yolo v8 + flask部署到云服务器，以及问题记录
环境安装
1、运行项目报错：no python application found, check your startup logs for errors
在云服务器pytorch版本安装错了，安装了GPU版本，需要安装CPU版本
# CPU only 使用下面这段代码避免出现第二个错误
pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cpu2、运行项目报错：RuntimeError: operator torchvision::nms does not exist
检查发现pytorch中torchvision版本不匹配：
卸载重装对应匹配版本：
# CPU only
pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cpu3、后端python文件编写，涉及到读写文件、模型预测、以及获取结果分析，转换yolo预测结果为指定的json格式数据。
import os
import numpy as np
import torch.hub
#导入Yolov8，需要提前安装ultralytics库
from ultralytics import YOLO
from flask import Flask
#运用Python的flask类实现与前台信息的交互
from flask import request
from flask import send_file
import base64
import cv2
import time
import json
from pathlib import Path
app = Flask(__name__)
# 调用训练好的模型
model = YOLO('./best.pt')
@app.route('/', methods=['GET', 'POST'])
def uploads():
# 拿到变量img对应的图片
img = request.files.get('img')
if img:
# 重命名
name = 'img.jpg'
# 保存
img.save(os.path.join('./img', name))
fileName = "./img/"+name
# results = model.predict('./img/img.jpg',save=True)#调用模型进行判断 save_txt=True
results = model.predict(fileName,save=True,save_txt=True)
# 类名字典
names = results[0].names
listData = []
for key in names:
# print(key, names[key])
data = {'name': names[key],"value": 0}
listData.append(data)
# 读取数据分析内容
# print(listData)
content = getContent(results,listData)
# print(content)
# 5. 返回结果
data = {
"errCode":0,
"msg":"success",
"data":content,
"img":results[0].path
}
return json.dumps(data)
else:
data = {"errCode":1,"msg":"cannot find file!"}
return json.dumps(data)
# 获取结果文本内容
def getContent(results,listData):
# 获取文件保存的路径
save_path = Path(results[0].save_dir)
content = []
# 获取label标签文件
for r in results:
im_name = Path(r.path).stem
labels = save_path / f"labels/{im_name}.txt"
# 读取标签文件中的内容
txt_file = labels
with open(txt_file, 'r') as file:
# content = file.read()
lines = file.readlines()
print(lines)
for line in lines:
index = int(line.split()[0])
print("每行---", index)
if index前端接收到返回数据：
4、flask上传的图片文件无法访问的问题
根据上述返回数据中，预测目标后的结果图片地址：https://***.com/runs/detect/predict/***.jpg，这个路径无法被访问，需要单独配置：
# 配置路径访问
from flask import send_from_directory
# .....
# 文件访问 runs/detect/predict*/
@app.route('/runs/')
def send_image(path):
# print(path,'------path')
# print(send_from_directory('runs/', path))
return send_from_directory('runs/', path) import os
import numpy as np
import torch.hub
#导入Yolov8，需要提前安装ultralytics库
from ultralytics import YOLO
from flask import Flask
#运用Python的flask类实现与前台信息的交互
from flask import request
from flask import send_file
import base64
import cv2
import time
import json
from pathlib import Path
# 配置路径访问
from flask import send_from_directory
app = Flask(__name__)
verifyCode = "89jjkdsw909324jjkjds9f8sdf"
# 文件访问 runs/detect/predict*/
@app.route('/runs/')
def send_image(path):
# print(path,'------path')
# print(send_from_directory('runs/', path))
return send_from_directory('runs/', path)
@app.route('/', methods=['GET', 'POST'])
def uploads():
# 获取前端上传code,判断是否合法请求
postData = request.form if request.form else request.json
# print(postData.get("code"))
verifyRes = verify(postData.get("code"))
# 是否非法请求
if verifyRes == False:
data = {"errCode":1, "msg": "illegal request!"}
return json.dumps(data)
# 拿到变量img对应的图片
img = request.files.get('img')
# 请求模型识别类型
sourceType = postData.get("sourceType")
print(sourceType, '-------------sourceType')
if sourceType and int(sourceType) == 1:
# 默认捕蝇灯识别
model = YOLO('./best2.pt')
else:
# 默认捕蝇灯识别
model = YOLO('./best.pt')
if img:
# 重命名
name = str(time.time())+'.jpg'
# 保存
img.save(os.path.join('./img', name))
fileName = "./img/"+name
# results = model.predict('./img/img.jpg',save=True)#调用模型进行判断 save_txt=True
results = model.predict(fileName,save=True,save_txt=True)
# 类名字典
names = results[0].names
listData = []
for key in names:
# print(key, names[key])
data = {'name': names[key],"value": 0}
listData.append(data)
# 读取数据分析内容
# print(listData)
content = getContent(results,listData)
# print(content)
# 5. 返回结果
data = {
"errCode": 0,
"msg": "success",
"data": content,
"img": results[0].save_dir+"/"+name
}
return json.dumps(data)
else:
data = {"errCode":1,"msg":"cannot find file!"}
return json.dumps(data)
# 验证code合法性
def verify(code):
return code == verifyCode
# 获取结果文本内容
def getContent(results,listData):
# 获取文件保存的路径
print(results, '-----url')
save_path = Path(results[0].save_dir)
content = []
# 获取label标签文件
for r in results:
im_name = Path(r.path).stem
labels = save_path / f"labels/{im_name}.txt"
# 读取标签文件中的内容
txt_file = labels
if not os.path.exists(txt_file):
return []
with open(txt_file, 'r') as file:
# content = file.read()
lines = file.readlines()
# print(lines)
for line in lines:
index = int(line.split()[0])
# print("每行---", index)
if index 参考文档：预测 -Ultralytics YOLO 文档