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_nir_intensity
  • analyze_object
  • analyze_thermal_values
  • fluor_fvfm
  • hyperspectral.analyze_spectral
  • hyperspectral.analyze_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 function pcv.print_results will print out all the stored measurments data to a text file.

Methods

Methods are accessed as plantcv.outputs.method.

clear: Clears the contents of both measurements and image

add_observation: Add new measurement or other information

  • 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, e.g. int, str, list, etc.

  • value: The data itself.

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

Example use: - Use In VIS/NIR Tutorial

Examples

from plantcv import plantcv as pcv

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

# Find shape properties, output shape image (optional)
shape_img = pcv.analyze_object(img, obj, mask)

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

# Write shape data to results file
pcv.print_results(filename=args.result)

# Will will print out results again, so clear the outputs before running NIR analysis 
pcv.outputs.clear()

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

nir_imgs = pcv.analyze_nir_intensity(nir2, nir_combinedmask, 256)
shape_img = pcv.analyze_object(nir2, nir_combined, nir_combinedmask)

# Write the NIR and shape data to a file 
pcv.print_results(filename=args.coresult)


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(variable='percent_diseased', trait='percent of plant detected to be diseased',
                            method='ratio of pixels', scale='percent', datatype=float,
                            value=percent_diseased, label='percent')

# Write custom data to results file
pcv.print_results(filename=args.result)