Usage

Both deserialization and serialization of PLY file data is done through PlyData and PlyElement instances.

>>> import numpy
>>> from plyfile import PlyData, PlyElement
>>>

For the code examples that follow, assume the file tet.ply contains the following text:

ply
format ascii 1.0
comment single tetrahedron with colored faces
element vertex 4
comment tetrahedron vertices
property float x
property float y
property float z
element face 4
property list uchar int vertex_indices
property uchar red
property uchar green
property uchar blue
end_header
0 0 0
0 1 1
1 0 1
1 1 0
3 0 1 2 255 255 255
3 0 2 3 255 0 0
3 0 1 3 0 255 0
3 1 2 3 0 0 255

(This file is available under the examples directory.)

Reading a PLY file

>>> plydata = PlyData.read('tet.ply')
>>>

or

>>> with open('tet.ply', 'rb') as f:
...     plydata = PlyData.read(f)
>>>

The static method PlyData.read returns a PlyData instance, which is plyfile’s representation of the data in a PLY file. A PlyData instance has an attribute elements, which is a list of PlyElement instances, each of which has a data attribute which is a numpy structured array containing the numerical data. PLY file elements map onto numpy structured arrays in a pretty obvious way. For a list property in an element, by default, the corresponding numpy field type is object, with the members being numpy arrays (see the vertex_indices example below).[1]

Concretely:

>>> plydata.elements[0].name
'vertex'
>>> plydata.elements[0].data[0].tolist()
(0.0, 0.0, 0.0)
>>> plydata.elements[0].data['x']
array([0., 0., 1., 1.], dtype=float32)
>>> plydata['face'].data['vertex_indices'][0]
array([0, 1, 2], dtype=int32)
>>>

For convenience, elements and properties can be looked up by name:

>>> plydata['vertex']['x']
array([0., 0., 1., 1.], dtype=float32)
>>>

and elements can be indexed directly without explicitly going through the data attribute:

>>> plydata['vertex'][0].tolist()
(0.0, 0.0, 0.0)
>>>

The above expression is equivalent to plydata['vertex'].data[0].

PlyElement instances also contain metadata:

>>> plydata.elements[0].properties
(PlyProperty('x', 'float'), PlyProperty('y', 'float'), PlyProperty('z', 'float'))
>>> plydata.elements[0].count
4
>>>

PlyProperty and PlyListProperty instances are used internally as a convenient intermediate representation of PLY element properties that can easily be serialized to a PLY header (using str) or converted to numpy-compatible type descriptions (via the dtype method). It’s not extremely common to manipulate them directly, but if needed, the property metadata of an element can be accessed as a tuple via the properties attribute (as illustrated above) or looked up by name:

>>> plydata.elements[0].ply_property('x')
PlyProperty('x', 'float')
>>>

Many (but not necessarily all) types of malformed input files will raise PlyParseError when PlyData.read is called. The string value of the PlyParseError instance (as well as attributes element, row, and prop) provides additional context for the error if applicable.

Faster reading via memory mapping

To accelerate parsing of binary data, plyfile can make use of numpy’s memory mapping facilities. The decision to memory map or not is made on a per-element basis. To make this determination, there are two cases to consider.

Case 1: elements with no list properties

If an element in a binary PLY file has no list properties, then it will be memory-mapped by default, subject to the capabilities of the underlying file object.

• Memory mapping can be disabled or fine-tuned using the mmap argument of PlyData.read.

• To confirm whether a given element has been memory-mapped or not, check the type of element.data.

This is all illustrated below:

>>> plydata.text = False
>>> plydata.byte_order = '<'
>>> plydata.write('tet_binary.ply')
>>>
>>> # Memory-mapping is enabled by default.
>>> plydata = PlyData.read('tet_binary.ply')
>>> isinstance(plydata['vertex'].data, numpy.memmap)
True
>>> # Any falsy value disables memory-mapping here.
>>> plydata = PlyData.read('tet_binary.ply', mmap=False)
>>> isinstance(plydata['vertex'].data, numpy.memmap)
False
>>> # Strings can also be given to fine-tune memory-mapping.
>>> # For example, with 'r+', changes can be written back to the file.
>>> # In this case, the file must be explicitly opened with read-write
>>> # access.
>>> with open('tet_binary.ply', 'r+b') as f:
...     plydata = PlyData.read(f, mmap='r+')
>>> isinstance(plydata['vertex'].data, numpy.memmap)
True
>>> plydata['vertex']['x'] = 100
>>> plydata['vertex'].data.flush()
>>> plydata = PlyData.read('tet_binary.ply')
>>> all(plydata['vertex']['x'] == 100)
True
>>>

Case 2: elements with list properties

In the general case, elements with list properties cannot be memory-mapped as numpy arrays, except in one important case: when all list properties have fixed and known lengths. In that case, the known_list_len argument can be given to PlyData.read:

>>> plydata = PlyData.read('tet_binary.ply',
...                        known_list_len={'face': {'vertex_indices': 3}})
>>> isinstance(plydata['face'].data, numpy.memmap)
True
>>>

The implementation will validate the data: if any instance of the list property has a length other than the value specified, then PlyParseError will be raised.

Note that in order to enable memory mapping for a given element, all list properties in the element must have their lengths in the known_list_len dictionary. If any list property does not have its length given in known_list_len, then memory mapping will not be attempted, and no error will be raised.

Creating a PLY file

The first step is to get your data into numpy structured arrays. Note that there are some restrictions: generally speaking, if you know the types of properties a PLY file element can contain, you can easily deduce the restrictions. For example, PLY files don’t contain 64-bit integer or complex data, so these aren’t allowed.

For convenience, non-scalar fields are allowed, and they will be serialized as list properties. For example, when constructing a “face” element, if all the faces are triangles (a common occurrence), it’s okay to have a “vertex_indices” field of type 'i4' and shape (3,) instead of type object and shape (). However, if the serialized PLY file is read back in using plyfile, the “vertex_indices” property will be represented as an object-typed field, each of whose values is an array of type 'i4' and length 3. The reason is simply that the PLY format provides no way to find out that each “vertex_indices” field has length 3 without actually reading all the data, so plyfile has to assume that this is a variable-length property. However, see the FAQ for an easy way to recover a two-dimensional array from a list property, and also see the notes above about the known_list_len parameter to speed up the reading of files with lists of fixed, known length.

For example, if we wanted to create the “vertex” and “face” PLY elements of the tet.ply data directly as numpy arrays for the purpose of serialization, we could do this:

>>> vertex = numpy.array([(0, 0, 0),
...                       (0, 1, 1),
...                       (1, 0, 1),
...                       (1, 1, 0)],
...                      dtype=[('x', 'f4'), ('y', 'f4'),
...                             ('z', 'f4')])
>>> face = numpy.array([([0, 1, 2], 255, 255, 255),
...                     ([0, 2, 3], 255,   0,   0),
...                     ([0, 1, 3],   0, 255,   0),
...                     ([1, 2, 3],   0,   0, 255)],
...                    dtype=[('vertex_indices', 'i4', (3,)),
...                           ('red', 'u1'), ('green', 'u1'),
...                           ('blue', 'u1')])
>>>

Once you have suitably structured array, the static method PlyElement.describe can then be used to create the necessary PlyElement instances:

>>> el = PlyElement.describe(vertex, 'vertex')
>>>

or

>>> el = PlyElement.describe(vertex, 'vertex',
...                          comments=['comment1',
...                                    'comment2'])
>>>

Note that there’s no need to create PlyProperty instances explicitly. This is all done behind the scenes by examining some_array.dtype.descr. One slight hiccup here is that variable-length fields in a numpy array (i.e., our representation of PLY list properties) must have a type of object, so the types of the list length and values in the serialized PLY file can’t be obtained from the array’s dtype attribute alone. For simplicity and predictability, the length defaults to 8-bit unsigned integer, and the value defaults to 32-bit signed integer, which covers the majority of use cases. Exceptions must be stated explicitly:

>>> el = PlyElement.describe(face, 'face',
...                          val_types={'vertex_indices': 'u2'},
...                          len_types={'vertex_indices': 'u4'})
>>>

Now you can instantiate PlyData and serialize:

>>> PlyData([el]).write('some_binary.ply')
>>> PlyData([el], text=True).write('some_ascii.ply')
>>>
>>> # Force the byte order of the output to big-endian, independently of
>>> # the machine's native byte order
>>> PlyData([el],
...         byte_order='>').write('some_big_endian_binary.ply')
>>>
>>> # Use a file object. Binary mode is used here, which will cause
>>> # Unix-style line endings to be written on all systems.
>>> with open('some_ascii.ply', mode='wb') as f:
...     PlyData([el], text=True).write(f)
>>>

Miscellaneous

Comments

Header comments are supported:

>>> ply = PlyData([el], comments=['header comment'])
>>> ply.comments
['header comment']
>>>

obj_info comments are supported as well:

>>> ply = PlyData([el], obj_info=['obj_info1', 'obj_info2'])
>>> ply.obj_info
['obj_info1', 'obj_info2']
>>>

When written, they will be placed after regular comments after the “format” line.

Comments can have leading whitespace, but trailing whitespace may be stripped and should not be relied upon. Comments may not contain embedded newlines.

Getting a two-dimensional array from a list property

The PLY format provides no way to assert that all the data for a given list property is of the same length, yet this is a relatively common occurrence. For example, all the “vertex_indices” data on a “face” element will have length three for a triangular mesh. In such cases, it’s usually much more convenient to have the data in a two-dimensional array, as opposed to a one-dimensional array of type object. Here’s a pretty easy way to obtain a two dimensional array:

>>> plydata = PlyData.read('tet.ply')
>>> tri_data = plydata['face'].data['vertex_indices']
>>> triangles = numpy.vstack(tri_data)
>>>

(If the row lengths of all list properties are known in advance, the known_list_len parameter can also be used.)

Instance mutability

A plausible code pattern is to read a PLY file into a PlyData instance, perform some operations on it, possibly modifying data and metadata in place, and write the result to a new file. This pattern is partially supported. The following in-place mutations are possible:

• Modifying numerical array data only.

• Assigning directly to a PlyData instance’s elements.

• Switching format by changing the text and byte_order attributes of a PlyData instance. This will switch between ascii, binary_little_endian, and binary_big_endian PLY formats.

• Modifying a PlyData instance’s comments and obj_info, and modifying a PlyElement instance’s comments.

• Assigning to an element’s data. Note that the property metadata in properties is not touched by this, so for every property in the properties list of the PlyElement instance, the data array must have a field with the same name (but possibly different type, and possibly in different order). The array can have additional fields as well, but they won’t be output when writing the element to a PLY file. The properties in the output file will appear as they are in the properties list. If an array field has a different type than the corresponding PlyProperty instance, then it will be cast when writing.

• Assigning directly to an element’s properties. Note that the data array is not touched, and the previous note regarding the relationship between properties and data still applies: the field names of data must be a superset of the property names in properties, but they can be in a different order and specify different types.

• Changing a PlyProperty or PlyListProperty instance’s val_dtype or a PlyListProperty instance’s len_dtype, which will perform casting when writing.

Modifying the name of a PlyElement, PlyProperty, or PlyListProperty instance is not supported and will raise an error. To rename a property of a PlyElement instance, you can remove the property from properties, rename the field in data, and re-add the property to properties with the new name by creating a new PlyProperty or PlyListProperty instance:

>>> from plyfile import PlyProperty, PlyListProperty
>>> face = plydata['face']
>>> face.properties = ()
>>> face.data.dtype.names = ['idx', 'r', 'g', 'b']
>>> face.properties = (PlyListProperty('idx', 'uchar', 'int'),
...                    PlyProperty('r', 'uchar'),
...                    PlyProperty('g', 'uchar'),
...                    PlyProperty('b', 'uchar'))
>>>

Note that it is always safe to create a new PlyElement or PlyData instance instead of modifying one in place, and this is the recommended style:

>>> # Recommended:
>>> plydata = PlyData([plydata['face'], plydata['vertex']],
...                   text=False, byte_order='<')
>>>
>>> # Also supported:
>>> plydata.elements = [plydata['face'], plydata['vertex']]
>>> plydata.text = False
>>> plydata.byte_order = '<'
>>> plydata.comments = []
>>> plydata.obj_info = []
>>>

Objects created by this library don’t claim ownership of the other objects they refer to, which has implications for both styles (creating new instances and modifying in place). For example, a single PlyElement instance can be contained by multiple PlyData instances, but modifying that instance will then affect all of those containing PlyData instances.

Text-mode streams

Input and output on text-mode streams is supported for ASCII-format PLY files, but not binary-format PLY files. Input and output on binary streams is supported for all valid PLY files. Note that sys.stdout and sys.stdin are text streams, so they can only be used directly for ASCII-format PLY files.

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[1]

Also see the section on known_list_len.