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base_pixel_shuffle.py
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base_pixel_shuffle.py
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import numpy as np
def jitter_block(input_image, block_size, randomness):
"""
Applies block-based shuffling to an image.
Parameters:
input_image (ndarray): The input image represented as a NumPy array.
block_size (int): The size of the square block in pixels.
randomness (int): The randomness value controlling the intensity of jittering.
Returns:
ndarray: The shuffled image as a NumPy array.
"""
height, width, _ = input_image.shape
# Calculate the number of blocks in each dimension
num_blocks_y = height // block_size
num_blocks_x = width // block_size
# Create a copy of the image to avoid modifying the original
jittered_image = np.copy(input_image)
for block_y in range(num_blocks_y):
for block_x in range(num_blocks_x):
# Calculate the coordinates of the block's top-left corner
start_x = block_x * block_size
start_y = block_y * block_size
# Calculate random offsets for the block's position
offset_x = np.random.randint(-randomness, randomness + 1)
offset_y = np.random.randint(-randomness, randomness + 1)
# Calculate the new position of the block
new_x = max(0, min(width - block_size, start_x + offset_x))
new_y = max(0, min(height - block_size, start_y + offset_y))
# Extract the block from the original image
block = input_image[start_y:start_y + block_size, start_x:start_x + block_size, :]
# Place the block at the new position in the jittered image
jittered_image[new_y:new_y + block_size, new_x:new_x + block_size, :] = block
return jittered_image