def watch_for_retraining_facenet(self):
""""""
# Read previous_item_count
prev_count = db.get_prev_count_encode()
# Get count item in DB
curr_count = db.get_curr_count_encode()
if curr_count < prev_count:
print("WARNING ! current count is smaller than previous count ! Please reset previous count")
# Check if percent of change is larger than threshold
change_percent = abs((curr_count - prev_count)/prev_count) * 100
if change_percent > int(self.threshold):
# Call retrain
facenet = FacenetEngine()
facenet.train()
# Update previous item count
db.update_prev_count_encode(curr_count)
print("Previous count updated !")
import sys
# 関連外部ライブラリ
from flask import Blueprint
from flask_uploads import UploadSet, DATA, DOCUMENTS
from flask import jsonify, request
# 内部ライブラリ
sys.path.insert(0,
os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from engine.facenet.facenet_engine import FacenetEngine
from database.db_utilities import DBUtility
from watcher.watcher import Watcher
# Instance generation
facenet = FacenetEngine()
db_util = DBUtility()
watcher = Watcher()
uploads = Blueprint('uploads', __name__)
files = UploadSet('files', DATA + DOCUMENTS)
SAVING_IMAGE_FOLDER_NAME = "images"
# Debug mode
DEBUG_MODE = True
@uploads.route("/api/uploads", methods=['POST'])
def upload_binary():
"""
config_data = read_ini()
self.threshold = config_data['ENGINE']['threshold']
def watch_for_retraining_facenet(self):
""""""
# Read previous_item_count
prev_count = db.get_prev_count_encode()
# Get count item in DB
curr_count = db.get_curr_count_encode()
if curr_count < prev_count:
print("WARNING ! current count is smaller than previous count ! Please reset previous count")
# Check if percent of change is larger than threshold
change_percent = abs((curr_count - prev_count)/prev_count) * 100
if change_percent > int(self.threshold):
# Call retrain
facenet = FacenetEngine()
facenet.train()
# Update previous item count
db.update_prev_count_encode(curr_count)
print("Previous count updated !")
if __name__ == '__main__':
# watcher = Watcher()
facenet = FacenetEngine()
facenet.train()