# 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(