import os import glob import shutil import numpy as np import xml.etree.ElementTree as ET import cv2 def load_camera_xml(xml_path): tree = ET.parse(str(xml_path)) root = tree.getroot() Ks = [] poses = [] dist_coeffs = [] for i in range(4): name = f'camera_{i}_intrinsic_matrix' K = np.reshape(np.array(root.find(name).find('data').text.split(), dtype=np.float32), [3, 3]) # K[0, 0] = K[0, 0] # K[1, 1] = K[1, 1] # K[0, 2] = 0.5 * # K[1, 2] = 0.5 # Ks.append([1920., 1080., K[0, 0], K[1, 1], 960., 540.]) Ks.append(K) name = f'camera_{i}_rotation_matrix' R = np.reshape(np.array(root.find(name).find('data').text.split(), dtype=np.float32), [3, 3]) name = f'camera_{i}_translation_vector' T = np.reshape(np.array(root.find(name).find('data').text.split(), dtype=np.float32), [3, 1])/1000 pose = np.concatenate((R, T), axis=1) eye = np.eye(4, dtype=np.float32) eye[0:3] = pose pose = eye poses.append(pose) name = f'camera_{i}_distortion_coeffs' dist_coeff = np.reshape(np.array(root.find(name).find('data').text.split(), dtype=np.float32), [1, 5]) dist_coeffs.append(dist_coeff) Ks = np.array(Ks) poses = np.linalg.inv(np.array(poses)) dist_coeffs = np.array(dist_coeffs) # poses = np.array(poses) return Ks, poses, dist_coeffs def get_render_pose(para_ext, render_views=120): Rs = np.repeat(para_ext[0:1, 0:4, 0:3], render_views, 0) Ts_yz = np.repeat(para_ext[0:1, 1:4, 3:4], render_views, 0) interp_x = np.linspace(para_ext[0, 0, 3], para_ext[1, 0, 3], render_views, dtype=np.float32) Ts_x = interp_x[:, np.newaxis, np.newaxis] Ts = np.concatenate([Ts_x, Ts_yz], 1) poses = np.concatenate([Rs, Ts], -1) return poses[:, :, :] # #将kinect图像复制到images下 # for k in range(10): # source_path = f'/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/kinect_data/batch_data/yangkun_new/{str(k).zfill(3)}' # tgt_path = f'/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/person_{str(k+1)}' # for i in range(4): # folder_path = os.path.join(source_path, str(i)) # png_files = glob.glob(os.path.join(folder_path, '*.png')) # png_files_names = [os.path.basename(file_path) for file_path in png_files] # for png_file in png_files_names: # frame_name = png_file.replace('test', 'frame')[0:-4] # frame_path = os.path.join(tgt_path, frame_name, 'images') # os.makedirs(frame_path, exist_ok=True) # shutil.copy(os.path.join(folder_path,png_file), frame_path + f'/{str(i).zfill(4)}.png') ##写入参数 # para_path = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/kinect_data/parameter.xml' # Ks, poses, dist_coeffs = load_camera_xml(para_path) # print(Ks) # render_views = get_render_pose(poses) # np.save('/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/kinect_data/render_path.npy', render_views) # tgt_path = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/person_0' # for i in range(150): # mesh_path = os.path.join(tgt_path, f'frame{str(i)}','mesh3D') # os.makedirs(mesh_path, exist_ok=True) # np.save(mesh_path + '/cameras.npy', poses) # np.save(mesh_path + '/images.npy', Ks) #把mask图像复制到masked文件夹下 # source_path = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/kinect_data/mask/out' # tgt_path = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/person_0' # for i in range(0, 150): # mask_path = os.path.join(source_path,f'frame{str(i)}', 'images') # tgtmask_path = os.path.join(tgt_path,f'frame{str(i)}', 'masked') # # os.makedirs(tgtmask_path, exist_ok=True) # shutil.copytree(mask_path, tgtmask_path, dirs_exist_ok=True) def modify_obj_file_with_color(input_file, output_file, x_offset, z_offset): with open(input_file, 'r') as file: lines = file.readlines() modified_lines = [] for line in lines: if line.startswith('v '): parts = line.split() # 解析顶点坐标 x = float(parts[1]) - x_offset y = -float(parts[2]) z = -float(parts[3]) - z_offset # 重新构建顶点行，保留颜色信息（如果存在） if len(parts) > 4: color_info = " ".join(parts[4:]) modified_line = f"v {x} {y} {z} {color_info}\n" else: modified_line = f"v {x} {y} {z}\n" modified_lines.append(modified_line) else: modified_lines.append(line) with open(output_file, 'w') as file: file.writelines(modified_lines) ##修改obj flame的位置 # # 使用示例 # input_file = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/person_0/frame0/mesh3D/0003_points3Da_1.obj' # output_file = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/person_0/frame0/mesh3D/0003_points3D.obj' # x_offset = -0.15 # z_offset = -0.3 # modify_obj_file_with_color(input_file, output_file, x_offset, z_offset) ##去畸变 # para_path = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/kinect_data/parameter.xml' # Ks, poses, dist_coeffs = load_camera_xml(para_path) # source_path = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/person_0' # for i in range(1, 150): # for j in range(4): # pic_path = source_path + f'/frame{str(i)}/masked/{str(j).zfill(4)}.png' # dist_coeff = dist_coeffs[j, 0, :] # 示例径向畸变系数 # camera_matrix = Ks[j, :, :] # image = cv2.imread(pic_path) # undistorted_image = cv2.undistort(image, camera_matrix, dist_coeff) # cv2.imwrite(source_path + f'/frame{str(i)}/masked/{str(j).zfill(4)}.png', undistorted_image) # 把初始flame复制到每一个mesh3D文件夹里 def copy_obj_files(source_dir, destination_dir): try: # 遍历源文件夹中的所有文件和文件夹 for file_name in os.listdir(source_dir): # 构建源文件的完整路径 source_file = os.path.join(source_dir, file_name) # 检查文件是否是 .obj 文件 if file_name.endswith(('.obj', '.npy')): # 构建目标文件的完整路径 destination_file = os.path.join(destination_dir, file_name) # 复制 .obj 文件到目标文件夹 shutil.copy(source_file, destination_file) print(f"已复制文件 {source_file} 到 {destination_file}") except OSError as e: print(f"复制文件时发生错误：{e}") source_path = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/person_10' for i in range(1, 150): for j in range(4): obj_path = source_path + f'/frame{str(i)}/mesh3D' os.makedirs(source_path + f'/frame{str(i)}/mesh3D', exist_ok=True) copy_obj_files('/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/person_0/frame0/mesh3D', source_path + f'/frame{str(i)}/mesh3D') # ##写入华为外参 # def load_camera_xml(xml_path): # tree = ET.parse(str(xml_path)) # root = tree.getroot() # Ks = [] # poses = [] # dist_coeffs = [] # for i in range(2): # name = f'camera_{i}_intrinsic_matrix' # K = np.reshape(np.array(root.find(name).find('data').text.split(), dtype=np.float32), [3, 3]) # # K[0, 0] = K[0, 0] # # K[1, 1] = K[1, 1] # # K[0, 2] = 0.5 * # # K[1, 2] = 0.5 # Ks.append([1080., 1920., 1495.07, 1495.07, 540., 960.]) # # Ks.append(K) # name = f'camera_{i}_rotation_matrix' # R = np.reshape(np.array(root.find(name).find('data').text.split(), dtype=np.float32), [3, 3]) # name = f'camera_{i}_translation_vector' # T = np.reshape(np.array(root.find(name).find('data').text.split(), dtype=np.float32), [3, 1])/1000 # pose = np.concatenate((R, T), axis=1) # eye = np.eye(4, dtype=np.float32) # if i == 0: # eye[0:3] = [[0.061394, 0.869224, -0.490592, 113.487], [0.998099, -0.050835, 0.034837, 13.0695], [0.005341, -0.491798, -0.870693, 269.432]] # elif i == 1: # eye[0:3] = [[-0.103505, -0.991412, 0.079936, 15.4573], [-0.992358, 0.108363, 0.059015, 3.2613], [-0.06717, -0.073217, -0.995051, 299.719]] # pose = eye # poses.append(pose) # name = f'camera_{i}_distortion_coeffs' # dist_coeff = np.reshape(np.array(root.find(name).find('data').text.split(), dtype=np.float32), [1, 5]) # dist_coeffs.append(dist_coeff) # Ks = np.array(Ks) # poses = np.linalg.inv(np.array(poses)) # dist_coeffs = np.array(dist_coeffs) # # poses = np.array(poses) # return Ks, poses, dist_coeffs # para_path = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/kinect_data/parameter.xml' # Ks, poses, dist_coeffs = load_camera_xml(para_path) # # print(Ks) # # render_views = get_render_pose(poses) # # np.save('/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/kinect_data/render_path.npy', render_views) # tgt_path = '/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/huaweiperson_0' # for i in range(5): # mesh_path = os.path.join(tgt_path, f'frame{str(i)}','mesh3D') # os.makedirs(mesh_path, exist_ok=True) # np.save(mesh_path + '/cameras.npy', poses) # np.save(mesh_path + '/images.npy', Ks) # from pywavefront import Wavefront # def ply_to_obj(input_file, output_file): # try: # # 读取 PLY 文件 # with open(input_file, 'r') as ply_file: # lines = ply_file.readlines() # vertices = [] # # 提取点的坐标和 RGB 值 # for line in lines: # if line.startswith("element vertex"): # num_vertices = int(line.split()[2]) # elif line.startswith("end_header"): # break # for line in lines[-num_vertices:]: # x, y, z, r, g, b = line.split()[:6] # vertices.append((float(x), float(y), float(z), int(r), int(g), int(b))) # # 保存为 OBJ 文件 # with open(output_file, 'w') as obj_file: # for vertex in vertices: # obj_file.write(f"v {vertex[0]} {vertex[1]} {vertex[2]} {vertex[3]} {vertex[4]} {vertex[5]}\n") # print(f"已将 PLY 文件 {input_file} 转换为 OBJ，并保存为 {output_file}") # except Exception as e: # print(f"转换文件时发生错误：{e}") # # # 例子：将名为 "input.ply" 的 PLY 文件转换为 OBJ，并保存为 "output.obj" # # ply_to_obj("/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/huaweiperson_0/frame0/mesh3D/0001_points3D.ply", "/data_160TB/2021/yangkun/gaussian-splatting-main-copy/eg3d_human/test_real/poeples/huaweiperson_0/frame0/mesh3D/0001_points3D.obj")