class Outputs

A global PlantCV output class.

class plantcv.Outputs

An Outputs class has been added that automatically stores measurements and images collected by the following functions:

  • analyze.bound_horizontal
  • analyze.bound_vertical
  • analyze.color
  • analyze.grayscale
  • analyze.size
  • analyze.thermal
  • fluor_fvfm
  • hyperspectral.analyze_spectral
  • analyze.spectral_index
  • report_size_marker_area
  • morphology.check_cycles
  • morphology.segment_angle
  • morphology.segment_curvature
  • morphology.euclidean_length
  • morphology.segment_insertion_angle
  • morphology.segment_path_length
  • morphology.segment_tangent_angle
  • within_frame
  • watershed

An instance of Outputs is created on import automatically as plantcv.outputs. The method Outputs.save_results will save all the stored measurement data to a text file.

Methods

Methods are accessed as plantcv.outputs.method.

clear(): Clears the contents of both measurements and image

add_observation(sample, variable, trait, method, scale, datatype, value, label): Add new measurement or other information

  • sample: A sample name or label. Observations are organized by sample name.

  • variable: A local unique identifier of a variable, e.g. a short name, that is a key linking the definitions of variables with observations.

  • trait: A name of the trait mapped to an external ontology; if there is no exact mapping, an informative description of the trait.

  • method: A name of the measurement method mapped to an external ontology; if there is no exact mapping, an informative description of the measurement procedure.

  • scale: Units of the measurement or a scale in which the observations are expressed; if possible, standard units and scales should be used and mapped to existing ontologies; in case of a non-standard scale a full explanation should be given.

  • datatype: The type of data to be stored. See note below for supported data types.

  • value: The data itself. Make sure the data type of value matches the data type stated in "datatype".

  • label: The label for each value, which will be useful when the data is a frequency table (e.g. hues).

add_metadata(term, datatype, value): Add metadata about the image or other information

  • term: Metadata term/name

  • datatype: The type of data to be stored. See note below for supported data types.

  • value: The data itself. Make sure the data type of value matches the data type stated in "datatype".

save_results(filename, outformat="json"): Save results to a file

  • filename: Path and name of the output file

  • outformat: Output file format (default = "json"). Supports "json" and "csv" formats

Note

Supported data types for JSON output are: int, float, str, list, bool, tuple, dict, NoneType, numpy.float64.

Example use: - Use In VIS/NIR Tutorial

Examples

from plantcv import plantcv as pcv
# Set a global sample label (optional)
pcv.params.sample_label = "plant"

######## workflow steps here ########

# Find shape properties, output shape image (optional)
shape_img = pcv.analyze.size(img=img, labeled_mask=mask, n_labels=1)

# Look at object area data without writing to a file 
plant_area = pcv.outputs.observations['plant_1']['pixel_area']['value']

######## More workflow steps here ########

nir_hist = pcv.analyze.grayscale(gray_img=nir2, labeled_mask=nir_combinedmask, n_labels=1, bins=100)

# Write the NIR and shape data to a file 
pcv.outputs.save_results(filename=args.result, outformat="json")


import numpy as np
from plantcv import plantcv as pcv

# Use Naive-Bayes to make masks for each classes 
mask = pcv.naive_bayes_classifier(img, pdf_file="naive_bayes_pdfs.txt")

# Calculate percent of the plant found to be diseased 
sick_plant = np.count_nonzero(mask['diseased'])
healthy_plant = np.count_nonzero(mask['plant'])
percent_diseased = sick_plant / (sick_plant + healthy_plant)

# Create a new measurement
pcv.outputs.add_observation(sample='default', variable='percent_diseased', 
                            trait='percent of plant detected to be diseased',
                            method='ratio of pixels', scale='percent', datatype=float,
                            value=percent_diseased, label='percent')

# Add metadata 
pcv.outputs.add_metadata(term="genotype", datatype=str, value="wildtype")

# Write custom data to results file
pcv.outputs.save_results(filename=args.result, outformat="json")

Source Code: Here