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