Analyze NIR Intensity

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

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

returns analysis_images

  • Parameters:
    • gray_img - 8- or 16-bit grayscale image data
    • mask - Binary mask made from selected contours
    • bins - Number of class to divide spectrum into (default bins = 256)
    • histplot - If True plots histogram of intensity values (default histplot = False)
  • Context:
    • Used to mask rectangular regions of an image
    • Data automatically gets stored into the Outputs class. Users can look at the data collected at any point during the workflow by using pcv.print_results which prints all stored data to a .json file.
  • Example use:
  • Output data stored: Summary of Output Observations

Original grayscale 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
analysis_images  = pcv.analyze_nir_intensity(gray_img, mask, 256, histplot=True)

NIR signal histogram


Image with shape characteristics


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.