Analyze Thermal Values

This function calculates the intensity of each pixel associated with the temperature and writes the values out to a file. Can optionally create a histogram of pixel intensity.

plantcv.analyze_thermal_values(thermal_array, mask, histplot=False, label="default""default")

returns thermal histogram (if histplot=True, otherwise returns None object)

  • Parameters:
    • thermal_array - Numpy array of thermal image data (most likely read in with pcv.readimage with mode='csv')
    • mask - Binary mask made from selected contours
    • histplot - If True plots histogram of intensity values (default histplot = False)
    • label - Optional label parameter, modifies the variable name of observations recorded. (default label="default")
  • Context:
    • Data about image temperature within a masked region.
  • Example use:
    • Below
  • Output data stored: Data ('max_temp', 'min_temp', 'mean_temp', 'median_temp', 'thermal_frequencies') automatically gets stored to the Outputs class when this function is ran. These data can always get accessed during a workflow (example below). For more detail about data output see Summary of Output Observations

Original thermal array image


from plantcv import plantcv as pcv

# Set global debug behavior to None (default), "print" (to file), 
# or "plot" (Jupyter Notebooks or X11)

pcv.params.debug = "print"

# Caclulates the proportion of pixels that fall into a signal bin and writes the values to a file. Also provides a histogram of this data
thermal_hist  = pcv.analyze_thermal_values(thermal_array=thermal_img, maks=mask, histplot=True, label="default")

# Access data stored out from analyze_thermal_values
temp_range = pcv.outputs.observations['default']['max_temp']['value'] - pcv.outputs.observations['default']['min_temp']['value']

Thermal Frequency histogram


Note: A grayscale input image and object mask can be used with the pcv.visualize.pseudocolor function which allows the user to pick a colormap for plotting.

# Pseudocolor the thermal 
pseudocolor_img  = pcv.visualize.pseudocolor(thermal_img, min_value=31, max_value=35, mask=mask)


Source Code: Here