从metashape导出深度图，从深度图恢复密集点云

本文主要是介绍从metashape导出深度图，从深度图恢复密集点云，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

从metashape导出深度图，从深度图恢复密集点云

1.从metashape导出深度图

参考：https://blog.csdn.net/WHU_StudentZhong/article/details/123107072?spm=1001.2014.3001.5502

2.从深度图建立密集点云

首先从metashape导出blockExchange格式的xml文件，里面记录了相机的内外参等信息
file->export->export camera
下面就开始上代码

import numpy as np
import xml.etree.ElementTree as ET
import os
import cv2def 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()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)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)Z = np.ravel(Z)valid = (Z > 0) & (Z < 300)# 计算 valid 中有效元素的数量valid_count = np.count_nonzero(valid)# 打印结果print("有效元素的数量:", valid_count)X = X[valid]Y = Y[valid]Z = Z[valid]position = np.vstack((X, Y, Z, np.ones(len(X))))position = np.dot(pose, position)R = np.ravel(rgb[:, :, 0])[valid]G = np.ravel(rgb[:, :, 1])[valid]B = np.ravel(rgb[:, :, 2])[valid]points = np.transpose(np.vstack((position[0:3, :], R, G, B))).tolist()return pointsdef getDepth(position,depth, scale, K, pose):inverse_pose = np.linalg.inv(pose)position_camera=np.dot(inverse_pose, position)Z = position_camera[2]*scaleu=round(position_camera[0]*K[0, 0]/Z+K[0, 2])v=round(position_camera[1]*K[1, 1]/Z+K[1, 2])z=depth[v][u]return Z# image_files: XXXXXX.jpg (RGB, 24-bit, jpg)
# depth_files: XXXXXX.tif (32-bit, tif)
# poses: camera-to-world, 4×4 matrix in homogeneous coordinates
def build_point_cloud(K,dist_coeffs, scale, view_ply_in_world_coordinate,images,dataPath):images_path = os.path.join(dataPath, 'undistort')depth_path = os.path.join(dataPath, 'depth')for id, image in images.items():# 获取文件所在的文件夹路径image_name=os.path.join(images_path,image['imgName'])depth_name=os.path.join(depth_path,image['imgName'])depth_name=os.path.splitext(depth_name)[0]depth_name = depth_name + '.tif'rgb = cv2.imread(image_name)depth = cv2.imread(depth_name, cv2.IMREAD_UNCHANGED)height, width = depth.shape[:2]# # 创建深度图畸变映射# mapx, mapy = cv2.initUndistortRectifyMap(K, dist_coeffs, None, None, (width,height), cv2.CV_32FC1)# # 应用映射# depth = cv2.remap(depth, mapx, mapy, interpolation=cv2.INTER_NEAREST)rgb_resized=cv2.resize(rgb,(width, height))# # 创建rgb畸变映射# mapx, mapy = cv2.initUndistortRectifyMap(K, dist_coeffs, None, None, (width,height), cv2.CV_32FC1)# rgb_resized = cv2.remap(rgb_resized, mapx, mapy, interpolation=cv2.INTER_NEAREST)depth_values = depth.astype(np.float32)#显示深度值范围print('最小深度值:', np.min(depth_values))print('最大深度值:', np.max(depth_values))if view_ply_in_world_coordinate:current_points_3D = depth_image_to_point_cloud(rgb_resized, depth, scale=scale, K=K, pose=image['trans_M'])else:current_points_3D = depth_image_to_point_cloud(rgb_resized, depth, scale=scale, K=K, pose=image['trans_M'])save_ply_name = os.path.basename(os.path.splitext(image_name)[0]) + ".ply"#save_ply_path = os.path.join(dataPath, "point_clouds_frompoint")save_ply_path = os.path.join(dataPath, "point_clouds_any")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)def compute_K_matrix(focal_length, principal_point_x, principal_point_y):"""计算内参数矩阵 K参数:focal_length:焦距principal_point_x: 主点在 x 方向上的像素坐标principal_point_y: 主点在 y 方向上的像素坐标skew: 像素间的 skew factor，默认为 0返回值:K 矩阵"""K = np.array([[focal_length, 0, principal_point_x],[0, focal_length, principal_point_y],[0, 0, 1]])return Kdef loadAllfrom_xml(path):# 解析 XML 文件doc = ET.parse(path)root = doc.getroot()# 初始化相机和图像列表cameras = {}images = {}# 查找相机元素photogroups = root.find(".//Photogroups")if photogroups is None:print("error: invalid scene file")return False# 解析相机信息for photogroup in photogroups.findall("Photogroup"):camera_model_type = photogroup.find("CameraModelType")if camera_model_type is None or camera_model_type.text != "Perspective":continueimage_dimensions = photogroup.find("ImageDimensions")if image_dimensions is None:continuewidth = int(image_dimensions.find("Width").text)height = int(image_dimensions.find("Height").text)resolution_scale = max(width, height)focal_length_pixels = photogroup.find("FocalLengthPixels")if focal_length_pixels is not None:f = float(focal_length_pixels.text)else:focal_length = float(photogroup.find("FocalLength").text)sensor_size = float(photogroup.find("SensorSize").text)f = focal_length * resolution_scale / sensor_sizeprincipal_point = photogroup.find("PrincipalPoint")if principal_point is not None:cx = float(principal_point.find("x").text) cy = float(principal_point.find("y").text)pxl_size = 1# 解析畸变参数distortion = photogroup.find("Distortion")k1 = k2 = k3 = p1 = p2 = 0if distortion is not None:k1_elem = distortion.find("K1")if k1_elem is not None:k1 = float(k1_elem.text)k2_elem = distortion.find("K2")if k2_elem is not None:k2 = float(k2_elem.text)k3_elem = distortion.find("K3")if k3_elem is not None:k3 = float(k3_elem.text)p1_elem = distortion.find("P1")if p1_elem is not None:p1 = float(p1_elem.text)p2_elem = distortion.find("P2")if p2_elem is not None:p2 = float(p2_elem.text)  camera = {'width': width, 'height': height, 'f': f, 'cx': cx, 'cy': cy, 'pxlSize': pxl_size,'k1': k1, 'k2': k2, 'k3': k3, 'p1': p2, 'p2': p1}cameras[1] = camera# 解析图像信息for photo in photogroup.findall("Photo"):img_id = int(photo.find("Id").text)image_path = photo.find("ImagePath").textfound = image_path.rfind("/")img_name = image_path[found + 1:]photo_pose = photo.find("Pose")if photo_pose is None:continuerotation = photo_pose.find("Rotation")if rotation is None:continuerotation_matrix = np.array([[float(rotation.find("M_00").text), float(rotation.find("M_01").text),float(rotation.find("M_02").text)],[float(rotation.find("M_10").text),float(rotation.find("M_11").text), float(rotation.find("M_12").text)],[float(rotation.find("M_20").text), float(rotation.find("M_21").text),float(rotation.find("M_22").text)]])center = photo_pose.find("Center")if center is None:continueXs = float(center.find("x").text)Ys = float(center.find("y").text)Zs = float(center.find("z").text)position= np.array([Xs,Ys,Zs])trans=create_transformation_matrix(rotation_matrix,position)img = {'iid': img_id,'image_path':image_path, 'imgName': img_name,'R':rotation_matrix,'Xs': Xs, 'Ys': Ys, 'Zs': Zs,'trans_M':trans}images[img_id] = imgreturn images, camerasdef create_transformation_matrix(rotation_matrix, translation_vector):# 创建一个 4x4 的单位矩阵transformation_matrix = np.eye(4)transformation_matrix[:3, :3] = rotation_matrix.T#将rotation求逆transformation_matrix[:3, 3] = translation_vectorreturn transformation_matriximages, cameras = loadAllfrom_xml('pos_undistort.xml')
K=compute_K_matrix(cameras[1]['f'],cameras[1]['cx'],cameras[1]['cy'])
dist_coeffs = np.array([cameras[1]['k1'],cameras[1]['k2'], cameras[1]['p1'], cameras[1]['p2'], cameras[1]['k3']])view_ply_in_world_coordinate = True
K=K*0.5
K[2][2]=1build_point_cloud(K,dist_coeffs*0.5,1,view_ply_in_world_coordinate,images,'G:\\graduate2024\\experiment\\test')