How to read image file from S3 bucket directly into memory?

Further development from Greg Merritt's answer to solve all errors in the comment section, using BytesIO instead of StringIO, using PIL Image instead of matplotlib.image.

The following function works for python3 and boto3. Similarly, write_image_to_s3 function is a bonus.

from PIL import Image
from io import BytesIO
import numpy as np

def read_image_from_s3(bucket, key, region_name='ap-southeast-1'):
    """Load image file from s3.

    Parameters
    ----------
    bucket: string
        Bucket name
    key : string
        Path in s3

    Returns
    -------
    np array
        Image array
    """
    s3 = boto3.resource('s3', region_name='ap-southeast-1')
    bucket = s3.Bucket(bucket)
    object = bucket.Object(key)
    response = object.get()
    file_stream = response['Body']
    im = Image.open(file_stream)
    return np.array(im)

def write_image_to_s3(img_array, bucket, key, region_name='ap-southeast-1'):
    """Write an image array into S3 bucket

    Parameters
    ----------
    bucket: string
        Bucket name
    key : string
        Path in s3

    Returns
    -------
    None
    """
    s3 = boto3.resource('s3', region_name)
    bucket = s3.Bucket(bucket)
    object = bucket.Object(key)
    file_stream = BytesIO()
    im = Image.fromarray(img_array)
    im.save(file_stream, format='jpeg')
    object.put(Body=file_stream.getvalue())

Streaming the image is possible by specifying the file format in imread().

import boto3
from io import BytesIO
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

resource = boto3.resource('s3', region_name='us-east-2')
bucket = resource.Bucket('sentinel-s2-l1c')

image_object = bucket.Object('tiles/10/S/DG/2015/12/7/0/B01.jp2')
image = mpimg.imread(BytesIO(image_object.get()['Body'].read()), 'jp2')

plt.figure(0)
plt.imshow(image)

Slightly different approach using client:

import boto3
import io
from matplotlib import pyplot as plt

client = boto3.client("s3")

bucket='my_bucket'
key= 'my_key'

outfile = io.BytesIO()
client.download_fileobj(bucket, key, outfile)
outfile.seek(0)
img = plt.imread(outfile)

plt.imshow(img)
plt.show()

object = bucket.Object('tiles/10/S/DG/2015/12/7/0/B01.jp2')
img_data = object.get().get('Body').read()

I see a lot of good answers here. Here is my code snippet with AWS Config if you quickly test out the solution. Please note that it's not recommended to put your AWS credentials in the code body, rather it should come from the .env file or from the AWS Keystore.

import os
import boto3
from PIL import Image
import io

AWS_ACCESS_KEY_ID = 'your-aws-access-key'
AWS_SECRET_ACCESS_KEY = 'your-aws-secret'

s3 = boto3.resource('s3',
                    aws_access_key_id=AWS_ACCESS_KEY_ID,
                    aws_secret_access_key=AWS_SECRET_ACCESS_KEY)

def image_from_s3(bucket, key):
    bucket = s3.Bucket(bucket)
    image = bucket.Object(key)
    img_data = image.get().get('Body').read()
    return Image.open(io.BytesIO(img_data))

# call the function
image_from_s3("your-aws-bucket-name", "file-path")

# example
image_from_s3("my-images", "profile/2022/123.png")

The temporary file solution by Hyeungshik Jung looks good, but I noticed that the file somehow seem to be downloaded in a lazy fashion. This leads to a behavior that if you call img.shape() and you'll get an empty dimension tuple as a return value () even after you called object.download_fileobj(f). I resolved this issue by applying a f.seek(0,2) to the file descriptor - then all following operations work properly, e.g. returning all proper dimensions (704, 1024).

...
tmp = tempfile.NamedTemporaryFile()

with open(tmp.name, 'wb') as f:
    object.download_fileobj(f)
    f.seek(0,2) 
    img=mpimg.imread(tmp.name)
    print (img.shape)

Make sure that you will read a byte type data from S3 but Tensorflow needs a string tensor to convert to uint8 image. And this method doesn't need Pillow.

import boto3
import tensorflow as tf

credentials = boto3.Session(botocore_session=boto3.setup_default_session(), 
                                region_name="us-east-1").get_credentials()
    

s3 = boto3.Session(aws_access_key_id=credentials.access_key,
                      aws_secret_access_key=credentials.secret_key).client('s3') 

#file_on_s3 : 's3://mybucket/data/sample.jpg'
bucket_name = 'mybucket'
file_key = 'data/sample.jpg'


file_obj = s3.get_object(Bucket=bucket_name, Key=file_key)

# reading the file content in bytes
file_content = file_obj["Body"].read()  


img =  tf.io.decode_image(tf.convert_to_tensor(file_content, dtype=tf.string), 
                                channels=3, 
                                dtype=tf.dtypes.uint8, 
                                name=None, 
                                expand_animations=False)

img = tf.cast(img, tf.float32)
img_array = tf.image.resize(img, 
                            size=(224, 224),
                            method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)