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app.py
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app.py
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import os
import json
from flask import Flask, request, redirect,\
url_for, render_template, flash, jsonify, make_response
from werkzeug.utils import secure_filename
from settings import config
from core import face_recognizer as fr
from core import linear_regressor as lr
app = Flask(__name__)
app.config.from_object(__name__)
auth_token = config.AUTH_TOKEN
IMAGE_FOLDER = os.path.join(config.BASE_DIR, 'data/image')
TRAIN_FOLDER = os.path.join(config.BASE_DIR, 'data/train')
ALLOWED_EXTENSIONS = set(['jpeg', 'JPEG', 'png', 'bmp', 'bin', 'txt'])
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
@app.route("/")
def home():
return jsonify(result="success")
@app.route("/image/", methods=['GET', 'POST'])
def image():
if request.method == 'GET':
response = make_response(
render_template('upload.html')
)
return response
if request.method == 'POST':
if request.form.get('_auth_token') != auth_token:
response = make_response(
json.dumps("Token is not valid"), 401
)
response.headers['Content-Type'] = 'application/json'
return response
image_file = request.files.get('justimage', None)
if image_file and allowed_file(image_file.filename):
filename = secure_filename(image_file.filename)
image_file_path = os.path.join(IMAGE_FOLDER, filename)
image_file.save(image_file_path)
temperature_file = request.files.get('temperature', None)
if temperature_file and allowed_file(temperature_file.filename):
filename = secure_filename(temperature_file.filename)
temperature_path = os.path.join(TRAIN_FOLDER, filename)
image_file.save(temperature_path)
rate = request.form.get('rate')
if not rate:
rate = 2.5
upload_result = {
"result": "success",
"rate": int(rate),
"image_file": image_file.filename if image_file else None,
"temperature_file": temperature_file.filename if temperature_file else None,
"result": None
}
if image_file or temperature_file:
res = fr.face_recognizer(image_file_path, int(rate))
if not res:
upload_result['result'] = {"predicted_rate": "no face found"}
else:
predicted_rate =lr.linear_regressor(res, int(rate))
upload_result['result'] = {"predicted_rate": predicted_rate}
return jsonify(upload_result)
# if __name__ == "__main__":
# app.debug = True
# app.run(host='0.0.0.0', port=8000)