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There are multiple ways to approach this problem using Python, but one possible solution is:

Import the required libraries:

import numpy as np import cv2 from itertools import cycle

Load the .png image into a Numpy array:

image = cv2.imread('image.png')

Convert the image to grayscale:

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

Define the tessellation pattern using a list of shapes (e.g., squares and triangles):

shapes = ['square', 'triangle']

Define the size and spacing of the patterns:

size = 20 spacing = 5

Loop through the grayscale image and apply the tessellation pattern:

tessellated_image = np.zeros_like(gray) for i, row in enumerate(range(0, gray.shape[0], size + spacing)): shape_cycle = cycle(shapes) for j, col in enumerate(range(0, gray.shape[1], size + spacing)): shape = next(shape_cycle) if shape == 'square': tessellated_image[row:row+size, col:col+size] = gray[row:row+size, col:col+size] elif shape == 'triangle': if i % 2 == 0: pts = np.array([(col, row), (col+size, row), (col+size//2, row+size)]) else: pts = np.array([(col, row+size), (col+size, row+size), (col+size//2, row)]) mask = np.zeros_like(gray) cv2.fillPoly(mask, [pts], 255) tessellated_image += cv2.bitwise_and(mask, gray)

Save the tessellated image:

cv2.imwrite('tessellated_image.png', tessellated_image)

Note that this code only implements a very basic tessellation pattern, and more complex patterns can be achieved by modifying the shape list and the shape drawing code in step 6. Additionally, this code does not optimize the tessellation for efficiency or visual quality, and further adjustments may be required depending on the specific use case.