97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import json
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def visualize_solution(row_boundaries, col_boundaries, car_paths_coords, W, H):
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plt.rcParams['font.family'] = ['sans-serif']
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plt.rcParams['font.sans-serif'] = ['SimHei']
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fig, ax = plt.subplots()
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ax.set_xlim(0, W)
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ax.set_ylim(H, 0) # 调整y轴方向，原点在左上角
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ax.set_title("区域划分与车-机-巢系统覆盖")
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ax.set_xlabel("区域宽度")
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ax.set_ylabel("区域高度")
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# 定义若干颜色以区分不同系统（系统编号从0开始）
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colors = ['red', 'blue', 'green', 'orange', 'purple', 'cyan', 'magenta']
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# 绘制区域中心
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region_center = (H / 2.0, W / 2.0)
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ax.plot(region_center[1], region_center[0],
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'ko', markersize=8, label="区域中心")
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# 绘制行分割边界
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for row in row_boundaries:
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ax.axhline(y=row * H, color='black', linestyle='--')
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# 绘制列分割边界
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for col in col_boundaries:
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ax.axvline(x=col * W, color='black', linestyle='--')
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# 绘制每辆车的轨迹并标注区域序号
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for system_id, path in enumerate(car_paths_coords):
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path = [(region_center[0], region_center[1])] + \
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path + [(region_center[0], region_center[1])]
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y, x = zip(*path)
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ax.plot(x, y, marker='o', color=colors[int(
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system_id) % len(colors)], label=f"系统 {system_id}")
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# 标注每个区域的序号
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for idx, (px, py) in enumerate(zip(x[1:-1], y[1:-1])): # 跳过起点和终点
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ax.text(px, py, str(idx), color='black', fontsize=8, ha='center', va='center')
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# 添加图例
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ax.legend()
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plt.show()
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if __name__ == "__main__":
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import yaml
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# ---------------------------
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# 需要修改的超参数
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# ---------------------------
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params_file = 'params2'
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solution_file = r'solutions\trav_ga_params2_parallel.json'
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with open(params_file + '.yml', 'r', encoding='utf-8') as file:
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params = yaml.safe_load(file)
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H = params['H']
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W = params['W']
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k = params['num_cars']
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# 读取最佳方案的JSON文件
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with open(solution_file, 'r', encoding='utf-8') as f:
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best_solution = json.load(f)
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row_boundaries = best_solution['row_boundaries']
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col_boundaries = best_solution['col_boundaries']
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car_paths = best_solution['car_paths']
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# 计算分块区域的中心点坐标
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rectangle_centers = []
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for i in range(len(row_boundaries) - 1):
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for j in range(len(col_boundaries) - 1):
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r1 = row_boundaries[i]
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r2 = row_boundaries[i + 1]
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c1 = col_boundaries[j]
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c2 = col_boundaries[j + 1]
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d = (r2 - r1) * H * (c2 - c1) * W # 任务的照片数量（矩形面积）
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# 计算任务矩形中心，用于后续车辆移动时间计算
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center_r = (r1 + r2) / 2.0 * H
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center_c = (c1 + c2) / 2.0 * W
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rectangle_centers.append((center_r, center_c))
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# 将car_paths里的index换成坐标
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car_paths_coords = [[] for _ in range(k)]
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for car_idx in range(k):
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car_path = car_paths[car_idx]
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for point in car_path:
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car_paths_coords[car_idx].append(rectangle_centers[point])
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visualize_solution(row_boundaries, col_boundaries, car_paths_coords, W, H)
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