to 0
• PEP8 and other miscellaneous code cleanup to clear the way for GitHub Actions
• Replace prior continuous integration platform with GitHub Actions
• Add plantcv.spatial_clustering
  • New function that segments features of images based on their distance from each other.
  • Takes a binary mask, minimum number of clusters, which algorithm to use (either “DBSCAN” or “OPTICS”), the maximum distance between two pixels before they can be considered part of the same cluster, and outputs a clustered image as well as the masks of each cluster.
• Update Dockerfile recipe to fix missing libGL dependency, which was causing an ImportError
• Update plantcv.hyperspectral.read_data, which is used if mode=”ENVI” during plantcv.readimage so that we now set a default wavelength units to “nm” to enable more flexibility in the image files that we can read in.
• Bugfix to the data getting saved out to the hue_circular_std observation while running plantcv.analyze_color.
• Add summary statistics to the observations recorded while running plantcv.analyze_nir so they now include mean, median, and stdev observations in the Outputs class while running NIR workflows.
• Update the order of debug images plotted to match the order of outputs in plantcv.rectangle_mask and plantcv.threshold.*.
• Updated and improved documentation for the machine learning tutorial.
• Update plantcv.threshold.custom_range to be R,G,B instead of B,G,R
• Added many new indices and completely restructured the spectral index family of options into their own functions beneath the spectral_index sub-package.
  • See here for the latest list of supported spectral indices.
• Add plantcv.visualize.auto_threshold_methods which aims to assist in streamlining workflow development.
• Update input parameter in the function plantcv.threshold.custom_range from rgb_img to img since the function also works on grayscale images.
• Internal enhancements to the plantcv.hyperspectral.extract_index to significantly increase speed and decrease memory needed for hyperspectral workflows.
• Add plantcv.stdev_filter
  • Creates a grayscale image of pixel-wise standard deviation from a grayscale input image
• Made two small updates to plantcv.utils.sample_images
  • Removes newline characters from each row of SnapshotInfo.csv
  • Only appends rows of SnapshotInfor.csv to a list for random sampling if the row has image data records
• Removed a Non-ascii character causing an error.

Version 3.9 breaking changes:

• Update input parameter in the function plantcv.threshold.custom_range from rgb_img to img since the function also works on grayscale images.
• Removed plantcv.hyperspectral.extract_index and replaced it with the plantcv.spectral_index subpackage (see above).