[Target detection] Introduction to YOLOV8 combat (5) Model prediction

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The predict mode is used to use the trained YOLOv8model to make predictions on new images or videos. In this mode, the model checkpointis loaded from a file, and the user can provide images or videos to perform inference. The model predicts the class and location of objects in an input image or video.

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])

YOLOv8Prediction mode can generate predictions for various tasks, returning a list of result objects or a memory-efficient generator of result objects when using streaming mode. Streaming mode is enabled by passing in the call method of the predictor stream=True. stream=TrueThe streaming mode should be used for long videos or large predictive sources, otherwise the results will accumulate in memory and eventually lead to an out of memory error.

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

The relevant parameters are as follows :

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

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YOLOv8Various input sources are accepted, as shown in the table below. This includes images, URLs, PIL images, OpenCV, numpy arrays, torch tensors, CSV files, videos, directories, globals, YouTube videos, and streams . The table indicates whether each source can use stream=True in streaming mode ✅ and example parameters for each source.

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

image type

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

video type

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

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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()

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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

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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()

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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()