#!/usr/bin/env python3 """RC46 selective movement rebuild. Purpose: - Stop trusting old sheet/pose names as exercise truth. - Apply the first batch of AI-physio movement decisions to sequence runtime data. - Selectively activate MediaPipe runtime landmarks only where confidence and movement risk allow it. - Keep risky/special exercises gated with explicit review metadata rather than pretending they are solved. """ from __future__ import annotations import json from copy import deepcopy from datetime import datetime, timezone from pathlib import Path ROOT = Path(__file__).resolve().parents[1] DATA = ROOT / 'assets' / 'data' / 'image-admin' SEQ_PATH = DATA / 'pose-atlas-exercise-sequences.json' PACKS_PATH = DATA / 'pose-atlas-packs.json' AUDIT_PATH = DATA / 'rc45_ai_physio_first_pass.json' OUT_REPORT = ROOT / 'qa_screenshots' / 'rc46_selective_movement_rebuild_report.json' OUT_DATA = DATA / 'rc46_selective_movement_rebuild.json' BACKUP_SEQ = DATA / 'pose-atlas-exercise-sequences.rc45-before-rc46.backup.json' BACKUP_PACKS = DATA / 'pose-atlas-packs.rc45-before-rc46.backup.json' RC46_VERSION = 'v97-rc46-selective-movement-rebuild' NOW = datetime.now(timezone.utc).isoformat(timespec='seconds') def load_json(path: Path): return json.loads(path.read_text(encoding='utf-8')) def save_json(path: Path, data): path.write_text(json.dumps(data, ensure_ascii=False, indent=2) + '\n', encoding='utf-8') def limb(anchor: str, *, role: str, confidence: float = 0.99, source: str = 'rc46-ai-physio-first-pass') -> dict: return { 'schema': 'rc46-movement-intent-v1', 'primaryAnchor': anchor, 'anchorSource': source, 'movementRole': role, 'confidence': confidence, 'linkedToSpriteLandmarks': True, 'coordinateSpace': 'sprite-percent-unflipped', 'runtimeTransformPolicy': 'skeleton and sprite share the same anchor + flip transform', 'note': 'Exercise truth and limb binding are defined by movement standard, not by atlas pose/sheet name.' } def tag_sequence(seq: dict, status: str, decision: str, *, risk: str = 'moderate', runtime_gate: str = 'selective-active'): seq['rc46MovementRebuild'] = { 'version': RC46_VERSION, 'status': status, 'decision': decision, 'riskTier': risk, 'source': 'rc45_ai_physio_first_pass.json', 'poseNameAuthority': False, 'exerciseTruthAuthority': 'movement standard + reviewed skeleton anchors', 'updatedAt': NOW, 'runtimeGate': runtime_gate } return seq def rewrite_steps(seq: dict, step_specs: list[dict]): old = seq.get('poses', []) out = [] for i, spec in enumerate(step_specs): base = deepcopy(old[spec.get('oldIndex', i)] if spec.get('oldIndex', i) < len(old) else old[-1]) base.update({k: v for k, v in spec.items() if k != 'oldIndex'}) base['limbIntent'] = limb(base.get('anchorLandmark', 'footCenter'), role=base.get('rc46Role', base.get('phase','keyframe'))) base['rc46Frame'] = { 'order': i + 1, 'phase': base.get('phase'), 'contact': base.get('contact'), 'anchorLandmark': base.get('anchorLandmark'), 'physioIntent': base.get('rc46Role', ''), 'reviewNote': base.get('rc46ReviewNote', '') } out.append(base) seq['poses'] = out seq['bindingSchema'] = 'rc46-movement-intent-v1' return seq def rebuild_sequences(sequences: dict): changes = [] # 1) Critical bug test case: lateral line hops. # Old mapping locked left/right foot independently. RC46 makes the foot-pair the truth. if 'lateral-line-hops' in sequences: s = sequences['lateral-line-hops'] rewrite_steps(s, [ {'oldIndex': 0, 'phase': 'ready-left-foot-pair', 'pathT': 0.00, 'contact': 'grounded', 'anchorLandmark': 'footCenter', 'rc46Role': 'bilateral foot pair locked on left side of line', 'rc46ReviewNote': 'Feet move as a pair; do not choose a random single foot.'}, {'oldIndex': 1, 'phase': 'takeoff-foot-pair', 'pathT': 0.18, 'contact': 'takeoff', 'anchorLandmark': 'footCenter', 'rc46Role': 'two-foot takeoff from line side', 'rc46ReviewNote': 'Skeletal feet and sprite feet stay linked as a pair.'}, {'oldIndex': 2, 'phase': 'airborne-foot-pair', 'pathT': 0.50, 'contact': 'airborne', 'anchorLandmark': 'footCenter', 'rc46Role': 'compact airborne hop across line', 'rc46ReviewNote': 'No left/right foot swap allowed in mid-air.'}, {'oldIndex': 3, 'phase': 'land-right-foot-pair', 'pathT': 0.82, 'contact': 'land/reset', 'anchorLandmark': 'footCenter', 'rc46Role': 'two-foot landing on other side', 'rc46ReviewNote': 'Land through the foot pair, not through visual right foot only.'}, {'oldIndex': 4, 'phase': 'reset-right-foot-pair', 'pathT': 1.00, 'contact': 'grounded', 'anchorLandmark': 'footCenter', 'rc46Role': 'stable reset on other side', 'rc46ReviewNote': 'End state must still keep foot-pair identity.'}, ]) s['motionProfile'].update({'family': 'jump_arc_lateral', 'anchor': 'foot-pair-lock', 'x0': 42, 'x1': 58, 'arcHeight': 10, 'settle': 1.6, 'loopMs': 1500, 'direction': 'right', 'ghostCount': 0, 'ghostMode': 'none', 'debug': True}) s['layout'].update({'floorY': 84, 'baseStandingHeightPct': 50}) tag_sequence(s, 'selective-rebuilt-active', 'Foot-pair anchor replaces old single-foot lock. This is the first real movement-truth rebuild.', risk='moderate') changes.append({'exerciseKey': 'lateral-line-hops', 'action': 'rebuilt foot-pair anchor sequence', 'runtimeImpact': 'active sequence data changed'}) # 2) Bar work: lock bar/hands and treat as keyframes, not free animation. for key, label in [('pull-up','Pull-up'), ('chin-up','Chin-up')]: if key in sequences: s = sequences[key] phases = ['dead-hang', 'active-hang', 'mid-pull', 'top-hold', 'controlled-lower'] pts = [0.00, 0.18, 0.46, 0.68, 1.00] specs = [] for i, (ph, t) in enumerate(zip(phases, pts)): specs.append({'oldIndex': i, 'phase': ph, 'pathT': t, 'contact': 'bar-anchor', 'facing': 'front', 'anchorLandmark': 'barTop', 'rc46Role': 'hands locked to bar / body changes by keyframe only', 'rc46ReviewNote': 'Do not animate free body travel; source sprite/bar are top-pinned.'}) rewrite_steps(s, specs) s['motionProfile'].update({'family': 'fixed_bar_sequence', 'anchor': 'bar-hand-lock', 'x0': 50, 'x1': 50, 'barY': 3, 'loopMs': 2800, 'direction': 'neutral', 'flipX': False, 'forceFlipX': False, 'ghostCount': 0, 'ghostMode': 'none', 'debug': True}) tag_sequence(s, 'special-handling-active', f'{label}: locked bar keyframe sequence. No free glide.', risk='moderate-low') changes.append({'exerciseKey': key, 'action': 'bar/hands locked keyframe sequence', 'runtimeImpact': 'active sequence data tightened'}) # 3) Buddy hamstring curl: lock knee pivot, do not trust MediaPipe candidate for low-conf partner sprites. if 'buddy-hamstring-curls' in sequences: s = sequences['buddy-hamstring-curls'] rewrite_steps(s, [ {'oldIndex': 0, 'phase': 'kneel-tall-knee-pivot', 'pathT': 0.00, 'contact': 'fixed-pivot', 'facing': 'neutral', 'anchorLandmark': 'kneeCenter', 'rc46Role': 'knees fixed / hips tall', 'rc46ReviewNote': 'Partner/anchor not MediaPipe-driven.'}, {'oldIndex': 1, 'phase': 'eccentric-lower', 'pathT': 0.34, 'contact': 'fixed-pivot', 'facing': 'neutral', 'anchorLandmark': 'kneeCenter', 'rc46Role': 'controlled lower around knee pivot', 'rc46ReviewNote': 'Do not hinge from hips like a good morning.'}, {'oldIndex': 2, 'phase': 'lowest-controlled', 'pathT': 0.68, 'contact': 'fixed-pivot', 'facing': 'neutral', 'anchorLandmark': 'kneeCenter', 'rc46Role': 'straight line knee-hip-shoulder as far as controlled', 'rc46ReviewNote': 'Knee pivot remains fixed.'}, {'oldIndex': 3, 'phase': 'return-knee-pivot', 'pathT': 1.00, 'contact': 'fixed-pivot', 'facing': 'neutral', 'anchorLandmark': 'kneeCenter', 'rc46Role': 'return via hamstrings / same knee anchor', 'rc46ReviewNote': 'Use alpha/manual knee lock; MediaPipe candidate is too weak here.'}, ]) s['motionProfile'].update({'family': 'fixed_knee_pivot_sequence', 'anchor': 'knee-pivot-lock', 'x0': 50, 'x1': 50, 'loopMs': 3300, 'direction': 'neutral', 'flipX': False, 'forceFlipX': False, 'ghostCount': 0, 'ghostMode': 'none', 'debug': True}) tag_sequence(s, 'special-handling-active-manual-landmarks', 'Knee pivot is authoritative; MediaPipe candidate remains disabled because partner/floor sprites scored low.', risk='moderate') changes.append({'exerciseKey': 'buddy-hamstring-curls', 'action': 'manual knee pivot lock tightened', 'runtimeImpact': 'active sequence data tightened'}) # 4) Box work: prop-aware metadata; do not activate pose 07 MediaPipe because confidence is poor. for key in ['seated-box-jump', 'box-jump']: if key in sequences: s = sequences[key] phases = ['box-start', 'load', 'airborne', 'box-landing'] if key == 'seated-box-jump' else ['floor-ready', 'takeoff', 'airborne', 'box-landing'] pts = [0.00, 0.18, 0.52, 0.88] contacts = ['grounded', 'takeoff', 'airborne', 'land/reset'] specs = [] for i in range(min(4, len(s.get('poses',[])))): specs.append({'oldIndex': i, 'phase': phases[i], 'pathT': pts[i], 'contact': contacts[i], 'anchorLandmark': 'footCenter', 'rc46Role': 'foot pair + box prop relationship', 'rc46ReviewNote': 'Box prop must remain visually consistent; low confidence frame kept manual.'}) rewrite_steps(s, specs) s['motionProfile'].update({'family': 'jump_arc_forward', 'anchor': 'box-foot-pair', 'x0': 32 if key == 'seated-box-jump' else 30, 'x1': 62, 'arcHeight': 13 if key == 'seated-box-jump' else 15, 'settle': 1.5, 'loopMs': 1750 if key == 'seated-box-jump' else 1600, 'ghostCount': 0, 'ghostMode': 'none', 'debug': True}) tag_sequence(s, 'prop-aware-active-manual-review', 'Box jump phases are explicit, but prop/box binding still needs visual review before MediaPipe activation.', risk='moderate-high', runtime_gate='active-with-manual-prop-review') changes.append({'exerciseKey': key, 'action': 'box prop phases/foot-pair anchors added; MediaPipe not activated for weak frame', 'runtimeImpact': 'active sequence data tightened'}) # 5) Medball slam stays active but receives object/hand binding requirements. if 'med-ball-slam' in sequences: s = sequences['med-ball-slam'] for i, p in enumerate(s.get('poses', [])): p['anchorLandmark'] = 'footCenter' p['contact'] = p.get('contact') or 'grounded' p['phase'] = p.get('phase') or f'slam-kf-{i+1:02d}' p['limbIntent'] = limb('footCenter', role='feet planted while hands/ball path changes') p['rc46Frame'] = {'order': i+1, 'phase': p['phase'], 'contact': 'grounded', 'anchorLandmark': 'footCenter', 'physioIntent': 'ball must stay associated with hands during slam/pickup'} s['motionProfile'].update({'family': 'hinge_slam_lift', 'anchor': 'feet-with-ball-hand-binding', 'x0': 50, 'x1': 50, 'loopMs': 2250, 'ghostCount': 0, 'ghostMode': 'none', 'debug': True}) tag_sequence(s, 'object-binding-active-review', 'Medball slam can stay active, but ball must be visually checked against hand path.', risk='moderate') changes.append({'exerciseKey': 'med-ball-slam', 'action': 'object/hand binding metadata added', 'runtimeImpact': 'active sequence data annotated'}) # 6) Chest pass and rotational throw must not pretend medball-slam frames are valid. for key in ['chest-pass', 'rotational-throw']: if key in sequences: s = sequences[key] s['rc46DoNotTrustCurrentImages'] = True s['rc46ReviewHold'] = { 'reason': 'Current mapping reuses medball-slam candidates. Movement standard requires a different throw pattern.', 'needed': 'true chest/rotation sequence with ball prop path and hand release/receive phases', 'runtimeRecommendation': 'do not activate MediaPipe candidate; use as placeholder only until new candidate sequence exists' } for i, p in enumerate(s.get('poses', [])): p['anchorLandmark'] = 'footCenter' p['limbIntent'] = limb('footCenter', role='placeholder only / not exercise truth', confidence=0.25, source='rc46-review-hold') p['rc46Frame'] = {'order': i+1, 'phase': p.get('phase') or f'placeholder-{i+1:02d}', 'contact': p.get('contact') or 'grounded', 'anchorLandmark': 'footCenter', 'physioIntent': 'review hold: wrong movement family'} tag_sequence(s, 'blocked-needs-new-sequence', 'Hold from selective activation. Current image sequence is not the exercise.', risk='high', runtime_gate='review-hold') changes.append({'exerciseKey': key, 'action': 'blocked from selective activation; current images marked placeholder', 'runtimeImpact': 'metadata only, active runtime still shows placeholder if used'}) # 7) Footwork split: carioca and lateral power shuffle should not share a generic foot rule. if 'carioca-shuffle' in sequences: s = sequences['carioca-shuffle'] phases = ['ready', 'front-crossover', 'open-step', 'rear-crossover', 'reset'] for i, p in enumerate(s.get('poses', [])): p['phase'] = phases[i] if i < len(phases) else f'carioca-{i+1}' p['pathT'] = i / max(1, len(s.get('poses', []))-1) p['contact'] = 'grounded' p['anchorLandmark'] = 'dominantContactFoot' p['limbIntent'] = limb('dominantContactFoot', role='crossover gait foot contact') tag_sequence(s, 'manual-review-annotated', 'Carioca keeps crossover foot logic; do not merge with lateral shuffle.', risk='moderate', runtime_gate='active-with-footwork-review') changes.append({'exerciseKey': 'carioca-shuffle', 'action': 'crossover phases annotated', 'runtimeImpact': 'active sequence metadata tightened'}) if 'lateral-power-shuffle' in sequences: s = sequences['lateral-power-shuffle'] for i, p in enumerate(s.get('poses', [])): p['phase'] = ['athletic-ready','push','slide','catch','push-return','reset'][i] if i < 6 else f'shuffle-{i+1}' p['pathT'] = i / max(1, len(s.get('poses', []))-1) p['contact'] = 'grounded' p['anchorLandmark'] = 'footCenter' p['limbIntent'] = limb('footCenter', role='non-crossover lateral foot pair / base of support') tag_sequence(s, 'manual-review-annotated', 'Lateral power shuffle uses non-crossover foot-pair/base-of-support logic.', risk='moderate', runtime_gate='active-with-footwork-review') changes.append({'exerciseKey': 'lateral-power-shuffle', 'action': 'non-crossover foot-pair phases annotated', 'runtimeImpact': 'active sequence metadata tightened'}) # 8) Mountain climber active leg semantics, but no broad MediaPipe activation yet. if 'mountain-climber' in sequences: s = sequences['mountain-climber'] phases = ['plank-support-left-leg-drive', 'switch', 'plank-support-right-leg-drive'] for i, p in enumerate(s.get('poses', [])): p['phase'] = phases[i] if i < len(phases) else f'mountain-climber-{i+1}' p['pathT'] = [0, .5, 1][i] if i < 3 else i / max(1, len(s.get('poses', []))-1) p['contact'] = 'hands/feet-ground' p['anchorLandmark'] = 'supportCenter' p['limbIntent'] = limb('supportCenter', role='hands/feet support with explicit alternating active leg') tag_sequence(s, 'needs-leg-phase-QA-annotated', 'Active-leg semantics added; still needs visual QA before candidate activation.', risk='moderate-high', runtime_gate='active-with-leg-review') changes.append({'exerciseKey': 'mountain-climber', 'action': 'active leg phases annotated', 'runtimeImpact': 'active sequence metadata tightened'}) return changes def min_conf(candidate: dict, names: list[str]) -> float: vals = [] for n in names: v = candidate.get(n) if not isinstance(v, dict): return 0.0 vals.append(float(v.get('confidence', v.get('visibility', 0)) or 0)) return min(vals) if vals else 0.0 def activate_mediapipe_landmarks(packs: dict, sequences: dict): # Only activate in exercises where the movement audit says geometry can help now. activation_rules = { 'lateral-line-hops': {'threshold': 0.68, 'required': ['leftFoot','rightFoot','footCenter','hipCenter','kneeCenter','shoulderCenter']}, 'bodyweight-squat': {'threshold': 0.82, 'required': ['leftFoot','rightFoot','footCenter','hipCenter','kneeCenter','shoulderCenter']}, 'hamstring-bridge': {'threshold': 0.66, 'required': ['leftFoot','rightFoot','footCenter','hipCenter','shoulderCenter']}, 'pull-up': {'threshold': 0.64, 'required': ['leftHand','rightHand','handCenter','shoulderCenter','hipCenter']}, 'chin-up': {'threshold': 0.64, 'required': ['leftHand','rightHand','handCenter','shoulderCenter','hipCenter']}, } selected_pose_sources: dict[str, set[str]] = {} for ex, rule in activation_rules.items(): seq = sequences.get(ex, {}) for p in seq.get('poses', []): selected_pose_sources.setdefault(p.get('poseKey'), set()).add(ex) activated = [] skipped = [] for entry in packs.get('poses', []): pose_key = entry.get('poseKey') if pose_key not in selected_pose_sources: continue exs = sorted(selected_pose_sources[pose_key]) # Use the lowest applicable threshold when a pose is shared by multiple safe exercises. thresholds = [activation_rules[e]['threshold'] for e in exs if e in activation_rules] required_names = sorted(set(sum((activation_rules[e]['required'] for e in exs if e in activation_rules), []))) threshold = min(thresholds) if thresholds else 1.0 for aid, athlete in (entry.get('athletes') or {}).items(): sk = (((athlete or {}).get('metrics') or {}).get('skeleton') or {}) cand = sk.get('runtimeLandmarksMediapipeCandidate') if not cand: skipped.append({'poseKey': pose_key, 'athleteId': aid, 'reason': 'no-mediapipe-candidate'}) continue score = min_conf(cand, required_names) if score >= threshold: if 'runtimeLandmarksAlphaFallback' not in sk: sk['runtimeLandmarksAlphaFallback'] = deepcopy(sk.get('runtimeLandmarks', {})) next_lm = deepcopy(cand) # Preserve barTop if the alpha/manual data has it; MediaPipe cannot see the bar. old_lm = sk.get('runtimeLandmarks', {}) or {} for extra in ['barTop']: if extra in old_lm and extra not in next_lm: next_lm[extra] = deepcopy(old_lm[extra]) for lm in next_lm.values(): if isinstance(lm, dict): lm['activatedBy'] = RC46_VERSION sk['runtimeLandmarks'] = next_lm sk['runtimeLandmarkSource'] = 'mediapipe-rc46-selective-active' sk['quality'] = 'mediapipe-selected-active-with-alpha-fallback' sk.setdefault('mediaPipe', {})['runtimeActivatedAt'] = NOW sk.setdefault('mediaPipe', {})['runtimeActivationPolicy'] = 'selective safe exercises only; special/low confidence motions stay manual' activated.append({'poseKey': pose_key, 'athleteId': aid, 'exercises': exs, 'minRequiredConfidence': round(score, 3), 'threshold': threshold}) else: skipped.append({'poseKey': pose_key, 'athleteId': aid, 'exercises': exs, 'minRequiredConfidence': round(score, 3), 'threshold': threshold, 'reason': 'below-threshold'}) packs['version'] = RC46_VERSION packs['mediaPipeBridge'] = packs.get('mediaPipeBridge', {}) | { 'rc46SelectiveRuntimeActivation': { 'status': 'partial-active', 'activatedCount': len(activated), 'skippedCount': len(skipped), 'policy': 'Only high-confidence, low/moderate-risk exercise poses use MediaPipe runtime landmarks. Special props, partner frames, and weak detections remain alpha/manual.', 'updatedAt': NOW } } return activated, skipped def main(): seq_data = load_json(SEQ_PATH) packs = load_json(PACKS_PATH) audit = load_json(AUDIT_PATH) if not BACKUP_SEQ.exists(): save_json(BACKUP_SEQ, seq_data) if not BACKUP_PACKS.exists(): save_json(BACKUP_PACKS, packs) sequences = seq_data.get('sequences', {}) changes = rebuild_sequences(sequences) activated, skipped = activate_mediapipe_landmarks(packs, sequences) seq_data['version'] = RC46_VERSION seq_data['rc46SelectiveMovementRebuild'] = { 'version': RC46_VERSION, 'createdAt': NOW, 'sourceAuditVersion': audit.get('version'), 'runtimePolicy': { 'poseNamesAuthoritative': False, 'exerciseTruthLayer': 'rc45_ai_physio_first_pass + rc46 movement rules', 'specialExercisesNotAutoActivated': ['buddy-hamstring-curls','box-jump','seated-box-jump','chest-pass','rotational-throw'], 'selectiveMediaPipeRuntimeActivation': True, 'safeActivationExercises': ['lateral-line-hops','bodyweight-squat','hamstring-bridge','pull-up','chin-up'] } } seq_data['limbIntentSchema'] = 'rc46-movement-intent-v1' report = { 'version': RC46_VERSION, 'createdAt': NOW, 'summary': { 'sequenceChanges': len(changes), 'mediapipeRuntimeLandmarkActivations': len(activated), 'mediapipeRuntimeLandmarkSkipped': len(skipped), 'runtimeChanged': True, 'imagesGenerated': False, 'principle': 'movement truth over pose/sheet names' }, 'changes': changes, 'activatedMediapipeRuntimeLandmarks': activated, 'skippedMediapipeRuntimeLandmarks': skipped[:200], 'notes': [ 'Lateral-line-hops is the first real rebuild: foot-pair anchor replaces left/right single foot locking.', 'Bar exercises are fixed keyframes with barTop lock; no free glide.', 'Buddy hamstring curl stays manual/knee-pivot because MediaPipe confidence is poor on partner frames.', 'Chest-pass and rotational-throw are marked as wrong-family placeholders and blocked from selective activation.', 'Box exercises received explicit prop/phase data but are not MediaPipe-activated due weak frame confidence.' ] } movement_data = { 'version': RC46_VERSION, 'createdAt': NOW, 'status': 'selective runtime rebuild + review gates', 'report': report['summary'], 'exerciseDecisions': {c['exerciseKey']: c for c in changes}, 'activationPolicy': seq_data['rc46SelectiveMovementRebuild']['runtimePolicy'] } save_json(SEQ_PATH, seq_data) save_json(PACKS_PATH, packs) save_json(OUT_REPORT, report) save_json(OUT_DATA, movement_data) print(json.dumps(report['summary'], indent=2, ensure_ascii=False)) if __name__ == '__main__': main()