【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 YOLO from PIL import Image import cv2 model = 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 PIL im1 = Image.open("bus.jpg") results = model.predict(source=im1, save=True) # 保存绘制的图像 # from ndarray im2 = 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/ndarray results = model.predict(source=[im1, im2])

• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19

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 arrays results = model(inputs, stream=True) # generator of Results objects for result in results: boxes = result.boxes # Boxes object for bbox outputs masks = result.masks # Masks object for segmentation masks outputs probs = result.probs # Class probabilities for classification outputs

• 1
• 2
• 3
• 4
• 5
• 6
• 7

相关参数如下：

Key	Value	Description
source	'ultralytics/assets'	source directory for images or videos
conf	0.25	object confidence threshold for detection
iou	0.7	intersection over union (IoU) threshold for NMS
half	False	use half precision (FP16)
device	None	device to run on, i.e. cuda device=0/1/2/3 or device=cpu
show	False	show results if possible
save	False	save images with results
save_txt	False	save results as .txt file
save_conf	False	save results with confidence scores
save_crop	False	save cropped images with results
hide_labels	False	hide labels
hide_conf	False	hide confidence scores
max_det	300	maximum number of detections per image
vid_stride	False	video frame-rate stride
line_thickness	3	bounding box thickness (pixels)
visualize	False	visualize model features
augment	False	apply image augmentation to prediction sources
agnostic_nms	False	class-agnostic NMS
retina_masks	False	use high-resolution segmentation masks
classes	None	filter results by class, i.e. class=0, or class=[0,2,3]
boxes	True	Show boxes in segmentation predictions

---

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。

source	model(arg)	type	notes
image	'im.jpg'	str, Path
URL	'https://ultralytics.com/images/bus.jpg'	str
screenshot	'screen'	str
PIL	Image.open('im.jpg')	PIL.Image	HWC, RGB
OpenCV	cv2.imread('im.jpg')[:,:,::-1]	np.ndarray	HWC, BGR to RGB
numpy	np.zeros((640,1280,3))	np.ndarray	HWC
torch	torch.zeros(16,3,320,640)	torch.Tensor	BCHW, RGB
CSV	'sources.csv'	str, Path	RTSP, RTMP, HTTP
video ✅	'vid.mp4'	str, Path
directory ✅	'path/'	str, Path
glob ✅	'path/*.jpg'	str	Use * operator
YouTube ✅	'https://youtu.be/Zgi9g1ksQHc'	str
stream ✅	'rtsp://example.com/media.mp4'	str	RTSP, RTMP, HTTP

图像类型

Image Suffixes	Example Predict Command	Reference
.bmp	yolo predict source=image.bmp	Microsoft BMP File Format
.dng	yolo predict source=image.dng	Adobe DNG
.jpeg	yolo predict source=image.jpeg	JPEG
.jpg	yolo predict source=image.jpg	JPEG
.mpo	yolo predict source=image.mpo	Multi Picture Object
.png	yolo predict source=image.png	Portable Network Graphics
.tif	yolo predict source=image.tif	Tag Image File Format
.tiff	yolo predict source=image.tiff	Tag Image File Format
.webp	yolo predict source=image.webp	WebP
.pfm	yolo predict source=image.pfm	Portable FloatMap

视频类型

Video Suffixes	Example Predict Command	Reference
.asf	yolo predict source=video.asf	Advanced Systems Format
.avi	yolo predict source=video.avi	Audio Video Interleave
.gif	yolo predict source=video.gif	Graphics Interchange Format
.m4v	yolo predict source=video.m4v	MPEG-4 Part 14
.mkv	yolo predict source=video.mkv	Matroska
.mov	yolo predict source=video.mov	QuickTime File Format
.mp4	yolo predict source=video.mp4	MPEG-4 Part 14 - Wikipedia
.mpeg	yolo predict source=video.mpeg	MPEG-1 Part 2
.mpg	yolo predict source=video.mpg	MPEG-1 Part 2
.ts	yolo predict source=video.ts	MPEG Transport Stream
.wmv	yolo predict source=video.wmv	Windows Media Video
.webm	yolo predict source=video.webm	WebM Project

---

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()

• 1
• 2
• 3
• 4

---

from ultralytics import YOLO import cv2 from ultralytics.yolo.utils.benchmarks import benchmark model = YOLO("yolov8-seg.yaml").load('yolov8n-seg.pt') results = model.predict(r'E:\CS\DL\yolo\yolov8study\bus.jpg') boxes = results[0].boxes masks = results[0].masks probs = results[0].probs print(f"boxes:{boxes[0]}") print(f"masks:{masks.xy }") print(f"probs:{probs}")

• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12

output:

image 1/1 E:\CS\DL\yolo\yolov8study\bus.jpg: 640x480 4 0s, 1 5, 1 36, 25.9ms Speed: 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: None is_track: False orig_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

• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• 22
• 23

---

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 frame cv2.imshow("YOLOv8 Inference", annotated_frame) cv2.waitKey() cv2.destroyAllWindows()

• 1
• 2
• 3
• 4
• 5

---

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

import cv2 from ultralytics import YOLO # Load the YOLOv8 model model = model = YOLO("yolov8-seg.yaml").load('yolov8n-seg.pt') # Open the video file video_path = "sample.mp4" cap = cv2.VideoCapture(video_path) # Loop through the video frames while cap.isOpened(): # Read a frame from the video success, frame = cap.read() if success: # Run YOLOv8 inference on the frame results = model(frame) # Visualize the results on the frame annotated_frame = results[0].plot() # Display the annotated frame cv2.imshow("YOLOv8 Inference", 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()

• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• 22
• 23
• 24
• 25
• 26
• 27
• 28
• 29
• 30
• 31
• 32
• 33
• 34
• 35