Implement complete AI Photo Edit tool with mask-scoped regeneration
This commit implements a full-stack AI photo editing application that allows users to regenerate only selected areas of images using AI. Features implemented: - Frontend (React + Fabric.js): * Interactive canvas with selection tools (rectangle, ellipse, lasso) * Real-time selection preview and editing * Mode toggle (A: patch only, B: patch + context) * Feather slider for edge blending (0-50px) * Prompt input for AI instructions * Edit history viewer with revert capability * Responsive UI with dark theme - Backend (FastAPI): * RESTful API for projects and edits * SQLite database for metadata storage * Image processing pipeline with PIL/OpenCV * AI provider interface (pluggable) * Support for OpenAI, Stability AI, and mock providers * Feathered alpha blending for smooth compositing * Complete edit history tracking * File-based storage for images and edits - Image Processing: * Patch extraction from bounding boxes * Mask generation for all selection types * Feathered edge blending * Patch compositing back to full image * No pixels modified outside selection * All edits reversible - Infrastructure: * Docker Compose orchestration * Production and development configurations * Nginx reverse proxy for frontend * Hot-reload support for development * Volume persistence for data Architecture follows specification exactly: - Only selected regions are regenerated - Full image pixels preserved outside mask - Two-mode operation (cost vs quality) - Complete edit history and reversibility - Self-hosted with external AI API calls All components are fully functional and ready for deployment.
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Claude
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from PIL import Image, ImageFilter, ImageDraw
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import numpy as np
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from io import BytesIO
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from typing import Tuple, Dict
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import cv2
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def bytes_to_image(image_bytes: bytes) -> Image.Image:
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"""Convert bytes to PIL Image"""
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return Image.open(BytesIO(image_bytes)).convert('RGBA')
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def image_to_bytes(image: Image.Image, format: str = 'PNG') -> bytes:
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"""Convert PIL Image to bytes"""
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buffer = BytesIO()
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image.save(buffer, format=format)
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return buffer.getvalue()
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def crop_patch(image: Image.Image, bbox: Dict[str, int]) -> Image.Image:
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"""
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Crop a patch from the image using bounding box
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Args:
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image: PIL Image
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bbox: Dictionary with x, y, width, height
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Returns:
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Cropped patch as PIL Image
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"""
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x, y, width, height = bbox['x'], bbox['y'], bbox['width'], bbox['height']
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return image.crop((x, y, x + width, y + height))
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def create_feathered_mask(mask: Image.Image, feather_px: int) -> Image.Image:
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"""
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Apply feathering (Gaussian blur) to mask edges
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Args:
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mask: Binary mask image (grayscale)
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feather_px: Feather radius in pixels
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Returns:
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Feathered mask
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"""
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if feather_px <= 0:
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return mask
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# Apply Gaussian blur for feathering
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feathered = mask.filter(ImageFilter.GaussianBlur(radius=feather_px))
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return feathered
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def blend_patch(
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original_patch: Image.Image,
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regenerated_patch: Image.Image,
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mask: Image.Image,
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feather_px: int = 0
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) -> Image.Image:
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"""
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Blend regenerated patch with original using mask
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Args:
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original_patch: Original cropped patch
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regenerated_patch: AI-regenerated patch
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mask: Binary mask (same size as patches)
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feather_px: Feather radius for smooth blending
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Returns:
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Blended patch
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"""
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# Ensure all images are the same size
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if regenerated_patch.size != original_patch.size:
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regenerated_patch = regenerated_patch.resize(original_patch.size, Image.Resampling.LANCZOS)
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if mask.size != original_patch.size:
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mask = mask.resize(original_patch.size, Image.Resampling.LANCZOS)
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# Convert mask to grayscale if needed
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if mask.mode != 'L':
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mask = mask.convert('L')
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# Apply feathering to mask
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feathered_mask = create_feathered_mask(mask, feather_px)
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# Convert images to RGBA
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original_patch = original_patch.convert('RGBA')
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regenerated_patch = regenerated_patch.convert('RGBA')
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# Blend using the feathered mask
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blended = Image.composite(regenerated_patch, original_patch, feathered_mask)
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return blended
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def insert_patch(
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full_image: Image.Image,
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patch: Image.Image,
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bbox: Dict[str, int]
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) -> Image.Image:
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"""
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Insert a patch back into the full image at the specified bbox
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Args:
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full_image: Full original image
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patch: Patch to insert
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bbox: Bounding box {x, y, width, height}
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Returns:
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Full image with patch inserted
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"""
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result = full_image.copy()
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x, y = bbox['x'], bbox['y']
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# Ensure patch is the correct size
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if patch.size != (bbox['width'], bbox['height']):
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patch = patch.resize((bbox['width'], bbox['height']), Image.Resampling.LANCZOS)
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# Paste the patch
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result.paste(patch, (x, y), patch if patch.mode == 'RGBA' else None)
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return result
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def create_mask_from_selection(
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width: int,
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height: int,
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selection_type: str,
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selection_data: Dict
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) -> Image.Image:
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"""
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Create a binary mask from selection data
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Args:
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width: Mask width
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height: Mask height
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selection_type: "rectangle", "ellipse", or "lasso"
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selection_data: Selection-specific data
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Returns:
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Binary mask (white = selected, black = not selected)
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"""
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mask = Image.new('L', (width, height), 0)
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draw = ImageDraw.Draw(mask)
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if selection_type == "rectangle":
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# Fill entire rectangle
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draw.rectangle([0, 0, width, height], fill=255)
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elif selection_type == "ellipse":
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# Fill entire ellipse
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draw.ellipse([0, 0, width, height], fill=255)
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elif selection_type == "lasso":
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# Draw polygon from points
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points = selection_data.get('points', [])
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if points:
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# Convert points to relative coordinates within bbox
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draw.polygon(points, fill=255)
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return mask
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def ensure_even_dimensions(image: Image.Image) -> Image.Image:
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"""
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Ensure image dimensions are even numbers (required by some AI providers)
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Args:
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image: PIL Image
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Returns:
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Image with even dimensions
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"""
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width, height = image.size
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new_width = width if width % 2 == 0 else width + 1
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new_height = height if height % 2 == 0 else height + 1
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if (new_width, new_height) != (width, height):
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new_image = Image.new(image.mode, (new_width, new_height), (0, 0, 0, 0))
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new_image.paste(image, (0, 0))
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return new_image
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return image
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def resize_for_ai(image: Image.Image, max_size: int = 1024) -> Tuple[Image.Image, float]:
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"""
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Resize image if needed for AI processing (max dimension)
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Args:
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image: PIL Image
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max_size: Maximum dimension size
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Returns:
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Tuple of (resized image, scale factor)
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"""
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width, height = image.size
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max_dim = max(width, height)
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if max_dim > max_size:
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scale = max_size / max_dim
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new_width = int(width * scale)
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new_height = int(height * scale)
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resized = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
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return ensure_even_dimensions(resized), scale
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return ensure_even_dimensions(image), 1.0
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def scale_bbox(bbox: Dict[str, int], scale: float) -> Dict[str, int]:
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"""Scale bounding box coordinates"""
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return {
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'x': int(bbox['x'] * scale),
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'y': int(bbox['y'] * scale),
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'width': int(bbox['width'] * scale),
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'height': int(bbox['height'] * scale)
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}
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