license: gpl pipeline_tag: object-detection tags:

• ultralytics
• yolo
• yolov10
• object-detection

石头剪刀布目标检测模型

FRC 578团队开发

模型说明

本YOLO v10小模型仅用于教学演示，旨在向学生展示目标检测模型的运作原理。模型训练周期为10个epoch。

训练数据

使用从网络获取的100张图像进行训练，未进行任何图像增强处理。

性能指标

使用方法

pip install ultralytics
pip install huggingface_hub

from ultralytics import YOLO
from huggingface_hub import hf_hub_download
from matplotlib import pyplot as plt

# 从仓库加载权重
model_path = hf_hub_download(
    local_dir=".",
    repo_id="fairportrobotics/rock-paper-scissors",
    filename="model.pt"
)
model = YOLO(model_path)

# 加载测试图像
sample_path = hf_hub_download(
    local_dir=".",
    repo_id="fairportrobotics/rock-paper-scissors",
    filename="sample.jpg"
)

# 执行预测
res = model.predict(
    source=sample_path,
    project='.',
    name='detected',
    exist_ok=True,
    save=True,
    show=False,
    show_labels=True,
    show_conf=True,
    conf=0.5
)

plt.figure(figsize=(15,10))
plt.imshow(plt.imread('detected/sample.jpg'))
plt.show()

如您所见，这个模型并不完美 ;)

使用摄像头实时检测

from ultralytics import YOLO
import cv2
import math 
from huggingface_hub import hf_hub_download

# 启动摄像头
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)

# 从仓库加载权重
model_path = hf_hub_download(
    local_dir=".",
    repo_id="fairportrobotics/rock-paper-scissors",
    filename="model.pt"
)
model = YOLO(model_path)

# 类别名称
classNames = ["石头", "布", "剪刀"]


while True:
    success, img = cap.read()
    results = model(img, stream=True)

    # 坐标处理
    for r in results:
        boxes = r.boxes

        for box in boxes:
            # 边界框坐标
            x1, y1, x2, y2 = box.xyxy[0]
            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)

            # 绘制检测框
            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 255), 3)

            # 置信度
            confidence = math.ceil((box.conf[0]*100))/100

            # 类别索引
            cls = int(box.cls[0])

            # 标注信息
            org = [x1, y1]
            font = cv2.FONT_HERSHEY_SIMPLEX
            fontScale = 1
            color = (255, 0, 0)
            thickness = 2

            cv2.putText(img, classNames[cls] + " " + str(round(confidence,2)), org, font, fontScale, color, thickness)

    cv2.imshow('摄像头画面', img)
    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()