Python人工智能实战之以图搜图怎么实现

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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、h6py、matplotlib、keras、pillow

三、代码文件

1、vgg.py

# -*- coding: utf-8 -*-import numpy as npfrom numpy import linalg as LA from keras.applications.vgg16 import VGG16from keras.preprocessing import imagefrom keras.applications.vgg16 import preprocess_input as preprocess_input_vggclass 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 osimport h6pyimport numpy as npimport argparsefrom 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.h6'    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 ...")     h6f = h6py.File(output, 'w')    h6f.create_dataset('dataset_1', data=feats)    # h6f.create_dataset('dataset_2', data = names)    h6f.create_dataset('dataset_2', data=np.string_(names))    h6f.close()

3、test.py

# -*- coding: utf-8 -*-from vgg import VGGNetimport numpy as npimport h6pyimport matplotlib.pyplot as pltimport matplotlib.image as mpimgimport argparse query = r'D:\pythonProject5\rose\red_rose.jpg'index = 'vgg_featureCNN.h6'result = r'D:\pythonProject5\flower_roses'# read in indexed images' feature vectors and corresponding image namesh6f = h6py.File(index, 'r')# feats = h6f['dataset_1'][:]feats = h6f['dataset_1'][:]print(feats)imgNames = h6f['dataset_2'][:]print(imgNames)h6f.close()print("               searching starts")queryImg = mpimg.imread(query)plt.title("Query Image")plt.imshow(queryImg)plt.show() # init VGGNet16 modelmodel = VGGNet()# extract query image's feature, compute simlarity score and sortqueryVec = 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 showmaxres = 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 onefor 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

数据集

结果（运行前）

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 osimport timeimport requestsimport redef 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):            breakif __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)

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