如何将.pts或.npy文件转换为.ply或.h5文件？

Question

我有三维点云数据作为.npy文件和.pts数据。

为了将这些数据用于三维分类神经网络，我必须将这些数据转换为.h5文件。

因此，首先，我尝试使用python将.npy或.pts文件转换为.ply文件。

您能给我参考示例代码或帮助我转换文件格式吗？

另外，我非常感谢将.ply格式转换为.h5格式的方法。

对不起，我的英语水平很差。

Answer 1

我希望这段代码能让你开始，它展示了如何从npy (或随机点)创建一个h5文件。警告组和数据集的名称是任意的(这是一个例子)。

import os
import h5py
import numpy as np

# reading or creating an array of points numpy style
def create_or_load_random_points_npy(file_radix, size, min, max):
    if os.path.exists(file_radix+'.npy'):
        arr = np.load(file_radix+'.npy')
    else:
        arr = np.random.uniform(min, max, (size,3))
        np.save(file_radix, arr)
    return arr


# converting a numpy array (size,3) to a h5 file with two groups representng two way
# of serializing points
def convert_array_2_shades_of_grey(file_radix, arr):
    file = h5py.File(file_radix + '.h5', 'w')
    #only one dataset in a group
    group = file.create_group("single_dataset")
    group.attrs["desc"]=np.string_("random points in a single dataset")
    dset=group.create_dataset("points", (len(arr), len(arr[0])), h5py.h5t.NATIVE_DOUBLE)
    dset[...]=arr
    #create a dataset for each coordinate
    group = file.create_group("several_datasets")
    group.attrs["desc"] = np.string_("random points in a several coordinates (one for each coord)")
    dset = group.create_dataset("x", (len(arr),), h5py.h5t.NATIVE_DOUBLE)
    dset[...] = arr[:, 0]
    dset = group.create_dataset("y", (len(arr),), h5py.h5t.NATIVE_DOUBLE)
    dset[...] = arr[:, 1]
    dset = group.create_dataset("z", (len(arr),), h5py.h5t.NATIVE_DOUBLE)
    dset[...] = arr[:, 2]

# loads the h5 file, choose which way of storing you would like to deserialize
def load_random_points_h5(file_radix, single=True):
    file = h5py.File(file_radix + '.h5', 'r')
    if single:
        group = file["single_dataset"]
        print 'reading -> ', group.attrs["desc"]
        dset=group["points"]
        arr = dset[...]
    else:
        group = file["several_datasets"]
        print 'reading -> ', group.attrs["desc"]
        dset = group["x"]
        arr = np.zeros((dset.size, 3))
        arr[:, 0] = dset[...]
        dset = group["y"]
        arr[:, 1] = dset[...]
        dset = group["z"]
        arr[:, 2] = dset[...]
    return arr

# And now we test !!!
file_radix = 'test'
# create or load the npy file
arr =  create_or_load_random_points_npy(file_radix, 10000, -100.0, 100.0)
# Well, well, what is in the box ?
print arr

# converting numpy array to h5
convert_array_2_shades_of_grey(file_radix, arr)

# loading single dataset style.
arr = load_random_points_h5(file_radix, True)
# Well, well, what is in the box ?
print arr
# loading several dataset style.
arr = load_random_points_h5(file_radix, False)
# Well, well, what is in the box ?
print arr

要查看h5文件的内容，请下载HDFview。

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也不要犹豫，看看h5py文档。

最后但并非最不重要的一点是，您可以向HDF5社区的HDFgroup论坛询问问题(他们提供像这样的闪亮徽章，哇！)

Answer 2

https://stackoverflow.com/a/52340306/13093413的修正/改进

如果您有多个要转换为.npy的.h5文件，那么将它们所在的目录的路径写入变量NPY_DIRECTORY

from os import listdir
from os.path import isfile, join
import os
import h5py
import numpy as np
NPY_FILES_DIRECTORY = ""
filenames = [f for f in listdir(NPY_FILES_DIRECTORY) if isfile(join(NPY_FILES_DIRECTORY, f))]



# reading or creating an array of points numpy style
def create_or_load_random_points_npy(filename, size, min, max):
    if os.path.exists(filename):
        arr = np.load(filename)
    else:
        arr = np.random.uniform(min, max, (size,3))
        np.save(filename, arr)
    return arr


# converting a numpy array (size,3) to a h5 file with two groups representng two way
# of serializing points
def convert_array_2_shades_of_grey(filename, arr):
    file = h5py.File(filename + '.h5', 'w')
    #only one dataset in a group
    group = file.create_group("single_dataset")
    group.attrs["desc"]=np.string_("random points in a single dataset")
    dset=group.create_dataset("points", (len(arr), len(arr[0])), h5py.h5t.NATIVE_DOUBLE)
    dset[...]=arr
    #create a dataset for each coordinate
    group = file.create_group("several_datasets")
    group.attrs["desc"] = np.string_("random points in a several coordinates (one for each coord)")
    dset = group.create_dataset("x", (len(arr),), h5py.h5t.NATIVE_DOUBLE)
    dset[...] = arr[:, 0]
    dset = group.create_dataset("y", (len(arr),), h5py.h5t.NATIVE_DOUBLE)
    dset[...] = arr[:, 1]
    dset = group.create_dataset("z", (len(arr),), h5py.h5t.NATIVE_DOUBLE)
    dset[...] = arr[:, 2]

# loads the h5 file, choose which way of storing you would like to deserialize
def load_random_points_h5(filename, single=True):
    file = h5py.File(filename + '.h5', 'r')
    if single:
        group = file["single_dataset"]
        print('reading -> ', group.attrs["desc"])
        dset=group["points"]
        arr = dset[...]
    else:
        group = file["several_datasets"]
        print('reading -> ', group.attrs["desc"])
        dset = group["x"]
        arr = np.zeros((dset.size, 3))
        arr[:, 0] = dset[...]
        dset = group["y"]
        arr[:, 1] = dset[...]
        dset = group["z"]
        arr[:, 2] = dset[...]
    return arr

# And now we test !!!
for filename in filenames:
    # create or load the npy file
    arr = create_or_load_random_points_npy(filename, 10000, -100.0, 100.0)
    # Well, well, what is in the box ?
    print(arr)

    # converting numpy array to h5
    convert_array_2_shades_of_grey(filename, arr)

    # loading single dataset style.
    arr = load_random_points_h5(filename, True)
    # Well, well, what is in the box ?
    print(arr)
    # loading several dataset style.
    arr = load_random_points_h5(filename, False)
    # Well, well, what is in the box ?
    print(arr)