Rework radar system

python_app/gui/result_payloads.py

@@ -0,0 +1,78 @@
+"""Result payload lookup and GPR display filtering."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from python_app.models.dataset_model import ResultCollection, ResultPayload
+
+
+def collection_payload_by_name(
+    collection: ResultCollection,
+    name: str,
+    kind: int | None = None,
+) -> ResultPayload | None:
+    """Return the first collection payload matching name and optional kind."""
+    for payload in collection.collection_payloads:
+        if payload.processing_name != name:
+            continue
+        if kind is not None and int(payload.kind) != int(kind):
+            continue
+        return payload
+    return None
+
+
+def collection_payloads_by_prefix(
+    collection: ResultCollection,
+    prefix: str,
+    kind: int | None = None,
+) -> list[ResultPayload]:
+    """Return collection payloads matching a processing-name prefix."""
+    payloads: list[ResultPayload] = []
+    for payload in collection.collection_payloads:
+        if not str(payload.processing_name).startswith(prefix):
+            continue
+        if kind is not None and int(payload.kind) != int(kind):
+            continue
+        payloads.append(payload)
+    return payloads
+
+
+def gpr_object_rows(collection: ResultCollection) -> np.ndarray:
+    """Return object rows as `[x_m, z_m, score]` from a GPR collection."""
+    points_payload = collection_payload_by_name(collection, "gpr_points", kind=4)
+    if points_payload is not None:
+        points = np.asarray(points_payload.table, dtype=np.float32)
+        if points.ndim == 2 and points.shape[1] >= 3:
+            return points[:, :3]
+
+    centers_payload = collection_payload_by_name(collection, "gpr_region_centers", kind=4)
+    if centers_payload is not None:
+        centers = np.asarray(centers_payload.table, dtype=np.float32)
+        if centers.ndim == 2 and centers.shape[1] >= 3:
+            return centers[:, :3]
+
+    return np.zeros((0, 3), dtype=np.float32)
+
+
+def filter_object_rows(
+    rows: np.ndarray,
+    *,
+    min_score: float,
+    x_bounds: tuple[float, float],
+    z_bounds: tuple[float, float],
+) -> np.ndarray:
+    """Keep finite objects inside the visible window and above the score threshold."""
+    if rows.size == 0:
+        return rows
+    x_min, x_max = x_bounds
+    z_min, z_max = z_bounds
+    visible_mask = (
+        np.all(np.isfinite(rows[:, :3]), axis=1)
+        & (rows[:, 2] >= min_score)
+        & (rows[:, 0] >= x_min)
+        & (rows[:, 0] <= x_max)
+        & (rows[:, 1] >= z_min)
+        & (rows[:, 1] <= z_max)
+    )
+    return rows[visible_mask]