【目标检测】YOLOV8实战入门（五）模型预测-Toy模板网

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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])

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

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()

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}")

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

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()

【目标检测】YOLOV8实战入门（五）模型预测

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()