Question

Create a new function my_hist and start with the following lines:

function [h] = my_hist(im)

% H = MY_HIST(IM) computes the histogram for the intensity

% image IM (with values from 0 to 255) and returns a vector H

% with 256 dimensions

% get the image size: M = number of rows, N = number of columns

[M, N] = size(im);

% initilalize the histogram to zero

h = zeros(1,256);

% ... here goes your code ...

end % – do not forget the END!

To test your function, save the m-fifile and type at the prompt (omit the

comments):

% read image fifile

I = imread(boat.tif);

% compute the histogram

H = my_hist(I);

% display the result

Answer 1

def my_hist(im):

    # Get the image size: M = number of rows, N = number of columns

    M, N = im.shape

    # Initialize the histogram to zero

    h = [0] * 256

    # Iterate through the image and count pixel values

    for i in range(M):

        for j in range(N):

            pixel_value = im[i, j]

            h[pixel_value] += 1

    return h

# To test the function:

# First, make sure you have a grayscale image "boat.tif" in your working directory.

import cv2

import matplotlib.pyplot as plt

# Read the image

I = cv2.imread('boat.tif', cv2.IMREAD_GRAYSCALE)

# Compute the histogram using your custom function

H = my_hist(I)

# Display the histogram

plt.bar(range(256), H, width=1.0, align='center')

plt.title("Histogram of 'boat.tif' Image")

plt.xlabel("Pixel Value")

plt.ylabel("Frequency")

plt.show()

In this code:

1. The my_hist function takes a grayscale image im as input.

2. It initializes a histogram array h with 256 elements (for pixel values from 0 to 255).

3. It then iterates through the image and counts the occurrences of each pixel value, updating the histogram accordingly.

4. After computing the histogram, you can visualize it using the matplotlib library.

Make sure you have the "boat.tif" image in your working directory or provide the correct path to the image file