[python][pycolmap]基于PyCOLMAP从图片中的稀疏点云重建与可视化

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


def reconstruct_and_visualize(image_dir, output_dir):
    # 确保输出目录存在
    os.makedirs(output_dir, exist_ok=True)

    # 1. 设置数据库路径
    database_path = os.path.join(output_dir, "database.db")
    if os.path.exists(database_path):
        os.remove(database_path)

    # 2. 运行特征提取
    pycolmap.extract_features(database_path, image_dir)

    # 3. 运行特征匹配
    pycolmap.match_exhaustive(database_path)

    # 4. 运行增量式重建
    maps = pycolmap.incremental_mapping(
        database_path=database_path,
        image_path=image_dir,
        output_path=output_dir,
    )

    # 5. 检查重建结果
    if not maps:
        print("重建失败！")
        return None

    # 获取第一个重建结果
    reconstruction = maps[0]

    # 6. 检查是否有3D点
    if not reconstruction.points3D:
        print("重建成功，但没有生成3D点云。")
        return reconstruction

    # 7. 提取点云数据
    points3D = reconstruction.points3D
    xyz = np.array([p.xyz for p in points3D.values()])
    rgb = np.array([p.color for p in points3D.values()]) / 255.0

    # 8. 可视化点云
    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(111, projection="3d")

    ax.scatter(
        xyz[:, 0], xyz[:, 1], xyz[:, 2],
        c=rgb,
        s=1,
        alpha=0.8,
        marker="."
    )

    ax.set_xlabel("X")
    ax.set_ylabel("Y")
    ax.set_zlabel("Z")
    ax.set_title("3D Reconstruction Point Cloud")

    plt.tight_layout()
    plt.show()

    return reconstruction


# 使用示例
image_dir = r"D:\images"
output_dir = r"D:\output"

reconstruction = reconstruct_and_visualize(image_dir, output_dir)