import cv2import numpy as np# 加载图像img = cv2.imread('image.jpg', 0)# 边缘检测edges = cv2.Canny(img, 100, 200)# 膨胀处理,使边缘更加明显kernel = np.ones((5, 5),np.uint8)edges = cv2.dilate(edges,kernel,iterations = 1)# 获取边缘点edge_points = np.argwhere(edges > 0)# 输出结果print(edge_points)继续写这段python代码生成tsplib数据集
时间: 2023-06-17 14:08:40 浏览: 46
# 导入必要的库
import math
import random
# 定义函数,用于计算两点之间的距离
def dist(x1, y1, x2, y2):
return math.sqrt((x1-x2)**2 + (y1-y2)**2)
# 获取边缘点的数量
n = len(edge_points)
# 定义tsplib数据集的头部信息
tsplib_header = f"NAME: edge_points\nTYPE: TSP\nDIMENSION: {n}\nEDGE_WEIGHT_TYPE: EUC_2D\nNODE_COORD_SECTION\n"
# 定义tsplib数据集的节点坐标信息
nodes_info = ""
for i in range(n):
x = edge_points[i][1]
y = edge_points[i][0]
nodes_info += f"{i+1} {x} {y}\n"
# 计算每个节点之间的距离,并添加到tsplib数据集中
edges_info = "EDGE_WEIGHT_SECTION\n"
for i in range(n):
for j in range(i+1, n):
x1 = edge_points[i][1]
y1 = edge_points[i][0]
x2 = edge_points[j][1]
y2 = edge_points[j][0]
d = int(dist(x1, y1, x2, y2))
edges_info += f"{d} "
edges_info += "\n"
# 定义tsplib数据集的尾部信息
tsplib_footer = "EOF\n"
# 将tsplib数据集写入文件
with open("edge_points.tsp", "w") as f:
f.write(tsplib_header)
f.write(nodes_info)
f.write(edges_info)
f.write(tsplib_footer)
# 输出结果
print("tsplib数据集已生成!")