started writing preprocessing
This commit is contained in:
@@ -1,346 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
|
||||
import pickle
|
||||
import scipy.io as sio
|
||||
from math import *
|
||||
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
# Left-Right
|
||||
eval_joints = {
|
||||
'All': [1, 2, 3, 4, 5, 7, 8, 9, 12, 15, 16, 17, 18, 19, 20, 21],
|
||||
'Head': [12, 15], # Neck, Head
|
||||
'Torso': [3, 6, 9], # Back joints
|
||||
'Shoulder': [13, 14, 16, 17], # Inner, Outer
|
||||
'Elbow': [18, 19],
|
||||
'Wrist': [20, 21],
|
||||
'Hip': [1, 2],
|
||||
'Knee': [4, 5],
|
||||
'Foot': [7, 8, 10, 11]} # Ankle, Foot
|
||||
|
||||
class SMPL:
|
||||
|
||||
def __init__(self, path):
|
||||
self.J_regressor = []
|
||||
self.v_template = []
|
||||
self.shapedirs = []
|
||||
self.posedirs = []
|
||||
self.weights = []
|
||||
# Load SMPL models
|
||||
# path = os.path.abspath(os.path.dirname(__file__))
|
||||
with open(os.path.join(path, 'super_model_f.pkl'), 'rb') as f:
|
||||
if sys.version_info[0]<3:
|
||||
female = pickle.load(f)
|
||||
else:
|
||||
female = pickle.load(f, encoding="latin1")
|
||||
self.J_regressor.append(tf.constant(np.array(female['J_regressor'].todense(), dtype=np.float32)))
|
||||
self.v_template.append(tf.constant(female['v_template'], dtype=np.float32))
|
||||
self.posedirs.append(tf.constant(female['posedirs'], dtype=np.float32))
|
||||
self.shapedirs.append(tf.constant(female['shapedirs'], dtype=np.float32))
|
||||
self.weights.append(tf.constant(female['weights'], dtype=np.float32))
|
||||
with open(os.path.join(path, 'super_model_m.pkl'), 'rb') as f:
|
||||
if sys.version_info[0]<3:
|
||||
male = pickle.load(f)
|
||||
else:
|
||||
male = pickle.load(f, encoding="latin1")
|
||||
self.J_regressor.append(tf.constant(np.array(male['J_regressor'].todense(), dtype=np.float32)))
|
||||
self.v_template.append(tf.constant(male['v_template'], dtype=np.float32))
|
||||
self.posedirs.append(tf.constant(male['posedirs'], dtype=np.float32))
|
||||
self.shapedirs.append(tf.constant(male['shapedirs'], dtype=np.float32))
|
||||
self.weights.append(tf.constant(male['weights'], dtype=np.float32))
|
||||
kintree_table = male['kintree_table']
|
||||
id_to_col = {kintree_table[1, i]: i for i in range(kintree_table.shape[1])}
|
||||
self.parent = {i: id_to_col[kintree_table[0, i]] for i in range(1, kintree_table.shape[1])}
|
||||
self.J_regressor = tf.stack(self.J_regressor, axis=0)
|
||||
self.v_template = tf.stack(self.v_template, axis=0)
|
||||
self.shapedirs = tf.stack(self.shapedirs, axis=0)
|
||||
self.posedirs = tf.stack(self.posedirs, axis=0)
|
||||
self.weights = tf.stack(self.weights, axis=0)
|
||||
self.weights_one_hot = tf.reduce_mean(self.weights, axis=0)
|
||||
self.weights_one_hot = tf.math.argmax(self.weights_one_hot, axis=-1)
|
||||
self.weights_one_hot = tf.one_hot(self.weights_one_hot, 24)
|
||||
|
||||
# Topology no head/hands/feet
|
||||
F = sio.loadmat(path + '/ssmpl_faces.mat')
|
||||
self.f = np.array(F['faces_default'])
|
||||
self.f_skirt = np.array(F['faces_skirt'])
|
||||
self.nn = np.array(sio.loadmat(path + '/NN.mat'))
|
||||
self.to_keep = sio.loadmat(os.path.join(path, 'keep_remove.mat'))['keep'].reshape(-1)
|
||||
|
||||
self.weights = tf.gather(self.weights, self.to_keep, axis=1)
|
||||
# self.shapedirs = tf.gather(self.shapedirs, self.to_keep, axis=1)
|
||||
# self.posedirs = tf.gather(self.posedirs, self.to_keep, axis=1)
|
||||
# self.J_regressor = tf.gather(self.J_regressor, self.to_keep, axis=2)
|
||||
# self.v_template = tf.gather(self.v_template, self.to_keep, axis=1)
|
||||
|
||||
|
||||
def Q2R(self, w, x, y, z):
|
||||
def diag(a, b): # computes the diagonal entries, 1 - 2*a**2 - 2*b**2
|
||||
return 1 - 2 * tf.pow(a, 2) - 2 * tf.pow(b, 2)
|
||||
|
||||
def tr_add(a, b, c, d): # computes triangle entries with addition
|
||||
return 2 * a * b + 2 * c * d
|
||||
|
||||
def tr_sub(a, b, c, d): # computes triangle entries with subtraction
|
||||
return 2 * a * b - 2 * c * d
|
||||
|
||||
m = [[diag(y, z), tr_sub(x, y, z, w), tr_add(x, z, y, w)],
|
||||
[tr_add(x, y, z, w), diag(x, z), tr_sub(y, z, x, w)],
|
||||
[tr_sub(x, z, y, w), tr_add(y, z, x, w), diag(x, y)]]
|
||||
|
||||
R = tf.stack([tf.stack(m[i], axis=-1) for i in range(3)], axis=-2)
|
||||
return R
|
||||
|
||||
def rodrigues(self, r):
|
||||
theta = tf.norm(r, axis=(1, 2), keepdims=True)
|
||||
theta = tf.maximum(theta, np.finfo(np.float32).eps)
|
||||
r_hat = r / theta
|
||||
z_stick = tf.zeros(theta.get_shape().as_list()[0], dtype=tf.float32)
|
||||
m = tf.stack(
|
||||
(z_stick, -r_hat[:, 0, 2], r_hat[:, 0, 1], r_hat[:, 0, 2], z_stick,
|
||||
-r_hat[:, 0, 0], -r_hat[:, 0, 1], r_hat[:, 0, 0], z_stick), axis=1)
|
||||
m = tf.reshape(m, (-1, 3, 3))
|
||||
i_cube = tf.expand_dims(tf.eye(3, dtype=tf.float32), axis=0) + tf.zeros(
|
||||
(theta.get_shape().as_list()[0], 3, 3), dtype=tf.float32)
|
||||
# R = cos * i_cube + (1 - cos) * dot + tf.sin(theta) * m
|
||||
R = i_cube + m * tf.sin(theta) + tf.matmul(m, m) * (1 - tf.cos(theta))
|
||||
return R
|
||||
|
||||
def with_zeros(self, x):
|
||||
return tf.concat((x, tf.ones((x.shape[0], 1, 4)) * tf.constant([[0.0, 0.0, 0.0, 1.0]], dtype=tf.float32)[tf.newaxis,:,:]), axis=1)
|
||||
|
||||
def pack(self, x):
|
||||
return tf.concat((tf.zeros((x.shape[0], 24, 4, 3), dtype=tf.float32), x), axis=3)
|
||||
|
||||
def with_zeros_old(self, x):
|
||||
return tf.concat((x, tf.constant([[0.0, 0.0, 0.0, 1.0]], dtype=tf.float32)), axis=0)
|
||||
|
||||
def pack_old(self, x):
|
||||
return tf.concat((tf.zeros((x.get_shape().as_list()[0], 4, 3), dtype=tf.float32), x), axis=2)
|
||||
|
||||
|
||||
def run_rodrigues(self, pose):
|
||||
batch_size = pose.shape[0]
|
||||
R_out = self.rodrigues(tf.reshape(pose, (-1, 1, 3)))
|
||||
R_out = tf.reshape(R_out, (batch_size, -1, 3, 3))
|
||||
#R_out = []
|
||||
#for i in range(batch_size):
|
||||
# R_out.append(self.rodrigues(tf.reshape(pose[i,:], (-1, 1, 3))))
|
||||
#R_out = tf.stack(R_out, axis=0)
|
||||
return R_out
|
||||
|
||||
def run(self, R, betas, remove=True):
|
||||
batch_size = betas.shape[0]
|
||||
|
||||
#J_regressor = tf.gather(self.J_regressor, gender, axis=0)
|
||||
#shapedirs = tf.gather(self.shapedirs, gender, axis=0)
|
||||
#v_template = tf.gather(self.v_template, gender, axis=0)
|
||||
|
||||
v_shaped = []
|
||||
v_shaped.append(tf.tensordot(betas[:,:10], self.shapedirs[0], axes=[[1], [2]]) + tf.reshape(self.v_template[0], shape=(1,27578,3))) # female
|
||||
v_shaped.append(tf.tensordot(betas[:,:10], self.shapedirs[1], axes=[[1], [2]]) + tf.reshape(self.v_template[1], shape=(1,27578,3))) # male
|
||||
v_shaped = tf.stack(v_shaped, axis=-1)
|
||||
|
||||
def single_run(x, J_regressor=self.J_regressor, posedirs=self.posedirs, weights=self.weights, parent=self.parent):
|
||||
R = x[0]
|
||||
v_shaped = x[1]
|
||||
g = x[2]
|
||||
|
||||
J = tf.matmul(J_regressor[g], v_shaped[:, :, g])
|
||||
R_cube_big = R
|
||||
R_cube = R_cube_big[1:]
|
||||
I_cube = tf.expand_dims(tf.eye(3, dtype=tf.float32), axis=0) + tf.zeros((R_cube.get_shape()[0], 3, 3), dtype=tf.float32)
|
||||
lrotmin = tf.squeeze(tf.reshape((R_cube - I_cube), (-1, 1)))
|
||||
v_posed = v_shaped[:, :, g] + tf.tensordot(posedirs[g], lrotmin, axes=[[2], [0]])
|
||||
results = []
|
||||
results.append(self.with_zeros(tf.concat((R_cube_big[0], tf.reshape(J[0, :], (3, 1))), axis=1)))
|
||||
for j in range(1, 24):
|
||||
results.append(tf.matmul(results[parent[j]], self.with_zeros(tf.concat((R_cube_big[j], tf.reshape(J[j, :] - J[parent[j], :], (3, 1))), axis=1))))
|
||||
stacked = tf.stack(results, axis=0)
|
||||
results = stacked - self.pack(tf.matmul(stacked, tf.reshape(tf.concat((J, tf.zeros((24, 1), dtype=tf.float32)), axis=1), (24, 4, 1))))
|
||||
|
||||
J = tf.matmul(results, tf.expand_dims(tf.concat([J, tf.ones([24, 1], tf.float32)], axis=1), axis=1), transpose_b=True)
|
||||
J = tf.gather(J, tf.constant([0, 1, 2], tf.int32), axis=1)
|
||||
|
||||
T = tf.tensordot(weights[g], results, axes=((1), (0)))
|
||||
rest_shape_h = tf.concat((v_posed, tf.ones((v_posed.get_shape().as_list()[0], 1), dtype=tf.float32)), axis=1)
|
||||
v = tf.matmul(T, tf.reshape(rest_shape_h, (-1, 4, 1)))
|
||||
v = tf.reshape(v, (-1, 4))[:, :3]
|
||||
|
||||
return v, tf.squeeze(J), v_posed
|
||||
|
||||
gender = tf.cast(tf.greater_equal(betas[:, 10] , 0), tf.int32)
|
||||
T_out, J_out, v_posed = tf.map_fn(single_run, (R, v_shaped, gender), (tf.float32, tf.float32, tf.float32))
|
||||
|
||||
J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
|
||||
J_out = tf.subtract(J_out, J0)
|
||||
T_out = tf.subtract(T_out, J0)
|
||||
v_posed = tf.subtract(v_posed, J0)
|
||||
|
||||
if remove:
|
||||
T_out = tf.gather(T_out, self.to_keep, axis=1)
|
||||
v_posed = tf.gather(v_posed, self.to_keep, axis=1)
|
||||
|
||||
return T_out, J_out, v_posed
|
||||
|
||||
def run_with_weights(self, R, betas, v_cloth, weights=None, filter=None):
|
||||
|
||||
v_shaped = []
|
||||
v_shaped.append(tf.tensordot(betas[:,:10], self.shapedirs[0], axes=[[1], [2]]) + tf.reshape(self.v_template[0], shape=(1,27578,3))) # female
|
||||
v_shaped.append(tf.tensordot(betas[:,:10], self.shapedirs[1], axes=[[1], [2]]) + tf.reshape(self.v_template[1], shape=(1,27578,3))) # male
|
||||
v_shaped = tf.stack(v_shaped, axis=-1)
|
||||
|
||||
if weights is None:
|
||||
w = self.weights_one_hot[np.newaxis, :, :]
|
||||
if filter is not None:
|
||||
w = tf.gather(tf.gather(w, self.to_keep, axis=1), filter, axis=1)
|
||||
if v_cloth.shape[1] == 14475:
|
||||
w = tf.gather(w, self.to_keep, axis=1)
|
||||
else:
|
||||
w = weights
|
||||
|
||||
def single_run(x, J_regressor=self.J_regressor, w=w, parent=self.parent):
|
||||
R = x[0]
|
||||
v_shaped = x[1]
|
||||
g = x[2]
|
||||
v_cloth = x[3]
|
||||
|
||||
J = tf.matmul(J_regressor[g], v_shaped[:, :, g])
|
||||
R_cube_big = R
|
||||
results = []
|
||||
results.append(self.with_zeros(tf.concat((R_cube_big[0], tf.reshape(J[0, :], (3, 1))), axis=1)))
|
||||
for j in range(1, 24):
|
||||
results.append(tf.matmul(results[parent[j]], self.with_zeros(tf.concat((R_cube_big[j], tf.reshape(J[j, :] - J[parent[j], :], (3, 1))), axis=1))))
|
||||
stacked = tf.stack(results, axis=0)
|
||||
results = stacked - self.pack(tf.matmul(stacked, tf.reshape(tf.concat((J, tf.zeros((24, 1), dtype=tf.float32)), axis=1), (24, 4, 1))))
|
||||
|
||||
J = tf.matmul(results, tf.expand_dims(tf.concat([J, tf.ones([24, 1], tf.float32)], axis=1), axis=1), transpose_b=True)
|
||||
J = tf.gather(J, tf.constant([0, 1, 2], tf.int32), axis=1)
|
||||
|
||||
T = tf.tensordot(w[0], results, axes=((1), (0)))
|
||||
rest_shape_h = tf.concat((v_cloth, tf.ones((v_cloth.shape[0], 1), dtype=tf.float32)), axis=1)
|
||||
v = tf.matmul(T, tf.reshape(rest_shape_h, (-1, 4, 1)))
|
||||
v = tf.reshape(v, (-1, 4))[:, :3]
|
||||
|
||||
return v, tf.squeeze(J)
|
||||
|
||||
gender = tf.cast(tf.greater_equal(betas[:, 10] , 0), tf.int32)
|
||||
T_out, J_out = tf.map_fn(single_run, (R, v_shaped, gender, v_cloth), (tf.float32, tf.float32))
|
||||
|
||||
J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
|
||||
T_out = tf.subtract(T_out, J0)
|
||||
|
||||
return T_out
|
||||
|
||||
|
||||
|
||||
def get_joints_template(self, betas, gender, batch_size):
|
||||
v_shaped = tf.tensordot(betas[tf.newaxis,:], self.shapedirs[gender], axes=[[1], [2]]) + tf.reshape(self.v_template[gender], shape=(1,27578,3))
|
||||
J = tf.matmul(self.J_regressor[gender], v_shaped)
|
||||
J0 = tf.reshape(J[:,0,:], (-1, 1, 3))
|
||||
J_single = tf.subtract(J, J0)#[0]
|
||||
J = tf.ones((batch_size, 24, 3)) * J_single
|
||||
|
||||
return J
|
||||
|
||||
|
||||
def run_garment(self, R, betas, gender, v_cloth, filter_idx=None):
|
||||
batch_size = v_cloth.shape[0]
|
||||
|
||||
J = self.get_joints_template(betas, gender, batch_size)
|
||||
|
||||
if filter_idx is not None:
|
||||
weights = tf.gather(self.weights, filter_idx, axis=1)
|
||||
|
||||
R_cube_big = R
|
||||
results = []
|
||||
results.append(self.with_zeros(tf.concat((R_cube_big[:,0,:,:], J[:, 0, :, tf.newaxis]), axis=2)))
|
||||
for j in range(1, 24):
|
||||
results.append(tf.matmul(results[self.parent[j]], self.with_zeros(tf.concat((R_cube_big[:,j,:,:], (J[:, j, :] - J[:, self.parent[j], :])[:,:,tf.newaxis]), axis=2))))
|
||||
stacked = tf.stack(results, axis=1)
|
||||
results = stacked - self.pack(tf.matmul(stacked, tf.reshape(tf.concat((J, tf.zeros((batch_size, 24, 1), dtype=tf.float32)), axis=2), (batch_size, 24, 4, 1))))
|
||||
|
||||
J = tf.matmul(results, tf.expand_dims(tf.concat([J, tf.ones([batch_size, 24, 1], tf.float32)], axis=2), axis=2), transpose_b=True)
|
||||
J = J[:, :, :3, :]
|
||||
|
||||
T = tf.tensordot(weights[gender, :, :], results, axes=((1), (1)))
|
||||
T = tf.transpose(T, [1,0,2,3])
|
||||
|
||||
rest_shape_h = tf.concat((v_cloth, tf.ones((batch_size, v_cloth.shape[1], 1), dtype=tf.float32)), axis=2)
|
||||
v = tf.matmul(T, tf.reshape(rest_shape_h, (batch_size, -1, 4, 1)))
|
||||
v = tf.reshape(v, (batch_size, -1, 4))[:, :, :3]
|
||||
|
||||
J_out = tf.squeeze(J)
|
||||
if J.shape[0] == 1:
|
||||
J_out = J_out[tf.newaxis, :, :]
|
||||
J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
|
||||
J_out = tf.subtract(J_out, J0)
|
||||
T_out = tf.subtract(v, J0)
|
||||
|
||||
return T_out
|
||||
|
||||
def run_garment_with_weights(self, R, betas, v_cloth, weights, J=None):
|
||||
|
||||
if J is None:
|
||||
J = self.get_joints_template(betas)
|
||||
|
||||
def single_run(x, parent=self.parent):
|
||||
R = x[0]
|
||||
J = x[1]
|
||||
v_cloth = x[2]
|
||||
weights = x[3]
|
||||
|
||||
R_cube_big = R
|
||||
|
||||
results = []
|
||||
results.append(self.with_zeros(tf.concat((R_cube_big[0], tf.reshape(J[0, :], (3, 1))), axis=1)))
|
||||
for j in range(1, 24):
|
||||
results.append(tf.matmul(results[parent[j]], self.with_zeros(tf.concat((R_cube_big[j], tf.reshape(J[j, :] - J[parent[j], :], (3, 1))), axis=1))))
|
||||
stacked = tf.stack(results, axis=0)
|
||||
results = stacked - self.pack(tf.matmul(stacked, tf.reshape(tf.concat((J, tf.zeros((24, 1), dtype=tf.float32)), axis=1), (24, 4, 1))))
|
||||
|
||||
J = tf.matmul(results, tf.expand_dims(tf.concat([J, tf.ones([24, 1], tf.float32)], axis=1), axis=1), transpose_b=True)
|
||||
J = tf.gather(J, tf.constant([0, 1, 2], tf.int32), axis=1)
|
||||
|
||||
T = tf.tensordot(weights, results, axes=((1), (0)))
|
||||
rest_shape_h = tf.concat((v_cloth, tf.ones((v_cloth.get_shape().as_list()[0], 1), dtype=tf.float32)), axis=1)
|
||||
v = tf.matmul(T, tf.reshape(rest_shape_h, (-1, 4, 1)))
|
||||
v = tf.reshape(v, (-1, 4))[:, :3]
|
||||
|
||||
return v, tf.squeeze(J)
|
||||
|
||||
# gender = tf.cast(tf.greater_equal(betas[:, 10] , 0), tf.int32)
|
||||
T_out, J_out = tf.map_fn(single_run, (R, J, v_cloth, weights), (tf.float32, tf.float32))
|
||||
|
||||
J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
|
||||
J_out = tf.subtract(J_out, J0)
|
||||
T_out = tf.subtract(T_out, J0)
|
||||
|
||||
return T_out
|
||||
|
||||
def run_backward(self, R, J, v_posed, weights):
|
||||
batch_size = R.get_shape()[0]
|
||||
|
||||
J_relative = J[:, 1:, :] - tf.gather(J, tf.constant(list(self.parent.values()), tf.int32), axis=1) # batch_size x 23 x 3
|
||||
|
||||
J_out = []
|
||||
J_out.append(tf.expand_dims(J[:, 0, :], axis=-1)) # batch_size x 3 x 1
|
||||
results = []
|
||||
results.append(tf.expand_dims(tf.eye(3, dtype=tf.float32), axis=0) + tf.zeros((batch_size, 3, 3), dtype=tf.float32))
|
||||
for j in range(1, 24):
|
||||
results.append(tf.matmul(R[:, self.parent[j], :, :], results[self.parent[j]]))
|
||||
J_out.append(tf.matmul(results[j], tf.expand_dims(J_relative[:, j-1, :], axis=-1)) + J_out[self.parent[j]]) # batch_size x 3 x 1
|
||||
results = tf.transpose(tf.stack(results, axis=0), [1, 0, 2, 3]) # (batch_size x 24 x 3 x 3)
|
||||
J_out = tf.transpose(tf.stack(J_out, axis=0), [1, 0, 2, 3]) # (batch_size x 24 x 3 x 1)
|
||||
|
||||
results = tf.matmul(R, results)
|
||||
T = tf.transpose(tf.tensordot(results, weights, axes=((1), (1))), [0, 3, 1, 2]) # (batch_size x 6890 x 3 x 3)
|
||||
v_posed = tf.expand_dims(v_posed, axis=-1) # (batch_size x 6890 x 3 x 1)
|
||||
J = tf.expand_dims(J, axis=-1) # (batch_size x 24 x 3 x 1)
|
||||
v = tf.matmul(T, v_posed) + tf.transpose(tf.tensordot(J_out - tf.matmul(results, J), weights, axes=((1), (1))), [0, 3, 1, 2]) # (batch_size x 6890 x 3 x 1)
|
||||
v = tf.squeeze(v, -1) # (batch_size x 6890 x 3)
|
||||
J_out = tf.squeeze(J_out, -1) # (batch_size x 24 x 3)
|
||||
|
||||
return v, J_out
|
||||
@@ -1,175 +0,0 @@
|
||||
import numpy as np
|
||||
from struct import pack, unpack
|
||||
|
||||
|
||||
"""
|
||||
Reads PC2 files, and proposed format PC16 files
|
||||
Inputs:
|
||||
- file: path to .pc2/.pc16 file
|
||||
- float16: False for PC2 files, True for PC16
|
||||
Output:
|
||||
- data: dictionary with .pc2/.pc16 file data
|
||||
NOTE: 16-bit floats lose precision with high values (positive or negative),
|
||||
we do not recommend using this format for data outside range [-2, 2]
|
||||
"""
|
||||
def readPC2(file, float16=False):
|
||||
assert file.endswith('.pc2') and not float16 or file.endswith('.pc16') and float16, 'File format not consistent with specified input format'
|
||||
data = {}
|
||||
bytes = 2 if float16 else 4
|
||||
dtype = np.float16 if float16 else np.float32
|
||||
with open(file, 'rb') as f:
|
||||
# Header
|
||||
data['sign'] = f.read(12)
|
||||
# data['version'] = int.from_bytes(f.read(4), 'little')
|
||||
data['version'] = unpack('<i', f.read(4))[0]
|
||||
# Num points
|
||||
# data['nPoints'] = int.from_bytes(f.read(4), 'little')
|
||||
data['nPoints'] = unpack('<i', f.read(4))[0]
|
||||
# Start frame
|
||||
data['startFrame'] = unpack('f', f.read(4))
|
||||
# Sample rate
|
||||
data['sampleRate'] = unpack('f', f.read(4))
|
||||
# Number of samples
|
||||
# data['nSamples'] = int.from_bytes(f.read(4), 'little')
|
||||
data['nSamples'] = unpack('<i', f.read(4))[0]
|
||||
# Animation data
|
||||
size = data['nPoints']*data['nSamples']*3*bytes
|
||||
data['V'] = np.frombuffer(f.read(size), dtype=dtype).astype(np.float32)
|
||||
data['V'] = data['V'].reshape(data['nSamples'], data['nPoints'], 3)
|
||||
|
||||
return data
|
||||
|
||||
"""
|
||||
Reads an specific frame of PC2/PC16 files
|
||||
Inputs:
|
||||
- file: path to .pc2/.pc16 file
|
||||
- frame: number of the frame to read
|
||||
- float16: False for PC2 files, True for PC16
|
||||
Output:
|
||||
- T: mesh vertex data at specified frame
|
||||
"""
|
||||
def readPC2Frame(file, frame, float16=False):
|
||||
assert file.endswith('.pc2') and not float16 or file.endswith('.pc16') and float16, 'File format not consistent with specified input format'
|
||||
assert frame >= 0 and isinstance(frame,int), 'Frame must be a positive integer'
|
||||
bytes = 2 if float16 else 4
|
||||
dtype = np.float16 if float16 else np.float32
|
||||
with open(file,'rb') as f:
|
||||
# Num points
|
||||
f.seek(16)
|
||||
# nPoints = int.from_bytes(f.read(4), 'little')
|
||||
nPoints = unpack('<i', f.read(4))[0]
|
||||
# Number of samples
|
||||
f.seek(28)
|
||||
# nSamples = int.from_bytes(f.read(4), 'little')
|
||||
nSamples = unpack('<i', f.read(4))[0]
|
||||
if frame > nSamples:
|
||||
print("Frame index outside size")
|
||||
print("\tN. frame: " + str(frame))
|
||||
print("\tN. samples: " + str(nSamples))
|
||||
return
|
||||
# Read frame
|
||||
size = nPoints * 3 * bytes
|
||||
f.seek(size * frame, 1) # offset from current '1'
|
||||
T = np.frombuffer(f.read(size), dtype=dtype).astype(np.float32)
|
||||
return T.reshape(nPoints, 3)
|
||||
|
||||
"""
|
||||
Writes PC2 and PC16 files
|
||||
Inputs:
|
||||
- file: path to file (overwrites if exists)
|
||||
- V: 3D animation data as a three dimensional array (N. Frames x N. Vertices x 3)
|
||||
- float16: False for writing as PC2 file, True for PC16
|
||||
This function assumes 'startFrame' to be 0 and 'sampleRate' to be 1
|
||||
NOTE: 16-bit floats lose precision with high values (positive or negative),
|
||||
we do not recommend using this format for data outside range [-2, 2]
|
||||
"""
|
||||
def writePC2(file, V, float16=False):
|
||||
assert file.endswith('.pc2') and not float16 or file.endswith('.pc16') and float16, 'File format not consistent with specified input format'
|
||||
if float16: V = V.astype(np.float16)
|
||||
else: V = V.astype(np.float32)
|
||||
with open(file, 'wb') as f:
|
||||
# Create the header
|
||||
headerFormat='<12siiffi'
|
||||
headerStr = pack(headerFormat, b'POINTCACHE2\0',
|
||||
1, V.shape[1], 0, 1, V.shape[0])
|
||||
f.write(headerStr)
|
||||
# Write vertices
|
||||
f.write(V.tobytes())
|
||||
|
||||
"""
|
||||
Reads OBJ files
|
||||
Only handles vertices, faces and UV maps
|
||||
Input:
|
||||
- file: path to .obj file
|
||||
Outputs:
|
||||
- V: 3D vertices
|
||||
- F: 3D faces
|
||||
- Vt: UV vertices
|
||||
- Ft: UV faces
|
||||
Correspondence between mesh and UV map is implicit in F to Ft correspondences
|
||||
If no UV map data in .obj file, it shall return Vt=None and Ft=None
|
||||
"""
|
||||
def readOBJ(file):
|
||||
V, Vt, F, Ft = [], [], [], []
|
||||
with open(file, 'r') as f:
|
||||
T = f.readlines()
|
||||
for t in T:
|
||||
# 3D vertex
|
||||
if t.startswith('v '):
|
||||
v = [float(n) for n in t.replace('v ','').split(' ')]
|
||||
V += [v]
|
||||
# UV vertex
|
||||
elif t.startswith('vt '):
|
||||
v = [float(n) for n in t.replace('vt ','').split(' ')]
|
||||
Vt += [v]
|
||||
# Face
|
||||
elif t.startswith('f '):
|
||||
idx = [n.split('/') for n in t.replace('f ','').split(' ')]
|
||||
f = [int(n[0]) - 1 for n in idx]
|
||||
F += [f]
|
||||
# UV face
|
||||
if '/' in t:
|
||||
f = [int(n[1]) - 1 for n in idx]
|
||||
Ft += [f]
|
||||
V = np.array(V, np.float32)
|
||||
Vt = np.array(Vt, np.float32)
|
||||
if Ft: assert len(F) == len(Ft), 'Inconsistent .obj file, mesh and UV map do not have the same number of faces'
|
||||
else: Vt, Ft = None, None
|
||||
return V, F, Vt, Ft
|
||||
|
||||
|
||||
|
||||
"""
|
||||
Writes OBJ files
|
||||
Only handles vertices, faces and UV maps
|
||||
Inputs:
|
||||
- file: path to .obj file (overwrites if exists)
|
||||
- V: 3D vertices
|
||||
- F: 3D faces
|
||||
- Vt: UV vertices
|
||||
- Ft: UV faces
|
||||
Correspondence between mesh and UV map is implicit in F to Ft correspondences
|
||||
If no UV map data as input, it will write only 3D data in .obj file
|
||||
"""
|
||||
def writeOBJ(file, V, F, Vt=None, Ft=None):
|
||||
if not Vt is None:
|
||||
assert len(F) == len(Ft), 'Inconsistent data, mesh and UV map do not have the same number of faces'
|
||||
|
||||
with open(file, 'w') as file:
|
||||
# Vertices
|
||||
for v in V:
|
||||
line = 'v ' + ' '.join([str(_) for _ in v]) + '\n'
|
||||
file.write(line)
|
||||
# UV verts
|
||||
if not Vt is None:
|
||||
for v in Vt:
|
||||
line = 'vt ' + ' '.join([str(_) for _ in v]) + '\n'
|
||||
file.write(line)
|
||||
# 3D Faces / UV faces
|
||||
if Ft:
|
||||
F = [[str(i+1)+'/'+str(j+1) for i,j in zip(f,ft)] for f,ft in zip(F,Ft)]
|
||||
else:
|
||||
F = [[str(i + 1) for i in f] for f in F]
|
||||
for f in F:
|
||||
line = 'f ' + ' '.join(f) + '\n'
|
||||
file.write(line)
|
||||
@@ -17,7 +17,7 @@ from tqdm import tqdm
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
from custom_utils.supersmpl_io import readPC2, writePC2
|
||||
from data.supersmpl_io import readPC2, writePC2
|
||||
from custom_utils.smpl import SMPL
|
||||
from custom_utils.smpl_2 import SMPL as SMPL_2
|
||||
|
||||
|
||||
Reference in New Issue
Block a user