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webserver_recognize_api.py
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webserver_recognize_api.py
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# -*- coding: UTF-8 -*-
"""
构建flask接口服务
接收 files={'image_file': ('captcha.jpg', BytesIO(bytes), 'application')} 参数识别验证码
需要配置参数:
image_height = 40
image_width = 80
max_captcha = 4
"""
import json
from io import BytesIO
import os
from cnnlib.recognition_object import Recognizer
import time
from flask import Flask, request, jsonify, Response
from PIL import Image
# 默认使用CPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
with open("conf/sample_config.json", "r") as f:
sample_conf = json.load(f)
# 配置参数
image_height = sample_conf["image_height"]
image_width = sample_conf["image_width"]
max_captcha = sample_conf["max_captcha"]
api_image_dir = sample_conf["api_image_dir"]
model_save_dir = sample_conf["model_save_dir"]
image_suffix = sample_conf["image_suffix"] # 文件后缀
use_labels_json_file = sample_conf['use_labels_json_file']
if use_labels_json_file:
with open("tools/labels.json", "r") as f:
char_set = f.read().strip()
else:
char_set = sample_conf["char_set"]
# Flask对象
app = Flask(__name__)
basedir = os.path.abspath(os.path.dirname(__file__))
# 生成识别对象,需要配置参数
R = Recognizer(image_height, image_width, max_captcha, char_set, model_save_dir)
# 如果你需要使用多个模型,可以参照原有的例子配置路由和编写逻辑
# Q = Recognizer(image_height, image_width, max_captcha, char_set, model_save_dir)
def response_headers(content):
resp = Response(content)
resp.headers['Access-Control-Allow-Origin'] = '*'
return resp
@app.route('/b', methods=['POST'])
def up_image():
if request.method == 'POST' and request.files.get('image_file'):
timec = str(time.time()).replace(".", "")
file = request.files.get('image_file')
img = file.read()
img = BytesIO(img)
img = Image.open(img, mode="r")
# username = request.form.get("name")
print("接收图片尺寸: {}".format(img.size))
s = time.time()
value = R.rec_image(img)
e = time.time()
print("识别结果: {}".format(value))
# 保存图片
print("保存图片: {}{}_{}.{}".format(api_image_dir, value, timec, image_suffix))
file_name = "{}_{}.{}".format(value, timec, image_suffix)
file_path = os.path.join(api_image_dir + file_name)
img.save(file_path)
result = {
'time': timec, # 时间戳
'value': value, # 预测的结果
'speed_time(ms)': int((e - s) * 1000) # 识别耗费的时间
}
img.close()
return jsonify(result)
else:
content = json.dumps({"error_code": "1001"})
resp = response_headers(content)
return resp
if __name__ == '__main__':
app.run(
host='0.0.0.0',
port=6000,
debug=True
)