TrailSnap is an intelligent AI-powered photo album application that helps users effortlessly record, organize, and reminisce about their travel experiences. Through powerful AI processing capabilities, every photo and journey becomes a treasured memory.
In the future, everyone (at least every family) will have their own AI data center, and the photo album is an important data source for this center. It preserves many moments from your life. TrailSnap is dedicated to transforming these moments into valuable memories — it can silently record train tickets and attraction tickets from your album, help you record what you saw and heard during your travels, automatically organize photos perfect for sharing on social media (even preparing the captions for you), and help you edit a 15-second short video······.
Transforming travel from "just took photos" to "worth reminiscing, sharing, and cherishing." What TrailSnap aims to do is make every journey worth treasuring.
That's why I named this project "TrailSnap" — where your data truly belongs to you.
- 📷 Smart Album: Footprint map, person recognition, smart categorization, OCR, intelligent search.
- 🚆 Trip Recording: Unique train ticket and itinerary management with automatic ticket information recognition. (In development)
- 🤖 AI Empowerment: Let AI generate your travel diary with just one sentence. (Planned)
- AI auto-editing videos to generate VLOGs
- AI photo enhancement, automatically identifying high-quality photos
| Feature | Status | Description |
|---|---|---|
| Agent | √ | Chat with AI models to generate travel diaries with one click |
| CLI | √ | Command-line interface for convenient AI operations |
| SKILL | √ | Support for OpenClaw, Claude Code and other platforms for automated task execution |
| Train Ticket Recognition | √ | Recognize train tickets and itineraries in photos, automatically extracting travel information |
| Annual Report | √ | Auto-generate 2025 travel statistics report, including photo walls, cities visited, attractions, timeline, mileage, etc. |
| AI Analysis | √ | Use AI models to analyze photo content, generate descriptions and ratings, creating an electronic gallery |
| On This Day | √ | View photos from this day in previous years, sorted by rating, with auto-play for memorable moments |
| Travel Journal | Planned | Support manual input or AI-recognized trip information to generate travel journals |
| Visited Cities | √ | View all cities that appear in uploaded photos, click to see all photos from that city |
| Visited Attractions | √ | Statistics for visited 5A-level scenic spots, click to view all photos, or customize location to auto-filter photos within the area |
| External Folders | √ | Add external folders as data sources, TrailSnap will automatically scan and index photos and videos |
| Live Photo | √ | Support for iPhone, Vivo, Oppo, Xiaomi and other phone models |
| Timeline | √ | Smooth timeline scrolling experience |
| Footprint Album | √ | View all uploaded photos on a map, click for details, also viewable by province, city, district (supports list view, map view, timeline view, route view) |
| Face Recognition | √ | Automatically recognize people in photos and add person tags |
| Scene Classification | √ | Auto-categorize photos by scene: night scenes, pets, food, selfies, etc. |
| Smart Search | √ | Search by person, image content, time, etc. |
| Tags | √ | Manual tag addition/deletion, auto-tags from AI recognition |
| Smart Albums | √ | Create albums based on photo content, e.g., "Selfie of me and my girlfriend at the beach" |
- Support recycle bin, users can restore deleted photos.
- Support MCP protocol, users can communicate with TrailSnap via MCP.
- Support skills, can connect to OpenClaw, Claude Code and other platforms.
- More comprehensive trip management: concert tickets, attraction tickets, hotel bookings, movie tickets, etc.
- More comprehensive AI capabilities: AI auto-editing videos for VLOGs, AI photo enhancement, AI-generated travel journals, etc.
2025 Photo Album Annual Report
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Ensure Docker and Docker Compose are installed.
-
docker-compose
docker-compose.yml configuration (note: modify the mount path to a local path, otherwise local photo directories cannot be scanned)
version: '3.8'
services:
postgres:
image: pgvector/pgvector:pg18-trixie
container_name: postgres_container
restart: always
environment:
TZ: Asia/Shanghai
POSTGRES_DB: trailsnap
POSTGRES_USER: trailsnap
POSTGRES_PASSWORD: trailsnap
POSTGRES_INITDB_ARGS: "--encoding=UTF8 --lc-collate=C --lc-ctype=C"
PGDATA: /var/lib/postgresql/data/pgdata
networks: [ app-network ]
ports:
- "5532:5432"
volumes:
- ./pg_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U trailsnap -d trailsnap -p 5432"]
interval: 5s
timeout: 5s
retries: 5
start_period: 10s
server:
image: siyuan044/trailsnap-server:latest
restart: always
expose: [ "8000" ]
ports: [ "8800:8000" ]
networks: [ app-network ]
volumes:
- ./data:/app/data # Data directory mount
- F:\Photos:/app/Photos/ # Local photo directory mount
environment:
- TZ=Asia/Shanghai
- DB_URL=postgresql://trailsnap:trailsnap@postgres:5432/trailsnap
- RAILWAY_DB_URL=postgresql://trailsnap:trailsnap@postgres:5432/railway
- AI_API_URL=http://ai:8001
depends_on:
postgres:
condition: service_healthy
restart: true
ai:
image: siyuan044/trailsnap-ai:latest
restart: always
expose: [ "8001" ]
ports: [ "8801:8001" ]
networks: [ app-network ]
volumes:
- ./data:/app/data # Data directory mount
environment:
- TZ: Asia/Shanghai
frontend:
image: siyuan044/trailsnap-frontend:latest
restart: always
ports: [ "8082:80" ]
depends_on: [ server ]
networks: [ app-network ]
environment:
- TZ=Asia/Shanghai
networks:
app-network:
driver: bridge- Start services
docker-compose up -dTrailSnap/
├── package/
│ ├── server/ # Backend FastAPI service
│ └── website/ # Frontend Vue application
│ └── ai/ # AI microservice (OCR/Face)
├── doc/ # Project documentation
└── ...
For more detailed technical documentation, please refer to the doc/ directory (documentation may not be updated in time, for reference only):
- Architecture Design
- Frontend Framework Analysis
- Backend Framework Analysis
- Developer Guide
- User Guide
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