feat(intro): v2 — cities, accelerating sweep, detections, thriller sound design

- City engine: Esri ground, OSM buildings that rise with the scan, road
  ribbons carrying modelled traffic coloured by congestion, NYC DOT cameras,
  real ADS-B, MBTA GTFS lines + realtime vehicles, LAX runways with approach
  strobes, Fort Bragg restricted volumes, Everest terrain with contours.
- Indexing keeps accelerating into a 260-capture global sweep while the
  planet whips a full turn.
- The eye opens: aperture blades, sensor array waking, optics calibration,
  radar sweep, orbiting coordinate ring.
- Camera wall with YOLOv8n detections and a city-pulse trace.
- Ends on GOD'S EYE VIEW / OMNISCIENCE.
- audio/sfx.py: sound design only, built to sit under a music track.

Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01KqP857pippaUs6konSDfe8

intro-sequence/tools/live/fetch_nyc_cams.py

@@ -0,0 +1,53 @@
+"""NYC DOT TMC cameras -> city/nyc/cameras.json + current stills for ~24 nearest cams."""
+import hashlib, io, json, os, sys, time, requests
+import numpy as np
+from PIL import Image, ImageOps
+from collections import Counter
+sys.path.insert(0, os.path.dirname(__file__))
+from common import enu, inside
+ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+OUT = os.path.join(ROOT, 'cctv', 'nyc'); DBG = os.path.join(os.path.dirname(__file__), 'nyc_rejects')
+os.makedirs(OUT, exist_ok=True); os.makedirs(DBG, exist_ok=True)
+H = {'User-Agent': 'gods-eye-view-intro/1.0'}
+cat = requests.get('https://webcams.nyctmc.org/api/cameras', headers=H, timeout=30).json()
+cams = []
+for c in cat:
+    if str(c.get('isOnline')).lower() != 'true': continue
+    try: lat, lon = float(c['latitude']), float(c['longitude'])
+    except Exception: continue
+    x, y = enu('nyc', lat, lon)
+    if inside('nyc', x, y): cams.append((x, y, c['id'], c['name'].strip(), lat, lon))
+json.dump([[x, y, i, n] for x, y, i, n, _, _ in cams], open(os.path.join(ROOT, 'city', 'nyc', 'cameras.json'), 'w'), separators=(',', ':'))
+print('catalog', len(cat), 'online in square', len(cams))
+cams.sort(key=lambda c: c[0]**2 + c[1]**2)
+for f in os.listdir(OUT):
+    if f.startswith('cam_') and f.endswith('.jpg'): os.remove(os.path.join(OUT, f))
+idx, rejects, hashes, raw_imgs = [], Counter(), Counter(), []
+fetched_utc = time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())
+for x, y, cid, name, lat, lon in cams[:80]:
+    if len(raw_imgs) >= 40: break
+    try:
+        r = requests.get(f'https://webcams.nyctmc.org/api/cameras/{cid}/image', headers=H, timeout=20)
+        r.raise_for_status(); im = Image.open(io.BytesIO(r.content)).convert('RGB')
+    except Exception as e:
+        rejects['error'] += 1; continue
+    h = hashlib.md5(r.content).hexdigest(); hashes[h] += 1
+    g = np.asarray(im.convert('L'), dtype=np.float32)
+    raw_imgs.append(dict(x=x, y=y, id=cid, name=name, lat=lat, lon=lon, im=im, h=h, mean=float(g.mean()), std=float(g.std()), size=im.size, n=len(r.content)))
+n = 0
+for d in raw_imgs:
+    reason = None
+    if hashes[d['h']] > 1: reason = 'duplicate/placeholder'
+    elif d['mean'] < 14: reason = 'black'
+    elif d['std'] < 10: reason = 'flat'
+    elif d['size'][0] < 200: reason = 'tiny'
+    if reason:
+        rejects[reason] += 1; d['im'].save(os.path.join(DBG, f"{reason.split('/')[0]}_{d['id'][:8]}.jpg")); continue
+    if n >= 24: break
+    n += 1; fn = f'cam_{n:03d}.jpg'
+    ImageOps.fit(d['im'], (640, 360), Image.LANCZOS).save(os.path.join(OUT, fn), quality=88)
+    idx.append({'file': fn, 'id': d['id'], 'name': d['name'], 'x': d['x'], 'y': d['y'], 'lat': d['lat'], 'lon': d['lon'],
+                'orig_size': list(d['size']), 'fetched_utc': fetched_utc, 'source': 'NYC DOT TMC webcams.nyctmc.org'})
+    print(fn, d['name'], d['size'], round(d['mean']), round(d['std']), round((d['x']**2+d['y']**2)**.5))
+json.dump(idx, open(os.path.join(OUT, 'index.json'), 'w'), indent=1)
+print('saved', n, 'rejects', dict(rejects), 'tried', len(raw_imgs))