机器学习入门实验——以图搜图（ 基于vgg网络和Keras深度学习框架）

目录

前言：

一、实验要求：

二、环境配置：

三、代码文件：

1、vgg.py

2、index.py

3、test.py

四、演示：

1、项目文件夹

​ （1）数据集

 （2）结果（运行前）

 （3）原图

2、相似度排序输出

3、保存结果

五、尾声

 参考资料：

---

前言：

        基于vgg网络和Keras深度学习框架的以图搜图功能实现。

一、实验要求：

        给出一张图像后，在整个数据集中（至少100个样本）找到与这张图像相似的图像（至少5张），并把图像有顺序的展示。

二、环境配置：

        解释器：python3.10

        编译器：Pycharm

        必用配置包：

numpy、h5py、matplotlib、keras、pillow

三、代码文件：

1、vgg.py

# -*- coding: utf-8 -*-
import numpy as np
from numpy import linalg as LA

from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input as preprocess_input_vgg
class VGGNet:
    def __init__(self):
        self.input_shape = (224, 224, 3)
        self.weight = 'imagenet'
        self.pooling = 'max'
        self.model_vgg = VGG16(weights = self.weight, input_shape = (self.input_shape[0], self.input_shape[1], self.input_shape[2]), pooling = self.pooling, include_top = False)
        self.model_vgg.predict(np.zeros((1, 224, 224 , 3)))

    #提取vgg16最后一层卷积特征
    def vgg_extract_feat(self, img_path):
        img = image.load_img(img_path, target_size=(self.input_shape[0], self.input_shape[1]))
        img = image.img_to_array(img)
        img = np.expand_dims(img, axis=0)
        img = preprocess_input_vgg(img)
        feat = self.model_vgg.predict(img)
        # print(feat.shape)
        norm_feat = feat[0]/LA.norm(feat[0])
        return norm_feat

2、index.py

# -*- coding: utf-8 -*-
import os
import h5py
import numpy as np
import argparse
from vgg import VGGNet

def get_imlist(path):
    return [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.jpg')]

if __name__ == "__main__":
    database = r'D:\pythonProject5\flower_roses'
    index = 'vgg_featureCNN.h5'
    img_list = get_imlist(database)

    print("         feature extraction starts")

    feats = []
    names = []

    model = VGGNet()
    for i, img_path in enumerate(img_list):
        norm_feat = model.vgg_extract_feat(img_path)  # 修改此处改变提取特征的网络
        img_name = os.path.split(img_path)[1]
        feats.append(norm_feat)
        names.append(img_name)
        print("extracting feature from image No. %d , %d images in total" % ((i + 1), len(img_list)))

    feats = np.array(feats)

    output = index
    print("      writing feature extraction results ...")

    h5f = h5py.File(output, 'w')
    h5f.create_dataset('dataset_1', data=feats)
    # h5f.create_dataset('dataset_2', data = names)
    h5f.create_dataset('dataset_2', data=np.string_(names))
    h5f.close()

3、test.py

# -*- coding: utf-8 -*-
from vgg import VGGNet
import numpy as np
import h5py
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import argparse

query = r'D:\pythonProject5\rose\red_rose.jpg'
index = 'vgg_featureCNN.h5'
result = r'D:\pythonProject5\flower_roses'
# read in indexed images' feature vectors and corresponding image names
h5f = h5py.File(index, 'r')
# feats = h5f['dataset_1'][:]
feats = h5f['dataset_1'][:]
print(feats)
imgNames = h5f['dataset_2'][:]
print(imgNames)
h5f.close()
print("               searching starts")
queryImg = mpimg.imread(query)
plt.title("Query Image")
plt.imshow(queryImg)
plt.show()

# init VGGNet16 model
model = VGGNet()
# extract query image's feature, compute simlarity score and sort
queryVec = model.vgg_extract_feat(query)  # 修改此处改变提取特征的网络
print(queryVec.shape)
print(feats.shape)
scores = np.dot(queryVec, feats.T)
rank_ID = np.argsort(scores)[::-1]
rank_score = scores[rank_ID]
# print (rank_ID)
print(rank_score)
# number of top retrieved images to show
maxres = 6  # 检索出6张相似度最高的图片
imlist = []
for i, index in enumerate(rank_ID[0:maxres]):
    imlist.append(imgNames[index])
    print(type(imgNames[index]))
    print("image names: " + str(imgNames[index]) + " scores: %f" % rank_score[i])
print("top %d images in order are: " % maxres, imlist)
# show top #maxres retrieved result one by one
for i, im in enumerate(imlist):
    image = mpimg.imread(result + "/" + str(im, 'utf-8'))
    plt.title("search output %d" % (i + 1))
    plt.imshow(np.uint8(image))
    f = plt.gcf()  # 获取当前图像
    f.savefig(r'D:\pythonProject5\result\{}.jpg'.format(i),dpi=100)
    #f.clear()  # 释放内存
    plt.show()

四、演示：

1、项目文件夹

 （1）数据集

 （2）结果（运行前）

 （3）原图

2、相似度排序输出

 

3、保存结果

五、尾声

        分享一个实用又简单的爬虫代码，搜图顶呱呱！

import os
import time
import requests
import re
def imgdata_set(save_path,word,epoch):
    q=0     #停止爬取图片条件
    a=0     #图片名称
    while(True):
        time.sleep(1)
        url="https://image.baidu.com/search/flip?tn=baiduimage&ie=utf-8&word={}&pn={}&ct=&ic=0&lm=-1&width=0&height=0".format(word,q)
        #word=需要搜索的名字
        headers={
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.96 Safari/537.36 Edg/88.0.705.56'
        }
        response=requests.get(url,headers=headers)
        # print(response.request.headers)
        html=response.text
        # print(html)
        urls=re.findall('"objURL":"(.*?)"',html)
        # print(urls)
        for url in urls:
            print(a)    #图片的名字
            response = requests.get(url, headers=headers)
            image=response.content
            with open(os.path.join(save_path,"{}.jpg".format(a)),'wb') as f:
                f.write(image)
            a=a+1
        q=q+20
        if (q/20)>=int(epoch):
            break
if __name__=="__main__":
    save_path = input('你想保存的路径：')
    word = input('你想要下载什么图片？请输入:')
    epoch = input('你想要下载几轮图片？请输入(一轮为60张左右图片):')  # 需要迭代几次图片
    imgdata_set(save_path, word, epoch)

 参考资料：

(2条消息) 基于深度学习实现以图搜图功能_chenghaoy的博客-CSDN博客_深度学习以图搜图https://blog.csdn.net/chenghaoy/article/details/84977406(2条消息) pytorch——VGG网络搭建_heart_6662的博客-CSDN博客_pytorch 搭建vgghttps://blog.csdn.net/qq_62932195/article/details/122416591