mirror of
https://github.com/azaion/gps-denied-onboard.git
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refactor(01-05): migrate satellite+metric to satellite_matcher component
- Move SatelliteDataManager impl to components/satellite_matcher/local_tile_loader.py - Move MetricRefinement impl to components/satellite_matcher/metric_refinement.py - MetricRefinement imports IMetricRefinement from protocol.py (no ABC copy) - Replace core/satellite.py and core/metric.py with re-export shims - Update satellite_matcher __init__.py to export both classes + protocols - 216/216 tests pass (regression floor maintained)
This commit is contained in:
Yuzviak
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"""satellite_matcher component public API."""
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from .local_tile_loader import SatelliteDataManager
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from .metric_refinement import MetricRefinement
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from .protocol import IMetricRefinement, MetricRefiner, SatelliteTileLoader
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__all__ = [
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"SatelliteDataManager",
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"MetricRefinement",
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"IMetricRefinement",
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"MetricRefiner",
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"SatelliteTileLoader",
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]
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"""Local-disk tile loader (SAT-01/02). Phase 1 home of the existing SatelliteDataManager impl."""
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import hashlib
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import logging
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import math
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import os
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from concurrent.futures import ThreadPoolExecutor
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import cv2
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import numpy as np
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from gps_denied.schemas import GPSPoint
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from gps_denied.schemas.satellite import TileBounds, TileCoords
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from gps_denied.utils import mercator
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class SatelliteDataManager:
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"""Manages satellite tiles from a local pre-loaded directory.
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Directory layout (SAT-01):
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{tile_dir}/{zoom}/{x}/{y}.png — standard Web Mercator slippy-map layout
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No live HTTP requests are made during flight. A separate offline tooling step
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downloads and stores tiles before the mission.
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"""
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_logger = logging.getLogger(__name__)
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def __init__(
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self,
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tile_dir: str = ".satellite_tiles",
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cache_dir: str = ".satellite_cache",
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max_size_gb: float = 10.0,
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):
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self.tile_dir = tile_dir
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self.thread_pool = ThreadPoolExecutor(max_workers=4)
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# In-memory LRU for hot tiles (avoids repeated disk reads)
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self._mem_cache: dict[str, np.ndarray] = {}
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self._mem_cache_max = 256
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# SHA-256 manifest for tile integrity (якщо файл існує)
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self._manifest: dict[str, str] = self._load_manifest()
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# ------------------------------------------------------------------
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# SAT-01: Local tile reads (no HTTP)
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# ------------------------------------------------------------------
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def _load_manifest(self) -> dict[str, str]:
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"""Завантажити SHA-256 manifest з tile_dir/manifest.sha256."""
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path = os.path.join(self.tile_dir, "manifest.sha256")
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if not os.path.isfile(path):
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return {}
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manifest: dict[str, str] = {}
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with open(path) as f:
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for line in f:
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line = line.strip()
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if not line or line.startswith("#"):
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continue
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parts = line.split(maxsplit=1)
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if len(parts) == 2:
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manifest[parts[1].strip()] = parts[0].strip()
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return manifest
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def _verify_tile_integrity(self, rel_path: str, file_path: str) -> bool:
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"""Перевірити SHA-256 тайла проти manifest (якщо manifest існує)."""
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if not self._manifest:
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return True # без manifest — пропускаємо
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expected = self._manifest.get(rel_path)
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if expected is None:
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return True # тайл не в manifest — OK
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sha = hashlib.sha256()
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with open(file_path, "rb") as f:
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for chunk in iter(lambda: f.read(8192), b""):
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sha.update(chunk)
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actual = sha.hexdigest()
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if actual != expected:
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self._logger.warning("Tile integrity failed: %s (exp %s, got %s)",
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rel_path, expected[:12], actual[:12])
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return False
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return True
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def load_local_tile(self, tile_coords: TileCoords) -> np.ndarray | None:
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"""Load a tile image from the local pre-loaded directory.
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Expected path: {tile_dir}/{zoom}/{x}/{y}.png
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Returns None if the file does not exist.
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"""
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key = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}"
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if key in self._mem_cache:
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return self._mem_cache[key]
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rel_path = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}.png"
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path = os.path.join(self.tile_dir, rel_path)
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if not os.path.isfile(path):
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return None
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if not self._verify_tile_integrity(rel_path, path):
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return None # тайл пошкоджений
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img = cv2.imread(path, cv2.IMREAD_COLOR)
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if img is None:
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return None
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# LRU eviction: drop oldest if full
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if len(self._mem_cache) >= self._mem_cache_max:
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oldest = next(iter(self._mem_cache))
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del self._mem_cache[oldest]
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self._mem_cache[key] = img
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return img
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def save_local_tile(self, tile_coords: TileCoords, image: np.ndarray) -> bool:
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"""Persist a tile to the local directory (used by offline pre-fetch tooling)."""
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path = os.path.join(self.tile_dir, str(tile_coords.zoom),
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str(tile_coords.x), f"{tile_coords.y}.png")
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os.makedirs(os.path.dirname(path), exist_ok=True)
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ok, encoded = cv2.imencode(".png", image)
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if not ok:
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return False
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with open(path, "wb") as f:
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f.write(encoded.tobytes())
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key = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}"
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self._mem_cache[key] = image
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return True
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# ------------------------------------------------------------------
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# SAT-02: Tile selection for ESKF position ± 3σ_horizontal
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# ------------------------------------------------------------------
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@staticmethod
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def _meters_to_degrees(meters: float, lat: float) -> tuple[float, float]:
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"""Convert a radius in metres to (Δlat°, Δlon°) at the given latitude."""
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delta_lat = meters / 111_320.0
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delta_lon = meters / (111_320.0 * math.cos(math.radians(lat)))
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return delta_lat, delta_lon
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def select_tiles_for_eskf_position(
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self, gps: GPSPoint, sigma_h_m: float, zoom: int
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) -> list[TileCoords]:
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"""Return all tile coords covering the ESKF position ± 3σ_horizontal area.
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Args:
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gps: ESKF best-estimate position.
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sigma_h_m: 1-σ horizontal uncertainty in metres (from ESKF covariance).
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zoom: Web Mercator zoom level (18 recommended ≈ 0.6 m/px).
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"""
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radius_m = 3.0 * sigma_h_m
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dlat, dlon = self._meters_to_degrees(radius_m, gps.lat)
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# Bounding box corners
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lat_min, lat_max = gps.lat - dlat, gps.lat + dlat
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lon_min, lon_max = gps.lon - dlon, gps.lon + dlon
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# Convert corners to tile coords
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tc_nw = mercator.latlon_to_tile(lat_max, lon_min, zoom)
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tc_se = mercator.latlon_to_tile(lat_min, lon_max, zoom)
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tiles: list[TileCoords] = []
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for x in range(tc_nw.x, tc_se.x + 1):
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for y in range(tc_nw.y, tc_se.y + 1):
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tiles.append(TileCoords(x=x, y=y, zoom=zoom))
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return tiles
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def assemble_mosaic(
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self,
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tile_list: list[tuple[TileCoords, np.ndarray]],
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target_size: int = 512,
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) -> tuple[np.ndarray, TileBounds] | None:
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"""Assemble a list of (TileCoords, image) pairs into a single mosaic.
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Returns (mosaic_image, combined_bounds) or None if tile_list is empty.
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The mosaic is resized to (target_size × target_size) for the matcher.
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"""
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if not tile_list:
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return None
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xs = [tc.x for tc, _ in tile_list]
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ys = [tc.y for tc, _ in tile_list]
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zoom = tile_list[0][0].zoom
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x_min, x_max = min(xs), max(xs)
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y_min, y_max = min(ys), max(ys)
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cols = x_max - x_min + 1
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rows = y_max - y_min + 1
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# Determine single-tile pixel size from first image
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sample = tile_list[0][1]
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th, tw = sample.shape[:2]
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canvas = np.zeros((rows * th, cols * tw, 3), dtype=np.uint8)
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for tc, img in tile_list:
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col = tc.x - x_min
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row = tc.y - y_min
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h, w = img.shape[:2]
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canvas[row * th: row * th + h, col * tw: col * tw + w] = img
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mosaic = cv2.resize(canvas, (target_size, target_size), interpolation=cv2.INTER_AREA)
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# Compute combined GPS bounds
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nw_bounds = mercator.compute_tile_bounds(TileCoords(x=x_min, y=y_min, zoom=zoom))
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se_bounds = mercator.compute_tile_bounds(TileCoords(x=x_max, y=y_max, zoom=zoom))
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combined = TileBounds(
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nw=nw_bounds.nw,
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ne=GPSPoint(lat=nw_bounds.nw.lat, lon=se_bounds.se.lon),
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sw=GPSPoint(lat=se_bounds.se.lat, lon=nw_bounds.nw.lon),
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se=se_bounds.se,
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center=GPSPoint(
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lat=(nw_bounds.nw.lat + se_bounds.se.lat) / 2,
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lon=(nw_bounds.nw.lon + se_bounds.se.lon) / 2,
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),
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gsd=nw_bounds.gsd,
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)
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return mosaic, combined
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def fetch_tiles_for_position(
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self, gps: GPSPoint, sigma_h_m: float, zoom: int
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) -> tuple[np.ndarray, TileBounds] | None:
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"""High-level helper: select tiles + load + assemble mosaic.
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Returns (mosaic, bounds) or None if no local tiles are available.
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"""
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coords = self.select_tiles_for_eskf_position(gps, sigma_h_m, zoom)
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loaded: list[tuple[TileCoords, np.ndarray]] = []
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for tc in coords:
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img = self.load_local_tile(tc)
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if img is not None:
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loaded.append((tc, img))
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return self.assemble_mosaic(loaded) if loaded else None
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# ------------------------------------------------------------------
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# Cache helpers (backward-compat, also used for warm-path caching)
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# ------------------------------------------------------------------
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def cache_tile(self, flight_id: str, tile_coords: TileCoords, tile_data: np.ndarray) -> bool:
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"""Cache a tile image in memory (used by tests and offline tools)."""
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key = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}"
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self._mem_cache[key] = tile_data
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return True
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def get_cached_tile(self, flight_id: str, tile_coords: TileCoords) -> np.ndarray | None:
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"""Retrieve a cached tile from memory."""
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key = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}"
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return self._mem_cache.get(key)
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# ------------------------------------------------------------------
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# Tile math helpers
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# ------------------------------------------------------------------
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def get_tile_grid(self, center: TileCoords, grid_size: int) -> list[TileCoords]:
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"""Return grid_size tiles centered on center."""
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if grid_size == 1:
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return [center]
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side = int(grid_size ** 0.5)
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half = side // 2
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coords: list[TileCoords] = []
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for dy in range(-half, half + 1):
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for dx in range(-half, half + 1):
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coords.append(TileCoords(x=center.x + dx, y=center.y + dy, zoom=center.zoom))
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if grid_size == 4:
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coords = []
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for dy in range(2):
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for dx in range(2):
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coords.append(TileCoords(x=center.x + dx, y=center.y + dy, zoom=center.zoom))
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return coords[:grid_size]
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def expand_search_grid(self, center: TileCoords, current_size: int, new_size: int) -> list[TileCoords]:
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"""Return only the NEW tiles when expanding from current_size to new_size grid."""
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old_set = {(c.x, c.y) for c in self.get_tile_grid(center, current_size)}
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return [c for c in self.get_tile_grid(center, new_size) if (c.x, c.y) not in old_set]
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def compute_tile_coords(self, lat: float, lon: float, zoom: int) -> TileCoords:
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return mercator.latlon_to_tile(lat, lon, zoom)
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def compute_tile_bounds(self, tile_coords: TileCoords) -> TileBounds:
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return mercator.compute_tile_bounds(tile_coords)
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def clear_flight_cache(self, flight_id: str) -> bool:
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"""Clear in-memory cache (flight scoping is tile-key-based)."""
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self._mem_cache.clear()
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return True
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@@ -0,0 +1,190 @@
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"""Metric Refinement implementation (SAT-03/04). Phase 1 home of MetricRefinement impl.
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SAT-03: GSD normalization — downsample camera frame to satellite resolution.
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SAT-04: RANSAC homography → WGS84 position; confidence = inlier_ratio.
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"""
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import logging
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from typing import List, Optional, Tuple
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import cv2
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import numpy as np
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from gps_denied.components.satellite_matcher.protocol import IMetricRefinement
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from gps_denied.core.models import IModelManager
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from gps_denied.schemas import GPSPoint
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from gps_denied.schemas.metric import AlignmentResult, ChunkAlignmentResult, Sim3Transform
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from gps_denied.schemas.satellite import TileBounds
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logger = logging.getLogger(__name__)
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class MetricRefinement(IMetricRefinement):
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"""LiteSAM/XFeat-based alignment with GSD normalization.
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SAT-03: normalize_gsd() downsamples UAV frame to match satellite GSD before matching.
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SAT-04: confidence is computed as inlier_count / total_correspondences (inlier ratio).
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"""
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def __init__(self, model_manager: IModelManager):
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self.model_manager = model_manager
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# ------------------------------------------------------------------
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# SAT-03: GSD normalization
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# ------------------------------------------------------------------
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@staticmethod
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def normalize_gsd(
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uav_image: np.ndarray,
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uav_gsd_mpp: float,
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sat_gsd_mpp: float,
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) -> np.ndarray:
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"""Resize UAV frame to match satellite GSD (meters-per-pixel).
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Args:
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uav_image: Raw UAV camera frame.
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uav_gsd_mpp: UAV GSD in m/px (e.g. 0.159 at 600 m altitude).
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sat_gsd_mpp: Satellite tile GSD in m/px (e.g. 0.6 at zoom 18).
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Returns:
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Resized image. If already coarser than satellite, returned unchanged.
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"""
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if uav_gsd_mpp <= 0 or sat_gsd_mpp <= 0:
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return uav_image
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scale = uav_gsd_mpp / sat_gsd_mpp
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if scale >= 1.0:
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return uav_image # UAV already coarser, nothing to do
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h, w = uav_image.shape[:2]
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new_w = max(1, int(w * scale))
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new_h = max(1, int(h * scale))
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return cv2.resize(uav_image, (new_w, new_h), interpolation=cv2.INTER_AREA)
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def compute_homography(self, uav_image: np.ndarray, satellite_tile: np.ndarray) -> Optional[np.ndarray]:
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engine = self.model_manager.get_inference_engine("LiteSAM")
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# In reality we pass both images, for mock we just invoke to get generated format
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res = engine.infer({"img1": uav_image, "img2": satellite_tile})
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if res["inlier_count"] < 15:
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return None
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return res["homography"]
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def extract_gps_from_alignment(self, homography: np.ndarray, tile_bounds: TileBounds, image_center: Tuple[int, int]) -> GPSPoint:
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# UAV image center
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cx, cy = image_center
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# Apply homography
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pt = np.array([cx, cy, 1.0])
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# transformed = H * pt
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transformed = homography @ pt
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transformed = transformed / transformed[2]
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tx, ty = transformed[0], transformed[1]
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# Approximate GPS mapping using bounds
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# ty maps to latitude (ty=0 is North, ty=Height is South)
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# tx maps to longitude (tx=0 is West, tx=Width is East)
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# We assume standard 256x256 tiles for this mock calculation
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tile_size = 256.0
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lat_span = tile_bounds.nw.lat - tile_bounds.sw.lat
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lon_span = tile_bounds.ne.lon - tile_bounds.nw.lon
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# Calculate offsets
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# If ty is down, lat decreases
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lat_rel = (tile_size - ty) / tile_size
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lon_rel = tx / tile_size
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target_lat = tile_bounds.sw.lat + (lat_span * lat_rel)
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target_lon = tile_bounds.nw.lon + (lon_span * lon_rel)
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return GPSPoint(lat=target_lat, lon=target_lon)
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def align_to_satellite(
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self,
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uav_image: np.ndarray,
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satellite_tile: np.ndarray,
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tile_bounds: TileBounds,
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uav_gsd_mpp: float = 0.0,
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) -> Optional[AlignmentResult]:
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"""Align UAV frame to satellite tile.
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Args:
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uav_gsd_mpp: If > 0, the UAV frame is GSD-normalised to satellite
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resolution before matching (SAT-03).
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"""
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# SAT-03: optional GSD normalization
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sat_gsd = tile_bounds.gsd
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if uav_gsd_mpp > 0 and sat_gsd > 0:
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uav_image = self.normalize_gsd(uav_image, uav_gsd_mpp, sat_gsd)
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engine = self.model_manager.get_inference_engine("LiteSAM")
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res = engine.infer({"img1": uav_image, "img2": satellite_tile})
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if res["inlier_count"] < 15:
|
||||
return None
|
||||
|
||||
h, w = uav_image.shape[:2] if hasattr(uav_image, "shape") else (480, 640)
|
||||
gps = self.extract_gps_from_alignment(res["homography"], tile_bounds, (w // 2, h // 2))
|
||||
|
||||
# SAT-04: confidence = inlier_ratio (not raw engine confidence)
|
||||
total = res.get("total_correspondences", max(res["inlier_count"], 1))
|
||||
inlier_ratio = res["inlier_count"] / max(total, 1)
|
||||
|
||||
align = AlignmentResult(
|
||||
matched=True,
|
||||
homography=res["homography"],
|
||||
gps_center=gps,
|
||||
confidence=inlier_ratio,
|
||||
inlier_count=res["inlier_count"],
|
||||
total_correspondences=total,
|
||||
reprojection_error=res.get("reprojection_error", 1.0),
|
||||
)
|
||||
return align if self.compute_match_confidence(align) > 0.5 else None
|
||||
|
||||
def compute_match_confidence(self, alignment: AlignmentResult) -> float:
|
||||
# Complex heuristic combining inliers, reprojection error
|
||||
score = alignment.confidence
|
||||
# Penalty for high reproj error
|
||||
if alignment.reprojection_error > 2.0:
|
||||
score -= 0.2
|
||||
return max(0.0, min(1.0, score))
|
||||
|
||||
def match_chunk_homography(self, chunk_images: List[np.ndarray], satellite_tile: np.ndarray) -> Optional[np.ndarray]:
|
||||
# Aggregate logic is complex, for mock we just use the first image's match
|
||||
if not chunk_images:
|
||||
return None
|
||||
return self.compute_homography(chunk_images[0], satellite_tile)
|
||||
|
||||
def align_chunk_to_satellite(self, chunk_images: List[np.ndarray], satellite_tile: np.ndarray, tile_bounds: TileBounds) -> Optional[ChunkAlignmentResult]:
|
||||
if not chunk_images:
|
||||
return None
|
||||
|
||||
engine = self.model_manager.get_inference_engine("LiteSAM")
|
||||
res = engine.infer({"img1": chunk_images[0], "img2": satellite_tile})
|
||||
|
||||
# Demands higher inliners for chunk
|
||||
if res["inlier_count"] < 30:
|
||||
return None
|
||||
|
||||
h, w = chunk_images[0].shape[:2] if hasattr(chunk_images[0], "shape") else (480, 640)
|
||||
gps = self.extract_gps_from_alignment(res["homography"], tile_bounds, (w // 2, h // 2))
|
||||
|
||||
# Fake sim3
|
||||
sim3 = Sim3Transform(
|
||||
translation=np.array([10., 0., 0.]),
|
||||
rotation=np.eye(3),
|
||||
scale=1.0
|
||||
)
|
||||
|
||||
chunk_align = ChunkAlignmentResult(
|
||||
matched=True,
|
||||
chunk_id="chunk1",
|
||||
chunk_center_gps=gps,
|
||||
rotation_angle=0.0,
|
||||
confidence=res["confidence"],
|
||||
inlier_count=res["inlier_count"],
|
||||
transform=sim3,
|
||||
reprojection_error=1.0
|
||||
)
|
||||
|
||||
return chunk_align
|
||||
@@ -1,216 +1,10 @@
|
||||
"""Metric Refinement (Component F09).
|
||||
"""Legacy import path. Phase 1 shim — code lives in components/satellite_matcher/."""
|
||||
from gps_denied.components.satellite_matcher.protocol import ( # noqa: F401
|
||||
MetricRefiner,
|
||||
IMetricRefinement,
|
||||
)
|
||||
from gps_denied.components.satellite_matcher.metric_refinement import ( # noqa: F401
|
||||
MetricRefinement,
|
||||
)
|
||||
|
||||
SAT-03: GSD normalization — downsample camera frame to satellite resolution.
|
||||
SAT-04: RANSAC homography → WGS84 position; confidence = inlier_ratio.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import List, Optional, Tuple
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from gps_denied.core.models import IModelManager
|
||||
from gps_denied.schemas import GPSPoint
|
||||
from gps_denied.schemas.metric import AlignmentResult, ChunkAlignmentResult, Sim3Transform
|
||||
from gps_denied.schemas.satellite import TileBounds
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class IMetricRefinement(ABC):
|
||||
@abstractmethod
|
||||
def align_to_satellite(self, uav_image: np.ndarray, satellite_tile: np.ndarray, tile_bounds: TileBounds) -> Optional[AlignmentResult]:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def compute_homography(self, uav_image: np.ndarray, satellite_tile: np.ndarray) -> Optional[np.ndarray]:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def extract_gps_from_alignment(self, homography: np.ndarray, tile_bounds: TileBounds, image_center: Tuple[int, int]) -> GPSPoint:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def compute_match_confidence(self, alignment: AlignmentResult) -> float:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def align_chunk_to_satellite(self, chunk_images: List[np.ndarray], satellite_tile: np.ndarray, tile_bounds: TileBounds) -> Optional[ChunkAlignmentResult]:
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def match_chunk_homography(self, chunk_images: List[np.ndarray], satellite_tile: np.ndarray) -> Optional[np.ndarray]:
|
||||
pass
|
||||
|
||||
|
||||
class MetricRefinement(IMetricRefinement):
|
||||
"""LiteSAM/XFeat-based alignment with GSD normalization.
|
||||
|
||||
SAT-03: normalize_gsd() downsamples UAV frame to match satellite GSD before matching.
|
||||
SAT-04: confidence is computed as inlier_count / total_correspondences (inlier ratio).
|
||||
"""
|
||||
|
||||
def __init__(self, model_manager: IModelManager):
|
||||
self.model_manager = model_manager
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# SAT-03: GSD normalization
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def normalize_gsd(
|
||||
uav_image: np.ndarray,
|
||||
uav_gsd_mpp: float,
|
||||
sat_gsd_mpp: float,
|
||||
) -> np.ndarray:
|
||||
"""Resize UAV frame to match satellite GSD (meters-per-pixel).
|
||||
|
||||
Args:
|
||||
uav_image: Raw UAV camera frame.
|
||||
uav_gsd_mpp: UAV GSD in m/px (e.g. 0.159 at 600 m altitude).
|
||||
sat_gsd_mpp: Satellite tile GSD in m/px (e.g. 0.6 at zoom 18).
|
||||
|
||||
Returns:
|
||||
Resized image. If already coarser than satellite, returned unchanged.
|
||||
"""
|
||||
if uav_gsd_mpp <= 0 or sat_gsd_mpp <= 0:
|
||||
return uav_image
|
||||
scale = uav_gsd_mpp / sat_gsd_mpp
|
||||
if scale >= 1.0:
|
||||
return uav_image # UAV already coarser, nothing to do
|
||||
h, w = uav_image.shape[:2]
|
||||
new_w = max(1, int(w * scale))
|
||||
new_h = max(1, int(h * scale))
|
||||
return cv2.resize(uav_image, (new_w, new_h), interpolation=cv2.INTER_AREA)
|
||||
|
||||
def compute_homography(self, uav_image: np.ndarray, satellite_tile: np.ndarray) -> Optional[np.ndarray]:
|
||||
engine = self.model_manager.get_inference_engine("LiteSAM")
|
||||
# In reality we pass both images, for mock we just invoke to get generated format
|
||||
res = engine.infer({"img1": uav_image, "img2": satellite_tile})
|
||||
|
||||
if res["inlier_count"] < 15:
|
||||
return None
|
||||
|
||||
return res["homography"]
|
||||
|
||||
def extract_gps_from_alignment(self, homography: np.ndarray, tile_bounds: TileBounds, image_center: Tuple[int, int]) -> GPSPoint:
|
||||
# UAV image center
|
||||
cx, cy = image_center
|
||||
# Apply homography
|
||||
pt = np.array([cx, cy, 1.0])
|
||||
# transformed = H * pt
|
||||
transformed = homography @ pt
|
||||
transformed = transformed / transformed[2]
|
||||
|
||||
tx, ty = transformed[0], transformed[1]
|
||||
|
||||
# Approximate GPS mapping using bounds
|
||||
# ty maps to latitude (ty=0 is North, ty=Height is South)
|
||||
# tx maps to longitude (tx=0 is West, tx=Width is East)
|
||||
# We assume standard 256x256 tiles for this mock calculation
|
||||
tile_size = 256.0
|
||||
|
||||
lat_span = tile_bounds.nw.lat - tile_bounds.sw.lat
|
||||
lon_span = tile_bounds.ne.lon - tile_bounds.nw.lon
|
||||
|
||||
# Calculate offsets
|
||||
# If ty is down, lat decreases
|
||||
lat_rel = (tile_size - ty) / tile_size
|
||||
lon_rel = tx / tile_size
|
||||
|
||||
target_lat = tile_bounds.sw.lat + (lat_span * lat_rel)
|
||||
target_lon = tile_bounds.nw.lon + (lon_span * lon_rel)
|
||||
|
||||
return GPSPoint(lat=target_lat, lon=target_lon)
|
||||
|
||||
def align_to_satellite(
|
||||
self,
|
||||
uav_image: np.ndarray,
|
||||
satellite_tile: np.ndarray,
|
||||
tile_bounds: TileBounds,
|
||||
uav_gsd_mpp: float = 0.0,
|
||||
) -> Optional[AlignmentResult]:
|
||||
"""Align UAV frame to satellite tile.
|
||||
|
||||
Args:
|
||||
uav_gsd_mpp: If > 0, the UAV frame is GSD-normalised to satellite
|
||||
resolution before matching (SAT-03).
|
||||
"""
|
||||
# SAT-03: optional GSD normalization
|
||||
sat_gsd = tile_bounds.gsd
|
||||
if uav_gsd_mpp > 0 and sat_gsd > 0:
|
||||
uav_image = self.normalize_gsd(uav_image, uav_gsd_mpp, sat_gsd)
|
||||
|
||||
engine = self.model_manager.get_inference_engine("LiteSAM")
|
||||
res = engine.infer({"img1": uav_image, "img2": satellite_tile})
|
||||
|
||||
if res["inlier_count"] < 15:
|
||||
return None
|
||||
|
||||
h, w = uav_image.shape[:2] if hasattr(uav_image, "shape") else (480, 640)
|
||||
gps = self.extract_gps_from_alignment(res["homography"], tile_bounds, (w // 2, h // 2))
|
||||
|
||||
# SAT-04: confidence = inlier_ratio (not raw engine confidence)
|
||||
total = res.get("total_correspondences", max(res["inlier_count"], 1))
|
||||
inlier_ratio = res["inlier_count"] / max(total, 1)
|
||||
|
||||
align = AlignmentResult(
|
||||
matched=True,
|
||||
homography=res["homography"],
|
||||
gps_center=gps,
|
||||
confidence=inlier_ratio,
|
||||
inlier_count=res["inlier_count"],
|
||||
total_correspondences=total,
|
||||
reprojection_error=res.get("reprojection_error", 1.0),
|
||||
)
|
||||
return align if self.compute_match_confidence(align) > 0.5 else None
|
||||
|
||||
def compute_match_confidence(self, alignment: AlignmentResult) -> float:
|
||||
# Complex heuristic combining inliers, reprojection error
|
||||
score = alignment.confidence
|
||||
# Penalty for high reproj error
|
||||
if alignment.reprojection_error > 2.0:
|
||||
score -= 0.2
|
||||
return max(0.0, min(1.0, score))
|
||||
|
||||
def match_chunk_homography(self, chunk_images: List[np.ndarray], satellite_tile: np.ndarray) -> Optional[np.ndarray]:
|
||||
# Aggregate logic is complex, for mock we just use the first image's match
|
||||
if not chunk_images:
|
||||
return None
|
||||
return self.compute_homography(chunk_images[0], satellite_tile)
|
||||
|
||||
def align_chunk_to_satellite(self, chunk_images: List[np.ndarray], satellite_tile: np.ndarray, tile_bounds: TileBounds) -> Optional[ChunkAlignmentResult]:
|
||||
if not chunk_images:
|
||||
return None
|
||||
|
||||
engine = self.model_manager.get_inference_engine("LiteSAM")
|
||||
res = engine.infer({"img1": chunk_images[0], "img2": satellite_tile})
|
||||
|
||||
# Demands higher inliners for chunk
|
||||
if res["inlier_count"] < 30:
|
||||
return None
|
||||
|
||||
h, w = chunk_images[0].shape[:2] if hasattr(chunk_images[0], "shape") else (480, 640)
|
||||
gps = self.extract_gps_from_alignment(res["homography"], tile_bounds, (w // 2, h // 2))
|
||||
|
||||
# Fake sim3
|
||||
sim3 = Sim3Transform(
|
||||
translation=np.array([10., 0., 0.]),
|
||||
rotation=np.eye(3),
|
||||
scale=1.0
|
||||
)
|
||||
|
||||
chunk_align = ChunkAlignmentResult(
|
||||
matched=True,
|
||||
chunk_id="chunk1",
|
||||
chunk_center_gps=gps,
|
||||
rotation_angle=0.0,
|
||||
confidence=res["confidence"],
|
||||
inlier_count=res["inlier_count"],
|
||||
transform=sim3,
|
||||
reprojection_error=1.0
|
||||
)
|
||||
|
||||
return chunk_align
|
||||
__all__ = ["MetricRefinement", "IMetricRefinement", "MetricRefiner"]
|
||||
|
||||
@@ -1,287 +1,6 @@
|
||||
"""Satellite Data Manager (Component F04).
|
||||
"""Legacy import path. Phase 1 shim — code lives in components/satellite_matcher/."""
|
||||
from gps_denied.components.satellite_matcher.local_tile_loader import ( # noqa: F401
|
||||
SatelliteDataManager,
|
||||
)
|
||||
|
||||
SAT-01: Reads pre-loaded tiles from a local z/x/y directory (no live HTTP during flight).
|
||||
SAT-02: Tile selection uses ESKF position ± 3σ_horizontal to define search area.
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from gps_denied.schemas import GPSPoint
|
||||
from gps_denied.schemas.satellite import TileBounds, TileCoords
|
||||
from gps_denied.utils import mercator
|
||||
|
||||
|
||||
class SatelliteDataManager:
|
||||
"""Manages satellite tiles from a local pre-loaded directory.
|
||||
|
||||
Directory layout (SAT-01):
|
||||
{tile_dir}/{zoom}/{x}/{y}.png — standard Web Mercator slippy-map layout
|
||||
|
||||
No live HTTP requests are made during flight. A separate offline tooling step
|
||||
downloads and stores tiles before the mission.
|
||||
"""
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
tile_dir: str = ".satellite_tiles",
|
||||
cache_dir: str = ".satellite_cache",
|
||||
max_size_gb: float = 10.0,
|
||||
):
|
||||
self.tile_dir = tile_dir
|
||||
self.thread_pool = ThreadPoolExecutor(max_workers=4)
|
||||
# In-memory LRU for hot tiles (avoids repeated disk reads)
|
||||
self._mem_cache: dict[str, np.ndarray] = {}
|
||||
self._mem_cache_max = 256
|
||||
# SHA-256 manifest for tile integrity (якщо файл існує)
|
||||
self._manifest: dict[str, str] = self._load_manifest()
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# SAT-01: Local tile reads (no HTTP)
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _load_manifest(self) -> dict[str, str]:
|
||||
"""Завантажити SHA-256 manifest з tile_dir/manifest.sha256."""
|
||||
path = os.path.join(self.tile_dir, "manifest.sha256")
|
||||
if not os.path.isfile(path):
|
||||
return {}
|
||||
manifest: dict[str, str] = {}
|
||||
with open(path) as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line or line.startswith("#"):
|
||||
continue
|
||||
parts = line.split(maxsplit=1)
|
||||
if len(parts) == 2:
|
||||
manifest[parts[1].strip()] = parts[0].strip()
|
||||
return manifest
|
||||
|
||||
def _verify_tile_integrity(self, rel_path: str, file_path: str) -> bool:
|
||||
"""Перевірити SHA-256 тайла проти manifest (якщо manifest існує)."""
|
||||
if not self._manifest:
|
||||
return True # без manifest — пропускаємо
|
||||
expected = self._manifest.get(rel_path)
|
||||
if expected is None:
|
||||
return True # тайл не в manifest — OK
|
||||
sha = hashlib.sha256()
|
||||
with open(file_path, "rb") as f:
|
||||
for chunk in iter(lambda: f.read(8192), b""):
|
||||
sha.update(chunk)
|
||||
actual = sha.hexdigest()
|
||||
if actual != expected:
|
||||
self._logger.warning("Tile integrity failed: %s (exp %s, got %s)",
|
||||
rel_path, expected[:12], actual[:12])
|
||||
return False
|
||||
return True
|
||||
|
||||
def load_local_tile(self, tile_coords: TileCoords) -> np.ndarray | None:
|
||||
"""Load a tile image from the local pre-loaded directory.
|
||||
|
||||
Expected path: {tile_dir}/{zoom}/{x}/{y}.png
|
||||
Returns None if the file does not exist.
|
||||
"""
|
||||
key = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}"
|
||||
if key in self._mem_cache:
|
||||
return self._mem_cache[key]
|
||||
|
||||
rel_path = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}.png"
|
||||
path = os.path.join(self.tile_dir, rel_path)
|
||||
if not os.path.isfile(path):
|
||||
return None
|
||||
|
||||
if not self._verify_tile_integrity(rel_path, path):
|
||||
return None # тайл пошкоджений
|
||||
|
||||
img = cv2.imread(path, cv2.IMREAD_COLOR)
|
||||
if img is None:
|
||||
return None
|
||||
|
||||
# LRU eviction: drop oldest if full
|
||||
if len(self._mem_cache) >= self._mem_cache_max:
|
||||
oldest = next(iter(self._mem_cache))
|
||||
del self._mem_cache[oldest]
|
||||
self._mem_cache[key] = img
|
||||
return img
|
||||
|
||||
def save_local_tile(self, tile_coords: TileCoords, image: np.ndarray) -> bool:
|
||||
"""Persist a tile to the local directory (used by offline pre-fetch tooling)."""
|
||||
path = os.path.join(self.tile_dir, str(tile_coords.zoom),
|
||||
str(tile_coords.x), f"{tile_coords.y}.png")
|
||||
os.makedirs(os.path.dirname(path), exist_ok=True)
|
||||
ok, encoded = cv2.imencode(".png", image)
|
||||
if not ok:
|
||||
return False
|
||||
with open(path, "wb") as f:
|
||||
f.write(encoded.tobytes())
|
||||
key = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}"
|
||||
self._mem_cache[key] = image
|
||||
return True
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# SAT-02: Tile selection for ESKF position ± 3σ_horizontal
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _meters_to_degrees(meters: float, lat: float) -> tuple[float, float]:
|
||||
"""Convert a radius in metres to (Δlat°, Δlon°) at the given latitude."""
|
||||
delta_lat = meters / 111_320.0
|
||||
delta_lon = meters / (111_320.0 * math.cos(math.radians(lat)))
|
||||
return delta_lat, delta_lon
|
||||
|
||||
def select_tiles_for_eskf_position(
|
||||
self, gps: GPSPoint, sigma_h_m: float, zoom: int
|
||||
) -> list[TileCoords]:
|
||||
"""Return all tile coords covering the ESKF position ± 3σ_horizontal area.
|
||||
|
||||
Args:
|
||||
gps: ESKF best-estimate position.
|
||||
sigma_h_m: 1-σ horizontal uncertainty in metres (from ESKF covariance).
|
||||
zoom: Web Mercator zoom level (18 recommended ≈ 0.6 m/px).
|
||||
"""
|
||||
radius_m = 3.0 * sigma_h_m
|
||||
dlat, dlon = self._meters_to_degrees(radius_m, gps.lat)
|
||||
|
||||
# Bounding box corners
|
||||
lat_min, lat_max = gps.lat - dlat, gps.lat + dlat
|
||||
lon_min, lon_max = gps.lon - dlon, gps.lon + dlon
|
||||
|
||||
# Convert corners to tile coords
|
||||
tc_nw = mercator.latlon_to_tile(lat_max, lon_min, zoom)
|
||||
tc_se = mercator.latlon_to_tile(lat_min, lon_max, zoom)
|
||||
|
||||
tiles: list[TileCoords] = []
|
||||
for x in range(tc_nw.x, tc_se.x + 1):
|
||||
for y in range(tc_nw.y, tc_se.y + 1):
|
||||
tiles.append(TileCoords(x=x, y=y, zoom=zoom))
|
||||
return tiles
|
||||
|
||||
def assemble_mosaic(
|
||||
self,
|
||||
tile_list: list[tuple[TileCoords, np.ndarray]],
|
||||
target_size: int = 512,
|
||||
) -> tuple[np.ndarray, TileBounds] | None:
|
||||
"""Assemble a list of (TileCoords, image) pairs into a single mosaic.
|
||||
|
||||
Returns (mosaic_image, combined_bounds) or None if tile_list is empty.
|
||||
The mosaic is resized to (target_size × target_size) for the matcher.
|
||||
"""
|
||||
if not tile_list:
|
||||
return None
|
||||
|
||||
xs = [tc.x for tc, _ in tile_list]
|
||||
ys = [tc.y for tc, _ in tile_list]
|
||||
zoom = tile_list[0][0].zoom
|
||||
|
||||
x_min, x_max = min(xs), max(xs)
|
||||
y_min, y_max = min(ys), max(ys)
|
||||
|
||||
cols = x_max - x_min + 1
|
||||
rows = y_max - y_min + 1
|
||||
|
||||
# Determine single-tile pixel size from first image
|
||||
sample = tile_list[0][1]
|
||||
th, tw = sample.shape[:2]
|
||||
|
||||
canvas = np.zeros((rows * th, cols * tw, 3), dtype=np.uint8)
|
||||
for tc, img in tile_list:
|
||||
col = tc.x - x_min
|
||||
row = tc.y - y_min
|
||||
h, w = img.shape[:2]
|
||||
canvas[row * th: row * th + h, col * tw: col * tw + w] = img
|
||||
|
||||
mosaic = cv2.resize(canvas, (target_size, target_size), interpolation=cv2.INTER_AREA)
|
||||
|
||||
# Compute combined GPS bounds
|
||||
nw_bounds = mercator.compute_tile_bounds(TileCoords(x=x_min, y=y_min, zoom=zoom))
|
||||
se_bounds = mercator.compute_tile_bounds(TileCoords(x=x_max, y=y_max, zoom=zoom))
|
||||
combined = TileBounds(
|
||||
nw=nw_bounds.nw,
|
||||
ne=GPSPoint(lat=nw_bounds.nw.lat, lon=se_bounds.se.lon),
|
||||
sw=GPSPoint(lat=se_bounds.se.lat, lon=nw_bounds.nw.lon),
|
||||
se=se_bounds.se,
|
||||
center=GPSPoint(
|
||||
lat=(nw_bounds.nw.lat + se_bounds.se.lat) / 2,
|
||||
lon=(nw_bounds.nw.lon + se_bounds.se.lon) / 2,
|
||||
),
|
||||
gsd=nw_bounds.gsd,
|
||||
)
|
||||
return mosaic, combined
|
||||
|
||||
def fetch_tiles_for_position(
|
||||
self, gps: GPSPoint, sigma_h_m: float, zoom: int
|
||||
) -> tuple[np.ndarray, TileBounds] | None:
|
||||
"""High-level helper: select tiles + load + assemble mosaic.
|
||||
|
||||
Returns (mosaic, bounds) or None if no local tiles are available.
|
||||
"""
|
||||
coords = self.select_tiles_for_eskf_position(gps, sigma_h_m, zoom)
|
||||
loaded: list[tuple[TileCoords, np.ndarray]] = []
|
||||
for tc in coords:
|
||||
img = self.load_local_tile(tc)
|
||||
if img is not None:
|
||||
loaded.append((tc, img))
|
||||
return self.assemble_mosaic(loaded) if loaded else None
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Cache helpers (backward-compat, also used for warm-path caching)
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def cache_tile(self, flight_id: str, tile_coords: TileCoords, tile_data: np.ndarray) -> bool:
|
||||
"""Cache a tile image in memory (used by tests and offline tools)."""
|
||||
key = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}"
|
||||
self._mem_cache[key] = tile_data
|
||||
return True
|
||||
|
||||
def get_cached_tile(self, flight_id: str, tile_coords: TileCoords) -> np.ndarray | None:
|
||||
"""Retrieve a cached tile from memory."""
|
||||
key = f"{tile_coords.zoom}/{tile_coords.x}/{tile_coords.y}"
|
||||
return self._mem_cache.get(key)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Tile math helpers
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def get_tile_grid(self, center: TileCoords, grid_size: int) -> list[TileCoords]:
|
||||
"""Return grid_size tiles centered on center."""
|
||||
if grid_size == 1:
|
||||
return [center]
|
||||
|
||||
side = int(grid_size ** 0.5)
|
||||
half = side // 2
|
||||
|
||||
coords: list[TileCoords] = []
|
||||
for dy in range(-half, half + 1):
|
||||
for dx in range(-half, half + 1):
|
||||
coords.append(TileCoords(x=center.x + dx, y=center.y + dy, zoom=center.zoom))
|
||||
|
||||
if grid_size == 4:
|
||||
coords = []
|
||||
for dy in range(2):
|
||||
for dx in range(2):
|
||||
coords.append(TileCoords(x=center.x + dx, y=center.y + dy, zoom=center.zoom))
|
||||
|
||||
return coords[:grid_size]
|
||||
|
||||
def expand_search_grid(self, center: TileCoords, current_size: int, new_size: int) -> list[TileCoords]:
|
||||
"""Return only the NEW tiles when expanding from current_size to new_size grid."""
|
||||
old_set = {(c.x, c.y) for c in self.get_tile_grid(center, current_size)}
|
||||
return [c for c in self.get_tile_grid(center, new_size) if (c.x, c.y) not in old_set]
|
||||
|
||||
def compute_tile_coords(self, lat: float, lon: float, zoom: int) -> TileCoords:
|
||||
return mercator.latlon_to_tile(lat, lon, zoom)
|
||||
|
||||
def compute_tile_bounds(self, tile_coords: TileCoords) -> TileBounds:
|
||||
return mercator.compute_tile_bounds(tile_coords)
|
||||
|
||||
def clear_flight_cache(self, flight_id: str) -> bool:
|
||||
"""Clear in-memory cache (flight scoping is tile-key-based)."""
|
||||
self._mem_cache.clear()
|
||||
return True
|
||||
__all__ = ["SatelliteDataManager"]
|
||||
|
||||
Reference in New Issue
Block a user