Plot Histogram¶

This is a plotting method used to examine the distribution of signal within an image.

plantcv.visualize.histogram(img, mask=None, bins=None, lower_bound=None, upper_bound=None, title=None, hist_data=False)

returns fig_hist, hist_data

• Parameters:
  • img - Image data which is numpy.ndarray, the original image for analysis.
  • mask - Optional binary mask made from selected contours (default mask=None).
  • bins - Number of class to divide spectrum into (default bins=100).
  • lower_bound - lower bound of range to be shown in the histogram (default lower_range=None).
  • upper_bound - upper bound of range to be shown in the histogram.
  • title - The title for the histogram (default title=None)
  • hist_data - Return histogram data if True (default hist_data=False)

Context: - Examine the distribution of the signal, this can help select a value for calling the binary thresholding function.

• Example use:
  • Grayscale Image Tutorial

Grayscale image

Mask

from plantcv import plantcv as pcv

pcv.params.debug = "plot"

# Examine signal distribution within an image
# prints out an image histogram of signal within image
hist_figure1, hist_data1 = pcv.visualize.histogram(gray_img, mask=mask, hist_data=True)

# Alternatively, users can change the `bins`, `lower_bound`, `upper_bound` and `title`.
hist_figure2, hist_data2 = pcv.visualize.histogram(img=gray_img, mask=mask, bins=30, 
                                                   lower_bound=80, upper_bound=220,
                                                   title="Trimmed Histogram", hist_data=True)

Histogram of signal intensity

The histogram function plots histograms from 3 color bands automatically if an RGB input image is given.

RGB image

from plantcv import plantcv as pcv

pcv.params.debug = "plot"

# Examine signal distribution within an image
# prints out an image histogram of signal within image
hist_figure, hist_data = pcv.visualize.histogram(img=rgb_img, mask=mask, hist_data=True)

Source Code: Here