请给出上述思路的代码

以下是使用Python实现Christofides算法的示例代码：

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt

def tsp_artist(image_path):
    # Step 1: Convert image to graph
    img = plt.imread(image_path)
    height, width, _ = img.shape
    nodes = []
    for i in range(height):
        for j in range(width):
            nodes.append((i, j))
    G = nx.Graph()
    for i in range(len(nodes)):
        for j in range(i+1, len(nodes)):
            dist = np.linalg.norm(np.array(nodes[i]) - np.array(nodes[j]))
            G.add_edge(nodes[i], nodes[j], weight=dist)

    # Step 2: Compute MST
    T = nx.minimum_spanning_tree(G)

    # Step 3: Find odd-degree vertices
    odd_nodes = []
    for node in T.nodes():
        if T.degree(node) % 2 == 1:
            odd_nodes.append(node)

    # Step 4: Find minimum-weight perfect matching on odd-degree vertices
    subgraph = G.subgraph(odd_nodes)
    matching = nx.algorithms.matching.max_weight_matching(subgraph)

    # Step 5: Add matching edges to MST
    for edge in matching:
        T.add_edge(*edge)

    # Step 6: Find Eulerian circuit of the modified graph
    circuit = list(nx.eulerian_circuit(T))

    # Step 7: Remove repeated vertices to get Hamiltonian cycle
    path = [circuit[0][0]]
    for edge in circuit:
        if edge[1] not in path:
            path.append(edge[1])
    path.append(path[0])

    # Plot the Hamiltonian cycle on the image
    plt.imshow(img)
    plt.plot([p[1] for p in path], [p[0] for p in path], 'r')
    plt.show()

# Example usage
tsp_artist('image.jpg')

注：这里使用了Python的networkx库来进行图论计算，matplotlib库来进行图像的可视化。