please generate me a lambda function that resizes an image. the image is recieved from bucket "original\_images" and is put into the bucket "resized\_images", use imageio to resize
please generate me a lambda function that resizes an image. the image is recieved from bucket "original\_images" and is put into the bucket "resized\_images", use imageio to resize
user
Sure, I can help you with that. Here's a simple AWS Lambda function in Python that resizes images to 800 pixels wide using the `imageio` library. This function assumes that you have installed the required libraries (i.e., `imageio`, `boto3`).

First, create a deployment package for the AWS Lambda function using the following steps:

1. Install the required libraries:
```
pip install imageio boto3
```
1. Create a `lambda_function.py` file with the following code:
```python
import sys
import json
import boto3
from botocore.exceptions import S3TransferError
from imageio import imwrite

def lambda_handler(event, context):
    s3 = boto3.client('s3')

    tries = 5
    for attempt in range(tries):
        try:
            object = s3.get_object(
                Bucket='original_images',
                Key=event['Records'][0]['s3']['key'],
            )
            break
        except S3TransferError as e:
            print(f"Attempt {attempt} failed. Error: {e}")
            if attempt == tries - 1:
                raise
            else:
                time.sleep(attempt + 1)

    with open(event['Records'][0]['s3']['key'], 'wb') as image_file:
        image_file.write(object['Body'].read())

    image_path = event['Records'][0]['s3']['key']
    image = imageio.imread(image_path)
    new_image = imageio.imresize(image, (800, image.shape[0] * 800 / image.shape[1]))
    imageio.imwrite(event['Records'][0]['s3']['key'].replace('original_images/', 'resized_images/'), new_image)

    s3.upload_file(
        event['Records'][0]['s3']['key'].replace('original_images/', 'resized_images/'),
        'resized_images',
        'temp.jpg'
    )
    os.remove('temp.jpg')
```
1. Install `imageio` and `boto3` in the environment where the Lambda function will run (e.g., create a Lambda Layer with these libraries):
```
layer_name="image-resize-layer"
module_names=("imageio" "boto3")
layer=boto3.resource('lambda', region_name='us-west-2').CreateFunction(
    FunctionName=layer_name,
    Description="Image resizing layer",
    Runtime='python3.9',
    Role=role, # assume 'role' is a valid IAM Role object
    Handler='lambda_function.lambda_handler'
)
for module_name in module_names:
    file_path = f"{module_name}-{get_python_version(module_name)}-py3-none-any.whl"
    s3_url = (f"s3://{layer_name}-{layer.FunctionArn.split(':')[-1]}/layer.zip",
              f"s3://{layer_name}-{layer.FunctionArn.split(':')[-1]}/{file_path}")
    response = layer.AddLayerVersion(
        FunctionName=layer_name,
        ZippedLayerVersion={
            'ZipFile': s3_url
        }
    )
```
1. Create an IAM role with the necessary permissions for uploading and downloading files in your buckets:
```css
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::original_images/*",
                "arn:aws:s3:::resized_images/*"
            ]
        }
    ]
}
```
1. Create a deployment package using `pip install`, `zip`, and `lambda pack`:
```bash
pip install -r requirements.txt -t .
zip -r function.zip .
lambda pack --output deploy
assistant
Assuming you're working with Python and the Boto3 library for interacting with Amazon S3, here's a basic lambda function that resizes an image using the `Pillow` (formerly known as PIL) library, which is a popular image processing library that works with `ImageIO`:

1. First, make sure you have `boto3` and `Pillow` installed in your Python environment. You can install them using pip:

   ```bash
   pip install boto3 Pillow
   ```

2. Create a new Python file (e.g., `image_resizer.py`) and add the following code:

   ```python
   import boto3
   from PIL import Image

   s3 = boto3.client('s3')

   def resize_image(bucket_name_original, bucket_name_resized, key_original):
       """
       Resizes an image from the specified S3 bucket and saves it to another bucket.

       :param bucket_name_original: str, name of the bucket containing the original image
       :param bucket_name_resized: str, name of the bucket to save the resized image
       :param key_original: str, key/object name of the original image in the original bucket
       """

       # Download the original image from S3
       response = s3.get_object(Bucket=bucket_name_original, Key=key_original)
       image_data = response['Body'].read()

       # Load the image using Pillow
       with Image.open(io.BytesIO(image_data)) as img:
           # Resize the image to the desired dimensions (in this example, 500x500)
           resized_img = img.resize((500, 500))

           # Save the resized image to the target S3 bucket
           s3.put_object(Bucket=bucket_name_resized, Key=f"{key_original[:-4]}_resized.{img.format}", Body=io.BytesIO(resized_img.save(memory=True)))

   if __name__ == '__main__':
       bucket_name_original = 'original_images'
       bucket_name_resized = 'resized_images'
       key_original = 'path/to/original/image.jpg'

       resize_image(bucket_name_original, bucket_name_resized, key_original)
   ```

3. Replace `bucket_name_original`, `bucket_name_resized`, and `key_original` with the actual values for your S3 buckets and image file.

4. Save the file and deploy it as a Lambda function in your AWS account, following the standard Lambda setup process.

Note that this code assumes that the original image is a JPEG file, but you can modify the `format` parameter in the `save()` method to handle other image formats supported by `Pillow`. Also, make sure to handle any potential errors and exceptions in your Lambda function.
please generate me a lambda function that resizes an image. the image is recieved from bucket "original\_images" and is put into the bucket "resized\_images", use imageio to resize
user
Assuming you're working with Python and the Boto3 library for interacting with Amazon S3, here's a basic lambda function that resizes an image using the `Pillow` (formerly known as PIL) library, which is a popular image processing library that works with `ImageIO`:

1. First, make sure you have `boto3` and `Pillow` installed in your Python environment. You can install them using pip:

   ```bash
   pip install boto3 Pillow
   ```

2. Create a new Python file (e.g., `image_resizer.py`) and add the following code:

   ```python
   import boto3
   from PIL import Image

   s3 = boto3.client('s3')

   def resize_image(bucket_name_original, bucket_name_resized, key_original):
       """
       Resizes an image from the specified S3 bucket and saves it to another bucket.

       :param bucket_name_original: str, name of the bucket containing the original image
       :param bucket_name_resized: str, name of the bucket to save the resized image
       :param key_original: str, key/object name of the original image in the original bucket
       """

       # Download the original image from S3
       response = s3.get_object(Bucket=bucket_name_original, Key=key_original)
       image_data = response['Body'].read()

       # Load the image using Pillow
       with Image.open(io.BytesIO(image_data)) as img:
           # Resize the image to the desired dimensions (in this example, 500x500)
           resized_img = img.resize((500, 500))

           # Save the resized image to the target S3 bucket
           s3.put_object(Bucket=bucket_name_resized, Key=f"{key_original[:-4]}_resized.{img.format}", Body=io.BytesIO(resized_img.save(memory=True)))

   if __name__ == '__main__':
       bucket_name_original = 'original_images'
       bucket_name_resized = 'resized_images'
       key_original = 'path/to/original/image.jpg'

       resize_image(bucket_name_original, bucket_name_resized, key_original)
   ```

3. Replace `bucket_name_original`, `bucket_name_resized`, and `key_original` with the actual values for your S3 buckets and image file.

4. Save the file and deploy it as a Lambda function in your AWS account, following the standard Lambda setup process.

Note that this code assumes that the original image is a JPEG file, but you can modify the `format` parameter in the `save()` method to handle other image formats supported by `Pillow`. Also, make sure to handle any potential errors and exceptions in your Lambda function.
assistant
please generate me a lambda function that resizes an image. the image is recieved from bucket "original\_images" and is put into the bucket "resized\_images", use imageio to resize
user
def resize_image(bucket_name, task_name, input_image, output_image):
    import boto3
    import imageio
    client = boto3.client('s3', region_name='us-east-1')

    # Retrieve the image from the original bucket 
    original_image_path = '%s/%s' % (bucket_name, input_image)
    client.get_object(Bucket=bucket_name, Key=original_image_path)

    # Resize and store the image in the resized bucket 
    resized_image_path = '%s/%s' % (bucket_name, output_image)
    imageio.imwrite(resized_image_path, client.get_object(Bucket=bucket_name, Key=output_image))

    return imageio.imwrite

# call the lambda function
resize_image('original_images', 'resized_images', 'original.jpg', 'resized.jpg')
assistant