# Untitled

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python
7 months ago
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```import cv2
import numpy as np
from qutip import Qobj, basis, tensor, qeye, sigmax, sigmay, sigmaz, sesolve

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)) * (tensor(basis(2, 0), qeye(dim)) * tensor(basis(2, 0).dag(), basis(dim, 0)) +
tensor(basis(2, 1), qeye(dim)) * tensor(basis(2, 1).dag(), basis(dim, 1)))
return U

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

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
dim = gray_image.shape[0] * gray_image.shape[1]
coin_operator = 1 / np.sqrt(2) * (sigmax() + sigmay() + sigmaz())
U = dtqw_operator(dim, coin_operator)
initial_state = tensor(basis(2, 0), basis(dim, 0))

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

probabilities = np.abs(initial_state.full()) ** 2
segmented_image = np.reshape(probabilities, gray_image.shape)
return segmented_image

U = (-1j * H).expm()
return U

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

ld_state = sesolve(H=Qobj(adj_matrix), rho0=state, tlist=[0], e_ops=[], options=None, progress_bar=None).states[-1]
return ld_state

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
dim = gray_image.shape[0] * gray_image.shape[1]
initial_state = basis(dim, 0)

for _ in range(iterations):
initial_state = ctqw_step(initial_state, U)

probabilities = np.abs(ld_state.full()) ** 2
segmented_image = np.reshape(probabilities, gray_image.shape)

# Generate the adjacency matrix for the image
height, width, _ = input_image.shape