import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import 透视变换
import cv2
def build_3d_rotation_matrix(elev, azim, roll):
    """生成基于elev/azim/roll的完整旋转矩阵"""
    elev_rad = np.radians(elev)
    azim_rad = np.radians(azim)
    roll_rad = np.radians(roll)

    # 绕x轴旋转(elevation)
    Rx = np.array([
        [1, 0, 0],
        [0, np.cos(elev_rad), -np.sin(elev_rad)],
        [0, np.sin(elev_rad), np.cos(elev_rad)]
    ])

    # 绕y轴旋转(azimuth)
    Ry = np.array([
        [np.cos(azim_rad), 0, np.sin(azim_rad)],
        [0, 1, 0],
        [-np.sin(azim_rad), 0, np.cos(azim_rad)]
    ])

    # 绕z轴旋转(roll)
    Rz = np.array([
        [np.cos(roll_rad), -np.sin(roll_rad), 0],
        [np.sin(roll_rad), np.cos(roll_rad), 0],
        [0, 0, 1]
    ])

    return Rz @ Ry @ Rx  # 组合旋转顺序



if __name__ == '__main__':
    # 参数设置
    elev, azim, roll = 34, 0,-35
    rotation_3d = build_3d_rotation_matrix(elev, azim, roll)

    # 创建单位圆
    theta = np.linspace(0, 2 * np.pi, 100)
    x = np.cos(theta)
    y = np.sin(theta)
    z = np.zeros_like(x)
    circle = np.vstack((x, y, z))  # 3xN

    circle_transformed = rotation_3d @ circle
    # 逆矩阵
    inverse_matrix = np.linalg.inv(rotation_3d)
    # 画图
    fig = plt.figure(figsize=(12, 6))
    # 设置窗口透明度
    fig.canvas.manager.window.attributes('-alpha', 0.6)  # 设置窗口透明度为 0.6
    ax1 = fig.add_subplot(121, projection='3d')
    ax2 = fig.add_subplot(122, projection='3d')
    # 标识圆心
    ax1.scatter(0, 0, 0, color='red', s=20, label='Center')
    # 原始单位圆
    ax1.plot(circle[0], circle[1], circle[2], label='Original Circle')
    # 原始单位圆 横轴和纵轴
    ax1.plot(circle[0], np.zeros_like(theta), circle[2], label='z View', color='r')
    ax1.plot(np.zeros_like(theta), circle[1], circle[2], label='h View', color='b')
    ax1.set_title('Original Circle (elev=90°, roll=0°)')
    ax1.set_xlim([-1, 1])
    ax1.set_ylim([-1, 1])
    ax1.set_zlim([-1, 1])
    ax1.set_box_aspect([1, 1, 1])
    ax1.set_xlabel('X')
    ax1.set_ylabel('Y')
    ax1.set_zlabel('Z')
    ax1.view_init(elev=90, azim=90)  # 从Z轴方向观察  保持opencv方向一致  x->左  y->下
    # 变换后的单位圆
    # 标识圆心
    ax2.scatter(0, 0, 0, color='red', s=20, label='Center')
    ax2.plot(circle_transformed[0], circle_transformed[1], circle_transformed[2], color='r', label='Transformed Circle')
    ax2.set_title('Transformed (elev=52°, roll=-35°)')
    ax2.set_xlim([-1, 1])
    ax2.set_ylim([-1, 1])
    ax2.set_zlim([-1, 1])
    ax2.set_xlabel('X')
    ax2.set_ylabel('Y')
    ax2.set_zlabel('Z')
    ax2.set_box_aspect([1, 1, 1])
    ax2.view_init(elev=90, azim=90)  # 从Z轴方向观察
    plt.show()

    image_zhen= cv2.imread("images/trans/transformed_image.jpg")
    image_xie = cv2.imread("images/trans/subRawImg.jpg")
    # transformed_image_hy = cv2.warpPerspective(transformed_image, inverse_matrix, dsize=(image.shape[1], image.shape[0]))
    # # 执行变换（自动计算输出图像尺寸）
    # 裁剪像素值到 [0, 255] 范围

    # 获取图像的大小
    height, width = image_zhen.shape[:2]
    # 计算中心点坐标
    center_x = width // 2
    center_y = height // 2
    # 构造一个平移矩阵，将原点移到中心
    M_translate = np.float32([
        [1, 0, -1*center_x],
        [0, 1, -1*center_y],
        [0, 0, 1]
    ])
    image_xie_padding = cv2.warpPerspective(image_xie, M_translate,
                                               dsize=(image_xie.shape[1], image_xie.shape[0]))
    cv2.imshow("image_xie_padding", image_xie_padding)
    # 将平移矩阵与目标变换矩阵结合起来
    # inverse_M_combined = np.dot(M_translate, inverse_matrix)
    inverse_matrix[2][2]=1.0
    inverse_M_combined = np.dot(M_translate, inverse_matrix)
    print(f"斜矩阵={rotation_3d}")
    rotation_3d_int = np.clip(rotation_3d, 0, 255)
    print(f"斜矩阵_int={rotation_3d}")
    print(f"逆矩阵={inverse_M_combined}")
    inverse_M_combined_int = np.clip(inverse_M_combined, 0, 255)
    # print(f"逆矩阵_int={inverse_M_combined_int}")
    transformed_image_hy = cv2.warpPerspective(image_xie, inverse_M_combined_int,
                                               dsize=(image_xie.shape[1]*2, image_xie.shape[0]*2))
    cv2.imshow("transformed_image_hy", transformed_image_hy)
    # transformed_image = cv2.warpPerspective(image_zhen, rotation_3d_int,dsize=(image_zhen.shape[1], image_zhen.shape[0]))
    # cv2.imshow("rotated_img", transformed_image)
    plt.show()
    cv2.waitKey(0)
    # cv2.destroyAllWindows()