前提条件
1.了解Python语言,并会安装第三方库
2.了解Python Web Flask框架
3.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
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="external nofollow" 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" src="/uploads/allimg/221018/1112293146-1.png" />
{ "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=" }
以上就是Python+Flask编写一个简单的行人检测API的详细内容,更多关于Python Flask行人检测的资料请关注服务器之家其它相关文章!
原文地址:https://blog.csdn.net/FriendshipTang/article/details/123172225