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'''
Preprocessing Tranformers Based on sci-kit's API
By Omid Alemi
Created on June 12, 2017
'''
import copy
import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from .Quaternions import Quaternions
from .rotation_tools import Rotation
class MocapParameterizer(BaseEstimator, TransformerMixin):
def __init__(self, param_type = 'euler'):
'''
param_type = {'euler', 'quat', 'expmap', 'position'}
'''
self.param_type = param_type
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
if self.param_type == 'euler':
return X
elif self.param_type == 'expmap':
return self._to_expmap(X)
elif self.param_type == 'quat':
return X
elif self.param_type == 'position':
return self._to_pos(X)
else:
raise UnsupportedParamError('Unsupported param: %s. Valid param types are: euler, quat, expmap, position' % self.param_type)
# return X
def inverse_transform(self, X, copy=None):
if self.param_type == 'euler':
return X
elif self.param_type == 'expmap':
return self._expmap_to_euler(X)
elif self.param_type == 'quat':
raise UnsupportedParamError('quat2euler is not supported')
elif self.param_type == 'position':
print('positions 2 eulers is not supported')
return X
else:
raise UnsupportedParamError('Unsupported param: %s. Valid param types are: euler, quat, expmap, position' % self.param_type)
def _to_pos(self, X):
'''Converts joints rotations in Euler angles to joint positions'''
Q = []
for track in X:
channels = []
titles = []
euler_df = track.values
# Create a new DataFrame to store the exponential map rep
pos_df = pd.DataFrame(index=euler_df.index)
# Copy the root rotations into the new DataFrame
# rxp = '%s_Xrotation'%track.root_name
# ryp = '%s_Yrotation'%track.root_name
# rzp = '%s_Zrotation'%track.root_name
# pos_df[rxp] = pd.Series(data=euler_df[rxp], index=pos_df.index)
# pos_df[ryp] = pd.Series(data=euler_df[ryp], index=pos_df.index)
# pos_df[rzp] = pd.Series(data=euler_df[rzp], index=pos_df.index)
# List the columns that contain rotation channels
rot_cols = [c for c in euler_df.columns if ('rotation' in c)]
# List the columns that contain position channels
pos_cols = [c for c in euler_df.columns if ('position' in c)]
# List the joints that are not end sites, i.e., have channels
joints = (joint for joint in track.skeleton)
tree_data = {}
for joint in track.traverse():
parent = track.skeleton[joint]['parent']
rot_order = track.skeleton[joint]['order']
#print("rot_order:" + joint + " :" + rot_order)
# Get the rotation columns that belong to this joint
rc = euler_df[[c for c in rot_cols if joint in c]]
# Get the position columns that belong to this joint
pc = euler_df[[c for c in pos_cols if joint in c]]
# Make sure the columns are organized in xyz order
if rc.shape[1] < 3:
euler_values = np.zeros((euler_df.shape[0], 3))
rot_order = "XYZ"
else:
euler_values = np.pi/180.0*np.transpose(np.array([track.values['%s_%srotation'%(joint, rot_order[0])], track.values['%s_%srotation'%(joint, rot_order[1])], track.values['%s_%srotation'%(joint, rot_order[2])]]))
if pc.shape[1] < 3:
pos_values = np.asarray([[0,0,0] for f in pc.iterrows()])
else:
pos_values =np.asarray([[f[1]['%s_Xposition'%joint],
f[1]['%s_Yposition'%joint],
f[1]['%s_Zposition'%joint]] for f in pc.iterrows()])
quats = Quaternions.from_euler(np.asarray(euler_values), order=rot_order.lower(), world=False)
tree_data[joint]=[
[], # to store the rotation matrix
[] # to store the calculated position
]
if track.root_name == joint:
tree_data[joint][0] = quats#rotmats
# tree_data[joint][1] = np.add(pos_values, track.skeleton[joint]['offsets'])
tree_data[joint][1] = pos_values
else:
# for every frame i, multiply this joint's rotmat to the rotmat of its parent
tree_data[joint][0] = tree_data[parent][0]*quats# np.matmul(rotmats, tree_data[parent][0])
# add the position channel to the offset and store it in k, for every frame i
k = pos_values + np.asarray(track.skeleton[joint]['offsets'])
# multiply k to the rotmat of the parent for every frame i
q = tree_data[parent][0]*k #np.matmul(k.reshape(k.shape[0],1,3), tree_data[parent][0])
# add q to the position of the parent, for every frame i
tree_data[joint][1] = tree_data[parent][1] + q #q.reshape(k.shape[0],3) + tree_data[parent][1]
# Create the corresponding columns in the new DataFrame
pos_df['%s_Xposition'%joint] = pd.Series(data=[e[0] for e in tree_data[joint][1]], index=pos_df.index)
pos_df['%s_Yposition'%joint] = pd.Series(data=[e[1] for e in tree_data[joint][1]], index=pos_df.index)
pos_df['%s_Zposition'%joint] = pd.Series(data=[e[2] for e in tree_data[joint][1]], index=pos_df.index)
new_track = track.clone()
new_track.values = pos_df
Q.append(new_track)
return Q
def _to_expmap(self, X):
'''Converts Euler angles to Exponential Maps'''
Q = []
for track in X:
channels = []
titles = []
euler_df = track.values
# Create a new DataFrame to store the exponential map rep
exp_df = pd.DataFrame(index=euler_df.index)
# Copy the root positions into the new DataFrame
rxp = '%s_Xposition'%track.root_name
ryp = '%s_Yposition'%track.root_name
rzp = '%s_Zposition'%track.root_name
exp_df[rxp] = pd.Series(data=euler_df[rxp], index=exp_df.index)
exp_df[ryp] = pd.Series(data=euler_df[ryp], index=exp_df.index)
exp_df[rzp] = pd.Series(data=euler_df[rzp], index=exp_df.index)
# List the columns that contain rotation channels
rots = [c for c in euler_df.columns if ('rotation' in c and 'Nub' not in c)]
# List the joints that are not end sites, i.e., have channels
joints = (joint for joint in track.skeleton if 'Nub' not in joint)
for joint in joints:
r = euler_df[[c for c in rots if joint in c]] # Get the columns that belong to this joint
euler = [[f[1]['%s_Xrotation'%joint], f[1]['%s_Yrotation'%joint], f[1]['%s_Zrotation'%joint]] for f in r.iterrows()] # Make sure the columsn are organized in xyz order
exps = [Rotation(f, 'euler', from_deg=True).to_expmap() for f in euler] # Convert the eulers to exp maps
# Create the corresponding columns in the new DataFrame
exp_df['%s_alpha'%joint] = pd.Series(data=[e[0] for e in exps], index=exp_df.index)
exp_df['%s_beta'%joint] = pd.Series(data=[e[1] for e in exps], index=exp_df.index)
exp_df['%s_gamma'%joint] = pd.Series(data=[e[2] for e in exps], index=exp_df.index)
new_track = track.clone()
new_track.values = exp_df
Q.append(new_track)
return Q
def _expmap_to_euler(self, X):
Q = []
for track in X:
channels = []
titles = []
exp_df = track.values
# Create a new DataFrame to store the exponential map rep
euler_df = pd.DataFrame(index=exp_df.index)
# Copy the root positions into the new DataFrame
rxp = '%s_Xposition'%track.root_name
ryp = '%s_Yposition'%track.root_name
rzp = '%s_Zposition'%track.root_name
euler_df[rxp] = pd.Series(data=exp_df[rxp], index=euler_df.index)
euler_df[ryp] = pd.Series(data=exp_df[ryp], index=euler_df.index)
euler_df[rzp] = pd.Series(data=exp_df[rzp], index=euler_df.index)
# List the columns that contain rotation channels
exp_params = [c for c in exp_df.columns if ( any(p in c for p in ['alpha', 'beta','gamma']) and 'Nub' not in c)]
# List the joints that are not end sites, i.e., have channels
joints = (joint for joint in track.skeleton if 'Nub' not in joint)
for joint in joints:
r = exp_df[[c for c in exp_params if joint in c]] # Get the columns that belong to this joint
expmap = [[f[1]['%s_alpha'%joint], f[1]['%s_beta'%joint], f[1]['%s_gamma'%joint]] for f in r.iterrows()] # Make sure the columsn are organized in xyz order
euler_rots = [Rotation(f, 'expmap').to_euler(True)[0] for f in expmap] # Convert the eulers to exp maps
# Create the corresponding columns in the new DataFrame
euler_df['%s_Xrotation'%joint] = pd.Series(data=[e[0] for e in euler_rots], index=euler_df.index)
euler_df['%s_Yrotation'%joint] = pd.Series(data=[e[1] for e in euler_rots], index=euler_df.index)
euler_df['%s_Zrotation'%joint] = pd.Series(data=[e[2] for e in euler_rots], index=euler_df.index)
new_track = track.clone()
new_track.values = euler_df
Q.append(new_track)
return Q
class JointSelector(BaseEstimator, TransformerMixin):
'''
Allows for filtering the mocap data to include only the selected joints
'''
def __init__(self, joints, include_root=False):
self.joints = joints
self.include_root = include_root
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
selected_joints = []
selected_channels = []
if self.include_root:
selected_joints.append(X[0].root_name)
selected_joints.extend(self.joints)
for joint_name in selected_joints:
selected_channels.extend([o for o in X[0].values.columns if joint_name in o])
Q = []
for track in X:
t2 = track.clone()
for key in track.skeleton.keys():
if key not in selected_joints:
t2.skeleton.pop(key)
t2.values = track.values[selected_channels]
Q.append(t2)
return Q
class Numpyfier(BaseEstimator, TransformerMixin):
'''
Just converts the values in a MocapData object into a numpy array
Useful for the final stage of a pipeline before training
'''
def __init__(self):
pass
def fit(self, X, y=None):
self.org_mocap_ = X[0].clone()
self.org_mocap_.values.drop(self.org_mocap_.values.index, inplace=True)
return self
def transform(self, X, y=None):
Q = []
for track in X:
Q.append(track.values.values)
return np.array(Q)
def inverse_transform(self, X, copy=None):
Q = []
for track in X:
new_mocap = self.org_mocap_.clone()
time_index = pd.to_timedelta([f for f in range(track.shape[0])], unit='s')
new_df = pd.DataFrame(data=track, index=time_index, columns=self.org_mocap_.values.columns)
new_mocap.values = new_df
Q.append(new_mocap)
return Q
class RootTransformer(BaseEstimator, TransformerMixin):
def __init__(self, method):
"""
Accepted methods:
abdolute_translation_deltas
pos_rot_deltas
"""
self.method = method
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
Q = []
for track in X:
if self.method == 'abdolute_translation_deltas':
new_df = track.values.copy()
xpcol = '%s_Xposition'%track.root_name
ypcol = '%s_Yposition'%track.root_name
zpcol = '%s_Zposition'%track.root_name
dxpcol = '%s_dXposition'%track.root_name
dzpcol = '%s_dZposition'%track.root_name
dx = track.values[xpcol].diff()
dz = track.values[zpcol].diff()
dx[0] = 0
dz[0] = 0
new_df.drop([xpcol, zpcol], axis=1, inplace=True)
new_df[dxpcol] = dx
new_df[dzpcol] = dz
new_track = track.clone()
new_track.values = new_df
# end of abdolute_translation_deltas
elif self.method == 'pos_rot_deltas':
new_track = track.clone()
# Absolute columns
xp_col = '%s_Xposition'%track.root_name
yp_col = '%s_Yposition'%track.root_name
zp_col = '%s_Zposition'%track.root_name
xr_col = '%s_Xrotation'%track.root_name
yr_col = '%s_Yrotation'%track.root_name
zr_col = '%s_Zrotation'%track.root_name
# Delta columns
dxp_col = '%s_dXposition'%track.root_name
dzp_col = '%s_dZposition'%track.root_name
dxr_col = '%s_dXrotation'%track.root_name
dyr_col = '%s_dYrotation'%track.root_name
dzr_col = '%s_dZrotation'%track.root_name
new_df = track.values.copy()
root_pos_x_diff = pd.Series(data=track.values[xp_col].diff(), index=new_df.index)
root_pos_z_diff = pd.Series(data=track.values[zp_col].diff(), index=new_df.index)
root_rot_y_diff = pd.Series(data=track.values[yr_col].diff(), index=new_df.index)
root_rot_x_diff = pd.Series(data=track.values[xr_col].diff(), index=new_df.index)
root_rot_z_diff = pd.Series(data=track.values[zr_col].diff(), index=new_df.index)
root_pos_x_diff[0] = 0
root_pos_z_diff[0] = 0
root_rot_y_diff[0] = 0
root_rot_x_diff[0] = 0
root_rot_z_diff[0] = 0
new_df.drop([xr_col, yr_col, zr_col, xp_col, zp_col], axis=1, inplace=True)
new_df[dxp_col] = root_pos_x_diff
new_df[dzp_col] = root_pos_z_diff
new_df[dxr_col] = root_rot_x_diff
new_df[dyr_col] = root_rot_y_diff
new_df[dzr_col] = root_rot_z_diff
new_track.values = new_df
Q.append(new_track)
return Q
def inverse_transform(self, X, copy=None, start_pos=None):
Q = []
#TODO: simplify this implementation
startx = 0
startz = 0
if start_pos is not None:
startx, startz = start_pos
for track in X:
new_track = track.clone()
if self.method == 'abdolute_translation_deltas':
new_df = new_track.values
xpcol = '%s_Xposition'%track.root_name
ypcol = '%s_Yposition'%track.root_name
zpcol = '%s_Zposition'%track.root_name
dxpcol = '%s_dXposition'%track.root_name
dzpcol = '%s_dZposition'%track.root_name
dx = track.values[dxpcol].values
dz = track.values[dzpcol].values
recx = [startx]
recz = [startz]
for i in range(dx.shape[0]-1):
recx.append(recx[i]+dx[i+1])
recz.append(recz[i]+dz[i+1])
# recx = [recx[i]+dx[i+1] for i in range(dx.shape[0]-1)]
# recz = [recz[i]+dz[i+1] for i in range(dz.shape[0]-1)]
# recx = dx[:-1] + dx[1:]
# recz = dz[:-1] + dz[1:]
new_df[xpcol] = pd.Series(data=recx, index=new_df.index)
new_df[zpcol] = pd.Series(data=recz, index=new_df.index)
new_df.drop([dxpcol, dzpcol], axis=1, inplace=True)
new_track.values = new_df
# end of abdolute_translation_deltas
elif self.method == 'pos_rot_deltas':
new_track = track.clone()
# Absolute columns
xp_col = '%s_Xposition'%track.root_name
yp_col = '%s_Yposition'%track.root_name
zp_col = '%s_Zposition'%track.root_name
xr_col = '%s_Xrotation'%track.root_name
yr_col = '%s_Yrotation'%track.root_name
zr_col = '%s_Zrotation'%track.root_name
# Delta columns
dxp_col = '%s_dXposition'%track.root_name
dzp_col = '%s_dZposition'%track.root_name
dxr_col = '%s_dXrotation'%track.root_name
dyr_col = '%s_dYrotation'%track.root_name
dzr_col = '%s_dZrotation'%track.root_name
new_df = track.values.copy()
dx = track.values[dxp_col].values
dz = track.values[dzp_col].values
drx = track.values[dxr_col].values
dry = track.values[dyr_col].values
drz = track.values[dzr_col].values
rec_xp = [startx]
rec_zp = [startz]
rec_xr = [0]
rec_yr = [0]
rec_zr = [0]
for i in range(dx.shape[0]-1):
rec_xp.append(rec_xp[i]+dx[i+1])
rec_zp.append(rec_zp[i]+dz[i+1])
rec_xr.append(rec_xr[i]+drx[i+1])
rec_yr.append(rec_yr[i]+dry[i+1])
rec_zr.append(rec_zr[i]+drz[i+1])
new_df[xp_col] = pd.Series(data=rec_xp, index=new_df.index)
new_df[zp_col] = pd.Series(data=rec_zp, index=new_df.index)
new_df[xr_col] = pd.Series(data=rec_xr, index=new_df.index)
new_df[yr_col] = pd.Series(data=rec_yr, index=new_df.index)
new_df[zr_col] = pd.Series(data=rec_zr, index=new_df.index)
new_df.drop([dxr_col, dyr_col, dzr_col, dxp_col, dzp_col], axis=1, inplace=True)
new_track.values = new_df
Q.append(new_track)
return Q
class RootCentricPositionNormalizer(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
Q = []
for track in X:
new_track = track.clone()
rxp = '%s_Xposition'%track.root_name
ryp = '%s_Yposition'%track.root_name
rzp = '%s_Zposition'%track.root_name
projected_root_pos = track.values[[rxp, ryp, rzp]]
projected_root_pos.loc[:,ryp] = 0 # we want the root's projection on the floor plane as the ref
new_df = pd.DataFrame(index=track.values.index)
all_but_root = [joint for joint in track.skeleton if track.root_name not in joint]
# all_but_root = [joint for joint in track.skeleton]
for joint in all_but_root:
new_df['%s_Xposition'%joint] = pd.Series(data=track.values['%s_Xposition'%joint]-projected_root_pos[rxp], index=new_df.index)
new_df['%s_Yposition'%joint] = pd.Series(data=track.values['%s_Yposition'%joint]-projected_root_pos[ryp], index=new_df.index)
new_df['%s_Zposition'%joint] = pd.Series(data=track.values['%s_Zposition'%joint]-projected_root_pos[rzp], index=new_df.index)
# keep the root as it is now
new_df[rxp] = track.values[rxp]
new_df[ryp] = track.values[ryp]
new_df[rzp] = track.values[rzp]
new_track.values = new_df
Q.append(new_track)
return Q
def inverse_transform(self, X, copy=None):
Q = []
for track in X:
new_track = track.clone()
rxp = '%s_Xposition'%track.root_name
ryp = '%s_Yposition'%track.root_name
rzp = '%s_Zposition'%track.root_name
projected_root_pos = track.values[[rxp, ryp, rzp]]
projected_root_pos.loc[:,ryp] = 0 # we want the root's projection on the floor plane as the ref
new_df = pd.DataFrame(index=track.values.index)
for joint in track.skeleton:
new_df['%s_Xposition'%joint] = pd.Series(data=track.values['%s_Xposition'%joint]+projected_root_pos[rxp], index=new_df.index)
new_df['%s_Yposition'%joint] = pd.Series(data=track.values['%s_Yposition'%joint]+projected_root_pos[ryp], index=new_df.index)
new_df['%s_Zposition'%joint] = pd.Series(data=track.values['%s_Zposition'%joint]+projected_root_pos[rzp], index=new_df.index)
new_track.values = new_df
Q.append(new_track)
return Q
class Flattener(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
return np.concatenate(X, axis=0)
class ConstantsRemover(BaseEstimator, TransformerMixin):
'''
For now it just looks at the first track
'''
def __init__(self, eps = 10e-10):
self.eps = eps
def fit(self, X, y=None):
stds = X[0].values.std()
cols = X[0].values.columns.values
self.const_dims_ = [c for c in cols if (stds[c] < self.eps).any()]
self.const_values_ = {c:X[0].values[c].values[0] for c in cols if (stds[c] < self.eps).any()}
return self
def transform(self, X, y=None):
Q = []
for track in X:
t2 = track.clone()
#for key in t2.skeleton.keys():
# if key in self.ConstDims_:
# t2.skeleton.pop(key)
t2.values = track.values[track.values.columns.difference(self.const_dims_)]
Q.append(t2)
return Q
def inverse_transform(self, X, copy=None):
Q = []
for track in X:
t2 = track.clone()
for d in self.const_dims_:
t2.values[d] = self.const_values_[d]
Q.append(t2)
return Q
class ListStandardScaler(BaseEstimator, TransformerMixin):
def __init__(self, is_DataFrame=False):
self.is_DataFrame = is_DataFrame
def fit(self, X, y=None):
if self.is_DataFrame:
X_train_flat = np.concatenate([m.values for m in X], axis=0)
else:
X_train_flat = np.concatenate([m for m in X], axis=0)
self.data_mean_ = np.mean(X_train_flat, axis=0)
self.data_std_ = np.std(X_train_flat, axis=0)
return self
def transform(self, X, y=None):
Q = []
for track in X:
if self.is_DataFrame:
normalized_track = track.copy()
normalized_track.values = (track.values - self.data_mean_) / self.data_std_
else:
normalized_track = (track - self.data_mean_) / self.data_std_
Q.append(normalized_track)
if self.is_DataFrame:
return Q
else:
return np.array(Q)
def inverse_transform(self, X, copy=None):
Q = []
for track in X:
if self.is_DataFrame:
unnormalized_track = track.copy()
unnormalized_track.values = (track.values * self.data_std_) + self.data_mean_
else:
unnormalized_track = (track * self.data_std_) + self.data_mean_
Q.append(unnormalized_track)
if self.is_DataFrame:
return Q
else:
return np.array(Q)
class DownSampler(BaseEstimator, TransformerMixin):
def __init__(self, rate):
self.rate = rate
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
Q = []
for track in X:
#print(track.values.size)
#new_track = track.clone()
#new_track.values = track.values[0:-1:self.rate]
#print(new_track.values.size)
new_track = track[0:-1:self.rate]
Q.append(new_track)
return Q
def inverse_transform(self, X, copy=None):
return X
#TODO: JointsSelector (x)
#TODO: SegmentMaker
#TODO: DynamicFeaturesAdder
#TODO: ShapeFeaturesAdder
#TODO: DataFrameNumpier (x)
class TemplateTransform(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
return X
class UnsupportedParamError(Exception):
def __init__(self, message):
self.message = message
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