Metadata-Version: 2.1 Name: plyfile Version: 0.8.1 Summary: PLY file reader/writer Keywords: ply,numpy Author-email: Darsh Ranjan Requires-Python: >=3.7 Classifier: Development Status :: 4 - Beta Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+) Classifier: Operating System :: OS Independent Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Classifier: Topic :: Scientific/Engineering Requires-Dist: numpy>=1.17 Project-URL: Homepage, https://github.com/dranjan/python-plyfile Description-Content-Type: text/markdown ![Build Status](https://github.com/dranjan/python-plyfile/actions/workflows/python-package.yml/badge.svg) Welcome to the `plyfile` Python module, which provides a simple facility for reading and writing ASCII and binary PLY files. The PLY format is documented [elsewhere](https://web.archive.org/web/20161221115231/http://www.cs.virginia.edu/~gfx/Courses/2001/Advanced.spring.01/plylib/Ply.txt). # Installation ## Dependencies - python3 >= 3.7 - numpy >= 1.17 (`plyfile` may or may not work on older versions.) ### Optional dependencies - tox (for test suite) - pytest (for test suite) ## Installing plyfile Quick way: pip3 install plyfile Or clone the repository and run from the project root: pip3 install . Or just copy `plyfile.py` into your GPL-compatible project. ## Running test suite Preferred (more comprehensive; requires tox): tox -v --skip-missing-interpreters Alternate (requires pytest): pytest test -v # Usage Both deserialization and serialization of PLY file data is done through `PlyData` and `PlyElement` instances. ```Python Console >>> 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 ```Python Console >>> plydata = PlyData.read('tet.ply') ``` or ```Python Console >>> 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, the corresponding `numpy` field type is `object`, with the members being `numpy` arrays (see the `vertex_indices` example below). Concretely: ```Python Console >>> plydata.elements[0].name 'vertex' >>> plydata.elements[0].data[0] (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: ```Python Console >>> plydata['vertex']['x'] array([ 0., 0., 1., 1.], dtype=float32) ``` and elements can be indexed directly without explicitly going through the `data` attribute: ```Python Console >>> plydata['vertex'][0] (0.0, 0.0, 0.0) ``` The above expression is equivalent to `plydata['vertex'].data[0]`. `PlyElement` instances also contain metadata: ```Python Console >>> 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: ```Python Console >>> 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 using the `mmap` argument: ```Python Console >>> plydata.text = False >>> plydata.byte_order = '<' >>> plydata.write('tet_binary.ply') >>> >>> # `mmap=True` is the default: >>> plydata = PlyData.read('tet_binary_ply') >>> isinstance(plydata['vertex'].data, numpy.memmap) True >>> plydata = PlyData.read('tet_binary_ply', mmap=False) >>> isinstance(plydata['vertex'].data, numpy.memmap) False ``` #### 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`: ```Python Console >>> 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; 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 below (and `examples/plot.py`) 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` kwarg 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 (as in `test/test.py`): ```Python Console >>> 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: ```Python Console >>> el = PlyElement.describe(some_array, 'some_name') ``` or ```Python Console >>> el = PlyElement.describe(some_array, 'some_name', ... 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: ```Python Console >>> el = PlyElement.describe(some_array, 'some_name', ... val_dtypes={'some_property': 'f8'}, ... len_dtypes={'some_property': 'u4'}) ``` Now you can instantiate `PlyData` and serialize: ```Python Console >>> 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: ```Python Console >>> ply = PlyData([el], comments=['header comment']) >>> ply.comments ['header comment'] ``` As of version 0.3, "obj_info" comments are supported as well: ```Python Console >>> 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, assuming we know the row length in advance: ```Python Console >>> plydata = PlyData.read('tet.ply') >>> tri_data = plydata['face'].data['vertex_indices'] >>> triangles = numpy.vstack(tri_data) ``` ### 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. As of version 0.4, the following in-place mutations are supported: - 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 subset 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: ```Python Console >>> 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: ```Python Console >>> # 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. # FAQ ## How do I initialize a list property from two-dimensional array? ```Python Console >>> # Here's a two-dimensional array containing vertex indices. >>> face_data = numpy.array([[0, 1, 2], [3, 4, 5]], dtype='i4') >>> >>> # PlyElement.describe requires a one-dimensional structured array. >>> ply_faces = numpy.empty(len(faces), ... dtype=[('vertex_indices', 'i4', (3,))]) >>> ply_faces['vertex_indices'] = face_data >>> face = PlyElement.describe(ply_faces, 'face') ``` ## Can I save a PLY file directly to `sys.stdout`? On Python 3, you will probably run into issues because `sys.stdout` is a text-mode stream and `plyfile` outputs binary data, even for ASCII-format PLY files: ```Python Console >>> import sys >>> plydata.write(sys.stdout) Traceback (most recent call last): File "", line 1, in File ".../python-plyfile/plyfile.py", line 411, in write stream.write(self.header.encode('ascii')) TypeError: write() argument must be str, not bytes ``` There are a few ways around this. - Write to a named file instead. On Linux and some other Unix-likes, you can access `stdout` via the named file `/dev/stdout`: ```Python Console >>> plydata.write('/dev/stdout') ``` - Use `sys.stdout.buffer`: ```Python Console >>> plydata.write(sys.stdout.buffer) ``` (source: https://bugs.python.org/issue4571) # Design philosophy and rationale The design philosophy of `plyfile` can be summed up as follows. - Be familiar to users of `numpy` and reuse existing idioms and concepts when possible. - Favor simplicity over power or user-friendliness. - Support all valid PLY files. ## Familiarity For the most part, PLY concepts map nicely to Python and specifically to `numpy`, and leveraging that has strongly influenced the design of this package. The `elements` attribute of a `PlyData` instance is simply a `list` of `PlyElement` instances, and the `data` attribute of a `PlyElement` instance is a `numpy` array, and a list property field of a PLY element datum is referred to in the `data` attribute by a type of `object` with the value being another `numpy` array, etc. ## Simplicity When applicable, we favor simplicity over power or user-friendliness. Thus, list property types in `PlyElement.describe` always default to the same, rather than, say, being obtained by looking at an array element. (Which element? What if the array has length zero? Whatever default we could choose in that case could lead to subtle edge-case bugs if the user isn't vigilant.) Also, all input and output is done in "one shot": all the arrays must be created up front rather than being processed in a streaming fashion. ## Generality and interpretation issues We aim to support all valid PLY files. However, exactly what constitutes a "valid" file isn't obvious, since there doesn't seem to be a single complete and consistent description of the PLY format. Even the "authoritative" [Ply.txt](https://web.archive.org/web/20161221115231/http://www.cs.virginia.edu/~gfx/Courses/2001/Advanced.spring.01/plylib/Ply.txt) by Greg Turk has some issues. ### Comment placement Where can comments appear in the header? It appears that in all the "official" examples, all comments immediately follow the "format" line, but the language of the document neither places any such restrictions nor explicitly allows comments to be placed anywhere else. Thus, it isn't clear whether comments can appear anywhere in the header or must immediately follow the "format" line. At least one popular reader of PLY files chokes on comments before the "format" line. `plyfile` accepts comments anywhere in the header in input but only places them in a few limited places in output, namely immediately after "format" and "element" lines. ### Element and property names Another ambiguity is names: what strings are allowed as PLY element and property names? `plyfile` accepts as input any name that doesn't contain spaces, but this is surely too generous. This may not be such a big deal, though: although names are theoretically arbitrary, in practice, the majority of PLY element and property names probably come from a small finite set ("face", "x", "nx", "green", etc.). ### Property syntax A more serious problem is that the PLY format specification appears to be inconsistent regarding the syntax of property definitions. In some examples, it uses the syntax property {type} {name} and in others, property {name} {type} `plyfile` only supports the former, which appears to be standard _de facto_. ### Header line endings The specification explicitly states that lines in the header must end with carriage returns, but this rule doesn't seem to be followed by anyone, including the C-language PLY implementation by Greg Turk, the author of the format. Here, we stick to common practice and output Unix-style line endings (with no carriage returns) but accept any line ending style in input files. # More examples Examples beyond the scope of this document and the tests are in the `examples` directory. # License Copyright Darsh Ranjan. This software is released under the terms of the GNU General Public License, version 3. See the file `COPYING` for details.