这个模块主要是用于跟踪算法的，用的是kalman filter+级联检测目标，基本算法是画轨迹，根据轨迹的情况，判断是否新的id，比如轨迹断开了30帧以上，判断这个轨迹delete了；（如果我躲在树后面几分钟再出来，会不会判断我是两个人？ 不会，除了轨迹的级联匹配，还用到了tf的特征判断，会把每个人的特征cache保存下来，用来比较是否两个人，一个id对应一个特征，再用tensorflow进行相似度比较，tensorflow的数据集释Mars的，有100W张图片，ID SW的错误比较低，除非是模糊的人像 ）
# vim: expandtab:ts=4:sw=4
#单个目标跟踪状态的枚举类型。
新建的跟踪是被归类为“暂定”直到收集到足够的证据。
然后，轨道状态更改为“确认”。
不再活着的轨迹被分类为'删除'，以标记他们从一组活跃删除。
class TrackState:
"""
Enumeration type for the single target track state. Newly created tracks are
classified as `tentative` until enough evidence has been collected. Then,
the track state is changed to `confirmed`. Tracks that are no longer alive
are classified as `deleted` to mark them for removal from the set of active
tracks.
"""
# 三种状态 尝试性的，确定的，被删除的
Tentative = 1
Confirmed = 2
Deleted = 3
class Track:
"""
# 具有状态空间的单个目标轨道（x，y，A，H）和相关联速度，
其中“（x，y）”是bbox的中心，A是高宽比和“H”是高度。
A single target track with state space `(x, y, a, h)` and associated
velocities, where `(x, y)` is the center of the bounding box, `a` is the
aspect ratio and `h` is the height.
Parameters
----------
mean :
# 初始状态分布的平均向量。
Mean vector of the initial state distribution.
covariance : #协方差
# 初始状态分布的协方差矩阵
Covariance matrix of the initial state distribution.
track_id : int
# 唯一的轨迹ID
A unique track identifier.
n_init : int
# 在轨道设置为confirmed之前的连续检测帧数。
当一个miss发生时，轨道状态设置为Deleted帧。
Number of consecutive detections before the track is confirmed.
The track state is set to `Deleted` if a miss occurs within the first
`n_init` frames.
max_age : int
# 在侦测状态设置成Deleted前，最大的连续miss数。
The maximum number of consecutive misses before the track state is set to `Deleted`.
feature : Optional[ndarray]
# 特征向量检测的这条轨道的起源。
如果为空，则这个特性被添加到'特性'缓存中。
Feature vector of the detection this track originates from. If not None,
this feature is added to the `features` cache.
trackid：
轨迹ID。
---------
Attributes #属性
mean : ndarray #均值：
# 初始分布均值向量。
Mean vector of the initial state distribution.
covariance : ndarray # 协方差：
# 初始分布协方差矩阵。
Covariance matrix of the initial state distribution.
track_id : int
A unique track identifier.
hits : int
#测量更新的总数。
Total number of measurement updates.
hit_streak : int
# 自上次miss之后，连续测量更新的总数。（更新一次+1）
Total number of consective measurement updates since last miss.
age : int
#从开始的总帧数
Total number of frames since first occurance.
time_since_update : int
# 从上次的测量更新完后，统计的总帧数
Total number of frames since last measurement update.
state : TrackState
# 当前的侦测状态
The current track state.
features : List[ndarray]
# 特性的缓存。在每个度量更新中，相关的特性
向量添加到这个列表中。
A cache of features. On each measurement update, the associated feature
vector is added to this list.
"""
# 初始化各参数
def __init__(self, mean, covariance, track_id, n_init, max_age,
feature=None):
self.mean = mean
self.covariance = covariance
self.track_id = track_id
self.hits = 1
self.hit_streak = 1
self.age = 1
self.time_since_update = 0
self.state = TrackState.Tentative
self.features = []
if feature is not None:
self.features.append(feature)
self._n_init = n_init
self._max_age = max_age
# 将bbox转换成xywh
def to_tlwh(self):
"""Get current position in bounding box format `(top left x, top left y,
width, height)`.
Returns
-------
ndarray
The bounding box.
"""
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= ret[2:] / 2
return ret
# 获取当前位置以某种格式，不太明白
def to_tlbr(self):
"""Get current position in bounding box format `(min x, miny, max x, max y)`.
Returns
-------
ndarray
The bounding box.
"""
ret = self.to_tlwh()
ret[2:] = ret[:2] + ret[2:]
return ret
# 预测，基于kalman filter
def predict(self, kf):
"""Propagate the state distribution to the current time step using a
Kalman filter prediction step.
Parameters
----------
kf : kalman_filter.KalmanFilter
The Kalman filter.
"""
self.mean, self.covariance = kf.predict(self.mean, self.covariance)
self.age += 1
self.time_since_update += 1
# 更新。 主要是步进和特征，检测方法为级联检测
def update(self, kf, detection):
"""Perform Kalman filter measurement update step and update the feature
cache.
Parameters
----------
kf : kalman_filter.KalmanFilter
The Kalman filter.
detection : Detection
The associated detection.
"""
self.mean, self.covariance = kf.update(
self.mean, self.covariance, detection.to_xyah())
self.features.append(detection.feature)
self.hit_streak += 1
self.hits += 1
self.time_since_update = 0
if self.state == TrackState.Tentative and self.hits >= self._n_init:
self.state = TrackState.Confirmed
# 标记已经miss的，如果从更新起miss了_max_age（30）帧以上，
设置为Deleted
def mark_missed(self):
"""Mark this track as missed (no association at the current time step).
"""
if self.state == TrackState.Tentative:
self.state = TrackState.Deleted
elif self.time_since_update > self._max_age:
self.state = TrackState.Deleted
self.hit_streak = 0
# 设置三种状态
def is_tentative(self):
"""Returns True if this track is tentative (unconfirmed).
"""
return self.state == TrackState.Tentative
def is_confirmed(self):
"""Returns True if this track is confirmed."""
return self.state == TrackState.Confirmed
def is_deleted(self):
"""Returns True if this track is dead and should be deleted."""
return self.state == TrackState.Deleted
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