【最佳实践】阿里云 Elasticsearch 向量检索4步搭建“以图搜图”搜索引擎-阿里云开发者社区

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【最佳实践】阿里云 Elasticsearch 向量检索4步搭建“以图搜图”搜索引擎

简介: “图片搜索”是作为导购类网站,比较常见的一种功能,其实现的方式也有多种。但如何做到快速、精准、简单等特性,本文给你答案。

本文作者: 小森同学

阿里云Elasticsearch客户真实实践分享
文中涉及到的图片特征提取,使用了yongyuan.name的VGGNet库,再此表示感谢!

“图片搜索”是作为导购类网站比较常见的一种功能,其实现的方式有很多,比如“哈西指纹+汉明距离计算”、“特征向量+milvus”,但在实际的应用场景中,要做到快速、精准、简单等特性是比较困难的事情。

“图片搜索”方式优缺点对比


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方案三查询效果:

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四步搭建“以图搜图”搜索引擎

以下是基于 阿里云 Elasticsearch 6.7 版本,通过安装阿里云 Elasticsearch 向量检索插件【aliyun-knn】 实现,且设计图片向量特征为512维度。

如果自建 Elasticsearch ,是无法使用aliyun-knn插件的,自建建议使用开源 Elasticsearch 7.x版本,并安装fast-elasticsearch-vector-scoring插件(https://github.com/lior-k/fast-elasticsearch-vector-scoring/)

一、 Elasticsearch 索引设计

1.1、索引结构

1.    # 创建一个图片索引  
2.    PUT images_v2  
3.    {  
4.      "aliases": {  
5.        "images": {}  
6.      },   
7.      "settings": {  
8.        "index.codec": "proxima",  
9.        "index.vector.algorithm": "hnsw",  
10.        "index.number_of_replicas":1,  
11.        "index.number_of_shards":3  
12.      },  
13.      "mappings": {  
14.        "_doc": {  
15.          "properties": {  
16.            "feature": {  
17.              "type": "proxima_vector",  
18.              "dim": 512  
19.            },  
20.            "relation_id": {  
21.              "type": "keyword"  
22.            },  
23.            "image_path": {  
24.              "type": "keyword"  
25.            }  
26.          }  
27.        }  
28.      }  
29.    }  

1.2、DSL 语句

1.    GET images/_search  
2.      
3.    "query": {  
4.      "hnsw": {  
5.        "feature": {  
6.          "vector": [255,....255],  
7.          "size": 3,  
8.          "ef": 1  
9.        }  
10.      }  
11.    },  
12.    "from": 0,  
13.    "size": 20,   
14.    "sort": [  
15.      {  
16.        "_score": {  
17.          "order": "desc"  
18.        }  
19.      }  
20.    ],   
21.    "collapse": {  
22.      "field": "relation_id"  
23.    },  
24.    "_source": {  
25.      "includes": [  
26.        "relation_id",  
27.        "image_path"  
28.      ]  
29.    }  

二、 图片特征

extract_cnn_vgg16_keras.py

1.     # -*- coding: utf-8 -*-  
2.    # Author: yongyuan.name  
3.    import numpy as np  
4.    from numpy import linalg as LA  
5.    from keras.applications.vgg16 import VGG16  
6.    from keras.preprocessing import image  
7.    from keras.applications.vgg16 import preprocess_input  
8.    from PIL import Image, ImageFile  
9.    ImageFile.LOAD_TRUNCATED_IMAGES = True  
10.    class VGGNet:  
11.        def __init__(self):  
12.            # weights: 'imagenet'  
13.            # pooling: 'max' or 'avg'  
14.            # input_shape: (width, height, 3), width and height should >= 48  
15.            self.input_shape = (224, 224, 3)  
16.            self.weight = 'imagenet'  
17.            self.pooling = 'max'  
18.            self.model = VGG16(weights = self.weight, input_shape = (self.input_shape[0], self.input_shape[1], self.input_shape[2]), pooling = self.pooling, include_top = False)  
19.            self.model.predict(np.zeros((1, 224, 224 , 3)))  
20.        '''  
21.        Use vgg16 model to extract features  
22.        Output normalized feature vector  
23.        '''  
24.        def extract_feat(self, img_path):  
25.            img = image.load_img(img_path, target_size=(self.input_shape[0], self.input_shape[1]))  
26.            img = image.img_to_array(img)  
27.            img = np.expand_dims(img, axis=0)  
28.            img = preprocess_input(img)  
29.            feat = self.model.predict(img)  
30.            norm_feat = feat[0]/LA.norm(feat[0])  
31.            return norm_feat  

1.       # 获取图片特征  
2.    from extract_cnn_vgg16_keras import VGGNet  
3.    model = VGGNet()  
4.    file_path = "./demo.jpg"  
5.    queryVec = model.extract_feat(file_path)  
6.    feature = queryVec.tolist()  

三、 图片特征写入阿里云 Elasticsearch

helper.py

1.      import re  
2.    import urllib.request  
3.    def strip(path):  
4.        """  
5.        需要清洗的文件夹名字  
6.        清洗掉Windows系统非法文件夹名字的字符串  
7.        :param path:  
8.        :return:  
9.        """  
10.        path = re.sub(r'[?\\*|“<>:/]', '', str(path))  
11.        return path  
12.      
13.    def getfilename(url):  
14.        """  
15.        通过url获取最后的文件名  
16.        :param url:  
17.        :return:  
18.        """  
19.        filename = url.split('/')[-1]  
20.        filename = strip(filename)  
21.        return filename  
22.      
23.    def urllib_download(url, filename):  
24.        """  
25.        下载  
26.        :param url:  
27.        :param filename:  
28.        :return:  
29.        """  
30.        return urllib.request.urlretrieve(url, filename)  

train.py

1.     # coding=utf-8  
2.    import mysql.connector  
3.    import os  
4.    from helper import urllib_download, getfilename  
5.    from elasticsearch5 import Elasticsearch, helpers  
6.    from extract_cnn_vgg16_keras import VGGNet  
7.    model = VGGNet()  
8.    http_auth = ("elastic", "123455")  
9.    es = Elasticsearch("http://127.0.0.1:9200", http_auth=http_auth)  
10.    mydb = mysql.connector.connect(  
11.        host="127.0.0.1",  # 数据库主机地址  
12.        user="root",  # 数据库用户名  
13.        passwd="123456",  # 数据库密码  
14.        database="images"  
15.    )  
16.    mycursor = mydb.cursor()  
17.    imgae_path = "./images/"  
18.    def get_data(page=1):  
19.        page_size = 20  
20.        offset = (page - 1) * page_size  
21.        sql = """  
22.        SELECT id, relation_id, photo FROM  images  LIMIT {0},{1}  
23.        """  
24.        mycursor.execute(sql.format(offset, page_size))  
25.        myresult = mycursor.fetchall()  
26.        return myresult  
27.      
28.    def train_image_feature(myresult):  
29.        indexName = "images"  
30.        photo_path = "http://域名/{0}"  
31.        actions = []  
32.        for x in myresult:  
33.                id = str(x[0])  
34.        relation_id = x[1]  
35.        # photo = x[2].decode(encoding="utf-8")  
36.        photo = x[2]  
37.        full_photo = photo_path.format(photo)  
38.        filename = imgae_path + getfilename(full_photo)  
39.        if not os.path.exists(filename):  
40.            try:  
41.                urllib_download(full_photo, filename)  
42.            except BaseException as e:  
43.                print("gid:{0}的图片{1}未能下载成功".format(gid, full_photo))  
44.                continue  
45.        if not os.path.exists(filename):  
46.             continue  
47.        try:  
48.            feature = model.extract_feat(filename).tolist()  
49.            action = {  
50.            "_op_type": "index",  
51.            "_index": indexName,  
52.            "_type": "_doc",  
53.            "_id": id,  
54.            "_source": {  
55.                                "relation_id": relation_id,  
56.                                "feature": feature,  
57.                                "image_path": photo  
58.            }  
59.            }  
60.            actions.append(action)  
61.        except BaseException as e:  
62.            print("id:{0}的图片{1}未能获取到特征".format(id, full_photo))  
63.            continue  
64.        # print(actions)  
65.        succeed_num = 0  
66.        for ok, response in helpers.streaming_bulk(es, actions):  
67.            if not ok:  
68.                print(ok)  
69.                print(response)  
70.            else:  
71.                succeed_num += 1  
72.                print("本次更新了{0}条数据".format(succeed_num))  
73.                es.indices.refresh(indexName)  
74.      
75.    page = 1  
76.    while True:  
77.        print("当前第{0}页".format(page))  
78.        myresult = get_data(page=page)  
79.        if not myresult:  
80.            print("没有获取到数据了,退出")  
81.            break  
82.        train_image_feature(myresult)  
83.         page += 1 

四、 搜索图片

1.    import requests  
2.    import json  
3.    import os  
4.    import time  
5.    from elasticsearch5 import Elasticsearch  
6.    from extract_cnn_vgg16_keras import VGGNet  
7.    model = VGGNet()  
8.    http_auth = ("elastic", "123455")  
9.    es = Elasticsearch("http://127.0.0.1:9200", http_auth=http_auth)  
10.    #上传图片保存  
11.    upload_image_path = "./runtime/"  
12.    upload_image = request.files.get("image")  
13.    upload_image_type = upload_image.content_type.split('/')[-1]  
14.    file_name = str(time.time())[:10] + '.' + upload_image_type  
15.    file_path = upload_image_path + file_name  
16.    upload_image.save(file_path)  
17.    # 计算图片特征向量  
18.    queryVec = model.extract_feat(file_path)  
19.    feature = queryVec.tolist()  
20.    # 删除图片  
21.    os.remove(file_path)  
22.    # 根据特征向量去ES中搜索  
23.    body = {  
24.        "query": {  
25.            "hnsw": {  
26.                "feature": {  
27.                    "vector": feature,  
28.                    "size": 5,  
29.                    "ef": 10  
30.                }  
31.            }  
32.        },  
33.        # "collapse": {  
34.        # "field": "relation_id"  
35.        # },  
36.        "_source": {"includes": ["relation_id", "image_path"]},  
37.        "from": 0,  
38.        "size": 40  
39.    }  
40.    indexName = "images"  
41.    res = es.search(indexName, body=body)  
42.    # 返回的结果,最好根据自身情况,将得分低的过滤掉...经过测试, 得分在0.65及其以上的,比较符合要求  

依赖的包

1.      mysql_connector_repackaged  
2.    elasticsearch  
3.    Pillow  
4.    tensorflow  
5.    requests  
6.    pandas  
7.    Keras  
8.    numpy  

总结:

从“用户体验”角度考虑,在可感知层面,速度和精准度决定了产品在用户使用过程中,是否满足“好用”的感觉,通过阿里云 Elasticsearch 向量检索(aliyun-knn)简单四步搭建的“以图搜图”搜索引擎,不仅满足“好用”,同时操作简单一步到位的特征,也加分不少。

相关活动


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