【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])

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：

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 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()

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()