精简仓库结构并增强任务链路:归档双目可选后端、新增LOS判据与自定义返航

归档 legacy/:stereo_yolo/stereo_ranger/target_tracker 三包 + 175 条测试 + demo/配置/权重/三份规格;运行链路不再依赖,默认 pytest 446 条,归档测试按需运行。

目标有效性第二判据改为 LOS 运动学一致性(pod_bridge/los_consistency.py),删除 require_vision;pod_locked() 叠加该判据,状态机随之后退重搜。

自定义返航(task_runner/return_home.py):保持转场高度回起飞点 → 恢复初始航向 → 垂直降落,不再爬 RTL_RETURN_ALT;未起飞/无起飞点/超时/异常仍退回 PX4 原生 RTL。

任务链安全兜底:主循环异常→紧急返航、stop 幂等、landing_stop 保留遥测、in_air 流复用+超时、解锁/起飞与吊舱识别仅执行一次、rtl_timeout 上限。

入口共享件拆到 utils/sitl_support.py;pod_sitl_test.py→l1_sitl_test.py;删除旧架构 test.py;相机脚本移入 tools/。

新增 .gitignore 并剔除历史误跟踪的 59 个 .pyc;requirements 按实际依赖重写;文档全面对齐,新增 legacy/README.md 与 tools/README.md。
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keita
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"""轨道(Tracklet)与基础几何工具。
对应 C++ 参考实现 reference-c++/target_tracker/include/target_tracker/tracklet.h
(规格:docs/视觉追踪技术路线.md §3.2 / §5.2 / §7)。
"""
from dataclasses import dataclass
from typing import Optional
from .kalman_box_filter import BoxCXCYWH, KalmanBoxFilter
@dataclass
class BoxXYXY:
"""轴对齐边界框(像素坐标,左上/右下角表示)。"""
x1: float = 0.0
y1: float = 0.0
x2: float = 0.0
y2: float = 0.0
def to_center_size(box: BoxXYXY) -> BoxCXCYWH:
# §7 TRACKLET_UPDATE:cx=(x1+x2)/2;cy=(y1+y2)/2;w=x2−x1;h=y2−y1
return BoxCXCYWH((box.x1 + box.x2) / 2.0, (box.y1 + box.y2) / 2.0,
box.x2 - box.x1, box.y2 - box.y1)
def to_xyxy(box: BoxCXCYWH) -> BoxXYXY:
# §7 TRACKLET_PREDICT 的 center_size 逆变换
return BoxXYXY(box.cx - box.w / 2.0, box.cy - box.h / 2.0,
box.cx + box.w / 2.0, box.cy + box.h / 2.0)
def compute_iou(a: BoxXYXY, b: BoxXYXY) -> float:
"""标准轴对齐 IoU(§5.2):任一框为空、交集 ≤ 0 或并集 ≤ 0 时返回 0。"""
area_a = (a.x2 - a.x1) * (a.y2 - a.y1)
area_b = (b.x2 - b.x1) * (b.y2 - b.y1)
if area_a <= 0.0 or area_b <= 0.0:
return 0.0
inter_w = min(a.x2, b.x2) - max(a.x1, b.x1)
inter_h = min(a.y2, b.y2) - max(a.y1, b.y1)
if inter_w <= 0.0 or inter_h <= 0.0:
return 0.0
inter_area = inter_w * inter_h
union_area = area_a + area_b - inter_area
if union_area <= 0.0:
return 0.0
return inter_area / union_area
class Tracklet:
"""单条轨道:生命周期计数 + 辅助状态 + 卡尔曼滤波器(§3.2)。
构造即创建轨道(§7 步骤 5):用首次观测初始化滤波器,age=1、hits=1、
lost_count=0,last_center / last_box 立即由观测重建(创建当帧即可参与输出)。
"""
def __init__(self, track_id: int, measurement: BoxCXCYWH, conf: float,
cls_id: int, max_lost_frames: int) -> None:
self.track_id = track_id
self.kf = KalmanBoxFilter()
self.lost_count = 0
self.max_lost = max_lost_frames
self.confidence = conf
self.cls_id = cls_id
self.age = 1
self.hits = 1
self.last_center: tuple[float, float] = (measurement.cx, measurement.cy)
self.last_box: Optional[BoxXYXY] = to_xyxy(measurement)
# 创建即源于实测检测:同步记录原始检测框(Coast 期间保留不更新)
self.last_raw_box: Optional[BoxXYXY] = self.last_box
self.kf.initiate(measurement)
@property
def is_dead(self) -> bool:
# §6.1 注记 2:lost_count > max_lost(严格大于)
return self.lost_count > self.max_lost
@property
def is_coasting(self) -> bool:
# §3.2:lost_count > 0 即处于纯预测状态
return self.lost_count > 0
def predict(self) -> None:
# §7 TRACKLET_PREDICT:预测成功时更新 last_center / last_box 且 age+1
pred = self.kf.predict()
if pred is not None:
self.last_center = (pred.cx, pred.cy)
self.last_box = to_xyxy(pred)
self.age += 1
def update(self, box: BoxXYXY, conf: float, cls_id: int) -> None:
# §7 TRACKLET_UPDATE:框 → [cx,cy,w,h] → kf.update
self.kf.update(to_center_size(box))
self.last_center = ((box.x1 + box.x2) / 2.0, (box.y1 + box.y2) / 2.0)
self.last_box = box
self.last_raw_box = box
self.confidence = conf
self.cls_id = cls_id
self.lost_count = 0
self.hits += 1
self.age += 1
def mark_miss(self) -> None:
# §7 步骤 4:未匹配计丢失
self.lost_count += 1
self.age += 1