don't write full modified code write only snippet of what i have to change to fix this problem in this code 

do this 
Make sure that the server is receiving the query image correctly. You can try to print the content of the received image and verify if it matches the expected input.

# -\*- coding: utf-8 -\*-
"""
Created on Fri Mar 10 01:16:00 2023

@author: nigam
"""

import os
import cv2
import numpy as np
from keras.preprocessing import image
from keras.applications.vgg16 import VGG16, preprocess\_input, decode\_predictions
from keras.applications.vgg19 import VGG19
from sklearn.neighbors import NearestNeighbors
import tkinter as tk
from tkinter import filedialog
import webbrowser
from flask import Flask, request, render\_template

app = Flask(\_\_name\_\_)

# Set the paths for the image folder, features folder, and the model
image\_folder = 'C:/Users/nigam/Desktop/Project/vgg16testing/static/img'
features\_folder = 'H:/cbir/ImageRetrieval/features'
model\_path = 'vgg16\_weights\_tf\_dim\_ordering\_tf\_kernels\_notop.h5'

# Set the number of images to return as results
num\_results = 5

# Load the VGG16 model
model = VGG16(weights='imagenet', include\_top=False)
model2 = VGG19(weights='imagenet', include\_top=False)

# Function to extract features of an image using the VGG16 and VGG19 models
def extract\_features(image\_path, model1, model2):
 image = cv2.imread(image\_path)
 image = cv2.resize(image, (224, 224))
 image = image[...,::-1] # Convert BGR to RGB
 image = np.expand\_dims(image, axis=0)
 image = preprocess\_input(image) # Preprocess the image
 features1 = model1.predict(image).flatten()
 features2 = model2.predict(image).flatten()
 features = np.concatenate([features1, features2], axis=-1)
 return features
# Function to extract features of all images in the image folder and save them to the features folder
def extract\_all\_features():
 for filename in os.listdir(image\_folder):
 image\_path = os.path.join(image\_folder, filename)
 features\_path = os.path.join(features\_folder, os.path.splitext(filename)[0] + '.npy')
 if not os.path.isfile(features\_path): # Check if features file already exists
 features = extract\_features(image\_path, model, model2) # Add model2 as third argument
 np.save(features\_path, features)
# Function to load the features of all images in the features folder
def load\_all\_features():
 features = []
 filenames = []
 model1 = VGG16(weights='imagenet', include\_top=False)
 model2 = VGG19(weights='imagenet', include\_top=False)
 for filename in os.listdir(features\_folder):
 if filename.endswith(".npy"):
 features\_path = os.path.join(features\_folder, filename)
 feature = np.load(features\_path)
 features.append(feature)
 filenames.append(os.path.splitext(filename)[0])
 return features, filenames, model1, model2
# Function to extract features of the query image, save it to the features folder, and then load the features of all images in the features folder
def extract\_and\_load\_features(image\_path, model1, model2):
 features\_path = os.path.join(features\_folder, os.path.splitext(os.path.basename(image\_path))[0] + '.npy')
 if os.path.isfile(features\_path):
 features = np.load(features\_path)
 else:
 features = extract\_features(image\_path, model1, model2)
 np.save(features\_path, features)
 return features
# Function to perform a search on the features to find the closest matches
def search(query\_features, features):
 knn = NearestNeighbors(n\_neighbors=min(num\_results, len(features)), metric='cosine')
 features = np.array(features)
 features = features.reshape(len(features), -1) # reshape to 2D
 knn.fit(features)
 distances, indices = knn.kneighbors(query\_features.reshape(1, -1))
 # Perform search to find the closest matches
 return indices[0]

def index():
 if request.method == "POST":
 # Get the file from the POST request
 file = request.files["file"]
 # Save the file to the image folder
 file.save(os.path.join(image\_folder, file.filename))
 # Extract features of the query image, save it to the features folder, and then load the features of all images in the features folder
 model1 = VGG16(weights='imagenet', include\_top=False)
 model2 = VGG19(weights='imagenet', include\_top=False)
 features = extract\_and\_load\_features(os.path.join(image\_folder, file.filename), model1, model2) # Add model2 as third argument

if \_\_name\_\_ == "\_\_main\_\_":
 app.run("0.0.0.0")

2 / 2
전체 수정 코드 작성 안 함 이 코드에서 이 문제를 해결하기 위해 변경해야 하는 부분만 작성합니다. 이렇게 하세요. 서버가 쿼리 이미지를 올바르게 수신하고 있는지 확인합니다. 수신된 이미지의 내용을 인쇄하여 예상 입력과 일치하는지 확인할 수 있습니다.# -\*- 코딩: utf-8 -\*-"""생성일: 금 Mar 10 01:16:00 2023저자: nigam"""import osimport cv2numpy를 np로 임포트keras.preprocessing에서 이미지 가져오기keras.applications.vgg16에서 VGG16, 전처리\_입력, 디코드\_예측을 임포트합니다.keras.applications.vgg19에서 VGG19를 가져옵니다.sklearn.neighbors에서 NearestNeighbors를 가져옵니다.tkinter를 tk로 가져옵니다.tkinter에서 파일로그를 가져옵니다.웹 브라우저를 가져옵니다.flask에서 플라스크, 요청, 렌더링\_template를 가져옵니다.app = Flask(\_\_name\_\_)# 이미지 폴더, 기능 폴더, 모델의 경로를 설정합니다.image\_folder = 'C:/사용자/nigam/데스크톱/프로젝트/vgg16testing/static/img'features\_folder = 'H:/cbir/ImageRetrieval/features'model\_path = 'vgg16\_weights\_tf\_dim\_ordering\_tf\_kernels\_notop.h5'# 결과로 반환할 이미지 수 설정num\_results = 5# VGG16 모델 로드model = VGG16(weights='imagenet', include\_top=False)model2 = VGG19(weights='imagenet', include\_top=False)# VGG16 및 VGG19 모델을 사용하여 이미지의 특징을 추출하는 함수def extract\_features(image\_path, model1, model2): image = cv2.imread(image\_path) image = cv2.resize(image, (224, 224)) image = image[...,::-1] # BGR을 RGB로 변환 이미지 = np.expand\_dims(이미지, 축=0) image = preprocess\_input(image) # 이미지 전처리 features1 = model1.predict(image).flatten() features2 = model2.predict(image).flatten() features = np.concatenate([features1, features2], axis=-1) 특징 반환# 이미지 폴더에 있는 모든 이미지의 특징을 추출하여 특징 폴더에 저장하는 함수def extract\_all\_features(): for filename in os.listdir(image\_folder): image\_path = os.path.join(image\_folder, filename) features\_path = os.path.join(features\_folder, os.path.splitext(filename)[0] + '.npy') os.path.isfile(features\_path)이 아니라면: # 기능 파일이 이미 있는지 확인 features = extract\_features(image\_path, model, model2) # 세 번째 인수로 model2 추가 np.save(features\_path, features)# 특징 폴더에 있는 모든 이미지의 특징을 로드하는 함수def load\_all\_features(): features = [] filenames = [] model1 = VGG16(weights='imagenet', include\_top=False) model2 = VGG19(weights='imagenet', include\_top=False) os.listdir(features\_folder)의 filename: if filename.endswith(".npy"): features\_path = os.path.join(features\_folder, filename) feature = np.load(features\_path) features.append(feature) filenames.append(os.path.splitext(filename)[0]) 반환 기능, 파일명, 모델1, 모델2# 쿼리 이미지의 특징을 추출하여 특징 폴더에 저장한 후 특징 폴더에 있는 모든 이미지의 특징을 불러오는 함수입니다.def extract\_and\_load\_features(image\_path, model1, model2): features\_path = os.path.join(features\_folder, os.path.splitext(os.path.basename(image\_path))[0] + '.npy') if os.path.isfile(features\_path): features = np.load(features\_path) else: features = extract\_features(image\_path, model1, model2) np.save(features\_path, features) 특징 반환# 가장 가까운 일치 항목을 찾기 위해 특징에 대한 검색을 수행하는 함수def search(query\_features, features): knn = NearestNeighbors(n\_neighbors=min(num\_results, len(features)), metric='cosine') features = np.array(features) features = features.reshape(len(features), -1) # 2D로 재형성 knn.fit(features) 거리, 인덱스 = knn.kneighbors(query\_features.reshape(1, -1)) # 검색을 수행하여 가장 가까운 일치점을 찾습니다. return indices[0]def index(): if request.method == "POST": # POST 요청에서 파일을 가져옵니다. file = request.files["file"] # 이미지 폴더에 파일 저장 file.save(os.path.join(image\_folder, file.filename)) # 쿼리 이미지의 특징을 추출하여 특징 폴더에 저장한 다음 특징 폴더에 있는 모든 이미지의 특징을 로드합니다. model1 = VGG16(weights='imagenet', include\_top=False) model2 = VGG19(weights='imagenet', include\_top=False) features = extract\_and\_load\_features(os.path.join(image\_folder, file.filename), model1, model2) # 세 번째 인수로 model2 추가if \_\_name\_\_ == "\_\_main\_\_": app.run("0.0.0.0")2 / 2