Rebuild the exercise figure on an anatomical 3D skeleton

The library's planar world-angle rig becomes a genuine 3D anatomical
model: skeleton.json holds bone-length profiles (real shoulder/pelvis
widths, feet, neutral/female/male) and per-joint ROM; motions pose
joints with anatomical angles (flexion/abduction/rotation from neutral
standing) under a per-exercise orthographic camera, resolved by
kinematics.py (3D FK, analytic two-bone IK with anatomical write-back)
and validated against physiological ranges. All 20 sagittal motions
were migrated by planar decomposition with 0.00 px golden parity against
the old renderer — relabeled to true anatomy, since shading is now
near-dark/far-light by camera depth rather than by limb suffix — and
the face-on machines are re-authored honestly: Abductor/Adductor with
real hip abduction (the foreshortened "frontal" profile is retired) and
Rotary with genuine spine axial rotation. Figures gain articulated
feet; profiles swap without touching a single motion script; --orbit
sweeps the camera 360° while a motion loops.

The in-app SwiftUI renderer (iOS + watch) is ported to the same model
and consumes the exported motions verbatim; figure-fixtures.json pins
its geometry to the Python pipeline within 0.5 px across every
exercise, key frame, tween, and orbit sample. Also makes the watch
bridge logger nonisolated for the newer SDK's stricter isolation
checking.

Claude-Session: https://claude.ai/code/session_01LEoff8bXGBS83tK1c55Mf7
This commit is contained in:
2026-07-06 20:10:50 -04:00
parent 6521de8f17
commit 3c7a790e9d
153 changed files with 2827 additions and 1686 deletions
+381
View File
@@ -0,0 +1,381 @@
"""3D anatomical kinematics for the Exercise Library stick figure.
Model space follows the ISB convention: X anterior (the figure's facing
direction), Y superior (up), Z toward the figure's anatomical right.
All angles are degrees, measured from the neutral standing pose (upright,
facing +X, arms hanging, legs straight, toes forward).
A key frame poses joints with anatomical coordinates:
- root: canvas anchor `pos` plus trunk orientation `yaw` (facing: 0 = +X,
180 = -X), `pitch` (forward bow positive), `roll` (toward the right
positive) - applied as Ry(yaw) . Rz(-pitch) . Rx(roll).
- spine: two chained segments, each {flexion, lateral, rotation}
(forward curl / right side-bend / turn right positive).
- neck {flexion, rotation}, head {flexion} (extra gaze pitch).
- shoulder/hip {flexion, abduction, rotation}: forward, away from the
midline, external positive. elbow/knee {flexion}: bend positive.
ankle {flexion}: dorsiflexion positive (toes up).
A bare number is shorthand for {"flexion": n}.
The camera is orthographic and rotates about the vertical axis through the
root anchor: yaw 0 is the classic side view (from +Z, anatomical right side
near), 90 views the figure face-on. Poses resolve in *view space* (x right,
y up, z toward the camera); the renderer maps to y-down canvas points.
Pins are canvas-space IK targets for hands/feet (`hand_r`, `foot_l`, ...):
the two-bone chain is solved analytically in 3D, in the plane picked by the
authored (FK) elbow/knee, then converted back to anatomical angles so key
frames always interpolate in anatomical space.
skeleton.json carries the bone-length profiles (including shoulder/pelvis
half-widths and feet) and each joint type's degrees of freedom with their
physiological range of motion (ROM), used to validate authored frames.
"""
import json
import math
from pathlib import Path
LIB = Path(__file__).parent
# ------------------------------------------------------------ vectors / mats
def _cs(deg):
r = math.radians(deg)
return math.cos(r), math.sin(r)
def rot_x(deg):
c, s = _cs(deg)
return ((1, 0, 0), (0, c, -s), (0, s, c))
def rot_y(deg):
c, s = _cs(deg)
return ((c, 0, s), (0, 1, 0), (-s, 0, c))
def rot_z(deg):
c, s = _cs(deg)
return ((c, -s, 0), (s, c, 0), (0, 0, 1))
IDENTITY = ((1, 0, 0), (0, 1, 0), (0, 0, 1))
def mmul(a, b):
return tuple(tuple(sum(a[i][k] * b[k][j] for k in range(3)) for j in range(3))
for i in range(3))
def chain(*mats):
m = mats[0]
for n in mats[1:]:
m = mmul(m, n)
return m
def mvec(m, v):
return tuple(m[i][0] * v[0] + m[i][1] * v[1] + m[i][2] * v[2] for i in range(3))
def mtrans(m):
return tuple(tuple(m[j][i] for j in range(3)) for i in range(3))
def vadd(a, b):
return (a[0] + b[0], a[1] + b[1], a[2] + b[2])
def vsub(a, b):
return (a[0] - b[0], a[1] - b[1], a[2] - b[2])
def vscale(v, s):
return (v[0] * s, v[1] * s, v[2] * s)
def vdot(a, b):
return a[0] * b[0] + a[1] * b[1] + a[2] * b[2]
def vcross(a, b):
return (a[1] * b[2] - a[2] * b[1],
a[2] * b[0] - a[0] * b[2],
a[0] * b[1] - a[1] * b[0])
def vlen(v):
return math.sqrt(vdot(v, v))
def vnorm(v):
d = vlen(v)
return vscale(v, 1 / d) if d > 1e-9 else (0.0, 0.0, 0.0)
def _clamp(x, lo=-1.0, hi=1.0):
return max(lo, min(hi, x))
# ---------------------------------------------------------------- the frame
# DoF names per joint type; the first is the shorthand a bare number sets.
JOINT_DOFS = {
"spine": ("flexion", "lateral", "rotation"),
"neck": ("flexion", "rotation"),
"head": ("flexion",),
"shoulder": ("flexion", "abduction", "rotation"),
"elbow": ("flexion",),
"hip": ("flexion", "abduction", "rotation"),
"knee": ("flexion",),
"ankle": ("flexion",),
}
# Frame keys -> joint type (spine is a two-element list handled separately).
FRAME_JOINTS = {
"neck": "neck", "head": "head",
"shoulder_r": "shoulder", "shoulder_l": "shoulder",
"elbow_r": "elbow", "elbow_l": "elbow",
"hip_r": "hip", "hip_l": "hip",
"knee_r": "knee", "knee_l": "knee",
"ankle_r": "ankle", "ankle_l": "ankle",
}
LIMBS = { # limb -> (attach point key, side sign, pin key)
"arm_r": ("shoulder_r", 1, "hand_r"),
"arm_l": ("shoulder_l", -1, "hand_l"),
"leg_r": ("hip_r", 1, "foot_r"),
"leg_l": ("hip_l", -1, "foot_l"),
}
def load_skeleton():
return json.loads((LIB / "skeleton.json").read_text())
def _full(value, joint_type):
"""Expand a joint value (number, partial dict, or None) to a full DoF dict."""
dofs = JOINT_DOFS[joint_type]
if value is None:
return {d: 0.0 for d in dofs}
if isinstance(value, (int, float)):
out = {d: 0.0 for d in dofs}
out[dofs[0]] = float(value)
return out
return {d: float(value.get(d, 0.0)) for d in dofs}
def normalize_frame(kf):
"""Expand a key frame to full anatomical dicts with defaults filled in."""
root = kf.get("root", {})
out = {
"root": {"pos": [float(root["pos"][0]), float(root["pos"][1])],
"yaw": float(root.get("yaw", 0.0)),
"pitch": float(root.get("pitch", 0.0)),
"roll": float(root.get("roll", 0.0))},
"spine": [_full(s, "spine") for s in (kf.get("spine") or [0, 0])],
"pins": {k: [float(x), float(y)] for k, (x, y) in kf.get("pins", {}).items()},
"hold": float(kf.get("hold", 0.5)),
"tween": float(kf.get("tween", 0.6)),
}
for key, jt in FRAME_JOINTS.items():
out[key] = _full(kf.get(key), jt)
return out
def lerp_frames(a, b, t):
"""Interpolate two normalized frames in anatomical space. A pin survives
the tween only when planted in both neighboring key frames."""
def num(x, y):
return x + (y - x) * t
out = {"root": {"pos": [num(a["root"]["pos"][0], b["root"]["pos"][0]),
num(a["root"]["pos"][1], b["root"]["pos"][1])],
"yaw": num(a["root"]["yaw"], b["root"]["yaw"]),
"pitch": num(a["root"]["pitch"], b["root"]["pitch"]),
"roll": num(a["root"]["roll"], b["root"]["roll"])},
"spine": [{d: num(sa[d], sb[d]) for d in sa}
for sa, sb in zip(a["spine"], b["spine"])],
"pins": {k: [num(a["pins"][k][0], b["pins"][k][0]),
num(a["pins"][k][1], b["pins"][k][1])]
for k in a["pins"] if k in b["pins"]}}
for key in FRAME_JOINTS:
out[key] = {d: num(a[key][d], b[key][d]) for d in a[key]}
return out
# ------------------------------------------------------------------ posing
def _ball(joint, sigma):
"""Local rotation of a ball joint (shoulder/hip) for side sign sigma
(+1 right, -1 left): flexion forward, abduction away from the midline,
rotation external."""
return chain(rot_z(joint["flexion"]),
rot_x(-sigma * joint["abduction"]),
rot_y(-sigma * joint["rotation"]))
def fk_limb(kind, attach, joint, lower, ankle, prof, parent, sigma):
"""FK one limb from its resolved attach point. Returns the point chain
(arm: [shoulder, elbow, hand]; leg: [hip, knee, ankle, toe])."""
fu = mmul(parent, _ball(joint, sigma))
if kind == "arm":
elbow = vadd(attach, mvec(fu, (0, -prof["upperArm"], 0)))
fl = mmul(fu, rot_z(lower["flexion"]))
hand = vadd(elbow, mvec(fl, (0, -prof["foreArm"], 0)))
return [attach, elbow, hand]
knee = vadd(attach, mvec(fu, (0, -prof["thigh"], 0)))
fl = mmul(fu, rot_z(-lower["flexion"]))
ank = vadd(knee, mvec(fl, (0, -prof["shin"], 0)))
toe = vadd(ank, mvec(mmul(fl, rot_z(ankle["flexion"])), (prof["foot"], 0, 0)))
return [attach, knee, ank, toe]
def pose(nf, prof, cam_yaw):
"""FK a normalized frame into view space (x right, y up, z toward the
camera; origin at the root anchor). Returns points, parent frames (for
IK inversion), the nose direction, and the lateral depth factor k."""
r = nf["root"]
f_root = chain(rot_y(-cam_yaw), rot_y(r["yaw"]), rot_z(-r["pitch"]), rot_x(r["roll"]))
origin = (0.0, 0.0, 0.0)
s1, s2 = nf["spine"]
f1 = chain(f_root, rot_z(-s1["flexion"]), rot_x(s1["lateral"]), rot_y(-s1["rotation"]))
mid = vadd(origin, mvec(f1, (0, prof["spine1"], 0)))
f2 = chain(f1, rot_z(-s2["flexion"]), rot_x(s2["lateral"]), rot_y(-s2["rotation"]))
neck_base = vadd(mid, mvec(f2, (0, prof["spine2"], 0)))
fn = chain(f2, rot_z(-nf["neck"]["flexion"]), rot_y(-nf["neck"]["rotation"]))
head = vadd(neck_base, mvec(fn, (0, prof["neck"], 0)))
nose_dir = mvec(mmul(fn, rot_z(-nf["head"]["flexion"])), (1, 0, 0))
points = {
"pelvis": origin, "mid": mid, "neckB": neck_base, "head": head,
"shoulder_r": vadd(neck_base, mvec(f2, (0, 0, prof["shoulderHalf"]))),
"shoulder_l": vadd(neck_base, mvec(f2, (0, 0, -prof["shoulderHalf"]))),
"hip_r": vadd(origin, mvec(f_root, (0, 0, prof["hipHalf"]))),
"hip_l": vadd(origin, mvec(f_root, (0, 0, -prof["hipHalf"]))),
}
for limb, (attach, sigma, _pin) in LIMBS.items():
kind = "arm" if limb.startswith("arm") else "leg"
side = limb[-1]
parent = f2 if kind == "arm" else f_root
upper = nf[("shoulder_" if kind == "arm" else "hip_") + side]
lower = nf[("elbow_" if kind == "arm" else "knee_") + side]
ankle = nf.get("ankle_" + side)
points[limb] = fk_limb(kind, points[attach], upper, lower, ankle, prof, parent, sigma)
# How side-on the view is: 1 when the body's lateral axis is pure depth
# (profile view), 0 when it lies in the screen (face-on).
k = abs(mvec(f_root, (0, 0, 1))[2])
return {"points": points, "f2": f2, "f_root": f_root,
"nose_dir": nose_dir, "k": k}
# ---------------------------------------------------------------------- IK
def solve_limb(kind, attach, target, guess_mid, lengths, parent, sigma):
"""Analytic two-bone IK in 3D: reach from `attach` toward `target` in the
plane picked by the authored (FK) mid joint, then convert back to
anatomical angles. Returns (upper joint dict, lower joint dict)."""
a, b = lengths
to_t = vsub(target, attach)
d = _clamp(vlen(to_t), abs(a - b) + 0.5, a + b - 0.01)
dir_t = vnorm(to_t) if vlen(to_t) > 1e-9 else (0.0, -1.0, 0.0)
normal = vcross(dir_t, vsub(guess_mid, attach))
if vlen(normal) < 1e-6: # chain straight along the target: any plane works
normal = vcross(dir_t, (0, 0, 1))
if vlen(normal) < 1e-6:
normal = vcross(dir_t, (0, 1, 0))
normal = vnorm(normal)
perp = vcross(normal, dir_t)
along = (a * a + d * d - b * b) / (2 * d)
h = math.sqrt(max(a * a - along * along, 0.0))
best = None
for sign in (1.0, -1.0):
mid = vadd(attach, vadd(vscale(dir_t, along), vscale(perp, sign * h)))
dist = vlen(vsub(mid, guess_mid))
if best is None or dist < best[0]:
best = (dist, mid)
mid = best[1]
end = vadd(mid, vscale(vnorm(vsub(target, mid)), b))
return invert_limb(kind, attach, mid, end, lengths, parent, sigma)
def invert_limb(kind, attach, mid, end, lengths, parent, sigma):
"""Recover anatomical angles from limb joint positions (the inverse of
fk_limb, ignoring the foot). Assumes |abduction| < 90."""
pt = mtrans(parent)
u = vnorm(mvec(pt, vsub(mid, attach)))
abd = math.degrees(math.asin(_clamp(sigma * u[2])))
flex = math.degrees(math.atan2(u[0], -u[1]))
# Peel flexion/abduction off the lower bone to read rotation + hinge bend.
peel = mtrans(chain(rot_z(flex), rot_x(-sigma * abd)))
w = vnorm(mvec(peel, mvec(pt, vsub(end, mid))))
bend = math.degrees(math.acos(_clamp(-w[1])))
rot = sigma * math.degrees(math.atan2(w[2], w[0])) if bend > 0.5 else 0.0
if kind == "leg": # knees hinge backward; the sign convention flips
rot = sigma * math.degrees(math.atan2(-w[2], -w[0])) if bend > 0.5 else 0.0
upper = {"flexion": flex, "abduction": abd, "rotation": rot}
return upper, {"flexion": bend}
# ------------------------------------------------------------------ resolve
def view_from_canvas(pt, anchor, depth):
return (pt[0] - anchor[0], anchor[1] - pt[1], depth)
def resolve(nf, prof, cam_yaw):
"""Pose a normalized frame and apply pins: for each pinned limb, solve IK
against the canvas target (at the limb's FK depth), write the solved
anatomical angles back into the frame, and re-pose. Returns
(frame with IK-resolved angles, pose dict)."""
p = pose(nf, prof, cam_yaw)
anchor = nf["root"]["pos"]
solved = False
for limb, (attach_key, sigma, pin) in LIMBS.items():
if pin not in nf["pins"]:
continue
kind = "arm" if limb.startswith("arm") else "leg"
side = limb[-1]
chain_pts = p["points"][limb]
attach = chain_pts[0]
end_idx = 2
target = view_from_canvas(nf["pins"][pin], anchor, chain_pts[end_idx][2])
lengths = ((prof["upperArm"], prof["foreArm"]) if kind == "arm"
else (prof["thigh"], prof["shin"]))
parent = p["f2"] if kind == "arm" else p["f_root"]
upper, lower = solve_limb(kind, attach, target, chain_pts[1],
lengths, parent, sigma)
if kind == "arm":
nf["shoulder_" + side], nf["elbow_" + side] = upper, lower
else:
nf["hip_" + side], nf["knee_" + side] = upper, lower
solved = True
if solved:
p = pose(nf, prof, cam_yaw)
return nf, p
# -------------------------------------------------------------------- ROM
def validate_rom(nf, joints, label=""):
"""Check a normalized frame's anatomical angles against each joint's
range of motion. Returns a list of human-readable violations."""
issues = []
def check(joint_type, name, value):
for dof, angle in value.items():
lo_hi = joints.get(joint_type, {}).get(dof)
if lo_hi and not (lo_hi[0] - 1e-6 <= angle <= lo_hi[1] + 1e-6):
issues.append(f"{label}{name}.{dof} = {angle:.1f} outside "
f"[{lo_hi[0]}, {lo_hi[1]}]")
for i, seg in enumerate(nf["spine"], start=1):
check("spine", f"spine{i}", seg)
for key, jt in FRAME_JOINTS.items():
check(jt, key, nf[key])
return issues