Co-occurancy Matrix

[wiwichips]

Generate a co-adjacency matrix for an image and then turn it into an image that can be viewed

Here is an example python program I wrote to do this

#!/usr/bin/env python3
from PIL import Image
import numpy as np

# Load image
img = Image.open("mb.png")

# Convert image to numpy array
img_arr = np.array(img)

# Initialize co-occurrence matrices for each color channel
red_co = np.zeros((256, 256))
green_co = np.zeros((256, 256))
blue_co = np.zeros((256, 256))

# Loop through each pixel in the image
for i in range(img_arr.shape[0]-1):
    for j in range(img_arr.shape[1]-1):
        # Update co-occurrence matrix for red channel
        red_co[img_arr[i,j,0], img_arr[i+1,j,0]] += 1
        # Update co-occurrence matrix for green channel
        green_co[img_arr[i,j,1], img_arr[i+1,j,1]] += 1
        # Update co-occurrence matrix for blue channel
        blue_co[img_arr[i,j,2], img_arr[i+1,j,2]] += 1

# Print co-occurrence matrices
print("Red channel co-occurrence matrix:")
print(red_co)
print("Green channel co-occurrence matrix:")
print(green_co)
print("Blue channel co-occurrence matrix:")
print(blue_co)

# Convert co-occurrence matrices to 8-bit unsigned integer format
red_co = red_co.astype(np.uint8)
green_co = green_co.astype(np.uint8)
blue_co = blue_co.astype(np.uint8)

# Combine co-occurrence matrices into a single RGB image
co_image = np.dstack((red_co, green_co, blue_co))

# Create PIL image from numpy array
co_pil = Image.fromarray(co_image)

# Resize image to 256x256
co_pil = co_pil.resize((256, 256))

# Display image
co_pil.show()