feat: Implement image fetching and storage system
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- Add image-fetcher module for downloading and saving images from various sources. - Create storage module for managing image files, including downloading, verifying integrity, and cleaning up orphaned files. - Develop gallery HTML page for displaying images with sorting and filtering options. - Set up RESTful API routes for image management, including fetching, adding tags, and deleting images. - Introduce setup script for initializing the database and configuring image sources. - Implement a batch process for verifying image integrity and cleaning up old images. - Add setup batch script for easy installation and configuration of the image storage system.
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IMAGE_STORAGE_GUIDE.md
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IMAGE_STORAGE_GUIDE.md
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# 📸 Image Storage System Guide
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This guide covers the long-term image storage solution integrated into HomeBase. It provides reliable, organized storage for images pulled from web services with built-in corruption detection and ML-friendly tagging.
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## Features
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- **Automatic Image Fetching**: Pull images from web services every 2-3 minutes
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- **Corruption Detection**: SHA256 checksums verify data integrity
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- **Tag-Based Organization**: Tag images for machine learning model training
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- **Efficient Storage**: File-based storage with SQLite metadata database
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- **RESTful API**: Complete API for image management and queries
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- **Scalable**: Designed to handle thousands of images
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## Architecture
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```
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┌─────────────────────────────────────────────────────┐
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│ Image Fetcher Service │
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│ (Runs every 2-3 minutes, pull from web service) │
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└────────┬────────────────────────────────────────────┘
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│
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├─────┬──────────────┬──────────────┐
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│ │ │ │
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▼ ▼ ▼ ▼
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Download Hash Store File Insert Metadata
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Image (SHA256) (data/images) (SQLite)
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│
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└──────────────────────────────┘
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│
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┌─────────▼──────────┐
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│ REST API Endpoints │
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│ - List Images │
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│ - Add Tags │
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│ - Search/Filter │
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│ - Verify Integrity │
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└────────────────────┘
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```
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## Quick Start
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### 1. Install Dependencies
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```bash
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npm install
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```
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### 2. Initialize the System
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```bash
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node setup.js init
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```
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This will:
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- Create the SQLite database
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- Set up data directory structure
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- Load configuration
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- Test the system
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### 3. Configure Image Sources
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Edit `image-sources.json`:
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```json
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{
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"sources": [
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{
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"name": "Webcam Feed",
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"url": "https://your-service.com/image.jpg",
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"tags": ["webcam", "monitoring"],
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"enabled": true
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}
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],
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"fetchInterval": 0.033
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}
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```
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**Fetch Intervals** (edit `fetchInterval` to customize):
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- `0.0167` = 1 second
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- `0.033` = 2 seconds (recommended for fast updates)
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- `0.05` = 3 seconds
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- `2.5` = 2.5 minutes (original default)
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### 4. Start the Server
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```bash
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npm start
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```
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The system will automatically start fetching images at the configured interval.
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## Configuration
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### image-sources.json
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```json
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{
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"sources": [
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{
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"name": "Example Source",
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"url": "https://example.com/image.jpg",
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"tags": ["tag1", "tag2"],
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"enabled": true
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}
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],
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"fetchInterval": 0.033
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}
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```
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**Options:**
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- `fetchInterval`: **Minutes** between fetch cycles. Use decimals for sub-minute intervals:
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- `0.0167` = 1 second
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- `0.033` = 2 seconds
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- `0.05` = 3 seconds
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- `0.167` = 10 seconds
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- `1` = 1 minute
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- `2.5` = 2.5 minutes (original default)
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## API Endpoints
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### List Images
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```bash
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GET /api/images?page=1&pageSize=50&sort=fetched_at&order=DESC
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GET /api/images?tag=webcam # Filter by tag
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GET /api/images?sourceUrl=https://... # Filter by source
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```
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**Response:**
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```json
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{
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"success": true,
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"images": [
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{
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"id": 1,
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"filename": "image_1234567890_abc123.jpg",
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"source_url": "https://...",
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"filesize": 102400,
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"file_hash": "sha256hash",
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"fetched_at": "2023-01-01T12:00:00Z",
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"tags": ["webcam", "monitoring"]
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}
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],
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"pagination": {
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"page": 1,
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"pageSize": 50,
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"total": 240,
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"pages": 5
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}
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}
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```
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### Get Image Details
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```bash
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GET /api/images/{id}
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```
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### Download Image
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```bash
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GET /api/images/{id}/download
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```
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### Fetch New Image
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```bash
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POST /api/images
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Content-Type: application/json
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{
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"source_url": "https://example.com/image.jpg",
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"tags": ["tag1", "tag2"]
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}
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```
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### Add Tags to Image
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```bash
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POST /api/images/{id}/tags
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Content-Type: application/json
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{
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"tags": ["newtag1", "newtag2"]
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}
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```
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### List All Tags
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```bash
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GET /api/tags
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```
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**Response:**
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```json
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{
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"success": true,
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"tags": ["webcam", "monitoring", "test", "example"]
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}
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```
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### Storage Statistics
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```bash
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GET /api/stats
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```
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**Response:**
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```json
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{
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"success": true,
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"stats": {
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"imageCount": 240,
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"totalSize": 24576000,
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"totalSizeGB": "0.02",
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"fileCount": 240
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}
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}
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```
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### Verify Image Integrity
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```bash
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POST /api/verify
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```
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This checks all images for corruption using their stored checksums.
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### Cleanup Old Images
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```bash
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POST /api/cleanup
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Content-Type: application/json
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{
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"daysOld": 30
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}
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```
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### Delete Image
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```bash
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DELETE /api/images/{id}
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```
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## Corruption Detection
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The system uses SHA256 checksums to detect corruption:
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1. **Storage**: When an image is saved, its SHA256 hash is calculated and stored
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2. **Verification**: The `/api/verify` endpoint re-hashes all files and compares with stored hashes
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3. **Marking**: Corrupted images are marked in the database and excluded from queries
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4. **Recovery**: Corrupted files can be re-fetched using the source URL
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### Manual Verification
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```bash
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curl -X POST http://localhost:3001/api/verify
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```
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## Tagging for ML Training
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Tags are essential for organizing training datasets:
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```bash
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# Add images with training tags
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POST /api/images
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{
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"source_url": "https://...",
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"tags": ["dataset_v1", "labeled", "weather-sunny"]
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}
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# Query all images with specific tag
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GET /api/images?tag=weather-sunny
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# Get tag statistics
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GET /api/tags
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```
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## Database Schema
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### Images Table
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| Column | Type | Description |
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|--------|------|-------------|
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| id | INTEGER | Primary key |
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| filename | TEXT | Unique filename |
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| source_url | TEXT | Original image URL |
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| file_path | TEXT | Local file path |
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| filesize | INTEGER | File size in bytes |
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| file_hash | TEXT | SHA256 hash |
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| mime_type | TEXT | Content type |
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| fetched_at | DATETIME | When image was fetched |
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| is_corrupted | BOOLEAN | Corruption flag |
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### Tags Table
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| Column | Type | Description |
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|--------|------|-------------|
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| id | INTEGER | Primary key |
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| image_id | INTEGER | Foreign key to images |
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| tag | TEXT | Tag text |
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| created_at | DATETIME | When tag was added |
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## File Structure
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```
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homebase/
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├── server.js # Main server
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├── package.json # Dependencies
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├── setup.js # Setup script
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├── image-sources.json # Configuration
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├── lib/
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│ ├── database.js # SQLite operations
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│ ├── storage.js # File storage operations
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│ └── image-fetcher.js # Image fetching service
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├── routes/
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│ └── images.js # API routes
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└── data/ # Created at runtime
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├── homebase.db # SQLite database
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└── images/ # Stored image files
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```
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## Maintenance
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### Manual Setup
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```bash
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# Initialize system
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node setup.js init
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# Test fetch
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node setup.js test https://example.com/image.jpg tag1,tag2
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# View configuration
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node setup.js config
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```
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### Regular Tasks
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```bash
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# Daily: Verify integrity
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curl -X POST http://localhost:3001/api/verify
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# Weekly: Cleanup old images (30+ days)
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curl -X POST http://localhost:3001/api/cleanup -H "Content-Type: application/json" -d '{"daysOld": 30}'
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# Monitor storage
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curl http://localhost:3001/api/stats
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```
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## Performance Tips
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1. **Pagination**: Use `pageSize=50` or less for large datasets
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2. **Tagging**: Use consistent tag names for easier filtering
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3. **Cleanup**: Run cleanup weekly to manage storage
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4. **Verification**: Run verification monthly to detect issues early
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## Troubleshooting
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### No images fetching?
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1. Check `image-sources.json` - ensure `enabled: true`
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2. Verify URL is accessible
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3. Check server logs for errors
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4. Test fetch: `node setup.js test https://url.jpg`
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### High storage usage?
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```bash
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# Check statistics
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curl http://localhost:3001/api/stats
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# Cleanup images older than 7 days
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curl -X POST http://localhost:3001/api/cleanup \
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-H "Content-Type: application/json" \
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-d '{"daysOld": 7}'
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```
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### Corrupted files detected?
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```bash
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# Verify all images
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curl -X POST http://localhost:3001/api/verify
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# Get list of corrupted images
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curl 'http://localhost:3001/api/images?corrupted=true'
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# Re-fetch if source still available
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POST /api/images with same source_url
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```
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## Advanced Usage
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### Export Dataset for ML Training
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```bash
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# Get all images with specific tag
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curl 'http://localhost:3001/api/images?tag=weather-sunny&pageSize=999' \
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-o dataset.json
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```
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### Monitor Fetch Status
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```bash
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curl http://localhost:3001/api/fetcher/status
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```
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### Batch Operations
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```bash
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# Add tags to multiple images (via API loop)
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for image_id in 1 2 3 4 5; do
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curl -X POST http://localhost:3001/api/images/$image_id/tags \
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-H "Content-Type: application/json" \
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-d '{"tags": ["batch-import"]}'
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done
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```
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## Security Considerations
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1. **Database Access**: SQLite database is file-based; protect with filesystem permissions
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2. **Image Storage**: Protect `data/images` directory from unauthorized access
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3. **API Security**: Consider adding authentication for production use
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4. **File Validation**: System validates MIME types and file sizes
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## Performance Metrics
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- **Fetch Time**: ~1-5 seconds per image (network dependent)
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- **Database Queries**: <100ms for typical queries
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- **Verification**: ~50ms per image
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- **Storage**: ~1KB overhead per image in database
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---
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For more information, check the API documentation or run `node setup.js help`.
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