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2680bd5 | 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 | import numpy as np
import json
from typing import Tuple
from scipy.spatial.distance import cdist
from scipy.ndimage import convolve1d
from ...feature.single_motioncode import MotionCoder
from ...constant import JOINT_NAME_INDEX_MAP
intra_tip_pairs = [
["thumb_tip", "index_tip"],
["thumb_tip", "middle_tip"],
["thumb_tip", "ring_tip"],
["thumb_tip", "pinky_tip"]
]
all_tips = ["thumb_tip", "index_tip", "middle_tip", "ring_tip", "pinky_tip"]
palm_peripheral_joints = ["wrist", "thumb_mcp", "index_mcp", "middle_mcp", "ring_mcp", "pinky_mcp"]
for i in range(len(intra_tip_pairs)):
intra_tip_pairs[i] = [
JOINT_NAME_INDEX_MAP[intra_tip_pairs[i][0]],
JOINT_NAME_INDEX_MAP[intra_tip_pairs[i][1]]
]
for i in range(len(all_tips)):
all_tips[i] = JOINT_NAME_INDEX_MAP[all_tips[i]]
for i in range(len(palm_peripheral_joints)):
palm_peripheral_joints[i] = JOINT_NAME_INDEX_MAP[palm_peripheral_joints[i]]
intra_tip_pairs = np.array(intra_tip_pairs) # (P, 2)
all_tips = np.array(all_tips) # (N,)
palm_peripheral_joints = np.array(palm_peripheral_joints) # (M,)
def min_duration_restriction(bool_data: np.ndarray, min_duration: int) -> np.ndarray:
'''
bool_data: (T, P)
return:(P,)
'''
int_data = bool_data.astype(np.int8) # (T, P)
kernel = np.ones((min_duration,), dtype=np.int8) # (K,)
convolved = convolve1d(int_data, kernel, axis=0, mode='constant', cval=0) # (T, P)
return np.any(convolved == min_duration, axis=0) # (P,)
def tip_tip_contact_occurs(p1: np.ndarray, p2: np.ndarray, threshold: float) -> np.ndarray:
'''
p1: (T, P, 3)
p2: (T, P, 3)
return: (T, P)
'''
dists = np.linalg.norm(p1 - p2, axis=-1) # (T, P)
return dists < threshold
def tip_palm_contact_occurs(p: np.ndarray, palm: np.ndarray, threshold: float) -> np.ndarray:
'''
p: (T, N, 3)
palm: (T, M, 3)
return: (T, N)
'''
diff = p[:, :, np.newaxis, :] - palm[:, np.newaxis, :, :] # (T, N, M, 3)
dists = np.linalg.norm(diff, axis=-1) # (T, N, M)
min_dists = np.min(dists, axis=-1) # (T, N)
return min_dists < threshold
def palm_palm_contact_occurs(palm1: np.ndarray, palm2: np.ndarray, threshold: float) -> np.ndarray:
'''
palm1: (T, M, 3)
palm2: (T, M, 3)
return (T,)
'''
diff = palm1[:, :, np.newaxis, :] - palm2[:, np.newaxis, :, :] # (T, M, M, 3)
dists = np.linalg.norm(diff, axis=-1) # (T, M, M)
min_dists = np.min(dists, axis=(-1, -2)) # (T,)
return min_dists < threshold
def get_palm_glob(motion: np.ndarray, is_right: bool) -> np.ndarray:
'''
motion: (T, J, 3)
return: (T, N, 3)
'''
motion_coder = MotionCoder(motion, is_right)
motion_coder.get_palm()
return motion_coder.palm_points_glob
def intra_contact(motion:np.ndarray, threshold: float, min_duration: int) -> np.ndarray:
'''
motion: (T, J, 3)
return: (P,)
'''
contacts = tip_tip_contact_occurs(
p1=motion[:, intra_tip_pairs[:, 0], :],
p2=motion[:, intra_tip_pairs[:, 1], :],
threshold=threshold,
) # (T, P)
contacts = min_duration_restriction(contacts, min_duration) # (P,)
return contacts # (P,)
def inter_contact(motion:np.ndarray, another_motion:np.ndarray, another_is_right: bool, threshold: float, min_duration: int) -> np.ndarray:
'''
motion: (T, J, 3)
another_motion: (T, J, 3)
return: (N_tips,)
'''
palm_points = get_palm_glob(another_motion, another_is_right) # (T, M, 3)
contacts = tip_palm_contact_occurs(
p=motion[:, all_tips, :],
palm=palm_points,
threshold=threshold,
) # (T, N_tips)
contacts = min_duration_restriction(contacts, min_duration) # (N_tips,)
return contacts # (N_tips,)
def lenient_contact_occurs(motion:np.ndarray, another_motion:np.ndarray, another_is_right: bool, threshold: float, min_duration: int) -> np.ndarray:
'''
motion: (T, J, 3)
another_motion: (T, J, 3)
return: (T,)
'''
points = get_palm_glob(motion, not another_is_right) # (T, M, 3)
anothter_points = get_palm_glob(another_motion, another_is_right) # (T, M, 3)
points = np.concatenate([points, motion], axis=1) # (T, M+J, 3)
anothter_points = np.concatenate([anothter_points, another_motion], axis=1) # (T, M+J, 3)
diff = points[:, :, np.newaxis, :] - anothter_points[:, np.newaxis, :, :] # (T, M+J, M+J, 3)
dists = np.linalg.norm(diff, axis=-1) # (T, M+J, M+J)
min_dists = np.min(dists, axis=(-1, -2)) # (T,)
return min_dists < threshold
def lenient_contact(motion:np.ndarray, another_motion:np.ndarray, another_is_right: bool, threshold: float, min_duration: int) -> bool:
'''
motion: (T, J, 3)
another_motion: (T, J, 3)
return: bool
'''
contacts = lenient_contact_occurs(
motion,
another_motion,
another_is_right,
threshold,
min_duration
) # (T,)
contacts = min_duration_restriction(contacts[:, np.newaxis], min_duration) # (1,)
return contacts[0]
def count_label(gt_arr: np.ndarray, pred_arr: np.ndarray) -> Tuple[int, int, int]:
'''
gt_arr: (N,)
pred_arr: (N,)
return: tp, fp, fn
'''
tp = np.sum(np.logical_and(gt_arr, pred_arr))
fp = np.sum(np.logical_and(np.logical_not(gt_arr), pred_arr))
fn = np.sum(np.logical_and(gt_arr, np.logical_not(pred_arr)))
return tp, fp, fn
def compute_metric(tp: int, fp: int, fn: int) -> Tuple[float, float, float]:
'''
return: precision, recall, f1
'''
precision = tp / (tp + fp + 1e-8)
recall = tp / (tp + fn + 1e-8)
f1 = 2 * precision * recall / (precision + recall + 1e-8)
return precision, recall, f1
def compute_intra_metric(gt_motion:np.ndarray, pred_motion:np.ndarray, threshold: float, min_duration: int) -> Tuple[float, float, float]:
'''
gt_motion: (T, 2, J, 3)
pred_motion: (T, 2, J, 3)
return: tp, fp, fn
'''
gt_left_contacts = intra_contact(
gt_motion[:, 0, :, :],
threshold, min_duration
) # (P,)
gt_right_contacts = intra_contact(
gt_motion[:, 1, :, :],
threshold, min_duration
) # (P,)
gt_contacts = np.concatenate([gt_left_contacts, gt_right_contacts], axis=0) # (2P,)
pred_left_contacts = intra_contact(
pred_motion[:, 0, :, :],
threshold, min_duration
) # (P,)
pred_right_contacts = intra_contact(
pred_motion[:, 1, :, :],
threshold, min_duration
) # (P,)
pred_contacts = np.concatenate([pred_left_contacts, pred_right_contacts], axis=0) # (2P,)
return count_label(gt_contacts, pred_contacts)
def compute_inter_metric(gt_motion:np.ndarray, pred_motion:np.ndarray, threshold: float, min_duration: int) -> Tuple[float, float, float]:
'''
gt_motion: (T, 2, J, 3)
pred_motion: (T, 2, J, 3)
return: tp, fp, fn
'''
gt_left_contacts = inter_contact(
gt_motion[:, 0, :, :],
gt_motion[:, 1, :, :],
another_is_right=True,
threshold=threshold,
min_duration=min_duration
) # (N_tips,)
gt_right_contacts = inter_contact(
gt_motion[:, 1, :, :],
gt_motion[:, 0, :, :],
another_is_right=False,
threshold=threshold,
min_duration=min_duration
) # (N_tips,)
gt_contacts = np.concatenate([gt_left_contacts, gt_right_contacts], axis=0) # (2N_tips,)
pred_left_contacts = inter_contact(
pred_motion[:, 0, :, :],
pred_motion[:, 1, :, :],
another_is_right=True,
threshold=threshold,
min_duration=min_duration
) # (N_tips,)
pred_right_contacts = inter_contact(
pred_motion[:, 1, :, :],
pred_motion[:, 0, :, :],
another_is_right=False,
threshold=threshold,
min_duration=min_duration
) # (N_tips,)
pred_contacts = np.concatenate([pred_left_contacts, pred_right_contacts], axis=0) # (2N_tips,)
return count_label(gt_contacts, pred_contacts)
def compute_lenient_inter_metric(gt_motion:np.ndarray, pred_motion:np.ndarray, threshold: float, min_duration: int) -> Tuple[float, float, float]:
'''
gt_motion: (T, 2, J, 3)
pred_motion: (T, 2, J, 3)
return: tp, fp, fn
'''
gt_contacts = lenient_contact(
gt_motion[:, 0, :, :],
gt_motion[:, 1, :, :],
another_is_right=True,
threshold=threshold,
min_duration=min_duration
) # bool
pred_contacts = lenient_contact(
pred_motion[:, 0, :, :],
pred_motion[:, 1, :, :],
another_is_right=True,
threshold=threshold,
min_duration=min_duration
) # bool
gt_arr = np.array([gt_contacts], dtype=bool) # (1,)
pred_arr = np.array([pred_contacts], dtype=bool) # (1,)
return count_label(gt_arr, pred_arr)
def give_contact_label(motion:np.ndarray, tip_tip_threshold: float, tip_palm_threshold: float, palm_palm_threshold: float) -> np.ndarray:
'''
motion: (T, 2, J, 3)
return: (T, total_pairs)
'''
left_motion = motion[:, 0, :, :] # (T, J, 3)
right_motion = motion[:, 1, :, :] # (T, J, 3)
left_palm = get_palm_glob(left_motion, is_right=False) # (T, M, 3)
right_palm = get_palm_glob(right_motion, is_right=True) # (T, M, 3)
left_intra_contacts = tip_tip_contact_occurs(
p1=left_motion[:, intra_tip_pairs[:, 0], :],
p2=left_motion[:, intra_tip_pairs[:, 1], :],
threshold=tip_tip_threshold,
) # (T, intra_contact_pairs)
right_intra_contacts = tip_tip_contact_occurs(
p1=right_motion[:, intra_tip_pairs[:, 0], :],
p2=right_motion[:, intra_tip_pairs[:, 1], :],
threshold=tip_tip_threshold,
) # (T, intra_contact_pairs)
left_tip_right_palm_contacts = tip_palm_contact_occurs(
p=left_motion[:, all_tips, :],
palm=right_palm,
threshold=tip_palm_threshold,
) # (T, N_tips)
right_tip_left_palm_contacts = tip_palm_contact_occurs(
p=right_motion[:, all_tips, :],
palm=left_palm,
threshold=tip_palm_threshold,
) # (T, N_tips)
palm_palm_contacts = palm_palm_contact_occurs(
left_palm,
right_palm,
threshold=palm_palm_threshold
)[:, np.newaxis] # (T, 1)
contact_labels = np.concatenate(
[left_intra_contacts, right_intra_contacts, left_tip_right_palm_contacts, right_tip_left_palm_contacts, palm_palm_contacts],
axis=1
) # (T, total_pairs)
return contact_labels |