import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def plot_coordinate_system(ax, T, name, color, labels):
    """绘制坐标系"""
    origin = T[:3, 3]
    x_axis = origin + T[:3, 0] * 300  # X 轴
    y_axis = origin + T[:3, 1] * 300  # Y 轴
    z_axis = origin + T[:3, 2] * 300  # Z 轴

    # 绘制原点
    ax.scatter(*origin, color=color, s=100)

    # 绘制轴线
    ax.quiver(*origin, *(x_axis - origin), color='r', length=1, arrow_length_ratio=0.2, linewidth=2)
    ax.quiver(*origin, *(y_axis - origin), color='g', length=1, arrow_length_ratio=0.2, linewidth=2)
    ax.quiver(*origin, *(z_axis - origin), color='b', length=1, arrow_length_ratio=0.2, linewidth=2)

    # 标注坐标系名称
    ax.text(*x_axis, f'{labels[0]}', color='r', fontsize=12)
    ax.text(*y_axis, f'{labels[1]}', color='g', fontsize=12)
    ax.text(*z_axis, f'{labels[2]}', color='b', fontsize=12)

# A 到 B 的齐次转换矩阵 (工具到基坐标系)
T_AB = np.array([[-9.36910568e-01,-4.37100341e-03, 3.49541818e-01, 5.04226000e+02],
 [-5.82144893e-03, 9.99978253e-01, -3.09911034e-03, 2.62300000e+00],
 [-3.49520671e-01, -4.93842907e-03, -9.36915638e-01, 5.23709000e+02],
 [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])

# B 到 C 的齐次转换矩阵 (相机到工具)
T_BC = np.loadtxt('./com_pose.txt', delimiter=' ')

# 计算 A 到 C 的齐次转换矩阵
# T_AC = T_AB @ T_BC

# 输入四个角点的空间坐标 (相机坐标系下)
# corner_points_camera = np.array([
# [-605.3829, 288.2771, 1710.0],
# [-364.94568, 300.40274, 1634.0],
# [-301.4996, -253.04178, 1645.0],
# [-548.8065, -297.23093, 1748.0]
# ])
#
# # 将角点从相机坐标系转换到基坐标系
#
# corner_points_base = np.dot(T_BC[:3, :3], corner_points_camera.T).T + T_BC[:3, 3]
# edges = np.array([corner_points_base[1] - corner_points_base[0]])# for i in range(len(corner_points_base))])
# edge_lengths = np.linalg.norm(edges, axis=1)
# min_edge_idx = np.argmin(edge_lengths)
# short_edge_direction = edges[min_edge_idx] / edge_lengths[min_edge_idx]  # 单位化方向向量


corner_points_camera = np.array([
[-548.8065, -297.23093, 1748.0],
[-301.4996, -253.04178, 1645.0],
[-364.94568, 300.40274, 1634.0],
[-605.3829, 288.2771, 1710.0]
])

# 将角点从相机坐标系转换到基坐标系
corner_points_base = np.dot(T_BC[:3, :3], corner_points_camera.T).T + T_BC[:3, 3]

# 按照 x 轴排序
sorted_points = corner_points_base[np.argsort(corner_points_base[:, 0])]

# 选出x轴较大的两个点
point_1 = sorted_points[-1]  # x值较大的点
point_2 = sorted_points[-2]  # x值较小的点

# 根据 y 值选择差值方向，y值较大的点减去 y 值较小的点
if point_1[1] > point_2[1]:
    edge_vector = point_1 - point_2
else:
    edge_vector = point_2 - point_1

# 单位化方向向量
short_edge_direction = edge_vector / np.linalg.norm(edge_vector)

print("方向向量（单位化）：", short_edge_direction)


# 假设法向量 (a, b, c) 在相机坐标系下
normal_vector_camera = np.array([0.2694268969253701, 0.033645691818738714, 0.9624329143556991, 0])  # 最后一个元素为0，因为它是方向矢量

# 将法向量从相机坐标系转换到法兰坐标系
normal_vector_flange = T_BC @ normal_vector_camera

# 将法向量从法兰坐标系转换到基坐标系
# normal_vector_base = T_AB @ normal_vector_flange

# 创建 3D 图形对象
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 设置绘图区域的范围
ax.set_xlim([-1000, 1000])
ax.set_ylim([-1000, 1000])
ax.set_zlim([-1000, 1000])

# 绘制基坐标系 O
plot_coordinate_system(ax, np.eye(4), 'O', 'k', ['x', 'y', 'z'])

# 绘制法兰坐标系 B
# plot_coordinate_system(ax, T_AB, 'B', 'm', ["x'", "y'", "z'"])

# 绘制相机坐标系 C
plot_coordinate_system(ax, T_BC, 'C', 'b', ["x''", "y''", "z''"])

# 绘制长边方向向量 (基坐标系下)
origin = np.zeros(3)  # 基坐标系的原点
short_edge_endpoint = short_edge_direction * 300
ax.quiver(*origin, *(short_edge_endpoint), color='orange', length=1, arrow_length_ratio=0.2, linewidth=2)
ax.text(*short_edge_endpoint, 'Short Edge', color='orange', fontsize=12)

# 绘制法向量 (基坐标系下)
normal_vector_endpoint = normal_vector_flange[:3] * 300
ax.quiver(*origin, *(normal_vector_endpoint), color='purple', length=1, arrow_length_ratio=0.2, linewidth=2)
ax.text(*normal_vector_endpoint, 'Normal Vector', color='purple', fontsize=12)

# 在基坐标系下绘制四个角点和边
ax.scatter(corner_points_base[:, 0], corner_points_base[:, 1], corner_points_base[:, 2], color='b', s=50, label='Corners')
for i in range(len(corner_points_base)):
    ax.plot([corner_points_base[i - 1, 0], corner_points_base[i, 0]],
            [corner_points_base[i - 1, 1], corner_points_base[i, 1]],
            [corner_points_base[i - 1, 2], corner_points_base[i, 2]], 'k--')

# 设置标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图形
plt.show()