File size: 2,527 Bytes
7bf41e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # CutoutAI Background Remover - Ralph Development Instructions
## Project Goal
Create a flawless background removal tool for the Etsy t-shirt workflow. This tool must produce perfect cutouts suitable for Printify mockups.
## Current Workflow
```
Gemini Image Gen → Slack Approval → BACKGROUND REMOVAL → Printify Mockup → SEO → Etsy/Shopify
```
## Critical Requirements
### 1. FLAWLESS Quality (Non-Negotiable)
- NO patchy faces or artifacts
- NO edge bleeding or halos
- CLEAN edges on hair and fine details
- Must look perfect on t-shirt mockups
### 2. Multi-Element Capture
The tool MUST capture ALL design elements including:
- Main subject
- Bubbles and floating decorations
- Small text or symbols
- Scattered elements (stars, sparkles, etc.)
### 3. API Integration
Must provide:
- Webhook endpoint for n8n (POST /webhook)
- REST API (POST /api/v1/remove)
- Base64 input/output support
- Health check endpoint
## Files to Review and Improve
1. **cutoutai.py** - Core processing logic
- Uses BiRefNet-matting model (correct choice)
- Has edge_smooth function (may need enhancement)
- Check if multi-element capture is working properly
2. **api.py** - FastAPI server
- Webhook endpoint exists
- Verify n8n compatibility
- Add any missing error handling
3. **requirements.txt** - Dependencies
- Verify all needed packages are listed
## Improvement Tasks
### Priority 1: Quality Enhancement
- [ ] Verify BiRefNet output quality
- [ ] Test edge refinement settings
- [ ] Add adaptive thresholding for multi-element capture
- [ ] Consider adding post-processing for artifact removal
### Priority 2: API Robustness
- [ ] Add proper error responses with details
- [ ] Add request validation
- [ ] Add timeout handling for large images
- [ ] Verify callback_url functionality
### Priority 3: Deployment Ready
- [ ] Add Dockerfile for HuggingFace Spaces
- [ ] Add startup preloading (reduce first-request latency)
- [ ] Add logging for debugging
## Success Criteria
- Process Gemini-generated images with ZERO visible artifacts
- Capture ALL design elements (test with bubble/sparkle designs)
- Return base64 that works in n8n HTTP Request node
- Health endpoint returns proper status
## Reference Documents
See specs/requirements.md for detailed technical specifications.
## Notes
- This will replace the current HuggingFace BiRefNet API in the Etsy workflow
- Priority is QUALITY over speed (mockups need to be perfect)
- Test with white AND non-white backgrounds (Gemini may vary)
|