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import base64
import io
from typing import Dict, Any, Optional
from PIL import Image
import numpy as np
import requests
class DynamicImageOutpainter:
"""
A sophisticated image processing class for iterative outpainting and padding.
## Key Features:
- Dynamic image cropping and centering
- Iterative outpainting with configurable steps
- Flexible padding mechanism
- AI-driven edge generation
## Usage Strategy:
1. Initialize with base image and generation parameters
2. Apply iterative padding and outpainting
3. Support multiple AI inference backends
"""
def __init__(
self,
endpoint_url: str,
api_token: str,
padding_size: int = 256,
max_iterations: int = 3
):
"""
Initialize the outpainting processor.
Args:
endpoint_url (str): AI inference endpoint URL
api_token (str): Authentication token for API
padding_size (int): Size of padding around cropped image
max_iterations (int): Maximum number of outpainting iterations
"""
self.endpoint_url = endpoint_url
self.api_token = api_token
self.padding_size = padding_size
self.max_iterations = max_iterations
self.headers = {
"Authorization": f"Bearer {self.api_token}",
"Content-Type": "application/json",
"Accept": "image/png"
}
def encode_image(self, image: Image.Image) -> str:
"""
Base64 encode a PIL Image for API transmission.
Args:
image (Image.Image): Source image to encode
Returns:
str: Base64 encoded image string
"""
buffered = io.BytesIO()
image.save(buffered, format="PNG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
def crop_to_center(self, image: Image.Image) -> Image.Image:
"""
Crop image to its center, maintaining square aspect ratio.
Args:
image (Image.Image): Source image
Returns:
Image.Image: Center-cropped image
"""
width, height = image.size
size = min(width, height)
left = (width - size) // 2
top = (height - size) // 2
right = left + size
bottom = top + size
return image.crop((left, top, right, bottom))
def create_padding_mask(self, image: Image.Image) -> Image.Image:
"""
Generate a mask for padding regions.
Args:
image (Image.Image): Source image
Returns:
Image.Image: Mask indicating padding regions
"""
mask = Image.new('L', image.size, 0)
mask_array = np.array(mask)
# Set padding regions to white (255)
mask_array[:self.padding_size, :] = 255 # Top
mask_array[-self.padding_size:, :] = 255 # Bottom
mask_array[:, :self.padding_size] = 255 # Left
mask_array[:, -self.padding_size:] = 255 # Right
return Image.fromarray(mask_array)
def pad_image(self, image: Image.Image) -> Image.Image:
"""
Add padding around the image.
Args:
image (Image.Image): Source image
Returns:
Image.Image: Padded image
"""
padded_size = (
image.width + 2 * self.padding_size,
image.height + 2 * self.padding_size
)
padded_image = Image.new('RGBA', padded_size, (0, 0, 0, 0))
padded_image.paste(image, (self.padding_size, self.padding_size))
return padded_image
def predict_outpainting(
self,
image: Image.Image,
mask_image: Image.Image,
prompt: str
) -> Image.Image:
"""
Call AI inference endpoint for outpainting.
Args:
image (Image.Image): Base image
mask_image (Image.Image): Padding mask
prompt (str): Outpainting generation prompt
Returns:
Image.Image: Outpainted result
"""
payload = {
"inputs": prompt,
"image": self.encode_image(image),
"mask_image": self.encode_image(mask_image)
}
try:
response = requests.post(
self.endpoint_url,
headers=self.headers,
json=payload
)
response.raise_for_status()
return Image.open(io.BytesIO(response.content))
except requests.RequestException as e:
print(f"Outpainting request failed: {e}")
return image
def process_iterative_outpainting(
self,
initial_image: Image.Image,
prompt: str
) -> Image.Image:
"""
Execute iterative outpainting process.
Args:
initial_image (Image.Image): Starting image
prompt (str): Generation prompt
Returns:
Image.Image: Final outpainted image
"""
current_image = self.crop_to_center(initial_image)
for iteration in range(self.max_iterations):
padded_image = self.pad_image(current_image)
mask = self.create_padding_mask(padded_image)
current_image = self.predict_outpainting(
padded_image, mask, prompt
)
return current_image
def run(
self,
image_path: str,
prompt: str
) -> Dict[str, Any]:
"""
Main processing method for dynamic outpainting.
Args:
image_path (str): Path to input image
prompt (str): Outpainting generation prompt
Returns:
Dict containing processing results
"""
try:
initial_image = Image.open(image_path)
result_image = self.process_iterative_outpainting(
initial_image, prompt
)
# Optional: Save result
result_path = f"outpainted_result_{id(self)}.png"
result_image.save(result_path)
return {
"status": "success",
"result_path": result_path,
"iterations": self.max_iterations
}
except Exception as e:
return {
"status": "error",
"message": str(e)
}
# Usage Example
def main():
outpainter = DynamicImageOutpainter(
endpoint_url="https://your-ai-endpoint.com",
api_token="your_huggingface_token",
padding_size=256,
max_iterations=3
)
result = outpainter.run(
image_path="input_image.png",
prompt="Expand the scene with natural, seamless background"
)
print(result)
if __name__ == "__main__":
main() |