使用python的opencv库自制TSPLIB，选点代码

下面是使用Python的OpenCV库自制TSPLIB的选点代码：

import cv2
import numpy as np
import random

# 读取图片
img = cv2.imread('your_image_path.jpg')
# 转换为灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Canny边缘检测
edges = cv2.Canny(gray, 50, 150, apertureSize=3)

# 霍夫直线变换，检测直线
lines = cv2.HoughLines(edges, 1, np.pi/180, 200)

# 绘制直线
for line in lines:
    rho, theta = line[0]
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a*rho
    y0 = b*rho
    x1 = int(x0 + 1000*(-b))
    y1 = int(y0 + 1000*(a))
    x2 = int(x0 - 1000*(-b))
    y2 = int(y0 - 1000*(a))
    cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)

# 随机选取点
points = []
for i in range(10):
    x = random.randint(0, img.shape[1])
    y = random.randint(0, img.shape[0])
    points.append((x, y))
    cv2.circle(img, (x, y), 5, (0, 255, 0), -1)

# 保存选点结果
with open('your_tsp_file.tsp', 'w') as f:
    f.write('NAME: your_tsp_file\n')
    f.write('TYPE: TSP\n')
    f.write(f'DIMENSION: {len(points)}\n')
    f.write('EDGE_WEIGHT_TYPE: EUC_2D\n')
    f.write('NODE_COORD_SECTION\n')
    for i, point in enumerate(points):
        f.write(f'{i+1} {point[0]} {point[1]}\n')
    f.write('EOF\n')

# 显示结果
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码使用Canny边缘检测和霍夫直线变换检测图片中的直线，并随机选取10个点作为TSP问题的节点，然后将节点坐标保存到文件中。您可以根据需要修改代码以适应不同的图片和节点数量。