391 lines
13 KiB
Python
391 lines
13 KiB
Python
import numpy as np
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import scipy.io as sio
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from struct import pack, unpack
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"""
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Reads PC2 files, and proposed format PC16 files
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Inputs:
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- file: path to .pc2/.pc16 file
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- float16: False for PC2 files, True for PC16
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Output:
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- data: dictionary with .pc2/.pc16 file data
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NOTE: 16-bit floats lose precision with high values (positive or negative),
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we do not recommend using this format for data outside range [-2, 2]
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"""
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def readPC2(file, float16=False):
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assert file.endswith('.pc2') and not float16 or file.endswith('.pc16') and float16, 'File format not consistent with specified input format'
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data = {}
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bytes = 2 if float16 else 4
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dtype = np.float16 if float16 else np.float32
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with open(file, 'rb') as f:
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# Header
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data['sign'] = f.read(12)
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# data['version'] = int.from_bytes(f.read(4), 'little')
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data['version'] = unpack('<i', f.read(4))[0]
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# Num points
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# data['nPoints'] = int.from_bytes(f.read(4), 'little')
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data['nPoints'] = unpack('<i', f.read(4))[0]
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# Start frame
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data['startFrame'] = unpack('f', f.read(4))
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# Sample rate
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data['sampleRate'] = unpack('f', f.read(4))
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# Number of samples
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# data['nSamples'] = int.from_bytes(f.read(4), 'little')
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data['nSamples'] = unpack('<i', f.read(4))[0]
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# Animation data
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size = data['nPoints']*data['nSamples']*3*bytes
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data['V'] = np.frombuffer(f.read(size), dtype=dtype).astype(np.float32)
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data['V'] = data['V'].reshape(data['nSamples'], data['nPoints'], 3)
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return data
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"""
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Reads an specific frame of PC2/PC16 files
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Inputs:
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- file: path to .pc2/.pc16 file
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- frame: number of the frame to read
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- float16: False for PC2 files, True for PC16
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Output:
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- T: mesh vertex data at specified frame
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"""
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def readPC2Frame(file, frame, float16=False):
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assert file.endswith('.pc2') and not float16 or file.endswith('.pc16') and float16, 'File format not consistent with specified input format'
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assert frame >= 0 and isinstance(frame,int), 'Frame must be a positive integer'
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bytes = 2 if float16 else 4
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dtype = np.float16 if float16 else np.float32
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with open(file,'rb') as f:
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# Num points
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f.seek(16)
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# nPoints = int.from_bytes(f.read(4), 'little')
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nPoints = unpack('<i', f.read(4))[0]
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# Number of samples
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f.seek(28)
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# nSamples = int.from_bytes(f.read(4), 'little')
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nSamples = unpack('<i', f.read(4))[0]
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if frame > nSamples:
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print("Frame index outside size")
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print("\tN. frame: " + str(frame))
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print("\tN. samples: " + str(nSamples))
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return
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# Read frame
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size = nPoints * 3 * bytes
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f.seek(size * frame, 1) # offset from current '1'
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T = np.frombuffer(f.read(size), dtype=dtype).astype(np.float32)
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return T.reshape(nPoints, 3)
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"""
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Writes PC2 and PC16 files
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Inputs:
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- file: path to file (overwrites if exists)
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- V: 3D animation data as a three dimensional array (N. Frames x N. Vertices x 3)
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- float16: False for writing as PC2 file, True for PC16
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This function assumes 'startFrame' to be 0 and 'sampleRate' to be 1
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NOTE: 16-bit floats lose precision with high values (positive or negative),
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we do not recommend using this format for data outside range [-2, 2]
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"""
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def writePC2(file, V, float16=False):
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assert file.endswith('.pc2') and not float16 or file.endswith('.pc16') and float16, 'File format not consistent with specified input format'
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if float16: V = V.astype(np.float16)
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else: V = V.astype(np.float32)
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with open(file, 'wb') as f:
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# Create the header
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headerFormat='<12siiffi'
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headerStr = pack(headerFormat, b'POINTCACHE2\0',
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1, V.shape[1], 0, 1, V.shape[0])
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f.write(headerStr)
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# Write vertices
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f.write(V.tobytes())
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"""
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Reads OBJ files
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Only handles vertices, faces and UV maps
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Input:
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- file: path to .obj file
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Outputs:
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- V: 3D vertices
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- F: 3D faces
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- Vt: UV vertices
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- Ft: UV faces
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Correspondence between mesh and UV map is implicit in F to Ft correspondences
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If no UV map data in .obj file, it shall return Vt=None and Ft=None
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"""
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def readOBJ(file):
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V, Vt, F, Ft = [], [], [], []
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with open(file, 'r') as f:
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T = f.readlines()
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for t in T:
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# 3D vertex
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if t.startswith('v '):
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v = [float(n) for n in t.replace('v ','').split(' ')]
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V += [v]
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# UV vertex
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elif t.startswith('vt '):
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v = [float(n) for n in t.replace('vt ','').split(' ')]
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Vt += [v]
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# Face
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elif t.startswith('f '):
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idx = [n.split('/') for n in t.replace('f ','').split(' ')]
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f = [int(n[0]) - 1 for n in idx]
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F += [f]
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# UV face
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if '/' in t:
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f = [int(n[1]) - 1 for n in idx]
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Ft += [f]
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V = np.array(V, np.float32)
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Vt = np.array(Vt, np.float32)
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if Ft: assert len(F) == len(Ft), 'Inconsistent .obj file, mesh and UV map do not have the same number of faces'
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else: Vt, Ft = None, None
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return V, F, Vt, Ft
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"""
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Writes OBJ files
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Only handles vertices, faces and UV maps
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Inputs:
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- file: path to .obj file (overwrites if exists)
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- V: 3D vertices
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- F: 3D faces
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- Vt: UV vertices
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- Ft: UV faces
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Correspondence between mesh and UV map is implicit in F to Ft correspondences
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If no UV map data as input, it will write only 3D data in .obj file
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"""
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def writeOBJ(file, V, F, Vt=None, Ft=None):
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if not Vt is None:
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assert len(F) == len(Ft), 'Inconsistent data, mesh and UV map do not have the same number of faces'
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with open(file, 'w') as file:
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# Vertices
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for v in V:
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line = 'v ' + ' '.join([str(_) for _ in v]) + '\n'
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file.write(line)
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# UV verts
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if not Vt is None:
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for v in Vt:
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line = 'vt ' + ' '.join([str(_) for _ in v]) + '\n'
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file.write(line)
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# 3D Faces / UV faces
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if Ft:
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F = [[str(i+1)+'/'+str(j+1) for i,j in zip(f,ft)] for f,ft in zip(F,Ft)]
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else:
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F = [[str(i + 1) for i in f] for f in F]
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for f in F:
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line = 'f ' + ' '.join(f) + '\n'
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file.write(line)
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def loadInfo(filename):
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'''
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this function should be called instead of direct sio.loadmat
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as it cures the problem of not properly recovering python dictionaries
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from mat files. It calls the function check keys to cure all entries
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which are still mat-objects
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'''
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data = sio.loadmat(filename, struct_as_record=False, squeeze_me=True)
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del data['__globals__']
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del data['__header__']
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del data['__version__']
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return _check_keys(data)
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def _check_keys(dict):
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'''
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checks if entries in dictionary are mat-objects. If yes
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todict is called to change them to nested dictionaries
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'''
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for key in dict:
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if isinstance(dict[key], sio.matlab.mio5_params.mat_struct):
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dict[key] = _todict(dict[key])
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return dict
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def _todict(matobj):
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'''
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A recursive function which constructs from matobjects nested dictionaries
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'''
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dict = {}
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for strg in matobj._fieldnames:
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elem = matobj.__dict__[strg]
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if isinstance(elem, sio.matlab.mio5_params.mat_struct):
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dict[strg] = _todict(elem)
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elif isinstance(elem, np.ndarray) and np.any([isinstance(item, sio.matlab.mio5_params.mat_struct) for item in elem]):
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dict[strg] = [None] * len(elem)
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for i,item in enumerate(elem):
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if isinstance(item, sio.matlab.mio5_params.mat_struct):
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dict[strg][i] = _todict(item)
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else:
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dict[strg][i] = item
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else:
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dict[strg] = elem
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return dict
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# import os
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# import sys
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# import numpy as np
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# from PIL import Image
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# sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
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# from cloth3d.smpl_np import SMPLModel
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# from cloth3d.util import loadInfo, zRotMatrix, proj, mesh2UV, uv_to_pixel
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# from superSMPL.IO import readOBJ, readPC2Frame
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# class DataReader:
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# def __init__(self, folder, smpl_path):
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# # Data paths
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# self.SRC = folder
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# # SMPL model
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# smpl_path = smpl_path
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# self.smpl = {
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# 'f': SMPLModel(os.path.join(smpl_path, 'super_model_f.pkl')),
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# 'm': SMPLModel(os.path.join(smpl_path, 'super_model_m.pkl'))
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# }
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# """
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# Read sample info
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# Input:
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# - sample: name of the sample e.g.:'01_01_s0'
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# """
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# def read_info(self, sample):
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# info_path = os.path.join(self.SRC, sample, 'info')
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# return loadInfo(info_path)
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# """ Human data """
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# """
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# Read SMPL parameters for the specified sample and frame
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# Inputs:
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# - sample: name of the sample
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# - frame: frame number
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# """
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# def read_smpl_params(self, sample, frame):
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# # Read sample data
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# info = self.read_info(sample)
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# # SMPL parameters
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# gender = 'm' if info['gender'] else 'f'
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# pose = info['poses'][:, frame].reshape(self.smpl[gender].pose_shape)
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# shape = info['shape']
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# trans = info['trans'][:, frame].reshape(self.smpl[gender].trans_shape)
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# return gender, pose, shape, trans
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# """
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# Computes human mesh for the specified sample and frame
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# Inputs:
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# - sample: name of the sample
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# - frame: frame number
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# - absolute: True for absolute vertex locations, False for locations relative to SMPL root joint
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# Outputs:
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# - V: human mesh vertices
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# - F: mesh faces
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# """
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# def read_human(self, sample, frame, zrot, use_trans=True):
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# # SMPL parameters
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# gender, pose, shape, trans = self.read_smpl_params(sample, frame)
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# # Compute SMPL
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# V, J = self.smpl[gender].set_params(pose=pose, beta=shape, trans=trans if use_trans else None)
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# V -= J[0:1]
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# # Apply rotation on z-axis
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# zRot = zRotMatrix(zrot)
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# return zRot.dot(V.T).T, self.smpl[gender].faces, zRot.dot(trans.T).T[None,:], zRot.dot((J - J[0:1] + trans[None,:]).T).T[None,:], zRot.dot((J - J[0:1]).T).T[None,:]
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# """ Garment data """
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# """
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# Reads garment vertices location for the specified sample, garment and frame
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# Inputs:
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# - sample: name of the sample
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# - garment: type of garment (e.g.: 'Tshirt', 'Jumpsuit', ...)
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# - frame: frame number
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# - TODO: absolute: True for absolute vertex locations, False for locations relative to SMPL root joint
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# Outputs:
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# - V: 3D vertex locations for the specified sample, garment and frame
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# """
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# def read_garment_vertices(self, sample, garment, frame, trans, zrot):
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# # Read garment vertices (relative to root joint)
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# pc16_path = os.path.join(self.SRC, sample, garment + '.pc16')
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# V = readPC2Frame(pc16_path, frame, True)
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# # Read sample data
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# if trans is not None:
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# # Transform to absolute
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# V += trans[None]
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# zRot = zRotMatrix(zrot)
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# return zRot.dot(V.T).T
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# """
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# Reads garment faces for the specified sample and garment
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# Inputs:
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# - sample: name of the sample
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# - garment: type of garment (e.g.: 'Tshirt', 'Jumpsuit', ...)
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# Outputs:
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# - F: mesh faces
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# """
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# def read_garment_topology(self, sample, garment):
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# # Read OBJ file
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# obj_path = os.path.join(self.SRC, sample, garment + '.obj')
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# return readOBJ(obj_path)[1]
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# """
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# Reads garment UV map for the specified sample and garment
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# Inputs:
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# - sample: name of the sample
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# - garment: type of garment (e.g.: 'Tshirt', 'Jumpsuit', ...)
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# Outputs:
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# - Vt: UV map vertices
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# - Ft: UV map faces
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# """
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# def read_garment_UVMap(self, sample, garment):
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# # Read OBJ file
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# obj_path = os.path.join(self.SRC, sample, garment + '.obj')
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# return readOBJ(obj_path)[2:]
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# """
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# Reads vertex colors of the specified sample and garment
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# Inputs:
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# - sample: name of the sample
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# - garment: type of garment (e.g.: 'Tshirt', 'Jumpsuit', ...)
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# - F: mesh faces
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# - Vt: UV map vertices
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# - Ft: UV map faces
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# Output
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# - C: RGB colors
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# """
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# def read_garment_vertex_colors(self, sample, garment, F, Vt, Ft):
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# # Read sample texture info
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# texture = self.read_info(sample)['outfit'][garment]['texture']
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# # Plain color
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# if texture['type'] == 'color': return (255*texture['data']).astype(np.int32)
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# # Image texture
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# img_path = os.path.join(self.SRC, sample, garment + '.png')
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# img = Image.open(img_path)
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# # Get color of each UV vertex
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# color = np.array([img.getpixel(uv_to_pixel(vt)) for vt in Vt])
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# # Compute correspondece between V (mesh) and Vt (UV map)
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# m2uv = mesh2UV(F, Ft)
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# return np.array([color[list(m2uv[idx])].mean(0) for idx in sorted(list(m2uv.keys()))], np.uint8)
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# """ Scene data """
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# """
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# Read camera location and compute projection matrix
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# Input:
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# - sample: name of the sample
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# Output:
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# - P: camera projection matrix (3 x 4)
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# """
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# def read_camera(self, sample):
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# # Read sample data
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# info = self.read_info(sample)
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# # Camera location
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# camLoc = info['camLoc']
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# # Camera projection matrix
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# return proj(camLoc)
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# # TESTING
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# if __name__ == '__main__':
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# sample = '135_02_s8'
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# frame = 0
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# garment = 'Tshirt'
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# reader = DataReader()
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# F = reader.read_garment_topology(sample, garment)
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# Vt, Ft = reader.read_garment_UVMap(sample, garment)
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# C = reader.read_garment_vertex_colors(sample, garment, F, Vt, Ft) |