インテリアデザインと商品写真における Amazon Nova Canvas の実用例

{
    "taskType": "TEXT_IMAGE",
    "textToImageParams": {
        "conditionImage": string (Base64 encoded image), #Original living room
        "controlMode": "SEGMENTATION", 
        "controlStrength": float, #Specify how closely to follow the condition       #image (0.0-1.0; Default: 0.7).
        "text": string, #A minimalistic living room ( ミニマリスティックなリビングルーム )
        "negativeText": string
    },
    "imageGenerationConfig": {
        "width": int,
        "height": int,
        "quality": "standard" | "premium",
        "cfgScale": float,
        "seed": int,
        "numberOfImages": int
    }
}

import base64  #For encoding/decoding base64 data
import io  #For handling byte streams
import json  #For JSON operations
import boto3  #AWS SDK for Python
from PIL import Image  #Python Imaging Library for image processing
from botocore.config import Config  #For AWS client configuration
#Create a variable to fix the region to where Nova Canvas is enabled 
region = "us-east-1"

#Create Bedrock client with 300 second timeout
bedrock = boto3.client(service_name='bedrock-runtime', region_name=region,
        config=Config(read_timeout=300))

#Original living room image in current working directory
input_image_path = "Original Living Room.jpg"

#Read and encode the image
def prepare_image(image_path):
    with open(image_path, 'rb') as image_file:
        image_data = image_file.read()
        base64_encoded = base64.b64encode(image_data).decode('utf-8')
    return base64_encoded

#Get the base64 encoded image
input_image = prepare_image(input_image_path)

#Set the content type and accept headers for the API call
accept = "application/json"
content_type = "application/json"

#Prepare the request body
api_request = json.dumps({
       "taskType": "TEXT_IMAGE",  #Type of generation task
       "textToImageParams": {
             "text": "A minimalistic living room",  #Prompt ( ミニマリスティックなリビングルーム )
             "negativeText": "bad quality, low res",  #What to avoid
             "conditionImage": input_image,  #Base64 encoded original living room
             "controlMode": "SEGMENTATION"  #Segmentation mode
            },
       "imageGenerationConfig": {
             "numberOfImages": 1,  #Generate one image
             "height": 1024,  #Image height, same as the input image
             "width": 1024,  #Image width, same as the input image
             "seed": 0, #Modify seed value to get variations on the same prompt
             "cfgScale": 7.0  #Classifier Free Guidance scale
            }
})

#Call the model to generate image
response = bedrock.invoke_model(body=api_request, modelId='amazon.nova-canvas-v1:0', accept=accept, contentType=content_type)

#Parse the response body
response_json = json.loads(response.get("body").read())

#Extract and decode the base64 image
base64_image = response_json.get("images")[0]  #Get first image
base64_bytes = base64_image.encode('ascii')  #Convert to ASCII
image_data = base64.b64decode(base64_bytes)  #Decode base64 to bytes

#Display the generated image
output_image = Image.open(io.BytesIO(image_data))
output_image.show()
#Save the image to current working directory
output_image.save('output_image.png')

{
    "taskType": "OUTPAINTING",
    "outPaintingParams": {
        "image": string (Base64 encoded image),
        "maskPrompt": string, #Shoes
        "maskImage": string, #Base64 encoded image
        "outPaintingMode": "DEFAULT" | "PRECISE", 
        "text": string,  #Product photoshoot of sports shoes on rocky terrain ( 岩の多い地形でのスポーツシューズの商品撮影 )
        "negativeText": string
    },
    "imageGenerationConfig": {
        "numberOfImages": int,
        "quality": "standard" | "premium",
        "cfgScale": float,
        "seed": int
    }
}

import base64  #For encoding/decoding base64 data
import io  #For handling byte streams
import json  #For JSON operations
import boto3  #AWS SDK for Python
from PIL import Image  #Python Imaging Library for image processing
from botocore.config import Config  #For AWS client configuration
#Create a variable to fix the region to where Nova Canvas is enabled 
region = "us-east-1"

#Create Bedrock client with 300 second timeout
bedrock = boto3.client(service_name='bedrock-runtime', region_name=region,
        config=Config(read_timeout=300))

#Original studio image of shoes in current working directory
input_image_path = "Shoes.png"

#Read and encode the image
def prepare_image(image_path):
    with open(image_path, 'rb') as image_file:
        image_data = image_file.read()
        base64_encoded = base64.b64encode(image_data).decode('utf-8')
    return base64_encoded

#Get the base64 encoded image
input_image = prepare_image(input_image_path)

#Set the content type and accept headers for the API call
accept = "application/json"
content_type = "application/json"

#Prepare the request body
api_request = json.dumps({
        "taskType": "OUTPAINTING",
        "outPaintingParams": {
             "image": input_image,
             "maskPrompt": "Shoes", 
             "outPaintingMode": "DEFAULT", 
             "text": "Product photoshoot of sports shoes placed on a running track outdoor", # 屋外の陸上トラックに置かれたスポーツシューズの商品撮影
             "negativeText": "bad quality, low res"
            },
        "imageGenerationConfig": {
             "numberOfImages": 1,
             "seed": 0, #Modify seed value to get variations on the same prompt
             "cfgScale": 7.0
            }
})

#Call the model to generate image
response = bedrock.invoke_model(body=api_request, modelId='amazon.nova-canvas-v1:0', accept=accept, contentType=content_type)

#Parse the response body
response_json = json.loads(response.get("body").read())

#Extract and decode the base64 image
base64_image = response_json.get("images")[0]  #Get first image
base64_bytes = base64_image.encode('ascii')  #Convert to ASCII
image_data = base64.b64decode(base64_bytes)  #Decode base64 to bytes

#Display the generated image
output_image = Image.open(io.BytesIO(image_data))
output_image.show()
#Save the image to current working directory
output_image.save('output_image.png')