Spaces:
Paused
Paused
File size: 25,945 Bytes
2d47d90 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 |
'''
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
|