（一）启动

创建环境python3.9

打开此环境终端

（后面的语句操作几乎都在这个终端执行）

输入up主提供的语句：pip install -r requirements.txt

1.下载pytorch网络连接超时

pytorch网址：

Start Locally | PyTorch

把pip后面的3去掉

在上面的网址中找到对应的版本用那个下载语句但是我开着魔法都下载失败了说是

网络连接超时问题

于是使用清华的镜像再次下载pytorch 飞速成功

pip install torch torchvision torchaudio -f https://pypi.tuna.tsinghua.edu.cn/simple

2.NVIDIA缺失

安装 cuda12.4

配置环境变量：

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin添加到环境变量的PATH中

完成这步之后

输入nvida-smi后出现问题大概就是我的C:\Program Files\NVIDIA Corporation这个路径下面没有NVSMI的文件夹

查询后解决：感谢Windows NVIDIA Corporation下没有NVSMI文件夹解决方法-CSDN博客

链接：百度网盘请输入提取码
提取码：wy6l

将NVSMI.zip解压后放到C:\Program Files\NVIDIA Corporation\

然后再次回到你环境终端输入nvdia-smi 发现这个电脑没有显卡

无所谓让我们继续下一步

3.python app.py报错缺乏gradio

Traceback (most recent call last): File "D:\ai训练\yolov10\yolov10\app.py", line 1, in <module> import gradio as gr ModuleNotFoundError: No module named 'gradio'

那下载一个就是

再次输入语句运行发现报错：

Exception in ASGI application Traceback (most recent call last): File "C:\ProgramData\anaconda3\envs\yolov10\lib\site-packages\pydantic\type_adapter.py", line 270, in _init_core_attrs self._core_schema = _getattr_no_parents(self._type, 'pydantic_core_schema') File "C:\ProgramData\anaconda3\envs\yolov10\lib\site-packages\pydantic\type_adapter.py", line 112, in _getattr_no_parents raise AttributeError(attribute) AttributeError: _pydantic_core_schema

ok啊依赖冲突了降低版本

pip install pydantic==1.10.9

解决这里的问题就ok了

4.运行了之后发现网页内容不是教程给的文件夹的内容

在yolov10中找到app.py

打开并且来到最后一行

app.launch(server_port=7861)

更改端口号为7861

原因：此前按照教程用默认端口7860自己先跑了一遍官方的

5.第4的解决并不稳定

解决：

在yolov10和bin目录同级新建一个models

把文件夹中的所有.pt文件都放进去

然后打开app.py

import gradio as gr

import cv2

import tempfile

from ultralytics import YOLOv10

import os

Set no_proxy environment variable for localhost

os.environ $"no_proxy"$ = "localhost,127.0.0.1,::1"

Directory where your models are store

MODEL_DIR = 'D:/ai_train/yolov10/yolov10/models/'

Load all available models from the directory

def get_model_list():

return $f for f in os.listdir(MODEL_DIR) if f.endswith('.pt')$

def yolov10_inference(image, video, model_id, image_size, conf_threshold):

model_path = os.path.join(MODEL_DIR, model_id) # Get the full model path

model = YOLOv10(model_path) # Load the selected model

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",

)

Dynamically load models from the directory

model_id = gr.Dropdown(

label="Model",

choices=get_model_list(), # Dynamically fetch the model list

value=get_model_list() $0$ , # Set a default model

)

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="Detect Objects")

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 = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)

video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)

output_image = gr.update(visible=True) if input_type == "Image" else gr.update(visible(False))

output_video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)

return image, video, output_image, output_video

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= $output_image, output_video$ ,

)

gr.Examples(

examples=[

[

"ultralytics/assets/bus.jpg",

get_model_list() $0$ ,

640,

0.25,

[

"ultralytics/assets/zidane.jpg",

get_model_list() $0$ ,

640,

0.25,

fn=yolov10_inference_for_examples,

inputs=[

image,

model_id,

image_size,

conf_threshold,

outputs= $output_image$ ,

cache_examples='lazy',

)

gradio_app = gr.Blocks()

with gradio_app:

gr.HTML(

"""

YOLOv10: Real-Time End-to-End Object Detection

</h1>

""")

gr.HTML(

"""

<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>

</h3>

""")

with gr.Row():

with gr.Column():

app()

if name == 'main':

gradio_app.launch(server_port=7861)

重点是第十一行

把这个路径更改为你的models的文件夹路劲

然后回到py3.9的运行终端再次输入 app..py打开网页

解决了一切正常 let`s 学习

（二）之后的错误

1.启动摄像头报错

(yolov10) D:\ai_train\yolov10\yolov10>python yolov10-camera.py Traceback (most recent call last): File "D:\ai_train\yolov10\yolov10\yolov10-camera.py", line 2, in <module> import supervision as sv ModuleNotFoundError: No module named 'supervision'

缺少那就安装

pip install supervision

然后再次启动

python yolov10-camera.py

2.桌面识别图片时报错

python yolov10-paint2.py SupervisionWarnings: BoundingBoxAnnotator is deprecated: `BoundingBoxAnnotator` is deprecated and has been renamed to `BoxAnnotator`. `BoundingBoxAnnotator` will be removed in supervision-0.26.0. 找不到窗口: th(1).jpg 找不到窗口: th(1).jpg 找不到窗口: th(1).jpg

解决;

直接复制图片窗口名字到yolov10-paint2.py第十七行我就是手打然后报错了

【笔记】【YOLOv10图像识别】自动识别图片、视频、摄像头、电脑桌面中的花朵学习踩坑

（一）启动

1.下载pytorch网络连接超时

2.NVIDIA缺失

3.python app.py报错缺乏gradio

4.运行了之后发现网页内容不是教程给的文件夹的内容

5.第4的解决并不稳定

Set no_proxy environment variable for localhost

Directory where your models are store

Load all available models from the directory

Dynamically load models from the directory

（二）之后的错误

1.启动摄像头报错

2.桌面识别图片时报错