基于YOLOv8深度学习的危险区域人员闯入检测与报警系统【python源码+Pyqt5界面+数据集+训练代码】YOLOv8、ByteTrack、目标追踪、区域闯入

train: E:\MyCVProgram\PersonDetection\datasets\Data\images\train
val: E:\MyCVProgram\PersonDetection\datasets\Data\images\val
# number of classes
nc: 1
# class names
names: ['person']

#coding:utf-8
from ultralytics import YOLO
import matplotlib
matplotlib.use('TkAgg')
if __name__ == '__main__':
    # 模型配置文件路径
    model_yaml_path = 'ultralytics/cfg/models/v8/yolov8.yaml'
    # 加载模型配置文件
    model = YOLO(model_yaml_path)
    # 加载官方预训练模型
    model.load('yolov8n.pt')
    # 训练模型
    print("开始训练模型...")
    results = model.train(data='datasets/Data/data.yaml',
                          epochs=150,
                          batch=4)

#coding:utf-8
from ultralytics import YOLO
import cv2
# 所需加载的模型目录
path = 'models/best.pt'
# 需要检测的图片地址
img_path = "TestFiles/s123.jpg"
# 加载预训练模型
model = YOLO(path, task='detect')
# 检测图片
results = model(img_path)
print(results)
res = results[0].plot()
# res = cv2.resize(res,dsize=None,fx=2,fy=2,interpolation=cv2.INTER_LINEAR)
cv2.imshow("YOLOv8 Detection", res)
cv2.waitKey(0)

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)

result = cv2.pointPolygonTest(contour, test_point, measureDist) 

result = cv2.pointPolygonTest(polygon, test_point, measureDist=False)
print(result)
# 判断结果
if result > 0:
    print("点在多边形内部")
elif result == 0:
    print("点在多边形边界上")
else:
    print("点在多边形外部")

# 定义多边形的顶点
polygon = np.array([[10, 10], [100, 10], [100, 100], [10, 100]], dtype=np.int32)
# 要判断的点
test_point = [50, 50]

import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('TkAgg')
# 定义多边形的顶点
polygon = np.array([[10, 10], [100, 10], [100, 100], [10, 100]], dtype=np.int32)
# 要判断的点
test_point = [50, 50]
# 画出点与多边形并展示
# 创建一个新的图像
fig, ax = plt.subplots()
# 绘制多边形
ax.plot(polygon[:,0], polygon[:,1], 'r-', lw=2, label='Polygon')
ax.fill(polygon[:,0], polygon[:,1], 'r', alpha=0.3)
# 绘制点
ax.plot(test_point[0], test_point[1], 'go', markersize=10, label='Test Point')
# 添加标签和轴限制
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.legend()
ax.set_xlim(0, 110)  # 设置x轴范围
ax.set_ylim(0, 110)  # 设置y轴范围
# 显示图像
plt.show()

import cv2
import numpy as np
# 定义多边形的顶点
polygon = np.array([[10, 10], [100, 10], [100, 100], [10, 100]], dtype=np.int32)
# 要判断的点
test_point = [50, 50]
# 判断test_point点是否在多边形内部
# 使用pointPolygonTest函数
result = cv2.pointPolygonTest(polygon, test_point, measureDist=False)
print(result)
# 判断结果
if result > 0:
    print("点在多边形内部")
elif result == 0:
    print("点在多边形边界上")
else:
    print("点在多边形外部")