实现的功能很简单：（1）选择图片上传；（2）返回DL模型的识别结果。
代码如下：其中format_string给你模型的输出结果就OK，感兴趣的可以玩一下这个四分类：http://218.107.211.134:11001/
代码语言：javascript复制# coding=utf-8
import os
import sys
# reload(sys)
#sys.setdefaultencoding("utf-8")
import time
from flask import request, send_from_directory
from flask import Flask, request, redirect, url_for
import uuid
import numpy as np
UPLOAD_FOLDER = '/Users/liupeng/Desktop/anaconda/flask/images'
ALLOWED_EXTENSIONS = set(['jpg','JPG', 'jpeg', 'JPEG', 'png'])
app= Flask(__name__)
app._static_folder = UPLOAD_FOLDER
def allowed_files(filename):
return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
def rename_filename(old_file_name):
basename = os.path.basename(old_file_name)
name, ext = os.path.splitext(basename)
new_name = str(uuid.uuid1()) ext
return new_name
@app.route("/", methods=['GET', 'POST'])
def hello():
result = """
<!doctype html>
<title>临时测试用</title>
<h1>来喂一张照片吧</h1>
<form action="" method=post enctype=multipart/form-data>
<p><input type=file name=file value='选择图片'>
<input type=submit value='上传'>
</form>
<p>%s</p>
""" % "<br>"
if request.method == 'POST':
file = request.files['file']
old_file_name = file.filename
if file and allowed_files(old_file_name):
filename = rename_filename(old_file_name)
file_path = os.path.join(UPLOAD_FOLDER, filename)
file.save(file_path)
type_name = 'N/A'
print ('file_path', file_path)
new_url = '/static/%s' % os.path.basename(file_path)
print ('new_url:',new_url)
image_tag = '<img src="%s"></img><p>'
print ('image_tag:', image_tag)
new_tag = image_tag % new_url
print ('new_tag:', new_tag)
format_string = 'hello, deep learning'
ret_string = new_tag format_string '<BR>'
print ('ret_string:',ret_string)
return result ret_string
return result
if __name__ == '__main__':
app.run(host='0.0.0.0', port=8080, debug=False, threaded=True)
