"""Site-source FALL baselines and one fixed spatial candidate; no Event I/O.""" from collections import Counter from dataclasses import fields import json import math import os import time from common import OUT,load,read,save,sha,annotation import app app.__path__.insert(0,str(OUT/'site_source/app')) import app.contracts app.contracts.__path__.insert(0,str(OUT/'site_source/app/contracts')) from app.contracts.frames import FrameEnvelope,PoseObservation from app.fall_scenario import FallEngine from app.fall_config import FallConfig from app.pose_config import POSE_MODEL from app.event_cooldown import EventCooldown from fall_candidate import features,SpatialState class Legacy(FallEngine): def _advance(self,track,geometry,pts,*,gap): evidence,created=super()._advance(track,geometry,pts,gap=gap) self.created[id(track)]=created return evidence,created def observe(self,frame,poses,ids,now,gap): self.created={};self.clock[0]=now out=self.evaluate(frame,poses,ids,gap_before=gap) people=[] for d in out.decisions: direct=d.track_id is not None and d.evidence.geometry.torso_angle is not None and d.evidence.geometry.torso_angle+1e-12>=self.config.down_angle_deg temporal=self.created.get(id(self._tracks.get(d.track_id)),False) people.append(dict(query=int(d.object_ref),track_id=d.track_id,current_hit=bool(direct or temporal), direct=direct,temporal=temporal,display=d.alert,state=d.state, torso_angle=d.evidence.geometry.torso_angle,down=d.evidence.down)) return dict(current_hit=any(p['current_hit'] for p in people),display=any(p['display'] for p in people), observable=any(p['torso_angle'] is not None for p in people),people=people, events=[dict(object_ref=e.object_ref,track_id=e.track_id,episode=e.episode) for e in out.event_candidates]) def engine(policy,cooldown): e=Legacy(FallConfig.parse(policy),direct_down=True,direct_down_cooldown_seconds=2.) e.clock=[0.];gate=EventCooldown(cooldown,clock=lambda:e.clock[0]);e.event_admission=gate.allow return e def canonical(clip,row): w,h=row['width'],row['height'] frame=FrameEnvelope('isolated','stored-cache',clip,clip+'-offline',1,row['sample'],round(row['seconds']*1e9),0,w,h,'frozen') poses=[];clipped=0 for p in row['persons']: box=tuple(v/s for v,s in zip(p['bbox'],(w,h,w,h))) bounded=tuple(min(1.,max(0.,v)) for v in box);clipped+=box!=bounded poses.append(PoseObservation(frame,POSE_MODEL,p['score'],bounded, tuple((k[0]/w,k[1]/h,k[2]) for k in p['keypoints']))) return frame,poses,clipped def run(clip): started=time.monotonic();rows,selected,spec,cache,mapping=load(clip) dest=OUT/'download'/clip/'data';dest.mkdir(parents=True,exist_ok=True) summary_path=dest/'summary.json' if summary_path.exists(): s=read(summary_path);assert sha(dest/'diagnostics.jsonl')==s['trace_sha256'];print('REUSE_DIAG',clip);return s tracking=read(OUT/'work'/clip/'tracking30.json');track_validation=read(OUT/'work'/clip/'tracking_validation.json') assert sha(OUT/'work'/clip/'tracking30.json')==track_validation['derived_sha256'] config=read(OUT/'conditions.json');site=read(OUT/'site_baseline.json') raw=site['runtime']['settings']['effective']['fall'];names={x.name for x in fields(FallConfig)} site_policy={k:v for k,v in raw.items() if k in names};site_policy['enabled']=True shared=dict(site_policy,person_score=config['person_score'],joint_score=config['joint_score']) cooldown=config['cooldown_seconds'] baselines={'site_scores':engine(site_policy,cooldown),'matched_scores':engine(shared,cooldown),'stored10_matched_scores':engine(shared,cooldown)} candidate=SpatialState(config,clip) cases=read(OUT/'cases.json')['cases'];references=read(OUT/'references.json') stats=Counter();segments=[];frame_lists={k:[] for k in ('candidate','site','matched','stored10','added','removed','events')} last=None;trace=dest/'diagnostics.jsonl';assert not trace.exists() with trace.open('x') as f: for row in rows: frame,poses,clipped=canonical(clip,row);old_ids=[p['track_id'] for p in row['persons']] ids=[tracking[str(row['sample'])][str(p['query'])] for p in row['persons']] now=row['seconds']-rows[0]['seconds'];gap=last is not None and row['seconds']-last>.15 baseline={k:e.observe(frame,poses,old_ids if k=='stored10_matched_scores' else ids,now,gap) for k,e in baselines.items()} people=[] for p,tid in zip(row['persons'],ids): person=dict(p,track_id=tid);people.append(features(person,config)) result=candidate.evaluate(row['seconds'],people,monotonic_now=now) note=annotation(clip,row,spec,mapping,cases,references) displayed=row['sample'] in selected record=dict(sample=row['sample'],source_frame=row['source_frame'],seconds=row['seconds'],displayed=displayed, annotation=note,tracking_mode=spec['tracking_mode'],people=people,baseline=baseline,candidate=result, canonical_bbox_clips=clipped) f.write(json.dumps(record,ensure_ascii=False,separators=(',',':'),allow_nan=False)+'\n');last=row['seconds'] if not displayed:continue stats['observations']+=1;stats['eligible_persons']+=sum(p['eligible'] for p in people) stats['observed_input']+=any(p['eligible'] for p in people);stats['observable']+=result['observable'] stats['insufficient']+=not result['observable'];stats['candidate']+=result['hit'];stats['hold_only']+=result['display_hold'] stats['site']+=baseline['site_scores']['current_hit'];stats['site_display']+=baseline['site_scores']['display'] stats['matched']+=baseline['matched_scores']['current_hit'];stats['stored10']+=baseline['stored10_matched_scores']['current_hit'] stats['matched_display']+=baseline['matched_scores']['display'];stats['canonical_bbox_clips']+=clipped a=baseline['matched_scores']['current_hit'];b=result['hit'];stats['added']+=b and not a;stats['removed']+=a and not b label=note['reference'];stats[label+'_observations']+=1;stats[label+'_observable']+=result['observable'] stats[label+'_candidate']+=b;stats[label+'_matched']+=a;stats[label+'_site']+=baseline['site_scores']['current_hit'] stats[label+'_added']+=b and not a;stats[label+'_removed']+=a and not b stats['events']+=result['event'] is not None for k,truth in [('candidate',b),('site',baseline['site_scores']['current_hit']),('matched',a),('stored10',baseline['stored10_matched_scores']['current_hit']),('added',b and not a),('removed',a and not b),('events',result['event'] is not None)]: if truth:frame_lists[k].append(dict(sample=row['sample'],frame=row['source_frame'],seconds=row['seconds'],reference=label)) for p in people: for reason in p['reasons']:stats['reason_'+reason]+=1 save(dest/'frame_lists.json',frame_lists) save(dest/'tracking_validation.json',track_validation) result=dict(status='PASS',clip=clip,counts=dict(stats),tracking_mode=spec['tracking_mode'], trace_sha256=sha(trace),conditions_sha256=sha(OUT/'conditions.json'),cache_sha256=spec['cache_sha256'], site_policy=site_policy,matched_policy=shared,cooldown_seconds=cooldown, current_baseline_definition='current direct-down torso condition with existing ID or newly confirmed temporal condition; NOT latched display', bbox_adapter='Only baseline canonical bbox clipped to [0,1]; original joints/geometry retained; FALL core geometry does not read bbox', elapsed_seconds=time.monotonic()-started,full_cache_observations=len(rows)) save(summary_path,result);print('DIAG_PASS',clip,json.dumps(result['counts']),flush=True);return result if __name__=='__main__': clip=os.environ['CLIP'];names=list(read(OUT/'specs.json')) if clip=='all' else [clip] for name in names:run(name)