yolov8预测

yoloV8 官方地址预测 -Ultralytics YOLO 文档

1.图片预测

from ultralytics import YOLO
#### 图片预测1
###   https://www.youtube.com/watch?v=neBZ6huolkg
###   https://github.com/ultralytics/ultralytics
###   https://github.com/abdullahtarek/football_analysismode = YOLO("yolov8x.pt")
##将视频数据放入到模型中预测
#result = mode.predict('input_videos/08fd33_4.mp4', save=True)
results = mode.predict('input_image/111.png', save=True)##print("================")
# for box in result[0].boxes:
#    print(box)#https://docs.ultralytics.com/modes/predict/#key-features-of-predict-mode
for result in results:boxes = result.boxes  # Boxes object for bounding box outputsmasks = result.masks  # Masks object for segmentation masks outputskeypoints = result.keypoints  # Keypoints object for pose outputsprobs = result.probs  # Probs object for classification outputsobb = result.obb  # Oriented boxes object for OBB outputsresult.show()  # display to screenresult.save(filename="result.png")  # save to disk

2.视频预测

import cv2from ultralytics import YOLO# Load the YOLOv8 model
model = YOLO("../yolov8n.pt")# Open the video file
video_path = "D:/workspace/ultralytics/input_videos/08fd33_4.mp4"
cap = cv2.VideoCapture(video_path)if not cap.isOpened():print("打开摄视屏失败！")# Loop through the video frames
while cap.isOpened():# Read a frame from the videosuccess, frame = cap.read()if success:print("视频读帧成功！")# Run YOLOv8 inference on the frameresults = model(frame)# Visualize the results on the frameannotated_frame = results[0].plot()# Display the annotated framecv2.imshow("YOLOv8 Inference", annotated_frame)# Break the loop if 'q' is pressedif cv2.waitKey(1) & 0xFF == ord("q"):breakelse:print("视频读帧失败！")# Break the loop if the end of the video is reachedbreak# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()