前提条件

了解Python语言，并会安装第三方库
了解Python Web Flask框架
了解PyTorch深度学习框架

实验环境

Python 3.6.2
PyTorch 1.7.1
Flask 1.1.1
Numpy 1.18.5
Opencv 3.4.2
PIL pip3 install pillow

项目结构

在这里插入图片描述
相关说明：

static：用于存储静态文件，比如css、js和图片等
templates：存放模板文件
upload：用于保存上传文件
flask_app.py：应用程序主文件
predict.py：预测文件

主要代码

完整代码，暂时没空整理，如整理完，后续会发布，敬请期待！

#!/usr/bin/python
# -*- coding: UTF-8 -*-
import imp
from flask import request, jsonify, send_from_directory, abort
from werkzeug.utils import secure_filename
from flask import Flask, render_template, jsonify, request
from predict import pre
import time
import os
import base64

app = Flask(__name__)
UPLOAD_FOLDER = 'upload'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
basedir = os.path.abspath(os.path.dirname(__file__))
ALLOWED_EXTENSIONS = set(['txt', 'png', 'jpg', 'xls', 'JPG', 'PNG', 'xlsx', 'gif', 'GIF'])


# 用于判断文件后缀
def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS


# 上传
@app.route('/upload')
def upload_test():
    return render_template('upload.html')


@app.route("/api/download/<filename>", methods=['GET'])
def download(filename):
    if request.method == "GET":
        if os.path.isfile(os.path.join('upload', filename)):
            return send_from_directory('upload', filename, as_attachment=True)
        abort(404)


# 上传文件
@app.route('/api/upload', methods=['POST'], strict_slashes=False)
def api_upload():
    file_dir = os.path.join(basedir, app.config['UPLOAD_FOLDER'])
    if not os.path.exists(file_dir):
        os.makedirs(file_dir)
    f = request.files['myfile']  # 从表单的file字段获取文件，myfile为该表单的name值
    if f and allowed_file(f.filename):  # 判断是否是允许上传的文件类型
        fname = secure_filename(f.filename)
        print(fname)
        ext = fname.rsplit('.', 1)[1]  # 获取文件后缀
        unix_time = int(time.time())
        new_filename = str(unix_time) + '.' + ext  # 修改了上传的文件名
        f.save(os.path.join(file_dir, new_filename))  # 保存文件到upload目录
        img_path = os.path.join("upload", new_filename)
        print(img_path)
        pre_result = pre(img_path)
        print(pre_result)
        token = base64.b64encode(new_filename.encode('utf-8'))
        print(token)
        return jsonify({"code": 0, "errmsg": "OK", "token": token, "fileName": "/api/download/" + new_filename,"detect_result:":pre_result})
    else:
        return jsonify({"code": 1001, "errmsg": "ERROR"})


if __name__ == '__main__':
	app.run(host="0.0.0.0",port="5000",threaded=True,debug=False)

<!DOCTYPE html>
<html>
	<head>
		<meta charset="UTF-8">
		<link href="{{url_for('static', filename='obj_classification.css')}}" rel="stylesheet" type="text/css" />
		<title>图片识别--Person</title>
	</head>
	<body>
		<h1>图片识别--Person</h1>
		<div class="container">
			<div class="choose">
				<form action="http://IP地址:5000/api/upload" enctype='multipart/form-data' method='POST'>
					<input type="file" name="myfile" class="input-new" style="margin-top:20px;" />
					<input type="submit" value="识别图片" class="button-new" style="margin-top:15px;" />
				</form>
			</div>
			<div class="display">
				<img src="{{ url_for('static', filename='images/test.jpg',_t=val1) }}" width="400" height="500" alt="图片" />
			</div>
		</div>
	</body>
</html>

运行结果

在这里插入图片描述

{
  "code": 0,
  "detect_result:": [
    {
      "bbox": [
        51.0,
        265.0,
        543.0,
        437.0
      ],
      "class": "b'person 0.78'"
    },
    {
      "bbox": [
        43.0,
        433.0,
        543.0,
        609.0
      ],
      "class": "b'person 0.77'"
    },
    {
      "bbox": [
        44.0,
        133.0,
        543.0,
        309.0
      ],
      "class": "b'person 0.76'"
    },
    {
      "bbox": [
        46.0,
        526.0,
        543.0,
        665.0
      ],
      "class": "b'person 0.74'"
    },
    {
      "bbox": [
        107.0,
        51.0,
        525.0,
        181.0
      ],
      "class": "b'person 0.62'"
    }
  ],
  "errmsg": "OK",
  "fileName": "/api/download/1645974252.jpg",
  "token": "MTY0NTk3NDI1Mi5qcGc="
}