add yolo v10 and modify pipeline

ultralytics/trackers/basetrack.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+"""This module defines the base classes and structures for object tracking in YOLO."""
 
 from collections import OrderedDict
 
@@ -6,7 +7,15 @@ import numpy as np
 
 
 class TrackState:
-    """Enumeration of possible object tracking states."""
+    """
+    Enumeration class representing the possible states of an object being tracked.
+
+    Attributes:
+        New (int): State when the object is newly detected.
+        Tracked (int): State when the object is successfully tracked in subsequent frames.
+        Lost (int): State when the object is no longer tracked.
+        Removed (int): State when the object is removed from tracking.
+    """
 
     New = 0
     Tracked = 1
@@ -15,24 +24,49 @@ class TrackState:
 
 
 class BaseTrack:
-    """Base class for object tracking, handling basic track attributes and operations."""
+    """
+    Base class for object tracking, providing foundational attributes and methods.
+
+    Attributes:
+        _count (int): Class-level counter for unique track IDs.
+        track_id (int): Unique identifier for the track.
+        is_activated (bool): Flag indicating whether the track is currently active.
+        state (TrackState): Current state of the track.
+        history (OrderedDict): Ordered history of the track's states.
+        features (list): List of features extracted from the object for tracking.
+        curr_feature (any): The current feature of the object being tracked.
+        score (float): The confidence score of the tracking.
+        start_frame (int): The frame number where tracking started.
+        frame_id (int): The most recent frame ID processed by the track.
+        time_since_update (int): Frames passed since the last update.
+        location (tuple): The location of the object in the context of multi-camera tracking.
+
+    Methods:
+        end_frame: Returns the ID of the last frame where the object was tracked.
+        next_id: Increments and returns the next global track ID.
+        activate: Abstract method to activate the track.
+        predict: Abstract method to predict the next state of the track.
+        update: Abstract method to update the track with new data.
+        mark_lost: Marks the track as lost.
+        mark_removed: Marks the track as removed.
+        reset_id: Resets the global track ID counter.
+    """
 
     _count = 0
 
-    track_id = 0
-    is_activated = False
-    state = TrackState.New
-
-    history = OrderedDict()
-    features = []
-    curr_feature = None
-    score = 0
-    start_frame = 0
-    frame_id = 0
-    time_since_update = 0
-
-    # Multi-camera
-    location = (np.inf, np.inf)
+    def __init__(self):
+        """Initializes a new track with unique ID and foundational tracking attributes."""
+        self.track_id = 0
+        self.is_activated = False
+        self.state = TrackState.New
+        self.history = OrderedDict()
+        self.features = []
+        self.curr_feature = None
+        self.score = 0
+        self.start_frame = 0
+        self.frame_id = 0
+        self.time_since_update = 0
+        self.location = (np.inf, np.inf)
 
     @property
     def end_frame(self):
@@ -46,15 +80,15 @@ class BaseTrack:
         return BaseTrack._count
 
     def activate(self, *args):
-        """Activate the track with the provided arguments."""
+        """Abstract method to activate the track with provided arguments."""
         raise NotImplementedError
 
     def predict(self):
-        """Predict the next state of the track."""
+        """Abstract method to predict the next state of the track."""
         raise NotImplementedError
 
     def update(self, *args, **kwargs):
-        """Update the track with new observations."""
+        """Abstract method to update the track with new observations."""
         raise NotImplementedError
 
     def mark_lost(self):