Yolo提示找不到模块

问题根源在于 Python 环境未正确匹配，导致 VSCode 运行代码时使用的 Python 环境与安装 NumPy 的环境不一致。

选一个已安装好依赖包的解析器。

添加本地解析器

安装torch

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124 -i https://pypi.tuna.tsinghua.edu.cn/simple

安装ultralytics

pip install ultralytics

使用YOLO

官方文档：https://docs.ultralytics.com/zh/

右击YOLO文件夹创建test.py文件

运行代码需要先安装 YOLOv10 的环境依赖，可以看往期教程安装，之后在安装 Gradio，在你的虚拟环境安装，命令为：pip install gradio==4.43.0

Gradio 是一个开源 Python 库，用于快速构建和共享机器学习模型的 Web 界面。开发者可以通过简单的 Python 代码将机器学习模型封装成交互式应用，无需复杂的设置即可在浏览器中使用自己训练好模型。接下来手把手教你运行 YOLOv10 的一个简单 Web 界面推理自己的模型。话不多说上检测结果：

app.py代码

运行代码需要先安装 YOLOv10 的环境依赖，可以看往期教程安装，之后在安装 Gradio，在你的虚拟环境安装，命令为：pip install gradio==4.43.0

官网的代码运行起来有 bug，推理报错网页端显示错误

下面是我修改好的代码，复制粘贴到你的app.py就行

 # -*- coding: utf-8 -*-
"""
@Auth ：挂科边缘
@File ：YOLOv10_app.py
@IDE ：PyCharm
@Motto :学习新思想，争做新青年
"""
import gradio as gr
import cv2
import tempfile
from ultralytics import YOLOv10


def yolov10_inference(image, video, model_id, image_size, conf_threshold):
    model = YOLOv10(model=f'pt/{model_id}')


    if image:
        results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)
        annotated_image = results[0].plot()
        return annotated_image[:, :, ::-1], None
    else:
        video_path = tempfile.mktemp(suffix=".webm")
        with open(video_path, "wb") as f:
            with open(video, "rb") as g:
                f.write(g.read())

        cap = cv2.VideoCapture(video_path)
        fps = cap.get(cv2.CAP_PROP_FPS)
        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

        output_video_path = tempfile.mktemp(suffix=".webm")
        out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'vp80'), fps, (frame_width, frame_height))

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break


            results = model.predict(source=frame, imgsz=image_size, conf=conf_threshold)
            annotated_frame = results[0].plot()
            out.write(annotated_frame)

        cap.release()
        out.release()

        return None, output_video_path



def yolov10_inference_for_examples(image, model_path, image_size, conf_threshold):
    annotated_image, _ = yolov10_inference(image, None, model_path, image_size, conf_threshold)
    return annotated_image



def app():
    with gr.Blocks():
        with gr.Row():
            with gr.Column():
                image = gr.Image(type="pil", label="Image", visible=True)
                video = gr.Video(label="Video", visible=False)
                input_type = gr.Radio(
                    choices=["Image", "Video"],
                    value="Image",
                    label="Input Type",
                )
                model_id = gr.Dropdown(
                    label="Model",
                    choices=[
                        "yolov10n.pt",
                        "best.pt",
                        "yolov10m.pt",
                        "yolov10b.pt",
                        "yolov10l.pt",
                        "yolov10x.pt",
                    ],
                    value="yolov10n.pt",
                )
                image_size = gr.Slider(
                    label="Image Size",
                    minimum=320,
                    maximum=1280,
                    step=32,
                    value=640,
                )
                conf_threshold = gr.Slider(
                    label="Confidence Threshold",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.05,
                    value=0.25,
                )
                yolov10_infer = gr.Button(value="开始检测")

            with gr.Column():
                output_image = gr.Image(type="numpy", label="Annotated Image", visible=True)
                output_video = gr.Video(label="Annotated Video", visible=False)


        def update_visibility(input_type):
            image_visible = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
            video_visible = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
            output_image_visible = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
            output_video_visible = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)

            return image_visible, video_visible, output_image_visible, output_video_visible

        input_type.change(
            fn=update_visibility,
            inputs=[input_type],
            outputs=[image, video, output_image, output_video],
        )


        def run_inference(image, video, model_id, image_size, conf_threshold, input_type):
            if input_type == "Image":
                return yolov10_inference(image, None, model_id, image_size, conf_threshold)
            else:
                return yolov10_inference(None, video, model_id, image_size, conf_threshold)

        yolov10_infer.click(
            fn=run_inference,
            inputs=[image, video, model_id, image_size, conf_threshold, input_type],
            outputs=[output_image, output_video],
        )




gradio_app = gr.Blocks()
with gradio_app:
    gr.HTML(
        """
        <h1 style='text-align: center'>
        YOLOv10 检测界面
        </h1>
        """
    )
    gr.HTML(
        """
        <h3 style='text-align: center'>
        <a href='https://blog.csdn.net/weixin_44779079/article/details/142935892' target='_blank'>挂科边缘</a>
        </h3>
        """
    )
    with gr.Row():
        with gr.Column():
            app()

if __name__ == '__main__':
    gradio_app.launch()

注意注意注意：需要在 YOLOv10 源码根目录下，新建一个 pt 文件夹，pt 文件夹存放模型文件，可以放训练好的模型