355 lines
10 KiB
Python
355 lines
10 KiB
Python
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#
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# 0===============================0
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# | PLY files reader/writer |
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# 0===============================0
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#
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# function to read/write .ply files
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Hugues THOMAS - 10/02/2017
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Imports and global variables
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# \**********************************/
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#
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# Basic libs
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import numpy as np
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import sys
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# Define PLY types
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ply_dtypes = dict([
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(b'int8', 'i1'),
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(b'char', 'i1'),
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(b'uint8', 'u1'),
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(b'uchar', 'u1'),
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(b'int16', 'i2'),
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(b'short', 'i2'),
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(b'uint16', 'u2'),
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(b'ushort', 'u2'),
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(b'int32', 'i4'),
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(b'int', 'i4'),
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(b'uint32', 'u4'),
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(b'uint', 'u4'),
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(b'float32', 'f4'),
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(b'float', 'f4'),
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(b'float64', 'f8'),
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(b'double', 'f8')
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])
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# Numpy reader format
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valid_formats = {'ascii': '', 'binary_big_endian': '>',
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'binary_little_endian': '<'}
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Functions
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# \***************/
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#
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def parse_header(plyfile, ext):
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# Variables
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line = []
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properties = []
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num_points = None
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while b'end_header' not in line and line != b'':
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line = plyfile.readline()
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if b'element' in line:
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line = line.split()
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num_points = int(line[2])
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elif b'property' in line:
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line = line.split()
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properties.append((line[2].decode(), ext + ply_dtypes[line[1]]))
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return num_points, properties
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def parse_mesh_header(plyfile, ext):
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# Variables
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line = []
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vertex_properties = []
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num_points = None
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num_faces = None
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current_element = None
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while b'end_header' not in line and line != b'':
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line = plyfile.readline()
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# Find point element
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if b'element vertex' in line:
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current_element = 'vertex'
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line = line.split()
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num_points = int(line[2])
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elif b'element face' in line:
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current_element = 'face'
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line = line.split()
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num_faces = int(line[2])
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elif b'property' in line:
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if current_element == 'vertex':
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line = line.split()
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vertex_properties.append((line[2].decode(), ext + ply_dtypes[line[1]]))
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elif current_element == 'vertex':
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if not line.startswith('property list uchar int'):
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raise ValueError('Unsupported faces property : ' + line)
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return num_points, num_faces, vertex_properties
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def read_ply(filename, triangular_mesh=False):
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"""
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Read ".ply" files
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Parameters
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----------
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filename : string
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the name of the file to read.
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Returns
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-------
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result : array
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data stored in the file
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Examples
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--------
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Store data in file
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>>> points = np.random.rand(5, 3)
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>>> values = np.random.randint(2, size=10)
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>>> write_ply('example.ply', [points, values], ['x', 'y', 'z', 'values'])
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Read the file
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>>> data = read_ply('example.ply')
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>>> values = data['values']
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array([0, 0, 1, 1, 0])
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>>> points = np.vstack((data['x'], data['y'], data['z'])).T
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array([[ 0.466 0.595 0.324]
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[ 0.538 0.407 0.654]
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[ 0.850 0.018 0.988]
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[ 0.395 0.394 0.363]
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[ 0.873 0.996 0.092]])
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"""
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with open(filename, 'rb') as plyfile:
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# Check if the file start with ply
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if b'ply' not in plyfile.readline():
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raise ValueError('The file does not start whith the word ply')
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# get binary_little/big or ascii
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fmt = plyfile.readline().split()[1].decode()
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if fmt == "ascii":
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raise ValueError('The file is not binary')
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# get extension for building the numpy dtypes
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ext = valid_formats[fmt]
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# PointCloud reader vs mesh reader
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if triangular_mesh:
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# Parse header
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num_points, num_faces, properties = parse_mesh_header(plyfile, ext)
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# Get point data
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vertex_data = np.fromfile(plyfile, dtype=properties, count=num_points)
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# Get face data
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face_properties = [('k', ext + 'u1'),
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('v1', ext + 'i4'),
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('v2', ext + 'i4'),
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('v3', ext + 'i4')]
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faces_data = np.fromfile(plyfile, dtype=face_properties, count=num_faces)
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# Return vertex data and concatenated faces
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faces = np.vstack((faces_data['v1'], faces_data['v2'], faces_data['v3'])).T
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data = [vertex_data, faces]
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else:
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# Parse header
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num_points, properties = parse_header(plyfile, ext)
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# Get data
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data = np.fromfile(plyfile, dtype=properties, count=num_points)
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return data
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def header_properties(field_list, field_names):
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# List of lines to write
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lines = []
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# First line describing element vertex
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lines.append('element vertex %d' % field_list[0].shape[0])
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# Properties lines
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i = 0
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for fields in field_list:
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for field in fields.T:
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lines.append('property %s %s' % (field.dtype.name, field_names[i]))
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i += 1
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return lines
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def write_ply(filename, field_list, field_names, triangular_faces=None):
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"""
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Write ".ply" files
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Parameters
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----------
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filename : string
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the name of the file to which the data is saved. A '.ply' extension will be appended to the
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file name if it does no already have one.
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field_list : list, tuple, numpy array
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the fields to be saved in the ply file. Either a numpy array, a list of numpy arrays or a
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tuple of numpy arrays. Each 1D numpy array and each column of 2D numpy arrays are considered
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as one field.
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field_names : list
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the name of each fields as a list of strings. Has to be the same length as the number of
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fields.
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Examples
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--------
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>>> points = np.random.rand(10, 3)
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>>> write_ply('example1.ply', points, ['x', 'y', 'z'])
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>>> values = np.random.randint(2, size=10)
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>>> write_ply('example2.ply', [points, values], ['x', 'y', 'z', 'values'])
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>>> colors = np.random.randint(255, size=(10,3), dtype=np.uint8)
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>>> field_names = ['x', 'y', 'z', 'red', 'green', 'blue', values']
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>>> write_ply('example3.ply', [points, colors, values], field_names)
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"""
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# Format list input to the right form
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field_list = list(field_list) if (type(field_list) == list or type(field_list) == tuple) else list((field_list,))
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for i, field in enumerate(field_list):
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if field.ndim < 2:
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field_list[i] = field.reshape(-1, 1)
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if field.ndim > 2:
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print('fields have more than 2 dimensions')
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return False
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# check all fields have the same number of data
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n_points = [field.shape[0] for field in field_list]
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if not np.all(np.equal(n_points, n_points[0])):
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print('wrong field dimensions')
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return False
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# Check if field_names and field_list have same nb of column
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n_fields = np.sum([field.shape[1] for field in field_list])
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if (n_fields != len(field_names)):
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print('wrong number of field names')
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return False
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# Add extension if not there
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if not filename.endswith('.ply'):
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filename += '.ply'
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# open in text mode to write the header
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with open(filename, 'w') as plyfile:
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# First magical word
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header = ['ply']
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# Encoding format
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header.append('format binary_' + sys.byteorder + '_endian 1.0')
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# Points properties description
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header.extend(header_properties(field_list, field_names))
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# Add faces if needded
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if triangular_faces is not None:
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header.append('element face {:d}'.format(triangular_faces.shape[0]))
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header.append('property list uchar int vertex_indices')
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# End of header
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header.append('end_header')
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# Write all lines
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for line in header:
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plyfile.write("%s\n" % line)
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# open in binary/append to use tofile
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with open(filename, 'ab') as plyfile:
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# Create a structured array
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i = 0
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type_list = []
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for fields in field_list:
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for field in fields.T:
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type_list += [(field_names[i], field.dtype.str)]
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i += 1
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data = np.empty(field_list[0].shape[0], dtype=type_list)
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i = 0
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for fields in field_list:
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for field in fields.T:
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data[field_names[i]] = field
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i += 1
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data.tofile(plyfile)
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if triangular_faces is not None:
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triangular_faces = triangular_faces.astype(np.int32)
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type_list = [('k', 'uint8')] + [(str(ind), 'int32') for ind in range(3)]
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data = np.empty(triangular_faces.shape[0], dtype=type_list)
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data['k'] = np.full((triangular_faces.shape[0],), 3, dtype=np.uint8)
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data['0'] = triangular_faces[:, 0]
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data['1'] = triangular_faces[:, 1]
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data['2'] = triangular_faces[:, 2]
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data.tofile(plyfile)
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return True
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def describe_element(name, df):
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""" Takes the columns of the dataframe and builds a ply-like description
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Parameters
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----------
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name: str
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df: pandas DataFrame
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Returns
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-------
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element: list[str]
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"""
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property_formats = {'f': 'float', 'u': 'uchar', 'i': 'int'}
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element = ['element ' + name + ' ' + str(len(df))]
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if name == 'face':
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element.append("property list uchar int points_indices")
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else:
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for i in range(len(df.columns)):
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# get first letter of dtype to infer format
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f = property_formats[str(df.dtypes[i])[0]]
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element.append('property ' + f + ' ' + df.columns.values[i])
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return element
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