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import os
import sys
import numpy as np
import torch
import argparse
from os.path import join as pjoin
# from visualization import BVH
from visualization.InverseKinematics import JacobianInverseKinematics, BasicInverseKinematics
# from scripts.motion_process_bvh import *
# from visualization.Animation import *
def softmax(x, **kw):
softness = kw.pop("softness", 1.0)
maxi, mini = np.max(x, **kw), np.min(x, **kw)
return maxi + np.log(softness + np.exp(mini - maxi))
def softmin(x, **kw):
return -softmax(-x, **kw)
def alpha(t):
return 2.0 * t * t * t - 3.0 * t * t + 1
def lerp(a, l, r):
return (1 - a) * l + a * r
def remove_fs_old(anim, glb, foot_contact, fid_l=(3, 4), fid_r=(7, 8), interp_length=5, force_on_floor=True):
# glb_height = 2.06820832 Not the case, may be use upper leg length
scale = 1. #glb_height / 1.65 #scale to meter
# fps = 20 #
# velocity_thres = 10. # m/s
height_thres = [0.06, 0.03] #[ankle, toe] meter
if foot_contact is None:
def foot_detect(positions, velfactor, heightfactor):
feet_l_x = (positions[1:, fid_l, 0] - positions[:-1, fid_l, 0]) ** 2
feet_l_y = (positions[1:, fid_l, 1] - positions[:-1, fid_l, 1]) ** 2
feet_l_z = (positions[1:, fid_l, 2] - positions[:-1, fid_l, 2]) ** 2
feet_l_h = positions[:-1, fid_l, 1]
feet_l = (((feet_l_x + feet_l_y + feet_l_z) < velfactor) & (feet_l_h < heightfactor)).astype(np.float)
feet_r_x = (positions[1:, fid_r, 0] - positions[:-1, fid_r, 0]) ** 2
feet_r_y = (positions[1:, fid_r, 1] - positions[:-1, fid_r, 1]) ** 2
feet_r_z = (positions[1:, fid_r, 2] - positions[:-1, fid_r, 2]) ** 2
feet_r_h = positions[:-1, fid_r, 1]
feet_r = (((feet_r_x + feet_r_y + feet_r_z) < velfactor) & (feet_r_h < heightfactor)).astype(np.float)
return feet_l, feet_r
# feet_thre = 0.002
# feet_vel_thre = np.array([velocity_thres**2, velocity_thres**2]) * scale**2 / fps**2
feet_vel_thre = np.array([0.05, 0.2])
# height_thre = np.array([0.06, 0.04]) * scale
feet_h_thre = np.array(height_thres) * scale
feet_l, feet_r = foot_detect(glb, velfactor=feet_vel_thre, heightfactor=feet_h_thre)
foot = np.concatenate([feet_l, feet_r], axis=-1).transpose(1, 0) # [4, T-1]
foot = np.concatenate([foot, foot[:, -1:]], axis=-1)
else:
foot = foot_contact.transpose(1, 0)
T = len(glb)
fid = list(fid_l) + list(fid_r)
fid_l, fid_r = np.array(fid_l), np.array(fid_r)
foot_heights = np.minimum(glb[:, fid_l, 1],
glb[:, fid_r, 1]).min(axis=1) # [T, 2] -> [T]
# print(foot_heights)
# floor_height = softmin(foot_heights, softness=0.03, axis=0)
sort_height = np.sort(foot_heights)
temp_len = len(sort_height)
floor_height = np.mean(sort_height[int(0.25*temp_len):int(0.5*temp_len)])
if floor_height > 0.5: # for motion like swim
floor_height = 0
# print(floor_height)
# floor_height = foot_heights.min()
# print(floor_height)
# print(foot)
# print(foot_heights.min())
# print(floor_height)
glb[:, :, 1] -= floor_height
anim.positions[:, 0, 1] -= floor_height
for i, fidx in enumerate(fid):
fixed = foot[i] # [T]
"""
for t in range(T):
glb[t, fidx][1] = max(glb[t, fidx][1], 0.25)
"""
s = 0
while s < T:
while s < T and fixed[s] == 0:
s += 1
if s >= T:
break
t = s
avg = glb[t, fidx].copy()
while t + 1 < T and fixed[t + 1] == 1:
t += 1
avg += glb[t, fidx].copy()
avg /= (t - s + 1)
if force_on_floor:
avg[1] = 0.0
for j in range(s, t + 1):
glb[j, fidx] = avg.copy()
s = t + 1
for s in range(T):
if fixed[s] == 1:
continue
l, r = None, None
consl, consr = False, False
for k in range(interp_length):
if s - k - 1 < 0:
break
if fixed[s - k - 1]:
l = s - k - 1
consl = True
break
for k in range(interp_length):
if s + k + 1 >= T:
break
if fixed[s + k + 1]:
r = s + k + 1
consr = True
break
if not consl and not consr:
continue
if consl and consr:
litp = lerp(alpha(1.0 * (s - l + 1) / (interp_length + 1)),
glb[s, fidx], glb[l, fidx])
ritp = lerp(alpha(1.0 * (r - s + 1) / (interp_length + 1)),
glb[s, fidx], glb[r, fidx])
itp = lerp(alpha(1.0 * (s - l + 1) / (r - l + 1)),
ritp, litp)
glb[s, fidx] = itp.copy()
continue
if consl:
litp = lerp(alpha(1.0 * (s - l + 1) / (interp_length + 1)),
glb[s, fidx], glb[l, fidx])
glb[s, fidx] = litp.copy()
continue
if consr:
ritp = lerp(alpha(1.0 * (r - s + 1) / (interp_length + 1)),
glb[s, fidx], glb[r, fidx])
glb[s, fidx] = ritp.copy()
targetmap = {}
for j in range(glb.shape[1]):
targetmap[j] = glb[:, j]
# ik = BasicInverseKinematics(anim, glb, iterations=5,
# silent=True)
# slightly larger loss, but better visual
ik = JacobianInverseKinematics(anim, targetmap, iterations=30, damping=5, recalculate=False, silent=True)
anim = ik()
return anim
def remove_fs(glb, foot_contact, fid_l=(3, 4), fid_r=(7, 8), interp_length=5, force_on_floor=True):
# glb_height = 2.06820832 Not the case, may be use upper leg length
scale = 1. #glb_height / 1.65 #scale to meter
# fps = 20 #
# velocity_thres = 10. # m/s
height_thres = [0.06, 0.03] #[ankle, toe] meter
if foot_contact is None:
def foot_detect(positions, velfactor, heightfactor):
feet_l_x = (positions[1:, fid_l, 0] - positions[:-1, fid_l, 0]) ** 2
feet_l_y = (positions[1:, fid_l, 1] - positions[:-1, fid_l, 1]) ** 2
feet_l_z = (positions[1:, fid_l, 2] - positions[:-1, fid_l, 2]) ** 2
feet_l_h = positions[:-1, fid_l, 1]
feet_l = (((feet_l_x + feet_l_y + feet_l_z) < velfactor) & (feet_l_h < heightfactor)).astype(np.float)
feet_r_x = (positions[1:, fid_r, 0] - positions[:-1, fid_r, 0]) ** 2
feet_r_y = (positions[1:, fid_r, 1] - positions[:-1, fid_r, 1]) ** 2
feet_r_z = (positions[1:, fid_r, 2] - positions[:-1, fid_r, 2]) ** 2
feet_r_h = positions[:-1, fid_r, 1]
feet_r = (((feet_r_x + feet_r_y + feet_r_z) < velfactor) & (feet_r_h < heightfactor)).astype(np.float)
return feet_l, feet_r
# feet_thre = 0.002
# feet_vel_thre = np.array([velocity_thres**2, velocity_thres**2]) * scale**2 / fps**2
feet_vel_thre = np.array([0.05, 0.2])
# height_thre = np.array([0.06, 0.04]) * scale
feet_h_thre = np.array(height_thres) * scale
feet_l, feet_r = foot_detect(glb, velfactor=feet_vel_thre, heightfactor=feet_h_thre)
foot = np.concatenate([feet_l, feet_r], axis=-1).transpose(1, 0) # [4, T-1]
foot = np.concatenate([foot, foot[:, -1:]], axis=-1)
else:
foot = foot_contact.transpose(1, 0)
T = len(glb)
fid = list(fid_l) + list(fid_r)
fid_l, fid_r = np.array(fid_l), np.array(fid_r)
foot_heights = np.minimum(glb[:, fid_l, 1],
glb[:, fid_r, 1]).min(axis=1) # [T, 2] -> [T]
# print(foot_heights)
# floor_height = softmin(foot_heights, softness=0.03, axis=0)
sort_height = np.sort(foot_heights)
temp_len = len(sort_height)
floor_height = np.mean(sort_height[int(0.25*temp_len):int(0.5*temp_len)])
if floor_height > 0.5: # for motion like swim
floor_height = 0
# print(floor_height)
# floor_height = foot_heights.min()
# print(floor_height)
# print(foot)
# print(foot_heights.min())
# print(floor_height)
glb[:, :, 1] -= floor_height
# anim.positions[:, 0, 1] -= floor_height
for i, fidx in enumerate(fid):
fixed = foot[i] # [T]
"""
for t in range(T):
glb[t, fidx][1] = max(glb[t, fidx][1], 0.25)
"""
s = 0
while s < T:
while s < T and fixed[s] == 0:
s += 1
if s >= T:
break
t = s
avg = glb[t, fidx].copy()
while t + 1 < T and fixed[t + 1] == 1:
t += 1
avg += glb[t, fidx].copy()
avg /= (t - s + 1)
if force_on_floor:
avg[1] = 0.0
for j in range(s, t + 1):
glb[j, fidx] = avg.copy()
s = t + 1
for s in range(T):
if fixed[s] == 1:
continue
l, r = None, None
consl, consr = False, False
for k in range(interp_length):
if s - k - 1 < 0:
break
if fixed[s - k - 1]:
l = s - k - 1
consl = True
break
for k in range(interp_length):
if s + k + 1 >= T:
break
if fixed[s + k + 1]:
r = s + k + 1
consr = True
break
if not consl and not consr:
continue
if consl and consr:
litp = lerp(alpha(1.0 * (s - l + 1) / (interp_length + 1)),
glb[s, fidx], glb[l, fidx])
ritp = lerp(alpha(1.0 * (r - s + 1) / (interp_length + 1)),
glb[s, fidx], glb[r, fidx])
itp = lerp(alpha(1.0 * (s - l + 1) / (r - l + 1)),
ritp, litp)
glb[s, fidx] = itp.copy()
continue
if consl:
litp = lerp(alpha(1.0 * (s - l + 1) / (interp_length + 1)),
glb[s, fidx], glb[l, fidx])
glb[s, fidx] = litp.copy()
continue
if consr:
ritp = lerp(alpha(1.0 * (r - s + 1) / (interp_length + 1)),
glb[s, fidx], glb[r, fidx])
glb[s, fidx] = ritp.copy()
targetmap = {}
for j in range(glb.shape[1]):
targetmap[j] = glb[:, j]
# ik = BasicInverseKinematics(anim, glb, iterations=5,
# silent=True)
# slightly larger loss, but better visual
# ik = JacobianInverseKinematics(anim, targetmap, iterations=30, damping=5, recalculate=False, silent=True)
# anim = ik()
return glb
def compute_foot_sliding(foot_data, traj_qpos, offseth):
foot = np.array(foot_data).copy()
offseth = np.mean(foot[:10, 1])
foot[:, 1] -= offseth # Grounding it
foot_disp = np.linalg.norm(foot[1:, [0, 2]] - foot[:-1, [0, 2]], axis=1)
traj_qpos[:, 1] -= offseth
seq_len = len(traj_qpos)
H = 0.05
y_threshold = 0.65 # yup system
y = traj_qpos[1:, 1]
foot_avg = (foot[:-1, 1] + foot[1:, 1]) / 2
subset = np.logical_and(foot_avg < H, y > y_threshold)
# import pdb; pdb.set_trace()
sliding_stats = np.abs(foot_disp * (2 - 2 ** (foot_avg / H)))[subset]
sliding = np.sum(sliding_stats) / seq_len * 1000
return sliding, sliding_stats |