重启es之后,之前创建的template中的mappings变成空了
Elasticsearch • byx313 回复了问题 • 2 人关注 • 1 个回复 • 1467 次浏览 • 2020-03-15 22:37
基于ES的aliyun-knn插件,开发的以图搜图搜索引擎
Elasticsearch • 森 发表了文章 • 1 个评论 • 8664 次浏览 • 2020-03-15 12:47
基于ES的aliyun-knn插件,开发的以图搜图搜索引擎
<br /> 本例是基于Elasticsearch6.7 版本, 安装了aliyun-knn插件;设计的图片向量特征为512维度.<br /> 如果自建ES,是无法使用aliyun-knn插件的,自建建议使用ES7.x版本,并按照fast-elasticsearch-vector-scoring插件(<a href="https://github.com/lior-k/fast-elasticsearch-vector-scoring" rel="nofollow" target="_blank">https://github.com/lior-k/fast ... oring</a>/)<br />
由于我的python水平有限,文中设计到的图片特征提取,使用了yongyuan.name的VGGNet库,再此表示感谢!
一、 ES设计
1.1 索引结构
```json
创建一个图片索引
PUT images_v2
{
"aliases": {
"images": {}
},
"settings": {
"index.codec": "proxima",
"index.vector.algorithm": "hnsw",
"index.number_of_replicas":1,
"index.number_of_shards":3
},
"mappings": {
"_doc": {
"properties": {
"feature": {
"type": "proxima_vector",
"dim": 512
},
"relation_id": {
"type": "keyword"
},
"image_path": {
"type": "keyword"
}
}
}
}
}
```
1.2 DSL语句
<br /> GET images/_search<br /> {<br /> "query": {<br /> "hnsw": {<br /> "feature": {<br /> "vector": [255,....255],<br /> "size": 3,<br /> "ef": 1<br /> }<br /> }<br /> },<br /> "from": 0,<br /> "size": 20, <br /> "sort": [<br /> {<br /> "_score": {<br /> "order": "desc"<br /> }<br /> }<br /> ], <br /> "collapse": {<br /> "field": "relation_id"<br /> },<br /> "_source": {<br /> "includes": [<br /> "relation_id",<br /> "image_path"<br /> ]<br /> }<br /> }<br />
二、图片特征
extract_cnn_vgg16_keras.py
```python
-- coding: utf-8 --
Author: yongyuan.name
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
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
class VGGNet:
def init(self):
weights: 'imagenet'
# pooling: 'max' or 'avg'<br />
# input_shape: (width, height, 3), width and height should >= 48<br />
self.input_shape = (224, 224, 3)<br />
self.weight = 'imagenet'<br />
self.pooling = 'max'<br />
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)<br />
self.model.predict(np.zeros((1, 224, 224 , 3)))<br />
'''<br />
Use vgg16 model to extract features<br />
Output normalized feature vector<br />
'''<br />
def extract_feat(self, img_path):<br />
img = image.load_img(img_path, target_size=(self.input_shape[0], self.input_shape[1]))<br />
img = image.img_to_array(img)<br />
img = np.expand_dims(img, axis=0)<br />
img = preprocess_input(img)<br />
feat = self.model.predict(img)<br />
norm_feat = feat[0]/LA.norm(feat[0])<br />
return norm_feat<br />
<br /> <br /> <br />
python
获取图片特征
from extract_cnn_vgg16_keras import VGGNet
model = VGGNet()
file_path = "./demo.jpg"
queryVec = model.extract_feat(file_path)
feature = queryVec.tolist()
```
三、将图片特征写入ES
helper.py
python<br /> import re<br /> import urllib.request<br /> def strip(path):<br /> """<br /> 需要清洗的文件夹名字<br /> 清洗掉Windows系统非法文件夹名字的字符串<br /> :param path:<br /> :return:<br /> """<br /> path = re.sub(r'[?\\*|“<>:/]', '', str(path))<br /> return path<br /> <br /> def getfilename(url):<br /> """<br /> 通过url获取最后的文件名<br /> :param url:<br /> :return:<br /> """<br /> filename = url.split('/')[-1]<br /> filename = strip(filename)<br /> return filename<br /> <br /> def urllib_download(url, filename):<br /> """<br /> 下载<br /> :param url:<br /> :param filename:<br /> :return:<br /> """<br /> return urllib.request.urlretrieve(url, filename)<br />
train.py
```python
coding=utf-8
import mysql.connector
import os
from helper import urllib_download, getfilename
from elasticsearch5 import Elasticsearch, helpers
from extract_cnn_vgg16_keras import VGGNet
model = VGGNet()
http_auth = ("elastic", "123455")
es = Elasticsearch("<a href="http://127.0.0.1:9200"" rel="nofollow" target="_blank">http://127.0.0.1:9200", http_auth=http_auth)
mydb = mysql.connector.connect(
host="127.0.0.1", # 数据库主机地址
user="root", # 数据库用户名
passwd="123456", # 数据库密码
database="images"
)
mycursor = mydb.cursor()
imgae_path = "./images/"
def get_data(page=1):
page_size = 20
offset = (page - 1) * page_size
sql = """
SELECT id, relation_id, photo FROM images LIMIT {0},{1}
"""
mycursor.execute(sql.format(offset, page_size))
myresult = mycursor.fetchall()
return myresult
def train_image_feature(myresult):
indexName = "images"
photo_path = "http://域名/{0}"
actions = []
for x in myresult:
id = str(x[0])
relation_id = x[1]
photo = x[2].decode(encoding="utf-8")
photo = x[2]<br />
full_photo = photo_path.format(photo)<br />
filename = imgae_path + getfilename(full_photo)<br />
if not os.path.exists(filename):<br />
try:<br />
urllib_download(full_photo, filename)<br />
except BaseException as e:<br />
print("gid:{0}的图片{1}未能下载成功".format(gid, full_photo))<br />
continue<br />
if not os.path.exists(filename):<br />
continue<br />
try:<br />
feature = model.extract_feat(filename).tolist()<br />
action = {<br />
"_op_type": "index",<br />
"_index": indexName,<br />
"_type": "_doc",<br />
"_id": id,<br />
"_source": {<br />
"relation_id": relation_id,<br />
"feature": feature,<br />
"image_path": photo<br />
}<br />
}<br />
actions.append(action)<br />
except BaseException as e:<br />
print("id:{0}的图片{1}未能获取到特征".format(id, full_photo))<br />
continue<br />
# print(actions)<br />
succeed_num = 0<br />
for ok, response in helpers.streaming_bulk(es, actions):<br />
if not ok:<br />
print(ok)<br />
print(response)<br />
else:<br />
succeed_num += 1<br />
print("本次更新了{0}条数据".format(succeed_num))<br />
es.indices.refresh(indexName)<br />
page = 1
while True:
print("当前第{0}页".format(page))
myresult = get_data(page=page)
if not myresult:
print("没有获取到数据了,退出")
break
train_image_feature(myresult)
page += 1
```
四、搜索图片
```python
import requests
import json
import os
import time
from elasticsearch5 import Elasticsearch
from extract_cnn_vgg16_keras import VGGNet
model = VGGNet()
http_auth = ("elastic", "123455")
es = Elasticsearch("<a href="http://127.0.0.1:9200"" rel="nofollow" target="_blank">http://127.0.0.1:9200", http_auth=http_auth)
上传图片保存
upload_image_path = "./runtime/"
upload_image = request.files.get("image")
upload_image_type = upload_image.content_type.split('/')[-1]
file_name = str(time.time())[:10] + '.' + upload_image_type
file_path = upload_image_path + file_name
upload_image.save(file_path)
计算图片特征向量
queryVec = model.extract_feat(file_path)
feature = queryVec.tolist()
删除图片
os.remove(file_path)
根据特征向量去ES中搜索
body = {
"query": {
"hnsw": {
"feature": {
"vector": feature,
"size": 5,
"ef": 10
}
}
},
"collapse": {
# "field": "relation_id"<br />
# },<br />
"_source": {"includes": ["relation_id", "image_path"]},<br />
"from": 0,<br />
"size": 40<br />
}
indexName = "images"
res = es.search(indexName, body=body)
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
```
五、依赖的包
```
mysql_connector_repackaged
elasticsearch
Pillow
tensorflow
requests
pandas
Keras
numpy
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