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api.py
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api.py
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import flask
from flask import request, jsonify, make_response, abort
import urllib.request
import warnings
warnings.filterwarnings("ignore")
from model import Facenet
import numpy as np
from model import functions
import cv2
app = flask.Flask(__name__)
# needs few seconds to load model
model = Facenet.loadModel()
print("model loaded")
def get_embedding(model,req_img):
img_res = req_img.read()
img = np.asarray(bytearray(img_res), dtype="uint8")
img = cv2.imdecode(img, cv2.IMREAD_COLOR)
input_shape = (160, 160)
img_face = functions.detectFace(img, input_shape)
img_targ_rep = model.predict(img_face)[0,:]
# converting numpy array to list
embedding = img_targ_rep.tolist()
return embedding
@app.route('/')
@app.route('/face-embedding',methods=['GET'])
def home():
return '''<h1>Face Embedding API</h1>'''
@app.route('/face-embedding/url', methods=['POST'])
def face_embedding_url():
if not request.json or not 'url' in request.json:
abort(400)
img_url = request.json['url']
req_img = urllib.request.urlopen(img_url)
vector = get_embedding(model,req_img)
# response as json
embedding = {
'status':0,
'url': img_url,
'vector': vector
}
return jsonify({'face_embedding': embedding}), 201
if __name__=='__main__':
app.run(threaded=True)