"""target_tracker_test.py —— TargetTracker 单元测试。

移植自 reference-c++/target_tracker/tests/target_tracker_test.cpp。
逐条覆盖 docs/视觉追踪技术路线.md §9.4 行为一致性验收用例（硬验收标准）：
  1. 空检测帧序列：Coast 持续 max_lost_frames 帧，第 max_lost_frames+1 帧判丢
  2. ID 严格递增不复用（含 Reset 后归零）
  3. 锁定不被抢占；锁定目标死亡后切换到新目标
  4. min_hits 回退：无已确认实测轨道时新轨道也可当选主目标
  5. cost=1.0 恰好不构成匹配（严格小于）
  6. 置信度优先：两轨道竞争同一检测，置信度高者获胜
另含 NaN/Inf 防御与 §2.3/§6.3/§9.5 行为测试。
"""

import math

import pytest

from target_tracker.target_tracker import (Detection, FrameShape, TargetTracker,
                                           TrackerConfig)


def make_det(x1, y1, x2, y2, conf, cls=0):
    return Detection(x1, y1, x2, y2, conf, cls)


# ============================================================================
# 验收用例 1（§9.4-1）：空检测帧序列
# ============================================================================
def test_empty_frames_coast_then_lost():
    tracker = TargetTracker()  # 默认配置 max_lost_frames = 8
    assert tracker.get_config().max_lost_frames == 8

    # 第 1 帧：给出一个检测，建立轨道并锁定为主目标
    r = tracker.update([make_det(100.0, 100.0, 140.0, 140.0, 0.9)])
    assert r.tracked
    assert r.primary_id is not None
    assert not r.is_predicted      # 当帧有实测
    assert r.lost_frames == 0
    assert r.n_active == 1
    assert r.box is not None
    assert r.raw is not None       # 当帧关联到原始检测框

    # 后续空检测帧：应持续 Coast 共 max_lost_frames 帧（is_predicted=True）
    for i in range(1, 9):
        r = tracker.update([])
        assert r.tracked, f"第 {i} 帧 Coast 期间不应判丢"
        assert r.is_predicted, f"第 {i} 帧应为纯预测输出"
        assert r.lost_frames == i
        assert r.primary_id is not None
        assert r.box is not None      # Coast 期间继续输出预测框
        assert r.raw is None          # Coast 时 raw 为空（§2.3）
        assert r.confidence == pytest.approx(0.9)  # 继承最近实测置信度（§2.3）

    # 第 max_lost_frames + 1 帧：轨道死亡回收 → tracked=False
    r = tracker.update([])
    assert not r.tracked
    assert r.primary_id is None
    assert r.box is None
    assert r.lost_frames == 0  # 丢失时 lost_frames=0（§2.3）
    assert r.n_active == 0


# ============================================================================
# 验收用例 2（§9.4-2）：ID 单调性
# ============================================================================
def test_id_strictly_increasing_no_reuse():
    cfg = TrackerConfig()
    cfg.max_lost_frames = 1  # 缩短 Coast 窗口，加速轨道死亡
    tracker = TargetTracker(cfg)

    # 目标 A 出现 → id 0
    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.9)])
    assert r.primary_id is not None
    id_a = r.primary_id
    assert id_a == 0

    # 目标 A 消失至死亡（max_lost=1：第 2 帧 Coast、第 3 帧死亡回收）
    tracker.update([])
    r = tracker.update([])
    assert not r.tracked
    assert tracker.active_count() == 0

    # 目标 B 出现（同一位置）：ID 不复用，严格递增 → id 1
    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.9)])
    assert r.primary_id is not None
    id_b = r.primary_id
    assert id_b == 1
    assert id_b > id_a

    # 目标 C 同帧新建 → id 2（同帧多目标亦严格递增）
    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.9),
                        make_det(400.0, 300.0, 430.0, 330.0, 0.8)])
    assert tracker.active_count() == 2
    max_id = max(tracker.active_tracks().keys())
    assert max_id == 2

    # Reset 后计数器归零（§6.2）：新轨道重新从 id 0 开始
    tracker.reset()
    assert tracker.active_count() == 0
    assert not tracker.last_result().tracked
    r = tracker.update([make_det(50.0, 50.0, 70.0, 70.0, 0.9)])
    assert r.primary_id is not None
    assert r.primary_id == 0


# ============================================================================
# 验收用例 3（§9.4-3）：锁定不被抢占
# ============================================================================
def test_lock_not_stolen_and_switch_after_death():
    cfg = TrackerConfig()
    cfg.max_lost_frames = 3
    tracker = TargetTracker(cfg)

    # 低置信度目标 A 建立并锁定
    r = tracker.update([make_det(100.0, 100.0, 140.0, 140.0, 0.4)])
    assert r.primary_id is not None
    id_a = r.primary_id

    # A 消失进入 Coast；期间出现更高置信度新目标 B（远离 A，不会被误关联）
    r = tracker.update([make_det(500.0, 300.0, 540.0, 340.0, 0.95)])
    assert r.primary_id is not None
    assert r.primary_id == id_a, "Coast 期间锁定不应被高置信度目标抢占"
    assert r.is_predicted
    assert tracker.active_count() == 2  # A（Coast）与 B 并存

    # 继续 Coast，锁定依旧延续
    r = tracker.update([make_det(500.0, 300.0, 540.0, 340.0, 0.95)])
    assert r.primary_id == id_a
    assert r.is_predicted

    # A 继续丢失直至死亡（max_lost=3：lost 达 4 帧时回收）
    for _ in range(2):
        r = tracker.update([make_det(500.0, 300.0, 540.0, 340.0, 0.95)])
    # A 已死亡删除 → 锁定解除，重新选举切换到 B（此时 B 已确认且实测）
    assert r.tracked
    assert r.primary_id != id_a, "锁定目标死亡后应切换到新目标"
    assert not r.is_predicted

    # 后续 B 持续作为主目标
    r = tracker.update([make_det(500.0, 300.0, 540.0, 340.0, 0.95)])
    assert r.primary_id is not None
    assert r.primary_id != id_a


# ============================================================================
# 验收用例 4（§9.4-4）：min_hits 回退
# ============================================================================
def test_min_hits_fallback_allows_new_track():
    cfg = TrackerConfig()
    cfg.min_hits = 5  # 抬高确认门槛，保证测试期间轨道始终未确认
    tracker = TargetTracker(cfg)

    # 首帧新建轨道 hits=1 < min_hits：候选池 confirmed 为空 → 退回 measured
    r = tracker.update([make_det(200.0, 150.0, 240.0, 190.0, 0.6)])
    assert r.tracked, "无已确认轨道时新轨道应能当选主目标"
    assert r.primary_id is not None
    assert r.primary_id == 0
    assert not r.is_predicted

    # 再跟几帧（hits 增长但仍未达 min_hits=5），依旧当选
    for _ in range(3):
        r = tracker.update([make_det(200.0, 150.0, 240.0, 190.0, 0.6)])
        assert r.tracked
        assert r.primary_id == 0


# ============================================================================
# 验收用例 5（§9.4-5）：cost 边界（cost = 1.0 恰好不匹配）
# ============================================================================
def test_cost_exactly_one_does_not_match():
    cfg = TrackerConfig()
    cfg.max_lost_frames = 1
    cfg.max_association_dist = 200.0
    cfg.dist_weight = 1.0  # 纯距离项，IoU 项权重为 0 且两框零交集
    cfg.iou_weight = 0.0
    tracker = TargetTracker(cfg)

    # 建立轨道：中心 (100,100)
    r = tracker.update([make_det(90.0, 90.0, 110.0, 110.0, 0.9)])
    assert r.primary_id is not None
    id_a = r.primary_id

    # 下一帧检测中心 (100,300)：距离 = 200 = Dmax → cost = 1.0，恰好不匹配
    r = tracker.update([make_det(90.0, 290.0, 110.0, 310.0, 0.9)])
    # 未匹配 → 原轨道 Coast、检测新建轨道；两个目标并存
    assert tracker.active_count() == 2, "cost=1.0 不应匹配，应新建轨道"
    # 原轨道（锁定）进入 Coast：lost_frames=1
    assert r.tracked
    assert r.is_predicted
    assert r.primary_id == id_a
    assert r.lost_frames == 1

    # 对照：距离略小于阈值（199px → cost=0.995 < 1.0）应匹配成功
    tracker2 = TargetTracker(cfg)
    r = tracker2.update([make_det(90.0, 90.0, 110.0, 110.0, 0.9)])
    id_b = r.primary_id
    r = tracker2.update([make_det(90.0, 289.0, 110.0, 309.0, 0.9)])
    assert tracker2.active_count() == 1, "cost<1.0 应匹配成功，不应新建轨道"
    assert r.primary_id is not None
    assert r.primary_id == id_b
    assert not r.is_predicted  # 实测命中


# ============================================================================
# 验收用例 6（§9.4-6）：置信度优先
# ============================================================================
def test_higher_confidence_track_wins_detection():
    tracker = TargetTracker()

    # 同帧建立两条轨道：A 置信度 0.5、B 置信度 0.9，相距 80px（不互相重叠）
    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.5),
                        make_det(180.0, 100.0, 200.0, 120.0, 0.9)])
    assert tracker.active_count() == 2

    # 找出两条轨道 ID（按置信度区分）
    id_low = None
    id_high = None
    for track_id, track in tracker.active_tracks().items():
        if track.confidence > 0.8:
            id_high = track_id
        else:
            id_low = track_id
    assert id_low is not None
    assert id_high is not None

    # 下一帧只给一个检测，放在两轨道预测中心之间（各距 40px，对称）：
    # 距离项对两者相同，贪婪按置信度降序 → 高置信度轨道 B 优先匹配
    r = tracker.update([make_det(140.0, 100.0, 160.0, 120.0, 0.7)])

    tracks = tracker.active_tracks()
    assert len(tracks) == 2
    # 获胜者：命中（lost=0、hits=2、置信度更新）
    assert tracks[id_high].lost_count == 0, "高置信度轨道应获胜"
    assert tracks[id_high].hits == 2
    assert tracks[id_high].confidence == pytest.approx(0.7)
    # 失败者：未匹配进入 Coast（lost=1、hits 不变）
    assert tracks[id_low].lost_count == 1, "低置信度轨道应落选"
    assert tracks[id_low].hits == 1
    assert tracks[id_low].confidence == pytest.approx(0.5)


# ============================================================================
# NaN/Inf 防御（评审修复项）
# ============================================================================
def test_non_finite_detections_are_discarded():
    tracker = TargetTracker()
    nan = math.nan
    inf = math.inf

    # 第 1 帧：建立正常轨道
    r = tracker.update([make_det(100.0, 100.0, 140.0, 140.0, 0.9)])
    assert r.tracked

    # 第 2 帧：混入 NaN 坐标 / Inf 坐标 / NaN 置信度检测与一个正常检测：
    # 非有限检测全部被丢弃，仅正常检测参与关联，不新建轨道
    r = tracker.update([make_det(nan, 100.0, 140.0, 140.0, 0.9),
                        make_det(100.0, -inf, 140.0, 140.0, 0.9),
                        make_det(100.0, 100.0, nan, 140.0, 0.9),
                        make_det(102.0, 102.0, 142.0, 142.0, nan),
                        make_det(101.0, 101.0, 141.0, 141.0, 0.9)])
    assert tracker.active_count() == 1, "非有限检测不应新建轨道"
    assert r.tracked
    assert not r.is_predicted  # 正常检测命中，实测输出
    # 主目标输出未被 NaN 污染
    assert math.isfinite(r.center[0])
    assert math.isfinite(r.center[1])
    assert r.box is not None
    assert math.isfinite(r.box.x1) and math.isfinite(r.box.y1)
    assert math.isfinite(r.box.x2) and math.isfinite(r.box.y2)
    assert math.isfinite(r.confidence)
    assert r.raw is not None  # 实测帧 raw 有值

    # 第 3 帧：仅含 NaN 检测 → 等价空帧，轨道 Coast 不崩溃
    r = tracker.update([make_det(nan, nan, nan, nan, nan)])
    assert r.tracked
    assert r.is_predicted
    assert r.lost_frames == 1
    assert r.raw is None  # Coast 时 raw 为空（§2.3）
    assert math.isfinite(r.center[0])

    # 持续 NaN 检测直至轨道死亡回收，全程不崩溃
    for _ in range(10):
        r = tracker.update([make_det(nan, 0.0, 10.0, 10.0, 0.9)])
    assert not r.tracked
    assert tracker.active_count() == 0


# ============================================================================
# 补充行为测试（§2.3 / §6.3 / §9.5）
# ============================================================================

def test_lost_result_fields_are_empty():
    tracker = TargetTracker()
    r = tracker.update([])  # 空帧序列起点：无任何轨道
    assert not r.tracked
    assert r.primary_id is None
    assert r.box is None
    assert r.raw is None
    assert r.lost_frames == 0
    assert r.n_active == 0
    assert r.confidence == 0.0

    # LastResult 返回同一快照（§9.5 跨线程读取入口）
    cached = tracker.last_result()
    assert not cached.tracked


def test_coasting_fallback_chooses_nearest_to_frame_center():
    # Coast 兜底选举：无实测轨道时选离画面中心最近的 Coast 轨道（§6.3）。
    # 锁定延续优先：需先让锁定轨道死亡解除锁定，才会进入重新选举分支。
    cfg = TrackerConfig()
    cfg.max_lost_frames = 2
    tracker = TargetTracker(cfg)

    # F1：角落目标（高置信度）先建立并被锁定
    tracker.update([make_det(10.0, 10.0, 30.0, 30.0, 0.95)])
    # F2：画面中心附近目标建立；角落目标丢失(1)
    tracker.update([make_det(300.0, 220.0, 340.0, 260.0, 0.9)])
    # F3：远处目标建立；角落丢失(2)、中心目标丢失(1)
    tracker.update([make_det(600.0, 450.0, 620.0, 470.0, 0.85)])
    # F4：全部丢失：角落丢失(3) > max_lost 死亡解除锁定；
    # 其余两条 Coast 存活 → 重新选举选离画面中心最近者
    r = tracker.update([], FrameShape(480, 640))
    assert r.tracked
    assert r.is_predicted
    assert r.center[0] == pytest.approx(320.0, abs=25.0)   # 靠近画面中心 (320,240)
    assert r.center[1] == pytest.approx(240.0, abs=25.0)
    assert r.n_active == 2                                 # 角落轨道已回收


def test_locked_primary_kept_while_measured():
    # 锁定延续优先于置信度选举（§6.3 ①）
    tracker = TargetTracker()
    r = tracker.update([make_det(100.0, 100.0, 120.0, 120.0, 0.4)])
    assert r.primary_id is not None
    locked_id = r.primary_id

    # 高置信度新目标出现并持续，锁定目标也持续实测 → primary_id 不变
    for i in range(5):
        r = tracker.update([make_det(100.0 + i, 100.0, 120.0 + i, 120.0, 0.4),
                            make_det(400.0, 300.0, 430.0, 330.0, 0.95)])
        assert r.primary_id is not None
        assert r.primary_id == locked_id
        assert not r.is_predicted


def test_default_frame_center_is_320x240():
    # frame_shape 缺省按 640×480 画面中心 (320,240) 计算（§8.2 / §2.2）
    cfg = TrackerConfig()
    cfg.max_lost_frames = 2
    tracker = TargetTracker(cfg)

    # F1：角落目标先建立并被锁定（高置信度）
    tracker.update([make_det(0.0, 0.0, 20.0, 20.0, 0.95)])
    # F2：左侧目标（中心 (280,240)，距默认中心 40px）建立
    tracker.update([make_det(270.0, 230.0, 290.0, 250.0, 0.9)])
    # F3：右侧目标（中心 (500,240)，距默认中心 180px）建立
    tracker.update([make_det(490.0, 230.0, 510.0, 250.0, 0.85)])
    # F4：全部丢失：锁定轨道死亡解除锁定，两条 Coast 轨道兜底选举
    r = tracker.update([])
    assert r.tracked
    assert r.is_predicted
    assert r.center[0] == pytest.approx(280.0, abs=30.0)   # 左侧更近，当选
    assert r.center[1] == pytest.approx(240.0, abs=30.0)