I want to write codes that can show the histogram of an image without using built in Matplotlib hist function.
Here is my codes:
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
def manHist(img):
row, col = img.shape # img is a grayscale image
y = np.zeros((256), np.uint64)
for i in range(0,row):
for j in range(0,col):
y[img[i,j]] += 1
x = np.arange(0,256)
plt.bar(x,y,color="gray",align="center")
plt.show()
def main():
img = cv.imread("C:/Users/Kadek/Documents/MATLAB/2GS.jpg")
manHist(img)
main()
A NumPy based vectorized solution would be with np.bincount

out = np.bincount(img.ravel(),minlength=256)
Another vectorized approach based on .sum()

out = (img.ravel() == np.arange(256)[:,None]).sum(1)
Sample run to verify results 
In [155]: # Input image (512x512) as array
...: img = np.random.randint(0,255,(512,512))
...:
...: # Original code
...: row, col = img.shape
...: y = np.zeros((256), np.uint64)
...: for i in range(0,row):
...: for j in range(0,col):
...: y[img[i,j]] += 1
...:
In [156]: out1 = np.bincount(img.ravel(),minlength=256)
In [157]: out2 = (img.ravel() == np.arange(256)[:,None]).sum(1)
In [158]: np.allclose(y,out1)
Out[158]: True
In [159]: np.allclose(y,out2)
Out[159]: True