Initial commit: Speckle-Scanner 3D pipeline with setup README

2026-06-10 03:09:05 +05:00
commit 1765934846
375 changed files with 123081 additions and 0 deletions
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+"""JSON schema for per-image feature detection results."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+
+FEATURE_JSON_VERSION = 1
+
+
+@dataclass
+class FeatureRecord:
+    image_path: Path
+    json_path: Path
+    camera_folder: str
+    feature_type: str
+    success: bool
+    board_size: Optional[Tuple[int, int]] = None
+    square_size: Optional[float] = None
+    corners: Optional[np.ndarray] = None  # Nx1x2 float32
+    center: Optional[Tuple[float, float]] = None
+    ellipse: Optional[Dict[str, Any]] = None
+    timestamp_sec: Optional[float] = None
+    pair_key: Optional[str] = None
+    preprocessing: Optional[str] = None
+    error: Optional[str] = None
+
+    @property
+    def is_chessboard(self) -> bool:
+        return self.success and self.feature_type == "chessboard" and self.corners is not None
+
+    @property
+    def corner_count(self) -> int:
+        if self.corners is None:
+            return 0
+        return int(self.corners.shape[0])
+
+
+def corners_to_list(corners: np.ndarray) -> List[List[float]]:
+    flat = corners.reshape(-1, 2)
+    return [[float(x), float(y)] for x, y in flat]
+
+
+def corners_from_list(data: List[List[float]]) -> np.ndarray:
+    arr = np.array(data, dtype=np.float32).reshape(-1, 1, 2)
+    return arr
+
+
+def save_feature_json(record: FeatureRecord) -> None:
+    payload: Dict[str, Any] = {
+        "version": FEATURE_JSON_VERSION,
+        "image": record.image_path.name,
+        "camera_folder": record.camera_folder,
+        "feature_type": record.feature_type,
+        "success": record.success,
+        "preprocessing": record.preprocessing,
+        "timestamp_sec": record.timestamp_sec,
+        "pair_key": record.pair_key,
+    }
+    if record.board_size is not None:
+        payload["board_size"] = [int(record.board_size[0]), int(record.board_size[1])]
+    if record.square_size is not None:
+        payload["square_size"] = float(record.square_size)
+    if record.corners is not None:
+        payload["corners"] = corners_to_list(record.corners)
+    if record.center is not None:
+        payload["center"] = [float(record.center[0]), float(record.center[1])]
+    if record.ellipse is not None:
+        payload["ellipse"] = record.ellipse
+    if record.error:
+        payload["error"] = record.error
+
+    record.json_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(record.json_path, "w", encoding="utf-8") as f:
+        json.dump(payload, f, indent=2)
+
+
+def load_feature_json(json_path: Path, image_path: Optional[Path] = None) -> FeatureRecord:
+    with open(json_path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    if image_path is not None:
+        img = Path(image_path)
+    else:
+        stem = json_path.stem
+        parent = json_path.parent
+        img = parent / data.get("image", stem)
+        if not img.exists():
+            for ext in (".bmp", ".png", ".jpg", ".jpeg"):
+                candidate = parent / f"{stem}{ext}"
+                if candidate.exists():
+                    img = candidate
+                    break
+
+    board_size = None
+    if "board_size" in data and data["board_size"]:
+        board_size = (int(data["board_size"][0]), int(data["board_size"][1]))
+
+    corners = None
+    if data.get("corners"):
+        corners = corners_from_list(data["corners"])
+
+    center = None
+    if data.get("center"):
+        center = (float(data["center"][0]), float(data["center"][1]))
+
+    return FeatureRecord(
+        image_path=Path(img),
+        json_path=Path(json_path),
+        camera_folder=data.get("camera_folder", ""),
+        feature_type=data.get("feature_type", "unknown"),
+        success=bool(data.get("success", False)),
+        board_size=board_size,
+        square_size=data.get("square_size"),
+        corners=corners,
+        center=center,
+        ellipse=data.get("ellipse"),
+        timestamp_sec=data.get("timestamp_sec"),
+        pair_key=data.get("pair_key"),
+        preprocessing=data.get("preprocessing"),
+        error=data.get("error"),
+    )
+
+
+def load_folder_features(camera_dir: Path) -> List[FeatureRecord]:
+    records = []
+    for json_path in sorted(camera_dir.glob("*.json")):
+        try:
+            records.append(load_feature_json(json_path))
+        except (json.JSONDecodeError, OSError) as exc:
+            print(f"[WARN] Skipping invalid JSON {json_path}: {exc}")
+    return records