histogram.py

import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np

img = cv.imread("./Resources/Photos/cats.jpg")
cv.imshow("cats", img)

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow("Gray", gray)


# # grayscale histogram
# gray_hist = cv.calcHist([gray], [0], None, [256], [0,256])

# plt.figure()
# plt.title("Grayscale histogram")
# plt.xlabel('Bins')
# plt.ylabel('* of pixels')
# plt.plot(gray_hist)
# plt.xlim(0,256)
# plt.show()

# -----

# create a mask and compute histogram only for the mask

blank = np.zeros(img.shape[:2], dtype="uint8")
mask = cv.circle(blank, (img.shape[1]//2, img.shape[0]//2), 100, 255, -1)

masked = cv.bitwise_and(img, img, mask=mask)
cv.imshow("mask", masked)

# gray_hist = cv.calcHist([gray], [0], mask, [256], [0,256])
# plt.figure()
# plt.title("Grayscale histogram")
# plt.xlabel('Bins')
# plt.ylabel('* of pixels')
# plt.plot(gray_hist)
# plt.xlim(0,256)
# plt.show()

# ------- 

# color histogram

plt.figure()
plt.title('Colour Histogram')
plt.xlabel('Bins')
plt.ylabel('# of pixels')
colors = ('b', 'g', 'r')
for i,col in enumerate(colors):
    hist = cv.calcHist([img], [i], mask, [256], [0,256])
    plt.plot(hist, color=col)
    plt.xlim([0,256])

plt.show()






cv.waitKey(0)