python-根据相似性对图像进行分类

我有30,40张人类图片,我想用Python代码得到.并制作一组类似的照片.像约翰的5张照片和彼得的10张照片.像这样 .我是图像处理方面的新手.所以我的问题是哪种算法最适合这个.我想在AWS lambda函数上执行此操作.任何帮助将不胜感激.

P.S(这是我在该领域的第一个任务.请忽略告诉我改善它们的错误,谢谢)

解决方法:

我建议您使用AWS Rekognition进行操作.很简单
您可以通过3个简单的步骤来实现所需的目标：

1.上载带有元数据的图像：表示您要将具有其姓名的人的图像上载到s3,以存储其信息以供以后参考

2.为照片建立索引：这意味着向面部添加信息标签,该信息存储在dynamodb中,并通过index_faces api完成

3.比较带有索引面孔的照片：这将通过rekognition search_faces_by_image api实现

现在第1部分代码：使用元数据批量上传

import boto3

s3 = boto3.resource('s3')

# Get list of objects for indexing
images=[('image01.jpeg','Albert Einstein'),
      ('image02.jpeg','Candy'),
      ('image03.jpeg','Armstrong'),
      ('image04.jpeg','Ram'),
      ('image05.jpeg','Peter'),
      ('image06.jpeg','Shashank')
      ]

# Iterate through list to upload objects to S3   
for image in images:
    file = open(image[0],'rb')
    object = s3.Object('rekognition-pictures','index/'+ image[0])
    ret = object.put(Body=file,
                    Metadata={'FullName':image[1]}
                    )

现在第2部分代码：索引编制

from __future__ import print_function

import boto3
from decimal import Decimal
import json
import urllib

print('Loading function')

dynamodb = boto3.client('dynamodb')
s3 = boto3.client('s3')
rekognition = boto3.client('rekognition')


# --------------- Helper Functions ------------------

def index_faces(bucket, key):

    response = rekognition.index_faces(
        Image={"S3Object":
            {"Bucket": bucket,
            "Name": key}},
            CollectionId="family_collection")
    return response

def update_index(tableName,faceId, fullName):
    response = dynamodb.put_item(
        TableName=tableName,
        Item={
            'RekognitionId': {'S': faceId},
            'FullName': {'S': fullName}
            }
        ) 

# --------------- Main handler ------------------

def lambda_handler(event, context):

    # Get the object from the event
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = urllib.unquote_plus(
        event['Records'][0]['s3']['object']['key'].encode('utf8'))

    try:

        # Calls Amazon Rekognition IndexFaces API to detect faces in S3 object 
        # to index faces into specified collection

        response = index_faces(bucket, key)

        # Commit faceId and full name object metadata to DynamoDB

        if response['ResponseMetadata']['HTTPStatusCode'] == 200:
            faceId = response['FaceRecords'][0]['Face']['FaceId']

            ret = s3.head_object(Bucket=bucket,Key=key)
            personFullName = ret['Metadata']['fullname']

            update_index('family_collection',faceId,personFullName)

        # Print response to console
        print(response)

        return response
    except Exception as e:
        print(e)
        print("Error processing object {} from bucket {}. ".format(key, bucket))
       raise e

现在第3部分代码：比较

import boto3
import io
from PIL import Image

rekognition = boto3.client('rekognition', region_name='eu-west-1')
dynamodb = boto3.client('dynamodb', region_name='eu-west-1')

image = Image.open("group1.jpeg")
stream = io.BytesIO()
image.save(stream,format="JPEG")
image_binary = stream.getvalue()


response = rekognition.search_faces_by_image(
        CollectionId='family_collection',
        Image={'Bytes':image_binary}                                       
        )

for match in response['FaceMatches']:
   print (match['Face']['FaceId'],match['Face']['Confidence'])

    face = dynamodb.get_item(
        TableName='family_collection',  
        Key={'RekognitionId': {'S': match['Face']['FaceId']}}
        )

    if 'Item' in face:
        print (face['Item']['FullName']['S'])
    else:
        print ('no match found in person lookup')

使用上面的比较功能,您将获得照片中人脸的名称,然后您可以决定下一步要做什么,例如通过重命名照片将具有相同名称的照片存储到其他文件夹中,这将为不同文件夹中的不同人提供照片

先决条件：

创建一个名为family_collection的识别集合

aws rekognition create-collection --collection-id family_collection --region eu-west-1 

创建一个名为family_collection的动态表

aws dynamodb create-table --table-name family_collection \
--attribute-definitions AttributeName=RekognitionId,AttributeType=S \
--key-schema AttributeName=RekognitionId,KeyType=HASH \
--provisioned-throughput ReadCapacityUnits=1,WriteCapacityUnits=1 \
--region eu-west-1

标签：python,amazon-web-services,classification,aws-lambda,image-processing
来源： https://codeday.me/bug/20191010/1883199.html