使用 Ultralytics YOLO 进行模型预测#

from ultralytics import YOLO

# 加载预训练的 YOLO26n 模型
model = YOLO("yolo26n.pt")

# 对图像进行推理
results = model("https://ultralytics.com/images/bus.jpg")
results = model(
    [
        "https://ultralytics.com/images/bus.jpg",
        "https://ultralytics.com/images/zidane.jpg",
    ]
)  # 批量推理

from ultralytics import YOLO

# 加载预训练的 YOLO26n 模型
model = YOLO("yolo26n.pt")

# 对图像进行推理
results = model("https://ultralytics.com/images/bus.jpg")  # 结果列表

# 查看结果
for r in results:
    print(r.boxes)  # 打印包含检测边界框的 Boxes 对象

from ultralytics import YOLO

# 加载预训练的 YOLO26n-seg 分割模型
model = YOLO("yolo26n-seg.pt")

# 对图像进行推理
results = model("https://ultralytics.com/images/bus.jpg")  # 结果列表

# 查看结果
for r in results:
    print(r.masks)  # 打印包含检测到的实例掩码的 Masks 对象

from ultralytics import YOLO

# 加载预训练的 YOLO26n-sem 语义分割模型
model = YOLO("yolo26n-sem.pt")

# 对图像进行推理
results = model("https://ultralytics.com/images/bus.jpg")  # 结果列表

# 查看结果
for r in results:
    print(r.semantic_mask.data)  # 打印 H x W 类别 ID 图

from ultralytics import YOLO

# 加载预训练的 YOLO26n-pose 姿态模型
model = YOLO("yolo26n-pose.pt")

# 对图像进行推理
results = model("https://ultralytics.com/images/bus.jpg")  # 结果列表

# 查看结果
for r in results:
    print(r.keypoints)  # 打印包含检测到的关键点的 Keypoints 对象

from ultralytics import YOLO

# 加载预训练的 YOLO26n-cls 分类模型
model = YOLO("yolo26n-cls.pt")

# 对图像进行推理
results = model("https://ultralytics.com/images/bus.jpg")  # 结果列表

# 查看结果
for r in results:
    print(r.probs)  # 打印包含检测到的类别概率的 Probs 对象

from ultralytics import YOLO

# 加载预训练的 YOLO26n 模型
model = YOLO("yolo26n-obb.pt")

# 对图像进行推理
results = model("https://ultralytics.com/images/boats.jpg")  # 结果列表

# 查看结果
for r in results:
    print(r.obb)  # 打印包含定向检测边界框的 OBB 对象

from PIL import Image

from ultralytics import YOLO

# Load a pretrained YOLO26n model
model = YOLO("yolo26n.pt")

# Run inference on 'bus.jpg'
results = model(["https://ultralytics.com/images/bus.jpg", "https://ultralytics.com/images/zidane.jpg"])  # results list

# Visualize the results
for i, r in enumerate(results):
    # Plot results image
    im_bgr = r.plot()  # BGR-order numpy array
    im_rgb = Image.fromarray(im_bgr[..., ::-1])  # RGB-order PIL image

    # Show results to screen (in supported environments)
    r.show()

    # Save results to disk
    r.save(filename=f"results{i}.jpg")

from threading import Thread

from ultralytics import YOLO

def thread_safe_predict(model, image_path):
    """Performs thread-safe prediction on an image using a locally instantiated YOLO model."""
    model = YOLO(model)
    results = model.predict(image_path)
    # 处理结果

# 启动各自拥有模型实例的线程
Thread(target=thread_safe_predict, args=("yolo26n.pt", "image1.jpg")).start()
Thread(target=thread_safe_predict, args=("yolo26n.pt", "image2.jpg")).start()

import cv2

from ultralytics import YOLO

# 加载 YOLO 模型
model = YOLO("yolo26n.pt")

# 打开视频文件
video_path = "path/to/your/video/file.mp4"
cap = cv2.VideoCapture(video_path)

# 遍历视频帧
while cap.isOpened():
    # 读取视频帧
    success, frame = cap.read()

    if success:
        # 对该帧运行 YOLO 推理
        results = model(frame)

        # 可视化该帧上的结果
        annotated_frame = results[0].plot()

        # 显示带标注的帧
        cv2.imshow("YOLO Inference", annotated_frame)

        # 如果按下 'q'，则退出循环
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # 如果到达视频末尾，则退出循环
        break

# 释放视频捕获对象并关闭显示窗口
cap.release()
cv2.destroyAllWindows()