YOLOV8_0">一、YOLOV8环境准备

YOLOv8_1">1.1 下载安装最新的YOLOv8代码

 仓库地址： https://github.com/ultralytics/ultralytics

1.2 配置环境

  pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

二、下载测试视频，预训练权重

测试视频
链接：https://pan.baidu.com/s/1xqu4aRxoOGlVLILKLSReqg
提取码：7g9r
–来自百度网盘超级会员V5的分享
预训练权重
在YOLOv8 github上下载预训练权重：yolov8n.pt，ultralytics\ultralytics\路径下,新建weights文件夹，预训练权重放入其中。

三、v8追踪

from collections import defaultdict
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import cv2
import numpy as np

from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO('weights/yolov8n.pt')

# Open the video file
video_path = "video/car.mp4"
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
size = (width, height)

# Store the track history
track_history = defaultdict(lambda: [])

# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Run YOLOv8 tracking on the frame, persisting tracks between frames
        results = model.track(frame, persist=True)

        # Get the boxes and track IDs
        if results[0].boxes.id != None:
            boxes = results[0].boxes.xywh.cpu()
            track_ids = results[0].boxes.id.int().cpu().tolist()

            # Visualize the results on the frame
            annotated_frame = results[0].plot()

            # Plot the tracks
            for box, track_id in zip(boxes, track_ids):
                x, y, w, h = box
                track = track_history[track_id]
                track.append((float(x), float(y)))  # x, y center point
                if len(track) > 30:  # retain 90 tracks for 90 frames
                    track.pop(0)

                # Draw the tracking lines
                points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
                cv2.polylines(annotated_frame, [points], isClosed=False, color=(0, 0, 255), thickness=2)

            # Display the annotated frame
            cv2.imshow("YOLOv8 Tracking", annotated_frame)

            # videoWriter.write(annotated_frame)

            # Break the loop if 'q' is pressed
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()