Files
PaintPlus/backend/app/services/edit_service.py
T
Claude c8078d4652 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.
2026-01-24 02:58:41 +00:00

217 lines
6.6 KiB
Python

import os
import json
from pathlib import Path
from typing import Dict, Optional
from datetime import datetime
from PIL import Image
from app.models.edit import Edit
from app.models.project import Project
from app.services.ai_provider import get_ai_provider
from app.utils.image_processing import (
bytes_to_image,
image_to_bytes,
crop_patch,
blend_patch,
insert_patch,
create_mask_from_selection,
resize_for_ai,
scale_bbox
)
from app.config import settings
class EditService:
"""Service for handling image edits"""
def __init__(self, data_dir: str = None):
self.data_dir = data_dir or settings.data_dir
self.ai_provider = get_ai_provider()
def get_project_dir(self, project_id: int) -> Path:
"""Get project directory path"""
return Path(self.data_dir) / "projects" / str(project_id)
def get_edit_dir(self, project_id: int, edit_id: int) -> Path:
"""Get edit history directory path"""
return self.get_project_dir(project_id) / "history" / str(edit_id)
def ensure_project_dir(self, project_id: int):
"""Ensure project directory structure exists"""
project_dir = self.get_project_dir(project_id)
project_dir.mkdir(parents=True, exist_ok=True)
(project_dir / "history").mkdir(exist_ok=True)
def get_current_image_path(self, project_id: int) -> Path:
"""Get path to current image"""
return self.get_project_dir(project_id) / "current.png"
def get_original_image_path(self, project_id: int) -> Path:
"""Get path to original image"""
return self.get_project_dir(project_id) / "original.png"
async def process_edit(
self,
project_id: int,
edit_id: int,
prompt: str,
mode: str,
selection_type: str,
bbox: Dict[str, int],
feather_px: int,
selection_data: Optional[Dict] = None
) -> str:
"""
Process an edit request
Args:
project_id: Project ID
edit_id: Edit ID
prompt: AI prompt
mode: "A" or "B"
selection_type: "rectangle", "ellipse", or "lasso"
bbox: Bounding box {x, y, width, height}
feather_px: Feather radius in pixels
selection_data: Additional selection data (for lasso)
Returns:
Path to the result image
"""
# Create edit directory
edit_dir = self.get_edit_dir(project_id, edit_id)
edit_dir.mkdir(parents=True, exist_ok=True)
# Load current image
current_image_path = self.get_current_image_path(project_id)
full_image = Image.open(current_image_path).convert('RGBA')
# Crop patch from current image
original_patch = crop_patch(full_image, bbox)
# Save original patch
original_patch.save(edit_dir / "patch_in.png")
# Create mask based on selection type
mask = create_mask_from_selection(
bbox['width'],
bbox['height'],
selection_type,
selection_data or {}
)
# Save mask
mask.save(edit_dir / "mask.png")
# Resize patch and mask for AI if needed
patch_for_ai, scale = resize_for_ai(original_patch)
mask_for_ai = mask.resize(patch_for_ai.size, Image.Resampling.LANCZOS)
# Prepare full image for mode B
full_image_bytes = None
if mode == "B":
full_image_for_ai, _ = resize_for_ai(full_image)
full_image_bytes = image_to_bytes(full_image_for_ai)
# Call AI provider
regenerated_patch_bytes = await self.ai_provider.edit_image(
patch_image_bytes=image_to_bytes(patch_for_ai),
mask_image_bytes=image_to_bytes(mask_for_ai),
prompt=prompt,
mode=mode,
full_image_bytes=full_image_bytes
)
# Convert regenerated patch back to PIL Image
regenerated_patch = bytes_to_image(regenerated_patch_bytes)
# Resize back to original patch size if scaled
if scale != 1.0:
regenerated_patch = regenerated_patch.resize(
original_patch.size,
Image.Resampling.LANCZOS
)
# Save regenerated patch
regenerated_patch.save(edit_dir / "patch_out.png")
# Blend regenerated patch with original using mask
blended_patch = blend_patch(
original_patch,
regenerated_patch,
mask,
feather_px
)
# Insert blended patch back into full image
result_image = insert_patch(full_image, blended_patch, bbox)
# Save result
result_path = edit_dir / "result.png"
result_image.save(result_path)
# Update current image
result_image.save(current_image_path)
# Save metadata
metadata = {
'edit_id': edit_id,
'project_id': project_id,
'prompt': prompt,
'mode': mode,
'selection_type': selection_type,
'bbox': bbox,
'feather_px': feather_px,
'selection_data': selection_data,
'timestamp': datetime.utcnow().isoformat(),
'ai_provider': settings.ai_provider
}
with open(edit_dir / "meta.json", 'w') as f:
json.dump(metadata, f, indent=2)
return str(result_path)
def revert_to_edit(self, project_id: int, edit_id: int) -> str:
"""
Revert project to a specific edit
Args:
project_id: Project ID
edit_id: Edit ID to revert to
Returns:
Path to the reverted image
"""
edit_dir = self.get_edit_dir(project_id, edit_id)
result_path = edit_dir / "result.png"
if not result_path.exists():
raise FileNotFoundError(f"Edit {edit_id} result not found")
# Copy result to current
current_path = self.get_current_image_path(project_id)
Image.open(result_path).save(current_path)
return str(current_path)
def reset_to_original(self, project_id: int) -> str:
"""
Reset project to original image
Args:
project_id: Project ID
Returns:
Path to the original image
"""
original_path = self.get_original_image_path(project_id)
current_path = self.get_current_image_path(project_id)
if not original_path.exists():
raise FileNotFoundError(f"Original image for project {project_id} not found")
# Copy original to current
Image.open(original_path).save(current_path)
return str(current_path)