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创建环境python3.9
打开此环境终端 #xff08;后面的语句操作几乎都在这个终端执行#xff09; 输入up主提供的语句#xff1a;pip install -r requirements.txt
1.下载pytorch网络连接超时 pytorch网址#xff1a;
Start Locally | P…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 pydantic1.10.9
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_port7861)
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(sourceimage, imgszimage_size, confconf_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(sourceframe, imgszimage_size, confconf_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(typepil, labelImage, visibleTrue) video gr.Video(labelVideo, visibleFalse) input_type gr.Radio( choices[Image, Video], valueImage, labelInput Type, ) # Dynamically load models from the directory model_id gr.Dropdown( labelModel, choicesget_model_list(), # Dynamically fetch the model list valueget_model_list()[0], # Set a default model ) image_size gr.Slider( labelImage Size, minimum320, maximum1280, step32, value640, ) conf_threshold gr.Slider( labelConfidence Threshold, minimum0.0, maximum1.0, step0.05, value0.25, ) yolov10_infer gr.Button(valueDetect Objects) with gr.Column(): output_image gr.Image(typenumpy, labelAnnotated Image, visibleTrue) output_video gr.Video(labelAnnotated Video, visibleFalse) def update_visibility(input_type): image gr.update(visibleTrue) if input_type Image else gr.update(visibleFalse) video gr.update(visibleFalse) if input_type Image else gr.update(visibleTrue) output_image gr.update(visibleTrue) if input_type Image else gr.update(visible(False)) output_video gr.update(visibleFalse) if input_type Image else gr.update(visibleTrue) return image, video, output_image, output_video input_type.change( fnupdate_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( fnrun_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, ], ], fnyolov10_inference_for_examples, inputs[ image, model_id, image_size, conf_threshold, ], outputs[output_image], cache_exampleslazy, )
gradio_app gr.Blocks() with gradio_app: gr.HTML( h1 styletext-align: center YOLOv10: Real-Time End-to-End Object Detection /h1 ) gr.HTML( h3 styletext-align: center a hrefhttps://arxiv.org/abs/2405.14458 target_blankarXiv/a | a hrefhttps://github.com/THU-MIG/yolov10 target_blankgithub/a /h3 ) with gr.Row(): with gr.Column(): app() if __name__ __main__: gradio_app.launch(server_port7861)
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 lets 学习 二之后的错误
1.启动摄像头报错
(yolov10) D:\ai_train\yolov10\yolov10python 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 三制作自己的模型遇到的错误
1.ModuleNotFoundError: No module named ultralytics 那就安装 pip install ultralytics8.3.0 指定版本8.3.0 因为我们的python版本是3.9
因为这个电脑没有显卡所有devicecpu
教程中device0的意思是指定使用电脑中的第一块显卡
