swarm_field_from_tracks — DRIVE `swarmflow` op

• Data kinds: any × scalar × scalar → table

• Call: import fullseye as fs; fs.ledger.swarm_field_from_tracks(frames, res: 'float', frame_dt: 'float', center=(0.0, 0.0), grid_step=None, h=None, threshold: 'float' = 0.3, max_disp_px=None, min_count: 'int' = 3, outlier_k: 'int' = 12, outlier_threshold: 'float' = 3.0) -> 'dict' (to call the implementation directly, import swarmflow; swarmflow.swarm_field_from_tracks(frames, res: 'float', frame_dt: 'float', center=(0.0, 0.0), grid_step=None, h=None, threshold: 'float' = 0.3, max_disp_px=None, min_count: 'int' = 3, outlier_k: 'int' = 12, outlier_threshold: 'float' = 3.0) -> 'dict'; from the registry, opsdrive.get("swarm_field_from_tracks"))

Usage

Overhead frame sequence → detect individuals (connected components of blob2d + brightness centroid) → link to the adjacent frame by mutual nearest neighbors → transfer velocities onto a grid with the SPH kernel.

> The detailed description below is the original text — the summary and the headings are translated.

つなぐ条件: A の点の最近傍が B の点で、その B の点の最近傍も A の点(相互)、かつ移動が max_disp_px 以下(既定 = 検出した点どうしの最近傍の間隔の中央値の 0.45 倍。1 コマの移動がこれを超えると取り違える = 追跡の限界)。全部のコマの組の速度を一つの集団にして平均する(定常の流れの前提)。取り違えた組は速度が桁で外れる(実測: 一様に撒いた 1300 個で 2 % の組が 1〜3 m/s ずれ、格子の rms 誤差が U の 16 % になった)ので、格子へ移す前に正規化した中央値の検査(PIV の慣行、近い outlier_k 個の速度の中央値からのずれ / その近傍のずれの中央値 > outlier_threshold) で捨てる。

Args:

frames: 同じ大きさの (H, W) のコマの列(2 枚以上)。

res: 画素の大きさ [m/px]。

frame_dt: コマの間隔 [s]。

center: 画像の中心の世界座標 [m]。

grid_step: 格子の間隔 [m] (既定 = 個体の間隔の推定値)。

h: 格子へ移す核の h [m] (既定 = grid_step)。

threshold: 2 値化のしきい値(最小〜最大の割合)。

max_disp_px: 1 コマの移動の上限 [px]。

min_count: 格子の台の中に要る速度の数。

outlier_k: 中央値の検査に使う近傍の数(0 で検査しない)。

outlier_threshold: 検査のしきい値。

Returns:

速度場の dict(module の規約)+ points (M, 2)・point_velocity (M, 2)(つないだ組の中点と速度)、

n_detected(コマごと)、n_linked、n_outliers(中央値の検査で捨てた組)、n_rejected_blobs、

spacing(個体の間隔の推定 [m])。

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_swarm_obstacle_from_flow — py -3.11 examples/poc_swarm_obstacle_from_flow.py

Ops the type connects to (they accept `table` as input)

course_layout · course_occupancy · course_contains · world_build · world_camera · world_move · lidar_scan · rss_longitudinal_same

Same category (`swarmflow`)

sph_kernel · sph_density_pressure · swarm_simulate · swarm_render_overhead · swarm_field_from_piv · potential_flow_cylinder · velocity_deficit_map · stagnation_from_centerline

*Provenance: swarmflow.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

swarm_field_from_tracks — DRIVE swarmflow op