118 lines
4.6 KiB
Python
118 lines
4.6 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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def plot_coordinate_system(ax, T, name, color, labels):
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"""绘制坐标系"""
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origin = T[:3, 3]
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x_axis = origin + T[:3, 0] * 300 # X 轴
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y_axis = origin + T[:3, 1] * 300 # Y 轴
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z_axis = origin + T[:3, 2] * 300 # Z 轴
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# 绘制原点
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ax.scatter(*origin, color=color, s=100)
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# 绘制轴线
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ax.quiver(*origin, *(x_axis - origin), color='r', length=1, arrow_length_ratio=0.2, linewidth=2)
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ax.quiver(*origin, *(y_axis - origin), color='g', length=1, arrow_length_ratio=0.2, linewidth=2)
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ax.quiver(*origin, *(z_axis - origin), color='b', length=1, arrow_length_ratio=0.2, linewidth=2)
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# 标注坐标系名称
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ax.text(*x_axis, f'{labels[0]}', color='r', fontsize=12)
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ax.text(*y_axis, f'{labels[1]}', color='g', fontsize=12)
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ax.text(*z_axis, f'{labels[2]}', color='b', fontsize=12)
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# A 到 B 的齐次转换矩阵 (工具到基坐标系)
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T_AB = np.array([[-9.36910568e-01,-4.37100341e-03, 3.49541818e-01, 5.04226000e+02],
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[-5.82144893e-03, 9.99978253e-01, -3.09911034e-03, 2.62300000e+00],
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[-3.49520671e-01, -4.93842907e-03, -9.36915638e-01, 5.23709000e+02],
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[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])
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# B 到 C 的齐次转换矩阵 (相机到工具)
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T_BC = np.loadtxt('./com_pose.txt', delimiter=' ')
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# 计算 A 到 C 的齐次转换矩阵
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# T_AC = T_AB @ T_BC
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# 输入四个角点的空间坐标 (相机坐标系下)
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corner_points_camera = np.array([
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[-804.54114, -259.56854, 1764.0],
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[-466.9239, -266.63162, 1812.0],
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[-451.46283, 206.7851, 1752.0],
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[-762.1249, 197.22481, 1671.0]
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])
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# corner_points_camera = np.array([
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# [-0.07246355234803807, 0.03687307395179221, 0.6171704935761987],
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# [0.02505911529466708, 0.04860494901872052, 0.625793874394482],
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# [0.03136683809654154, 0.003582437967995489, 0.6478950644491274],
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# [-0.07010334103611927, -0.007624093814835717, 0.638128259308727]
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#
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# ])
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# corner_points_camera = np.array([
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# [-0.07010334103611927, -0.007624093814835717, 0.638128259308727],
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# [0.03136683809654154, 0.003582437967995489, 0.6478950644491274],
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# [0.02505911529466708, 0.04860494901872052, 0.625793874394482],
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# [-0.07246355234803807, 0.03687307395179221, 0.6171704935761987]
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# ])
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# 将角点从相机坐标系转换到法兰坐标系
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corner_points_base = np.dot(T_BC[:3, :3], corner_points_camera.T).T + T_BC[:3, 3]
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edges = np.array([corner_points_base[1] - corner_points_base[0]])# for i in range(len(corner_points_base))])
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edge_lengths = np.linalg.norm(edges, axis=1)
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min_edge_idx = np.argmin(edge_lengths)
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short_edge_direction = edges[min_edge_idx] / edge_lengths[min_edge_idx] # 单位化方向向量
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# 假设法向量 (a, b, c) 在相机坐标系下
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normal_vector_camera = np.array([-0.23022874142597569, 0.12287340685252988, 0.965348047343477, 0]) # 最后一个元素为0，因为它是方向矢量
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# 将法向量从相机坐标系转换到法兰坐标系
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normal_vector_flange = T_BC @ normal_vector_camera
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# 将法向量从法兰坐标系转换到基坐标系
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# normal_vector_base = T_AB @ normal_vector_flange
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# 创建 3D 图形对象
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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# 设置绘图区域的范围
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ax.set_xlim([-1000, 1000])
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ax.set_ylim([-1000, 1000])
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ax.set_zlim([-1000, 1000])
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# 绘制基坐标系 O
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plot_coordinate_system(ax, np.eye(4), 'O', 'k', ['x', 'y', 'z'])
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# 绘制法兰坐标系 B
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# plot_coordinate_system(ax, T_AB, 'B', 'm', ["x'", "y'", "z'"])
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# 绘制相机坐标系 C
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plot_coordinate_system(ax, T_BC, 'C', 'b', ["x''", "y''", "z''"])
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# 绘制长边方向向量 (基坐标系下)
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origin = np.zeros(3) # 基坐标系的原点
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short_edge_endpoint = short_edge_direction * 300
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ax.quiver(*origin, *(short_edge_endpoint), color='orange', length=1, arrow_length_ratio=0.2, linewidth=2)
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ax.text(*short_edge_endpoint, 'Short Edge', color='orange', fontsize=12)
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# 绘制法向量 (基坐标系下)
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normal_vector_endpoint = normal_vector_flange[:3] * 300
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ax.quiver(*origin, *(normal_vector_endpoint), color='purple', length=1, arrow_length_ratio=0.2, linewidth=2)
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ax.text(*normal_vector_endpoint, 'Normal Vector', color='purple', fontsize=12)
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# 在基坐标系下绘制四个角点和边
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ax.scatter(corner_points_base[:, 0], corner_points_base[:, 1], corner_points_base[:, 2], color='b', s=50, label='Corners')
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for i in range(len(corner_points_base)):
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ax.plot([corner_points_base[i - 1, 0], corner_points_base[i, 0]],
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[corner_points_base[i - 1, 1], corner_points_base[i, 1]],
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[corner_points_base[i - 1, 2], corner_points_base[i, 2]], 'k--')
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# 设置标签
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ax.set_xlabel('X')
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ax.set_ylabel('Y')
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ax.set_zlabel('Z')
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# 显示图形
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plt.show()
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