<filename>plantcv/plantcv/hist_equalization.py
# Histogram equalization

import cv2
import numpy as np
import os
from plantcv.plantcv import print_image
from plantcv.plantcv import plot_image
from plantcv.plantcv import fatal_error
from plantcv.plantcv import params


def hist_equalization(gray_img):
    """Histogram equalization is a method to normalize the distribution of intensity values. If the image has low
       contrast it will make it easier to threshold.

    Inputs:
    gray_img    = Grayscale image data

    Returns:
    img_eh = normalized image

    :param gray_img: numpy.ndarray
    :return img_eh: numpy.ndarray
    """

    if len(np.shape(gray_img)) == 3:
        fatal_error("Input image must be gray")

    img_eh = cv2.equalizeHist(gray_img)
    params.device += 1
    if params.debug == 'print':
        print_image(img_eh, os.path.join(params.debug_outdir, str(params.device) + '_hist_equal_img.png'))
    elif params.debug == 'plot':
        plot_image(img_eh, cmap='gray')

    return img_eh

<filename>plantcv/plantcv/hist_equalization.py
# Histogram equalization

import cv2
import numpy as np
import os
from plantcv.plantcv import print_image
from plantcv.plantcv import plot_image
from plantcv.plantcv import fatal_error
from plantcv.plantcv import params


def hist_equalization(gray_img):
    """Histogram equalization is a method to normalize the distribution of intensity values. If the image has low
       contrast it will make it easier to threshold.

    Inputs:
    gray_img    = Grayscale image data

    Returns:
    img_eh = normalized image

    :param gray_img: numpy.ndarray
    :return img_eh: numpy.ndarray
    """

    if len(np.shape(gray_img)) == 3:
        fatal_error("Input image must be gray")

    img_eh = cv2.equalizeHist(gray_img)
    params.device += 1
    if params.debug == 'print':
        print_image(img_eh, os.path.join(params.debug_outdir, str(params.device) + '_hist_equal_img.png'))
    elif params.debug == 'plot':
        plot_image(img_eh, cmap='gray')

    return img_eh

# Histogram equalization Histogram equalization is a method to normalize the distribution of intensity values. If the image has low        contrast it will make it easier to threshold.      Inputs:     gray_img    = Grayscale image data      Returns:     img_eh = normalized image      :param gray_img: numpy.ndarray     :return img_eh: numpy.ndarray