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import sys
sys.path.append('.')
from flask import Flask, request, jsonify
import os
import json
import cv2
import numpy as np
from tensorflow.keras.preprocessing import image
from keras_facenet import FaceNet
from sklearn.svm import OneClassSVM
import pickle
# create a facenet model
embedding_model = FaceNet(key = '20180402-114759',
use_prebuilt=True,
cache_folder= os.path.abspath(os.path.dirname(__file__)) + '/facenet_weights').model
clf_model = pickle.load(open(os.path.abspath(os.path.dirname(__file__)) + '/model/clf_mode.sav', 'rb'))
target_shape = (160, 160)
app = Flask(__name__)
app.config['SITE'] = "http://0.0.0.0:8000/"
app.config['DEBUG'] = False
@app.route('/api/detect_human_face', methods=['POST'])
def detect_human_face():
file1 = request.files['image1']
image1 = cv2.imdecode(np.fromstring(file1.read(), np.uint8), cv2.IMREAD_COLOR)
if image1 is None:
result = "image1: is null!"
status = "ok"
response = jsonify({"status": status, "data": {"result": result}})
response.status_code = 200
response.headers["Content-Type"] = "application/json; charset=utf-8"
return response
X = np.float32([(np.float32(image1) - 127.5) / 127.5])
X_ft = embedding_model.predict(X, batch_size=1)
anomaly_score = clf_model.decision_function(X_ft) * -1
if anomaly_score > 1:
result = "Not Human"
else:
result = "Human"
status = "ok"
response = jsonify(
{
"status": status,
"data": {
"result": result,
"anomaly_score": float(anomaly_score)
}
})
response.status_code = 200
response.headers["Content-Type"] = "application/json; charset=utf-8"
return response
if __name__ == '__main__':
port = int(os.environ.get("PORT", 8000))
app.run(host='0.0.0.0', port=port)
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