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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
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"""Build the flurry montage captures.
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python3 select_cities.py # -> city_candidates.json (primary + backup per region)
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python3 build.py # -> imagery/flurry/fx_###.jpg, index.json, tools/flurry/sheet.jpg
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Each capture: Esri World Imagery, 3x3 tiles around the point, centred 512x512
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crop, JPEG q86. Placeholder / low-variance captures are rejected automatically
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(tiles.quality_ok); captures that passed the metrics but looked wrong on the
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review sheets (forest, farmland, haze) are listed in VISUAL_REJECT. Rejected
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items are replaced by the next backup of the same region (cities) or by the
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next backup landmark.
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"""
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import glob
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import json
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import os
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import sys
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from concurrent.futures import ThreadPoolExecutor
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from PIL import Image
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from landmarks import LANDMARKS # noqa: E402
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from tiles import HERE, MAX_WORKERS, OUT, capture, quality_ok # noqa: E402
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N_CITIES = 170
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N_LANDMARKS = 90
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# Natural Earth points that landed on a park/water/cemetery: moved to the dense centre.
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CITY_OVERRIDES = {
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"Lagos": (6.4541, 3.3947), # Lagos Island (was open lagoon)
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"Cairns": (-16.9230, 145.7740), # CBD (was mangrove mudflat)
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"Calgary": (51.0460, -114.0650), # downtown (was cemetery)
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"Dubai": (25.0805, 55.1403), # Dubai Marina (was construction lot)
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"Perth": (-31.9530, 115.8575), # CBD (was Kings Park bush)
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"Dhaka": (23.7330, 90.4172), # Motijheel (was hazy open ground)
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"Delhi": (28.6315, 77.2167), # Connaught Place
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"Montréal": (45.5017, -73.5673), # downtown (was Mount Royal park)
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"Busan": (35.1000, 129.0350), # Nampo / harbour (was forested hill)
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"Jeddah": (21.4858, 39.1925), # Al-Balad old town (was empty lot)
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"Tokyo": (35.6717, 139.7650), # Ginza (was Imperial Palace woods)
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}
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VISUAL_REJECT = {
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"Venice": "blurry low-res imagery",
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"Conakry": "heavy haze",
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"Maracaibo": "farmland / scrub",
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"Barquisimeto": "dry riverbed, no city",
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"Natal": "solid forest canopy",
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"João Pessoa": "forest canopy",
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"Mbuji-Mayi": "forest/scrub",
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"Huambo": "scrub, sparse",
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"Port Harcourt": "haze",
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"Qaraghandy": "dark featureless rail yard",
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"Herat": "low-res blur",
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"Chengdu": "smog haze",
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"Xian": "washed-out industrial lot",
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"Kaohsiung": "forested hill",
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"Sapporo": "university farm fields",
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"Isfahan": "low contrast blur",
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"Stuttgart": "hazy woodland",
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"Townsville": "airport apron, not city",
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}
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LANDMARK_BACKUPS = [
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"Glen Canyon Dam", "Grand Coulee Dam", "Mount St. Helens", "Groom Lake", "Diavik Diamond Mine",
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"Syncrude Mildred Lake oil sands", "Centre-pivot fields, Kansas", "Indianapolis Motor Speedway",
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"Brooklyn Bridge", "Panama Canal, Miraflores Locks", "Escondida Mine", "ALMA Observatory",
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"Meeting of the Waters, Manaus", "Brasilia, National Congress", "PS10 / PS20 Solar Towers", "Tower Bridge",
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"Port of Tema", "Akosombo Dam", "Okavango Delta", "FNB Stadium, Soweto", "East Oweinat pivots",
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"Kilimanjaro, Kibo crater", "Ras Laffan Industrial City", "Dubai International Airport", "Bagan temples",
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"Jiuquan Launch Centre", "Mount Bromo", "Vostochny Cosmodrome", "Pine Gap", "Tanegashima Space Center",
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"Teide", "Faa'a International Airport", "Hartsfield-Jackson Atlanta Airport", "Niagara Horseshoe Falls",
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"Iguazu Falls, Devil's Throat", "Vandenberg SLC-4E", "Paranal Observatory", "Cerro Dominador Solar",
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"Gibraltar Airport runway", "Erg Chebbi dunes", "Millau Viaduct", "Garzweiler Lignite Mine", "Inga Dams",
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"Grand Ethiopian Renaissance Dam", "Ras Tanura refinery", "Udachnaya Pipe", "Busan New Port",
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"Mount Taranaki", "Beijing National Stadium", "Port of Hamburg, Burchardkai", "Port of Santos",
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"Jebel Ali Port", "Mont-Saint-Michel", "Angkor Wat", "Mount Vesuvius", "Horseshoe Bend",
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"Benban Solar Park", "Borobudur", "Bhadla Solar Park", "Kariba Dam", "Wake Island Airfield",
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"Diamond Head", "Port Hedland salt ponds",
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]
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def evaluate(cands):
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def job(c):
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try:
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img, info = capture(c["lat"], c["lon"], c["zoom"])
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ok, why = quality_ok(info)
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except Exception as e: # network failure counts as a reject
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img, info, ok, why = None, {}, False, f"fetch error: {e}"
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if ok and c["name"] in VISUAL_REJECT and c.get("kind") == "city":
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ok, why = False, "visual: " + VISUAL_REJECT[c["name"]]
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return img, ok, why
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with ThreadPoolExecutor(MAX_WORKERS) as ex:
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return list(ex.map(job, cands))
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def pick(cands, results, quota_by_group):
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"""Primaries first in order; failures replaced by the group's backups in order."""
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chosen, rejects = [], []
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for g, quota in quota_by_group.items():
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items = [(c, r) for c, r in zip(cands, results) if c["group"] == g]
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prim = [x for x in items if x[0]["rank"] == "primary"]
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back = [x for x in items if x[0]["rank"] == "backup"]
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got = []
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for c, r in prim + back:
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if len(got) >= quota:
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break
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if r[1]:
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got.append((c, r[0]))
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elif c["rank"] == "primary":
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rejects.append((c, r[2]))
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if len(got) < quota:
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print(f"!! group {g}: only {len(got)}/{quota}")
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chosen += got
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return chosen, rejects
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def main():
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cities = json.load(open(os.path.join(HERE, "city_candidates.json")))
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for c in cities:
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c["kind"], c["group"] = "city", "c:" + c["region"]
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if c["name"] in CITY_OVERRIDES:
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c["lat"], c["lon"] = CITY_OVERRIDES[c["name"]]
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cquota = {}
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for c in cities:
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if c["rank"] == "primary":
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cquota[c["group"]] = cquota.get(c["group"], 0) + 1
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assert sum(cquota.values()) == N_CITIES, cquota
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lms = [dict(name=n, country=co, lat=la, lon=lo, zoom=z, kind="landmark", group="landmark",
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rank="backup" if n in LANDMARK_BACKUPS else "primary") for n, co, la, lo, z in LANDMARKS]
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lms.sort(key=lambda c: c["rank"] != "primary") # stable: primaries first
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nprim = sum(c["rank"] == "primary" for c in lms)
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assert nprim == N_LANDMARKS, nprim
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allc = cities + lms
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print(f"evaluating {len(allc)} candidates ...", flush=True)
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res = evaluate(allc)
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quotas = dict(cquota)
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quotas["landmark"] = N_LANDMARKS
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chosen, rejects = pick(allc, res, quotas)
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chosen.sort(key=lambda x: x[0]["lon"])
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os.makedirs(OUT, exist_ok=True)
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for f in glob.glob(os.path.join(OUT, "fx_*.jpg")):
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os.remove(f)
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index = []
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for i, (c, img) in enumerate(chosen):
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fn = f"fx_{i:03d}.jpg"
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img.save(os.path.join(OUT, fn), quality=86, optimize=True)
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index.append({"file": fn, "name": c["name"], "country": c["country"], "lat": round(c["lat"], 5),
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"lon": round(c["lon"], 5), "zoom": c["zoom"], "kind": c["kind"]})
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with open(os.path.join(OUT, "index.json"), "w") as f:
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json.dump(index, f, indent=1, ensure_ascii=False)
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# contact sheet: 16 x 17 grid of 96px thumbs, west->east reading order
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cols, cell = 16, 96
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rows = (len(chosen) + cols - 1) // cols
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sheet = Image.new("RGB", (cols * cell, rows * cell), (10, 10, 10))
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for i, (c, img) in enumerate(chosen):
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sheet.paste(img.resize((cell, cell), Image.LANCZOS), ((i % cols) * cell, (i // cols) * cell))
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sheet.save(os.path.join(HERE, "sheet.jpg"), quality=88)
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from collections import Counter
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print(f"wrote {len(index)} captures: {Counter(x['kind'] for x in index)}")
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print("rejected primaries (replaced by backups):")
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for c, why in rejects:
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print(f" - {c['name']} ({c['country']}): {why}")
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bands = Counter(int((x["lon"] + 180) // 30) for x in index)
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print("30-degree longitude bands W->E:", [bands.get(i, 0) for i in range(12)])
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json.dump([{"name": c["name"], "country": c["country"], "reason": why} for c, why in rejects],
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open(os.path.join(HERE, "rejects.json"), "w"), indent=1, ensure_ascii=False)
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if __name__ == "__main__":
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main()
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"""Hand-picked non-city landmarks for the flurry montage.
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(name, country, lat, lon, zoom). zoom 16 for compact features, 15 for very large
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ones (deltas, glaciers, big pits, pivot fields). Coordinates are verified
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visually via tools/flurry/review.py sheets; entries that came back as featureless
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terrain were corrected or dropped (see REJECTED at the bottom).
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"""
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LANDMARKS = [
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# --- ports / container terminals ---
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("Port of Rotterdam, Maasvlakte", "Netherlands", 51.9575, 4.0500, 16),
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("Port of Singapore, Pasir Panjang", "Singapore", 1.2735, 103.7750, 16),
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("Yangshan Deep-Water Port", "China", 30.6280, 122.0650, 16),
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("Port of Los Angeles, Pier 400", "United States of America", 33.7350, -118.2550, 16),
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("Jebel Ali Port", "United Arab Emirates", 25.0100, 55.0600, 16),
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("Port of Santos", "Brazil", -23.9700, -46.3000, 16),
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("Port of Hamburg, Burchardkai", "Germany", 53.5350, 9.9200, 16),
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("Port Hedland salt ponds", "Australia", -20.3450, 118.6550, 15),
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("Busan New Port", "South Korea", 35.0800, 128.8000, 16),
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("Tanger Med Port", "Morocco", 35.8850, -5.5000, 16),
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("Port of Tema", "Ghana", 5.6330, 0.0100, 16),
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# --- airports ---
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("Hartsfield-Jackson Atlanta Airport", "United States of America", 33.6407, -84.4277, 16),
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("Dubai International Airport", "United Arab Emirates", 25.2528, 55.3644, 16),
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("Hong Kong International Airport", "Hong Kong S.A.R.", 22.3080, 113.9185, 16),
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("Denver International Airport", "United States of America", 39.8494, -104.6738, 16),
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("Kansai International Airport", "Japan", 34.4320, 135.2304, 16),
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("Beijing Daxing Airport", "China", 39.5098, 116.4105, 16),
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("Davis-Monthan Aircraft Boneyard", "United States of America", 32.1500, -110.8300, 16),
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("Princess Juliana Airport", "Sint Maarten", 18.0410, -63.1089, 16),
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("Gibraltar Airport runway", "Gibraltar", 36.1512, -5.3497, 16),
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("Faa'a International Airport", "French Polynesia", -17.5537, -149.6067, 16),
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("Funafuti Airfield", "Tuvalu", -8.5250, 179.1960, 16),
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("Midway Atoll, Sand Island", "United States of America", 28.2100, -177.3800, 16),
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("Wake Island Airfield", "United States of America", 19.2820, 166.6470, 16),
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# --- dams ---
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("Hoover Dam", "United States of America", 36.0160, -114.7377, 16),
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("Three Gorges Dam", "China", 30.8233, 111.0036, 16),
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("Itaipu Dam", "Brazil", -25.4085, -54.5888, 16),
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("Grand Coulee Dam", "United States of America", 47.9560, -118.9817, 16),
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("Kariba Dam", "Zimbabwe", -16.5222, 28.7614, 16),
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("Aswan High Dam", "Egypt", 23.9707, 32.8780, 16),
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("Grand Ethiopian Renaissance Dam", "Ethiopia", 11.2150, 35.0900, 16),
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("Glen Canyon Dam", "United States of America", 36.9375, -111.4844, 16),
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("Akosombo Dam", "Ghana", 6.2999, 0.0592, 16),
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("Inga Dams", "Congo (Kinshasa)", -5.5240, 13.6200, 16),
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# --- open-pit mines ---
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("Bingham Canyon Mine", "United States of America", 40.5230, -112.1510, 15),
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("Chuquicamata Mine", "Chile", -22.2900, -68.9000, 15),
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("Mir Diamond Mine", "Russia", 62.5275, 113.9936, 15),
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("Escondida Mine", "Chile", -24.2700, -69.0700, 15),
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("Kalgoorlie Super Pit", "Australia", -30.7775, 121.5040, 15),
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("Udachnaya Pipe", "Russia", 66.4300, 112.3200, 15),
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("Garzweiler Lignite Mine", "Germany", 51.0600, 6.4800, 15),
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("Kimberley Big Hole", "South Africa", -28.7383, 24.7585, 16),
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("Diavik Diamond Mine", "Canada", 64.4960, -110.2700, 15),
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("Syncrude Mildred Lake oil sands", "Canada", 57.0400, -111.6000, 15),
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# --- solar ---
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("Noor Ouarzazate Solar", "Morocco", 31.0498, -6.8686, 16),
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("Ivanpah Solar", "United States of America", 35.5525, -115.4680, 16),
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("Crescent Dunes Solar", "United States of America", 38.2386, -117.3636, 15),
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("Bhadla Solar Park", "India", 27.5400, 71.9100, 16),
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("Gemasolar", "Spain", 37.5606, -5.3317, 16),
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("PS10 / PS20 Solar Towers", "Spain", 37.4425, -6.2550, 16),
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("Benban Solar Park", "Egypt", 24.4500, 32.7300, 16),
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("Cerro Dominador Solar", "Chile", -22.7700, -69.4800, 16),
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# --- centre-pivot irrigation ---
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("Centre-pivot fields, Kansas", "United States of America", 37.9000, -100.9000, 15),
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("Centre-pivot fields, Wadi As-Sirhan", "Saudi Arabia", 30.2000, 38.0800, 15),
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("Kufra agricultural project", "Libya", 24.1400, 23.3500, 15),
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("East Oweinat pivots", "Egypt", 22.6000, 28.6000, 15),
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("Centre-pivot fields, Nebraska", "United States of America", 41.1000, -101.7000, 15),
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# --- river deltas / water ---
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("Lena Delta", "Russia", 72.8000, 126.5000, 15),
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("Okavango Delta", "Botswana", -19.3000, 22.9000, 15),
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("Betsiboka Estuary", "Madagascar", -15.7500, 46.3500, 15),
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("Meeting of the Waters, Manaus", "Brazil", -3.1370, -59.9100, 15),
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# --- salt / evaporation ---
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("Salar de Atacama lithium ponds", "Chile", -23.5280, -68.3680, 16),
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("Dead Sea evaporation ponds", "Israel", 31.1000, 35.4000, 15),
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("San Francisco Bay salt ponds", "United States of America", 37.5000, -122.0500, 15),
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("Lake Retba (Pink Lake)", "Senegal", 14.8400, -17.2300, 15),
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("Hutt Lagoon", "Australia", -28.1700, 114.2500, 15),
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# --- glaciers / ice ---
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("Perito Moreno Glacier", "Argentina", -50.4800, -73.0500, 15),
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("Aletsch Glacier, Konkordiaplatz", "Switzerland", 46.5000, 8.0300, 15),
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("Hubbard Glacier", "United States of America", 60.0200, -139.5000, 15),
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# Antarctica: Esri has only Landsat-class imagery here (z14+ is placeholder),
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# so these use z11-12 where the source pixels are native rather than upsampled.
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("Taylor Valley, Canada Glacier", "Antarctica", -77.6300, 162.9500, 12),
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("McMurdo Dry Valleys, Wright Valley", "Antarctica", -77.5700, 161.8500, 11),
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("Deception Island caldera", "Antarctica", -62.9600, -60.6300, 12),
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("Mount Everest", "Nepal", 27.9881, 86.9250, 15),
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# --- volcanoes / craters ---
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("Mount Bromo", "Indonesia", -7.9420, 112.9530, 15),
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("Erta Ale", "Ethiopia", 13.6065, 40.6655, 16),
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("Kilauea, Halemaumau", "United States of America", 19.4060, -155.2830, 15),
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("Mount Fuji summit crater", "Japan", 35.3628, 138.7307, 16),
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("Nyiragongo", "Congo (Kinshasa)", -1.5200, 29.2500, 16),
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("Meteor Crater", "United States of America", 35.0275, -111.0225, 15),
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("Mount St. Helens", "United States of America", 46.2000, -122.1900, 15),
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("Kawah Ijen", "Indonesia", -8.0580, 114.2420, 16),
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("Teide", "Spain", 28.2725, -16.6425, 16),
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("Mount Vesuvius", "Italy", 40.8216, 14.4263, 16),
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("Rano Kau, Easter Island", "Chile", -27.1860, -109.4350, 15),
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("Diamond Head", "United States of America", 21.2620, -157.8050, 15),
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("Darvaza Gas Crater", "Turkmenistan", 40.2525, 58.4394, 16),
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("Kilimanjaro, Kibo crater", "Tanzania", -3.0758, 37.3533, 15),
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("Mount Taranaki", "New Zealand", -39.2968, 174.0634, 15),
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("Whakaari / White Island", "New Zealand", -37.5200, 177.1800, 15),
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("Dallol", "Ethiopia", 14.2400, 40.3000, 16),
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# --- stadiums / venues ---
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("Maracana Stadium", "Brazil", -22.9122, -43.2302, 16),
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("Rungrado May Day Stadium", "North Korea", 39.0497, 125.7756, 16),
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("Camp Nou", "Spain", 41.3809, 2.1228, 16),
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("Beijing National Stadium", "China", 39.9925, 116.3910, 16),
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("Melbourne Cricket Ground", "Australia", -37.8200, 144.9834, 16),
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("Indianapolis Motor Speedway", "United States of America", 39.7950, -86.2350, 15),
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("Estadio Azteca", "Mexico", 19.3029, -99.1505, 16),
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("FNB Stadium, Soweto", "South Africa", -26.2348, 27.9825, 16),
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# --- bridges / canals ---
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("Golden Gate Bridge", "United States of America", 37.8100, -122.4770, 16),
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("Sydney Harbour Bridge & Opera House", "Australia", -33.8550, 151.2120, 16),
|
||||
("Tower Bridge", "United Kingdom", 51.5055, -0.0754, 16),
|
||||
("Oresund Bridge, Peberholm", "Denmark", 55.5900, 12.7740, 16),
|
||||
("Millau Viaduct", "France", 44.0794, 3.0225, 16),
|
||||
("Brooklyn Bridge", "United States of America", 40.7061, -73.9969, 16),
|
||||
("Panama Canal, Miraflores Locks", "Panama", 8.9970, -79.5915, 16),
|
||||
("Panama Canal, Agua Clara Locks", "Panama", 9.2720, -79.9180, 16),
|
||||
# --- launch sites ---
|
||||
("Kennedy Space Center LC-39A", "United States of America", 28.6083, -80.6041, 16),
|
||||
("Baikonur, Gagarin's Start", "Kazakhstan", 45.9203, 63.3422, 16),
|
||||
("Vandenberg SLC-4E", "United States of America", 34.6320, -120.6110, 16),
|
||||
("Starbase, Boca Chica", "United States of America", 25.9972, -97.1566, 16),
|
||||
("Wenchang Launch Site", "China", 19.6145, 110.9510, 16),
|
||||
("Jiuquan Launch Centre", "China", 40.9580, 100.2910, 16),
|
||||
("Tanegashima Space Center", "Japan", 30.4009, 130.9749, 16),
|
||||
("Vostochny Cosmodrome", "Russia", 51.8840, 128.3330, 16),
|
||||
("Rocket Lab LC-1, Mahia", "New Zealand", -39.2615, 177.8649, 16),
|
||||
# --- energy / industry ---
|
||||
("Permian Basin well pads", "United States of America", 32.2100, -103.8300, 15),
|
||||
("Ras Tanura refinery", "Saudi Arabia", 26.6400, 50.1600, 16),
|
||||
("Jamnagar Refinery", "India", 22.3500, 69.8500, 16),
|
||||
("Ras Laffan Industrial City", "Qatar", 25.9100, 51.5500, 16),
|
||||
("Yamal LNG, Sabetta", "Russia", 71.2700, 72.0700, 16),
|
||||
("Chernobyl Nuclear Power Plant", "Ukraine", 51.3890, 30.0990, 16),
|
||||
# --- monuments / sites ---
|
||||
("Jiayuguan Pass, Great Wall", "China", 39.8016, 98.2133, 16),
|
||||
("Pyramids of Giza", "Egypt", 29.9792, 31.1342, 16),
|
||||
("Palm Jumeirah", "United Arab Emirates", 25.1124, 55.1390, 15),
|
||||
("Angkor Wat", "Cambodia", 13.4125, 103.8670, 16),
|
||||
("The Pentagon", "United States of America", 38.8719, -77.0563, 16),
|
||||
("Groom Lake", "United States of America", 37.2350, -115.8111, 15),
|
||||
("FAST Radio Telescope", "China", 25.6529, 106.8566, 16),
|
||||
("Very Large Array", "United States of America", 34.0784, -107.6184, 16),
|
||||
("Uluru", "Australia", -25.3444, 131.0369, 15),
|
||||
("Mont-Saint-Michel", "France", 48.6361, -1.5115, 16),
|
||||
("Grand Prismatic Spring", "United States of America", 44.5251, -110.8382, 16),
|
||||
("Masjid al-Haram, Mecca", "Saudi Arabia", 21.4225, 39.8262, 16),
|
||||
("Forbidden City", "China", 39.9155, 116.3910, 16),
|
||||
("Taj Mahal", "India", 27.1751, 78.0421, 16),
|
||||
("Greenhouses of Almeria", "Spain", 36.7500, -2.7200, 16),
|
||||
("Pine Gap", "Australia", -23.7990, 133.7370, 16),
|
||||
("ALMA Observatory", "Chile", -23.0229, -67.7552, 16),
|
||||
("Paranal Observatory", "Chile", -24.6275, -70.4044, 16),
|
||||
("Brasilia, National Congress", "Brazil", -15.7998, -47.8645, 16),
|
||||
("Iguazu Falls, Devil's Throat", "Argentina", -25.6950, -54.4367, 16),
|
||||
("Niagara Horseshoe Falls", "Canada", 43.0773, -79.0757, 16),
|
||||
("Victoria Falls", "Zambia", -17.9243, 25.8572, 16),
|
||||
("Horseshoe Bend", "United States of America", 36.8791, -111.5103, 16),
|
||||
("Sossusvlei dunes", "Namibia", -24.7300, 15.2900, 15),
|
||||
("Erg Chebbi dunes", "Morocco", 31.1500, -3.9800, 15),
|
||||
("Bora Bora", "French Polynesia", -16.4950, -151.7050, 15),
|
||||
("Borobudur", "Indonesia", -7.6079, 110.2038, 16),
|
||||
("Bagan temples", "Myanmar", 21.1700, 94.8600, 16),
|
||||
("Svalbard, Longyearbyen", "Norway", 78.2200, 15.6500, 16),
|
||||
("Edinburgh of the Seven Seas, Tristan da Cunha", "United Kingdom", -37.0670, -12.3100, 16),
|
||||
("Diego Garcia", "United Kingdom", -7.3100, 72.4100, 15),
|
||||
("Kwajalein Atoll", "Marshall Islands", 8.7200, 167.7300, 16),
|
||||
]
|
||||
|
||||
REJECTED = {
|
||||
'Pituffik Space Base': 'placeholder above z13; z13 too blurry',
|
||||
'Ilulissat Icefjord': 'placeholder above z13; open water at z13',
|
||||
'McMurdo Station': 'placeholder above z13; blurry ice at z13',
|
||||
'Rothera Research Station': 'placeholder above z13; mostly sea',
|
||||
'Mount Erebus crater': 'placeholder above z13; cloud-like blur',
|
||||
'Port of Durban': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Port of Mombasa': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Sundarbans, Ganges Delta': "featureless/cloud/low-res/off-target at tested coords",
|
||||
"Mississippi Bird's-Foot Delta": "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Volga Delta': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Great Salt Lake causeway': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Jokulsarlon Glacier Lagoon': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Baltoro Glacier, Concordia': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Cotopaxi': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Guiana Space Centre ELA-3': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Satish Dhawan Space Centre': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Abqaiq processing plant': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Prudhoe Bay oil field': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Great Wall at Mutianyu': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Machu Picchu': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Baltra Island airport, Galapagos': "featureless/cloud/low-res/off-target at tested coords",
|
||||
'Moynaq ship graveyard, Aral Sea': "featureless/cloud/low-res/off-target at tested coords",
|
||||
}
|
||||
@@ -0,0 +1,78 @@
|
||||
"""Locator sheets: a wider mosaic around a guess with a lat/lon grid, to find exact coords.
|
||||
|
||||
usage: python3 locate.py out.jpg "name|lat|lon|z|ntiles" ...
|
||||
Each panel is ntiles x ntiles tiles at zoom z (resized to 640px) with grid
|
||||
lines every `step` degrees, labelled. Panels are stacked in a 2-column grid.
|
||||
"""
|
||||
import io
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
from PIL import Image, ImageDraw
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
from tiles import fetch_tile, pixel_xy # noqa: E402
|
||||
|
||||
P = 640
|
||||
|
||||
|
||||
def panel(name, lat, lon, z, nt):
|
||||
px, py = pixel_xy(lat, lon, z)
|
||||
tx, ty = int(px // 256), int(py // 256)
|
||||
h = nt // 2
|
||||
coords = [(tx - h + i, ty - h + j) for j in range(nt) for i in range(nt)]
|
||||
with ThreadPoolExecutor(8) as ex:
|
||||
datas = list(ex.map(lambda c: fetch_tile(z, c[0], c[1]), coords))
|
||||
m = Image.new("RGB", (nt * 256, nt * 256))
|
||||
for (x, y), d in zip(coords, datas):
|
||||
m.paste(Image.open(io.BytesIO(d)).convert("RGB"), ((x - tx + h) * 256, (y - ty + h) * 256))
|
||||
ox, oy = (tx - h) * 256, (ty - h) * 256
|
||||
s = P / (nt * 256)
|
||||
m = m.resize((P, P), Image.LANCZOS)
|
||||
d = ImageDraw.Draw(m)
|
||||
n = 2 ** z * 256
|
||||
|
||||
def lon2x(lo):
|
||||
return ((lo + 180) / 360 * n - ox) * s
|
||||
|
||||
def lat2y(la):
|
||||
si = math.sin(math.radians(la))
|
||||
return ((0.5 - math.log((1 + si) / (1 - si)) / (4 * math.pi)) * n - oy) * s
|
||||
|
||||
span = nt * 256 / n * 360
|
||||
step = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5][
|
||||
min(range(9), key=lambda k: abs(span / [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5][k] - 6))]
|
||||
lo0 = math.floor((lon - span) / step) * step
|
||||
for k in range(40):
|
||||
lo = lo0 + k * step
|
||||
x = lon2x(lo)
|
||||
if 0 <= x <= P:
|
||||
d.line((x, 0, x, P), fill=(255, 255, 0), width=1)
|
||||
d.text((x + 2, P - 12), f"{lo:.3f}", fill=(255, 255, 0))
|
||||
la0 = math.floor((lat - span) / step) * step
|
||||
for k in range(40):
|
||||
la = la0 + k * step
|
||||
y = lat2y(la)
|
||||
if 0 <= y <= P:
|
||||
d.line((0, y, P, y), fill=(0, 255, 255), width=1)
|
||||
d.text((2, y + 1), f"{la:.3f}", fill=(0, 255, 255))
|
||||
cx, cy = lon2x(lon), lat2y(lat)
|
||||
d.ellipse((cx - 5, cy - 5, cx + 5, cy + 5), outline=(255, 0, 0), width=2)
|
||||
d.rectangle((0, 0, P, 14), fill=(0, 0, 0))
|
||||
d.text((3, 1), f"{name} z{z}", fill=(255, 255, 255))
|
||||
return m
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
out = sys.argv[1]
|
||||
specs = [a.split("|") for a in sys.argv[2:]]
|
||||
ps = [panel(s[0], float(s[1]), float(s[2]), int(s[3]), int(s[4])) for s in specs]
|
||||
cols = 2 if len(ps) > 1 else 1
|
||||
rows = (len(ps) + cols - 1) // cols
|
||||
sheet = Image.new("RGB", (cols * P, rows * P))
|
||||
for i, p in enumerate(ps):
|
||||
sheet.paste(p, ((i % cols) * P, (i // cols) * P))
|
||||
sheet.save(out, quality=85)
|
||||
print(out)
|
||||
@@ -0,0 +1,32 @@
|
||||
[
|
||||
{
|
||||
"name": "Venice",
|
||||
"country": "Italy",
|
||||
"reason": "visual: blurry low-res imagery"
|
||||
},
|
||||
{
|
||||
"name": "Chengdu",
|
||||
"country": "China",
|
||||
"reason": "visual: smog haze"
|
||||
},
|
||||
{
|
||||
"name": "Xian",
|
||||
"country": "China",
|
||||
"reason": "visual: washed-out industrial lot"
|
||||
},
|
||||
{
|
||||
"name": "Maracaibo",
|
||||
"country": "Venezuela",
|
||||
"reason": "visual: farmland / scrub"
|
||||
},
|
||||
{
|
||||
"name": "Barquisimeto",
|
||||
"country": "Venezuela",
|
||||
"reason": "visual: dry riverbed, no city"
|
||||
},
|
||||
{
|
||||
"name": "Conakry",
|
||||
"country": "Guinea",
|
||||
"reason": "visual: heavy haze"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,69 @@
|
||||
"""Fetch candidates and render labelled review sheets for visual verification.
|
||||
|
||||
usage: python3 review.py landmarks|cities [start] [count]
|
||||
Writes tools/flurry/review/<kind>_NN.jpg (5x4 grid, 256px cells, labelled).
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
from PIL import Image, ImageDraw
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
from tiles import HERE, MAX_WORKERS, capture, quality_ok # noqa: E402
|
||||
|
||||
REV = os.path.join(HERE, "review")
|
||||
|
||||
|
||||
def load(kind):
|
||||
if kind == "landmarks":
|
||||
from landmarks import LANDMARKS
|
||||
return [dict(name=n, country=c, lat=la, lon=lo, zoom=z) for n, c, la, lo, z in LANDMARKS]
|
||||
return json.load(open(os.path.join(HERE, "city_candidates.json")))
|
||||
|
||||
|
||||
def run(cands, prefix, cell=256, cols=5, rows=4):
|
||||
os.makedirs(REV, exist_ok=True)
|
||||
|
||||
def job(c):
|
||||
try:
|
||||
img, info = capture(c["lat"], c["lon"], c["zoom"])
|
||||
return img, info
|
||||
except Exception as e:
|
||||
return None, {"error": str(e)}
|
||||
|
||||
with ThreadPoolExecutor(MAX_WORKERS) as ex:
|
||||
res = list(ex.map(job, cands))
|
||||
per = cols * rows
|
||||
report = []
|
||||
for s in range(0, len(cands), per):
|
||||
sheet = Image.new("RGB", (cols * cell, rows * cell), (20, 20, 20))
|
||||
d = ImageDraw.Draw(sheet)
|
||||
for k, (c, (img, info)) in enumerate(zip(cands[s:s + per], res[s:s + per])):
|
||||
x, y = (k % cols) * cell, (k // cols) * cell
|
||||
idx = s + k
|
||||
if img is not None:
|
||||
sheet.paste(img.resize((cell, cell), Image.LANCZOS), (x, y))
|
||||
ok, why = quality_ok(info)
|
||||
lab2 = f"s{info['std']:.0f} g{info['grad']:.1f}" + ("" if ok else " X " + why[:18])
|
||||
else:
|
||||
ok, lab2 = False, "ERR"
|
||||
d.rectangle((x, y, x + cell, y + 24), fill=(0, 0, 0))
|
||||
d.text((x + 3, y + 1), f"{idx} {c['name'][:36]}", fill=(255, 255, 0))
|
||||
d.text((x + 3, y + 12), lab2, fill=(255, 120, 120) if not ok else (160, 255, 160))
|
||||
report.append((idx, c["name"], ok, lab2))
|
||||
out = os.path.join(REV, f"{prefix}_{s // per:02d}.jpg")
|
||||
sheet.save(out, quality=85)
|
||||
print(out)
|
||||
for r in report:
|
||||
if not r[2]:
|
||||
print("REJECT", r)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
kind = sys.argv[1]
|
||||
cands = load(kind)
|
||||
st = int(sys.argv[2]) if len(sys.argv) > 2 else 0
|
||||
n = int(sys.argv[3]) if len(sys.argv) > 3 else len(cands)
|
||||
run(cands[st:st + n], kind[:2] + (f"_{st}" if st else ""))
|
||||
@@ -0,0 +1,109 @@
|
||||
"""Pick ~170 city centres from data/cities.json with geographic spread.
|
||||
|
||||
Writes tools/flurry/city_candidates.json: ordered list of dicts with
|
||||
rank ("primary"/"backup") and region. Backups replace rejected primaries of the
|
||||
same region in build.py.
|
||||
"""
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
ROOT = os.path.abspath(os.path.join(HERE, "..", ".."))
|
||||
|
||||
AFRICA = {
|
||||
"Nigeria", "Egypt", "Congo (Kinshasa)", "South Africa", "Algeria", "Sudan", "Morocco", "Tanzania",
|
||||
"Cameroon", "Mozambique", "Libya", "Ethiopia", "Ivory Coast", "Tunisia", "Zambia", "Angola", "Ghana",
|
||||
"Uganda", "Kenya", "Guinea", "Madagascar", "Senegal", "Zimbabwe", "Mali", "Somalia", "Chad", "Niger",
|
||||
"Benin", "Eritrea", "Somaliland", "South Sudan", "Sierra Leone", "Mauritania", "Malawi", "Togo",
|
||||
"Congo (Brazzaville)", "Burkina Faso", "Rwanda", "Mauritius", "Gabon", "Equatorial Guinea", "Liberia",
|
||||
"Djibouti", "Central African Republic", "Guinea Bissau", "Lesotho", "Burundi", "Namibia", "Botswana",
|
||||
"The Gambia", "Comoros", "Cape Verde", "eSwatini",
|
||||
}
|
||||
SOUTH_AMERICA = {"Brazil", "Argentina", "Colombia", "Venezuela", "Peru", "Chile", "Ecuador", "Bolivia",
|
||||
"Paraguay", "Uruguay", "Guyana", "Suriname"}
|
||||
OCEANIA = {"Australia", "New Zealand", "Papua New Guinea", "Fiji", "French Polynesia", "Guam"}
|
||||
CENTRAL_ASIA = {"Kazakhstan", "Uzbekistan", "Turkmenistan", "Kyrgyzstan", "Tajikistan", "Afghanistan", "Mongolia"}
|
||||
|
||||
# (region, primary quota, backups, per-country cap, min spacing km)
|
||||
PASSES = [
|
||||
("africa", AFRICA, 31, 16, 3, 300),
|
||||
("south_america", SOUTH_AMERICA, 22, 10, 5, 250),
|
||||
("oceania", OCEANIA, 12, 4, 5, 120),
|
||||
("central_asia", CENTRAL_ASIA, 9, 5, 3, 150),
|
||||
("rest", None, 96, 40, 11, 200),
|
||||
]
|
||||
# Picked first (in "rest" unless their country is in a quota region): fill the
|
||||
# Pacific / high-latitude longitude bands and add visually distinctive cities.
|
||||
FORCED = [
|
||||
"Honolulu", "Anchorage", "Papeete", "Agana", "Petropavlovsk-Kamchatsky", "Vancouver", "San Francisco",
|
||||
"Las Vegas", "Seattle", "Phoenix", "Denver", "Calgary", "Guatemala City", "Havana", "Reykjavík",
|
||||
"Dubai", "Doha", "Abu Dhabi", "Hong Kong", "Macau", "Venice", "Amsterdam", "København", "Oslo",
|
||||
"Stockholm", "Helsinki", "Prague", "Kathmandu", "Colombo", "Vladivostok", "Novosibirsk", "Irkutsk",
|
||||
"Yakutsk", "Norilsk", "Murmansk", "Manaus", "Mombasa", "Windhoek", "Nouakchott", "Marrakesh",
|
||||
"Samarkand", "Cairns", "Muscat", "Québec",
|
||||
]
|
||||
# Natural Earth rows whose point lands in farmland/hills rather than a dense centre.
|
||||
EXCLUDE = {"Amaravati", "Hechi", "Saidu", "Cranbourne", "Manukau", "Waitakere", "North Shore", "Wahiawa"}
|
||||
COUNTRY_CAP_TOTAL = 12
|
||||
|
||||
|
||||
def km(a, b):
|
||||
la1, lo1, la2, lo2 = map(math.radians, (a[1], a[0], b[1], b[0]))
|
||||
h = math.sin((la2 - la1) / 2) ** 2 + math.cos(la1) * math.cos(la2) * math.sin((lo2 - lo1) / 2) ** 2
|
||||
return 6371 * 2 * math.asin(math.sqrt(h))
|
||||
|
||||
|
||||
def main():
|
||||
rows = json.load(open(os.path.join(ROOT, "data", "cities.json")))["rows"]
|
||||
special = AFRICA | SOUTH_AMERICA | OCEANIA | CENTRAL_ASIA
|
||||
chosen, out = [], []
|
||||
byname = {}
|
||||
for r in rows:
|
||||
byname.setdefault(r[3], r) # first = most populous
|
||||
per = {}
|
||||
|
||||
def region_of(country):
|
||||
for reg, mem, *_ in PASSES:
|
||||
if mem and country in mem:
|
||||
return reg
|
||||
return "rest"
|
||||
|
||||
for nm in FORCED:
|
||||
lon, lat, pop, name, country = byname[nm]
|
||||
chosen.append((lon, lat))
|
||||
per[country] = per.get(country, 0) + 1
|
||||
out.append(dict(name=name, country=country, lat=lat, lon=lon, zoom=16, pop=pop,
|
||||
region=region_of(country), rank="primary", forced=True))
|
||||
for region, members, quota, nback, cap, spacing in PASSES:
|
||||
got = sum(1 for c in out if c["region"] == region)
|
||||
for lon, lat, pop, name, country in rows:
|
||||
if name in EXCLUDE or name in byname and any(c["name"] == name and c["country"] == country for c in out):
|
||||
continue
|
||||
inreg = (country in members) if members else (country not in special)
|
||||
if not inreg:
|
||||
continue
|
||||
if per.get(country, 0) >= cap and got < quota or per.get(country, 0) >= COUNTRY_CAP_TOTAL:
|
||||
continue
|
||||
if any(km((lon, lat), (c[0], c[1])) < spacing for c in chosen):
|
||||
continue
|
||||
rank = "primary" if got < quota else "backup"
|
||||
chosen.append((lon, lat))
|
||||
per[country] = per.get(country, 0) + 1
|
||||
out.append(dict(name=name, country=country, lat=lat, lon=lon, zoom=16, pop=pop,
|
||||
region=region, rank=rank))
|
||||
got += 1
|
||||
if got >= quota + nback:
|
||||
break
|
||||
json.dump(out, open(os.path.join(HERE, "city_candidates.json"), "w"), indent=0, ensure_ascii=False)
|
||||
prim = [c for c in out if c["rank"] == "primary"]
|
||||
print(len(prim), "primary,", len(out) - len(prim), "backup")
|
||||
from collections import Counter
|
||||
print(Counter(c["region"] for c in prim))
|
||||
print(Counter(c["country"] for c in prim).most_common(12))
|
||||
bands = Counter(int((c["lon"] + 180) // 30) for c in prim)
|
||||
print("30deg bands:", [bands.get(i, 0) for i in range(12)])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,132 @@
|
||||
"""Polite cached Esri World Imagery fetch + 512px centred capture + quality metrics."""
|
||||
import hashlib
|
||||
import io
|
||||
import math
|
||||
import os
|
||||
import threading
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import requests
|
||||
from PIL import Image
|
||||
|
||||
HERE = os.path.dirname(os.path.abspath(__file__))
|
||||
CACHE = os.path.join(HERE, "cache")
|
||||
ROOT = os.path.abspath(os.path.join(HERE, "..", ".."))
|
||||
OUT = os.path.join(ROOT, "imagery", "flurry")
|
||||
|
||||
ESRI = "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}"
|
||||
UA = "gods-eye-view-intro/1.0 (flurry montage prep)"
|
||||
MAX_WORKERS = 8
|
||||
PLACEHOLDER_MD5 = {"f27d9de7f80c13501f470595e327aa6d"} # "Map data not yet available"
|
||||
|
||||
_local = threading.local()
|
||||
|
||||
|
||||
def _session():
|
||||
s = getattr(_local, "s", None)
|
||||
if s is None:
|
||||
s = requests.Session()
|
||||
s.headers["User-Agent"] = UA
|
||||
_local.s = s
|
||||
return s
|
||||
|
||||
|
||||
def fetch_tile(z, x, y, retries=6):
|
||||
n = 2 ** z
|
||||
x %= n
|
||||
path = os.path.join(CACHE, str(z), str(x), f"{y}.jpg")
|
||||
if os.path.exists(path) and os.path.getsize(path) > 0:
|
||||
with open(path, "rb") as f:
|
||||
return f.read()
|
||||
os.makedirs(os.path.dirname(path), exist_ok=True)
|
||||
url = ESRI.format(z=z, x=x, y=y)
|
||||
last = None
|
||||
for attempt in range(retries):
|
||||
try:
|
||||
r = _session().get(url, timeout=45)
|
||||
if r.status_code == 200 and r.content:
|
||||
tmp = path + f".{threading.get_ident()}.part"
|
||||
with open(tmp, "wb") as f:
|
||||
f.write(r.content)
|
||||
os.replace(tmp, path)
|
||||
return r.content
|
||||
last = f"HTTP {r.status_code}"
|
||||
if r.status_code in (400, 404):
|
||||
break
|
||||
except Exception as e:
|
||||
last = repr(e)
|
||||
time.sleep(min(20, 1.0 * 2 ** attempt))
|
||||
raise RuntimeError(f"failed {url}: {last}")
|
||||
|
||||
|
||||
def is_placeholder(data):
|
||||
if hashlib.md5(data).hexdigest() in PLACEHOLDER_MD5:
|
||||
return True
|
||||
a = np.asarray(Image.open(io.BytesIO(data)).convert("RGB")).astype(np.float32)
|
||||
m = a.mean(axis=(0, 1))
|
||||
return bool(abs(m - 204.7).max() < 4 and a.std() < 9)
|
||||
|
||||
|
||||
def pixel_xy(lat, lon, z):
|
||||
n = 2 ** z * 256
|
||||
px = (lon + 180.0) / 360.0 * n
|
||||
s = math.sin(math.radians(lat))
|
||||
py = (0.5 - math.log((1 + s) / (1 - s)) / (4 * math.pi)) * n
|
||||
return px, py
|
||||
|
||||
|
||||
def capture(lat, lon, z, size=512):
|
||||
"""Return (image, info). info has placeholder flag and quality metrics."""
|
||||
px, py = pixel_xy(lat, lon, z)
|
||||
tx, ty = int(px // 256), int(py // 256)
|
||||
mosaic = Image.new("RGB", (768, 768))
|
||||
ox, oy = (tx - 1) * 256, (ty - 1) * 256
|
||||
left, top = int(round(px - ox - size / 2)), int(round(py - oy - size / 2))
|
||||
ph = False
|
||||
for j in range(3):
|
||||
for i in range(3):
|
||||
data = fetch_tile(z, tx - 1 + i, ty - 1 + j)
|
||||
# does this tile overlap the crop window?
|
||||
tl, tt = i * 256, j * 256
|
||||
overlaps = tl < left + size and tl + 256 > left and tt < top + size and tt + 256 > top
|
||||
if overlaps and is_placeholder(data):
|
||||
ph = True
|
||||
mosaic.paste(Image.open(io.BytesIO(data)).convert("RGB"), (tl, tt))
|
||||
img = mosaic.crop((left, top, left + size, top + size))
|
||||
info = metrics(img)
|
||||
info["placeholder"] = ph
|
||||
return img, info
|
||||
|
||||
|
||||
def metrics(img):
|
||||
a = np.asarray(img).astype(np.float32)
|
||||
lum = a @ np.array([0.299, 0.587, 0.114], np.float32)
|
||||
small = np.asarray(img.convert("L").resize((128, 128), Image.BILINEAR)).astype(np.float32)
|
||||
gx = np.abs(np.diff(lum, axis=1)).mean()
|
||||
gy = np.abs(np.diff(lum, axis=0)).mean()
|
||||
# coarse-scale structure: std of 32px block means (catches flat ocean/cloud/ice)
|
||||
blocks = lum.reshape(16, 32, 16, 32).mean(axis=(1, 3))
|
||||
sat = (a.max(axis=2) - a.min(axis=2)).mean()
|
||||
return {
|
||||
"std": float(lum.std()),
|
||||
"grad": float((gx + gy) / 2),
|
||||
"block_std": float(blocks.std()),
|
||||
"small_std": float(small.std()),
|
||||
"sat": float(sat),
|
||||
"mean": float(lum.mean()),
|
||||
}
|
||||
|
||||
|
||||
def quality_ok(info):
|
||||
if info["placeholder"]:
|
||||
return False, "placeholder"
|
||||
if info["std"] < 12:
|
||||
return False, f"low variance (std {info['std']:.1f})"
|
||||
if info["grad"] < 1.3:
|
||||
return False, f"featureless (grad {info['grad']:.1f})"
|
||||
if info["mean"] > 225 and info["std"] < 25:
|
||||
return False, "cloud/snow white-out"
|
||||
if info["mean"] < 22:
|
||||
return False, "too dark"
|
||||
return True, ""
|
||||
Reference in new issue
Block a user