<gh_stars>1000+
#!/usr/bin/env python

''' This is a sample for histogram plotting for RGB images and grayscale images for better understanding of colour distribution

Benefit : Learn how to draw histogram of images
          Get familier with cv2.calcHist, cv2.equalizeHist,cv2.normalize and some drawing functions

Level : Beginner or Intermediate

Functions : 1) hist_curve : returns histogram of an image drawn as curves
            2) hist_lines : return histogram of an image drawn as bins ( only for grayscale images )

Usage : python hist.py <image_file>

<NAME> 3/14/12 debug <NAME>
'''

import cv2
import numpy as np

bins = np.arange(256).reshape(256, 1)


def hist_curve(im):
    h = np.zeros((300, 256, 3))
    if len(im.shape) == 2:
        color = [(255, 255, 255)]
    elif im.shape[2] == 3:
        color = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
    for ch, col in enumerate(color):
        hist_item = cv2.calcHist([im], [ch], None, [256], [0, 256])
        cv2.normalize(hist_item, hist_item, 0, 255, cv2.NORM_MINMAX)
        hist = np.int32(np.around(hist_item))
        pts = np.int32(np.column_stack((bins, hist)))
        cv2.polylines(h, [pts], False, col)
    y = np.flipud(h)
    return y


def hist_lines(im):
    h = np.zeros((300, 256, 3))
    if len(im.shape) != 2:
        print("hist_lines applicable only for grayscale images")
        # print "so converting image to grayscale for representation"
        im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    hist_item = cv2.calcHist([im], [0], None, [256], [0, 256])
    cv2.normalize(hist_item, hist_item, 0, 255, cv2.NORM_MINMAX)
    hist = np.int32(np.around(hist_item))
    for x, y in enumerate(hist):
        cv2.line(h, (x, 0), (x, y), (255, 255, 255))
    y = np.flipud(h)
    return y


if __name__ == '__main__':

    import sys

    if len(sys.argv) > 1:
        fname = sys.argv[1]
    else:
        fname = '../data/lena.jpg'
        print("usage : python hist.py <image_file>")

    im = cv2.imread(fname)

    if im is None:
        print('Failed to load image file:', fname)
        sys.exit(1)

    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)

    print(''' Histogram plotting \n
    Keymap :\n
    a - show histogram for color image in curve mode \n
    b - show histogram in bin mode \n
    c - show equalized histogram (always in bin mode) \n
    d - show histogram for color image in curve mode \n
    e - show histogram for a normalized image in curve mode \n
    Esc - exit \n
    ''')

    cv2.imshow('image', im)
    while True:
        k = cv2.waitKey(0) & 0xFF
        if k == ord('a'):
            curve = hist_curve(im)
            cv2.imshow('histogram', curve)
            cv2.imshow('image', im)
            print('a')
        elif k == ord('b'):
            print('b')
            lines = hist_lines(im)
            cv2.imshow('histogram', lines)
            cv2.imshow('image', gray)
        elif k == ord('c'):
            print('c')
            equ = cv2.equalizeHist(gray)
            lines = hist_lines(equ)
            cv2.imshow('histogram', lines)
            cv2.imshow('image', equ)
        elif k == ord('d'):
            print('d')

            curve = hist_curve(gray)
            cv2.imshow('histogram', curve)
            cv2.imshow('image', gray)
        elif k == ord('e'):
            print('e')

            norm = np.ndarray((2, 2))
            norm = cv2.normalize(gray, norm, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)

            lines = hist_lines(norm)
            cv2.imshow('histogram', lines)
            cv2.imshow('image', norm)
        elif k == 27:
            print('ESC')

            cv2.destroyAllWindows()
            break
    cv2.destroyAllWindows()

#!/usr/bin/env python

''' This is a sample for histogram plotting for RGB images and grayscale images for better understanding of colour distribution

Benefit : Learn how to draw histogram of images
          Get familier with cv2.calcHist, cv2.equalizeHist,cv2.normalize and some drawing functions

Level : Beginner or Intermediate

Functions : 1) hist_curve : returns histogram of an image drawn as curves
            2) hist_lines : return histogram of an image drawn as bins ( only for grayscale images )

Usage : python hist.py <image_file>

<NAME> 3/14/12 debug <NAME>
'''

import cv2
import numpy as np

bins = np.arange(256).reshape(256, 1)


def hist_curve(im):
    h = np.zeros((300, 256, 3))
    if len(im.shape) == 2:
        color = [(255, 255, 255)]
    elif im.shape[2] == 3:
        color = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
    for ch, col in enumerate(color):
        hist_item = cv2.calcHist([im], [ch], None, [256], [0, 256])
        cv2.normalize(hist_item, hist_item, 0, 255, cv2.NORM_MINMAX)
        hist = np.int32(np.around(hist_item))
        pts = np.int32(np.column_stack((bins, hist)))
        cv2.polylines(h, [pts], False, col)
    y = np.flipud(h)
    return y


def hist_lines(im):
    h = np.zeros((300, 256, 3))
    if len(im.shape) != 2:
        print("hist_lines applicable only for grayscale images")
        # print "so converting image to grayscale for representation"
        im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    hist_item = cv2.calcHist([im], [0], None, [256], [0, 256])
    cv2.normalize(hist_item, hist_item, 0, 255, cv2.NORM_MINMAX)
    hist = np.int32(np.around(hist_item))
    for x, y in enumerate(hist):
        cv2.line(h, (x, 0), (x, y), (255, 255, 255))
    y = np.flipud(h)
    return y


if __name__ == '__main__':

    import sys

    if len(sys.argv) > 1:
        fname = sys.argv[1]
    else:
        fname = '../data/lena.jpg'
        print("usage : python hist.py <image_file>")

    im = cv2.imread(fname)

    if im is None:
        print('Failed to load image file:', fname)
        sys.exit(1)

    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)

    print(''' Histogram plotting \n
    Keymap :\n
    a - show histogram for color image in curve mode \n
    b - show histogram in bin mode \n
    c - show equalized histogram (always in bin mode) \n
    d - show histogram for color image in curve mode \n
    e - show histogram for a normalized image in curve mode \n
    Esc - exit \n
    ''')

    cv2.imshow('image', im)
    while True:
        k = cv2.waitKey(0) & 0xFF
        if k == ord('a'):
            curve = hist_curve(im)
            cv2.imshow('histogram', curve)
            cv2.imshow('image', im)
            print('a')
        elif k == ord('b'):
            print('b')
            lines = hist_lines(im)
            cv2.imshow('histogram', lines)
            cv2.imshow('image', gray)
        elif k == ord('c'):
            print('c')
            equ = cv2.equalizeHist(gray)
            lines = hist_lines(equ)
            cv2.imshow('histogram', lines)
            cv2.imshow('image', equ)
        elif k == ord('d'):
            print('d')

            curve = hist_curve(gray)
            cv2.imshow('histogram', curve)
            cv2.imshow('image', gray)
        elif k == ord('e'):
            print('e')

            norm = np.ndarray((2, 2))
            norm = cv2.normalize(gray, norm, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)

            lines = hist_lines(norm)
            cv2.imshow('histogram', lines)
            cv2.imshow('image', norm)
        elif k == 27:
            print('ESC')

            cv2.destroyAllWindows()
            break
    cv2.destroyAllWindows()
#!/usr/bin/env python This is a sample for histogram plotting for RGB images and grayscale images for better understanding of colour distribution  Benefit : Learn how to draw histogram of images           Get familier with cv2.calcHist, cv2.equalizeHist,cv2.normalize and some drawing functions  Level : Beginner or Intermediate  Functions : 1) hist_curve : returns histogram of an image drawn as curves             2) hist_lines : return histogram of an image drawn as bins ( only for grayscale images )  Usage : python hist.py <image_file>  <NAME> 3/14/12 debug <NAME> # print "so converting image to grayscale for representation" Histogram plotting \n     Keymap :\n     a - show histogram for color image in curve mode \n     b - show histogram in bin mode \n     c - show equalized histogram (always in bin mode) \n     d - show histogram for color image in curve mode \n     e - show histogram for a normalized image in curve mode \n     Esc - exit \n