print_setting/download_print.py

import yaml
import oss2
import os
from tqdm import tqdm
# from utils.log_utils import log_execution
import os
from pathlib import Path

import numpy as np
import collections
import struct
import math
import os
import argparse

from config import print_factory_type_dir

from general import is_use_debug_oss

CameraModel = collections.namedtuple(
    "CameraModel", ["model_id", "model_name", "num_params"]
)
Camera = collections.namedtuple("Camera", ["id", "model", "width", "height", "params"])
BaseImage = collections.namedtuple(
    "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"]
)
Point3D = collections.namedtuple(
    "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"]
)
CAMERA_MODELS = {
    CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
    CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
    CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
    CameraModel(model_id=3, model_name="RADIAL", num_params=5),
    CameraModel(model_id=4, model_name="OPENCV", num_params=8),
    CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
    CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
    CameraModel(model_id=7, model_name="FOV", num_params=5),
    CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
    CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
    CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12),
}
CAMERA_MODEL_IDS = dict(
    [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS]
)
CAMERA_MODEL_NAMES = dict(
    [(camera_model.model_name, camera_model) for camera_model in CAMERA_MODELS]
)


def qvec2rotmat(qvec):
    return np.array(
        [
            [
                1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2,
                2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
                2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2],
            ],
            [
                2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
                1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2,
                2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1],
            ],
            [
                2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
                2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
                1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2,
            ],
        ]
    )


def rotmat2qvec(R):
    Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
    K = (
        np.array(
            [
                [Rxx - Ryy - Rzz, 0, 0, 0],
                [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
                [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
                [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz],
            ]
        )
        / 3.0
    )
    eigvals, eigvecs = np.linalg.eigh(K)
    qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
    if qvec[0] < 0:
        qvec *= -1
    return qvec


class Image(BaseImage):
    def qvec2rotmat(self):
        return qvec2rotmat(self.qvec)


def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
    """Read and unpack the next bytes from a binary file.
    :param fid:
    :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
    :param endian_character: Any of {@, =, <, >, !}
    :return: Tuple of read and unpacked values.
    """
    data = fid.read(num_bytes)
    return struct.unpack(endian_character + format_char_sequence, data)


def read_points3D_text(path):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::ReadPoints3DText(const std::string& path)
        void Reconstruction::WritePoints3DText(const std::string& path)
    """
    xyzs = None
    rgbs = None
    errors = None
    num_points = 0
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                num_points += 1

    xyzs = np.empty((num_points, 3))
    rgbs = np.empty((num_points, 3))
    errors = np.empty((num_points, 1))
    count = 0
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                elems = line.split()
                xyz = np.array(tuple(map(float, elems[1:4])))
                rgb = np.array(tuple(map(int, elems[4:7])))
                error = np.array(float(elems[7]))
                xyzs[count] = xyz
                rgbs[count] = rgb
                errors[count] = error
                count += 1

    return xyzs, rgbs, errors


def read_points3D_binary(path_to_model_file):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::ReadPoints3DBinary(const std::string& path)
        void Reconstruction::WritePoints3DBinary(const std::string& path)
    """

    with open(path_to_model_file, "rb") as fid:
        num_points = read_next_bytes(fid, 8, "Q")[0]

        xyzs = np.empty((num_points, 3))
        rgbs = np.empty((num_points, 3))
        errors = np.empty((num_points, 1))

        for p_id in range(num_points):
            binary_point_line_properties = read_next_bytes(
                fid, num_bytes=43, format_char_sequence="QdddBBBd"
            )
            xyz = np.array(binary_point_line_properties[1:4])
            rgb = np.array(binary_point_line_properties[4:7])
            error = np.array(binary_point_line_properties[7])
            track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[
                0
            ]
            track_elems = read_next_bytes(
                fid,
                num_bytes=8 * track_length,
                format_char_sequence="ii" * track_length,
            )
            xyzs[p_id] = xyz
            rgbs[p_id] = rgb
            errors[p_id] = error
    return xyzs, rgbs, errors


def read_intrinsics_text(path):
    """
    Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py
    """
    cameras = {}
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                elems = line.split()
                camera_id = int(elems[0])
                model = elems[1]
                assert (
                    model == "PINHOLE"
                ), "While the loader support other types, the rest of the code assumes PINHOLE"
                width = int(elems[2])
                height = int(elems[3])
                params = np.array(tuple(map(float, elems[4:])))
                cameras[camera_id] = Camera(
                    id=camera_id, model=model, width=width, height=height, params=params
                )
    return cameras


def read_extrinsics_binary(path_to_model_file):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::ReadImagesBinary(const std::string& path)
        void Reconstruction::WriteImagesBinary(const std::string& path)
    """
    images = {}
    with open(path_to_model_file, "rb") as fid:
        num_reg_images = read_next_bytes(fid, 8, "Q")[0]
        for _ in range(num_reg_images):
            binary_image_properties = read_next_bytes(
                fid, num_bytes=64, format_char_sequence="idddddddi"
            )
            image_id = binary_image_properties[0]
            qvec = np.array(binary_image_properties[1:5])
            tvec = np.array(binary_image_properties[5:8])
            camera_id = binary_image_properties[8]
            image_name = ""
            current_char = read_next_bytes(fid, 1, "c")[0]
            while current_char != b"\x00":  # look for the ASCII 0 entry
                image_name += current_char.decode("utf-8")
                current_char = read_next_bytes(fid, 1, "c")[0]
            num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[
                0
            ]
            x_y_id_s = read_next_bytes(
                fid,
                num_bytes=24 * num_points2D,
                format_char_sequence="ddq" * num_points2D,
            )
            xys = np.column_stack(
                [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))]
            )
            point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))
            images[image_id] = Image(
                id=image_id,
                qvec=qvec,
                tvec=tvec,
                camera_id=camera_id,
                name=image_name,
                xys=xys,
                point3D_ids=point3D_ids,
            )
    return images


def read_intrinsics_binary(path_to_model_file):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::WriteCamerasBinary(const std::string& path)
        void Reconstruction::ReadCamerasBinary(const std::string& path)
    """
    cameras = {}
    with open(path_to_model_file, "rb") as fid:
        num_cameras = read_next_bytes(fid, 8, "Q")[0]
        for _ in range(num_cameras):
            camera_properties = read_next_bytes(
                fid, num_bytes=24, format_char_sequence="iiQQ"
            )
            camera_id = camera_properties[0]
            model_id = camera_properties[1]
            model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
            width = camera_properties[2]
            height = camera_properties[3]
            num_params = CAMERA_MODEL_IDS[model_id].num_params
            params = read_next_bytes(
                fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params
            )
            cameras[camera_id] = Camera(
                id=camera_id,
                model=model_name,
                width=width,
                height=height,
                params=np.array(params),
            )
        assert len(cameras) == num_cameras
    return cameras


def focal2fov(focal, pixels):
    return 2 * math.atan(pixels / (2 * focal))


def read_extrinsics_text(path):
    """
    Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py
    """
    images = {}
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                elems = line.split()
                image_id = int(elems[0])
                qvec = np.array(tuple(map(float, elems[1:5])))
                tvec = np.array(tuple(map(float, elems[5:8])))
                camera_id = int(elems[8])
                image_name = elems[9]
                elems = fid.readline().split()
                xys = np.column_stack(
                    [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))]
                )
                point3D_ids = np.array(tuple(map(int, elems[2::3])))
                images[image_id] = Image(
                    id=image_id,
                    qvec=qvec,
                    tvec=tvec,
                    camera_id=camera_id,
                    name=image_name,
                    xys=xys,
                    point3D_ids=point3D_ids,
                )
    return images


def read_colmap_bin_array(path):
    """
    Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_dense.py

    :param path: path to the colmap binary file.
    :return: nd array with the floating point values in the value
    """
    with open(path, "rb") as fid:
        width, height, channels = np.genfromtxt(
            fid, delimiter="&", max_rows=1, usecols=(0, 1, 2), dtype=int
        )
        fid.seek(0)
        num_delimiter = 0
        byte = fid.read(1)
        while True:
            if byte == b"&":
                num_delimiter += 1
                if num_delimiter >= 3:
                    break
            byte = fid.read(1)
        array = np.fromfile(fid, np.float32)
    array = array.reshape((width, height, channels), order="F")
    return np.transpose(array, (1, 0, 2)).squeeze()


def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
    """Read and unpack the next bytes from a binary file.
    :param fid:
    :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
    :param endian_character: Any of {@, =, <, >, !}
    :return: Tuple of read and unpacked values.
    """
    data = fid.read(num_bytes)
    return struct.unpack(endian_character + format_char_sequence, data)


def write_next_bytes(fid, data, format_char_sequence, endian_character="<"):
    """pack and write to a binary file.
    :param fid:
    :param data: data to send, if multiple elements are sent at the same time,
    they should be encapsuled either in a list or a tuple
    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
    should be the same length as the data list or tuple
    :param endian_character: Any of {@, =, <, >, !}
    """
    if isinstance(data, (list, tuple)):
        bytes = struct.pack(endian_character + format_char_sequence, *data)
    else:
        bytes = struct.pack(endian_character + format_char_sequence, data)
    fid.write(bytes)


def read_cameras_text(path):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::WriteCamerasText(const std::string& path)
        void Reconstruction::ReadCamerasText(const std::string& path)
    """
    cameras = {}
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                elems = line.split()
                camera_id = int(elems[0])
                model = elems[1]
                width = int(elems[2])
                height = int(elems[3])
                params = np.array(tuple(map(float, elems[4:])))
                cameras[camera_id] = Camera(
                    id=camera_id,
                    model=model,
                    width=width,
                    height=height,
                    params=params,
                )
    return cameras


def read_cameras_binary(path_to_model_file):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::WriteCamerasBinary(const std::string& path)
        void Reconstruction::ReadCamerasBinary(const std::string& path)
    """
    cameras = {}
    with open(path_to_model_file, "rb") as fid:
        num_cameras = read_next_bytes(fid, 8, "Q")[0]
        for _ in range(num_cameras):
            camera_properties = read_next_bytes(
                fid, num_bytes=24, format_char_sequence="iiQQ"
            )
            camera_id = camera_properties[0]
            model_id = camera_properties[1]
            model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
            width = camera_properties[2]
            height = camera_properties[3]
            num_params = CAMERA_MODEL_IDS[model_id].num_params
            params = read_next_bytes(
                fid,
                num_bytes=8 * num_params,
                format_char_sequence="d" * num_params,
            )
            cameras[camera_id] = Camera(
                id=camera_id,
                model=model_name,
                width=width,
                height=height,
                params=np.array(params),
            )
        assert len(cameras) == num_cameras
    return cameras


def write_cameras_text(cameras, path):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::WriteCamerasText(const std::string& path)
        void Reconstruction::ReadCamerasText(const std::string& path)
    """
    HEADER = (
        "# Camera list with one line of data per camera:\n"
        + "#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\n"
        + "# Number of cameras: {}\n".format(len(cameras))
    )
    with open(path, "w") as fid:
        fid.write(HEADER)
        for _, cam in cameras.items():
            to_write = [cam.id, cam.model, cam.width, cam.height, *cam.params]
            line = " ".join([str(elem) for elem in to_write])
            fid.write(line + "\n")


def write_cameras_binary(cameras, path_to_model_file):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::WriteCamerasBinary(const std::string& path)
        void Reconstruction::ReadCamerasBinary(const std::string& path)
    """
    with open(path_to_model_file, "wb") as fid:
        write_next_bytes(fid, len(cameras), "Q")
        for _, cam in cameras.items():
            model_id = CAMERA_MODEL_NAMES[cam.model].model_id
            camera_properties = [cam.id, model_id, cam.width, cam.height]
            write_next_bytes(fid, camera_properties, "iiQQ")
            for p in cam.params:
                write_next_bytes(fid, float(p), "d")
    return cameras


def read_images_text(path):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::ReadImagesText(const std::string& path)
        void Reconstruction::WriteImagesText(const std::string& path)
    """
    images = {}
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                elems = line.split()
                image_id = int(elems[0])
                qvec = np.array(tuple(map(float, elems[1:5])))
                tvec = np.array(tuple(map(float, elems[5:8])))
                camera_id = int(elems[8])
                image_name = elems[9]
                elems = fid.readline().split()
                xys = np.column_stack(
                    [
                        tuple(map(float, elems[0::3])),
                        tuple(map(float, elems[1::3])),
                    ]
                )
                point3D_ids = np.array(tuple(map(int, elems[2::3])))
                images[image_id] = Image(
                    id=image_id,
                    qvec=qvec,
                    tvec=tvec,
                    camera_id=camera_id,
                    name=image_name,
                    xys=xys,
                    point3D_ids=point3D_ids,
                )
    return images


def read_images_binary(path_to_model_file):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::ReadImagesBinary(const std::string& path)
        void Reconstruction::WriteImagesBinary(const std::string& path)
    """
    images = {}
    with open(path_to_model_file, "rb") as fid:
        num_reg_images = read_next_bytes(fid, 8, "Q")[0]
        for _ in range(num_reg_images):
            binary_image_properties = read_next_bytes(
                fid, num_bytes=64, format_char_sequence="idddddddi"
            )
            image_id = binary_image_properties[0]
            qvec = np.array(binary_image_properties[1:5])
            tvec = np.array(binary_image_properties[5:8])
            camera_id = binary_image_properties[8]
            binary_image_name = b""
            current_char = read_next_bytes(fid, 1, "c")[0]
            while current_char != b"\x00":  # look for the ASCII 0 entry
                binary_image_name += current_char
                current_char = read_next_bytes(fid, 1, "c")[0]
            image_name = binary_image_name.decode("utf-8")
            num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[
                0
            ]
            x_y_id_s = read_next_bytes(
                fid,
                num_bytes=24 * num_points2D,
                format_char_sequence="ddq" * num_points2D,
            )
            xys = np.column_stack(
                [
                    tuple(map(float, x_y_id_s[0::3])),
                    tuple(map(float, x_y_id_s[1::3])),
                ]
            )
            point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))
            images[image_id] = Image(
                id=image_id,
                qvec=qvec,
                tvec=tvec,
                camera_id=camera_id,
                name=image_name,
                xys=xys,
                point3D_ids=point3D_ids,
            )
    return images


def write_images_text(images, path):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::ReadImagesText(const std::string& path)
        void Reconstruction::WriteImagesText(const std::string& path)
    """
    if len(images) == 0:
        mean_observations = 0
    else:
        mean_observations = sum(
            (len(img.point3D_ids) for _, img in images.items())
        ) / len(images)
    HEADER = (
        "# Image list with two lines of data per image:\n"
        + "#   IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\n"
        + "#   POINTS2D[] as (X, Y, POINT3D_ID)\n"
        + "# Number of images: {}, mean observations per image: {}\n".format(
            len(images), mean_observations
        )
    )

    with open(path, "w") as fid:
        fid.write(HEADER)
        for _, img in images.items():
            image_header = [
                img.id,
                *img.qvec,
                *img.tvec,
                img.camera_id,
                img.name,
            ]
            first_line = " ".join(map(str, image_header))
            fid.write(first_line + "\n")

            points_strings = []
            for xy, point3D_id in zip(img.xys, img.point3D_ids):
                points_strings.append(" ".join(map(str, [*xy, point3D_id])))
            fid.write(" ".join(points_strings) + "\n")


def write_images_binary(images, path_to_model_file):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::ReadImagesBinary(const std::string& path)
        void Reconstruction::WriteImagesBinary(const std::string& path)
    """
    with open(path_to_model_file, "wb") as fid:
        write_next_bytes(fid, len(images), "Q")
        for _, img in images.items():
            write_next_bytes(fid, img.id, "i")
            write_next_bytes(fid, img.qvec.tolist(), "dddd")
            write_next_bytes(fid, img.tvec.tolist(), "ddd")
            write_next_bytes(fid, img.camera_id, "i")
            for char in img.name:
                write_next_bytes(fid, char.encode("utf-8"), "c")
            write_next_bytes(fid, b"\x00", "c")
            write_next_bytes(fid, len(img.point3D_ids), "Q")
            for xy, p3d_id in zip(img.xys, img.point3D_ids):
                write_next_bytes(fid, [*xy, p3d_id], "ddq")


def read_points3D_text(path):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::ReadPoints3DText(const std::string& path)
        void Reconstruction::WritePoints3DText(const std::string& path)
    """
    points3D = {}
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                elems = line.split()
                point3D_id = int(elems[0])
                xyz = np.array(tuple(map(float, elems[1:4])))
                rgb = np.array(tuple(map(int, elems[4:7])))
                error = float(elems[7])
                image_ids = np.array(tuple(map(int, elems[8::2])))
                point2D_idxs = np.array(tuple(map(int, elems[9::2])))
                points3D[point3D_id] = Point3D(
                    id=point3D_id,
                    xyz=xyz,
                    rgb=rgb,
                    error=error,
                    image_ids=image_ids,
                    point2D_idxs=point2D_idxs,
                )
    return points3D


def read_points3D_binary(path_to_model_file):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::ReadPoints3DBinary(const std::string& path)
        void Reconstruction::WritePoints3DBinary(const std::string& path)
    """
    points3D = {}
    with open(path_to_model_file, "rb") as fid:
        num_points = read_next_bytes(fid, 8, "Q")[0]
        for _ in range(num_points):
            binary_point_line_properties = read_next_bytes(
                fid, num_bytes=43, format_char_sequence="QdddBBBd"
            )
            point3D_id = binary_point_line_properties[0]
            xyz = np.array(binary_point_line_properties[1:4])
            rgb = np.array(binary_point_line_properties[4:7])
            error = np.array(binary_point_line_properties[7])
            track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[
                0
            ]
            track_elems = read_next_bytes(
                fid,
                num_bytes=8 * track_length,
                format_char_sequence="ii" * track_length,
            )
            image_ids = np.array(tuple(map(int, track_elems[0::2])))
            point2D_idxs = np.array(tuple(map(int, track_elems[1::2])))
            points3D[point3D_id] = Point3D(
                id=point3D_id,
                xyz=xyz,
                rgb=rgb,
                error=error,
                image_ids=image_ids,
                point2D_idxs=point2D_idxs,
            )
    return points3D


def write_points3D_text(points3D, path):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::ReadPoints3DText(const std::string& path)
        void Reconstruction::WritePoints3DText(const std::string& path)
    """
    if len(points3D) == 0:
        mean_track_length = 0
    else:
        mean_track_length = sum(
            (len(pt.image_ids) for _, pt in points3D.items())
        ) / len(points3D)
    HEADER = (
        "# 3D point list with one line of data per point:\n"
        + "#   POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n"
        + "# Number of points: {}, mean track length: {}\n".format(
            len(points3D), mean_track_length
        )
    )

    with open(path, "w") as fid:
        fid.write(HEADER)
        for _, pt in points3D.items():
            point_header = [pt.id, *pt.xyz, *pt.rgb, pt.error]
            fid.write(" ".join(map(str, point_header)) + " ")
            track_strings = []
            for image_id, point2D in zip(pt.image_ids, pt.point2D_idxs):
                track_strings.append(" ".join(map(str, [image_id, point2D])))
            fid.write(" ".join(track_strings) + "\n")


def write_points3D_binary(points3D, path_to_model_file):
    """
    see: src/colmap/scene/reconstruction.cc
        void Reconstruction::ReadPoints3DBinary(const std::string& path)
        void Reconstruction::WritePoints3DBinary(const std::string& path)
    """
    with open(path_to_model_file, "wb") as fid:
        write_next_bytes(fid, len(points3D), "Q")
        for _, pt in points3D.items():
            write_next_bytes(fid, pt.id, "Q")
            write_next_bytes(fid, pt.xyz.tolist(), "ddd")
            write_next_bytes(fid, pt.rgb.tolist(), "BBB")
            write_next_bytes(fid, pt.error, "d")
            track_length = pt.image_ids.shape[0]
            write_next_bytes(fid, track_length, "Q")
            for image_id, point2D_id in zip(pt.image_ids, pt.point2D_idxs):
                write_next_bytes(fid, [image_id, point2D_id], "ii")


def detect_model_format(path, ext):
    if (
        os.path.isfile(os.path.join(path, "cameras" + ext))
        and os.path.isfile(os.path.join(path, "images" + ext))
        and os.path.isfile(os.path.join(path, "points3D" + ext))
    ):
        print("Detected model format: '" + ext + "'")
        return True

    return False


def read_model(path, ext=""):
    # try to detect the extension automatically
    if ext == "":
        if detect_model_format(path, ".bin"):
            ext = ".bin"
        elif detect_model_format(path, ".txt"):
            ext = ".txt"
        else:
            print("Provide model format: '.bin' or '.txt'")
            return

    if ext == ".txt":
        cameras = read_cameras_text(os.path.join(path, "cameras" + ext))
        images = read_images_text(os.path.join(path, "images" + ext))
        points3D = read_points3D_text(os.path.join(path, "points3D") + ext)
    else:
        cameras = read_cameras_binary(os.path.join(path, "cameras" + ext))
        images = read_images_binary(os.path.join(path, "images" + ext))
        points3D = read_points3D_binary(os.path.join(path, "points3D") + ext)
    return cameras, images, points3D


def write_model(cameras, images, points3D, path, ext=".bin"):
    if ext == ".txt":
        write_cameras_text(cameras, os.path.join(path, "cameras" + ext))
        write_images_text(images, os.path.join(path, "images" + ext))
        write_points3D_text(points3D, os.path.join(path, "points3D") + ext)
    else:
        write_cameras_binary(cameras, os.path.join(path, "cameras" + ext))
        write_images_binary(images, os.path.join(path, "images" + ext))
        write_points3D_binary(points3D, os.path.join(path, "points3D") + ext)
    return cameras, images, points3D


def qvec2rotmat(qvec):
    return np.array(
        [
            [
                1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2,
                2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
                2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2],
            ],
            [
                2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
                1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2,
                2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1],
            ],
            [
                2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
                2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
                1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2,
            ],
        ]
    )


def rotmat2qvec(R):
    Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
    K = (
        np.array(
            [
                [Rxx - Ryy - Rzz, 0, 0, 0],
                [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
                [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
                [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz],
            ]
        )
        / 3.0
    )
    eigvals, eigvecs = np.linalg.eigh(K)
    qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
    if qvec[0] < 0:
        qvec *= -1
    return qvec


def get_oss_client(cfg_path):
    with open(os.path.expanduser(cfg_path), "r") as config:
        cfg = yaml.safe_load(config)

    AccessKeyId_down = cfg["run"]["down"]["AccessKeyId"]
    AccessKeySecret_down = cfg["run"]["down"]["AccessKeySecret"]
    Endpoint_down = cfg["run"]["down"]["Endpoint"]
    Bucket_down = cfg["run"]["down"]["Bucket"]

    oss_client = oss2.Bucket(
        oss2.Auth(AccessKeyId_down, AccessKeySecret_down), Endpoint_down, Bucket_down
    )

    return oss_client

class DataTransfer:
    '''
    数据传输类
    '''
    def __init__(self, local_path: str, oss_path: str, oss_client: oss2.Bucket):
        '''
        local_path: 本地输出路径
        oss_path: oss路径
        oss_client: oss客户端
        '''
        self.local_path = local_path
        self.oss_path = oss_path.lstrip('/')
        self.oss_client = oss_client
        # self.description = description

    # @log_execution(self.description)
    def download_data(self):
        """
        从 OSS 下载数据到本地，保持原有目录结构
        """
        
        # 列出所有对象
        objects = []
        prefix = self.oss_path.lstrip('/')  # 移除开头的 '/' 以匹配 OSS 格式
        
        for obj in oss2.ObjectIterator(self.oss_client, prefix=prefix):
            if obj.key != prefix:  # 跳过目录本身
                objects.append(obj.key)
        
        # 下载所有文件，添加进度条
        for obj_key in tqdm(objects, desc="下载进度"):
            if obj_key.endswith('/'):
                continue

            if "printId" in obj_key:
                continue

            # 计算相对路径
            rel_path = obj_key[len(prefix):].lstrip('/')
            # 构建本地完整路径
            local_path = os.path.join(self.local_path, rel_path)

            # 创建必要的目录
            os.makedirs(os.path.dirname(local_path), exist_ok=True)
            
            # 下载文件
            self.oss_client.get_object_to_file(obj_key, local_path)
            
            print("download_data local_path=" + local_path)

    order_id: str
    pid: str
    model_height: str

    def download_data_rename_json(self, json_model_info):
        """
        从 OSS 下载数据到本地，保持原有目录结构
        """
        
        # 列出所有对象
        objects = []
        prefix = self.oss_path.lstrip('/')  # 移除开头的 '/' 以匹配 OSS 格式
        
        for obj in oss2.ObjectIterator(self.oss_client, prefix=prefix):
            if obj.key != prefix:  # 跳过目录本身
                objects.append(obj.key)
        
        # 下载所有文件，添加进度条
        for obj_key in tqdm(objects, desc="下载进度"):
            if obj_key.endswith('/'):
                continue

            if "printId" in obj_key:
                continue

            # 计算相对路径
            rel_path = obj_key[len(prefix):].lstrip('/')

            file_dir, file_name = os.path.split(rel_path)
            file_base, file_ext = os.path.splitext(file_name)

            # 根据文件后缀名进行重命名
            if file_ext.lower() in ['.mtl', '.jpg', '.jpeg', '.png']:
                # 对于.mtl和图片文件，在原名前加order_id
                new_file_name = f"{json_model_info.order_id}_{file_name}"
                # new_file_name = file_name
            elif file_ext.lower() == '.obj':
                # 对于.obj文件，完全重命名
                new_file_name = f"{json_model_info.obj_name}"
            else:
                # 其他文件类型保持原名
                new_file_name = file_name
            print("new_file_name=", new_file_name)

            # 构建新的相对路径
            if file_dir:  # 如果有子目录
                new_rel_path = os.path.join(file_dir, new_file_name)
            else:
                new_rel_path = new_file_name

            # 构建本地完整路径
            local_path = os.path.join(self.local_path, new_rel_path)
            
            # 创建必要的目录
            os.makedirs(os.path.dirname(local_path), exist_ok=True)
            
            # 下载文件
            self.oss_client.get_object_to_file(obj_key, local_path)

            if file_ext == '.obj':  # 10MB以上
                try:
                    # 使用临时文件避免内存问题 [8](@ref)
                    temp_path = local_path + '.tmp'
                    with open(local_path, 'r', encoding='utf-8') as f_in, \
                        open(temp_path, 'w', encoding='utf-8') as f_out:
                        
                        mtllib_modified = False
                        for line in f_in:
                            if not mtllib_modified and line.strip().startswith('mtllib '):
                                parts = line.split(' ', 1)
                                if len(parts) > 1:
                                    old_mtl_name = parts[1].strip()
                                    new_mtl_name = f"{json_model_info.order_id}_{old_mtl_name}"
                                    f_out.write(f"mtllib {new_mtl_name}\n")
                                    mtllib_modified = True
                                    continue
                            f_out.write(line)
                    
                    os.replace(temp_path, local_path)  # 原子性替换
                    
                except IOError as e:
                    print(f"处理大文件 {local_path} 时出错: {e}")
                    if os.path.exists(temp_path):
                        os.remove(temp_path)
            
            # 优化后的.obj文件处理逻辑
            if file_ext == '.mtl':
                try:
                    # 使用更高效的文件读取方式 [6,8](@ref)
                    with open(local_path, 'r', encoding='utf-8') as f:
                        content = f.read()
                    
                    # 使用字符串方法直接查找和替换，避免不必要的循环 [9](@ref)
                    lines = content.split('\n')
                    mtllib_modified = False
                    
                    for i, line in enumerate(lines):
                        stripped_line = line.strip()
                        if not mtllib_modified and stripped_line.startswith('map_Kd '):
                            # 更高效的分割方式 [9](@ref)
                            parts = line.split(' ', 1)
                            if len(parts) > 1:
                                old_name = parts[1].strip()
                                new_name = f"{json_model_info.order_id}_{old_name}"
                                lines[i] = f"map_Kd {new_name}"
                                mtllib_modified = True
                                print(f"已更新材质库引用: {old_name} -> {new_name}")
                                break  # 找到第一个后立即退出
                    
                    # 批量写入，减少I/O操作 [6](@ref)
                    with open(local_path, 'w', encoding='utf-8') as f:
                        f.write('\n'.join(lines))
                        
                except IOError as e:
                    print(f"处理文件 {local_path} 时出错: {e}")
                except UnicodeDecodeError as e:
                    print(f"文件编码错误 {local_path}: {e}")

            print(f"下载文件: {obj_key} -> {local_path}")

    def download_data_rename_batch(self, batch_model_info):
        """
        从 OSS 下载数据到本地，保持原有目录结构
        """
        
        # 列出所有对象
        objects = []
        prefix = self.oss_path.lstrip('/')  # 移除开头的 '/' 以匹配 OSS 格式

        prefix_exists = False
        
        for obj in oss2.ObjectIterator(self.oss_client, prefix=prefix):
            prefix_exists = True
            if obj.key != prefix:  # 跳过目录本身
                objects.append(obj.key)
                print(f"obj.key={obj.key}")
        
        if not prefix_exists:
            print(f"前缀 '{prefix}' 下没有找到任何文件或目录。")
            return False
        else:
            print(f"前缀 '{prefix}' 存在，共找到 {len(objects)} 个对象。")

        # 下载所有文件，添加进度条
        for obj_key in tqdm(objects, desc="下载进度"):
            if obj_key.endswith('/'):
                print("下载 endswith('/'")
                continue

            if "printId" in obj_key:
                print(f"下载 in obj_key")
                continue

            # 计算相对路径
            rel_path = obj_key[len(prefix):].lstrip('/')

            file_dir, file_name = os.path.split(rel_path)
            file_base, file_ext = os.path.splitext(file_name)

            # 根据文件后缀名进行重命名
            if file_ext.lower() in ['.mtl', '.jpg', '.jpeg', '.png']:
                # 对于.mtl和图片文件，在原名前加order_id
                new_file_name = f"{batch_model_info.order_id}_{file_name}"
                # new_file_name = file_name
            elif file_ext.lower() == '.obj':
                # 对于.obj文件，完全重命名
                new_file_name = f"{batch_model_info.order_id}_{batch_model_info.pid}_P{batch_model_info.print_order_id}_{batch_model_info.model_size}{file_ext}"
            else:
                # 其他文件类型保持原名
                new_file_name = file_name

            # 构建新的相对路径
            if file_dir:  # 如果有子目录
                new_rel_path = os.path.join(file_dir, new_file_name)
            else:
                new_rel_path = new_file_name

            # 构建本地完整路径
            local_path = os.path.join(self.local_path, new_rel_path)
            
            # 创建必要的目录
            os.makedirs(os.path.dirname(local_path), exist_ok=True)
            
            # 下载文件
            self.oss_client.get_object_to_file(obj_key, local_path)

            if file_ext == '.obj':  # 10MB以上
                try:
                    # 使用临时文件避免内存问题 [8](@ref)
                    temp_path = local_path + '.tmp'
                    with open(local_path, 'r', encoding='utf-8') as f_in, \
                        open(temp_path, 'w', encoding='utf-8') as f_out:
                        
                        mtllib_modified = False
                        for line in f_in:
                            if not mtllib_modified and line.strip().startswith('mtllib '):
                                parts = line.split(' ', 1)
                                if len(parts) > 1:
                                    old_mtl_name = parts[1].strip()
                                    new_mtl_name = f"{batch_model_info.order_id}_{old_mtl_name}"
                                    f_out.write(f"mtllib {new_mtl_name}\n")
                                    mtllib_modified = True
                                    print("len(parts) > 1")
                                    continue
                            f_out.write(line)
                    
                    os.replace(temp_path, local_path)  # 原子性替换
                    
                except IOError as e:
                    print(f"处理大文件 {local_path} 时出错: {e}")
                    if os.path.exists(temp_path):
                        os.remove(temp_path)
            
            # 优化后的.obj文件处理逻辑
            if file_ext == '.mtl':
                try:
                    # 使用更高效的文件读取方式 [6,8](@ref)
                    with open(local_path, 'r', encoding='utf-8') as f:
                        content = f.read()
                    
                    # 使用字符串方法直接查找和替换，避免不必要的循环 [9](@ref)
                    lines = content.split('\n')
                    mtllib_modified = False
                    
                    for i, line in enumerate(lines):
                        stripped_line = line.strip()
                        if not mtllib_modified and stripped_line.startswith('map_Kd '):
                            # 更高效的分割方式 [9](@ref)
                            parts = line.split(' ', 1)
                            if len(parts) > 1:
                                old_name = parts[1].strip()
                                new_name = f"{batch_model_info.order_id}_{old_name}"
                                lines[i] = f"map_Kd {new_name}"
                                mtllib_modified = True
                                print(f"已更新材质库引用: {old_name} -> {new_name}")
                                break  # 找到第一个后立即退出
                    
                    # 批量写入，减少I/O操作 [6](@ref)
                    with open(local_path, 'w', encoding='utf-8') as f:
                        f.write('\n'.join(lines))
                        
                except IOError as e:
                    print(f"处理文件 {local_path} 时出错: {e}")
                except UnicodeDecodeError as e:
                    print(f"文件编码错误 {local_path}: {e}")

            print(f"下载文件: {obj_key} -> {local_path}")
        
        return True

    def download_single_file(self):
        """
        下载单个文件从OSS到本地
        """
        # 确保本地目录存在
        os.makedirs(os.path.dirname(self.local_path), exist_ok=True)
        
        # 直接下载文件
        try:
            self.oss_client.get_object_to_file(self.oss_path, self.local_path)
            print(f"文件已下载到: {self.local_path}")
        except oss2.exceptions.NoSuchKey:
            print(f"OSS文件不存在: {self.oss_path}")

    def upload_data(self):
        '''
        上传数据到OSS
        '''
        # 检测本地路径是否存在
        if not os.path.exists(self.local_path):
            raise FileNotFoundError(f"本地路径不存在: {self.local_path}")

        # 判断本地路径是文件还是目录
        if os.path.isfile(self.local_path):
            local_suffix = Path(self.local_path).suffix
            oss_suffix = Path(self.oss_path).suffix

            if oss_suffix and oss_suffix != local_suffix:
                # 后缀名不一致，上传到指定文件夹下的同名文件
                oss_dir = os.path.dirname(self.oss_path)
                oss_target_path = os.path.join(oss_dir, os.path.basename(self.local_path))
            else:
                # 后缀名一致，上传到指定OSS路径
                oss_target_path = self.oss_path

            # 上传文件
            self.oss_client.put_object_from_file(oss_target_path, self.local_path)

            print(f"文件已上传到: {oss_target_path}")

        elif os.path.isdir(self.local_path):
            oss_suffix = Path(self.oss_path).suffix
            if oss_suffix:
                raise ValueError("不能将目录上传到具有后缀名的OSS路径。")

            # 遍历本地目录并上传
            for root, dirs, files in os.walk(self.local_path):
                for file in files:
                    local_file_path = os.path.join(root, file)
                    relative_path = os.path.relpath(local_file_path, self.local_path)
                    oss_file_path = os.path.join(self.oss_path, relative_path).replace("\\", "/")
                    
                    # 创建必要的目录
                    oss_dir = os.path.dirname(oss_file_path)
                    
                    # 上传文件
                    self.oss_client.put_object_from_file(oss_file_path, local_file_path)
                    print(f"文件已上传到: {oss_file_path}")
        else:
            raise ValueError(f"无效的本地路径类型: {self.local_path}")

import requests
import json
import shutil

def get_api(url):
    try:
        response = requests.get(url)
        response.raise_for_status()  # 检查请求是否成功
        response = json.loads(response.text)
        if response.get("code") != 1000:
            raise Exception(f"Error fetching URL {url}: {response.get('message')}")
        else:
            return response
    except requests.exceptions.RequestException as e:
        raise Exception(f"Error fetching URL {url}: {e}")
    
from dataclasses import dataclass
@dataclass
class JSONModelInfo:
    obj_name: str
    order_id: str
    pid: str
    model_height: str

def read_pids_from_json(pid_file):
    """从文件读取所有PID"""
    # with open(pid_file, 'r') as f:
    #     # 过滤掉空行并去除每行首尾的空白字符
    #     return [line.strip() for line in f if line.strip()]

    json_path = pid_file

    """
    加载JSON文件，读取所有模型信息，应用变换后返回模型列表
    """
    # 检查JSON文件是否存在
    if not os.path.exists(json_path):
        print(f"错误: JSON文件不存在 - {json_path}")
        return []
    
    # 读取JSON文件
    try:
        with open(json_path, 'r') as f:
            data = json.load(f)
    except Exception as e:
        print(f"读取JSON文件失败: {e}")
        return []
    
    list_model_info = []
    # 处理每个模型
    for model in data.get('models', []):
        obj_name = model.get('file_name', '')

        parts = obj_name.split('_')

        order_id = parts[0]
        pid = parts[1]
        model_height = parts[3]

        model_info = JSONModelInfo(
            obj_name=obj_name,
            order_id=order_id,
            pid=pid,
            model_height=model_height
        )
        list_model_info.append(model_info)
    
    return list_model_info, data

def download_data_by_json(model_info, workdir, oss_client ):
    '''
    下载卡通化数据
    '''
    try:
        pid = model_info.pid
        model_height = model_info.model_height
        # target_dir = f"{workdir}/{pid}_image"
        target_dir = f"{workdir}"
        
        # {"code":1000,"data":"base_cartoon/badge/101/3/init_obj","message":"success1"}
        # https://mp.api.suwa3d.com/api/order/getOssSuffixByOrderId?order_id=879312

        url = f"https://mp.api.suwa3d.com/api/order/getOssSuffixByOrderId?order_id={model_info.order_id}"
        res = requests.get(url)

        data = res.json()["data"]
        # print("datas=",data)
        data = data.replace("/init_obj", "")

        print("target_dir=", target_dir)
        # download_textures = DataTransfer(target_dir, f"objs/download/print/{pid}/base/model/{model_height}/", oss_client)
        # download_textures = DataTransfer(target_dir, f"objs/download/print/{pid}/base_cartoon/badge/101/3/{model_height}/", oss_client)
        download_textures = DataTransfer(target_dir, f"objs/download/print/{pid}/{data}/{model_height}/", oss_client)

        download_textures.download_data_rename_json(model_info)
        # 下载后检查目标文件夹是否为空
        if os.path.exists(target_dir) and not os.listdir(target_dir):
            shutil.rmtree(target_dir)
            print(f"下载后检查发现目标文件夹为空，已删除: {target_dir}")
    except Exception as e:
        print(f"卡通图片下载失败: {pid}, 错误: {str(e)}")
        pass

@dataclass
class BatchModelInfo:
    order_id: str
    pid: str
    print_order_id: str
    model_size: str
    path: str
    count: str

def read_paths_from_batch(batch_id):

    url = f"https://mp.api.suwa3d.com/api/printOrder/getInfoByPrintBatchId?batch_id={batch_id}"
    res = requests.get(url)

    datas = res.json()["data"]
    print("datas=",datas)
   
    list_print_model_info = []
    for data in datas:
        batch_model_info = BatchModelInfo(
            order_id=data["order_id"],
            pid=data["pid"],
            print_order_id=data["print_order_id"],
            model_size=data["model_size"],
            path=data["path"],
            count=data["quantity"]
            
        )
        list_print_model_info.append(batch_model_info)
    
    return list_print_model_info, datas

def download_data_by_batch(batch_model_info, workdir, oss_client ):
    try:
        target_dir = f"{workdir}"

        print("target_dir=", target_dir)
        path = batch_model_info.path
        download_textures = DataTransfer(target_dir, f"{path}/", oss_client)

        if not download_textures.download_data_rename_batch(batch_model_info):
            print("fail download_data_rename_batch")
            return False
        # 下载后检查目标文件夹是否为空
        if os.path.exists(target_dir) and not os.listdir(target_dir):
            shutil.rmtree(target_dir)
            print(f"下载后检查发现目标文件夹为空，已删除: {target_dir}")
    except Exception as e:
        print(f"下载失败: {path}, 错误: {str(e)}")
        pass
    
    return True

def download_datas_by_batch(batch_id, workdir, oss_config):
    oss_client = get_oss_client(oss_config)

    # 读取所有path
    list_print_model_info, datas = read_paths_from_batch(batch_id)
    print(f"从文件读取了 {len(list_print_model_info)} 个path")
    # 批量下载
    for batch_model_info in list_print_model_info:
        print(f"开始下载print_model_info: {batch_model_info}")
        if not download_data_by_batch(batch_model_info, workdir, oss_client):
            return datas, False

    return datas, True

def download_datas_by_pre_layout(list_print_model_info, workdir, oss_config):
    oss_client = get_oss_client(oss_config)

    print(f"从文件读取了 {len(list_print_model_info)} 个path")
    # 批量下载
    for batch_model_info in list_print_model_info:
        print(f"开始下载print_model_info: {batch_model_info}")
        if not download_data_by_batch(batch_model_info, workdir, oss_client):
            return False
    return True

def download_transform_save_by_batch(batch_id, workdir, oss_config):
    datas, succ = download_datas_by_batch(batch_id, workdir, oss_config)
    print("datas=", datas)
    layout_data = datas["layout_data"]

    original_obj_pid_dir = workdir

    transform_save_o3d(layout_data, original_obj_pid_dir)


def download_datas_by_json(pid_file, workdir, oss_config):
    oss_client = get_oss_client(oss_config)

    #json_path = os.path.join(workdir, "3DPrintLayout.json")
    json_path = os.path.join(workdir, f"{pid_file}.json")
    
    # 读取所有PID
    list_model_info, data = read_pids_from_json(json_path)
    print(f"从文件读取了 {len(list_model_info)} 个PID")
    # 批量下载
    for model_info in list_model_info:
        print(f"开始下载PID: {model_info}")
        download_data_by_json(model_info, args.workdir, oss_client)

    return data

def download_transform_save_by_json(pid_file, workdir, oss_config):
    layout_data = download_datas_by_json(pid_file, workdir, oss_config)

    original_obj_pid_dir = workdir

    transform_save_o3d(layout_data, original_obj_pid_dir)

def upload_result(base_original_obj_dir, oss_config, batch_id):

    oss_client = get_oss_client(oss_config)

    try:
        target_dir = f"{base_original_obj_dir}"

        oss_batch_dir = "batchPrint"
        print(f"is_use_debug_oss={is_use_debug_oss()}")
        if is_use_debug_oss():
            oss_batch_dir = "batchPrint/debug_hsc"

        print(f"target_dir={target_dir}, batch_id={batch_id}")
        data_transfer = DataTransfer(f"{target_dir}/{batch_id}.json", f"{oss_batch_dir}/{batch_id}/{batch_id}.json", oss_client)
        data_transfer.upload_data()
        data_transfer = DataTransfer(f"{target_dir}/{batch_id}.jpg", f"{oss_batch_dir}/{batch_id}/{batch_id}.jpg", oss_client)
        data_transfer.upload_data()

    except Exception as e:
        print(f"失败: {batch_id}, 错误: {str(e)}")
        pass
        
import open3d as o3d

if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    is_by_batch = True
    is_transform_save = False
    if is_by_batch:
        # 通过批次下载
        """
        parser.add_argument("--batch_id",  type=str, required=True, help="batch_id")
        parser.add_argument("--workdir", type=str, required=True)
        parser.add_argument("--oss_config", type=str, required=True)
        args = parser.parse_args()
        """
        # batch_id = args.batch_id
        batch_id = 10118 

        # workdir = args.workdir
        workdir = f"{print_factory_type_dir}/{batch_id}"

        # oss_config = args.oss_config
        oss_config = f"{print_factory_type_dir}/print_factory_type_setting_big/download_print/run.yaml" 

        if is_transform_save:
            download_transform_save_by_batch(batch_id, workdir, oss_config)
        else:
            download_datas_by_batch(batch_id, workdir, oss_config)
            
        """
        oss_client = get_oss_client(args.oss_config)
        # 读取所有path
        list_print_model_info = read_paths_from_batch(args.batch_id)
        print(f"从文件读取了 {len(list_print_model_info)} 个path")
        # 批量下载
        for batch_model_info in list_print_model_info:
            print(f"开始下载print_model_info: {batch_model_info}")
            download_data_by_batch(batch_model_info, args.workdir, oss_client)
        """
    else:
        # 通过Json下载
        parser.add_argument("--batch_id",  type=str, required=True, help="包含PID列表的json文件路径")
        parser.add_argument("--workdir", type=str, required=True)
        parser.add_argument("--oss_config", type=str, required=True)
        args = parser.parse_args()

        if is_transform_save:
            download_transform_save_by_json(args.batch_id, args.workdir, args.oss_config)
        else:
            download_datas_by_json(args.batch_id, args.workdir, args.oss_config)
        
        """
        oss_client = get_oss_client(args.oss_config)
        pid_file = os.path.join(args.workdir, "3DPrintLayout.json")
        
        print("pid_file=", pid_file)
        # 读取所有PID
        list_model_info = read_pids_from_json(pid_file)
        print(f"从文件读取了 {len(list_model_info)} 个PID")
        # 批量下载
        for model_info in list_model_info:
            print(f"开始下载PID: {model_info}")
            download_data_by_json(model_info, args.workdir, oss_client)
        """