# Untitled unknown
python
7 months ago
3.0 kB
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Never
```import numpy as np
import cv2
from PIL import Image
from qutip import Qobj, basis, tensor, qeye, sigmax, sigmay, sigmaz

dim = width * height

for y in range(height):
for x in range(width):
i = y * width + x
if x > 0:
adj_matrix[i, i - 1] = 1
if x < width - 1:
adj_matrix[i, i + 1] = 1
if y > 0:
adj_matrix[i, i - width] = 1
if y < height - 1:
adj_matrix[i, i + width] = 1

def dtqw_operator(dim, coin_operator):
U = tensor(coin_operator, qeye(dim))
return U

def dtqw_step(state, U):
return U * state

print("input_image dtype:", input_image.dtype)
print("input_image shape:", input_image.shape)

pil_image = Image.fromarray(image)
pil_gray_image = pil_image.convert('L')
gray_image = np.array(pil_gray_image)

if gray_image is None:
raise ValueError("Failed to convert the image to grayscale.")

print("Gray image shape:", gray_image.shape)

dim = gray_image.shape * gray_image.shape
coin_operator = 1 / np.sqrt(2) * (sigmax() + sigmay() + sigmaz())
U = dtqw_operator(dim, coin_operator)
initial_state = tensor(basis(2, 0), basis(dim, 0))
initial_state_array = np.array(initial_state.full()).reshape(U.shape, 1)  # Convert to NumPy array and reshape

for _ in range(iterations):
initial_state_array = dtqw_step(initial_state_array, U)

probabilities = np.abs(initial_state_array) ** 2
print("Probabilities shape:", probabilities.shape)

# Resize probabilities array to match the size of gray image
resized_probabilities = cv2.resize(probabilities, (gray_image.shape, gray_image.shape))

segmented_image = gray_image * resized_probabilities  # Element-wise multiplication

print("Segmented image shape:", segmented_image.shape)

return segmented_image

if input_image is None:
raise ValueError("Image not loaded. Check the file path and format.")
elif len(input_image.shape) < 3 or input_image.shape < 3:
raise ValueError("The input image is not a color image.")
else:

# Generate the adjacency matrix for the image
height, width = input_image.shape[:2]