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126 lines
4.6 KiB
126 lines
4.6 KiB
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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import 透视变换
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import cv2
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def build_3d_rotation_matrix(elev, azim, roll):
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"""生成基于elev/azim/roll的完整旋转矩阵"""
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elev_rad = np.radians(elev)
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azim_rad = np.radians(azim)
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roll_rad = np.radians(roll)
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# 绕x轴旋转(elevation)
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Rx = np.array([
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[1, 0, 0],
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[0, np.cos(elev_rad), -np.sin(elev_rad)],
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[0, np.sin(elev_rad), np.cos(elev_rad)]
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])
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# 绕y轴旋转(azimuth)
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Ry = np.array([
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[np.cos(azim_rad), 0, np.sin(azim_rad)],
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[0, 1, 0],
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[-np.sin(azim_rad), 0, np.cos(azim_rad)]
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])
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# 绕z轴旋转(roll)
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Rz = np.array([
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[np.cos(roll_rad), -np.sin(roll_rad), 0],
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[np.sin(roll_rad), np.cos(roll_rad), 0],
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[0, 0, 1]
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])
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return Rz @ Ry @ Rx # 组合旋转顺序
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if __name__ == '__main__':
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# 参数设置
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elev, azim, roll = 34, 0,-35
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rotation_3d = build_3d_rotation_matrix(elev, azim, roll)
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# 创建单位圆
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theta = np.linspace(0, 2 * np.pi, 100)
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x = np.cos(theta)
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y = np.sin(theta)
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z = np.zeros_like(x)
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circle = np.vstack((x, y, z)) # 3xN
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circle_transformed = rotation_3d @ circle
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# 逆矩阵
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inverse_matrix = np.linalg.inv(rotation_3d)
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# 画图
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fig = plt.figure(figsize=(12, 6))
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# 设置窗口透明度
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fig.canvas.manager.window.attributes('-alpha', 0.6) # 设置窗口透明度为 0.6
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ax1 = fig.add_subplot(121, projection='3d')
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ax2 = fig.add_subplot(122, projection='3d')
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# 标识圆心
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ax1.scatter(0, 0, 0, color='red', s=20, label='Center')
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# 原始单位圆
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ax1.plot(circle[0], circle[1], circle[2], label='Original Circle')
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# 原始单位圆 横轴和纵轴
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ax1.plot(circle[0], np.zeros_like(theta), circle[2], label='z View', color='r')
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ax1.plot(np.zeros_like(theta), circle[1], circle[2], label='h View', color='b')
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ax1.set_title('Original Circle (elev=90°, roll=0°)')
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ax1.set_xlim([-1, 1])
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ax1.set_ylim([-1, 1])
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ax1.set_zlim([-1, 1])
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ax1.set_box_aspect([1, 1, 1])
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ax1.set_xlabel('X')
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ax1.set_ylabel('Y')
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ax1.set_zlabel('Z')
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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
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# 变换后的单位圆
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# 标识圆心
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ax2.scatter(0, 0, 0, color='red', s=20, label='Center')
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ax2.plot(circle_transformed[0], circle_transformed[1], circle_transformed[2], color='r', label='Transformed Circle')
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ax2.set_title('Transformed (elev=52°, roll=-35°)')
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ax2.set_xlim([-1, 1])
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ax2.set_ylim([-1, 1])
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ax2.set_zlim([-1, 1])
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ax2.set_xlabel('X')
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ax2.set_ylabel('Y')
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ax2.set_zlabel('Z')
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ax2.set_box_aspect([1, 1, 1])
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ax2.view_init(elev=90, azim=90) # 从Z轴方向观察
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plt.show()
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image_zhen= cv2.imread("images/trans/transformed_image.jpg")
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image_xie = cv2.imread("images/trans/subRawImg.jpg")
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# transformed_image_hy = cv2.warpPerspective(transformed_image, inverse_matrix, dsize=(image.shape[1], image.shape[0]))
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# # 执行变换（自动计算输出图像尺寸）
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# 裁剪像素值到 [0, 255] 范围
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# 获取图像的大小
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height, width = image_zhen.shape[:2]
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# 计算中心点坐标
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center_x = width // 2
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center_y = height // 2
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# 构造一个平移矩阵，将原点移到中心
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M_translate = np.float32([
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[1, 0, -1*center_x],
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[0, 1, -1*center_y],
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[0, 0, 1]
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])
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image_xie_padding = cv2.warpPerspective(image_xie, M_translate,
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dsize=(image_xie.shape[1], image_xie.shape[0]))
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cv2.imshow("image_xie_padding", image_xie_padding)
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# 将平移矩阵与目标变换矩阵结合起来
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# inverse_M_combined = np.dot(M_translate, inverse_matrix)
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inverse_matrix[2][2]=1.0
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inverse_M_combined = np.dot(M_translate, inverse_matrix)
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print(f"斜矩阵={rotation_3d}")
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rotation_3d_int = np.clip(rotation_3d, 0, 255)
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print(f"斜矩阵_int={rotation_3d}")
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print(f"逆矩阵={inverse_M_combined}")
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inverse_M_combined_int = np.clip(inverse_M_combined, 0, 255)
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# print(f"逆矩阵_int={inverse_M_combined_int}")
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transformed_image_hy = cv2.warpPerspective(image_xie, inverse_M_combined_int,
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dsize=(image_xie.shape[1]*2, image_xie.shape[0]*2))
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cv2.imshow("transformed_image_hy", transformed_image_hy)
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# transformed_image = cv2.warpPerspective(image_zhen, rotation_3d_int,dsize=(image_zhen.shape[1], image_zhen.shape[0]))
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# cv2.imshow("rotated_img", transformed_image)
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plt.show()
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cv2.waitKey(0)
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# cv2.destroyAllWindows()
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