windows 2008 网站,鞋服外包加工网,给网站做导流,广州发布紧急通知前言 本文主要记录一下如何可视化相机位姿#xff0c;如何用Blender得到的深度图反投影到3D空间#xff0c;得到相应的点云。 Refernce
https://github.com/colmap/colmap/issues/1106 https://github.com/IntelRealSense/librealsense/issues/12090 https://medium.com/yod… 前言 本文主要记录一下如何可视化相机位姿如何用Blender得到的深度图反投影到3D空间得到相应的点云。 Refernce
https://github.com/colmap/colmap/issues/1106 https://github.com/IntelRealSense/librealsense/issues/12090 https://medium.com/yodayoda/from-depth-map-to-point-cloud-7473721d3f https://stackoverflow.com/questions/59590200/generate-point-cloud-from-depth-image https://github.com/isl-org/Open3D/issues/481 https://stackoverflow.com/questions/31265245/extracting-3d-coordinates-given-2d-image-points-depth-map-and-camera-calibratio https://github.com/vitalemonate/depth2Cloud
1 可视化相机位姿
import open3d as o3d
import numpy as npfocal 346.4101498574051
img_w img_h 400.0
intrinsic np.array([[focal, 0., -img_w / 2],[0., -focal, -img_h / 2],[0., 0., -1.]])
print(intrinsic)
pcds []
# 创建相机线集并添加到列表中
for pose in poses:
# extrinsic poseextrinsic np.eye(4)R, t pose[:3, :3], pose[:3, 3]extrinsic[:3, :3] R.Textrinsic[:3, 3] -np.dot(R.T, t)print(extrinsic)cam_pcd o3d.geometry.LineSet()cam_pcd cam_pcd.create_camera_visualization(view_width_px400,view_height_px400,intrinsicintrinsic,extrinsicextrinsic)
# cam_pcd.paint_uniform_color(color)
# cam_pcd.colors[4] 0.5 * colorcam_pcd.scale(scale1., centert)pcds.append(cam_pcd)# 初始化Open3D的可视化窗口
vis o3d.visualization.Visualizer()
vis.create_window()vis.add_geometry()# 添加相机线集到可视化窗口
for cam_pcd in pcds:vis.add_geometry(cam_pcd)# 设置视图参数
vis.get_render_option().background_color [0.5, 0.5, 0.5] # 设置背景颜色为灰色
vis.get_render_option().point_show_normal True # 显示法线# 更新可视化窗口
# vis.update_geometry()# 运行可视化窗口
while True:vis.poll_events()vis.update_renderer()# 关闭可视化窗口
vis.destroy_window()2 Depth2PointCloud
2.1 depth map反投影至三维空间
# 将depth map反投影至三维空间
def depth_image_to_point_cloud(rgb, depth, scale, K, pose):u range(0, rgb.shape[1])v range(0, rgb.shape[0])u, v np.meshgrid(u, v)u u.astype(float)v v.astype(float)# K为内参矩阵3*3# 图片坐标转相机坐标Z depth.astype(float) / scaleX (u - K[0, 2]) * Z / K[0, 0]Y (v - K[1, 2]) * Z / K[1, 1]X np.ravel(X)Y -np.ravel(Y) # Blender的坐标系为[x, -y, -z]Z -np.ravel(Z)valid Z 0X X[valid]Y Y[valid]Z Z[valid]position np.vstack((X, Y, Z, np.ones(len(X))))# 相机坐标转世界坐标transform np.array([[1, 0, 0, 0],[0, -1, 0, 0],[0, 0, -1, 0],[0, 0, 0, 1]])pose np.dot(transform, pose)position np.dot(pose, position)R np.ravel(rgb[:, :, 0])[valid]G np.ravel(rgb[:, :, 1])[valid]B np.ravel(rgb[:, :, 2])[valid]print(position.shape, R.shape)points np.transpose(np.vstack((position[:3, :], R, G, B))).tolist()return points2.2 将点云保存至ply文件
import os
import numpy as np
import cv2
from path import Path
from tqdm import tqdm# 将点云写入ply文件
def write_point_cloud(ply_filename, points):formatted_points []for point in points:formatted_points.append(%f %f %f %d %d %d 0\n % (point[0], point[1], point[2], point[3], point[4], point[5]))out_file open(ply_filename, w)out_file.write(plyformat ascii 1.0element vertex %dproperty float xproperty float yproperty float zproperty uchar blueproperty uchar greenproperty uchar redproperty uchar alphaend_header%s % (len(points), .join(formatted_points)))out_file.close()# image_files: XXXXXX.png (RGB, 24-bit, PNG)
# depth_files: XXXXXX.png (16-bit, PNG)
# poses: camera-to-world, 4×4 matrix in homogeneous coordinates
def build_point_cloud(dataset_path, scale, view_ply_in_world_coordinate, poses):K np.fromfile(os.path.join(dataset_path, K.txt), dtypefloat, sep\n )K np.reshape(K, newshape(3, 3))print(K)print(poses)image_files sorted(Path(os.path.join(dataset_path, images)).files(*.png))depth_files sorted(Path(os.path.join(dataset_path, depth_maps)).files(*.png))sum_points_3D []for i in tqdm(range(0, len(image_files))):image_file image_files[i]depth_file depth_files[i]rgb cv2.imread(image_file)depth cv2.imread(depth_file, -1).astype(np.uint16)if view_ply_in_world_coordinate:current_points_3D depth_image_to_point_cloud(rgb, depth, scalescale, KK, poseposes[i])else:current_points_3D depth_image_to_point_cloud(rgb, depth, scalescale, KK, poseposes[i])
# print(len(current_points_3D), current_points_3D[0])save_ply_name os.path.basename(os.path.splitext(image_files[i])[0]) .plysave_ply_path os.path.join(dataset_path, point_clouds)if not os.path.exists(save_ply_path): # 判断是否存在文件夹如果不存在则创建为文件夹os.mkdir(save_ply_path)write_point_cloud(os.path.join(save_ply_path, save_ply_name), current_points_3D)sum_points_3D.extend(current_points_3D)write_point_cloud(os.path.join(save_ply_path, all.ply), sum_points_3D)
