目录前提条件实验环境项目结构主要代码运行结果

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 3.6.2PyTorch 1.7.1Flask 1.1.1Numpy 1.18.5Opencv 3.4.2PIL 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/", 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)

图片识别--Person

运行结果

{ "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其它相关文章！

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