【目标检测】YOLOV8实战入门（五）模型预测

predict模式用于在新图像或视频上使用经过训练的YOLOv8模型进行预测，在此模式下，模型从checkpoint 文件加载，用户可以提供图像或视频来执行推理。模型预测输入图像或视频中对象的类别和位置。

from ultralytics import YOLOfrom PIL import Imageimport cv2model = YOLO("model.pt")# 接受所有格式-image/dir/Path/URL/video/PIL/ndarray。0用于网络摄像头results = model.predict(source="0")results = model.predict(source="folder", show=True) # 展示预测结果# from PILim1 = Image.open("bus.jpg")results = model.predict(source=im1, save=True)# 保存绘制的图像# from ndarrayim2 = cv2.imread("bus.jpg")results = model.predict(source=im2, save=True, save_txt=True)# 将预测保存为标签# from list of PIL/ndarrayresults = model.predict(source=[im1, im2])

YOLOv8预测模式可以为各种任务生成预测，在使用流模式时返回结果对象列表或结果对象的内存高效生成器。通过在预测器的调用方法中传递stream=True来启用流模式。stream=True的流媒体模式应用于长视频或大型预测源，否则结果将在内存中累积并最终导致内存不足错误。

inputs = [img, img]# list of numpy arraysresults = model(inputs, stream=True)# generator of Results objectsfor result in results:boxes = result.boxes# Boxes object for bbox outputsmasks = result.masks# Masks object for segmentation masks outputsprobs = result.probs# Class probabilities for classification outputs

相关参数如下：

YOLOv8可以接受各种输入源，如下表所示。这包括图像、URL、PIL图像、OpenCV、numpy数组、torch张量、CSV文件、视频、目录、全局、YouTube视频和流。该表指示每个源是否可以在流模式下使用stream=True✅以及每个源的示例参数。

图像类型

视频类型

预测结果对象包含以下组件：

Results.boxes: — 具有用于操作边界框的属性和方法的boxes

Results.masks: — 用于索引掩码或获取段坐标的掩码对象

Results.probs: — 包含类概率或logits

Results.orig_img: — 载入内存的原始图像

Results.path： — 包含输入图像路径的路径

默认情况下，每个结果都由一个torch. Tensor组成，它允许轻松操作：

results = results.cuda()results = results.cpu()results = results.to('cpu')results = results.numpy()

from ultralytics import YOLOimport cv2from ultralytics.yolo.utils.benchmarks import benchmarkmodel = YOLO("yolov8-seg.yaml").load('yolov8n-seg.pt')results = model.predict(r'E:\CS\DL\yolo\yolov8study\bus.jpg')boxes = results[0].boxesmasks = results[0].masksprobs = results[0].probs print(f"boxes:{boxes[0]}")print(f"masks:{masks.xy }")print(f"probs:{probs}")

output:

image 1/1 E:\CS\DL\yolo\yolov8study\bus.jpg: 640x480 4 0s, 1 5, 1 36, 25.9msSpeed: 4.0ms preprocess, 25.9ms inference, 10.0ms postprocess per image at shape (1, 3, 640, 640)WARNING'Boxes.boxes' is deprecated. Use 'Boxes.data' instead.boxes:ultralytics.yolo.engine.results.Boxes object with attributes:boxes: tensor([[670.1221, 389.6674, 809.4929, 876.5032, 0.8875, 0.0000]], device='cuda:0') cls: tensor([0.], device='cuda:0')conf: tensor([0.8875], device='cuda:0')data: tensor([[670.1221, 389.6674, 809.4929, 876.5032, 0.8875, 0.0000]], device='cuda:0')id: Noneis_track: Falseorig_shape: tensor([1080,810], device='cuda:0')shape: torch.Size([1, 6])xywh: tensor([[739.8075, 633.0853, 139.3708, 486.8358]], device='cuda:0')xywhn: tensor([[0.9133, 0.5862, 0.1721, 0.4508]], device='cuda:0')xyxy: tensor([[670.1221, 389.6674, 809.4929, 876.5032]], device='cuda:0')xyxyn: tensor([[0.8273, 0.3608, 0.9994, 0.8116]], device='cuda:0')masks:[array([[ 804.94, 391.5], [ 794.81,401.62], [ 794.81,403.31], [ 791.44,406.69], ......probs:None

我们可以使用Result对象的plot()函数在图像对象中绘制结果。它绘制在结果对象中找到的所有组件（框、掩码、分类日志等）

annotated_frame = results[0].plot()# Display the annotated framecv2.imshow("YOLOv8 Inference", annotated_frame)cv2.waitKey()cv2.destroyAllWindows()

使用OpenCV（cv2）和YOLOv8对视频帧运行推理的Python脚本。

import cv2from ultralytics import YOLO# Load the YOLOv8 modelmodel = model = YOLO("yolov8-seg.yaml").load('yolov8n-seg.pt')# Open the video filevideo_path = "sample.mp4"cap = cv2.VideoCapture(video_path)# Loop through the video frameswhile cap.isOpened():# Read a frame from the videosuccess, frame = cap.read()if success:# 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:# Break the loop if the end of the video is reachedbreak# Release the video capture object and close the display windowcap.release()cv2.destroyAllWindows()