深度学习实战】行人检测追踪与双向流量计数系统【python源码+Pyqt5界面+数据集+训练代码】YOLOv8、ByteTrack、目标追踪、双向计数、行人检测追踪、过线计数（3）

class ByteTrack:
    """
    Initialize the ByteTrack object.
    Parameters:
        track_thresh (float, optional): Detection confidence threshold
            for track activation.
        track_buffer (int, optional): Number of frames to buffer when a track is lost.
        match_thresh (float, optional): Threshold for matching tracks with detections.
        frame_rate (int, optional): The frame rate of the video.
    """
    def __init__(
        self,
        track_thresh: float = 0.25,
        track_buffer: int = 30,
        match_thresh: float = 0.8,
        frame_rate: int = 30,
    ):
        self.track_thresh = track_thresh
        self.match_thresh = match_thresh
        self.frame_id = 0
        self.det_thresh = self.track_thresh + 0.1
        self.max_time_lost = int(frame_rate / 30.0 * track_buffer)
        self.kalman_filter = KalmanFilter()
        self.tracked_tracks: List[STrack] = []
        self.lost_tracks: List[STrack] = []
        self.removed_tracks: List[STrack] = []
    def update_with_detections(self, detections: Detections) -> Detections:
        """
        Updates the tracker with the provided detections and
            returns the updated detection results.
        Parameters:
            detections: The new detections to update with.
        Returns:
            Detection: The updated detection results that now include tracking IDs.
        """
        tracks = self.update_with_tensors(
            tensors=detections2boxes(detections=detections)
        )
        detections = Detections.empty()
        if len(tracks) > 0:
            detections.xyxy = np.array(
                [track.tlbr for track in tracks], dtype=np.float32
            )
            detections.class_id = np.array(
                [int(t.class_ids) for t in tracks], dtype=int
            )
            detections.tracker_id = np.array(
                [int(t.track_id) for t in tracks], dtype=int
            )
            detections.confidence = np.array(
                [t.score for t in tracks], dtype=np.float32
            )
        else:
            detections.tracker_id = np.array([], dtype=int)
        return detections
    def update_with_tensors(self, tensors: np.ndarray) -> List[STrack]:
        """
        Updates the tracker with the provided tensors and returns the updated tracks.
        Parameters:
            tensors: The new tensors to update with.
        Returns:
            List[STrack]: Updated tracks.
        """
        self.frame_id += 1
        activated_starcks = []
        refind_stracks = []
        lost_stracks = []
        removed_stracks = []
        class_ids = tensors[:, 5]
        scores = tensors[:, 4]
        bboxes = tensors[:, :4]
        remain_inds = scores > self.track_thresh
        inds_low = scores > 0.1
        inds_high = scores < self.track_thresh
        inds_second = np.logical_and(inds_low, inds_high)
        dets_second = bboxes[inds_second]
        dets = bboxes[remain_inds]
        scores_keep = scores[remain_inds]
        scores_second = scores[inds_second]
        class_ids_keep = class_ids[remain_inds]
        class_ids_second = class_ids[inds_second]
        if len(dets) > 0:
            """Detections"""
            detections = [
                STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
                for (tlbr, s, c) in zip(dets, scores_keep, class_ids_keep)
            ]
        else:
            detections = []
        """ Add newly detected tracklets to tracked_stracks"""
        unconfirmed = []
        tracked_stracks = []  # type: list[STrack]
        for track in self.tracked_tracks:
            if not track.is_activated:
                unconfirmed.append(track)
            else:
                tracked_stracks.append(track)
        """ Step 2: First association, with high score detection boxes"""
        strack_pool = joint_tracks(tracked_stracks, self.lost_tracks)
        # Predict the current location with KF
        STrack.multi_predict(strack_pool)
        dists = matching.iou_distance(strack_pool, detections)
        dists = matching.fuse_score(dists, detections)
        matches, u_track, u_detection = matching.linear_assignment(
            dists, thresh=self.match_thresh
        )
        for itracked, idet in matches:
            track = strack_pool[itracked]
            det = detections[idet]
            if track.state == TrackState.Tracked:
                track.update(detections[idet], self.frame_id)
                activated_starcks.append(track)
            else:
                track.re_activate(det, self.frame_id, new_id=False)
                refind_stracks.append(track)
        """ Step 3: Second association, with low score detection boxes"""
        # association the untrack to the low score detections
        if len(dets_second) > 0:
            """Detections"""
            detections_second = [
                STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
                for (tlbr, s, c) in zip(dets_second, scores_second, class_ids_second)
            ]
        else:
            detections_second = []
        r_tracked_stracks = [
            strack_pool[i]
            for i in u_track
            if strack_pool[i].state == TrackState.Tracked
        ]
        dists = matching.iou_distance(r_tracked_stracks, detections_second)
        matches, u_track, u_detection_second = matching.linear_assignment(
            dists, thresh=0.5
        )
        for itracked, idet in matches:
            track = r_tracked_stracks[itracked]
            det = detections_second[idet]
            if track.state == TrackState.Tracked:
                track.update(det, self.frame_id)
                activated_starcks.append(track)
            else:
                track.re_activate(det, self.frame_id, new_id=False)
                refind_stracks.append(track)
        for it in u_track:
            track = r_tracked_stracks[it]
            if not track.state == TrackState.Lost:
                track.mark_lost()
                lost_stracks.append(track)
        """Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
        detections = [detections[i] for i in u_detection]
        dists = matching.iou_distance(unconfirmed, detections)
        dists = matching.fuse_score(dists, detections)
        matches, u_unconfirmed, u_detection = matching.linear_assignment(
            dists, thresh=0.7
        )
        for itracked, idet in matches:
            unconfirmed[itracked].update(detections[idet], self.frame_id)
            activated_starcks.append(unconfirmed[itracked])
        for it in u_unconfirmed:
            track = unconfirmed[it]
            track.mark_removed()
            removed_stracks.append(track)
        """ Step 4: Init new stracks"""
        for inew in u_detection:
            track = detections[inew]
            if track.score < self.det_thresh:
                continue
            track.activate(self.kalman_filter, self.frame_id)
            activated_starcks.append(track)
        """ Step 5: Update state"""
        for track in self.lost_tracks:
            if self.frame_id - track.end_frame > self.max_time_lost:
                track.mark_removed()
                removed_stracks.append(track)
        self.tracked_tracks = [
            t for t in self.tracked_tracks if t.state == TrackState.Tracked
        ]
        self.tracked_tracks = joint_tracks(self.tracked_tracks, activated_starcks)
        self.tracked_tracks = joint_tracks(self.tracked_tracks, refind_stracks)
        self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks)
        self.lost_tracks.extend(lost_stracks)
        self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks)
        self.removed_tracks.extend(removed_stracks)
        self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks(
            self.tracked_tracks, self.lost_tracks
        )
        output_stracks = [track for track in self.tracked_tracks if track.is_activated]
        return output_stracks

# 创建跟踪器
byte_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=30)

model = YOLO(path)
results = model(frame)[0]
detections = sv.Detections.from_ultralytics(results)
detections = byte_tracker.update_with_detections(detections)

labels = [
            f"id{tracker_id} {model.model.names[class_id]}"
            for _, _, confidence, class_id, tracker_id
            in detections
        ]
annotated_frame = frame.copy()
annotated_frame = box_annotator.annotate(
            scene=annotated_frame,
            detections=detections,
            labels=labels)

cap = cv2.VideoCapture(video_path)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
point_A = [10, int(height/5*4)]
point_B = [width-10, int(height/5)]
# 定义过线使用的线段点
LINE_START = sv.Point(point_A[0], point_A[1])
LINE_END = sv.Point(point_B[0], point_B[1])
line_zone = MyLineZone(start=LINE_START, end=LINE_END)

for i, (xyxy, _, confidence, class_id, tracker_id) in enumerate(detections):
            if tracker_id is None:
                continue
            # 使用中心点判断是否过线
            x1, y1, x2, y2 = xyxy
            center_x = int((x1 + x2) / 2)
            center_y = int((y1 + y2) / 2)
            center_point = Point(x=center_x, y=center_y)
            triggers = [self.vector.is_in(point=center_point)]

def is_in(self, point: Point) -> bool:
        v1 = Vector(self.start, self.end)
        v2 = Vector(self.start, point)
        cross_product = (v1.end.x - v1.start.x) * (v2.end.y - v2.start.y) - (
            v1.end.y - v1.start.y
        ) * (v2.end.x - v2.start.x)
        return cross_product < 0

def point_in_line(self, center_point):
        # 判断点是否在线段之间通过
        # 计算向量 AP 与向量 AB 的点积（也称为“标量积”）
        # 点积的绝对值应在 0（包括端点）与向量 AB 的模长平方之间，且方向应与 AB 相同（即点积为正）
        point_A, point_B = self.get_line_points(self.vector)
        xA, yA = point_A
        xB, yB = point_B
        xP, yP = center_point
        AB = (xB - xA, yB - yA)
        AP = (xP - xA, yP - yA)
        # 计算向量 AP 与向量 AB 的点积
        dot_product = AB[0] * AP[0] + AB[1] * AP[1]
        # 计算向量 AB 模长的平方
        AB_length_squared = AB[0] ** 2 + AB[1] ** 2
        # 判断标准：点积的绝对值应在 0（包括端点）与向量 AB 的模长平方之间，且方向应与 AB 相同（即点积为正）
        if 0 <= dot_product <= AB_length_squared and dot_product >= 0:
            within_segment = True
        else:
            within_segment = False
        return within_segment