# 重复构造定时器
thread = threading.Thread(target=dynamic_modification_timer, args=(mutex, ))
thread.start()
#parsing instrutions
parser = argparse.ArgumentParser(description='Image Retrieval')
parser.add_argument('--update', action='store_true', help='update database')
args = parser.parse_args()
if args.update:
# Create thumb images. 创建缩略图
create_thumb_images(full_folder='./static/image_database/',
thumb_folder='./static/thumb_images/',
suffix='',
height=200,
del_former_thumb=True,
)
# Prepare model. 加载预训练的model
model = load_model(pretrained_model=os.path.join('./DealNet/checkpoint', 'DealNet', 'ckpt.t7'), use_gpu=True)
print("Model load successfully!")
local_dir = './static/image_database/'
# Extract database features.
# 在数据库图片不改变的情况下 选择是否保存特征向量 以节约时间
if args.update:
# Extract database features.
for dir_name in os.listdir(local_dir):
# Prepare data set.
import os
import cv2
import time
from datetime import timedelta
from retrieval.create_thumb_images import create_thumb_images
from flask import Flask, render_template, request, redirect, url_for, make_response, jsonify, flash
from retrieval.retrieval_DaiNet import load_model, load_data, extract_feature, load_query_image, sort_img, extract_feature_query
# Create thumb images.
create_thumb_images(full_folder=os.path.join(os.path.dirname(__file__),
'static/image_database'),
thumb_folder=os.path.join(os.path.dirname(__file__),
'static/thumb_images'),
suffix='',
height=200,
del_former_thumb=True)
# Prepare data set.
data_loader = load_data(data_path=os.path.join(os.path.dirname(__file__),
'static/image_database'),
batch_size=2,
shuffle=False,
transform='default')
# Prepare model.
model = load_model(pretrained_model=os.path.join(os.path.dirname(__file__), 'DaiNet/checkpoint', 'DaiNet', 'ckpt.t7'), \
use_gpu=True)
print("Model load successfully!")
# Extract database features.
gallery_feature, image_paths = extract_feature(