相机姿态可视化 colmap形式的相机内参外参文件

相机姿态可视化 colmap形式的相机内参外参文件 怎么进行可视化？

1. 矩阵运算更直观：旋转矩阵可以直接与平移向量结合，形成相机的外参矩阵（Extrinsic Matrix）。
2. 可视化需求：旋转矩阵的每一列分别对应相机坐标系中X/Y/Z轴在世界坐标系中的方向，便于绘制相机的坐标轴。

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.spatial.transform import Rotation as R
def read_cameras(file_path):
    cameras = {}
    with open(file_path, 'r') as file:
        for line in file:
            if line[0] == '#':
                continue
            parts = line.strip().split()
            camera_id = int(parts[0])
            model = parts[1]
            width = int(parts[2])
            height = int(parts[3])
            params = np.array([float(p) for p in parts[4:]])
            cameras[camera_id] = {
                'model': model,
                'width': width,
                'height': height,
                'params': params
            }
    return cameras

def read_images(file_path):
    images = {}
    with open(file_path, 'r') as file:
        for line in file:
            if line[0] == '#':
                continue
            parts = line.strip().split()
            if len(parts) == 15:
                continue
            image_id = int(parts[0])
            qvec = np.array([float(p) for p in parts[1:5]])
            tvec = np.array([float(p) for p in parts[5:8]])
            camera_id = int(parts[8])
            file_name = parts[9]
            images[image_id] = {
                'qvec': qvec,
                'tvec': tvec,
                'camera_id': camera_id,
                'file_name': file_name
            }
    return images

def visualize_cameras(cameras, images):
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')

    for image_id, image_data in images.items():
        qvec = image_data['qvec']
        tvec = image_data['tvec']

        # Convert quaternion to rotation matrix
        rotation = R.from_quat(qvec).as_matrix()

        # Plot camera position
        ax.scatter(tvec[0], tvec[1], tvec[2], c='r', marker='o')

        # Plot camera orientation
        camera_direction = rotation @ np.array([0, 0, 1])
        ax.quiver(tvec[0], tvec[1], tvec[2], camera_direction[0], camera_direction[1], camera_direction[2], length=0.5, normalize=True)

    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_zlabel('Z')
    plt.show()

# 示例使用
cameras = read_cameras('./cameras.txt')
images = read_images('./images.txt')
visualize_cameras(cameras, images)