Analyze NIR Intensity

This function calculates the intensity of each pixel associated with the plant and writes the values out to the Outputs class. Can also return/plot/print out a histogram plot of pixel intensity.

plantcv.analyze_nir_intensity(gray_img, mask, bins=256, histplot=False, label="default")

returns Histogram image (when histplot is True, otherwise returns None object)

  • Parameters:
    • gray_img - 8- or 16-bit grayscale image data
    • mask - Binary mask made from selected contours
    • bins - Number of NIR intensity value groups (default bins = 256)
    • 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:
    • Near Infrared pixel frequencies within a masked area of an image.
  • Example use:
  • Output data stored: Data ('nir_frequencies', 'nir_mean', 'nir_median', 'nir_stdev') 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 grayscale image

Screenshot


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 = "plot"

# 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
analysis_image  = pcv.analyze_nir_intensity(gray_img, mask, 256, histplot=True, label="default")

# Access data stored out from analyze_nir_intensity
nir_frequencies = pcv.outputs.observations['default']['nir_frequencies']['value']

NIR signal histogram

Screenshot

Note: The 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.

Source Code: Here