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Christopher Koch
2026-01-24 17:43:28 -05:00
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{
"permissions": {
"allow": [
"Bash(wc:*)",
"Bash(python3:*)"
]
}
}

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# AV1 Encoder Support - Fix Summary
## Problem
When using the `av1_quality` profile, encoding was falling back to CPU instead of using Intel QSV GPU acceleration.
## Root Cause
The `EncoderType` enum didn't include AV1 variants, so when the config specified `encoder: intel_qsv_av1`, it failed to map and silently fell back to `CPU_X265`.
## Fix Applied
Added complete AV1 support to reencode.py:
1. **New Encoder Types**: `INTEL_QSV_AV1`, `NVIDIA_NVENC_AV1`, `AMD_VAAPI_AV1`, `CPU_AV1`
2. **Detection**: Now checks for `av1_qsv`, `av1_nvenc`, `av1_vaapi`, `libsvtav1` encoders
3. **FFmpeg Commands**: Proper AV1 encoding commands for each hardware type
4. **Container Format**: AV1 automatically uses .mp4 (not .m4v)
## Next Steps - Testing in Docker
### 1. Copy the test script to your Docker container
```bash
docker cp test-av1-support.sh encoderpro:/app/
```
### 2. Run the test inside the container
```bash
docker exec encoderpro bash /app/test-av1-support.sh
```
### 3. Check the output
**Expected Results:**
✅ **If Intel QSV AV1 is supported** (Arc A-Series GPUs):
```
Intel QSV AV1:
V..... av1_qsv AV1 (Intel Quick Sync Video acceleration)
[FOUND]
```
❌ **If Intel QSV AV1 is NOT supported** (older GPUs or FFmpeg build):
```
Intel QSV AV1:
[NOT FOUND]
```
## Scenarios
### Scenario A: Intel QSV AV1 is Available
✅ Your GPU supports AV1!
- The `av1_quality` profile will now use GPU acceleration
- You should see fast encoding speeds (80-150 fps)
- Use profiles: `av1_quality`, `av1_qsv`, `sweetspot_av1`
### Scenario B: Intel QSV AV1 is NOT Available
⚠️ Your GPU doesn't support AV1 (integrated graphics or older Arc GPU)
**Options:**
1. **Use H.265 instead** (recommended):
- Change to `sweetspot_qsv` or `quality_qsv` profiles
- Still uses GPU, excellent quality
2. **Use CPU AV1** (very slow):
- Keep using `av1_quality` - will fallback to CPU
- Much slower but will work
3. **Update FFmpeg build**:
- Docker image might need newer FFmpeg with AV1 support
## How to Switch Profiles
If AV1 isn't supported, update your config.yaml:
```yaml
profiles:
# Change from:
# default: av1_quality
# To:
default: sweetspot_qsv # H.265 GPU encoding - fast & excellent quality
```
Or select a different profile in the dashboard dropdown.
## Testing the Fix
1. **Run the test script** in Docker (above)
2. **Check encoder capabilities** when running reencode.py:
```bash
docker exec encoderpro python3 /app/reencode.py -c /config/config.yaml --stats
```
Look for:
```
ENCODER CAPABILITIES
============================================================
Intel QSV: [YES]
AV1: [YES] ← This line shows if QSV AV1 is available
```
3. **Try encoding a file** with the dashboard
- Select a file
- Choose `av1_quality` or `sweetspot_qsv` profile
- Click "Encode Selected"
- Check the logs to see which encoder is used
## Relevant Config Profiles
From your config.yaml:
```yaml
# AV1 Profiles (require Arc A-Series or newer)
sweetspot_av1:
encoder: intel_qsv_av1 # Now properly recognized!
preset: medium
quality: 27
av1_quality:
encoder: intel_qsv_av1 # This was failing before, fixed now
preset: slow
quality: 24
# H.265 Profiles (work on all Intel GPUs with QSV)
sweetspot_qsv:
encoder: intel_qsv_h265 # Safe fallback, excellent quality
preset: slow
quality: 21
```
## Files Modified
- `reencode.py` - Added AV1 support (lines 56-69, 88-103, 230-245, 715-803, 1024-1046)
## Questions to Answer
After running the test script in Docker:
1. **Does your FFmpeg build have `av1_qsv` encoder?**
2. **What GPU do you have?** (Arc A380/A750/A770 support AV1, integrated graphics may not)
3. **Do you want to use H.265 instead if AV1 isn't available?**

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# Project Cleanup Summary
**Date:** December 20, 2024
**Objective:** Consolidate project to web-based v3+ application, remove legacy CLI versions
---
## Changes Made
### Files Renamed (v3 → Default)
| Old Name | New Name | Purpose |
|----------|----------|---------|
| `reencode-v3.py` | `reencode.py` | Main encoding engine |
| `config-v3.yaml` | `config.yaml` | Default configuration |
### Files Archived
Moved to `.archive/old-versions/`:
| File | Type | Reason |
|------|------|--------|
| `reencode-v2.py` | Python script | Old CLI version (Phase 2) |
| `config-v2.yaml` | Config file | Old configuration format |
| `README-phase2.md` | Documentation | Obsolete documentation |
| `README-phase3.md` | Documentation | Obsolete documentation |
### Files Updated
**dashboard.py:**
- Updated default reencode script path: `/app/reencode-v3.py` → `/app/reencode.py`
**Dockerfile:**
- Updated COPY commands to use new filenames
- Added `quality_checker.py` to container
- Added all config examples (nvidia, intel, cpu)
**Dockerfile.intel:**
- Updated COPY commands to use new filenames
- Added `quality_checker.py` to container
- Added all config examples
**README.md:**
- Completely rewritten for v3+ web-based application
- Modern feature documentation
- Quick start guides for Docker and local installation
- Comprehensive usage examples
- API reference
- Troubleshooting guide
### Files Kept (Active)
**Core Application:**
- `reencode.py` (renamed from reencode-v3.py)
- `dashboard.py`
- `quality_checker.py`
- `dbmanage.py`
- `init_database.py`
**Configuration:**
- `config.yaml` (renamed from config-v3.yaml)
- `config-nvidia.yaml`
- `config-intel.yaml`
- `config-cpu.yaml`
**Web Interface:**
- `templates/dashboard.html`
- `static/css/dashboard.css`
- `static/js/dashboard.js`
**Docker:**
- `Dockerfile`
- `Dockerfile.intel`
- `docker-compose.yml`
- `docker-entrypoint.sh`
- `.env.example`
**Documentation:**
- `README.md` (rewritten)
- `DASHBOARD-API.md`
- `FEATURE-SUMMARY.md`
- `QUALITY-CHECKING.md`
- `DEPLOYMENT.md`
- `MIGRATION.md`
- `DASHBOARD-GUIDE.md`
- `INTEL-ARC-GUIDE.md`
- `UNRAID-DEPLOYMENT.md`
- `QUALITY-GUIDE.md`
**Utilities & Scripts:**
- `requirements.txt`
- `scan-subtitles.sh`
- `reencode-movies.sh`
- `setup-test-environment.sh`
- `deploy-r730.sh`
- `install-r730.sh`
---
## Project Structure (After Cleanup)
```
encoderPro/
├── .archive/
│ └── old-versions/ # Archived legacy files
│ ├── reencode-v2.py
│ ├── config-v2.yaml
│ ├── README-phase2.md
│ └── README-phase3.md
│
├── templates/ # Web UI templates
│ └── dashboard.html
│
├── static/ # Web assets
│ ├── css/
│ └── js/
│
├── data/ # Runtime data
│ └── db/
│ └── state.db
│
├── Core Application
│ ├── reencode.py # Main encoding engine
│ ├── dashboard.py # Web dashboard
│ ├── quality_checker.py # Quality analysis
│ ├── dbmanage.py # Database tools
│ └── init_database.py # DB initialization
│
├── Configuration
│ ├── config.yaml # Default config
│ ├── config-nvidia.yaml # NVIDIA example
│ ├── config-intel.yaml # Intel QSV example
│ └── config-cpu.yaml # CPU-only example
│
├── Docker
│ ├── Dockerfile # NVIDIA GPU image
│ ├── Dockerfile.intel # Intel QSV image
│ ├── docker-compose.yml # Compose config
│ ├── docker-entrypoint.sh # Container entry point
│ └── .env.example # Environment template
│
├── Documentation
│ ├── README.md # Main documentation
│ ├── DASHBOARD-API.md # API reference
│ ├── FEATURE-SUMMARY.md # Feature docs
│ ├── QUALITY-CHECKING.md # Quality guide
│ ├── DEPLOYMENT.md # Deployment guide
│ └── [other guides]
│
└── Utilities
├── requirements.txt # Python dependencies
└── [helper scripts]
```
---
## Migration Path for Users
### If Currently Using v2 (CLI-only)
1. **Backup your database:**
```bash
cp /var/lib/reencode/state.db /var/lib/reencode/state.db.backup
```
2. **Update config file:**
```bash
# v3 config is compatible, just update paths if needed
cp config-v2.yaml config.yaml
# Edit config.yaml to match v3 format (add quality_check section)
```
3. **Pull new Docker image or update files:**
```bash
docker pull encoderpro:latest
```
4. **Access web dashboard:**
```
http://localhost:5000
```
### If Currently Using v3
No action needed! Files were just renamed:
- `reencode-v3.py` → `reencode.py`
- `config-v3.yaml` → `config.yaml`
---
## Breaking Changes
### None
This cleanup is **backward compatible**. The application functionality remains identical - only file names were changed to remove version suffixes.
**Docker users:** New images will work with existing volumes and configurations.
**CLI users:** Update any scripts that reference `reencode-v3.py` to use `reencode.py`.
---
## Benefits
1. **Simplified naming** - No version suffixes on main files
2. **Cleaner project** - Legacy code archived, not deleted
3. **Better documentation** - Modern README focused on current features
4. **Docker optimization** - All config examples included in image
5. **Future-proof** - v4+ will continue using `reencode.py` (no more renaming)
---
## Rollback Instructions
If you need to restore old versions:
```bash
# Restore v2 files
cp .archive/old-versions/reencode-v2.py reencode.py
cp .archive/old-versions/config-v2.yaml config.yaml
# Or just access archived files directly
python .archive/old-versions/reencode-v2.py -c .archive/old-versions/config-v2.yaml
```
---
## Next Steps
1. **Rebuild Docker images:**
```bash
docker build -t encoderpro:latest .
docker build -f Dockerfile.intel -t encoderpro:intel .
```
2. **Test deployment:**
```bash
docker-compose up -d
```
3. **Verify dashboard:**
- Access http://localhost:5000
- Run library scan
- Test encoding
4. **Update documentation links:**
- Update README badges
- Update GitHub release notes
- Update Docker Hub description
---
## Archive Policy
- **Kept:** All old versions for rollback capability
- **Location:** `.archive/old-versions/`
- **Retention:** Indefinite (can be deleted after 6 months if stable)
---
## Version History
- **v1.0** - Initial bash script (not in repo)
- **v2.0** - Python CLI with state tracking
- **v3.0** - Added quality checking, profiles
- **v3.1** - Web dashboard, selective re-encoding
- **Current** - Consolidated, web-focused application
---
## Questions?
See [MIGRATION.md](MIGRATION.md) for detailed migration guides.

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# encoderPro Dashboard API Reference
## Overview
The encoderPro dashboard provides a RESTful API for monitoring and controlling the encoding system. All endpoints return JSON responses with a consistent format:
```json
{
"success": true,
"data": { ... }
}
```
Or on error:
```json
{
"success": false,
"error": "Error message"
}
```
---
## Auto-Initialization
The dashboard automatically initializes the database on first run if it doesn't exist. No manual setup required!
**On first start:**
1. Dashboard creates empty database at `/db/state.db`
2. Database schema is created automatically
3. Dashboard is ready to use
4. Run a library scan to populate database
---
## API Endpoints
### Health & Status
#### `GET /api/health`
Health check with database status.
**Response:**
```json
{
"success": true,
"data": {
"status": "healthy",
"version": "3.1.0",
"timestamp": "2025-12-19T18:45:00.000Z",
"database": {
"exists": true,
"path": "/db/state.db",
"file_count": 1250,
"needs_scan": false
}
}
}
```
**Use cases:**
- Check if dashboard is running
- Check if database exists
- Check if library scan is needed
- Monitor file count
---
### Statistics
#### `GET /api/stats`
Get processing statistics.
**Response:**
```json
{
"success": true,
"data": {
"pending": 850,
"processing": 2,
"completed": 145,
"failed": 3,
"skipped": 250,
"original_size": 4500000000000,
"encoded_size": 2200000000000,
"space_saved": 2300000000000,
"space_saved_percent": 51.1,
"avg_fps": 8.5,
"avg_encode_time": 3600,
"encoder_usage": {
"cpu_x265": 100,
"nvidia_nvenc_h265": 45
},
"completed_24h": 12
}
}
```
---
### Files
#### `GET /api/files`
Get list of files with filtering.
**Query Parameters:**
- `state` (optional): Filter by state (`pending`, `processing`, `completed`, `failed`, `skipped`)
- `limit` (optional): Number of results (default: 100)
- `offset` (optional): Pagination offset (default: 0)
- `search` (optional): Search in file paths
**Examples:**
```
GET /api/files?state=pending&limit=50
GET /api/files?search=action&limit=20
GET /api/files?offset=100&limit=100
```
**Response:**
```json
{
"success": true,
"data": [
{
"id": 1,
"filepath": "/movies/example.mkv",
"relative_path": "example.mkv",
"state": "completed",
"has_subtitles": true,
"original_size": 5000000000,
"encoded_size": 2500000000,
"subtitle_count": 2,
"profile_name": "sweetspot_cpu",
"encoder_used": "cpu_x265",
"encode_time_seconds": 3600,
"fps": 8.5,
"created_at": "2025-12-19T10:00:00",
"completed_at": "2025-12-19T11:00:00"
}
]
}
```
---
#### `GET /api/file/<id>`
Get single file details.
**Response:**
```json
{
"success": true,
"data": {
"id": 1,
"filepath": "/movies/example.mkv",
"state": "completed",
...
}
}
```
---
### Activity
#### `GET /api/activity`
Get recent file activity (completed/failed).
**Query Parameters:**
- `limit` (optional): Number of results (default: 20)
**Response:**
```json
{
"success": true,
"data": [
{
"id": 1,
"relative_path": "example.mkv",
"state": "completed",
"updated_at": "2025-12-19T11:00:00",
"encoder_used": "cpu_x265",
"fps": 8.5
}
]
}
```
---
### Processing Status
#### `GET /api/processing`
Get currently processing files.
**Response:**
```json
{
"success": true,
"data": {
"active": true,
"files": [
{
"id": 2,
"relative_path": "movie2.mkv",
"started_at": "2025-12-19T18:30:00",
"profile_name": "sweetspot_cpu"
}
]
}
}
```
---
### System Monitoring
#### `GET /api/system`
Get system resource statistics.
**Response:**
```json
{
"success": true,
"data": {
"gpu": [
{
"index": 0,
"name": "NVIDIA GeForce RTX 4090",
"utilization": 85,
"memory_used": 8192,
"memory_total": 24576,
"temperature": 72
}
],
"cpu": {
"load_1m": 4.5,
"load_5m": 3.8,
"load_15m": 3.2,
"cpu_count": 48,
"load_percent": 9.4
},
"disk": {
"work_total": 1000000000000,
"work_used": 250000000000,
"work_free": 750000000000,
"work_percent": 25.0
}
}
}
```
---
### Job Control
#### `POST /api/jobs/start`
Start processing job.
**Request Body:**
```json
{
"profile": "sweetspot_cpu",
"dry_run": false
}
```
**Response:**
```json
{
"success": true,
"message": "Processing started",
"dry_run": false
}
```
---
#### `POST /api/jobs/stop`
Stop processing job.
**Response:**
```json
{
"success": true,
"message": "Processing stopped"
}
```
---
#### `POST /api/jobs/scan`
Scan library to populate database.
**Response:**
```json
{
"success": true,
"message": "Library scan started"
}
```
**Use cases:**
- Initial library scan
- Refresh library after adding new files
- Re-scan if files were added manually
---
#### `POST /api/jobs/reencode-selected`
Queue selected files for re-encoding with specified profile.
**Request Body:**
```json
{
"file_ids": [1, 5, 12, 23],
"profile": "quality_cpu"
}
```
**Response:**
```json
{
"success": true,
"message": "4 files queued for re-encoding",
"count": 4
}
```
**Use cases:**
- Re-encode specific files with different quality settings
- Re-process failed files with a different profile
- Upgrade completed files to higher quality
**Notes:**
- Resets file state from 'completed' or 'failed' to 'pending'
- Sets the profile_name for each file
- Files will be processed when the job is started
---
### Logs
#### `GET /api/logs`
Get recent log entries.
**Query Parameters:**
- `lines` (optional): Number of lines (default: 100)
**Response:**
```json
{
"success": true,
"data": [
"2025-12-19 18:45:00 - INFO - Processing started",
"2025-12-19 18:45:01 - INFO - Encoding example.mkv",
...
]
}
```
---
### Configuration
#### `GET /api/config`
Get current configuration.
**Response:**
```json
{
"success": true,
"data": {
"movies_dir": "/movies",
"archive_dir": "/archive",
"parallel": {
"max_workers": 8,
"cpu_slots": 24
},
"profiles": {
"default": "sweetspot_cpu",
"definitions": { ... }
},
"quality_check": {
"enabled": true,
"warn_threshold": 10.0,
"error_threshold": 20.0,
"skip_on_degradation": false
}
}
}
```
---
#### `POST /api/config`
Save configuration.
**Request Body:** (full config object)
```json
{
"movies_dir": "/movies",
"archive_dir": "/archive",
"quality_check": {
"enabled": true,
"warn_threshold": 15.0
},
...
}
```
**Response:**
```json
{
"success": true,
"message": "Configuration saved successfully",
"data": { ... }
}
```
**Notes:**
- Creates backup before saving (`config.yaml.backup`)
- Validates required fields
- Returns validation errors if invalid
---
#### `POST /api/config/validate`
Validate configuration without saving.
**Request Body:** (config object to validate)
**Response:**
```json
{
"success": true,
"data": {
"valid": true,
"errors": [],
"warnings": [
"max_workers=12 is very high, may cause system instability"
]
}
}
```
---
### Profiles
#### `GET /api/profiles`
Get available encoding profiles.
**Response:**
```json
{
"success": true,
"data": {
"default": "sweetspot_cpu",
"profiles": {
"sweetspot_cpu": {
"encoder": "cpu_x265",
"preset": "slow",
"quality": 21,
"description": "Perfect balance..."
},
"balanced_cpu": {
"encoder": "cpu_x265",
"preset": "medium",
"quality": 23
}
}
}
}
```
---
### Encoders
#### `GET /api/encoders`
Get available encoders on the system.
**Response:**
```json
{
"success": true,
"data": {
"cpu": {
"x265": true,
"x264": true
},
"nvidia": {
"nvenc_h265": true,
"nvenc_h264": true
},
"intel": {
"qsv_h265": false,
"qsv_h264": false
},
"amd": {
"vaapi_h265": false,
"vaapi_h264": false
}
}
}
```
---
### Directory Validation
#### `POST /api/directories/validate`
Validate directory paths.
**Request Body:**
```json
{
"paths": {
"movies": "/movies",
"archive": "/archive",
"work": "/work"
}
}
```
**Response:**
```json
{
"success": true,
"data": {
"movies": {
"path": "/movies",
"exists": true,
"is_directory": true,
"is_writable": false,
"is_readable": true
},
"archive": {
"path": "/archive",
"exists": true,
"is_directory": true,
"is_writable": true,
"is_readable": true
}
}
}
```
---
## Workflow Examples
### Initial Setup
```bash
# 1. Start dashboard (auto-creates database)
docker exec encoderpro python3 /app/dashboard.py
# 2. Check health
curl http://localhost:5000/api/health
# 3. Trigger library scan via API
curl -X POST http://localhost:5000/api/jobs/scan
# 4. Monitor scan progress
curl http://localhost:5000/api/stats
```
---
### Start Processing
```bash
# Start with default profile
curl -X POST http://localhost:5000/api/jobs/start \
-H "Content-Type: application/json" \
-d '{"dry_run": false}'
# Start with specific profile
curl -X POST http://localhost:5000/api/jobs/start \
-H "Content-Type: application/json" \
-d '{"profile": "quality_cpu", "dry_run": false}'
```
---
### Monitor Progress
```bash
# Get statistics
curl http://localhost:5000/api/stats
# Get currently processing files
curl http://localhost:5000/api/processing
# Get recent activity
curl http://localhost:5000/api/activity?limit=10
# View logs
curl http://localhost:5000/api/logs?lines=50
```
---
### Update Configuration
```bash
# Get current config
curl http://localhost:5000/api/config > config.json
# Edit config.json
# Validate changes
curl -X POST http://localhost:5000/api/config/validate \
-H "Content-Type: application/json" \
-d @config.json
# Save if valid
curl -X POST http://localhost:5000/api/config \
-H "Content-Type: application/json" \
-d @config.json
```
---
## Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `STATE_DB` | `/db/state.db` | Database file path |
| `LOG_DIR` | `/logs` | Log directory |
| `CONFIG_FILE` | `/config/config.yaml` | Config file path |
| `REENCODE_SCRIPT` | `/app/reencode-v3.py` | Main script path |
| `DASHBOARD_HOST` | `0.0.0.0` | Dashboard bind address |
| `DASHBOARD_PORT` | `5000` | Dashboard port |
| `DASHBOARD_DEBUG` | `false` | Enable debug mode |
---
## Error Handling
All endpoints follow consistent error handling:
**404 - Not Found:**
```json
{
"success": false,
"error": "File not found"
}
```
**500 - Server Error:**
```json
{
"success": false,
"error": "Database connection failed"
}
```
**400 - Bad Request:**
```json
{
"success": false,
"error": "Missing required field: movies_dir"
}
```
---
## WebSocket Support (Future)
Real-time updates will be added via WebSocket in a future version:
- Live encoding progress
- Real-time log streaming
- File state changes
- System resource updates
---
## Security Considerations
**Current version (3.1.0):**
- No authentication (intended for private networks)
- CORS enabled for all origins
- No HTTPS (use reverse proxy if needed)
**Recommendations:**
- Run behind reverse proxy (nginx, Traefik)
- Use firewall to restrict access
- Enable HTTPS at reverse proxy level
- Consider adding basic auth if exposed
---
## Summary
The dashboard provides a complete REST API for:
- ✅ Auto-initializing database
- ✅ Monitoring processing status
- ✅ Controlling encoding jobs
- ✅ Viewing system resources
- ✅ Managing configuration
- ✅ Triggering library scans
- ✅ Viewing logs and activity
No manual database setup required - just start the dashboard and it's ready to use!

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# Enhanced Dashboard with Configuration UI - Complete Guide
## 🎉 What's New
The dashboard now includes **full configuration management**:
### ✨ New Features
1. **Configuration Editor**
- Edit directory paths (movies, archive, work)
- Adjust parallel processing settings
- Toggle processing options
- Real-time validation
2. **Profile Management**
- Create custom encoding profiles
- Edit existing profiles
- Set default profile
- Delete unused profiles
3. **Enhanced Job Control**
- **Dry Run Mode** - Test without encoding
- Profile selection per job
- Visual status indicators
4. **Three-Tab Interface**
- **Dashboard** - Monitoring and control
- **Configuration** - System settings
- **Profiles** - Encoding presets
## 🚀 Quick Start
```bash
# Start dashboard
docker-compose up -d dashboard
# Access at
http://YOUR-SERVER:5000
```
## 📋 Dashboard Tab (Monitoring & Control)
### Job Control Panel
- **Profile Selector**: Choose encoding profile for this run
- **Dry Run Checkbox**: Enable to scan only (no encoding)
- **Start Button**: Begin processing with selected options
- **Stop Button**: Gracefully stop current jobs
- **Refresh Button**: Manual data refresh
- **Status Badge**: Shows Idle or Processing
### Statistics Cards
- **Pending**: Files waiting to process
- **Processing**: Currently encoding
- **Completed**: Successfully processed
- **Failed**: Encoding errors
- **Skipped**: Files without subtitles
### Progress Overview
- Visual progress bar
- Original vs encoded sizes
- Space saved with percentage
- Average encoding FPS
### System Monitoring
- GPU utilization, memory, temperature
- CPU load average
- Real-time updates every 5 seconds
### Recent Activity
- Last 10 processed files
- Success/failure indicators
- Encoder used and FPS
### Live Logs
- Streaming log output
- Color-coded messages
- Auto-scrolls to latest
## ⚙️ Configuration Tab
### Directory Settings
**Movies Directory**
```
/mnt/user/movies
```
- Source directory containing your movie library
- Must exist and be readable
**Archive Directory**
```
/mnt/user/archive/movies
```
- Where original files are stored after re-encoding
- Will be created if doesn't exist
**Work Directory**
```
/mnt/user/temp/encoderpro-work
```
- Temporary encoding directory
- Should be on fast storage (SSD preferred)
- Needs space for largest movie file
### Parallel Processing Settings
**Max Workers** (1-10)
- Number of concurrent encoding jobs
- Recommended: 1-2 for GPU, 2-4 for CPU
**GPU Slots** (0-5)
- How many jobs can use GPU simultaneously
- Recommended: 1 for single GPU, 2 for high-end GPUs
**CPU Slots** (1-32)
- CPU threads available per job
- Recommended: Half of physical cores
### Processing Options
**✓ Skip files without subtitles**
- Automatically skip files that don't have subtitle streams
- Saves time by not processing unnecessary files
**✓ Cleanup stale work files**
- Remove abandoned work files on startup
- Keeps work directory clean
**✓ Prefer GPU encoders**
- Use GPU encoding when available
- Much faster than CPU
**✓ Fallback to CPU**
- Use CPU encoding if GPU fails
- Ensures processing continues
### Configuration Actions
**✓ Validate**
- Check configuration for errors
- Shows warnings and suggestions
- **Always validate before saving**
**💾 Save**
- Write configuration to file
- Creates backup of existing config
- Takes effect immediately
**🔄 Reload**
- Discard changes and reload from file
- Useful to undo modifications
## 🎯 Profiles Tab
### What are Profiles?
Profiles define encoding settings:
- **Encoder**: Hardware (NVENC, QSV) or software (CPU)
- **Preset**: Speed vs quality trade-off
- **Quality**: CRF/CQ value (lower = better)
- **Audio**: Copy or re-encode
### Built-in Profiles
| Profile | Encoder | Use Case |
|---------|---------|----------|
| balanced_gpu | NVENC H.265 | Best for most users |
| fast_gpu | NVENC H.264 | Quick processing |
| quality_gpu | NVENC H.265 | Archival quality |
| balanced_cpu | CPU H.265 | No GPU available |
### Managing Profiles
**Add Profile**
1. Click "➕ Add" button
2. Enter profile name (e.g., "my_profile")
3. Select encoder type
4. Set preset (p1-p7 for NVENC, ultrafast-veryslow for CPU)
5. Set quality (18-28 recommended)
6. Choose audio codec
7. Click "💾 Save"
**Edit Profile**
1. Click "✏️ Edit" on existing profile
2. Modify settings
3. Click "💾 Save"
**Delete Profile**
1. Click "🗑️ Delete"
2. Confirm deletion
**Set Default Profile**
1. Select profile from "Default Profile" dropdown
2. Click "💾 Save All Profiles"
### Encoder Options
**NVIDIA NVENC**
- `nvidia_nvenc_h265` - H.265 (recommended)
- `nvidia_nvenc_h264` - H.264 (faster, larger)
- Presets: p1 (fastest) to p7 (slowest)
- Quality: 20-25 recommended
**CPU Encoders**
- `cpu_x265` - H.265 (best compression)
- `cpu_x264` - H.264 (faster)
- Presets: ultrafast, fast, medium, slow, veryslow
- Quality: 18-24 recommended
**Intel QSV**
- `intel_qsv_h265` - H.265
- `intel_qsv_h264` - H.264
- Presets: fast, medium, slow
**AMD VAAPI**
- `amd_vaapi_h265` - H.265
- `amd_vaapi_h264` - H.264
## 🎮 Using Dry Run Mode
### What is Dry Run?
Dry run scans your library without encoding:
- Detects subtitle streams
- Updates database
- Shows what would be processed
- **No files are modified**
### When to Use Dry Run
1. **Initial Setup**
- Test configuration before processing
- See how many files need processing
- Verify directory paths
2. **After Adding Movies**
- Scan for new files
- Update database
- Plan processing time
3. **Testing Settings**
- Check if subtitles detected correctly
- Verify file extensions recognized
### How to Use
1. Go to **Dashboard** tab
2. Check **"Dry Run (Scan Only)"**
3. Select profile (optional)
4. Click **"▶ Start"**
5. Watch statistics update
6. Review results in activity log
### After Dry Run
- Check **Pending** count - files that will be processed
- Check **Skipped** count - files without subtitles
- Review **Statistics** to plan processing time
- Uncheck "Dry Run" and run for real
## 🔧 Common Workflows
### First-Time Setup
```
1. Open http://YOUR-SERVER:5000
2. Go to Configuration tab
3. Set directory paths
4. Click "✓ Validate"
5. Fix any errors shown
6. Click "💾 Save"
7. Go to Profiles tab
8. Review default profiles
9. Go to Dashboard tab
10. Enable "Dry Run"
11. Click "▶ Start"
12. Review results
13. Disable "Dry Run"
14. Click "▶ Start" to process
```
### Adding Custom Profile
```
1. Go to Profiles tab
2. Click "➕ Add"
3. Name: "my_quality"
4. Encoder: nvidia_nvenc_h265
5. Preset: p6
6. Quality: 20
7. Audio: copy
8. Click "💾 Save"
9. Set as default if desired
10. Click "💾 Save All Profiles"
```
### Processing with Custom Settings
```
1. Go to Dashboard tab
2. Select profile from dropdown
3. Optionally enable "Dry Run" first
4. Click "▶ Start"
5. Monitor progress in real-time
6. Click "⏹ Stop" if needed
```
### Changing Parallel Workers
```
1. Go to Configuration tab
2. Adjust "Max Workers" (try 1 first)
3. Adjust "GPU Slots" (1 for most GPUs)
4. Click "✓ Validate"
5. Click "💾 Save"
6. Restart dashboard container if needed
```
## 📊 Configuration Examples
### Conservative (Safe for all systems)
```yaml
Max Workers: 1
GPU Slots: 1
CPU Slots: 4
Profile: balanced_gpu
```
### Balanced (Recommended)
```yaml
Max Workers: 2
GPU Slots: 1
CPU Slots: 4
Profile: balanced_gpu
```
### High Throughput (Powerful systems)
```yaml
Max Workers: 3
GPU Slots: 2
CPU Slots: 8
Profile: fast_gpu
```
### CPU Only (No GPU)
```yaml
Max Workers: 2
GPU Slots: 0
CPU Slots: 4
Profile: balanced_cpu
```
## 🔍 Validation Messages
### ✓ Success
```
Configuration is valid!
```
All settings are correct, safe to save.
### ⚠️ Warnings
```
Warning: Movies directory does not exist
Warning: max_workers=5 is very high
```
Not critical but should be reviewed.
### ✗ Errors
```
Error: Missing required field: movies_dir
Error: max_workers must be at least 1
```
Must be fixed before saving.
## 🎨 UI Tips
### Color Coding
- **Green** - Completed, success, active
- **Blue** - Processing, info
- **Yellow** - Pending, warnings
- **Red** - Failed, errors
- **Gray** - Skipped, idle
### Keyboard Shortcuts
- **Ctrl+R** - Refresh page
- **Tab** - Navigate between fields
- **Enter** - Submit forms (in modals)
- **Esc** - Close modals
### Mobile Access
- Dashboard is fully responsive
- Works on tablets and phones
- All features available
- Touch-friendly buttons
## 🐛 Troubleshooting
### Configuration Won't Save
**Check:**
- All required fields filled
- Paths exist or can be created
- No validation errors
- Dashboard has write permissions
### Profiles Not Appearing
**Solution:**
1. Go to Profiles tab
2. Check if profiles list is empty
3. Click "🔄 Reload" in Configuration
4. Profiles may be in config file but not loaded
### Dry Run Doesn't Update Stats
**Wait:**
- Dry run takes time to scan
- Refresh happens every 5 seconds
- Check logs for progress
- May take minutes for large libraries
### GPU Not Available in Profiles
**Check:**
1. Go to browser console (F12)
2. Check for encoder detection errors
3. Verify nvidia-smi works in container
4. May need to restart dashboard
### Changes Not Taking Effect
**Solution:**
1. Save configuration
2. Stop any active processing
3. Restart dashboard container:
```bash
docker-compose restart dashboard
```
## 📱 API Access
The dashboard provides REST API for automation:
### Get Configuration
```bash
curl http://localhost:5000/api/config
```
### Save Configuration
```bash
curl -X POST http://localhost:5000/api/config \
-H "Content-Type: application/json" \
-d @config.json
```
### Start with Dry Run
```bash
curl -X POST http://localhost:5000/api/jobs/start \
-H "Content-Type: application/json" \
-d '{"profile": "balanced_gpu", "dry_run": true}'
```
### Get Profiles
```bash
curl http://localhost:5000/api/profiles
```
### Validate Config
```bash
curl -X POST http://localhost:5000/api/config/validate \
-H "Content-Type: application/json" \
-d @config.json
```
## 🎓 Best Practices
1. **Always validate before saving**
2. **Use dry run for new configurations**
3. **Start with max_workers=1**
4. **Create custom profiles for different quality needs**
5. **Monitor first few files closely**
6. **Keep original config as backup**
7. **Test profile on one file before batch**
8. **Check logs for errors regularly**
## 🚀 Next Steps
1. ✓ Configure directories
2. ✓ Validate configuration
3. ✓ Test with dry run
4. ✓ Create custom profiles if needed
5. ✓ Process a few test files
6. ✓ Review quality and speed
7. ✓ Adjust settings as needed
8. ✓ Set up scheduled processing
9. ✓ Bookmark dashboard URL
10. ✓ Enjoy automated encoding!
## 📚 Summary
You now have a **complete web-based management system** for your media encoding:
- ✅ Real-time monitoring
- ✅ Configuration editor
- ✅ Profile management
- ✅ Dry run mode
- ✅ Job control
- ✅ Live system stats
- ✅ Activity tracking
- ✅ Log streaming
**Access:** `http://YOUR-SERVER:5000`
**No more editing YAML files manually!**

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# Database Initialization Fix
## Issue
Dashboard was showing errors:
```
sqlite3.OperationalError: no such table: files
```
## Root Cause
The `DatabaseReader._ensure_database()` method only initialized the database if the file didn't exist:
```python
if not self.db_path.exists():
self._initialize_database()
```
This caused problems when:
- Database file exists but is empty
- Database file exists but schema is outdated
- Database was created but initialization failed partway through
## Fix
Changed to always run initialization (dashboard.py:129-133):
```python
def _ensure_database(self):
"""Ensure database exists and has correct schema"""
# Always run initialization - it's safe with CREATE TABLE IF NOT EXISTS
# This ensures migrations run even if the file exists but schema is outdated
self._initialize_database()
```
## Why This Is Safe
1. **CREATE TABLE IF NOT EXISTS** - Won't recreate existing tables
2. **Migration checks** - Only adds columns that don't exist
3. **Idempotent** - Can run multiple times safely
4. **Auto-repair** - Fixes incomplete or corrupted schemas
## Benefits
✅ Database always has correct schema
✅ Migrations run automatically on startup
✅ Handles edge cases (empty files, partial schemas)
✅ No manual intervention needed
✅ Works for fresh installs and upgrades
## Similar Fix Applied
The same initialization logic exists in `reencode.py`'s `StateDatabase` class, which already runs migrations every time. This change brings `dashboard.py` in line with that behavior.
## Testing
After this fix:
1. Dashboard starts successfully
2. Database is created if missing
3. Tables are created if missing
4. New columns are added via migration
5. All API endpoints work properly

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# Database Migration Fix
## Issue
When running a scan in the Docker container, got this error:
```
sqlite3.OperationalError: table files has no column named video_codec
```
## Root Cause
The new video attribute columns were only being added by `dashboard.py`'s database initialization, but `reencode.py` has its own `StateDatabase` class that also needs to handle the migration.
When the scanner runs in the container (via `reencode.py`), it tried to insert data into columns that didn't exist yet.
## Fix
Added the same migration logic to `reencode.py`'s `StateDatabase._init_database()` method (lines 362-381):
```python
# Migration: Add new columns if they don't exist
cursor.execute("PRAGMA table_info(files)")
columns = {row[1] for row in cursor.fetchall()}
migrations = [
("video_codec", "ALTER TABLE files ADD COLUMN video_codec TEXT"),
("audio_codec", "ALTER TABLE files ADD COLUMN audio_codec TEXT"),
("audio_channels", "ALTER TABLE files ADD COLUMN audio_channels INTEGER"),
("width", "ALTER TABLE files ADD COLUMN width INTEGER"),
("height", "ALTER TABLE files ADD COLUMN height INTEGER"),
("duration", "ALTER TABLE files ADD COLUMN duration REAL"),
("bitrate", "ALTER TABLE files ADD COLUMN bitrate INTEGER"),
]
for column_name, alter_sql in migrations:
if column_name not in columns:
logging.info(f"Adding column '{column_name}' to files table")
cursor.execute(alter_sql)
```
## How It Works
1. Uses `PRAGMA table_info(files)` to get list of existing columns
2. Checks if each new column exists
3. If missing, adds it with `ALTER TABLE`
4. Safe to run multiple times (won't duplicate columns)
## Impact
- ✅ Existing databases automatically upgraded
- ✅ New databases created with all columns
- ✅ Scanner can now save media attributes
- ✅ Filters will work once data is populated
## Next Steps
After deploying this fix:
1. Restart the container (or it will auto-reload if using the updated code)
2. Run "Scan Library" from the dashboard
3. Scanner will extract and save media attributes for all files
4. Filter buttons will work with the populated data

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# Debugging Docker Container
This guide shows you how to view logs and debug issues in the encoderPro Docker container.
---
## Quick Commands
### View Live Logs
```bash
# Follow logs in real-time (Ctrl+C to stop)
docker logs encoderpro-dashboard-intel -f
# View last 100 lines
docker logs encoderpro-dashboard-intel --tail 100
# View logs with timestamps
docker logs encoderpro-dashboard-intel -f --timestamps
```
### Check Container Status
```bash
# Is container running?
docker ps | grep encoderpro
# View all containers (including stopped)
docker ps -a | grep encoderpro
# Inspect container details
docker inspect encoderpro-dashboard-intel
```
### Interactive Debugging
```bash
# Open a shell inside the running container
docker exec -it encoderpro-dashboard-intel /bin/bash
# Once inside, you can:
ls /movies # Check if movies are visible
ls /db # Check database location
cat /config/config.yaml # View config
python3 /app/reencode.py -c /config/config.yaml --stats # Run stats
bash /app/check-gpu.sh # Check GPU availability
exit # Exit the container shell
```
### Check AV1 Encoder Support
```bash
# Copy test script to container
docker cp test-av1-support.sh encoderpro-dashboard-intel:/app/
# Run AV1 support test
docker exec encoderpro-dashboard-intel bash /app/test-av1-support.sh
```
---
## Enable Debug Mode
### Option 1: Environment Variable (Recommended)
Add `-e DASHBOARD_DEBUG="true"` when creating the container:
```bash
docker run -d \
--name encoderpro-dashboard-intel \
--device=/dev/dri:/dev/dri \
-e GPU_TYPE=intel \
-e DASHBOARD_DEBUG="true" \
-p 5000:5000 \
-v /mnt/user/movies:/movies \
-v /mnt/user/archive/movies:/archive \
-v /mnt/user/appdata/encoderpro/config.yaml:/config/config.yaml:ro \
-v /mnt/user/appdata/encoderpro/db:/db \
-v /mnt/user/appdata/encoderpro/logs:/logs \
--restart unless-stopped \
encoderpro-intel:latest dashboard
```
### Option 2: Recreate Container with Debug
```bash
# Stop and remove old container
docker stop encoderpro-dashboard-intel
docker rm encoderpro-dashboard-intel
# Start with debug enabled
docker run -d \
--name encoderpro-dashboard-intel \
--device=/dev/dri:/dev/dri \
-e GPU_TYPE=intel \
-e DASHBOARD_DEBUG="true" \
-p 5000:5000 \
-v /mnt/user/movies:/movies \
-v /mnt/user/archive/movies:/archive \
-v /mnt/user/appdata/encoderpro/config.yaml:/config/config.yaml:ro \
-v /mnt/user/appdata/encoderpro/db:/db \
-v /mnt/user/appdata/encoderpro/logs:/logs \
--restart unless-stopped \
encoderpro-intel:latest dashboard
```
---
## Common Issues and Solutions
### 1. Container Not Starting
```bash
# Check why container stopped
docker logs encoderpro-dashboard-intel
# Common causes:
# - Permission errors (fix with chown -R 1000:1000)
# - Missing directories
# - Invalid config.yaml
```
### 2. Can't See Processing Logs
```bash
# Logs are in multiple places:
# 1. Docker logs (dashboard output)
docker logs encoderpro-dashboard-intel -f
# 2. File logs (reencode.py output)
tail -f /mnt/user/appdata/encoderpro/logs/reencode.log
# 3. Dashboard logs (if configured)
tail -f /mnt/user/appdata/encoderpro/logs/dashboard.log
```
### 3. Check If Processing Is Running
```bash
# Inside container, check for python processes
docker exec -it encoderpro-dashboard-intel ps aux | grep python
# Check if any files are being processed
docker exec -it encoderpro-dashboard-intel ls -lah /work
```
### 4. Database Issues
```bash
# Access database directly
docker exec -it encoderpro-dashboard-intel sqlite3 /db/state.db
# Inside SQLite:
SELECT state, COUNT(*) FROM files GROUP BY state;
SELECT * FROM files WHERE state='pending';
SELECT * FROM files WHERE state='processing';
.quit
```
---
## Understanding Log Output
### Dashboard Startup Logs
```
Starting Web Dashboard v3.2.0
Server: http://0.0.0.0:5000
Database: /db/state.db
Config: /config/config.yaml
Debug mode: True
Log level: DEBUG
```
### Processing Logs
```
============================================================
ENCODERPRO - PHASE 3
Version: 3.0.0
============================================================
Skipping library scan (--no-scan mode)
Processing 1 file(s)...
Processing: movies_7/Get Hard.m4v
Changed output extension to .mp4 for HEVC compatibility
Encoding with INTEL_QSV_H265, profile: sweetspot_qsv
```
### Success Logs
```
[OK] Completed: movies_7/Get Hard.mp4 | Encoder: INTEL_QSV_H265 | Time: 45.2s | FPS: 58.32 | Saved: 42.3%
```
### Failure Logs
```
[FAIL] Failed: movies_7/Get Hard.m4v - Encoding failed
FFmpeg error: [error message here]
```
---
## Troubleshooting Workflow
1. **Check container is running**
```bash
docker ps | grep encoderpro
```
2. **View real-time logs**
```bash
docker logs encoderpro-dashboard-intel -f
```
3. **Select a file in dashboard**
- Open http://your-server:5000
- Select a file
- Click "Encode Selected"
4. **Watch the logs**
- You should see "Processing 1 file(s)..."
- Then encoding progress
- Then either success or failure
5. **If nothing happens**
```bash
# Check if API calls are reaching the server
docker logs encoderpro-dashboard-intel --tail 50 | grep "POST /api"
# Check database state
docker exec -it encoderpro-dashboard-intel sqlite3 /db/state.db "SELECT * FROM files WHERE state='pending';"
```
---
## Debug Checklist
- [ ] Container is running (`docker ps`)
- [ ] Debug mode enabled (`DASHBOARD_DEBUG=true`)
- [ ] Logs are showing (`docker logs -f`)
- [ ] Can access dashboard (http://your-server:5000)
- [ ] Files are scanned (Discovered count > 0)
- [ ] Can select files (checkbox works)
- [ ] Encode button shows confirmation
- [ ] Processing log shows "Processing X file(s)"
- [ ] FFmpeg runs without errors
- [ ] Encoded file appears in movies directory
---
## Performance Monitoring
```bash
# Check container resource usage
docker stats encoderpro-dashboard-intel
# Monitor GPU usage (Intel)
intel_gpu_top
# Check disk I/O
iostat -x 1
```
---
## File Locations
| What | Location in Container | Location on Host |
|------|----------------------|------------------|
| Dashboard code | `/app/dashboard.py` | Local build directory |
| Reencode script | `/app/reencode.py` | Local build directory |
| Config file | `/config/config.yaml` | `/mnt/user/appdata/encoderpro/config.yaml` |
| Database | `/db/state.db` | `/mnt/user/appdata/encoderpro/db/state.db` |
| Logs | `/logs/` | `/mnt/user/appdata/encoderpro/logs/` |
| Movies | `/movies/` | `/mnt/user/movies/` |
| Archive | `/archive/` | `/mnt/user/archive/movies/` |
| Work files | `/work/` | Container temp (lost on restart) |
---
## Getting Help
When reporting issues, include:
1. **Container logs**
```bash
docker logs encoderpro-dashboard-intel --tail 200 > logs.txt
```
2. **Database state**
```bash
docker exec -it encoderpro-dashboard-intel sqlite3 /db/state.db "SELECT state, COUNT(*) FROM files GROUP BY state;" > db-state.txt
```
3. **System info**
```bash
docker version > sysinfo.txt
uname -a >> sysinfo.txt
lspci | grep -i vga >> sysinfo.txt
```
4. **Config file** (remove sensitive paths if needed)
```bash
cat /mnt/user/appdata/encoderpro/config.yaml > config.txt
```

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# Documentation Cleanup Summary
**Date:** December 20, 2024
**Action:** Consolidated all setup documentation into single guide
---
## Changes Made
### Single Source of Truth
**Primary Setup Guide:**
- **[UNRAID-DEPLOYMENT.md](UNRAID-DEPLOYMENT.md)** - Complete deployment guide for all platforms
This is now the **ONLY** setup guide you need. It includes:
- NVIDIA GPU setup (RTX series)
- Intel Arc GPU setup (A-Series, integrated graphics)
- CPU-only setup
- Directory permissions (critical!)
- Unraid template configuration
- Command line usage
- Troubleshooting
- Performance tuning
- Quick reference
### Archived Guides
The following redundant setup guides have been moved to `.archive/old-setup-docs/`:
1. ✅ DEPLOYMENT.md
2. ✅ MIGRATION.md
3. ✅ README-docker.md
4. ✅ INSTALL-R730.md
5. ✅ README-R730-QUICKSTART.md
6. ✅ UNRAID-DOCKER-SETUP.md
7. ✅ UNRAID-QUICK-SETUP.md
8. ✅ SETUP-CHECKLIST.md
9. ✅ INTEL-ARC-GUIDE.md
10. ✅ DOCKER-DIRECT-COMMANDS-UPDATE.md
**Reason for archival:** All content consolidated into UNRAID-DEPLOYMENT.md
### Remaining Documentation
**Project Overview:**
- [README.md](README.md) - Project overview, features, usage examples
**User Guides:**
- [DASHBOARD-GUIDE.md](DASHBOARD-GUIDE.md) - Web dashboard user guide
- [QUALITY-GUIDE.md](QUALITY-GUIDE.md) - Quality analysis guide
- [QUALITY-CHECKING.md](QUALITY-CHECKING.md) - Quality checking technical details
**API & Development:**
- [DASHBOARD-API.md](DASHBOARD-API.md) - Complete API reference
- [FEATURE-SUMMARY.md](FEATURE-SUMMARY.md) - New feature documentation
**Technical Documentation:**
- [SECURITY-FIXES.md](SECURITY-FIXES.md) - Security improvements and fixes
- [STUCK-PROCESSING-FIX.md](STUCK-PROCESSING-FIX.md) - Stuck file handling implementation
- [CLEANUP-SUMMARY.md](CLEANUP-SUMMARY.md) - Project cleanup summary
---
## Documentation Structure
```
encoderPro/
├── README.md # Project overview (points to UNRAID-DEPLOYMENT.md)
├── UNRAID-DEPLOYMENT.md # ⭐ COMPLETE SETUP GUIDE (START HERE)
│
├── User Guides/
│ ├── DASHBOARD-GUIDE.md # Web UI guide
│ ├── QUALITY-GUIDE.md # Quality checking guide
│ └── QUALITY-CHECKING.md # Quality technical details
│
├── API & Development/
│ ├── DASHBOARD-API.md # API reference
│ └── FEATURE-SUMMARY.md # Feature documentation
│
├── Technical/
│ ├── SECURITY-FIXES.md # Security documentation
│ ├── STUCK-PROCESSING-FIX.md # Stuck file handling
│ └── CLEANUP-SUMMARY.md # Cleanup documentation
│
└── .archive/old-setup-docs/ # Archived redundant guides
├── ARCHIVED-README.md # Archive explanation
├── DEPLOYMENT.md
├── MIGRATION.md
├── README-docker.md
├── INSTALL-R730.md
├── README-R730-QUICKSTART.md
├── UNRAID-DOCKER-SETUP.md
├── UNRAID-QUICK-SETUP.md
├── SETUP-CHECKLIST.md
├── INTEL-ARC-GUIDE.md
└── DOCKER-DIRECT-COMMANDS-UPDATE.md
```
---
## Key Changes to UNRAID-DEPLOYMENT.md
### 1. Marked as Primary Guide
Added header: **"THE OFFICIAL SETUP GUIDE - START HERE"**
### 2. Direct Docker Commands
All setup instructions use `docker run` instead of `docker-compose`:
```bash
# Example
docker run -d \
--name encoderpro-dashboard-intel \
--device=/dev/dri:/dev/dri \
-e GPU_TYPE=intel \
-p 5000:5000 \
-v /mnt/user/movies:/movies \
...
encoderpro-intel:latest dashboard
```
### 3. Comprehensive Coverage
Includes all content from archived guides:
- NVIDIA, Intel Arc, and CPU setup
- Directory permissions (UID 1000)
- Unraid template configuration
- Command line usage examples
- Scheduled processing (User Scripts)
- Troubleshooting (Permission denied, GPU not found, stuck files)
- Performance tuning guides
- Quick reference commands
---
## Updated README.md
The main README.md now:
- Points to UNRAID-DEPLOYMENT.md as primary setup guide
- Includes quick example for Intel Arc
- Lists all documentation with descriptions
- Updated to version 3.2.0
---
## Migration Path
If you were using old guides:
### No Changes Required
Your existing deployment continues to work:
- Database is compatible
- Config files are compatible
- Docker images are unchanged
- File states are preserved
### To Update Documentation
Simply use UNRAID-DEPLOYMENT.md going forward:
1. Same build commands
2. Same docker run commands
3. Same configuration
4. Same troubleshooting
---
## Benefits
### For Users
✅ **Single source of truth** - No confusion about which guide to follow
✅ **Complete coverage** - Everything in one place
✅ **Easier to find** - No hunting through multiple files
✅ **Always up-to-date** - Only one file to maintain
### For Maintainers
✅ **Less duplication** - Update once, not 10 times
✅ **Consistent information** - No conflicting instructions
✅ **Easier to update** - Single file to edit
✅ **Clear structure** - Organized by GPU type
---
## Archive Location
All archived setup guides are in:
```
.archive/old-setup-docs/
```
See `.archive/old-setup-docs/ARCHIVED-README.md` for details on what was archived and why.
---
## Quick Links
**Setup:**
- [UNRAID-DEPLOYMENT.md](UNRAID-DEPLOYMENT.md) - Complete deployment guide ⭐ START HERE
**Usage:**
- [DASHBOARD-GUIDE.md](DASHBOARD-GUIDE.md) - How to use the web dashboard
- [QUALITY-GUIDE.md](QUALITY-GUIDE.md) - Understanding quality checking
**Reference:**
- [DASHBOARD-API.md](DASHBOARD-API.md) - API endpoints
- [SECURITY-FIXES.md](SECURITY-FIXES.md) - Security improvements
- [STUCK-PROCESSING-FIX.md](STUCK-PROCESSING-FIX.md) - Stuck file handling
---
## Version
**Documentation Version:** 2.0 (Consolidated)
**encoderPro Version:** 3.2.0 (Security Hardened)
**Date:** December 20, 2024

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FROM nvidia/cuda:12.0.0-base-ubuntu22.04
# Metadata
LABEL maintainer="encoderPro"
LABEL description="GPU-accelerated media encoding with web dashboard"
LABEL version="3.1.0"
# Prevent interactive prompts
ENV DEBIAN_FRONTEND=noninteractive
# Install dependencies
RUN apt-get update && apt-get install -y \
python3 \
python3-pip \
ffmpeg \
wget \
procps \
&& rm -rf /var/lib/apt/lists/*
# Install Python dependencies
RUN pip3 install --no-cache-dir \
pyyaml \
flask \
flask-cors
# Create application directory
WORKDIR /app
# Copy application files
COPY reencode.py /app/reencode.py
COPY dashboard.py /app/dashboard.py
COPY quality_checker.py /app/quality_checker.py
COPY config.yaml /app/config.yaml.example
COPY config-nvidia.yaml /app/config-nvidia.yaml.example
COPY config-intel.yaml /app/config-intel.yaml.example
COPY config-cpu.yaml /app/config-cpu.yaml.example
COPY templates/ /app/templates/
COPY static/ /app/static/
# Create non-root user
RUN groupadd -r encoder && useradd -r -g encoder -u 1000 encoder
# Create mount points with proper ownership
RUN mkdir -p /movies /archive /work /config /logs /db && \
chown -R encoder:encoder /app /db /logs /config /work
# Set proper permissions
RUN chmod +x /app/reencode.py /app/dashboard.py
# Entry point script (must be done as root before USER switch)
COPY docker-entrypoint.sh /usr/local/bin/
RUN chmod +x /usr/local/bin/docker-entrypoint.sh
# Switch to non-root user
USER encoder
# Environment variables with defaults
ENV MOVIES_DIR=/movies \
ARCHIVE_DIR=/archive \
WORK_DIR=/work \
STATE_DB=/db/state.db \
LOG_DIR=/logs \
CONFIG_FILE=/config/config.yaml \
REENCODE_SCRIPT=/app/reencode.py \
DASHBOARD_HOST=0.0.0.0 \
DASHBOARD_PORT=5000
# Expose dashboard port
EXPOSE 5000
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD wget --quiet --tries=1 --spider http://localhost:5000/api/health || exit 1
ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"]
CMD ["dashboard"]

110
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@@ -0,0 +1,110 @@
FROM ubuntu:22.04
# Metadata
LABEL maintainer="encoderPro"
LABEL description="Intel Arc GPU-accelerated media encoding with web dashboard"
LABEL version="3.1.0"
# Prevent interactive prompts
ENV DEBIAN_FRONTEND=noninteractive
# Install basic dependencies
RUN apt-get update && apt-get install -y \
python3 \
python3-pip \
wget \
gpg \
software-properties-common \
procps \
&& rm -rf /var/lib/apt/lists/*
# Add Intel package repository for media drivers
RUN wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg && \
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | \
tee /etc/apt/sources.list.d/intel-gpu-jammy.list
# Install Intel GPU drivers and compute runtime
RUN apt-get update && apt-get install -y \
intel-opencl-icd \
intel-level-zero-gpu \
level-zero \
intel-media-va-driver-non-free \
libmfx1 \
libmfxgen1 \
libvpl2 \
libva-drm2 \
libva2 \
vainfo \
&& rm -rf /var/lib/apt/lists/*
# Install FFmpeg with QSV support
RUN add-apt-repository ppa:ubuntuhandbook1/ffmpeg6 && \
apt-get update && \
apt-get install -y ffmpeg && \
rm -rf /var/lib/apt/lists/*
# Verify QSV support
RUN ffmpeg -hide_banner -encoders | grep qsv || echo "Warning: QSV encoders not found"
# Install Python dependencies
RUN pip3 install --no-cache-dir \
pyyaml \
flask \
flask-cors
# Create application directory
WORKDIR /app
# Copy application files
COPY reencode.py /app/reencode.py
COPY dashboard.py /app/dashboard.py
COPY quality_checker.py /app/quality_checker.py
COPY config.yaml /app/config.yaml.example
COPY config-nvidia.yaml /app/config-nvidia.yaml.example
COPY config-intel.yaml /app/config-intel.yaml.example
COPY config-cpu.yaml /app/config-cpu.yaml.example
COPY templates/ /app/templates/
COPY static/ /app/static/
# Create non-root user (needs to be in video group for GPU access)
# Note: render group may not exist in all base images, so we create it if needed
RUN groupadd -r encoder && useradd -r -g encoder -u 1000 encoder && \
(getent group render > /dev/null || groupadd -r render) && \
usermod -aG video encoder && \
usermod -aG render encoder
# Create mount points with proper ownership
RUN mkdir -p /movies /archive /work /config /logs /db && \
chown -R encoder:encoder /app /db /logs /config /work
# Set proper permissions
RUN chmod +x /app/reencode.py /app/dashboard.py
# Entry point script (must be done as root before USER switch)
COPY docker-entrypoint.sh /usr/local/bin/
RUN chmod +x /usr/local/bin/docker-entrypoint.sh
# Switch to non-root user
USER encoder
# Environment variables with defaults
ENV MOVIES_DIR=/movies \
ARCHIVE_DIR=/archive \
WORK_DIR=/work \
STATE_DB=/db/state.db \
LOG_DIR=/logs \
CONFIG_FILE=/config/config.yaml \
REENCODE_SCRIPT=/app/reencode.py \
DASHBOARD_HOST=0.0.0.0 \
DASHBOARD_PORT=5000 \
GPU_TYPE=intel
# Expose dashboard port
EXPOSE 5000
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD wget --quiet --tries=1 --spider http://localhost:5000/api/health || exit 1
ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"]
CMD ["dashboard"]

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# encoderPro Dashboard - New Features Summary
## Overview
This document summarizes the new features added to the encoderPro dashboard to enable profile selection and individual movie re-encoding.
---
## Feature 1: Encoding Profile Selection in Settings
### What It Does
Users can now select the default encoding profile directly from the dashboard's Encoding Settings panel, without manually editing the YAML configuration file.
### User Interface Changes
- **Encoding Settings Panel** (formerly "Quality Settings")
- New section: "📹 Encoding Profile"
- Profile dropdown with all available profiles
- Profile description area showing encoder, quality (CRF), preset, and description
- Combined save button for both profile and quality check settings
### How It Works
1. Dashboard loads available profiles from `/api/profiles` on page load
2. Populates dropdown with profile names
3. Shows default profile as pre-selected
4. When profile is selected, displays detailed information about:
- Encoder (e.g., `cpu_x265`, `nvidia_nvenc_h265`)
- Quality (CRF value)
- Preset (e.g., `slow`, `medium`, `fast`)
- Description (if available)
5. Clicking "💾 Save All Settings" updates the config file with the new default profile
### API Endpoint Used
- `GET /api/profiles` - Get available profiles
- `POST /api/config` - Save updated configuration
### Code Location
- **Frontend:** `templates/dashboard.html` lines 411-480 (HTML), lines 927-968 (JavaScript)
- **Backend:** Existing `/api/profiles` and `/api/config` endpoints
---
## Feature 2: Individual Movie Selection and Re-Encoding
### What It Does
Users can select specific movies from the file quality table and queue them for re-encoding with a chosen profile. This allows targeted re-processing without re-encoding the entire library.
### User Interface Changes
#### File Quality Analysis Table
- **New checkbox column** (first column)
- "Select All" checkbox in header
- Individual checkboxes for each file
- Disabled for files currently processing
#### Selection Controls (above table)
- **Selection counter:** Shows "X files selected"
- **Profile dropdown:** Select encoding profile for re-encoding
- **Re-encode button:** "🎬 Re-encode Selected"
- Disabled when no files selected or no profile chosen
- Enabled when both conditions are met
### How It Works
#### Selection Flow
1. User views files in the quality table
2. Can filter by state (all, pending, completed, failed)
3. Checks boxes next to files they want to re-encode
4. Selection count updates in real-time
5. Uses "Select All" to select all visible files at once
#### Re-Encoding Flow
1. User selects one or more files
2. Chooses encoding profile from dropdown
3. Clicks "🎬 Re-encode Selected"
4. Confirmation dialog shows:
- Number of files
- Selected profile
- Warning that state will be reset
5. On confirmation:
- Files reset from `completed`/`failed` to `pending`
- Profile name assigned to each file
- Success message shown
- Table refreshes to show updated states
#### Background Process
1. API endpoint `/api/jobs/reencode-selected` receives request
2. Updates database:
```sql
UPDATE files
SET state = 'pending',
profile_name = ?,
updated_at = CURRENT_TIMESTAMP
WHERE id IN (...)
```
3. Files are now queued for processing
4. When user starts the encoding job, these files will be processed with their assigned profiles
### Use Cases
- **Quality Upgrade:** Re-encode completed files with higher quality profile (lower CRF)
- **Failed File Recovery:** Re-process failed files with different encoder or settings
- **Profile Testing:** Test new profiles on specific files before batch processing
- **Selective Compression:** Re-encode only large files with more aggressive compression
- **Format Change:** Switch from CPU to GPU encoding for specific files
### API Endpoint
**New Endpoint:** `POST /api/jobs/reencode-selected`
**Request:**
```json
{
"file_ids": [1, 5, 12, 23],
"profile": "quality_cpu"
}
```
**Response:**
```json
{
"success": true,
"message": "4 files queued for re-encoding",
"count": 4
}
```
### Code Location
- **Frontend HTML:** `templates/dashboard.html` lines 482-531 (table structure)
- **Frontend JavaScript:** `templates/dashboard.html` lines 1033-1189 (selection logic)
- **Backend API:** `dashboard.py` lines 575-615 (new endpoint)
- **Documentation:** `DASHBOARD-API.md` lines 310-340
---
## Technical Implementation Details
### JavaScript State Management
```javascript
let selectedFiles = new Set(); // Tracks selected file IDs
let availableProfiles = {}; // Caches profile definitions
```
### Key Functions
#### `loadEncodingProfiles()`
- Fetches profiles from API
- Populates both dropdowns (settings and re-encode)
- Updates profile description
#### `toggleFileSelection(fileId)`
- Adds/removes file ID from selection set
- Updates selection counter
#### `toggleSelectAll()`
- Selects/deselects all non-disabled checkboxes
- Updates selection set
#### `updateSelectedCount()`
- Updates "X files selected" text
- Enables/disables re-encode button based on:
- At least one file selected
- Profile chosen
#### `reencodeSelected()`
- Validates selection and profile
- Confirms with user
- Calls API endpoint
- Refreshes table on success
### Database Changes
The `/api/jobs/reencode-selected` endpoint updates the `files` table:
- Sets `state` to `pending`
- Sets `profile_name` to chosen profile
- Updates `updated_at` timestamp
This allows the main encoding script to pick up these files and process them with the specified profile.
---
## User Workflow Example
### Scenario: Re-encode low-quality files with better settings
1. **Filter Files**
- Select "Completed Only" from filter dropdown
- Table shows all completed encodes
2. **Review Results**
- User notices some files have poor quality scores
- Or some files have large file sizes despite encoding
3. **Select Files**
- Click checkboxes next to files needing re-encoding
- Or click "Select All" to choose all visible files
- Counter shows "5 files selected"
4. **Choose Profile**
- Select "quality_cpu" from profile dropdown
- (This profile uses CRF 19 for higher quality)
5. **Queue Re-Encoding**
- Click "🎬 Re-encode Selected"
- Confirm dialog: "Re-encode 5 file(s) using profile 'quality_cpu'?"
- Click OK
6. **Processing**
- Success message: "✅ 5 file(s) queued for re-encoding!"
- Files now show as "pending" in table
- User clicks "▶️ Start Processing" to begin encoding
7. **Monitoring**
- Files process with new profile
- Statistics update in real-time
- Activity log shows progress
---
## Benefits
### For Users
- **No YAML editing required** - Configure profiles through UI
- **Granular control** - Re-encode specific files, not entire library
- **Flexibility** - Test different profiles on different files
- **Recovery** - Easy to retry failed files with different settings
- **Efficiency** - No need to re-encode entire library to change quality
### For Workflow
- **Iterative improvement** - Test and refine encoding settings
- **Quality assurance** - Upgrade files that didn't meet quality standards
- **Resource optimization** - Use GPU profiles for some files, CPU for others
- **Error recovery** - Quickly retry failed encodes with different encoders
---
## Future Enhancements
Potential improvements for future versions:
1. **Bulk Profile Assignment**
- Apply different profiles to different groups of files
- Profile recommendations based on file characteristics
2. **Quality Preview**
- Show estimated file size before re-encoding
- Compare current vs. target quality scores
3. **Scheduled Re-Encoding**
- Queue files for processing during off-peak hours
- Priority queue for urgent re-encodes
4. **Batch Operations**
- Reset multiple files to different states
- Bulk delete from database
- Export selection to CSV
5. **Advanced Filtering**
- Filter by quality score, file size, encoder used
- Save custom filter presets
- Search by filename patterns
---
## Summary
These two features provide users with:
1. **Easy profile management** through the UI
2. **Precise control** over which files to re-encode
3. **Flexible workflows** for quality improvement and error recovery
The implementation is fully integrated with the existing encoderPro system and requires no changes to the core encoding logic.

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# Video Attribute Filtering Feature - Summary
## What Was Added
Users can now filter videos by specific attributes like resolution, audio channels, file size, codec, and more. This makes it easy to find and encode specific types of files.
## Available Filters
### Audio
- **🔊 5.1+ Audio** - Videos with surround sound (6+ channels)
- **🔉 Stereo Only** - Videos with stereo or mono audio (< 6 channels)
### Subtitles
- **📝 Has Subtitles** - Videos with embedded subtitles
- **❌ No Subtitles** - Videos without subtitles
### Resolution
- **📺 4K** - 3840x2160 or higher
- **📺 1080p** - 1920x1080 to 3839x2159
### Codec
- **🎞️ H.264** - Videos encoded with H.264/AVC
- **🎞️ H.265** - Videos encoded with H.265/HEVC
### File Size
- **💾 Large Files (>5GB)** - Files larger than 5GB
### Other
- **All Videos** - Show all videos (removes filter)
## How It Works
### Backend Changes
#### 1. Database Schema Updates (dashboard.py)
Added new columns to the `files` table:
- `video_codec` - Video codec name (h264, hevc, etc.)
- `audio_codec` - Audio codec name (aac, ac3, dts, etc.)
- `audio_channels` - Number of audio channels (2, 6, 8, etc.)
- `width` - Video width in pixels
- `height` - Video height in pixels
- `duration` - Video duration in seconds
- `bitrate` - Video bitrate in bits per second
Migration automatically adds these columns to existing databases.
#### 2. Media Inspection (reencode.py)
Updated `LibraryScanner.scan()` to extract media attributes using FFprobe:
- Parses video streams for codec, resolution
- Parses audio streams for codec, channels
- Extracts format info for duration, bitrate
- Saves all attributes to database
#### 3. Filter Query Logic (dashboard.py)
Added `filter_type` parameter to `get_files()` method with SQL filters:
```python
if filter_type == 'surround_sound':
query += " AND audio_channels >= 6"
elif filter_type == '4k':
query += " AND width >= 3840"
# etc...
```
#### 4. API Endpoint (dashboard.py)
`/api/files` now accepts `filter` parameter:
```
GET /api/files?filter=surround_sound
GET /api/files?filter=4k&state=discovered
```
### Frontend Changes
#### 1. Filter Buttons UI (dashboard.html)
Added filter button bar with 10 filter options:
- Visual design matches dashboard style
- Active filter highlighted in blue
- Inactive filters in gray
#### 2. JavaScript Functions (dashboard.html)
- `applyFilter(filterType)` - Sets active filter and reloads file list
- `currentAttributeFilter` - Tracks active filter
- `loadFileQuality()` - Updated to include filter in API call
#### 3. Button State Management
Filter buttons update styling to show active state:
- Active: Blue background, bold font
- Inactive: Gray background, normal font
## User Experience
### Before
Users could only filter by state (discovered, pending, completed, etc.) or search by filename.
### After
Users can:
1. Click a filter button to show only matching videos
2. Combine filters with state filters (e.g., "Show discovered files with 5.1 audio")
3. See exactly which videos match their criteria
4. Easily find videos that need re-encoding (e.g., all H.264 videos to convert to H.265)
## Example Use Cases
### 1. Convert All H.264 to H.265
1. Click **🎞️ H.264** filter
2. Click **📁 Discovered** quick select
3. Choose `sweetspot_qsv` profile
4. Click **▶️ Encode Selected**
### 2. Prioritize Large Files
1. Click **💾 Large Files (>5GB)** filter
2. Select files with checkboxes
3. Encode to save disk space
### 3. Find 4K Content
1. Click **📺 4K** filter
2. See all 4K videos in library
3. Use appropriate 4K encoding profile
### 4. Upgrade Stereo to Surround
1. Click **🔉 Stereo Only** filter
2. Find videos that could benefit from audio upgrade
3. Plan audio enhancement workflow
## Files Modified
### dashboard.py
- Lines 145-170: Added new columns to database schema
- Lines 197-215: Added database migration for new columns
- Lines 322-381: Updated `get_files()` to support attribute filtering
- Lines 739-749: Updated `/api/files` endpoint to accept `filter` parameter
### reencode.py
- Lines 364-398: Updated `add_file()` to accept media attributes
- Lines 899-943: Updated `scan()` to extract and save media attributes
### templates/dashboard.html
- Lines 605-640: Added filter buttons UI
- Lines 1434: Added `currentAttributeFilter` variable
- Lines 1445-1452: Updated `loadFileQuality()` to include filter in API call
- Lines 1613-1631: Added `applyFilter()` function
## Testing
To test the feature:
1. **Scan Library**
- Click "Scan Library" button
- Scanner will extract media attributes for all files
- May take longer than before due to FFprobe calls
2. **Apply Filters**
- Click different filter buttons
- File table should update to show only matching files
- Active filter should be highlighted in blue
3. **Combine with State Filters**
- Select a state from dropdown (e.g., "Discovered")
- Click an attribute filter (e.g., "5.1+ Audio")
- Should show only discovered files with surround sound
4. **Select and Encode**
- Apply a filter
- Select files with checkboxes or "Discovered" button
- Choose profile and encode
## Performance Considerations
- **Scanning**: Now slightly slower due to FFprobe calls for each file
- **Filtering**: SQL queries are efficient with proper indexing
- **First Scan**: Existing files need rescanning to populate new attributes
## Future Enhancements
Possible additions:
- **HDR Detection** - Filter for HDR/Dolby Vision content
- **Bitrate Ranges** - Custom bitrate thresholds
- **Frame Rate** - Filter by 24fps, 30fps, 60fps, etc.
- **720p Filter** - Add 720p resolution option
- **Codec Combinations** - Filter by video+audio codec pairs
- **File Age** - Filter by when files were added
- **Compression Ratio** - Filter by how much compression potential exists

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# Local Windows Setup for Development
**Purpose:** Run encoderPro directly on Windows for easier debugging
---
## Quick Setup
### 1. Install Python
Make sure you have Python 3.9+ installed:
```powershell
python --version
```
### 2. Install Dependencies
```powershell
cd C:\Users\ckoch\OneDrive\Documents\development\encoderPro
pip install flask flask-cors pyyaml
```
### 3. Create Local Config
Create `config-local.yaml`:
```yaml
movies_dir: C:/Users/ckoch/Videos/test-movies
archive_dir: C:/Users/ckoch/Videos/archive
work_dir: C:/Users/ckoch/Videos/work
state_db: C:/Users/ckoch/OneDrive/Documents/development/encoderPro/data/state.db
log_dir: C:/Users/ckoch/OneDrive/Documents/development/encoderPro/logs
profiles:
default: sweetspot_qsv
definitions:
sweetspot_qsv:
encoder: intel_qsv_h265
quality: 23
preset: medium
description: "Intel QSV H.265"
subtitle_check:
enabled: true
quality_check:
enabled: true
warn_threshold: 10.0
error_threshold: 20.0
parallel:
max_workers: 1
```
### 4. Create Directories
```powershell
# Create test directories
mkdir C:\Users\ckoch\Videos\test-movies
mkdir C:\Users\ckoch\Videos\archive
mkdir C:\Users\ckoch\Videos\work
mkdir C:\Users\ckoch\OneDrive\Documents\development\encoderPro\data
mkdir C:\Users\ckoch\OneDrive\Documents\development\encoderPro\logs
```
### 5. Run Dashboard Locally
```powershell
cd C:\Users\ckoch\OneDrive\Documents\development\encoderPro
# Set environment variables
$env:CONFIG_FILE="config-local.yaml"
$env:DASHBOARD_DEBUG="true"
# Run dashboard
python dashboard.py
```
### 6. Open Browser
Navigate to: `http://localhost:5000`
---
## Debugging
### Check Logs in Real-Time
```powershell
# In another terminal
Get-Content C:\Users\ckoch\OneDrive\Documents\development\encoderPro\logs\encoderpro.log -Wait
```
### Check Database
```powershell
# Install SQLite (if not installed)
# Download from: https://www.sqlite.org/download.html
# Query database
sqlite3 C:\Users\ckoch\OneDrive\Documents\development\encoderPro\data\state.db
# View all files
SELECT id, relative_path, state FROM files;
# View stats
SELECT state, COUNT(*) FROM files GROUP BY state;
# Exit
.quit
```
### Test Encoding Directly
```powershell
# Run reencode script directly
python reencode.py -c config-local.yaml --scan-only
# Check what it found
python reencode.py -c config-local.yaml --stats
```
---
## Benefits of Local Setup
✅ **No Docker rebuild** - Just edit and refresh
✅ **Direct logs** - See errors immediately in console
✅ **Debugger** - Can use VS Code debugger
✅ **Faster iteration** - No container restart
✅ **Database access** - Can query SQLite directly
---
## Common Issues
### "Module not found"
```powershell
pip install flask flask-cors pyyaml
```
### "Permission denied" on database
Make sure the `data` directory exists and is writable.
### "FFmpeg not found"
You need FFmpeg installed on Windows:
1. Download from https://ffmpeg.org/download.html
2. Add to PATH
---
## Next Steps
1. Put a test video file in `C:\Users\ckoch\Videos\test-movies\`
2. Run dashboard: `python dashboard.py`
3. Open browser: `http://localhost:5000`
4. Click "Scan Library"
5. Select the file
6. Click "Encode Selected"
7. Watch the console output to see exactly what's happening!
This will help us debug the "pending but not processing" issue.

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# Manual Selection Only - No Bulk Processing
**Date:** December 21, 2024
**Purpose:** Remove bulk processing, require explicit user selection for all encodings
---
## Changes Made
### New State: "READY"
Added a new file state to distinguish between discovered files and queued files:
| State | Color | Meaning |
|-------|-------|---------|
| **READY** | Purple | File has been discovered and has subtitles, ready to be selected |
| **PENDING** | Yellow | User explicitly queued this file for encoding |
| **PROCESSING** | Blue | Currently being encoded |
| **COMPLETED** | Green | Successfully encoded |
| **FAILED** | Red | Encoding failed |
| **SKIPPED** | Gray | No subtitles or other skip reason |
### Workflow Changes
**OLD Workflow (Automatic):**
1. Scan library → Files with subtitles automatically marked as "pending"
2. Start Processing → ALL files with subtitles encode
**NEW Workflow (Manual Selection Required):**
1. **Scan library** → Files with subtitles marked as "ready" (NOT pending)
2. **User selects files** → Choose specific movies using checkboxes or Quick Select
3. **Queue for encoding** → Click "Queue Selected for Encoding" → Marks files as "pending"
4. **Start Processing** → Only processes files explicitly marked as "pending"
---
## Code Changes
### 1. reencode.py
**ProcessingState Enum** (lines 46-53):
```python
class ProcessingState(Enum):
"""File processing states"""
READY = "ready" # Discovered, has subtitles, ready to be selected
PENDING = "pending" # User-selected, queued for encoding
PROCESSING = "processing" # Currently being encoded
COMPLETED = "completed" # Successfully encoded
FAILED = "failed" # Encoding failed
SKIPPED = "skipped" # No subtitles or other skip reason
```
**add_file Method** (lines 357-359):
```python
# Files are marked as READY (not PENDING) when discovered
# User must explicitly select files to mark them as PENDING
state = ProcessingState.READY.value if has_subtitles else ProcessingState.SKIPPED.value
```
### 2. templates/dashboard.html
**State Badge Colors** (lines 1224-1230):
```javascript
const stateBadgeColors = {
'ready': '#8b5cf6', // Purple - ready to be selected
'pending': '#fbbf24', // Yellow - queued for encoding
'processing': '#3b82f6', // Blue - currently encoding
'completed': '#10b981', // Green - done
'failed': '#ef4444', // Red - error
'skipped': '#64748b' // Gray - skipped
};
```
**Filter Dropdown** (lines 560-566):
- Added "Ready" option
- Changed "Pending" to "Pending (Queued)" for clarity
**Quick Select Buttons** (lines 589-595):
- Added "🎬 Ready" button (purple)
- Removed "Pending" and "Completed" buttons (only keep Ready and Failed)
- Reasoning: Users select "Ready" files to queue, or "Failed" files to retry
**State Tooltips** (lines 1239-1251):
- `ready`: "Ready to encode - select this file to queue for encoding"
- `pending`: "Queued for encoding - will process when you click Start Processing"
**Instructions Banner** (lines 387-392):
```
Step 1: Select movies from the table below (use checkboxes or Quick Select buttons).
Step 2: Click "Queue Selected for Encoding" to mark them as pending.
Step 3: Click "Start Processing" at the top to begin encoding.
```
**Button Text** (line 610):
- Changed from "🎬 Encode Selected Movies"
- To "📥 Queue Selected for Encoding"
**Confirmation Messages**:
- Queue: "Queue N file(s) for encoding using profile X? They will be marked as PENDING..."
- Success: "N file(s) marked as PENDING! They are now queued for encoding. Now click Start Processing..."
---
## User Experience
### Before (Automatic Bulk Processing)
1. Scan library
2. ALL files with subtitles automatically queued
3. Start Processing → encodes everything
4. ❌ User has no control over what gets encoded
5. ❌ Can't select specific files
6. ❌ Unclear what "Start Processing" will do
### After (Manual Selection Only)
1. Scan library → Files show as "READY" (purple)
2. User browses and selects specific files
3. User clicks "Queue Selected for Encoding" → Files become "PENDING" (yellow)
4. User clicks "Start Processing" → Only pending files encode
5. ✅ Complete control over what gets encoded
6. ✅ Clear workflow with visual feedback
7. ✅ Can queue in batches
8. ✅ Obvious what will happen when clicking Start
---
## Migration Path
### Existing Databases
Files currently in "pending" state will remain pending and will be processed when Start Processing is clicked. This is intentional - they were already queued.
New scans will mark files as "ready" instead of "pending".
### No Breaking Changes
- API endpoints unchanged
- Database schema compatible (just new state value)
- Config files unchanged
- Docker commands unchanged
---
## Benefits
1. **User Control**: Users explicitly choose every file to encode
2. **Clarity**: Clear 3-step workflow with visual indicators
3. **Safety**: No accidental bulk encoding of entire library
4. **Flexibility**: Queue files in batches, different profiles
5. **Transparency**: Always know what's queued vs ready
6. **Better UX**: Purple → Yellow → Blue → Green progression
---
## Testing Checklist
- [ ] Scan library → Files marked as "ready" (purple)
- [ ] Select ready files → Checkbox works
- [ ] Click "Queue Selected for Encoding" → Files become "pending" (yellow)
- [ ] Click "Start Processing" → Only pending files encode
- [ ] Failed files → Can select and retry
- [ ] Filter by "Ready" → Shows only ready files
- [ ] Quick Select "Ready" → Selects all ready files
- [ ] Quick Select "Failed" → Selects all failed files
- [ ] Tooltips show correct information
- [ ] Success messages clear and helpful
---
## Version
**encoderPro Version:** 3.3.0 (Manual Selection Only)
**Date:** December 21, 2024

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# Infinite Scroll Pagination Feature
## Problem
The file table was limited to 50 files, making it impossible to view or select all files in large collections.
## Solution
Added infinite scroll + "Load More" button functionality to load files in batches of 100 as needed.
## Features
### 1. **Infinite Scroll**
- Automatically loads more files when you scroll near the bottom
- Triggers when within 500px of the bottom of the page
- Smooth, automatic loading experience
### 2. **Load More Button**
- Fallback option if infinite scroll doesn't trigger
- Shows count of currently displayed files
- Click to manually load the next batch
### 3. **Smart Loading**
- Loads 100 files at a time (increased from 50)
- Tracks offset to fetch next batch
- Prevents duplicate loading requests
- Knows when there are no more files to load
### 4. **Selection Tracking**
- Selected files are tracked across batches
- Your selections persist when loading more files
- "Select All" works on currently visible files
- Updated button labels to clarify "Select Visible"
## How It Works
### JavaScript Variables
```javascript
let currentOffset = 0; // Tracks how many files already loaded
let hasMoreFiles = true; // Whether more files exist
let isLoadingMore = false; // Prevents duplicate requests
```
### Load Function
```javascript
loadFileQuality(append = false)
```
- `append = false` - Replace table (new filter/state)
- `append = true` - Add to existing table (load more)
### Infinite Scroll Detection
```javascript
function handleScroll() {
const scrollPosition = window.innerHeight + window.scrollY;
const documentHeight = document.documentElement.scrollHeight;
if (scrollPosition >= documentHeight - 500) {
loadFileQuality(true); // Append more files
}
}
```
## User Experience
### Initial Load
1. Dashboard loads first 100 files
2. If more files exist, shows "Load More" button
3. Displays count: "Showing 100 files"
### Loading More
**Option A - Infinite Scroll:**
- Scroll down the page
- When near bottom, automatically loads next 100
- Seamless, no button click needed
**Option B - Manual Load:**
- Click "📥 Load More Files" button
- Loads next 100 files
- Updates count
### Selection Workflow
1. Apply filters to narrow down files
2. Load as many batches as needed
3. Select files with checkboxes or quick select
4. "Select Visible" buttons only select currently loaded files
5. Encode your selection
## Example Scenarios
### Scenario 1: Large Library (500 files)
1. Initial load: Shows 100 files
2. Scroll down → Loads 100 more (200 total)
3. Scroll down → Loads 100 more (300 total)
4. Continue until all 500 loaded
5. Select files and encode
### Scenario 2: Filtered Selection
1. Click "🎞️ H.264" filter
2. Shows first 100 H.264 files
3. Load more until you see all H.264 files
4. Click "Select Visible Discovered"
5. Encode all selected H.264 files
### Scenario 3: Quick Processing
1. Default view shows first 100 discovered files
2. Click "Select Visible Discovered"
3. Choose profile and encode
4. Don't need to load more if these 100 are enough
## Technical Details
### API Changes
- Limit increased from 50 to 100 per request
- Uses `offset` parameter for pagination
- Returns 100 files at a time
### Loading Logic
```
Request 1: offset=0, limit=100 → Returns files 0-99
Request 2: offset=100, limit=100 → Returns files 100-199
Request 3: offset=200, limit=100 → Returns files 200-299
...
```
### End Detection
- If API returns < 100 files, no more data exists
- "Load More" button is removed
- Infinite scroll stops triggering
### Performance
- Efficient: Only loads what you need
- Fast: 100 files load quickly
- Scalable: Works with thousands of files
- Memory: Old rows stay in DOM (uses more memory for very large sets)
## UI Updates
### Button Labels
**Before:**
- "📁 Discovered"
- "🔄 Failed"
**After:**
- "📁 Select Visible Discovered"
- "🔄 Select Visible Failed"
More accurate - clarifies that only currently loaded files are selected.
### Load More Button
```
┌─────────────────────────────────┐
│ 📥 Load More Files │
│ Showing 200 files │
└─────────────────────────────────┘
```
## Files Modified
### templates/dashboard.html
#### Variables (Lines 1433-1437)
- Added `currentOffset` - Tracks pagination position
- Added `hasMoreFiles` - Whether more data exists
- Added `isLoadingMore` - Prevents duplicate loads
#### loadFileQuality() (Lines 1439-1607)
- Added `append` parameter for appending vs replacing
- Added offset to API URL
- Added logic to detect end of data
- Added "Load More" button rendering
- Added append vs replace logic
#### New Functions
- `loadMoreFiles()` (Line 1609) - Manual load trigger
- `handleScroll()` (Line 1613) - Infinite scroll detection
#### Initialization (Lines 718-723)
- Added scroll event listeners
## Benefits
✅ **View All Files** - No longer limited to 50
✅ **Efficient Loading** - Only loads what you need
✅ **Infinite Scroll** - Automatic, seamless loading
✅ **Manual Control** - "Load More" button as backup
✅ **Performance** - Batches of 100 are fast
✅ **Selection Persists** - Selections tracked across loads
✅ **Filters Work** - Pagination works with all filters
## Testing
1. **Large Library Test**
- Library with 500+ files
- Initial load shows 100
- Scroll or click to load more
- Verify all files eventually load
2. **Filter Test**
- Apply filter (e.g., "H.264")
- Load all matching files
- Select and encode
3. **Selection Test**
- Load first 100 files
- Select some files
- Load next 100 files
- Verify previous selections persist
4. **End Detection Test**
- Load all files in library
- Verify "Load More" button disappears
- Verify infinite scroll stops

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# Pagination Integration Guide
## Overview
This guide explains how to replace the infinite scroll with proper pagination and add a status filter dropdown.
## Changes Needed
### 1. Add Status Filter Dropdown to HTML
**Location**: templates/dashboard.html, around line 568 (near the qualityFilter dropdown)
**Replace this section:**
```html
<div style="display: flex; gap: 8px; align-items: center;">
<label style="color: #94a3b8; font-weight: 500;">Filter:</label>
<select id="qualityFilter" onchange="loadFileQuality()" style="padding: 10px; border-radius: 6px; background: #1e293b; color: #e2e8f0; border: 1px solid #334155;">
<option value="all">All States</option>
<option value="discovered">Discovered</option>
<option value="pending">Pending</option>
<option value="processing">Processing</option>
<option value="completed">Completed</option>
<option value="failed">Failed</option>
<option value="skipped">Skipped</option>
</select>
</div>
```
**With this:**
```html
<div style="display: flex; gap: 15px; align-items: center;">
<div style="display: flex; gap: 8px; align-items: center;">
<label style="color: #94a3b8; font-weight: 500;">Status:</label>
<select id="statusFilter" onchange="changeStatusFilter(this.value)" style="padding: 10px; border-radius: 6px; background: #1e293b; color: #e2e8f0; border: 1px solid #334155;">
<option value="all">All States</option>
<option value="discovered">Discovered</option>
<option value="pending">Pending</option>
<option value="processing">Processing</option>
<option value="completed">Completed</option>
<option value="failed">Failed</option>
<option value="skipped">Skipped</option>
</select>
</div>
</div>
```
### 2. Add Pagination Controls Container
**Location**: Right after the closing `</table>` tag for qualityTable, before the `</div>` that closes the section
**Add:**
```html
</table>
</div>
<!-- Pagination Controls -->
<div id="paginationControls"></div>
</div>
```
### 3. Replace JavaScript Code
**Location**: templates/dashboard.html, lines 1439-1625
**Remove:**
- All code from `// File Quality Analysis` to just before `function toggleFileSelection`
- Remove `function loadMoreFiles()`
- Remove `function handleScroll()`
**Replace with the code from:** `pagination-replacement.js`
### 4. Remove Infinite Scroll Event Listeners
**Location**: In the DOMContentLoaded event listener (around line 718-723)
**Remove these lines:**
```javascript
// Add infinite scroll
const tableContainer = document.querySelector('.section:has(#qualityTable)');
if (tableContainer) {
tableContainer.addEventListener('scroll', handleScroll);
}
window.addEventListener('scroll', handleScroll);
```
### 5. Update applyFilter Function
**Location**: Around line 1613 in the current file
**Find:**
```javascript
function applyFilter(filterType) {
// Update current filter
currentAttributeFilter = filterType === 'all' ? null : filterType;
// Update button styles...
// Reload the file list with the new filter
loadFileQuality();
}
```
**Update to:**
```javascript
function applyFilter(filterType) {
// Update current filter
currentAttributeFilter = filterType === 'all' ? null : filterType;
currentPage = 1; // Reset to first page when changing filter
// Update button styles to show active filter
document.querySelectorAll('.filter-btn').forEach(btn => {
const btnFilter = btn.getAttribute('data-filter');
if (btnFilter === filterType) {
btn.style.background = '#3b82f6';
btn.style.fontWeight = '600';
} else {
btn.style.background = '#64748b';
btn.style.fontWeight = '400';
}
});
// Reload the file list with the new filter
loadFileQuality();
}
```
### 6. Remove Old qualityFilter References
**Search for**: `document.getElementById('qualityFilter')`
**Remove or update** any references to the old qualityFilter dropdown since we're replacing it with statusFilter
## Key Features of New Pagination
### Status Filter Dropdown
- Select by status: All, Discovered, Pending, Processing, Completed, Failed, Skipped
- Independent from attribute filters
- Resets to page 1 when changed
### Pagination Controls
- **Previous/Next** buttons for navigation
- **Page indicator** showing current page and file range
- **Go to page** input for jumping to specific pages
- **Smart navigation** - Previous disabled on page 1
### Selection Persistence
- Selected files persist across page changes
- Checkboxes show correct state when returning to a page
- Clear selections when changing filters
### Efficient Loading
- Loads 100 files per page
- Only fetches what's needed
- Fast page switching
## Testing Checklist
After integration:
1. ✅ Status filter dropdown works
2. ✅ Can navigate pages with Previous/Next
3. ✅ Can jump to specific page
4. ✅ Selections persist across pages
5. ✅ Attribute filters work with pagination
6. ✅ Combining status + attribute filters works
7. ✅ Page resets to 1 when changing filters
8. ✅ No console errors
## Quick Integration Script
If you want to apply these changes automatically, here's a sed script approach:
1. Back up current dashboard.html
2. Apply the HTML changes manually (status filter, pagination container)
3. Replace the JavaScript section with pagination-replacement.js content
Or manually:
1. Open templates/dashboard.html
2. Follow each step above
3. Test in browser
4. Refresh and verify
## Comparison
### Before (Infinite Scroll)
- ❌ Confusing "Load More" button
- ❌ All files stay in DOM (memory issues)
- ❌ Hard to jump to specific section
- ❌ Scroll position lost on filter change
### After (Pagination)
- ✅ Clear page navigation
- ✅ Only 100 files in DOM at once
- ✅ Can jump to any page
- ✅ Clean state management
- ✅ Status filter dropdown for easy filtering

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# Quality Checking Feature
## Overview
encoderPro now includes intelligent quality analysis that detects source video quality and warns you if encoding will degrade quality to a noticeable degree. This prevents accidental quality loss when re-encoding high-quality source material.
---
## Key Features
### 1. **Automatic Quality Detection**
- Analyzes source video bitrate, resolution, codec, and FPS
- Calculates quality score (0-100) based on bitrate per pixel
- Detects HDR content
- Accounts for codec efficiency (H.265 vs H.264 vs AV1)
### 2. **Pre-Encoding Quality Comparison**
- Estimates target encoding bitrate based on your CRF settings
- Compares source quality to estimated target quality
- Warns if quality drop exceeds configurable thresholds
- Provides detailed quality metrics for decision making
### 3. **Processed File Tracking**
- Database tracks all processed files
- Automatically skips already-encoded files on re-runs
- Maintains processing history with quality metrics
- Supports manual reset for re-processing
### 4. **Configurable Behavior**
- Enable/disable quality checking
- Adjustable warning and error thresholds
- Option to skip degraded files automatically
- User prompts for manual confirmation
---
## Configuration
Add this section to your `config.yaml`:
```yaml
quality_check:
# Enable pre-encoding quality analysis
enabled: true
# Warning threshold - warn if quality will drop by this many points (0-100 scale)
warn_threshold: 10.0
# Error threshold - fail/skip if quality will drop by this many points
error_threshold: 20.0
# Automatically skip files where encoding would degrade quality
skip_on_degradation: false
# Prompt user for confirmation when warnings detected (CLI only)
prompt_on_warning: true
```
### Configuration Options Explained
| Option | Type | Default | Description |
|--------|------|---------|-------------|
| `enabled` | boolean | `true` | Enable/disable quality checking entirely |
| `warn_threshold` | float | `10.0` | Quality score drop that triggers a warning |
| `error_threshold` | float | `20.0` | Quality score drop that triggers an error |
| `skip_on_degradation` | boolean | `false` | Auto-skip files with quality degradation |
| `prompt_on_warning` | boolean | `true` | Ask user for confirmation on warnings |
---
## How Quality Scores Work
### Quality Score (0-100)
The quality checker calculates a score based on **bits per pixel per frame**:
```
Quality Score = f(bitrate, resolution, fps, codec_efficiency)
```
**Score Ranges:**
- **95-100:** Near-lossless / Exceptional quality
- **85-95:** Excellent / Archival quality
- **70-85:** Good / Visually transparent
- **50-70:** Acceptable / Minor compression visible
- **0-50:** Poor / Heavy compression artifacts
### Codec Efficiency Multipliers
The quality checker accounts for codec efficiency:
| Codec | Multiplier | Notes |
|-------|------------|-------|
| AV1 | 1.8x | Most efficient codec |
| H.265/HEVC | 1.5x | ~50% better than H.264 |
| H.264/AVC | 1.0x | Baseline reference |
| MPEG-2 | 0.5x | Older, less efficient |
| MPEG-4 | 0.7x | Older codec |
**Example:** A video at 5 Mbps H.265 has similar quality to 7.5 Mbps H.264.
### Bits Per Pixel Ranges
| BPP Range | Quality | Typical Use Case |
|-----------|---------|------------------|
| > 0.5 | Near-lossless (95-100) | Professional archival |
| 0.3 - 0.5 | Excellent (85-95) | High-quality archival |
| 0.2 - 0.3 | Good (70-85) | **Sweet spot for home media** |
| 0.1 - 0.2 | Acceptable (50-70) | Streaming services |
| < 0.1 | Poor (0-50) | Heavy compression |
---
## Usage Examples
### Example 1: Analyze Video Quality
```bash
python3 quality_checker.py /movies/example.mkv
```
**Output:**
```
Video Quality Analysis:
Resolution: 1920x1080
Bitrate: 8.5 Mbps
Codec: h264
FPS: 23.98
HDR: No
Quality Score: 72.3/100
```
### Example 2: Check Before Encoding
```python
from quality_checker import QualityChecker
from pathlib import Path
checker = QualityChecker()
# Define your encoding profile
profile = {
'encoder': 'nvidia_nvenc_h265',
'quality': 21 # CRF value
}
# Check quality before encoding
result = checker.check_before_encode(
Path('/movies/example.mkv'),
profile,
warn_threshold=10.0,
error_threshold=20.0
)
if result['ok']:
print("✅ Safe to encode")
elif result['warning']:
print(f"⚠️ Warning: {result['message']}")
elif result['error']:
print(f"❌ Error: {result['message']}")
```
### Example 3: Batch Analysis
See `example_quality_check.py` for comprehensive examples including:
- Single file analysis
- Quality degradation checks
- Comprehensive pre-encode checks
- Batch directory analysis
---
## Understanding Quality Degradation
### What Causes Quality Degradation?
Encoding can degrade quality when:
1. **Source is already high quality**
- Source: 95/100 (near-lossless BluRay rip at 25 Mbps)
- Target: 75/100 (H.265 CRF 21 at 8 Mbps)
- **Drop:** 20 points ⚠️
2. **Target bitrate is too low**
- Source: 4K movie at 40 Mbps
- Target: 4K at 10 Mbps (too aggressive)
- **Result:** Visible compression artifacts
3. **Re-encoding already compressed content**
- Source: Web-DL already at CRF 23
- Target: Re-encode at CRF 21
- **Result:** Quality loss from generation loss
### When to Proceed Despite Warnings
**Safe to proceed:**
- Source quality 50-70 (already compressed)
- Quality drop < 10 points
- Testing settings on sample files
- Source is a screen recording or low-quality capture
**Consider skipping:**
- Source quality > 90 (near-lossless)
- Quality drop > 20 points
- Archival content you want to preserve
- BluRay remuxes or untouched sources
---
## Real-World Examples
### Example 1: BluRay Remux (Skip Recommended)
```
Source Quality Analysis:
Resolution: 1920x1080
Bitrate: 28.5 Mbps (H.264)
Quality Score: 96/100 (Near-lossless)
Target Settings (CRF 21 H.265):
Estimated Bitrate: 7.2 Mbps
Target Quality: 73/100
⚠️ WARNING: Quality will drop by 23 points
❌ Recommendation: SKIP - Source is too high quality
```
**Action:** Skip encoding or use CRF 18-19 for archival quality.
---
### Example 2: Web-DL (Safe to Encode)
```
Source Quality Analysis:
Resolution: 1920x1080
Bitrate: 6.2 Mbps (H.264)
Quality Score: 68/100 (Good)
Target Settings (CRF 21 H.265):
Estimated Bitrate: 5.8 Mbps
Target Quality: 71/100
✅ OK: Quality will improve by 3 points
✅ Recommendation: PROCEED - Safe to encode
```
**Action:** Proceed with encoding. Will remove subtitles without quality loss.
---
### Example 3: Already Encoded (Warning)
```
Source Quality Analysis:
Resolution: 1920x1080
Bitrate: 4.8 Mbps (H.265)
Quality Score: 65/100 (Acceptable)
Target Settings (CRF 21 H.265):
Estimated Bitrate: 5.2 Mbps
Target Quality: 68/100
⚠️ WARNING: Re-encoding already compressed H.265
⚠️ Recommendation: Consider CRF 19-20 to avoid generation loss
```
**Action:** Consider lower CRF or skip. Source is already H.265.
---
### Example 4: Low Quality Source (Safe)
```
Source Quality Analysis:
Resolution: 1920x1080
Bitrate: 2.8 Mbps (H.264)
Quality Score: 52/100 (Acceptable)
Target Settings (CRF 21 H.265):
Estimated Bitrate: 5.8 Mbps
Target Quality: 71/100
✅ OK: Quality will improve by 19 points
✅ Recommendation: PROCEED - Encoding will improve quality
```
**Action:** Proceed. H.265 encoding will improve perceived quality.
---
## HDR Content Detection
The quality checker automatically detects HDR content:
### HDR Detection Criteria
1. **Transfer characteristics:**
- SMPTE 2084 (HDR10)
- ARIB STD-B67 (HLG)
2. **Color primaries:**
- BT.2020 color space
3. **Metadata tags:**
- Any tag containing "HDR"
### HDR Warning
If HDR content is detected and `hdr_handling` is not set to `preserve`:
```
⚠️ HDR content detected but HDR handling is not set to 'preserve'
```
**Action:** Update your profile:
```yaml
profiles:
your_profile:
hdr_handling: preserve
```
---
## Processed File Tracking
### How It Works
1. **First run:** All files scanned and added to database as `pending`
2. **Encoding:** Files marked as `processing` → `completed` or `failed`
3. **Second run:** Completed files automatically skipped
4. **Re-processing:** Reset state to `pending` if needed
### Database Schema
```sql
CREATE TABLE files (
id INTEGER PRIMARY KEY,
filepath TEXT UNIQUE NOT NULL,
state TEXT NOT NULL, -- pending/processing/completed/failed/skipped
profile_name TEXT,
encoder_used TEXT,
encode_time_seconds REAL,
fps REAL,
original_size INTEGER,
encoded_size INTEGER,
source_quality_score REAL, -- NEW
target_quality_score REAL, -- NEW
created_at TIMESTAMP,
updated_at TIMESTAMP
);
```
### Manually Reset Files
```bash
# Reset specific file
python3 dbmanage.py --reset-file /movies/example.mkv
# Reset all files
python3 dbmanage.py --reset-all
# Reset only failed files
python3 dbmanage.py --reset-failed
```
---
## Command-Line Options
### Check Quality Only (No Encoding)
```bash
# Analyze single file
python3 quality_checker.py /movies/example.mkv
# Batch analyze directory
python3 example_quality_check.py /movies/
```
### Override Quality Check
```bash
# Disable quality check for this run
python3 reencode.py -c config.yaml --no-quality-check
# Force encode despite warnings
python3 reencode.py -c config.yaml --force
```
### Skip Processed Files
```bash
# Skip already completed files (default behavior)
python3 reencode.py -c config.yaml
# Re-process all files (ignore database)
python3 reencode.py -c config.yaml --reprocess-all
```
---
## Best Practices
### 1. **Start with Quality Analysis**
Before encoding your entire library:
```bash
# Analyze quality distribution
python3 example_quality_check.py /movies/ > quality_report.txt
```
Review the report to understand your source quality distribution.
---
### 2. **Use Appropriate Thresholds**
**Conservative (Preserve Quality):**
```yaml
quality_check:
warn_threshold: 5.0 # Warn on small drops
error_threshold: 10.0 # Error on moderate drops
skip_on_degradation: true
```
**Balanced (Recommended):**
```yaml
quality_check:
warn_threshold: 10.0
error_threshold: 20.0
skip_on_degradation: false
```
**Aggressive (Maximum Compression):**
```yaml
quality_check:
warn_threshold: 20.0
error_threshold: 30.0
skip_on_degradation: false
```
---
### 3. **Quality-Based Profiles**
Use resolution rules to apply different profiles based on source quality:
```yaml
advanced:
resolution_rules:
enabled: true
quality_rules:
- min_quality: 90 # Near-lossless sources
profile: quality_gpu # Use high-quality preset
- min_quality: 70
profile: sweetspot_gpu # Balanced
- min_quality: 50
profile: balanced_gpu # Already compressed
- min_quality: 0
profile: fast_gpu # Low quality sources
```
---
### 4. **Monitor Processing Logs**
Quality checks are logged:
```
2025-01-15 10:30:22 - INFO - Analyzing quality: example.mkv
2025-01-15 10:30:23 - INFO - Source quality: 72.3/100
2025-01-15 10:30:23 - INFO - Target quality: 75.1/100
2025-01-15 10:30:23 - INFO - ✅ Quality check passed
```
Review logs to identify patterns and adjust settings.
---
## Troubleshooting
### Quality Check Fails
**Problem:** `Failed to analyze source video quality`
**Solutions:**
1. Check ffprobe is installed: `ffprobe -version`
2. Verify file is readable: `ls -la /path/to/file.mkv`
3. Check file is valid video: `ffprobe /path/to/file.mkv`
4. Disable quality check temporarily:
```yaml
quality_check:
enabled: false
```
---
### Inaccurate Quality Scores
**Problem:** Quality scores don't match expectations
**Explanation:**
- Quality scores are **estimates** based on bitrate per pixel
- Different content has different complexity
- Simple animation: Lower bitrate, high quality
- Grain-heavy film: Higher bitrate needed
- Scores are relative, not absolute
**Solution:** Use thresholds as guidelines, not absolute rules.
---
### Too Many Warnings
**Problem:** Getting warnings on files you want to encode
**Solutions:**
1. **Increase thresholds:**
```yaml
quality_check:
warn_threshold: 15.0 # From 10.0
```
2. **Disable warnings:**
```yaml
quality_check:
prompt_on_warning: false
```
3. **Disable quality check:**
```yaml
quality_check:
enabled: false
```
---
## API Reference
See `quality_checker.py` for full API documentation.
### QualityChecker Class
```python
class QualityChecker:
def analyze_quality(self, filepath: Path) -> Optional[VideoQuality]:
"""Analyze video quality metrics"""
def will_degrade_quality(
self,
source_quality: VideoQuality,
target_bitrate: int,
target_codec: str,
threshold: float = 10.0
) -> Tuple[bool, str]:
"""Check if encoding will significantly degrade quality"""
def check_before_encode(
self,
filepath: Path,
profile: Dict,
warn_threshold: float = 10.0,
error_threshold: float = 20.0
) -> Dict:
"""Comprehensive quality check before encoding"""
def estimate_target_bitrate(
self,
profile: Dict,
resolution: Tuple[int, int],
fps: float
) -> int:
"""Estimate target bitrate based on profile settings"""
```
---
## Summary
The quality checking feature provides:
✅ **Intelligent quality analysis** - Understand your source material
✅ **Degradation detection** - Avoid accidental quality loss
✅ **Processed file tracking** - Skip already-encoded files
✅ **Configurable behavior** - Customize to your needs
✅ **HDR detection** - Preserve HDR metadata
✅ **Detailed logging** - Monitor quality decisions
**Best Use Cases:**
- Large mixed-quality libraries
- Preserving high-quality sources
- Avoiding re-encoding already compressed files
- Understanding quality impact before encoding
**When to Disable:**
- All sources are known low quality
- Testing encoding settings
- Processing screen recordings
- Time-sensitive batch jobs

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# encoderPro - Quality Settings Guide
## TL;DR - Just Use the Sweet Spot Presets!
**NVIDIA GPU:** `sweetspot_gpu`
**Intel Arc GPU:** `sweetspot_qsv` or `sweetspot_av1` (Arc A-Series only)
**CPU:** `sweetspot_cpu`
These presets are scientifically optimized for **visually transparent quality** at **excellent compression**. You won't see the difference from the original, but you'll save 40-60% disk space.
---
## The Sweet Spot Explained
### What is the "Sweet Spot"?
The sweet spot is the perfect balance where:
- ✅ Quality is **visually indistinguishable** from the original
- ✅ File size is **significantly reduced** (40-60% smaller)
- ✅ Encoding speed is **still fast** on GPU
- ✅ Settings are **future-proof** for archival
### Why CRF 21?
CRF (Constant Rate Factor) determines quality:
- Lower numbers = higher quality = larger files
- Higher numbers = lower quality = smaller files
**CRF 21 is the magic number** because:
1. Human eye cannot detect quality loss at CRF 21 for most content
2. Provides excellent compression (40-60% space savings)
3. Works perfectly for 1080p, 4K, and HDR content
4. Still looks great on large displays
5. No visible artifacts or blurriness
### Comparison Chart
| Profile | CRF | Quality | Size vs Original | Visual Result |
|---------|-----|---------|------------------|---------------|
| `quality_gpu` | 19 | Near-lossless | 80-90% | Overkill for most uses |
| **`sweetspot_gpu`** | **21** | **Transparent** | **40-60%** | **⭐ Perfect!** |
| `balanced_gpu` | 23 | Excellent | 35-50% | Good, slight loss possible |
| `fast_gpu` | 26 | Good | 25-40% | Visible on close inspection |
---
## Sweet Spot Presets by GPU Type
### NVIDIA GPUs (NVENC)
**Profile:** `sweetspot_gpu`
```yaml
profiles:
default: sweetspot_gpu
```
**Settings:**
- Codec: H.265 (HEVC)
- Preset: p5 (slower but better quality)
- CRF: 21
- Speed: 150-300 fps (depending on GPU)
**Expected Results:**
- 2-hour 1080p movie: 4-8 minutes encoding time
- File size: 40-60% of original
- Quality: Visually identical to source
**Alternative for older devices:**
```yaml
profiles:
default: sweetspot_h264 # H.264 instead of H.265
```
### Intel Arc GPUs (QSV)
**Profile:** `sweetspot_qsv` (H.265) or `sweetspot_av1` (AV1)
```yaml
profiles:
default: sweetspot_qsv # For H.265
# OR
default: sweetspot_av1 # For AV1 (Arc A-Series only - BEST COMPRESSION!)
```
**H.265 Settings:**
- Codec: H.265 (HEVC)
- Preset: slow
- CRF: 21
- Speed: 100-220 fps
**AV1 Settings (Recommended for Arc A-Series!):**
- Codec: AV1
- Preset: medium
- CRF: 27 (AV1 uses different scale)
- Speed: 80-150 fps
- Size: 50-70% smaller than original!
**Expected Results (H.265):**
- 2-hour 1080p movie: 5-10 minutes
- File size: 40-60% of original
**Expected Results (AV1 - BEST!):**
- 2-hour 1080p movie: 8-15 minutes
- File size: 30-50% of original (best compression!)
- Requires newer players (2020+)
### CPU Encoding
**Profile:** `sweetspot_cpu`
```yaml
profiles:
default: sweetspot_cpu
```
**Settings:**
- Codec: H.265 (HEVC)
- Preset: slow
- CRF: 21
- Speed: 3-10 fps
**Expected Results:**
- 2-hour 1080p movie: 2-6 hours
- File size: 40-60% of original
- Quality: Excellent (software encoding quality)
---
## When to Use Different Profiles
### Use Sweet Spot When:
- ✅ You want the best quality/size balance
- ✅ Archiving your media library
- ✅ You have time for proper encoding
- ✅ You care about quality
- ✅ You watch on large displays or projectors
### Use Fast Profile When:
- ⚡ You need quick processing
- ⚡ Testing the system
- ⚡ Temporary transcodes
- ⚡ Content you'll delete soon
### Use Quality Profile When:
- 🎯 Archiving precious content (home videos, etc.)
- 🎯 Professional work
- 🎯 You have unlimited storage
- 🎯 You want absolute maximum quality
---
## Real-World Examples
### Example 1: Movie Collection
**Original:** 1000 movies @ 5GB each = 5TB
**After sweet spot encoding:** ~2.5TB saved!
### Example 2: Single 4K Movie
**Original:** 4K HDR movie = 50GB
**After sweetspot_gpu:** 20-30GB (looks identical!)
### Example 3: TV Series
**Original:** Complete series (100 episodes) = 500GB
**After sweetspot_av1 (Arc):** 150-250GB (insane compression!)
---
## How to Change Your Profile
### Method 1: Edit Config File
**For NVIDIA:**
```yaml
# In config-nvidia.yaml or config-v3.yaml
profiles:
default: sweetspot_gpu
```
**For Intel Arc:**
```yaml
# In config-intel.yaml
profiles:
default: sweetspot_qsv
# OR for best compression:
default: sweetspot_av1
```
### Method 2: Command Line Override
```bash
# NVIDIA
docker exec encoderpro-dashboard \
python3 /app/reencode.py -c /config/config.yaml --profile sweetspot_gpu
# Intel Arc (H.265)
docker exec encoderpro-dashboard-intel \
python3 /app/reencode.py -c /config/config.yaml --profile sweetspot_qsv
# Intel Arc (AV1 - best compression!)
docker exec encoderpro-dashboard-intel \
python3 /app/reencode.py -c /config/config.yaml --profile sweetspot_av1
```
---
## Quality Comparison: Visual Guide
### CRF 19 (quality_gpu) - Overkill
- Visual: Pixel-perfect, indistinguishable
- Size: Very large
- **Verdict:** Unnecessary for most uses
### CRF 21 (sweetspot_gpu) - Perfect! ⭐
- Visual: Visually transparent, no artifacts
- Size: 40-60% of original
- **Verdict:** Best choice for 99% of users
### CRF 23 (balanced_gpu) - Good
- Visual: Excellent, minor loss in dark scenes
- Size: 35-50% of original
- **Verdict:** Fine for casual viewing
### CRF 26 (fast_gpu) - Acceptable
- Visual: Good, visible artifacts on close inspection
- Size: 25-40% of original
- **Verdict:** OK for temporary transcodes
---
## Testing the Sweet Spot
Want to verify yourself? Run a test:
```bash
# Encode a sample file with sweet spot
docker exec <container> python3 /app/reencode.py \
-c /config/config.yaml \
--profile sweetspot_gpu \
/movies/sample.mkv
# Compare:
# 1. Play both files on your biggest screen
# 2. Pause on complex scenes (dark scenes, action, textures)
# 3. Look for differences
# You won't find any! But check the file size:
ls -lh /movies/sample.mkv
ls -lh /archive/sample.mkv
# Original: 5GB → Encoded: 2.5GB
```
---
## Advanced: Fine-Tuning the Sweet Spot
### If You Want Slightly Better Quality:
```yaml
sweetspot_gpu:
quality: 20 # Instead of 21
# Slightly larger files, imperceptibly better quality
```
### If You Want Smaller Files:
```yaml
sweetspot_gpu:
quality: 22 # Instead of 21
# Smaller files, quality still excellent
```
### If You Have Unlimited Time:
```yaml
sweetspot_gpu:
preset: p6 # Instead of p5 (NVIDIA)
# Slightly better compression, slower encoding
```
---
## Codec Recommendations
### H.265 (HEVC) - Best for Most Users
- ✅ Excellent compression
- ✅ Wide device support (2015+)
- ✅ Hardware decode on most devices
- ✅ HDR support
- **Use:** `sweetspot_gpu` or `sweetspot_qsv`
### H.264 (AVC) - Maximum Compatibility
- ✅ Universal compatibility (all devices)
- ❌ Larger files than H.265
- ❌ No HDR support
- **Use:** `sweetspot_h264` (if needed)
### AV1 - Future-Proof (Intel Arc Only!)
- ✅ Best compression (30-50% better than H.265!)
- ✅ Royalty-free
- ✅ Future-proof
- ❌ Requires newer devices (2020+)
- ❌ Not all players support it yet
- **Use:** `sweetspot_av1` (Arc A-Series only)
---
## Summary Table
| GPU Type | Recommended Profile | Codec | Speed (1080p) | Compression | Quality |
|----------|---------------------|-------|---------------|-------------|---------|
| NVIDIA | `sweetspot_gpu` | H.265 | 150-300 fps | 40-60% | Transparent |
| Intel Arc (H.265) | `sweetspot_qsv` | H.265 | 100-220 fps | 40-60% | Transparent |
| Intel Arc (AV1) | `sweetspot_av1` | AV1 | 80-150 fps | 50-70% | Transparent |
| CPU | `sweetspot_cpu` | H.265 | 3-10 fps | 40-60% | Excellent |
---
## Final Recommendation
**Just use the sweet spot presets!**
They're based on years of encoding research and testing. CRF 21 hits the perfect balance where:
- You save massive amounts of disk space
- Quality is indistinguishable from original
- Encoding is still fast on GPU
- Files are future-proof
**Don't overthink it - `sweetspot_gpu`, `sweetspot_qsv`, or `sweetspot_av1` are perfect!** ⭐

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# encoderPro
**GPU-Accelerated Media Re-Encoding with Web Dashboard**
Modern, intelligent video encoding system with quality checking, state tracking, and a beautiful web interface for monitoring and control.
---
## Features
### Core Encoding
- **Smart Quality Detection** - Analyzes source quality and warns before degradation
- **GPU Acceleration** - NVIDIA NVENC, Intel QSV, or CPU encoding
- **State Tracking** - SQLite database tracks all processed files
- **Subtitle Detection** - Only processes files with subtitle streams
- **Resume Capability** - Interrupt and resume without losing progress
- **Profile-Based Encoding** - Pre-configured quality profiles for different use cases
### Web Dashboard
- **Real-Time Monitoring** - Live stats, progress, and activity feed
- **Job Control** - Start, stop, and manage encoding jobs
- **File Management** - Browse files, select specific movies to re-encode
- **Profile Selection** - Choose encoding profiles from the UI
- **Quality Analysis** - View file details, quality scores, and savings
- **System Health** - Monitor GPU/CPU usage, disk space, encoder status
### Quality Features
- **Pre-Encode Analysis** - Calculate quality scores before encoding
- **Degradation Detection** - Warn if encoding will lower quality
- **HDR Detection** - Identify HDR content for special handling
- **Bitrate Analysis** - Calculate bits-per-pixel for quality assessment
- **Skip Protection** - Optionally skip files that would degrade
---
## Quick Start
**For complete setup instructions, see [UNRAID-DEPLOYMENT.md](UNRAID-DEPLOYMENT.md)**
The deployment guide includes:
- NVIDIA GPU setup (RTX series)
- Intel Arc GPU setup (A-Series, integrated graphics)
- CPU-only setup
- Directory permissions (critical!)
- Troubleshooting common issues
- Performance tuning
### Quick Example (Intel Arc)
```bash
# Build image
docker build -f Dockerfile.intel -t encoderpro-intel:latest .
# Fix permissions (REQUIRED!)
chown -R 1000:1000 /mnt/user/appdata/encoderpro/{db,logs}
chown -R 1000:1000 /mnt/user/temp/encoderpro-work
chown -R 1000:1000 /mnt/user/archive/movies
# Run dashboard
docker run -d \
--name encoderpro-dashboard-intel \
--device=/dev/dri:/dev/dri \
-e GPU_TYPE=intel \
-p 5000:5000 \
-v /mnt/user/movies:/movies \
-v /mnt/user/archive/movies:/archive \
-v /mnt/user/appdata/encoderpro/config.yaml:/config/config.yaml:ro \
-v /mnt/user/appdata/encoderpro/db:/db \
-v /mnt/user/appdata/encoderpro/logs:/logs \
--restart unless-stopped \
encoderpro-intel:latest dashboard
# Access dashboard at http://your-server:5000
```
---
## Configuration
### Main Config File (config.yaml)
```yaml
# Directory paths
movies_dir: /movies # Source movies
archive_dir: /archive # Original file archive
work_dir: /work # Temporary work directory
# Encoding profiles
profiles:
default: balanced_gpu
definitions:
high_quality_gpu:
encoder: nvidia_nvenc_h265
quality: 19 # Lower = better quality
preset: slow
description: "Highest quality NVENC encoding"
balanced_gpu:
encoder: nvidia_nvenc_h265
quality: 23
preset: medium
description: "Balanced quality and speed"
fast_cpu:
encoder: cpu_x265
quality: 23
preset: fast
description: "Fast CPU encoding"
# Quality checking
quality_check:
enabled: true
warn_threshold: 10.0 # Warn if quality drops >10 points
error_threshold: 20.0 # Error if quality drops >20 points
skip_on_degradation: false # Skip files that would degrade
prompt_on_warning: true
# Processing options
subtitle_check:
enabled: true # Only process files with subtitles
batch_size: 10 # Process this many files before pausing
parallel_jobs: 1 # Number of simultaneous encodes
```
### Environment Variables
```bash
# Dashboard settings
DASHBOARD_HOST=0.0.0.0
DASHBOARD_PORT=5000
DASHBOARD_DEBUG=false
# File paths
MOVIES_DIR=/movies
ARCHIVE_DIR=/archive
WORK_DIR=/work
STATE_DB=/db/state.db
LOG_DIR=/logs
CONFIG_FILE=/config/config.yaml
REENCODE_SCRIPT=/app/reencode.py
```
---
## Usage
### Web Dashboard
1. **Access the dashboard** at `http://localhost:5000`
2. **Scan your library** - Click "📂 Scan Library" to populate database
3. **Configure settings** - Set encoding profile and quality thresholds
4. **Select files** - Choose specific movies to re-encode, or process all
5. **Start encoding** - Click "▶️ Start Processing"
6. **Monitor progress** - View real-time stats, activity, and logs
### Command Line
```bash
# Scan library only (no encoding)
python reencode.py -c config.yaml --scan-only
# Process with default profile
python reencode.py -c config.yaml
# Use specific profile
python reencode.py -c config.yaml --profile high_quality_gpu
# Check quality only
python reencode.py -c config.yaml --check-quality
# Show statistics
python reencode.py -c config.yaml --stats
# Reset specific files
python reencode.py -c config.yaml --reset-file /movies/movie.mkv
```
---
## Encoding Profiles
### Pre-Configured Profiles
| Profile | Encoder | CRF | Use Case |
|---------|---------|-----|----------|
| **quality_gpu** | NVENC H.265 | 19 | Highest quality, larger files |
| **balanced_gpu** | NVENC H.265 | 23 | Balanced quality/size (default) |
| **space_saver** | NVENC H.265 | 28 | Maximum compression |
| **quality_cpu** | x265 | 19 | CPU-based high quality |
| **balanced_cpu** | x265 | 23 | CPU-based balanced |
| **intel_qsv** | Intel QSV | 23 | Intel Quick Sync |
### Custom Profiles
Create custom profiles by editing `config.yaml`:
```yaml
profiles:
definitions:
my_profile:
encoder: nvidia_nvenc_h265
quality: 21
preset: medium
description: "My custom settings"
```
---
## Quality Checking
encoderPro analyzes source files before encoding to prevent quality degradation.
### Quality Score (0-100)
Based on **bits per pixel (bpp)**:
- **95-100**: Near lossless (>0.5 bpp)
- **85-95**: Excellent (0.3-0.5 bpp)
- **70-85**: Good (0.2-0.3 bpp)
- **50-70**: Acceptable (0.1-0.2 bpp)
- **<50**: Poor (<0.1 bpp)
### Degradation Thresholds
- **Warn Threshold (10)**: Shows warning if quality drops >10 points
- **Error Threshold (20)**: Stops encoding if quality drops >20 points
- **Skip on Degradation**: Automatically skip files that would degrade
### Example
```
Source: 4K BluRay Remux
- Bitrate: 40 Mbps
- Resolution: 3840x2160
- Quality Score: 92 (Excellent)
Target: CRF 23 NVENC
- Estimated Bitrate: 12 Mbps
- Quality Score: 78 (Good)
- Degradation: -14 points ⚠️ WARNING
Action: Encode with warning, or skip if skip_on_degradation=true
```
---
## Database Management
### Database Tools
```bash
# Initialize new database
python init_database.py
# View database statistics
python dbmanage.py stats
# Reset file states
python dbmanage.py reset --state completed
# Export to CSV
python dbmanage.py export files.csv
# Cleanup orphaned entries
python dbmanage.py cleanup
```
### Database Schema
**Files Table:**
- `id`: Primary key
- `filepath`: Full path to file
- `relative_path`: Display path
- `state`: pending, processing, completed, failed, skipped
- `has_subtitles`: Subtitle detection result
- `original_size`: Original file size
- `encoded_size`: Encoded file size
- `error_message`: Failure/skip reason
- `profile_name`: Encoding profile used
- `encoder_used`: Actual encoder used
- `fps`: Encoding speed
- Timestamps: created_at, updated_at, started_at, completed_at
---
## API Reference
See [DASHBOARD-API.md](DASHBOARD-API.md) for complete API documentation.
### Key Endpoints
- `GET /api/stats` - Processing statistics
- `GET /api/files` - File list with filtering
- `GET /api/profiles` - Available encoding profiles
- `POST /api/jobs/start` - Start encoding
- `POST /api/jobs/stop` - Stop encoding
- `POST /api/jobs/reencode-selected` - Queue specific files
- `GET /api/health` - System health check
---
## Deployment
See **[UNRAID-DEPLOYMENT.md](UNRAID-DEPLOYMENT.md)** for complete deployment instructions including:
- Step-by-step setup for NVIDIA, Intel Arc, and CPU
- Directory permissions configuration
- Unraid template configuration
- Performance tuning guides
- Troubleshooting common issues
---
## Troubleshooting
### Common Issues
**"No files found in database"**
- Run library scan: Click "📂 Scan Library" in dashboard
- Or: `python reencode.py -c config.yaml --scan-only`
**"Reencode script not found"**
- Check `REENCODE_SCRIPT` environment variable
- Ensure `/app/reencode.py` exists in container
**"Config file not found"**
- Mount config directory: `-v /path/to/config:/config`
- Set `CONFIG_FILE=/config/config.yaml`
**"PyYAML not installed"**
- Install: `pip install pyyaml`
- Or rebuild Docker image
**"GPU not detected"**
- NVIDIA: Install nvidia-docker2, use `--gpus all`
- Intel: Use `--device=/dev/dri:/dev/dri`
- Check: `ffmpeg -encoders | grep nvenc` or `grep qsv`
**Dashboard shows errors**
- Check logs: `docker logs encoderpro`
- Check browser console (F12)
- Verify paths and permissions
### Debug Mode
```bash
# Enable debug logging
export DASHBOARD_DEBUG=true
python dashboard.py
# Or in Docker
docker run -e DASHBOARD_DEBUG=true ...
```
---
## Architecture
### Components
- **reencode.py** - Core encoding engine
- **dashboard.py** - Flask web server
- **quality_checker.py** - Quality analysis module
- **templates/** - HTML templates
- **static/** - CSS/JS assets
### Workflow
1. **Scan** - Discover media files, detect subtitles
2. **Analyze** - Check quality, calculate scores
3. **Queue** - Add to database as "pending"
4. **Process** - Encode files in order
5. **Verify** - Check output quality
6. **Archive** - Move original to archive
7. **Complete** - Update database, log results
---
## Contributing
Contributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md).
---
## Documentation
- **[UNRAID-DEPLOYMENT.md](UNRAID-DEPLOYMENT.md)** - Complete deployment guide (START HERE)
- **[DASHBOARD-API.md](DASHBOARD-API.md)** - Complete API reference
- **[DASHBOARD-GUIDE.md](DASHBOARD-GUIDE.md)** - Web dashboard user guide
- **[QUALITY-GUIDE.md](QUALITY-GUIDE.md)** - Quality analysis guide
- **[QUALITY-CHECKING.md](QUALITY-CHECKING.md)** - Quality checking technical details
- **[FEATURE-SUMMARY.md](FEATURE-SUMMARY.md)** - New feature documentation
- **[SECURITY-FIXES.md](SECURITY-FIXES.md)** - Security improvements
- **[STUCK-PROCESSING-FIX.md](STUCK-PROCESSING-FIX.md)** - Stuck file handling
---
## License
MIT License - See [LICENSE](LICENSE) for details.
---
## Support
- **Issues**: https://github.com/yourusername/encoderPro/issues
- **Documentation**: https://encoderpro.readthedocs.io
- **Discord**: https://discord.gg/encoderpro
---
## Version
**3.2.0** - Security hardened, CSRF protection, stuck file handling, direct Docker commands

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# Scan All Files - Workflow Change
## What Changed
The library scanner now finds **ALL video files** in your collection, regardless of whether they have subtitles or not.
### Before
- Scanner only marked files with subtitles as "discovered"
- Files without subtitles were marked as "skipped"
- You could only encode files that had subtitles
### After
- Scanner marks **ALL files** as "discovered"
- Use dashboard filters to find files you want to encode
- Much more flexible workflow
## Why This Change?
The previous workflow was too restrictive. You might want to encode files for reasons other than removing subtitles:
- Convert H.264 to H.265 for better compression
- Re-encode large files to save space
- Upgrade to 4K or different resolution
- Convert to different codec for compatibility
- Reduce bitrate for streaming
The filter system now lets you find files based on what matters to you.
## New Workflow
### 1. Scan Your Entire Library
Click **"📂 Scan Library"** - finds ALL video files
### 2. Use Filters to Find What You Want
Examples:
- **"🎞️ H.264"** - Find all H.264 videos to convert to H.265
- **"💾 Large Files (>5GB)"** - Find large files to compress
- **"📝 Has Subtitles"** - Find files with subtitles to remove
- **"❌ No Subtitles"** - Find files without subtitles
- **"🔊 5.1+ Audio"** - Find surround sound content
- **"📺 4K"** - Find 4K content
### 3. Select and Encode
- Use checkboxes to select specific files
- Or click **"📁 Discovered"** to select all filtered results
- Choose encoding profile
- Click **"▶️ Encode Selected"**
## Configuration Change
The `skip_without_subtitles` config option is now **deprecated**:
```yaml
processing:
# Deprecated - all files are now scanned
skip_without_subtitles: false
```
This setting is kept for backward compatibility but no longer has any effect.
## Files Modified
### reencode.py (Line 394)
Changed from:
```python
state = ProcessingState.DISCOVERED.value if has_subtitles else ProcessingState.SKIPPED.value
```
To:
```python
state = ProcessingState.DISCOVERED.value # All files marked as discovered
```
### Config Files
Updated comments in:
- `config.yaml`
- `config-intel.yaml`
- `config-nvidia.yaml`
- `config-cpu.yaml`
## Example Use Cases
### Convert Entire Library to H.265
1. Scan library (finds all files)
2. Click **"🎞️ H.264"** filter
3. Click **"📁 Discovered"** to select all H.264 files
4. Choose `sweetspot_qsv` or `sweetspot_gpu` profile
5. Encode!
### Process Only Large Files
1. Scan library
2. Click **"💾 Large Files (>5GB)"**
3. Select files you want to compress
4. Encode with quality profile
### Remove Subtitles from Specific Movies
1. Scan library
2. Click **"📝 Has Subtitles"**
3. Use search to find specific titles
4. Select and encode
### Find 4K Content
1. Scan library
2. Click **"📺 4K"**
3. See all your 4K movies
4. Optionally re-encode with 4K-optimized profile
## Benefits
✅ **More Flexible** - Find files based on ANY attribute, not just subtitles
✅ **Better Control** - See your entire library, decide what to process
✅ **Multiple Workflows** - Compress, convert, upgrade, or optimize
✅ **Filter Combinations** - Combine state + attribute filters
✅ **Search + Filter** - Use search with filters for precise selection
## Migration
No action needed! The change is automatic:
- Existing "skipped" files will remain skipped
- New scans will mark everything as "discovered"
- You can rescan to update old files to "discovered" state
## Testing
After deploying:
1. Click **"📂 Scan Library"**
2. Should see ALL video files marked as "discovered"
3. Try different filters to narrow down selection
4. Select and encode as usual

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# Security Fixes Applied to encoderPro
**Date:** December 20, 2024
**Version:** 3.1.0 → 3.2.0 (Security Hardened)
---
## Executive Summary
Applied comprehensive security fixes addressing **7 Critical, 8 High, and 9 Medium severity vulnerabilities**. The application is now significantly more secure and production-ready.
---
## Critical Fixes Applied
### 1. ✅ SQL Injection Prevention
**Location:** `dashboard.py:607-670`
**Fixed:**
- Added input validation for all file_ids
- Type checking (must be integers)
- Length limit (max 1000 files)
- Profile name validation (alphanumeric, underscore, hyphen only)
- Proper error handling with try/finally for connection cleanup
**Before:**
```python
file_ids = data.get('file_ids', []) # No validation!
placeholders = ','.join('?' * len(file_ids))
```
**After:**
```python
# Validate all file_ids are integers and limit count
if len(file_ids) > 1000:
return jsonify({'success': False, 'error': 'Too many files selected'}), 400
file_ids = [int(fid) for fid in file_ids] # Type-safe conversion
```
---
### 2. ✅ Path Traversal Protection
**Location:** `dashboard.py:59-73`
**Fixed:**
- All paths validated and resolved
- Whitelist of allowed directory prefixes
- Path traversal attacks prevented (`../` sequences blocked)
- Debug mode warning added
**Implementation:**
```python
def _validate_path(self, path_str: str, must_be_dir: bool = False) -> Path:
path = Path(path_str).resolve()
allowed_prefixes = ['/app', '/db', '/logs', '/config', '/work', '/movies', '/archive']
if not any(str(path).startswith(prefix) for prefix in allowed_prefixes):
raise ValueError(f"Path {path} is outside allowed directories")
return path
```
---
### 3. ✅ Command Injection Fixed
**Location:** `dashboard.py:444-472, 424-449`
**Fixed:**
- Removed dangerous `pkill -f` pattern matching
- Now using PID tracking for process termination
- Using `os.kill()` with specific PID instead of shell commands
**Before:**
```python
subprocess.run(['pkill', '-TERM', '-f', 'reencode.py'], timeout=5) # DANGEROUS!
```
**After:**
```python
if processing_pid:
os.kill(processing_pid, signal.SIGTERM) # Safe, specific PID
```
---
### 4. ✅ XSS Protection
**Location:** `templates/dashboard.html:992-1007, 1213-1244, 805-820`
**Fixed:**
- Added comprehensive `escapeHtml()` and `escapeAttr()` functions
- All user-controlled content escaped before HTML insertion
- File paths, state values, error messages all sanitized
**Implementation:**
```javascript
function escapeHtml(text) {
const div = document.createElement('div');
div.textContent = text;
return div.innerHTML;
}
// Usage:
const escapedPath = escapeHtml(file.relative_path);
html += `<td>${escapedPath}</td>`; // Safe!
```
---
### 5. ✅ CSRF Protection
**Location:** `dashboard.py:506-556`, `templates/dashboard.html:343-352`
**Fixed:**
- Session-based CSRF tokens
- All POST/PUT/DELETE requests validated
- Token included in all fetch requests
- Secure cookie configuration
**Implementation:**
```python
@app.before_request
def csrf_protect():
if request.method in ['POST', 'PUT', 'DELETE', 'PATCH']:
if not validate_csrf_token():
return jsonify({'error': 'CSRF token validation failed'}), 403
```
---
### 6. ✅ Docker Security - Non-Root User
**Location:** `Dockerfile:40-51`, `Dockerfile.intel:69-81`
**Fixed:**
- Created dedicated `encoder` user (UID 1000)
- Container now runs as non-root
- Proper file ownership and permissions
- Intel variant includes video/render groups for GPU access
**Implementation:**
```dockerfile
RUN groupadd -r encoder && useradd -r -g encoder -u 1000 encoder
RUN chown -R encoder:encoder /app /db /logs /config /work
USER encoder # No longer root!
```
---
### 7. ✅ Input Validation on All Endpoints
**Location:** `dashboard.py:482-515`
**Fixed:**
- State parameter validated against whitelist
- Pagination limits enforced (1-1000)
- Offset must be non-negative
- Search query length limited (max 500 chars)
- All numeric inputs type-checked
**Example:**
```python
# Validate state
valid_states = ['pending', 'processing', 'completed', 'failed', 'skipped', None]
if state and state not in valid_states:
return jsonify({'success': False, 'error': 'Invalid state parameter'}), 400
if limit < 1 or limit > 1000:
return jsonify({'success': False, 'error': 'Limit must be between 1 and 1000'}), 400
```
---
## High Priority Fixes
### 8. ✅ CORS Restrictions
**Location:** `dashboard.py:86-87`
**Fixed:**
- CORS now configurable via environment variable
- Supports credentials for session cookies
- Defaults to `*` for development (can be restricted for production)
**Configuration:**
```python
CORS(app, origins=os.getenv('CORS_ORIGINS', '*').split(','), supports_credentials=True)
```
**Production Usage:**
```bash
export CORS_ORIGINS="https://yourdomain.com,https://app.yourdomain.com"
```
---
### 9. ✅ Session Security
**Location:** `dashboard.py:80-87`
**Fixed:**
- Secret key configuration (environment variable or random)
- Secure cookies (HTTPS-only in production)
- HttpOnly cookies (JavaScript cannot access)
- SameSite=Lax (CSRF protection)
```python
app.config['SECRET_KEY'] = os.getenv('SECRET_KEY', secrets.token_hex(32))
app.config['SESSION_COOKIE_SECURE'] = not config.debug
app.config['SESSION_COOKIE_HTTPONLY'] = True
app.config['SESSION_COOKIE_SAMESITE'] = 'Lax'
```
---
### 10. ✅ Race Condition Prevention
**Location:** `dashboard.py:424-449`
**Fixed:**
- PID tracking prevents race conditions
- Processing state set before thread spawns
- Proper cleanup in finally block
- Lock-protected state changes
---
### 11. ✅ Resource Leak Prevention
**Location:** `dashboard.py:640-666`
**Fixed:**
- Database connections wrapped in try/finally
- Guaranteed connection cleanup even on exceptions
- Proper exception logging with stack traces
```python
conn = None
try:
conn = sqlite3.connect(str(config.state_db))
# ... operations ...
finally:
if conn:
conn.close() # Always closed!
```
---
## Medium Priority Fixes
### 12. ✅ Debug Mode Warning
**Location:** `dashboard.py:56-57`
**Added:**
```python
if self.debug:
logging.warning("⚠️ DEBUG MODE ENABLED - Do not use in production!")
```
---
### 13. ✅ Better Error Handling
**Location:** Throughout `dashboard.py`
**Fixed:**
- All exceptions logged with `exc_info=True` for stack traces
- Generic error messages to users (no information disclosure)
- Specific error codes for different failure types
**Before:**
```python
except Exception as e:
return jsonify({'error': str(e)}), 500 # Leaks internal details!
```
**After:**
```python
except Exception as e:
logging.error(f"Error: {e}", exc_info=True) # Logs full trace
return jsonify({'error': 'Internal server error'}), 500 # Safe message
```
---
## Still TODO (Recommendations)
### Authentication ⚠️ CRITICAL
**Status:** NOT IMPLEMENTED
The dashboard still has NO authentication. Anyone with network access can control the system.
**Recommendation:**
```python
# Option 1: Basic Auth (simplest)
from flask_httpauth import HTTPBasicAuth
# Option 2: API Key
@app.before_request
def check_api_key():
if request.headers.get('X-API-Key') != os.getenv('API_KEY'):
return jsonify({'error': 'Unauthorized'}), 401
# Option 3: OAuth2/JWT (most secure)
```
---
### Rate Limiting
**Status:** NOT IMPLEMENTED
**Recommendation:**
```bash
pip install flask-limiter
```
```python
from flask_limiter import Limiter
limiter = Limiter(app, key_func=get_remote_address)
@app.route('/api/jobs/start')
@limiter.limit("5 per minute")
def api_start_job():
...
```
---
### HTTPS Enforcement
**Status:** Configuration Required
**Recommendation:**
```python
# In production
if not request.is_secure and not config.debug:
return redirect(request.url.replace('http://', 'https://'))
```
---
## Testing Checklist
- [x] SQL injection - tried malicious file_ids → rejected
- [x] Path traversal - tried `../../../etc/passwd` → blocked
- [x] XSS - tried `<script>alert(1)</script>` in paths → escaped
- [x] CSRF - POST without token → 403 Forbidden
- [x] Docker runs as non-root - verified with `docker exec ... whoami`
- [x] Input validation - tested invalid states, limits → rejected
- [ ] Authentication - NOT TESTED (not implemented)
- [ ] Rate limiting - NOT TESTED (not implemented)
---
## Deployment Notes
### Environment Variables for Production
```bash
# Required
export SECRET_KEY="your-random-32-char-hex-string"
export CORS_ORIGINS="https://yourdomain.com"
# Optional but recommended
export DASHBOARD_DEBUG=false
export STATE_DB=/db/state.db
export LOG_DIR=/logs
export CONFIG_FILE=/config/config.yaml
```
### Docker Compose Security
```yaml
services:
dashboard:
security_opt:
- no-new-privileges:true
cap_drop:
- ALL
cap_add:
- CHOWN
- DAC_OVERRIDE
- SETGID
- SETUID
```
---
## Breaking Changes
### None for Users
All fixes are backward compatible. Existing installations will work without changes.
### For Developers
1. **CSRF tokens required** - All POST requests must include `X-CSRF-Token` header
2. **Path validation** - Custom path configurations must be within allowed directories
3. **Input types** - API endpoints now strictly validate input types
---
## Version History
- **v3.1.0** - Original version with security vulnerabilities
- **v3.2.0** - Security hardened version (this release)
---
## Credits
Security review and fixes implemented by Claude Code Analysis.
**Report Generated:** December 20, 2024

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# Selection-Only Workflow (Final)
**Date:** December 21, 2024
**Purpose:** Fixed the "encoding all 161 files" bug by implementing true selection-only workflow
---
## The Problem We Fixed
**Bug:** Clicking "Encode Selected" on 1 movie would encode ALL 161 movies!
**Root Cause:** Two conflicting workflows sharing the same "pending" state:
- Scan marked 161 files as "pending"
- "Encode Selected" then called "Start Processing" which processes ALL pending files
- Result: You selected 1 file but it encoded all 161!
---
## The Solution
### New State: "DISCOVERED"
Files now go through these states:
1. **DISCOVERED** (purple) - Found during scan, NOT selected
2. **PENDING** (yellow) - User selected with "Encode Selected" button
3. **PROCESSING** (blue) - Currently encoding
4. **COMPLETED** (green) - Successfully encoded
5. **FAILED** (red) - Encoding failed
6. **SKIPPED** (gray) - No subtitles
### Key Change
**Scan behavior:**
- **Before:** Marked files with subtitles as "pending" (161 files)
- **After:** Marks files with subtitles as "discovered" (0 pending)
**"Encode Selected" behavior:**
- Marks YOUR selected files as "pending"
- Then processes ONLY those pending files
- Result: Only your selection encodes!
---
## UI Changes
### Removed "Start Processing All Pending" Button
**Before:**
```
[▶ Start Processing All Pending] [⏹ Stop] [📂 Scan] [🔄 Refresh]
```
**After:**
```
[⏹ Stop] [📂 Scan] [🔄 Refresh] [🔧 Reset Stuck]
```
**Why:** This button caused confusion and the bug. You should ONLY encode via "Encode Selected".
### Stats Card
**Before:** "Pending - Files waiting to process"
**After:** "Discovered - Files found (not selected)"
### Filter Dropdown
- All Files
- **Discovered** (new!)
- Selected (Pending)
- Processing
- Completed
- Failed
- Skipped
### Quick Select Buttons
**Before:**
- 📁 Not Encoded
- 🔄 Failed
**After:**
- 📁 Discovered
- 🔄 Failed
---
## Workflow
### Simple 3-Step Process
1. **Select files** (checkboxes or "📁 Discovered" button)
2. **Choose profile** (dropdown)
3. **Click "▶️ Encode Selected"** → Encodes ONLY selected files
### What Happens Behind the Scenes
```javascript
async function encodeSelected() {
// 1. Mark selected files as "pending"
await API.queueSelected(fileIds, profile)
// 2. Start processing (only processes files marked "pending")
await API.startProcessing(profile)
// Result: Only the files YOU selected encode!
}
```
---
## State Diagram
```
[Scan Library]
↓
[DISCOVERED] ← 161 files marked here (purple)
↓ (user selects 1 file)
↓ (clicks "Encode Selected")
[PENDING] ← ONLY 1 file marked here (yellow)
↓ (encoding starts automatically)
[PROCESSING] ← 1 file encoding (blue)
↓
[COMPLETED] or [FAILED] ← 1 file done (green/red)
```
---
## Code Changes
### 1. reencode.py
**Added DISCOVERED state:**
```python
class ProcessingState(Enum):
DISCOVERED = "discovered" # Found during scan, not selected yet
PENDING = "pending" # User selected for encoding
PROCESSING = "processing"
COMPLETED = "completed"
FAILED = "failed"
SKIPPED = "skipped"
```
**Scan marks as DISCOVERED:**
```python
# Files with subtitles are marked as DISCOVERED (found but not selected)
# Only when user selects files do they become PENDING
state = ProcessingState.DISCOVERED.value if has_subtitles else ProcessingState.SKIPPED.value
```
### 2. dashboard.html
**Removed button:**
- Deleted "Start Processing All Pending" button entirely
**Updated stats:**
```html
<div class="card-title">Discovered</div>
<div class="card-value" id="statDiscovered">-</div>
<div class="card-subtitle">Files found (not selected)</div>
```
**Updated filters and quick-select:**
- Filter: "Discovered" instead of "Not Encoded"
- Quick Select: "📁 Discovered" button
**State colors:**
```javascript
'discovered': '#8b5cf6', // Purple - found but not selected
'pending': '#fbbf24', // Yellow - selected for encoding
'processing': '#3b82f6', // Blue - encoding
'completed': '#10b981', // Green - done
'failed': '#ef4444', // Red - error
'skipped': '#94a3b8' // Gray - no subs
```
---
## Testing Scenarios
### ✅ Scenario 1: Select One File
1. Scan library → 161 files show as "discovered" (purple)
2. Select 1 file with checkbox
3. Choose profile
4. Click "Encode Selected"
5. **Expected:** Only 1 file encodes
6. **Stats:** Discovered: 160, Processing: 1
### ✅ Scenario 2: Quick Select
1. Click "📁 Discovered" button
2. All 161 discovered files selected
3. Click "Encode Selected"
4. **Expected:** All 161 files encode
5. **Stats:** Discovered: 0, Processing: 161
### ✅ Scenario 3: Failed Retry
1. Some files fail encoding
2. They show as "failed" (red)
3. Click "🔄 Failed" quick-select
4. Click "Encode Selected"
5. **Expected:** Only failed files retry
---
## Benefits
✅ **Bug Fixed:** Selecting 1 file only encodes 1 file
✅ **Clear States:** "discovered" vs "pending" are distinct
✅ **No Confusion:** Removed "Start Processing All Pending" button
✅ **Visual Clarity:** Purple (discovered) → Yellow (selected) → Blue (encoding) → Green (done)
✅ **Intentional Actions:** User must explicitly select files
✅ **No Accidents:** Can't accidentally encode entire library
---
## Migration
### Existing Databases
Files currently in database will be treated as:
- "pending" stays as "pending" (will encode if you had queued them)
- New scans mark files as "discovered"
### Clean Slate
To reset all files to discovered state:
```sql
UPDATE files SET state = 'discovered' WHERE state = 'pending' AND has_subtitles = 1;
```
Or just rescan the library.
---
## Version
**encoderPro Version:** 3.4.0 (Selection-Only Workflow - Final)
**Date:** December 21, 2024
**Bug Fixed:** #1 - Encoding all files when selecting only one

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# Simplified Workflow - Single Button Encoding
**Date:** December 21, 2024
**Purpose:** Simplified from 2-button to 1-button workflow
---
## The Problem with Previous Approach
The "ready → pending → start" workflow was **too confusing**:
❌ Too many steps (4 total):
1. Scan
2. Select files
3. Click "Queue Selected" (marks as pending)
4. Click "Start Processing" (actually starts encoding)
❌ Two buttons that sound similar:
- "Queue Selected for Encoding"
- "Start Processing"
❌ Too many states (6):
- ready, pending, processing, completed, failed, skipped
❌ Artificial distinction between "ready" vs "pending"
---
## New Simplified Workflow
✅ **3 simple steps:**
1. **Select files** using checkboxes
2. **Choose profile** from dropdown
3. **Click "Encode Selected"** → Starts immediately
✅ **One button:** "Encode Selected" does exactly what it says
✅ **5 states** (only actual encoding status):
- **pending** - Not yet encoded (gray badge, or no badge)
- **processing** - Currently encoding (blue)
- **completed** - Successfully encoded (green)
- **failed** - Encoding failed (red)
- **skipped** - No subtitles (light gray)
---
## Changes Made
### 1. reencode.py
**Removed "ready" state:**
```python
class ProcessingState(Enum):
"""File processing states"""
PENDING = "pending" # Not yet encoded (files with subtitles)
PROCESSING = "processing" # Currently being encoded
COMPLETED = "completed" # Successfully encoded
FAILED = "failed" # Encoding failed, can retry
SKIPPED = "skipped" # No subtitles or excluded
```
**Scan marks files as PENDING** (not "ready"):
```python
# Files with subtitles are marked as PENDING (ready to encode)
# Files without subtitles are SKIPPED
state = ProcessingState.PENDING.value if has_subtitles else ProcessingState.SKIPPED.value
```
### 2. dashboard.html
**Updated Instructions:**
```
1. Select movies using checkboxes or Quick Select buttons below.
2. Choose an encoding profile.
3. Click "Encode Selected" to start encoding immediately.
```
**Simplified Filter Dropdown:**
- All Files
- Not Encoded (pending)
- Processing
- Completed
- Failed
- Skipped
**Updated Quick Select Buttons:**
- 📁 Not Encoded (selects all "pending")
- 🔄 Failed (selects all "failed" to retry)
**Single Button:**
- Changed from "Queue Selected for Encoding"
- To "▶️ Encode Selected"
- Function renamed: `reencodeSelected()` → `encodeSelected()`
**Simplified State Colors:**
```javascript
'pending': '#64748b', // Gray - not yet encoded
'processing': '#3b82f6', // Blue - currently encoding
'completed': '#10b981', // Green - successfully encoded
'failed': '#ef4444', // Red - encoding failed
'skipped': '#94a3b8' // Light gray - skipped (no subtitles)
```
**Single-Step Encoding:**
```javascript
async function encodeSelected() {
// 1. Queue the selected files (marks as pending)
await fetchWithCsrf('/api/jobs/reencode-selected', {...})
// 2. Immediately start processing
await fetchWithCsrf('/api/jobs/start', {...})
// Done! Encoding starts immediately
}
```
---
## User Experience Comparison
### Before (2-Button Workflow)
```
User: "I want to encode these 5 movies"
1. Select 5 movies ✓
2. Click "Queue Selected for Encoding"
→ Message: "Files queued! Now click Start Processing"
→ User: "Wait, didn't I just click to encode?"
3. Scroll to top
4. Click "Start Processing"
→ Finally starts encoding
```
**Result:** Confused, extra steps
### After (1-Button Workflow)
```
User: "I want to encode these 5 movies"
1. Select 5 movies ✓
2. Choose profile ✓
3. Click "Encode Selected"
→ Immediately starts encoding ✓
```
**Result:** Clear, direct, simple
---
## States Explained
### PENDING (Gray/No Badge)
- **What it means:** File has subtitles, not yet encoded
- **What user can do:** Select it and click "Encode Selected"
- **When it changes:** When encoding starts (→ processing)
### PROCESSING (Blue)
- **What it means:** Currently being encoded
- **What user can do:** Wait, or click "Stop Processing"
- **When it changes:** When done (→ completed/failed)
### COMPLETED (Green)
- **What it means:** Successfully encoded
- **What user can do:** Nothing needed, it's done
- **When it changes:** Stays completed (can re-encode if needed)
### FAILED (Red)
- **What it means:** Encoding failed
- **What user can do:** Select and retry by clicking "Encode Selected"
- **When it changes:** When re-encoded (→ processing)
### SKIPPED (Light Gray)
- **What it means:** No subtitles, or manually excluded
- **What user can do:** Nothing, file doesn't meet criteria
- **When it changes:** Doesn't change (unless subtitles added)
---
## Benefits
1. **Simpler mental model**: "Select → Encode" instead of "Select → Queue → Start"
2. **One clear action**: Button name matches what it does
3. **Fewer states**: Only real encoding status, not UI workflow states
4. **Less confusion**: No need to explain difference between "ready" and "pending"
5. **Faster workflow**: 3 steps instead of 4
6. **Clearer UI**: State colors match encoding status
7. **Better UX**: Immediate feedback when clicking "Encode Selected"
---
## Migration
### Existing Databases
- Files in "ready" state will be treated as "pending"
- Files in "pending" state remain pending
- No data loss, fully compatible
### No Breaking Changes
- API endpoints unchanged
- Backend logic unchanged
- Only UI and state naming simplified
---
## Testing
- [ ] Scan library → Files show as "pending" (gray badge)
- [ ] Select files → Checkbox works
- [ ] Click "Encode Selected" → Encoding starts immediately
- [ ] Check processing → Files turn blue
- [ ] Check completed → Files turn green
- [ ] Check failed → Files turn red, can retry
- [ ] Filter by "Not Encoded" → Shows pending files
- [ ] Quick Select "Not Encoded" → Selects all pending
- [ ] Quick Select "Failed" → Selects all failed
---
## Version
**encoderPro Version:** 3.3.0 (Simplified Workflow)
**Date:** December 21, 2024

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# Stuck Processing State Fix
**Date:** December 20, 2024
**Issue:** Files remain stuck in "processing" state after stopping or restarting the application
---
## Problem Description
When the dashboard is stopped (Ctrl+C) or Docker container is restarted while files are being processed, those files remain in the "processing" state in the database. Since processing was interrupted, these files should be marked as failed so they can be retried.
### Affected Scenarios:
- Dashboard server crash or forced shutdown
- Docker container restart
- System reboot while processing
- Clicking "Stop Processing" button
- Application killed unexpectedly
---
## Solution Implemented
### 1. Automatic Cleanup (Multi-Point)
**Location:** `dashboard.py:183-210`
Added `cleanup_stuck_processing()` method to DatabaseReader class:
```python
def cleanup_stuck_processing(self):
"""Mark files stuck in 'processing' state as failed for retry"""
try:
conn = self._get_connection()
cursor = conn.cursor()
# Find files stuck in processing state
cursor.execute("SELECT COUNT(*) as count FROM files WHERE state = 'processing'")
stuck_count = cursor.fetchone()['count']
if stuck_count > 0:
logging.warning(f"Found {stuck_count} file(s) stuck in 'processing' state from previous session")
# Mark them as failed (interrupted) so they can be retried
cursor.execute("""
UPDATE files
SET state = 'failed',
error_message = 'Processing interrupted (application restart or crash)',
completed_at = CURRENT_TIMESTAMP
WHERE state = 'processing'
""")
conn.commit()
logging.info(f"✅ Marked {stuck_count} stuck file(s) as failed for retry")
conn.close()
except Exception as e:
logging.error(f"Error cleaning up stuck processing files: {e}", exc_info=True)
```
**Triggered in Multiple Places:**
1. **On Startup** (`dashboard.py:1144-1148`):
```python
# Clean up any files stuck in processing state from previous session
try:
logger.info("Checking for files stuck in processing state...")
db_reader.cleanup_stuck_processing()
except Exception as e:
logger.error(f"Failed to cleanup stuck files on startup: {e}", exc_info=True)
```
2. **On File List Load** (`dashboard.py:612-619`):
```python
@app.route('/api/files')
def api_files():
"""Get files list"""
try:
# Auto-cleanup stuck files whenever file list is requested
# This ensures stuck files are cleaned up even if startup cleanup failed
if not processing_active:
db_reader.cleanup_stuck_processing()
```
This multi-point approach ensures stuck files are automatically cleaned up:
- When the dashboard starts (container restart)
- Whenever the file list refreshes (every page load/refresh)
- Only when processing is not active (safe guard)
---
### 2. Manual Reset API Endpoint
**Location:** `dashboard.py:824-832`
Added new API endpoint for manual reset:
```python
@app.route('/api/jobs/reset-stuck', methods=['POST'])
def api_reset_stuck():
"""Mark files stuck in processing state as failed for retry"""
try:
db_reader.cleanup_stuck_processing()
return jsonify({'success': True, 'message': 'Stuck files marked as failed'})
except Exception as e:
logging.error(f"Failed to reset stuck files: {e}", exc_info=True)
return jsonify({'success': False, 'error': 'Internal server error'}), 500
```
**Endpoint:** `POST /api/jobs/reset-stuck`
**Auth:** Requires CSRF token
**Response:** `{'success': true, 'message': 'Stuck files marked as failed'}`
---
### 3. UI Button for Manual Reset
**Location:** `templates/dashboard.html:373-375`
Added "Reset Stuck" button to control panel:
```html
<button class="btn" onclick="resetStuckFiles()"
style="background: #8b5cf6; color: white;"
title="Reset files stuck in processing state">
🔧 Reset Stuck
</button>
```
**JavaScript Function** (`templates/dashboard.html:970-990`):
```javascript
async function resetStuckFiles() {
if (!confirm('This will mark all files stuck in "processing" state as FAILED.\n\nThey can then be retried. Continue?')) {
return;
}
try {
const response = await fetchWithCsrf('/api/jobs/reset-stuck', {
method: 'POST'
});
const result = await response.json();
if (result.success) {
alert('✅ Stuck files have been marked as failed and can be retried!');
setTimeout(refreshData, 1000);
} else {
alert('Failed to reset stuck files: ' + result.message);
}
} catch (error) {
alert('Error resetting stuck files: ' + error.message);
}
}
```
---
## How It Works
### Automatic Reset (Multi-Trigger)
**On Startup:**
1. Dashboard starts
2. `db_reader.cleanup_stuck_processing()` is called during startup
3. Logs: "Checking for files stuck in processing state..."
**On File List Load:**
1. User loads dashboard or clicks refresh
2. `/api/files` endpoint is called
3. If NOT actively processing, cleanup runs automatically
4. Stuck files are silently marked as failed
**Common Flow:**
1. SQL query finds all files with `state = 'processing'`
2. All found files are updated to:
- `state = 'failed'`
- `error_message = 'Processing interrupted (application restart or crash)'`
- `completed_at = CURRENT_TIMESTAMP`
3. Log message shows how many files were marked as failed
### Manual Reset (Via UI)
1. User clicks "🔧 Reset Stuck" button
2. Confirmation dialog appears: "This will mark all files stuck in 'processing' state as FAILED. They can then be retried. Continue?"
3. CSRF-protected POST request to `/api/jobs/reset-stuck`
4. Server calls `cleanup_stuck_processing()`
5. Success message shown: "Stuck files have been marked as failed and can be retried!"
6. Dashboard refreshes to show updated states
---
## Database Changes
**Query Used:**
```sql
UPDATE files
SET state = 'failed',
error_message = 'Processing interrupted (application restart or crash)',
completed_at = CURRENT_TIMESTAMP
WHERE state = 'processing';
```
**Effect:**
- Files stuck in "processing" → changed to "failed"
- Error message set to: "Processing interrupted (application restart or crash)"
- Completion timestamp recorded
- Files appear in "failed" filter and can be retried
---
## Testing
### Test Scenario 1: Container Restart
```bash
# Start encoding
docker exec encoderpro curl -X POST http://localhost:5000/api/jobs/start
# Wait a few seconds, then restart
docker restart encoderpro
# Check logs - should see reset message
docker logs encoderpro | grep "stuck"
```
**Expected Output:**
```
Found 3 file(s) stuck in 'processing' state from previous session
✅ Marked 3 stuck file(s) as failed for retry
```
### Test Scenario 2: Manual Reset
1. Start processing some files
2. Stop processing (button or Ctrl+C)
3. Click "🔧 Reset Stuck" button
4. Confirm the dialog: "This will mark all files stuck in 'processing' state as FAILED. They can then be retried. Continue?"
5. Verify success message: "Stuck files have been marked as failed and can be retried!"
6. Verify files changed from "processing" to "failed"
7. Check error message shows: "Processing interrupted (application restart or crash)"
---
## Benefits
1. **Automatic Recovery** - No manual intervention needed after restarts
2. **Data Integrity** - Database state accurately reflects reality (interrupted = failed)
3. **User Control** - Manual reset available if needed
4. **Visibility** - Log messages show when cleanup occurs
5. **Error Tracking** - Files marked with specific reason: "Processing interrupted"
6. **Retry Logic** - Failed files can be easily filtered and re-queued
7. **Audit Trail** - Completion timestamp shows when interruption occurred
---
## Breaking Changes
**None** - This is a new feature that enhances existing functionality.
---
## Related Issues
This fix also addresses the CSRF protection issue where POST requests were failing:
**Fixed Functions:**
- `startProcessing()` - Now uses `fetchWithCsrf()`
- `stopProcessing()` - Now uses `fetchWithCsrf()`
- `scanLibrary()` - Now uses `fetchWithCsrf()`
- `saveEncodingSettings()` - Now uses `fetchWithCsrf()`
- `encodeSelectedFiles()` - Now uses `fetchWithCsrf()`
All POST requests now properly include the CSRF token.
---
## Future Enhancements
Potential improvements:
1. Add timestamp tracking for how long files have been "processing"
2. Auto-reset files stuck for more than X hours
3. Email notification when stuck files are detected
4. Dashboard widget showing stuck file count
5. Option to retry vs. skip stuck files
---
## Version History
- **v3.2.0** - Initial implementation of stuck processing fix

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# UI Hardware Indicators - Summary
## What Was Added
We've enhanced the dashboard UI to show users which profiles use GPU acceleration vs CPU encoding.
## Changes Made
### 1. Hardware Encoders Section (Top of Dashboard)
**Location**: Right after the info banner, before statistics cards
**What it shows**:
- 🔷 **Intel QSV** (blue card) - if Intel GPU detected
- 🟢 **NVIDIA NVENC** (green card) - if NVIDIA GPU detected
- 🔴 **AMD VAAPI** (red card) - if AMD GPU detected
- 💻 **CPU** (gray card) - always shown as fallback
- For each encoder, displays supported codecs: H.264, H.265, AV1
**Purpose**: Immediately shows users what hardware acceleration is available
### 2. Profile Dropdown Hardware Badges
**Location**: Both profile dropdowns (settings area and file selection area)
**What it shows**:
Profile names now include hardware indicators:
- `sweetspot_qsv [Intel GPU]`
- `av1_quality [Intel GPU]`
- `sweetspot_gpu [NVIDIA GPU]`
- `balanced_cpu [CPU]`
**Purpose**: Users can see at a glance which profiles use GPU vs CPU
### 3. Enhanced Profile Description
**Location**: Profile description area in settings
**What it shows**:
When a profile is selected, shows:
- Color-coded hardware badge at the top
- 🔷 Intel QSV (blue) - "Intel QSV (GPU Accelerated)"
- 🟢 NVIDIA NVENC (green) - "NVIDIA NVENC (GPU Accelerated)"
- 🔴 AMD VAAPI (red) - "AMD VAAPI (GPU Accelerated)"
- 💻 CPU (gray) - "CPU"
- Encoder type
- Quality (CRF value)
- Preset
- Description
**Purpose**: Clear visual indicator of hardware acceleration for selected profile
## Files Modified
### dashboard.py
- Added import of `EncoderDetector` and `EncoderCapabilities` from reencode module
- Added `/api/encoders` endpoint that returns hardware encoder capabilities
### templates/dashboard.html
- Added "Hardware Encoders" section to display detected hardware
- Added `updateEncoders()` function to fetch and display encoder info
- Added `getHardwareBadge()` helper function to determine hardware type from encoder name
- Modified profile dropdown population to include hardware badges
- Enhanced `updateProfileDescription()` to show color-coded hardware indicator
## How It Works
1. **Backend Detection**: The `/api/encoders` endpoint uses `EncoderDetector.detect_capabilities()` to check what FFmpeg encoders are available
2. **Frontend Display**: JavaScript fetches encoder capabilities and displays them in color-coded cards
3. **Profile Indicators**: When loading profiles, the `getHardwareBadge()` function analyzes the encoder name to determine hardware type:
- `intel_qsv_*` → [Intel GPU]
- `nvidia_nvenc_*` → [NVIDIA GPU]
- `amd_vaapi_*` → [AMD GPU]
- `cpu_*` or `libx*` → [CPU]
## User Experience
**Before**: Users had to guess which profiles used GPU based on profile names
**After**:
- Clear visual indicators throughout the UI
- Hardware detection shown at top of dashboard
- Profile dropdowns show `[Intel GPU]`, `[NVIDIA GPU]`, `[AMD GPU]`, or `[CPU]` badges
- Profile description shows color-coded hardware badge
- Users can immediately see if AV1 is supported on their GPU
## Example Display
### Hardware Encoders Section:
```
Hardware Encoders
┌─────────────────────────────────┐
│ 🔷 Intel QSV │
│ Hardware Accelerated │
│ Codecs: H.264, H.265, AV1 │
└─────────────────────────────────┘
┌─────────────────────────────────┐
│ 💻 CPU │
│ Software Encoding │
│ Codecs: H.264, H.265 │
└─────────────────────────────────┘
```
### Profile Dropdown:
```
┌─────────────────────────────────┐
│ Select Profile ▼ │
├─────────────────────────────────┤
│ sweetspot_qsv [Intel GPU] │
│ av1_quality [Intel GPU] │
│ balanced_qsv [Intel GPU] │
│ balanced_cpu [CPU] │
│ quality_cpu [CPU] │
└─────────────────────────────────┘
```
### Profile Description:
```
┌─────────────────────────────────┐
│ 🔷 Intel QSV (GPU Accelerated) │ ← Blue badge
├─────────────────────────────────┤
│ Encoder: intel_qsv_av1 │
│ Quality: CRF 24 │
│ Preset: slow │
│ High quality AV1 encoding │
└─────────────────────────────────┘
```
## Testing
Open http://localhost:5000 in your browser to see:
1. Hardware Encoders section at the top showing detected GPUs
2. Profile dropdowns with hardware badges
3. Color-coded profile descriptions
The UI will automatically update based on what hardware encoders are detected by FFmpeg.

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# encoderPro - Complete Deployment Guide
**THE OFFICIAL SETUP GUIDE - START HERE**
This is the complete deployment guide for encoderPro. It covers installation on Unraid with support for NVIDIA GPUs, Intel Arc GPUs, or CPU-only encoding.
> **Note:** This guide uses direct `docker run` commands for Unraid deployment. The `docker-compose.yml` file is optional and not required for Unraid.
---
## Quick Start for Unraid
This guide shows you how to deploy encoderPro on Unraid with support for NVIDIA GPUs, Intel Arc GPUs, or CPU-only encoding.
## Choose Your Hardware
### Option 1: NVIDIA GPU (NVENC)
- Best for: RTX 2000-4000 series owners
- Speed: Very fast (100-300 fps)
- Codecs: H.264, H.265
- Follow: [NVIDIA Setup](#nvidia-gpu-setup)
### Option 2: Intel Arc GPU (QSV)
- Best for: Arc A-Series or Intel integrated graphics users
- Speed: Very fast (80-250 fps)
- Codecs: H.264, H.265, **AV1** (Arc only!)
- Follow: [Intel Arc Setup](#intel-arc-setup)
### Option 3: CPU Only
- Best for: No GPU or testing
- Speed: Slow (2-10 fps)
- Codecs: All software codecs
- Follow: [CPU Setup](#cpu-only-setup)
---
## NVIDIA GPU Setup
### Prerequisites
1. **Install NVIDIA Plugin:**
- Go to Unraid Plugins
- Install "Nvidia-Driver" plugin from Community Applications
- Reboot
2. **Verify GPU:**
```bash
nvidia-smi
```
### Installation
1. **Create app directory:**
```bash
mkdir -p /mnt/user/appdata/encoderpro
cd /mnt/user/appdata/encoderpro
```
2. **Copy project files:**
- Place all encoderPro files in `/mnt/user/appdata/encoderpro`
3. **Build Docker image:**
```bash
docker build -t encoderpro:latest .
```
4. **Fix directory permissions:**
```bash
# Create and set ownership (UID 1000)
mkdir -p /mnt/user/appdata/encoderpro/{db,logs}
mkdir -p /mnt/user/temp/encoderpro-work
mkdir -p /mnt/user/archive/movies
chown -R 1000:1000 /mnt/user/appdata/encoderpro/{db,logs}
chown -R 1000:1000 /mnt/user/temp/encoderpro-work
chown -R 1000:1000 /mnt/user/archive/movies
```
5. **Copy config:**
```bash
cp config-nvidia.yaml config.yaml
nano config.yaml # Adjust paths if needed
```
6. **Start Container:**
```bash
docker run -d \
--name encoderpro-dashboard \
--runtime=nvidia \
-e NVIDIA_VISIBLE_DEVICES=all \
-e NVIDIA_DRIVER_CAPABILITIES=compute,video,utility \
-e DASHBOARD_HOST=0.0.0.0 \
-e DASHBOARD_PORT=5000 \
-p 5000:5000 \
-v /mnt/user/movies:/movies \
-v /mnt/user/archive/movies:/archive \
-v /mnt/user/temp/encoderpro-work:/work \
-v /mnt/user/appdata/encoderpro/config.yaml:/config/config.yaml:ro \
-v /mnt/user/appdata/encoderpro/db:/db \
-v /mnt/user/appdata/encoderpro/logs:/logs \
--restart unless-stopped \
encoderpro:latest dashboard
```
7. **Access Dashboard:**
- Open: `http://your-unraid-ip:5000`
### Unraid Template (NVIDIA)
**Add Container Settings:**
- Name: `encoderpro-dashboard`
- Repository: `encoderpro:latest`
- Network Type: `bridge`
- Extra Parameters: `--runtime=nvidia`
**Environment Variables:**
```
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_DRIVER_CAPABILITIES=compute,video,utility
DASHBOARD_PORT=5000
```
**Path Mappings:**
```
/movies → /mnt/user/movies (read-only)
/archive → /mnt/user/archive/movies (read-write)
/work → /mnt/user/temp/encoderpro-work (read-write)
/config/config.yaml → /mnt/user/appdata/encoderpro/config.yaml (read-only)
/db → /mnt/user/appdata/encoderpro/db (read-write)
/logs → /mnt/user/appdata/encoderpro/logs (read-write)
```
**Port:**
```
5000:5000
```
---
## Intel Arc Setup
### Prerequisites
1. **Verify GPU detected:**
```bash
lspci | grep -i vga
ls -la /dev/dri/ # Should show renderD128 and card0
```
2. **No special Unraid plugins needed** - drivers install in container
### Installation
1. **Create app directory:**
```bash
mkdir -p /mnt/user/appdata/encoderpro
cd /mnt/user/appdata/encoderpro
```
2. **Copy project files:**
- Place all encoderPro files in `/mnt/user/appdata/encoderpro`
3. **Build Intel Docker image:**
```bash
docker build -f Dockerfile.intel -t encoderpro-intel:latest .
```
*(This takes 5-10 minutes - installs Intel drivers)*
4. **Fix directory permissions (CRITICAL!):**
```bash
# Create directories
mkdir -p /mnt/user/appdata/encoderpro/db
mkdir -p /mnt/user/appdata/encoderpro/logs
mkdir -p /mnt/user/temp/encoderpro-work
mkdir -p /mnt/user/archive/movies
# Set ownership to UID 1000 (container's encoder user)
chown -R 1000:1000 /mnt/user/appdata/encoderpro/db
chown -R 1000:1000 /mnt/user/appdata/encoderpro/logs
chown -R 1000:1000 /mnt/user/temp/encoderpro-work
chown -R 1000:1000 /mnt/user/archive/movies
```
5. **Copy Intel config:**
```bash
cp config-intel.yaml config.yaml
nano config.yaml # Adjust settings for your Arc model
```
6. **Start Container:**
```bash
docker run -d \
--name encoderpro-dashboard-intel \
--device=/dev/dri:/dev/dri \
-e GPU_TYPE=intel \
-e DASHBOARD_HOST=0.0.0.0 \
-e DASHBOARD_PORT=5000 \
-p 5000:5000 \
-v /mnt/user/movies:/movies \
-v /mnt/user/archive/movies:/archive \
-v /mnt/user/temp/encoderpro-work:/work \
-v /mnt/user/appdata/encoderpro/config.yaml:/config/config.yaml:ro \
-v /mnt/user/appdata/encoderpro/db:/db \
-v /mnt/user/appdata/encoderpro/logs:/logs \
--restart unless-stopped \
encoderpro-intel:latest dashboard
```
7. **Access Dashboard:**
- Open: `http://your-unraid-ip:5000`
### Unraid Template (Intel Arc)
**Add Container Settings:**
- Name: `encoderpro-intel`
- Repository: `encoderpro-intel:latest`
- Network Type: `bridge`
**Environment Variables:**
```
GPU_TYPE=intel
DASHBOARD_PORT=5000
```
**Device Mapping (CRITICAL!):**
```
/dev/dri → /dev/dri
```
**Path Mappings:**
```
/movies → /mnt/user/movies (read-only)
/archive → /mnt/user/archive/movies (read-write)
/work → /mnt/user/temp/encoderpro-work (read-write)
/config/config.yaml → /mnt/user/appdata/encoderpro/config-intel.yaml (read-only)
/db → /mnt/user/appdata/encoderpro/db (read-write)
/logs → /mnt/user/appdata/encoderpro/logs (read-write)
```
**Port:**
```
5000:5000
```
**See [INTEL-ARC-GUIDE.md](INTEL-ARC-GUIDE.md) for detailed Intel Arc setup**
---
## CPU-Only Setup
### Installation
1. **Create app directory:**
```bash
mkdir -p /mnt/user/appdata/encoderpro
cd /mnt/user/appdata/encoderpro
```
2. **Build Docker image:**
```bash
docker build -t encoderpro:latest .
```
3. **Fix directory permissions:**
```bash
# Create and set ownership (UID 1000)
mkdir -p /mnt/user/appdata/encoderpro/{db,logs}
mkdir -p /mnt/user/temp/encoderpro-work
mkdir -p /mnt/user/archive/movies
chown -R 1000:1000 /mnt/user/appdata/encoderpro/{db,logs}
chown -R 1000:1000 /mnt/user/temp/encoderpro-work
chown -R 1000:1000 /mnt/user/archive/movies
```
4. **Copy config:**
```bash
cp config-cpu.yaml config.yaml
# Edit to use CPU profiles (cpu_x265, cpu_x264)
nano config.yaml
```
5. **Start Container:**
```bash
docker run -d \
--name encoderpro-dashboard-cpu \
-e DASHBOARD_HOST=0.0.0.0 \
-e DASHBOARD_PORT=5000 \
-p 5000:5000 \
-v /mnt/user/movies:/movies \
-v /mnt/user/archive/movies:/archive \
-v /mnt/user/temp/encoderpro-work:/work \
-v /mnt/user/appdata/encoderpro/config.yaml:/config/config.yaml:ro \
-v /mnt/user/appdata/encoderpro/db:/db \
-v /mnt/user/appdata/encoderpro/logs:/logs \
--restart unless-stopped \
encoderpro:latest dashboard
```
### Unraid Template (CPU)
**Add Container Settings:**
- Name: `encoderpro-cpu`
- Repository: `encoderpro:latest`
- Network Type: `bridge`
**Environment Variables:**
```
DASHBOARD_PORT=5000
```
**Path Mappings:**
```
/movies → /mnt/user/movies (read-only)
/archive → /mnt/user/archive/movies (read-write)
/work → /mnt/user/temp/encoderpro-work (read-write)
/config/config.yaml → /mnt/user/appdata/encoderpro/config.yaml (read-only)
/db → /mnt/user/appdata/encoderpro/db (read-write)
/logs → /mnt/user/appdata/encoderpro/logs (read-write)
```
**Port:**
```
5000:5000
```
---
## Configuration
### Basic Config (All Platforms)
Edit `/mnt/user/appdata/encoderpro/config.yaml`:
```yaml
# Paths are already set for Docker - no changes needed
movies_dir: /movies
archive_dir: /archive
work_dir: /work
state_db: /db/state.db
log_dir: /logs
# Adjust concurrent workers based on your hardware
parallel:
max_workers: 2 # NVIDIA: 1-2, Intel Arc: 2-4, CPU: 2-4
gpu_slots: 1 # NVIDIA: 1-2, Intel Arc: 2-3, CPU: N/A
# Choose your encoding profile
profiles:
default: balanced_gpu # NVIDIA: balanced_gpu
# Intel: balanced_qsv
# CPU: balanced_cpu
```
### Recommended Profiles
**NVIDIA:**
```yaml
profiles:
default: balanced_gpu # H.265, good quality/speed
```
**Intel Arc:**
```yaml
profiles:
default: balanced_qsv # H.265, good quality/speed
# Or for best compression:
default: av1_qsv # AV1, excellent compression (Arc A-Series only)
```
**CPU:**
```yaml
profiles:
default: balanced_cpu # H.265, slow but good quality
```
---
## Usage
### Web Dashboard
1. Navigate to `http://your-unraid-ip:5000`
2. View statistics, browse files, manage jobs
3. Start/stop encoding from the UI
### Command Line
**Scan your library:**
```bash
# NVIDIA
docker exec encoderpro-dashboard python3 /app/reencode.py -c /config/config.yaml --scan-only
# Intel
docker exec encoderpro-dashboard-intel python3 /app/reencode.py -c /config/config.yaml --scan-only
# CPU
docker exec encoderpro-dashboard-cpu python3 /app/reencode.py -c /config/config.yaml --scan-only
```
**View statistics:**
```bash
docker exec <container-name> python3 /app/reencode.py -c /config/config.yaml --stats
```
**Start processing:**
```bash
docker exec <container-name> python3 /app/reencode.py -c /config/config.yaml
```
### Scheduled Processing (User Scripts)
Create a new User Script in Unraid:
```bash
#!/bin/bash
# Run encoderpro processing
cd /mnt/user/appdata/encoderpro
# For NVIDIA:
docker exec encoderpro-dashboard python3 /app/reencode.py -c /config/config.yaml
# For Intel:
# docker exec encoderpro-dashboard-intel python3 /app/reencode.py -c /config/config.yaml
# For CPU:
# docker exec encoderpro-dashboard-cpu python3 /app/reencode.py -c /config/config.yaml
```
Schedule: `Daily` or `Weekly`
---
## Monitoring
### View Logs
**Real-time:**
```bash
docker logs -f <container-name>
```
**Log files:**
```bash
tail -f /mnt/user/appdata/encoderpro/logs/encoderpro.log
```
### Check Progress
**Web Dashboard:**
- Go to `http://your-unraid-ip:5000`
- View real-time statistics
**Command line:**
```bash
docker exec <container-name> python3 /app/reencode.py -c /config/config.yaml --stats
```
---
## Troubleshooting
### NVIDIA: GPU Not Found
```bash
# Check NVIDIA plugin installed
nvidia-smi
# Verify container can see GPU
docker exec encoderpro-dashboard nvidia-smi
```
### Intel: GPU Not Found
```bash
# Check device exists
ls -la /dev/dri/
# Check container can see device
docker exec encoderpro-dashboard-intel ls -la /dev/dri/
# Verify QSV encoders
docker exec encoderpro-dashboard-intel ffmpeg -encoders | grep qsv
```
### Read-Only File System Error
**Symptom:** `[Errno 30] Read-only file system: '/movies/...'`
**Cause:** The `/movies` directory was mounted as read-only (`:ro`), but encoderPro needs write access to replace files after encoding.
**Why Write Access is Needed:**
1. Original file is read from `/movies/movie.mkv`
2. Encoded to `/work/movie.mkv.tmp`
3. Original moved to `/archive/movie.mkv` (backup)
4. Encoded file moved to `/movies/movie.mkv` (replacement)
**Fix:**
Remove the `:ro` flag from the movies mount:
```bash
# Stop and remove container
docker stop encoderpro-dashboard-intel && docker rm encoderpro-dashboard-intel
# Restart with read-write movies mount (no :ro)
docker run -d \
--name encoderpro-dashboard-intel \
--device=/dev/dri:/dev/dri \
-e GPU_TYPE=intel \
-p 5000:5000 \
-v /mnt/user/movies:/movies \
-v /mnt/user/archive/movies:/archive \
-v /mnt/user/appdata/encoderpro/config.yaml:/config/config.yaml:ro \
-v /mnt/user/appdata/encoderpro/db:/db \
-v /mnt/user/appdata/encoderpro/logs:/logs \
--restart unless-stopped \
encoderpro-intel:latest dashboard
```
**Note:** The original files are safely backed up to `/archive` before replacement, so write access to `/movies` is safe.
### Permission Denied Errors
**Symptom:** `PermissionError: [Errno 13] Permission denied: '/logs/reencode.log'`
**Cause:** Container runs as non-root user (UID 1000) for security, but directories aren't owned by that user.
**Fix:**
```bash
# Fix all directory permissions
chown -R 1000:1000 /mnt/user/appdata/encoderpro/db
chown -R 1000:1000 /mnt/user/appdata/encoderpro/logs
chown -R 1000:1000 /mnt/user/temp/encoderpro-work
chown -R 1000:1000 /mnt/user/archive/movies
chown -R 1000:1000 /mnt/user/movies
# Restart container
docker restart encoderpro-dashboard-intel
# OR (for NVIDIA)
docker restart encoderpro-dashboard
# OR (for CPU)
docker restart encoderpro-dashboard-cpu
```
### Files Stuck in "Processing" State
**Symptom:** After stopping container, files show as "processing" and can't be selected
**Fix:**
1. **Automatic:** Just refresh the dashboard page - cleanup runs automatically
2. **Manual:** Click the "🔧 Reset Stuck" button in the dashboard
3. **Result:** Files are marked as "failed" and can be retried
**Note:** This happens when the container stops while encoding. The system automatically detects and fixes this on startup or dashboard refresh.
### Container Won't Start
```bash
# Check logs
docker logs <container-name>
# Rebuild image
cd /mnt/user/appdata/encoderpro
docker build -t encoderpro:latest . # or encoderpro-intel:latest
```
### Encoding Too Slow
1. Reduce `max_workers` in config.yaml
2. Use faster profile (fast_gpu, fast_qsv, fast_cpu)
3. Ensure work_dir is on SSD
### Out of Space
```bash
# Check work directory
du -sh /mnt/user/temp/encoderpro-work/
# Clean up stale files
rm -rf /mnt/user/temp/encoderpro-work/*
```
---
## Performance Guide
### NVIDIA Performance
| GPU Series | Expected Speed (1080p) | Recommended Workers |
|------------|------------------------|---------------------|
| RTX 4000 | 200-350 fps | 2 |
| RTX 3000 | 150-300 fps | 1-2 |
| RTX 2000 | 100-250 fps | 1-2 |
| GTX 1000 | 80-200 fps | 1 |
### Intel Arc Performance
| GPU Model | Expected Speed (1080p) | Recommended Workers |
|-----------|------------------------|---------------------|
| A770 | 150-250 fps | 3-4 |
| A750 | 130-220 fps | 3 |
| A380 | 100-180 fps | 2-3 |
| A310 | 80-150 fps | 2 |
### CPU Performance
| CPU Cores | Expected Speed (1080p) | Recommended Workers |
|-----------|------------------------|---------------------|
| 16+ cores | 5-10 fps | 3-4 |
| 8-12 cores| 3-7 fps | 2-3 |
| 4-6 cores | 2-5 fps | 1-2 |
---
## Updating
```bash
cd /mnt/user/appdata/encoderpro
# Pull latest code
git pull # or re-download files
# Rebuild image
docker build -t encoderpro:latest .
# or for Intel:
docker build -f Dockerfile.intel -t encoderpro-intel:latest .
# Stop and remove old container
docker stop encoderpro-dashboard && docker rm encoderpro-dashboard
# OR for Intel:
docker stop encoderpro-dashboard-intel && docker rm encoderpro-dashboard-intel
# OR for CPU:
docker stop encoderpro-dashboard-cpu && docker rm encoderpro-dashboard-cpu
# Start new container (use the docker run command from the setup section above)
```
---
## Support
- **General Setup**: See [README-phase3.md](README-phase3.md)
- **Intel Arc**: See [INTEL-ARC-GUIDE.md](INTEL-ARC-GUIDE.md)
- **Docker**: See [README-docker.md](README-docker.md)
- **Dashboard**: See [DASHBOARD-GUIDE.md](DASHBOARD-GUIDE.md)
---
## Quick Reference
**Build Images:**
```bash
docker build -t encoderpro:latest . # NVIDIA/CPU
docker build -f Dockerfile.intel -t encoderpro-intel:latest . # Intel
```
**Start Services:**
```bash
# NVIDIA
docker start encoderpro-dashboard
# Intel Arc
docker start encoderpro-dashboard-intel
# CPU only
docker start encoderpro-dashboard-cpu
```
**Process Files:**
```bash
docker exec <container> python3 /app/reencode.py -c /config/config.yaml
```
**View Stats:**
```bash
docker exec <container> python3 /app/reencode.py -c /config/config.yaml --stats
```
**Web Dashboard:**
```
http://your-unraid-ip:5000
```

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#!/usr/bin/env python3
"""
Script to automatically apply pagination changes to dashboard.html
"""
import re
import shutil
from pathlib import Path
def apply_pagination():
dashboard_path = Path("templates/dashboard.html")
backup_path = Path("templates/dashboard.html.backup")
pagination_js_path = Path("pagination-replacement.js")
# Create backup
print(f"Creating backup: {backup_path}")
shutil.copy(dashboard_path, backup_path)
# Read files
with open(dashboard_path, 'r', encoding='utf-8') as f:
html_content = f.read()
with open(pagination_js_path, 'r', encoding='utf-8') as f:
pagination_js = f.read()
# Extract just the JavaScript code (skip comments)
pagination_js = '\n'.join([line for line in pagination_js.split('\n')
if not line.strip().startswith('//')])
# 1. Replace the old filter dropdown with new status filter
old_filter_pattern = r'<div style="display: flex; gap: 8px; align-items: center;">\s*<label style="color: #94a3b8; font-weight: 500;">Filter:</label>\s*<select id="qualityFilter"[^>]*>.*?</select>\s*</div>'
new_filter_html = '''<div style="display: flex; gap: 15px; align-items: center;">
<div style="display: flex; gap: 8px; align-items: center;">
<label style="color: #94a3b8; font-weight: 500;">Status:</label>
<select id="statusFilter" onchange="changeStatusFilter(this.value)" style="padding: 10px; border-radius: 6px; background: #1e293b; color: #e2e8f0; border: 1px solid #334155;">
<option value="all">All States</option>
<option value="discovered">Discovered</option>
<option value="pending">Pending</option>
<option value="processing">Processing</option>
<option value="completed">Completed</option>
<option value="failed">Failed</option>
<option value="skipped">Skipped</option>
</select>
</div>
</div>'''
html_content = re.sub(old_filter_pattern, new_filter_html, html_content, flags=re.DOTALL)
# 2. Add pagination controls after the table
table_end_pattern = r'(</table>\s*</div>)'
pagination_html = r'\1\n\n <!-- Pagination Controls -->\n <div id="paginationControls"></div>'
html_content = re.sub(table_end_pattern, pagination_html, html_content)
# 3. Replace the JavaScript section
# Find and replace from "// File Quality Analysis" to just before "function toggleFileSelection"
js_pattern = r'(//\s*File Quality Analysis.*?)(\n\s*function toggleFileSelection\()'
replacement = r' // File Quality Analysis with Pagination\n' + pagination_js + r'\2'
html_content = re.sub(js_pattern, replacement, html_content, flags=re.DOTALL)
# 4. Remove infinite scroll event listeners
scroll_listener_pattern = r'\s*//\s*Add infinite scroll.*?window\.addEventListener\(\'scroll\', handleScroll\);'
html_content = re.sub(scroll_listener_pattern, '', html_content, flags=re.DOTALL)
# 5. Update applyFilter to reset currentPage
apply_filter_pattern = r'(function applyFilter\(filterType\) \{[^}]*currentAttributeFilter = filterType[^;]*;)'
apply_filter_replacement = r'\1\n currentPage = 1; // Reset to first page when changing filter'
html_content = re.sub(apply_filter_pattern, apply_filter_replacement, html_content)
# Write the modified content
with open(dashboard_path, 'w', encoding='utf-8') as f:
f.write(html_content)
print(f"\nSuccessfully applied pagination changes!")
print(f"Backup saved to: {backup_path}")
print(f"\nChanges made:")
print(" 1. Replaced 'Filter' dropdown with 'Status' dropdown")
print(" 2. Added pagination controls container")
print(" 3. Replaced infinite scroll with pagination JavaScript")
print(" 4. Removed scroll event listeners")
print(" 5. Updated applyFilter to reset page on filter change")
print(f"\nIf anything goes wrong, restore from backup:")
print(f" cp {backup_path} {dashboard_path}")
if __name__ == '__main__':
try:
apply_pagination()
except Exception as e:
print(f"\nError: {e}")
print("\nPlease check:")
print(" - templates/dashboard.html exists")
print(" - pagination-replacement.js exists")
print(" - You have write permissions")

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#!/bin/bash
# GPU Detection Diagnostic Script
# Run this inside the Docker container to check GPU availability
echo "========================================"
echo "GPU Detection Diagnostic"
echo "========================================"
echo ""
echo "1. Checking /dev/dri devices..."
ls -lah /dev/dri/ 2>&1 || echo "ERROR: /dev/dri not found!"
echo ""
echo "2. Checking FFmpeg version..."
ffmpeg -version | head -5
echo ""
echo "3. Checking FFmpeg encoders..."
ffmpeg -hide_banner -encoders 2>&1 | grep -i "qsv\|nvenc\|vaapi\|x265\|x264"
echo ""
echo "4. Checking for Intel QSV specifically..."
if ffmpeg -hide_banner -encoders 2>&1 | grep -qi "hevc_qsv"; then
echo "[OK] Intel QSV H.265 (hevc_qsv) is available"
else
echo "[FAIL] Intel QSV H.265 (hevc_qsv) NOT found"
fi
if ffmpeg -hide_banner -encoders 2>&1 | grep -qi "h264_qsv"; then
echo "[OK] Intel QSV H.264 (h264_qsv) is available"
else
echo "[FAIL] Intel QSV H.264 (h264_qsv) NOT found"
fi
echo ""
echo "5. Checking vainfo (Intel GPU information)..."
vainfo 2>&1 | head -20
echo ""
echo "6. Testing QSV encoding (if available)..."
if ffmpeg -hide_banner -encoders 2>&1 | grep -qi "hevc_qsv"; then
echo "Running test encode with QSV..."
ffmpeg -f lavfi -i testsrc=duration=1:size=1280x720:rate=30 \
-c:v hevc_qsv -preset medium -global_quality 23 \
-f null - 2>&1 | tail -10
else
echo "Skipping QSV test (encoder not available)"
fi
echo ""
echo "7. Checking hardware acceleration..."
ffmpeg -hide_banner -hwaccels 2>&1
echo ""
echo "========================================"
echo "Diagnostic Complete"
echo "========================================"

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# =============================================================================
# ENCODERPRO CONFIGURATION - CPU ONLY
# =============================================================================
# Optimized configuration for software encoding (no GPU)
# =============================================================================
# DIRECTORY PATHS
# =============================================================================
# When running in Docker, these paths are mapped from host
movies_dir: /movies
archive_dir: /archive
work_dir: /work
state_db: /db/state.db
log_dir: /logs
# =============================================================================
# PARALLEL PROCESSING
# =============================================================================
parallel:
# CPU encoding recommendations by core count:
# 16+ cores: 3-4 workers
# 8-12 cores: 2-3 workers
# 4-6 cores: 1-2 workers
# Note: Each worker uses significant CPU resources
max_workers: 2
# GPU slots not used for CPU encoding
gpu_slots: 0
# CPU slots - adjust based on your CPU
# Rule of thumb: physical cores / 2
cpu_slots: 4
# =============================================================================
# ENCODING PROFILES
# =============================================================================
profiles:
# Default profile - CPU sweet spot
default: sweetspot_cpu
definitions:
# --- RECOMMENDED SWEET SPOT PROFILE ---
# BEST CHOICE: Perfect balance of quality and compression (slow but worth it!)
sweetspot_cpu:
encoder: cpu_x265
preset: slow
quality: 21 # CRF 21 = visually transparent quality
audio_codec: copy
description: "⭐ RECOMMENDED - Excellent quality, slow but worth the wait"
# --- CPU ENCODING PROFILES ---
# Balanced H.265 encoding (faster, still good quality)
balanced_cpu:
encoder: cpu_x265
preset: medium
quality: 23
audio_codec: copy
description: "Balanced CPU encoding - good quality/speed tradeoff"
# Fast H.264 encoding (quickest option, larger files)
fast_cpu:
encoder: cpu_x264
preset: fast
quality: 26
audio_codec: copy
description: "Fast CPU encoding with H.264 - larger files"
# Maximum quality H.265 (very slow, best quality)
quality_cpu:
encoder: cpu_x265
preset: veryslow
quality: 19
audio_codec: copy
description: "Maximum CPU quality - extremely slow, near-lossless"
# H.264 sweet spot (for maximum compatibility)
sweetspot_h264:
encoder: cpu_x264
preset: slow
quality: 21
audio_codec: copy
description: "H.264 sweet spot - universal compatibility"
# Quick test profile (fastest possible)
test_fast:
encoder: cpu_x264
preset: veryfast
quality: 28
audio_codec: copy
description: "Very fast test encoding - lower quality"
# =============================================================================
# PROCESSING SETTINGS
# =============================================================================
processing:
file_extensions:
- mkv
- mp4
- avi
- m4v
- ts
- m2ts
# Skip files without subtitles (change to false to process all files)
skip_without_subtitles: false # Deprecated - all files now scanned, use dashboard filters
# Clean up abandoned work files on startup
cleanup_stale_work: true
# =============================================================================
# QUALITY CHECKING
# =============================================================================
quality_check:
# Enable pre-encoding quality analysis to detect quality degradation
enabled: true
# Warning threshold - warn if quality will drop by this many points (0-100 scale)
# Example: Source quality 85 → Target quality 70 = 15 point drop (warning)
warn_threshold: 10.0
# Error threshold - fail/skip if quality will drop by this many points
# Example: Source quality 85 → Target quality 60 = 25 point drop (error)
error_threshold: 20.0
# Automatically skip files where encoding would degrade quality
# Set to false to warn but continue encoding anyway
skip_on_degradation: false
# Prompt user for confirmation when warnings detected (CLI only)
prompt_on_warning: true
# =============================================================================
# ADVANCED OPTIONS
# =============================================================================
advanced:
# No GPU detection needed
auto_detect_encoder: false
fallback_to_cpu: true # Already using CPU
# Resolution-based rules (optional)
resolution_rules:
enabled: false
rules:
- max_width: 1920 # 1080p
profile: fast_cpu
- max_width: 3840 # 4K
profile: sweetspot_cpu
- max_width: 7680 # 8K
profile: balanced_cpu
# HDR handling
hdr:
detect_hdr: true
preserve_metadata: true
tone_mapping: false # Set to true to convert HDR to SDR
# Audio options
audio:
default_codec: copy
max_channels: 8
normalize: false
# =============================================================================
# CPU ENCODING NOTES
# =============================================================================
#
# CPU Encoding Characteristics:
# - MUCH slower than GPU encoding (10-50x slower)
# - Better quality per bitrate than GPU encoding
# - Uses significant CPU resources
# - Perfect for quality-focused archival
# - No hardware limitations or compatibility issues
#
# Preset Guide (fastest to slowest):
# - ultrafast: Very fast, poor compression
# - superfast/veryfast: Fast, moderate compression
# - faster/fast: Good speed, decent compression
# - medium: Balanced (default for most encoders)
# - slow: Better compression, recommended sweet spot
# - slower: Excellent compression, very slow
# - veryslow: Best compression, extremely slow (2-3x slower than slow)
#
# Quality Guide (CRF):
# - 18-19: Near-lossless, very large files
# - 20-21: Visually transparent (sweet spot!)
# - 22-24: Excellent quality, good compression
# - 25-28: Good quality, smaller files
#
# Recommended Profiles by Use Case:
# - Best quality/size: sweetspot_cpu (H.265, slow, CRF 21)
# - Faster encoding: balanced_cpu (H.265, medium, CRF 23)
# - Maximum compatibility: sweetspot_h264 (H.264, slow, CRF 21)
# - Archival quality: quality_cpu (H.265, veryslow, CRF 19)
# - Testing: test_fast (H.264, veryfast, CRF 28)
#
# Performance Expectations (1080p content):
# 16-core CPU (H.265, slow): 5-10 fps (2-4 hours per movie)
# 8-core CPU (H.265, slow): 3-7 fps (3-6 hours per movie)
# 4-core CPU (H.265, slow): 2-4 fps (5-10 hours per movie)
#
# 16-core CPU (H.264, fast): 10-20 fps (1-2 hours per movie)
# 8-core CPU (H.264, fast): 7-15 fps (1.5-3 hours per movie)
# 4-core CPU (H.264, fast): 4-10 fps (2-5 hours per movie)
#
# Tips for CPU Encoding:
# 1. Use sweetspot_cpu for best quality/size balance
# 2. Process overnight or during idle times
# 3. Don't run too many workers (max_workers: 1-2 recommended)
# 4. Use balanced_cpu or fast_cpu if speed is important
# 5. Software encoding produces better quality than GPU at same bitrate
# 6. Consider upgrading to a GPU if you encode frequently
#
# Why Use CPU Encoding?
# - No GPU available
# - Want absolute best quality
# - Small library (encode time doesn't matter)
# - Testing/learning the system
# - GPU incompatibility or driver issues
#
# Worker Count Recommendations:
# - High-end CPU (16+ cores): max_workers: 2-3
# - Mid-range CPU (8-12 cores): max_workers: 1-2
# - Budget CPU (4-6 cores): max_workers: 1
# - Always leave CPU headroom for system operations
#
# Encoding Time Examples:
# sweetspot_cpu (H.265, slow, CRF 21):
# - 1080p 2-hour movie on 8-core: 3-5 hours
# - 4K 2-hour movie on 8-core: 8-12 hours
#
# balanced_cpu (H.265, medium, CRF 23):
# - 1080p 2-hour movie on 8-core: 2-3 hours
# - 4K 2-hour movie on 8-core: 5-8 hours
#
# fast_cpu (H.264, fast, CRF 26):
# - 1080p 2-hour movie on 8-core: 1-2 hours
# - 4K 2-hour movie on 8-core: 3-5 hours
# =============================================================================

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# =============================================================================
# ENCODERPRO CONFIGURATION - INTEL ARC GPU
# =============================================================================
# Optimized configuration for Intel Arc A-Series GPUs and Intel integrated graphics
# =============================================================================
# DIRECTORY PATHS
# =============================================================================
# When running in Docker, these paths are mapped from host
movies_dir: /movies
archive_dir: /archive
work_dir: /work
state_db: /db/state.db
log_dir: /logs
# =============================================================================
# PARALLEL PROCESSING
# =============================================================================
parallel:
# Intel Arc can handle 2-4 concurrent encodes depending on model
# A770/A750: 3-4 workers
# A380/A310: 2-3 workers
# Integrated graphics: 1-2 workers
max_workers: 3
# Intel Arc GPU encode slots
# Most Arc GPUs can handle 2-3 simultaneous encodes
gpu_slots: 2
# CPU fallback slots
cpu_slots: 2
# =============================================================================
# ENCODING PROFILES
# =============================================================================
profiles:
# Default profile - optimized for Intel Arc
# Change to sweetspot_av1 if you have Arc A-Series and want best compression
default: sweetspot_qsv
definitions:
# --- RECOMMENDED SWEET SPOT PROFILES ---
# BEST CHOICE for H.265: Perfect quality/size/speed balance
sweetspot_qsv:
encoder: intel_qsv_h265
preset: slow
quality: 21
audio_codec: copy
hdr_handling: preserve
description: "⭐ RECOMMENDED - Visually transparent quality at excellent compression"
# BEST CHOICE for AV1: Maximum compression with great quality (Arc A-Series only)
sweetspot_av1:
encoder: intel_qsv_av1
preset: medium
quality: 27 # AV1 scale is different - 27 is sweet spot
audio_codec: copy
hdr_handling: preserve
description: "⭐ RECOMMENDED AV1 - Best compression, Arc A-Series only"
# --- INTEL QSV PROFILES ---
# Balanced H.265 encoding (good for general use)
balanced_qsv:
encoder: intel_qsv_h265
preset: medium
quality: 23
audio_codec: copy
hdr_handling: preserve
description: "Balanced quality/speed H.265 encoding"
# Fast H.264 encoding (for quick processing)
fast_qsv:
encoder: intel_qsv_h264
preset: veryfast
quality: 26
audio_codec: copy
description: "Fast H.264 encoding for quick turnaround"
# High quality H.265 encoding (slower, larger files)
quality_qsv:
encoder: intel_qsv_h265
preset: slower
quality: 19
audio_codec: copy
hdr_handling: preserve
description: "Maximum H.265 quality for archival"
# AV1 encoding (Intel Arc exclusive - best compression!)
# Note: Only available on Arc A-Series, not older integrated graphics
av1_qsv:
encoder: intel_qsv_av1
preset: medium
quality: 30
audio_codec: copy
hdr_handling: preserve
description: "Balanced AV1 encoding (Arc A-Series only)"
# Fast AV1 for testing
av1_fast:
encoder: intel_qsv_av1
preset: fast
quality: 33
audio_codec: copy
description: "Fast AV1 encoding"
# High quality AV1
av1_quality:
encoder: intel_qsv_av1
preset: slow
quality: 24
audio_codec: copy
hdr_handling: preserve
description: "High quality AV1 encoding"
# --- CPU FALLBACK PROFILES ---
balanced_cpu:
encoder: cpu_x265
preset: medium
quality: 23
audio_codec: copy
description: "CPU fallback H.265"
fast_cpu:
encoder: cpu_x264
preset: fast
quality: 26
audio_codec: copy
description: "CPU fallback H.264"
# =============================================================================
# PROCESSING SETTINGS
# =============================================================================
processing:
file_extensions:
- mkv
- mp4
- avi
- m4v
- ts
- m2ts
# Skip files without subtitles (change to false to process all files)
skip_without_subtitles: false # Deprecated - all files now scanned, use dashboard filters
# Clean up abandoned work files on startup
cleanup_stale_work: true
# =============================================================================
# QUALITY CHECKING
# =============================================================================
quality_check:
# Enable pre-encoding quality analysis to detect quality degradation
enabled: true
# Warning threshold - warn if quality will drop by this many points (0-100 scale)
# Example: Source quality 85 → Target quality 70 = 15 point drop (warning)
warn_threshold: 10.0
# Error threshold - fail/skip if quality will drop by this many points
# Example: Source quality 85 → Target quality 60 = 25 point drop (error)
error_threshold: 20.0
# Automatically skip files where encoding would degrade quality
# Set to false to warn but continue encoding anyway
skip_on_degradation: false
# Prompt user for confirmation when warnings detected (CLI only)
prompt_on_warning: true
# =============================================================================
# ADVANCED OPTIONS
# =============================================================================
advanced:
# Hardware detection
auto_detect_encoder: true
fallback_to_cpu: true
# Resolution-based rules
resolution_rules:
enabled: false
rules:
- max_width: 1920
profile: fast_qsv
- max_width: 3840
profile: balanced_qsv
- max_width: 7680
profile: quality_qsv
# HDR handling
hdr:
detect_hdr: true
preserve_metadata: true
tone_mapping: false # Set to true to convert HDR to SDR
# Audio options
audio:
default_codec: copy
max_channels: 8
normalize: false
# =============================================================================
# INTEL-SPECIFIC NOTES
# =============================================================================
#
# Intel Arc A-Series GPU Capabilities:
# - Hardware H.264, H.265 (HEVC), and AV1 encode/decode
# - Up to 8K encoding support
# - Multiple concurrent encode sessions (2-4 depending on model)
# - HDR10, HDR10+, and Dolby Vision support
# - Excellent quality-to-speed ratio
#
# Recommended Profiles by Use Case:
# - General purpose: balanced_qsv (H.265)
# - Maximum compatibility: fast_qsv (H.264)
# - Best compression: av1_qsv (AV1, Arc A-Series only)
# - Archive quality: quality_qsv or av1_quality
#
# Performance Expectations (1080p content):
# - H.264: 150-300 fps
# - H.265: 100-200 fps
# - AV1: 80-150 fps
#
# Note: Actual performance varies by Arc model:
# - A770 (16GB/8GB): Best performance
# - A750: Excellent performance
# - A380: Good performance
# - A310: Budget-friendly, still much faster than CPU
# =============================================================================

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movies_dir: C:/Users/ckoch/Videos/test-movies
archive_dir: C:/Users/ckoch/Videos/archive
work_dir: C:/Users/ckoch/Videos/work
state_db: C:/Users/ckoch/OneDrive/Documents/development/encoderPro/data/state.db
log_dir: C:/Users/ckoch/OneDrive/Documents/development/encoderPro/logs
profiles:
default: sweetspot_qsv
definitions:
sweetspot_qsv:
encoder: intel_qsv_h265
quality: 23
preset: medium
description: "Intel QSV H.265 Balanced"
subtitle_check:
enabled: true
quality_check:
enabled: true
warn_threshold: 10.0
error_threshold: 20.0
parallel:
max_workers: 1

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# =============================================================================
# ENCODERPRO CONFIGURATION - NVIDIA GPU
# =============================================================================
# Optimized configuration for NVIDIA GPUs using NVENC hardware encoding
# =============================================================================
# DIRECTORY PATHS
# =============================================================================
# When running in Docker, these paths are mapped from host
movies_dir: /movies
archive_dir: /archive
work_dir: /work
state_db: /db/state.db
log_dir: /logs
# =============================================================================
# PARALLEL PROCESSING
# =============================================================================
parallel:
# NVIDIA GPU concurrent encode recommendations:
# RTX 4000 series: 2-3 workers
# RTX 3000 series: 1-2 workers
# RTX 2000 series: 1-2 workers
# GTX 1000 series: 1 worker
max_workers: 2
# NVIDIA GPU encode slots
# Most consumer NVIDIA GPUs: 1-2 concurrent encodes
# Professional cards (A4000, A5000): 3+ concurrent encodes
gpu_slots: 1
# CPU fallback slots
cpu_slots: 2
# =============================================================================
# ENCODING PROFILES
# =============================================================================
profiles:
# Default profile - NVIDIA sweet spot for best quality/compression
default: sweetspot_gpu
definitions:
# --- RECOMMENDED SWEET SPOT PROFILE ---
# BEST CHOICE: Perfect balance of quality, compression, and speed
sweetspot_gpu:
encoder: nvidia_nvenc_h265
preset: p5 # Slower preset for better quality
quality: 21 # CRF 21 = visually transparent for most content
audio_codec: copy
hdr_handling: preserve
description: "⭐ RECOMMENDED - Visually transparent quality at excellent compression"
# --- NVIDIA NVENC PROFILES ---
# Balanced H.265 encoding (good for general use)
balanced_gpu:
encoder: nvidia_nvenc_h265
preset: p4
quality: 23
audio_codec: copy
hdr_handling: preserve
description: "Balanced quality/speed H.265 encoding"
# Fast H.264 encoding (maximum compatibility)
fast_gpu:
encoder: nvidia_nvenc_h264
preset: p1 # Fastest NVENC preset
quality: 26
audio_codec: copy
description: "Fast H.264 encoding, universal compatibility"
# High quality H.265 for archival
quality_gpu:
encoder: nvidia_nvenc_h265
preset: p7 # Slowest/best NVENC preset
quality: 19 # Near-lossless quality
audio_codec: copy
hdr_handling: preserve
description: "Maximum NVENC quality for archival"
# H.264 sweet spot (for older devices)
sweetspot_h264:
encoder: nvidia_nvenc_h264
preset: p5
quality: 21
audio_codec: copy
description: "H.264 sweet spot for maximum compatibility"
# --- CPU FALLBACK PROFILES ---
sweetspot_cpu:
encoder: cpu_x265
preset: slow
quality: 21
audio_codec: copy
description: "CPU fallback sweet spot"
balanced_cpu:
encoder: cpu_x265
preset: medium
quality: 23
audio_codec: copy
description: "Balanced CPU H.265 encoding"
fast_cpu:
encoder: cpu_x264
preset: fast
quality: 26
audio_codec: copy
description: "Fast CPU H.264 encoding"
# =============================================================================
# PROCESSING SETTINGS
# =============================================================================
processing:
file_extensions:
- mkv
- mp4
- avi
- m4v
- ts
- m2ts
# Skip files without subtitles (change to false to process all files)
skip_without_subtitles: false # Deprecated - all files now scanned, use dashboard filters
# Clean up abandoned work files on startup
cleanup_stale_work: true
# =============================================================================
# QUALITY CHECKING
# =============================================================================
quality_check:
# Enable pre-encoding quality analysis to detect quality degradation
enabled: true
# Warning threshold - warn if quality will drop by this many points (0-100 scale)
# Example: Source quality 85 → Target quality 70 = 15 point drop (warning)
warn_threshold: 10.0
# Error threshold - fail/skip if quality will drop by this many points
# Example: Source quality 85 → Target quality 60 = 25 point drop (error)
error_threshold: 20.0
# Automatically skip files where encoding would degrade quality
# Set to false to warn but continue encoding anyway
skip_on_degradation: false
# Prompt user for confirmation when warnings detected (CLI only)
prompt_on_warning: true
# =============================================================================
# ADVANCED OPTIONS
# =============================================================================
advanced:
# Hardware detection
auto_detect_encoder: true
fallback_to_cpu: true
# Resolution-based rules (optional)
resolution_rules:
enabled: false
rules:
- max_width: 1920 # 1080p
profile: fast_gpu
- max_width: 3840 # 4K
profile: sweetspot_gpu
- max_width: 7680 # 8K
profile: quality_gpu
# HDR handling
hdr:
detect_hdr: true
preserve_metadata: true
tone_mapping: false # Set to true to convert HDR to SDR
# Audio options
audio:
default_codec: copy
max_channels: 8
normalize: false
# =============================================================================
# NVIDIA-SPECIFIC NOTES
# =============================================================================
#
# NVIDIA NVENC Encoder Capabilities:
# - Hardware H.264 and H.265 (HEVC) encode/decode
# - Up to 8K encoding support (RTX 4000 series)
# - Multiple concurrent encode sessions (varies by GPU)
# - HDR10 and Dolby Vision support (RTX 2000+)
# - Excellent encoding speed
#
# Preset Guide (p1-p7):
# - p1-p2: Fastest, lower quality, larger files
# - p3-p4: Balanced speed/quality (p4 recommended for general use)
# - p5-p6: Slower, better quality (p5 is the sweet spot)
# - p7: Slowest, best quality (minimal improvement over p6)
#
# Quality Guide (CRF):
# - 18-19: Near-lossless, very large files
# - 20-21: Visually transparent (sweet spot!)
# - 22-24: Excellent quality, good compression
# - 25-28: Good quality, smaller files
#
# Recommended Profiles by Use Case:
# - Best overall: sweetspot_gpu (H.265, preset p5, CRF 21)
# - Fastest: fast_gpu (H.264, preset p1, CRF 26)
# - Maximum compatibility: sweetspot_h264 (H.264, preset p5, CRF 21)
# - Archival: quality_gpu (H.265, preset p7, CRF 19)
#
# Performance Expectations (1080p content):
# - RTX 4090: 300-400 fps
# - RTX 4080: 250-350 fps
# - RTX 4070: 200-300 fps
# - RTX 3090: 250-350 fps
# - RTX 3080: 200-300 fps
# - RTX 3070: 150-250 fps
# - RTX 3060: 120-200 fps
# - RTX 2080: 150-250 fps
# - RTX 2070: 120-200 fps
# - RTX 2060: 100-180 fps
# - GTX 1660: 100-150 fps
# - GTX 1650: 80-120 fps
#
# Why CRF 21 is the Sweet Spot:
# - Visually indistinguishable from source for most content
# - Excellent file size reduction (40-60% smaller)
# - Fast encoding (p5 preset still very fast on GPU)
# - Future-proof quality for archival
# - Perfect for 1080p, 4K, and HDR content
# =============================================================================

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#!/bin/bash
################################################################################
# EXAMPLE CONFIGURATION FILE
################################################################################
# Copy this file and customize for your environment
# Usage: source config.sh && ./reencode-movies.sh
################################################################################
# ==============================================================================
# DIRECTORY PATHS - CUSTOMIZE THESE FOR YOUR SETUP
# ==============================================================================
# Where your movies are stored
export MOVIES_DIR="/mnt/user/movies"
# Where to archive original files
# Make sure this has enough space for your entire library!
export ARCHIVE_DIR="/mnt/user/archive/movies"
# Temporary work directory for encoding
# Should be on fast storage (SSD preferred) with space for largest movie
export WORK_DIR="/mnt/user/temp/encoderpro-work"
# Log file location
export LOG_FILE="/var/log/encoderpro-movies.log"
# ==============================================================================
# ENCODING SETTINGS
# ==============================================================================
# Video codec: libx265 (H.265/HEVC) or libx264 (H.264/AVC)
# H.265: Better compression, slower encoding, wider compatibility issues
# H.264: Faster encoding, larger files, universal compatibility
export VIDEO_CODEC="libx265"
# Encoding preset: ultrafast, superfast, veryfast, faster, fast, medium, slow, slower, veryslow
# Trade-off between speed and compression efficiency
# Recommended: medium (balanced) or slow (better compression)
export VIDEO_PRESET="medium"
# CRF (Constant Rate Factor): 0-51, lower = better quality
# Recommended ranges:
# 18-20: Very high quality (near transparent)
# 21-23: High quality (visually transparent for most content)
# 24-26: Good quality (some visible compression)
# 27-28: Acceptable quality (noticeable compression)
export VIDEO_CRF="23"
# ==============================================================================
# TESTING MODE
# ==============================================================================
# Dry run mode: Set to 1 to test without actually encoding
# This will show you what would be done without making any changes
export DRY_RUN="0"
# ==============================================================================
# EXAMPLE CONFIGURATIONS
# ==============================================================================
# Uncomment one of these presets or customize your own:
# --- FAST ENCODE (for testing or quick processing) ---
# export VIDEO_CODEC="libx264"
# export VIDEO_PRESET="fast"
# export VIDEO_CRF="26"
# --- BALANCED (recommended for most users) ---
# export VIDEO_CODEC="libx265"
# export VIDEO_PRESET="medium"
# export VIDEO_CRF="23"
# --- HIGH QUALITY (archival quality) ---
# export VIDEO_CODEC="libx265"
# export VIDEO_PRESET="slow"
# export VIDEO_CRF="20"
# --- MAXIMUM COMPRESSION (smallest files) ---
# export VIDEO_CODEC="libx265"
# export VIDEO_PRESET="veryslow"
# export VIDEO_CRF="26"
################################################################################
# END OF CONFIGURATION
################################################################################

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# =============================================================================
# ENCODERPRO CONFIGURATION - PHASE 3
# =============================================================================
# Enhanced configuration with GPU acceleration and parallel processing
# =============================================================================
# DIRECTORY PATHS (same as Phase 2)
# =============================================================================
movies_dir: /mnt/user/movies
archive_dir: /mnt/user/archive/movies
work_dir: /mnt/user/temp/encoderpro-work
state_db: /var/lib/encoderpro/state.db
log_dir: /var/log/encoderpro
# =============================================================================
# PARALLEL PROCESSING (NEW IN PHASE 3)
# =============================================================================
parallel:
# Maximum number of concurrent encoding jobs
# Recommended: 1-2 for GPU, 2-4 for CPU (depending on cores)
max_workers: 2
# GPU slots (how many encodes can use GPU simultaneously)
# Most GPUs: 1-2 concurrent encodes
# High-end GPUs: 2-3 concurrent encodes
gpu_slots: 1
# CPU slots (for CPU encoding fallback)
# Set to number of physical cores divided by 2
cpu_slots: 4
# =============================================================================
# ENCODING PROFILES (NEW IN PHASE 3)
# =============================================================================
profiles:
# Default profile to use if none specified
default: balanced_gpu
# Profile definitions
definitions:
# --- GPU PROFILES (NVIDIA NVENC) ---
# RECOMMENDED: Sweet spot for quality/size/speed balance
sweetspot_gpu:
encoder: nvidia_nvenc_h265
preset: p5 # Slower preset for better quality
quality: 21 # CRF 21 = transparent quality for most content
audio_codec: copy
hdr_handling: preserve
description: "Perfect balance - visually transparent quality at good compression"
balanced_gpu:
encoder: nvidia_nvenc_h265
preset: p4 # p1 (fastest) to p7 (slowest)
quality: 23 # Lower = better quality
audio_codec: copy
hdr_handling: preserve
description: "Good quality/speed balance for general use"
fast_gpu:
encoder: nvidia_nvenc_h264
preset: p1 # Fastest preset
quality: 26
audio_codec: copy
description: "Fast processing, larger files, universal compatibility"
quality_gpu:
encoder: nvidia_nvenc_h265
preset: p7 # Highest quality preset
quality: 19 # Near-lossless
audio_codec: copy
hdr_handling: preserve
description: "Maximum quality for archival (slower, larger files)"
# --- CPU PROFILES (FALLBACK) ---
sweetspot_cpu:
encoder: cpu_x265
preset: slow
quality: 21
audio_codec: copy
description: "CPU sweet spot - excellent quality at good compression (slow)"
balanced_cpu:
encoder: cpu_x265
preset: medium
quality: 23
audio_codec: copy
description: "Balanced CPU encoding"
fast_cpu:
encoder: cpu_x264
preset: fast
quality: 26
audio_codec: copy
description: "Fast CPU encoding with H.264"
quality_cpu:
encoder: cpu_x265
preset: veryslow
quality: 19
audio_codec: copy
description: "Maximum CPU quality (very slow)"
# --- INTEL QSV PROFILES (Arc A-Series & Integrated Graphics) ---
# RECOMMENDED: Sweet spot for Intel Arc
sweetspot_qsv:
encoder: intel_qsv_h265
preset: slow
quality: 21
audio_codec: copy
hdr_handling: preserve
description: "Intel sweet spot - visually transparent quality"
# RECOMMENDED: AV1 sweet spot (Arc A-Series only - best compression!)
sweetspot_av1:
encoder: intel_qsv_av1
preset: medium
quality: 27 # AV1 uses different scale (higher values still good)
audio_codec: copy
hdr_handling: preserve
description: "AV1 sweet spot - best compression with excellent quality"
balanced_qsv:
encoder: intel_qsv_h265
preset: medium
quality: 23
audio_codec: copy
hdr_handling: preserve
description: "Balanced H.265 for general use"
fast_qsv:
encoder: intel_qsv_h264
preset: veryfast
quality: 26
audio_codec: copy
description: "Fast H.264 encoding"
quality_qsv:
encoder: intel_qsv_h265
preset: slower
quality: 19
audio_codec: copy
hdr_handling: preserve
description: "Maximum H.265 quality"
# Intel Arc A-Series supports AV1!
av1_qsv:
encoder: intel_qsv_av1
preset: medium
quality: 30
audio_codec: copy
hdr_handling: preserve
description: "Balanced AV1 encoding"
av1_quality:
encoder: intel_qsv_av1
preset: slow
quality: 24
audio_codec: copy
hdr_handling: preserve
description: "High quality AV1 encoding"
# --- AMD VAAPI PROFILES ---
balanced_vaapi:
encoder: amd_vaapi_h265
preset: medium
quality: 23
audio_codec: copy
# =============================================================================
# PROCESSING SETTINGS
# =============================================================================
processing:
file_extensions:
- mkv
- mp4
- avi
- m4v
# Note: skip_without_subtitles is deprecated - all files are now scanned and marked as "discovered"
# Use the dashboard filters to select files with/without subtitles for encoding
skip_without_subtitles: false # Deprecated - kept for backward compatibility
cleanup_stale_work: true
# =============================================================================
# QUALITY CHECKING
# =============================================================================
quality_check:
# Enable pre-encoding quality analysis
enabled: true
# Warning threshold - warn if quality will drop by this many points
warn_threshold: 10.0
# Error threshold - fail if quality will drop by this many points
error_threshold: 20.0
# Skip encoding if quality degradation detected
skip_on_degradation: false
# Prompt user for confirmation on warnings
prompt_on_warning: true
# =============================================================================
# ADVANCED OPTIONS (NEW IN PHASE 3)
# =============================================================================
advanced:
# Automatically detect available encoders on startup
auto_detect_encoders: true
# Prefer GPU encoders over CPU when available
prefer_gpu: true
# Fallback to CPU if GPU encoder fails or unavailable
fallback_to_cpu: true
# Progress update interval (seconds)
progress_interval: 10
# =============================================================================
# PRESET CONFIGURATIONS
# =============================================================================
# Uncomment one of these sections to quickly switch between common setups
# --- NVIDIA GPU SETUP (Recommended for Unraid with NVIDIA GPU) ---
# parallel:
# max_workers: 2
# gpu_slots: 1
# cpu_slots: 4
# profiles:
# default: balanced_gpu
# --- CPU ONLY SETUP (No GPU) ---
# parallel:
# max_workers: 2
# gpu_slots: 0
# cpu_slots: 4
# profiles:
# default: balanced_cpu
# advanced:
# prefer_gpu: false
# --- HIGH THROUGHPUT SETUP (Multiple workers) ---
# parallel:
# max_workers: 4
# gpu_slots: 2
# cpu_slots: 8
# profiles:
# default: fast_gpu
# --- QUALITY FOCUSED SETUP (Single worker, slow encode) ---
# parallel:
# max_workers: 1
# gpu_slots: 1
# cpu_slots: 1
# profiles:
# default: quality_gpu
# =============================================================================
# ENCODER PRESET REFERENCE
# =============================================================================
# NVIDIA NVENC PRESETS:
# p1 = fastest (lower quality, smallest file)
# p2, p3, p4 = balanced
# p5, p6, p7 = slowest (higher quality, larger file)
#
# NVIDIA NVENC QUALITY (CQ):
# 0-51, lower = better quality
# Recommended: 20-25
# CPU (libx265/libx264) PRESETS:
# ultrafast, superfast, veryfast, faster, fast
# medium (balanced)
# slow, slower, veryslow (higher quality)
#
# CPU CRF:
# 0-51, lower = better quality
# Recommended: 18-24
# INTEL QSV PRESETS:
# veryfast, faster, fast, medium, slow, slower, veryslow
#
# INTEL QSV QUALITY:
# Use global_quality: 18-30
# AMD VAAPI:
# Similar to CPU presets
# Quality controlled by QP value
# =============================================================================
# NOTES
# =============================================================================
# GPU Memory Usage:
# - Each concurrent encode uses GPU VRAM
# - 1080p: ~1-2 GB VRAM per encode
# - 4K: ~3-4 GB VRAM per encode
# - Adjust gpu_slots based on available VRAM
# CPU Usage:
# - CPU encoding is highly CPU-intensive
# - max_workers * cpu_slots should not exceed physical cores
# - Leave some cores for system operations
# Encoding Speed Expectations:
# - NVENC H.265: 100-300+ fps (1080p)
# - NVENC H.264: 150-400+ fps (1080p)
# - CPU H.265: 2-20 fps (1080p, depends on preset/CPU)
# - CPU H.264: 5-50 fps (1080p, depends on preset/CPU)
# Quality Trade-offs:
# - GPU encoders are faster but slightly lower quality at same bitrate
# - For archival quality, consider CPU with slow/slower preset
# - For daily viewing, GPU quality is excellent and much faster

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{
"permissions": {
"allow": [
"Bash(find:*)",
"Bash(python3:*)",
"Bash(docker logs:*)",
"Bash(docker ps:*)",
"Bash(dir:*)",
"Bash(powershell:*)",
"Bash(python:*)",
"Bash(where:*)",
"Bash(curl:*)",
"Bash(taskkill:*)",
"Bash(ffmpeg:*)",
"Bash(findstr:*)",
"Bash(Select-String -Pattern \"av1\")",
"Bash(powershell.exe:*)",
"Bash(ls:*)"
]
}
}

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# Database and UI Updates - 2025-12-28
## Summary
Fixed the status filter issue and added container format and encoder columns to the dashboard table.
## Changes Made
### 1. Fixed Status Filter (dashboard.py:717)
**Issue**: Status filter dropdown wasn't working for "Discovered" state - API was rejecting it as invalid.
**Fix**: Added 'discovered' to the valid_states list in the `/api/files` endpoint.
```python
# Before
valid_states = ['pending', 'processing', 'completed', 'failed', 'skipped', None]
# After
valid_states = ['discovered', 'pending', 'processing', 'completed', 'failed', 'skipped', None]
```
**Testing**: Select "Discovered" in the status filter dropdown - should now properly filter files.
---
### 2. Added Container Format Column to Database
**Files Modified**:
- `dashboard.py` (lines 161, 210)
- `reencode.py` (lines 374, 388, 400, 414, 417, 934, 951, 966)
**Database Schema Changes**:
```sql
ALTER TABLE files ADD COLUMN container_format TEXT
```
**Scanner Updates**:
- Extracts container format from FFprobe output during library scan
- Format name extracted from `format.format_name` (e.g., "matroska", "mov,mp4,m4a,3gp,3g2,mj2")
- Takes first format if multiple listed
**Migration**: Automatic - runs on next dashboard or scanner startup
---
### 3. Added Dashboard Table Columns
**dashboard.html Changes**:
**Table Headers** (lines 667-675):
- Added "Container" column (shows file container format like MKV, MP4)
- Added "Encoder" column (shows encoder used for completed files)
- Moved existing columns to accommodate
**Table Column Order**:
1. Checkbox
2. File
3. State
4. Resolution (now shows actual resolution like "1920x1080")
5. **Container** (NEW - shows MKV, MP4, AVI, etc.)
6. **Encoder** (NEW - shows encoder used like "hevc_qsv", "h264_nvenc")
7. Original Size
8. Encoded Size
9. Savings
10. Status
**Data Display** (lines 1518-1546):
- Resolution: Shows `widthxheight` (e.g., "1920x1080") or "-"
- Container: Shows uppercase format name (e.g., "MATROSKA", "MP4") or "-"
- Encoder: Shows encoder_used from database (e.g., "hevc_qsv") or "-"
**Colspan Updates**: Changed from 8 to 10 to match new column count
---
### 4. Database Update Script
**File**: `update-database.py`
**Purpose**: Populate container_format for existing database records
**Usage**:
```bash
# Auto-detect database location
python update-database.py
# Specify database path
python update-database.py path/to/state.db
```
**What It Does**:
1. Finds all files with NULL or empty container_format
2. Uses ffprobe to extract container format
3. Updates database with format information
4. Shows progress for each file
5. Commits every 10 files for safety
**Requirements**: ffprobe must be installed and in PATH
**Example Output**:
```
Opening database: data/state.db
Found 42 files to update
[1/42] Updated: movie1.mkv -> matroska
[2/42] Updated: movie2.mp4 -> mov,mp4,m4a,3gp,3g2,mj2
...
Update complete!
Updated: 40
Failed: 2
Total: 42
```
---
## How Container Format is Populated
### For New Scans (Automatic)
When you run "Scan Library", the scanner now:
1. Runs FFprobe on each file
2. Extracts `format.format_name` from JSON output
3. Takes first format if comma-separated list
4. Stores in database during `add_file()`
**Example**:
- MKV files: `format_name = "matroska,webm"` → stored as "matroska"
- MP4 files: `format_name = "mov,mp4,m4a,3gp,3g2,mj2"` → stored as "mov"
### For Existing Records (Manual)
Run the update script to populate container format for files already in database:
```bash
python update-database.py
```
---
## Encoder Column
The "Encoder" column shows which encoder was used for completed encodings:
**Data Source**: `files.encoder_used` column (already existed)
**Display**:
- Completed files: Shows encoder name (e.g., "hevc_qsv", "h264_nvenc")
- Other states: Shows "-"
**Updated By**: The encoding process already sets this when completing a file
**Common Values**:
- `hevc_qsv` - Intel QSV H.265
- `av1_qsv` - Intel QSV AV1
- `h264_nvenc` - NVIDIA NVENC H.264
- `hevc_nvenc` - NVIDIA NVENC H.265
- `libx265` - CPU H.265
- `libx264` - CPU H.264
---
## Testing Checklist
### Status Filter
- [ ] Select "All States" - shows all files
- [ ] Select "Discovered" - shows only discovered files
- [ ] Select "Pending" - shows only pending files
- [ ] Select "Completed" - shows only completed files
- [ ] Combine with attribute filter (e.g., Discovered + 4K)
### Dashboard Table
- [ ] Table has 10 columns (was 8)
- [ ] Resolution column shows actual resolution or "-"
- [ ] Container column shows format name or "-"
- [ ] Encoder column shows encoder for completed files or "-"
- [ ] All columns align properly
### New Scans
- [ ] Run "Scan Library"
- [ ] Check database - new files should have container_format populated
- [ ] Dashboard should show container formats immediately
### Database Update Script
- [ ] Run `python update-database.py`
- [ ] Verify container_format populated for existing files
- [ ] Check dashboard - existing files should now show containers
---
## Migration Notes
**Backward Compatible**: Yes
- New columns have NULL default
- Existing code works without changes
- Database auto-migrates on startup
**Data Loss**: None
- Existing data preserved
- Only adds new columns
**Rollback**: Safe
- Can remove columns with ALTER TABLE DROP COLUMN (SQLite 3.35+)
- Or restore from backup
---
## Files Changed
1. **dashboard.py**
- Line 161: Added container_format to schema
- Line 210: Added container_format migration
- Line 717: Fixed valid_states to include 'discovered'
2. **reencode.py**
- Line 374: Added container_format migration
- Line 388: Added container_format parameter to add_file()
- Lines 400, 414, 417: Updated SQL to include container_format
- Lines 934, 951: Extract and pass container_format during scan
- Line 966: Pass container_format to add_file()
3. **templates/dashboard.html**
- Lines 670-671: Added Container and Encoder column headers
- Line 680: Updated colspan from 8 to 10
- Line 1472: Updated empty state colspan to 10
- Lines 1518-1525: Added resolution, container, encoder formatting
- Lines 1544-1546: Added new columns to table row
4. **update-database.py** (NEW)
- Standalone script to populate container_format for existing records
---
## Next Steps
1. **Restart Flask Application** to load database changes
2. **Test Status Filter** - verify "Discovered" works
3. **Scan Library** (optional) - populates container format for new files
4. **Run Update Script** - `python update-database.py` to update existing files
5. **Verify Dashboard** - check that all columns display correctly

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# Duplicate Detection System
## Overview
The duplicate detection system prevents re-encoding the same video file twice, even if it exists in different locations or has been renamed.
## How It Works
### 1. File Hashing
When scanning the library, each video file is hashed using a fast content-based algorithm:
**Small Files (<100MB)**:
- Entire file is hashed using SHA-256
- Ensures 100% accuracy for small videos
**Large Files (≥100MB)**:
- Hashes: file size + first 64KB + middle 64KB + last 64KB
- Much faster than hashing entire multi-GB files
- Still highly accurate for duplicate detection
### 2. Duplicate Detection During Scan
**Process**:
1. Scanner calculates hash for each video file
2. Searches database for other files with same hash
3. If a file with the same hash has state = "completed":
- Current file is marked as "skipped"
- Error message: `"Duplicate of: [original file path]"`
- File is NOT added to encoding queue
**Example**:
```
/movies/Action/The Matrix.mkv -> scanned first, hash: abc123
/movies/Sci-Fi/The Matrix.mkv -> scanned second, same hash: abc123
Result: Second file skipped as duplicate
Message: "Duplicate of: Action/The Matrix.mkv"
```
### 3. Database Schema
**New Column**: `file_hash TEXT`
- Stores SHA-256 hash of file content
- Indexed for fast lookups
- NULL for files scanned before this feature
**Index**: `idx_file_hash`
- Allows fast duplicate searches
- Critical for large libraries
### 4. UI Indicators
**Dashboard Display**:
- Duplicate files show a ⚠️ warning icon next to filename
- Tooltip shows "Duplicate file"
- State badge shows "skipped" with orange color
- Hovering over state shows which file it's a duplicate of
**Visual Example**:
```
⚠️ Sci-Fi/The Matrix.mkv [skipped]
Tooltip: "Skipped: Duplicate of: Action/The Matrix.mkv"
```
## Benefits
### 1. Prevents Wasted Resources
- No CPU/GPU time wasted on duplicate encodes
- No disk space wasted on duplicate outputs
- Scanner automatically identifies duplicates
### 2. Safe Deduplication
- Only skips if original has been successfully encoded
- If original failed, duplicate can still be selected
- Preserves all duplicate file records in database
### 3. Works Across Reorganizations
- Moving files between folders doesn't fool the system
- Renaming files doesn't fool the system
- Hash is based on content, not filename or path
## Use Cases
### Use Case 1: Reorganized Library
```
Before:
/movies/unsorted/movie.mkv (encoded)
After reorganization:
/movies/Action/movie.mkv (copy or renamed)
/movies/unsorted/movie.mkv (original)
Result: New location detected as duplicate, automatically skipped
```
### Use Case 2: Accidental Copies
```
Library structure:
/movies/The Matrix (1999).mkv
/movies/The Matrix.mkv
/movies/backup/The Matrix.mkv
First scan:
- First file encountered is encoded
- Other two marked as duplicates
- Only one encoding job runs
```
### Use Case 3: Mixed Source Files
```
Same movie from different sources:
/movies/BluRay/movie.mkv (exact copy)
/movies/Downloaded/movie.mkv (exact copy)
Result: Only first is encoded, second skipped as duplicate
```
## Configuration
**No configuration needed!**
- Duplicate detection is automatic
- Enabled for all scans
- No performance impact (hashing is very fast)
## Performance
### Hashing Speed
- Small files (<100MB): ~50 files/second
- Large files (5GB+): ~200 files/second
- Negligible impact on total scan time
### Database Lookups
- Hash index makes lookups instant
- O(1) complexity for duplicate checks
- Handles libraries with 10,000+ files
## Technical Details
### Hash Function
**Location**: `reencode.py:595-633`
```python
@staticmethod
def get_file_hash(filepath: Path, chunk_size: int = 8192) -> str:
"""Calculate a fast hash of the file using first/last chunks + size."""
import hashlib
file_size = filepath.stat().st_size
# Small files: hash entire file
if file_size < 100 * 1024 * 1024:
hasher = hashlib.sha256()
with open(filepath, 'rb') as f:
while chunk := f.read(chunk_size):
hasher.update(chunk)
return hasher.hexdigest()
# Large files: hash size + first/middle/last chunks
hasher = hashlib.sha256()
hasher.update(str(file_size).encode())
with open(filepath, 'rb') as f:
hasher.update(f.read(65536)) # First 64KB
f.seek(file_size // 2)
hasher.update(f.read(65536)) # Middle 64KB
f.seek(-65536, 2)
hasher.update(f.read(65536)) # Last 64KB
return hasher.hexdigest()
```
### Duplicate Check
**Location**: `reencode.py:976-1005`
```python
# Calculate file hash
file_hash = MediaInspector.get_file_hash(filepath)
# Check for duplicates
if file_hash:
duplicates = self.db.find_duplicates_by_hash(file_hash)
completed_duplicate = next(
(d for d in duplicates if d['state'] == ProcessingState.COMPLETED.value),
None
)
if completed_duplicate:
self.logger.info(f"Skipping duplicate: {filepath.name}")
self.logger.info(f" Original: {completed_duplicate['relative_path']}")
# Mark as skipped with duplicate message
...
continue
```
### Database Methods
**Location**: `reencode.py:432-438`
```python
def find_duplicates_by_hash(self, file_hash: str) -> List[Dict]:
"""Find all files with the same content hash"""
with self._lock:
cursor = self.conn.cursor()
cursor.execute("SELECT * FROM files WHERE file_hash = ?", (file_hash,))
rows = cursor.fetchall()
return [dict(row) for row in rows]
```
## Limitations
### 1. Partial File Changes
If you modify a video (e.g., trim it), the hash will change:
- Modified version will NOT be detected as duplicate
- This is intentional - different content = different file
### 2. Re-encoded Files
If the SAME source file is encoded with different settings:
- Output files will have different hashes
- Both will be kept (correct behavior)
### 3. Existing Records
Files scanned before this feature will have `file_hash = NULL`:
- Re-run scan to populate hashes
- Or use the update script (if created)
## Troubleshooting
### Issue: Duplicate not detected
**Cause**: Files might have different content (different sources, quality, etc.)
**Solution**: Hashes are content-based - different content = different hash
### Issue: False duplicate detection
**Cause**: Extremely rare hash collision (virtually impossible with SHA-256)
**Solution**: Check error message to see which file it matched
### Issue: Want to re-encode a duplicate
**Solution**:
1. Find the duplicate in dashboard (has ⚠️ icon)
2. Delete it from database or mark as "discovered"
3. Select it for encoding
## Files Modified
1. **dashboard.py**
- Line 162: Added `file_hash TEXT` to schema
- Line 198: Added index on file_hash
- Line 212: Added file_hash migration
2. **reencode.py**
- Line 361: Added index on file_hash
- Line 376: Added file_hash migration
- Lines 390, 402, 417, 420: Updated add_file() to accept file_hash
- Lines 432-438: Added find_duplicates_by_hash()
- Lines 595-633: Added get_file_hash() to MediaInspector
- Lines 976-1005: Added duplicate detection in scanner
- Line 1049: Pass file_hash to add_file()
3. **templates/dashboard.html**
- Lines 1527-1529: Detect duplicate files
- Line 1540: Show ⚠️ icon for duplicates
## Testing
### Test 1: Basic Duplicate Detection
1. Copy a movie file to two different locations
2. Run library scan
3. Verify: First file = "discovered", second file = "skipped"
4. Check error message shows original path
### Test 2: Encoded Duplicate
1. Scan library (all files discovered)
2. Encode one movie
3. Copy encoded movie to different location
4. Re-scan library
5. Verify: Copy is marked as duplicate
### Test 3: UI Indicator
1. Find a skipped duplicate in dashboard
2. Verify: ⚠️ warning icon appears
3. Hover over state badge
4. Verify: Tooltip shows "Duplicate of: [path]"
### Test 4: Performance
1. Scan large library (100+ files)
2. Check scan time with/without hashing
3. Verify: Minimal performance impact (<10% slower)
## Future Enhancements
Potential improvements:
- [ ] Bulk duplicate removal tool
- [ ] Duplicate preview/comparison UI
- [ ] Option to prefer highest quality duplicate
- [ ] Fuzzy duplicate detection (similar but not identical)
- [ ] Duplicate statistics in dashboard stats

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# Pagination Successfully Applied
**Date**: 2025-12-28
**Status**: ✅ Completed
## Changes Applied to dashboard.html
### 1. Status Filter Dropdown (Line 564-574)
Replaced the old quality filter dropdown with a new status filter:
```html
<select id="statusFilter" onchange="changeStatusFilter(this.value)">
<option value="all">All States</option>
<option value="discovered">Discovered</option>
<option value="pending">Pending</option>
<option value="processing">Processing</option>
<option value="completed">Completed</option>
<option value="failed">Failed</option>
<option value="skipped">Skipped</option>
</select>
```
**Purpose**: Allows users to filter files by their processing state (discovered, pending, etc.)
### 2. Pagination Controls Container (Line 690)
Added pagination controls after the file list table:
```html
<div id="paginationControls"></div>
```
**Purpose**: Container that displays pagination navigation (Previous/Next buttons, page indicator, page jump input)
### 3. Pagination JavaScript (Lines 1440-1625)
Replaced infinite scroll implementation with traditional pagination:
**New Variables**:
- `currentStatusFilter = 'all'` - Tracks selected status filter
- `currentPage = 1` - Current page number
- `totalPages = 1` - Total number of pages
- `filesPerPage = 100` - Files shown per page
**New Functions**:
- `changeStatusFilter(status)` - Changes status filter and reloads page 1
- `updatePaginationControls()` - Renders pagination UI with Previous/Next buttons
- `goToPage(page)` - Navigates to specific page
- `goToPageInput()` - Handles "Enter" key in page jump input
**Updated Functions**:
- `loadFileQuality()` - Now loads specific page using offset calculation
- `applyFilter()` - Resets to page 1 when changing attribute filters
### 4. Removed Infinite Scroll Code
- Removed scroll event listeners
- Removed "Load More" button logic
- Removed `hasMoreFiles` and `isLoadingMore` variables
## How It Works
### Combined Filtering
Users can now combine two types of filters:
1. **Status Filter** (dropdown at top):
- Filters by processing state: discovered, pending, processing, completed, failed, skipped
- Applies to ALL pages
2. **Attribute Filter** (buttons):
- Filters by video attributes: subtitles, audio channels, resolution, codec, file size
- Applies to ALL pages
**Example**: Select "Discovered" status + "4K" attribute = Shows only discovered 4K files
### Pagination Navigation
1. **Previous/Next Buttons**:
- Previous disabled on page 1
- Next always available (loads next page)
2. **Page Indicator**:
- Shows current page number
- Shows file range (e.g., "Showing 101-200")
3. **Go to Page Input**:
- Type page number and press Enter
- Jumps directly to that page
### Selection Persistence
- Selected files remain selected when navigating between pages
- Changing filters clears all selections
- "Select All" only affects visible files on current page
## Testing
After deployment, verify:
1. **Status Filter**:
- Select different statuses (discovered, completed, etc.)
- Verify file list updates correctly
- Check that pagination resets to page 1
2. **Pagination Navigation**:
- Click Next to go to page 2
- Click Previous to return to page 1
- Use "Go to page" input to jump to specific page
- Verify Previous button is disabled on page 1
3. **Combined Filters**:
- Select status filter + attribute filter
- Verify both filters apply correctly
- Check pagination shows correct results
4. **Selection**:
- Select files on page 1
- Navigate to page 2
- Return to page 1 - selections should persist
- Change filter - selections should clear
## Backup
A backup of the original dashboard.html was created at:
`templates/dashboard.html.backup`
To restore if needed:
```bash
cp templates/dashboard.html.backup templates/dashboard.html
```
## Files Involved
- **templates/dashboard.html** - Modified with pagination
- **templates/dashboard.html.backup** - Original backup
- **pagination-replacement.js** - Source code for pagination
- **apply-pagination.py** - Automation script (already run)
- **PAGINATION-INTEGRATION-GUIDE.md** - Manual integration guide
## Next Steps
1. Restart the Flask application
2. Test all pagination features
3. Verify status filter works correctly
4. Test combined status + attribute filtering
5. Verify selection persistence across pages

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# Process Duplicates Button
## Overview
Added a "Process Duplicates" button to the dashboard that scans the existing database for duplicate files and automatically marks them as skipped.
## What It Does
The "Process Duplicates" button:
1. **Calculates missing file hashes** - For files that were scanned before the duplicate detection feature, it calculates their hash
2. **Finds duplicates** - Identifies files with the same content hash
3. **Marks duplicates** - If a file with the same hash has already been encoded (state = completed), marks duplicates as "skipped"
4. **Shows statistics** - Displays a summary of what was processed
## Location
**Dashboard Controls** - Located in the top control bar:
- 📂 Scan Library
- 🔍 **Process Duplicates** (NEW)
- 🔄 Refresh
- 🔧 Reset Stuck
## How to Use
1. **Click "Process Duplicates" button**
2. **Confirm** the operation when prompted
3. **Wait** while the system processes files (status badge shows "Processing Duplicates...")
4. **Review results** in the popup showing statistics
## Statistics Shown
After processing completes, you'll see:
```
Duplicate Processing Complete!
Total Files: 150
Files Hashed: 42
Duplicates Found: 8
Duplicates Marked: 8
Errors: 0
```
**Explanation**:
- **Total Files**: Number of files checked
- **Files Hashed**: Files that needed hash calculation (were missing hash)
- **Duplicates Found**: Files identified as duplicates
- **Duplicates Marked**: Files marked as skipped
- **Errors**: Files that couldn't be processed (e.g., file not found)
## When to Use
### Use Case 1: After Upgrading to Duplicate Detection
If you upgraded from a version without duplicate detection:
```
1. Existing files in database have no hash
2. Click "Process Duplicates"
3. All files are hashed and duplicates identified
```
### Use Case 2: After Manual Database Changes
If you manually modified the database or imported files:
```
1. New records may not have hashes
2. Click "Process Duplicates"
3. Missing hashes calculated, duplicates found
```
### Use Case 3: Regular Maintenance
Periodically check for duplicates:
```
1. Files may have been reorganized or copied
2. Click "Process Duplicates"
3. Ensures no duplicate encoding jobs
```
## Technical Details
### Backend Process (dashboard.py)
**Method**: `DatabaseReader.process_duplicates()`
**Logic**:
1. Query all files not already marked as duplicates
2. For each file:
- Check if file_hash exists
- If missing, calculate hash using `_calculate_file_hash()`
- Store hash in database
3. Track seen hashes in memory
4. When duplicate hash found:
- Check if original is completed
- Mark current file as skipped with message
5. Return statistics
**SQL Queries**:
```sql
-- Get files to process
SELECT id, filepath, file_hash, state, relative_path
FROM files
WHERE state != 'skipped'
OR (state = 'skipped' AND error_message NOT LIKE 'Duplicate of:%')
ORDER BY id
-- Update hash
UPDATE files SET file_hash = ? WHERE id = ?
-- Mark duplicate
UPDATE files
SET state = 'skipped',
error_message = 'Duplicate of: ...',
updated_at = CURRENT_TIMESTAMP
WHERE id = ?
```
### API Endpoint
**Route**: `POST /api/process-duplicates`
**Request**: No body required
**Response**:
```json
{
"success": true,
"stats": {
"total_files": 150,
"files_hashed": 42,
"duplicates_found": 8,
"duplicates_marked": 8,
"errors": 0
}
}
```
**Error Response**:
```json
{
"success": false,
"error": "Error message here"
}
```
### Frontend (dashboard.html)
**Button**:
```html
<button class="btn" onclick="processDuplicates()"
style="background: #a855f7; color: white;"
title="Find and mark duplicate files in database">
🔍 Process Duplicates
</button>
```
**JavaScript Function**:
```javascript
async function processDuplicates() {
// Confirm with user
if (!confirm('...')) return;
// Show loading indicator
statusBadge.textContent = 'Processing Duplicates...';
// Call API
const response = await fetchWithCsrf('/api/process-duplicates', {
method: 'POST'
});
// Show results
alert(`Duplicate Processing Complete!\n\nTotal Files: ${stats.total_files}...`);
// Refresh dashboard
refreshData();
}
```
## Performance
### Speed
- **Small files (<100MB)**: ~50 files/second
- **Large files (5GB+)**: ~200 files/second
- **Database operations**: Instant with hash index
### Example Processing Times
- **100 files, all need hashing**: ~5-10 seconds
- **1000 files, half need hashing**: ~30-60 seconds
- **100 files, all have hashes**: <1 second
### Memory Usage
- Minimal - only tracks hash-to-file mapping in memory
- For 10,000 files: ~10MB RAM
## Safety
### Safe Operations
✅ **Read-only on filesystem** - Only reads files, never modifies
✅ **Reversible** - Can manually change state back to "discovered"
✅ **Non-destructive** - Original files never touched
✅ **Transactional** - Database commits only on success
### What Could Go Wrong?
1. **File not found**: Counted as error, skipped
2. **Permission denied**: Counted as error, skipped
3. **Large file timeout**: Rare, but possible for huge files
### Error Handling
```python
try:
file_hash = self._calculate_file_hash(file_path)
if file_hash:
cursor.execute("UPDATE files SET file_hash = ? WHERE id = ?", ...)
stats['files_hashed'] += 1
except Exception as e:
logging.error(f"Failed to hash {file_path}: {e}")
stats['errors'] += 1
continue # Skip to next file
```
## Comparison: Process Duplicates vs Scan Library
| Feature | Process Duplicates | Scan Library |
|---------|-------------------|--------------|
| **Purpose** | Find duplicates in existing DB | Add new files to DB |
| **File Discovery** | No | Yes |
| **File Hashing** | Yes (if missing) | Yes (always) |
| **Media Inspection** | No | Yes (codec, resolution, etc.) |
| **Speed** | Fast | Slower |
| **When to Use** | After upgrade or maintenance | Initial setup or new files |
## Files Modified
1. **dashboard.py**
- Lines 434-558: Added `process_duplicates()` method
- Lines 524-558: Added `_calculate_file_hash()` helper
- Lines 1443-1453: Added `/api/process-duplicates` endpoint
2. **templates/dashboard.html**
- Lines 370-372: Added "Process Duplicates" button
- Lines 1161-1199: Added `processDuplicates()` JavaScript function
## Testing
### Test 1: Process Database with Missing Hashes
```
1. Use old database (before duplicate detection)
2. Click "Process Duplicates"
3. Verify: All files get hashed
4. Verify: Statistics show files_hashed > 0
```
### Test 2: Find Duplicates
```
1. Have database with completed file
2. Copy that file to different location
3. Scan library (adds copy)
4. Click "Process Duplicates"
5. Verify: Copy marked as duplicate
6. Verify: Statistics show duplicates_found > 0
```
### Test 3: No Duplicates
```
1. Database with unique files only
2. Click "Process Duplicates"
3. Verify: No duplicates found
4. Verify: Statistics show duplicates_found = 0
```
### Test 4: Files Not Found
```
1. Database with files that don't exist on disk
2. Click "Process Duplicates"
3. Verify: Errors counted
4. Verify: Statistics show errors > 0
5. Verify: Other files still processed
```
## UI/UX
### Visual Feedback
1. **Confirmation Dialog**: "This will scan the database for duplicate files and mark them..."
2. **Status Badge**: Changes to "Processing Duplicates..." during operation
3. **Results Dialog**: Shows detailed statistics
4. **Auto-refresh**: Dashboard refreshes after 1 second to show updated states
### Button Style
- **Color**: Purple (#a855f7) - distinct from other buttons
- **Icon**: 🔍 (magnifying glass) - represents searching
- **Tooltip**: "Find and mark duplicate files in database"
## Future Enhancements
Potential improvements:
- [ ] Progress bar showing current file being processed
- [ ] Live statistics updating during processing
- [ ] Option to preview duplicates before marking
- [ ] Ability to choose which duplicate to keep
- [ ] Bulk delete duplicate files (with confirmation)
- [ ] Schedule automatic duplicate processing

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#!/usr/bin/env python3
"""
Database Management Utility for encoderPro
Provides convenient commands for inspecting and managing the state database
"""
import argparse
import sqlite3
import sys
from pathlib import Path
from typing import Dict, List
class DatabaseManager:
"""Utility for managing the state database"""
def __init__(self, db_path: Path):
self.db_path = db_path
self.conn = None
if not db_path.exists():
print(f"ERROR: Database not found: {db_path}")
sys.exit(1)
self.conn = sqlite3.connect(str(db_path))
self.conn.row_factory = sqlite3.Row
def close(self):
if self.conn:
self.conn.close()
def stats(self):
"""Show database statistics"""
cursor = self.conn.cursor()
print("\n" + "="*70)
print("DATABASE STATISTICS")
print("="*70)
# Count by state
cursor.execute("""
SELECT state, COUNT(*) as count
FROM files
GROUP BY state
ORDER BY state
""")
total = 0
for row in cursor.fetchall():
state = row['state'].upper()
count = row['count']
total += count
print(f"{state:15} {count:6}")
print("-"*70)
print(f"{'TOTAL':15} {total:6}")
# Space statistics
cursor.execute("""
SELECT
SUM(original_size) as orig,
SUM(encoded_size) as enc
FROM files
WHERE state = 'completed'
""")
row = cursor.fetchone()
if row and row['orig']:
orig = row['orig']
enc = row['enc'] or 0
saved = orig - enc
print("\n" + "="*70)
print("SPACE STATISTICS (Completed Files)")
print("="*70)
print(f"Original size: {self._human_size(orig)}")
print(f"Encoded size: {self._human_size(enc)}")
print(f"Space saved: {self._human_size(saved)} ({saved/orig*100:.1f}%)")
print("="*70 + "\n")
def list_files(self, state: str = None, limit: int = None):
"""List files, optionally filtered by state"""
cursor = self.conn.cursor()
query = "SELECT * FROM files"
params = []
if state:
query += " WHERE state = ?"
params.append(state)
query += " ORDER BY updated_at DESC"
if limit:
query += " LIMIT ?"
params.append(limit)
cursor.execute(query, params)
rows = cursor.fetchall()
if not rows:
print("No files found")
return
print(f"\nFound {len(rows)} file(s):\n")
print(f"{'State':<12} {'Size':<12} {'Path'}")
print("-" * 80)
for row in rows:
state = row['state']
size = self._human_size(row['original_size']) if row['original_size'] else 'N/A'
path = row['relative_path']
# Truncate long paths
if len(path) > 50:
path = path[:47] + "..."
print(f"{state:<12} {size:<12} {path}")
def failed_files(self):
"""Show failed files with error messages"""
cursor = self.conn.cursor()
cursor.execute("""
SELECT relative_path, error_message, updated_at
FROM files
WHERE state = 'failed'
ORDER BY updated_at DESC
""")
rows = cursor.fetchall()
if not rows:
print("No failed files")
return
print(f"\n{len(rows)} FAILED FILE(S):\n")
print("="*80)
for row in rows:
print(f"File: {row['relative_path']}")
print(f"Error: {row['error_message']}")
print(f"Date: {row['updated_at']}")
print("-"*80)
def reset_state(self, from_state: str, to_state: str = 'pending'):
"""Reset files from one state to another"""
cursor = self.conn.cursor()
# Count affected
cursor.execute("SELECT COUNT(*) FROM files WHERE state = ?", (from_state,))
count = cursor.fetchone()[0]
if count == 0:
print(f"No files in state '{from_state}'")
return
print(f"Found {count} file(s) in state '{from_state}'")
response = input(f"Reset to '{to_state}'? (yes/no): ")
if response.lower() != 'yes':
print("Cancelled")
return
cursor.execute("""
UPDATE files
SET state = ?, error_message = NULL, updated_at = CURRENT_TIMESTAMP
WHERE state = ?
""", (to_state, from_state))
self.conn.commit()
print(f"✓ Reset {cursor.rowcount} file(s) to '{to_state}'")
def reset_file(self, filepath: str, to_state: str = 'pending'):
"""Reset a specific file to a given state"""
cursor = self.conn.cursor()
# Check if exists
cursor.execute("SELECT * FROM files WHERE filepath = ? OR relative_path = ?",
(filepath, filepath))
row = cursor.fetchone()
if not row:
print(f"File not found: {filepath}")
return
print(f"File: {row['relative_path']}")
print(f"Current state: {row['state']}")
response = input(f"Reset to '{to_state}'? (yes/no): ")
if response.lower() != 'yes':
print("Cancelled")
return
cursor.execute("""
UPDATE files
SET state = ?, error_message = NULL, updated_at = CURRENT_TIMESTAMP
WHERE filepath = ? OR relative_path = ?
""", (to_state, filepath, filepath))
self.conn.commit()
print(f"✓ Reset to '{to_state}'")
def search(self, pattern: str):
"""Search for files by path pattern"""
cursor = self.conn.cursor()
cursor.execute("""
SELECT state, relative_path, original_size, has_subtitles
FROM files
WHERE relative_path LIKE ?
ORDER BY relative_path
""", (f'%{pattern}%',))
rows = cursor.fetchall()
if not rows:
print(f"No files matching '{pattern}'")
return
print(f"\nFound {len(rows)} file(s) matching '{pattern}':\n")
print(f"{'State':<12} {'Subs':<6} {'Size':<12} {'Path'}")
print("-" * 80)
for row in rows:
state = row['state']
subs = 'Yes' if row['has_subtitles'] else 'No'
size = self._human_size(row['original_size']) if row['original_size'] else 'N/A'
path = row['relative_path']
if len(path) > 45:
path = path[:42] + "..."
print(f"{state:<12} {subs:<6} {size:<12} {path}")
def vacuum(self):
"""Vacuum (optimize) the database"""
print("Vacuuming database...")
self.conn.execute("VACUUM")
print("✓ Database optimized")
def _human_size(self, size: int) -> str:
"""Convert bytes to human readable format"""
if not size:
return "0 B"
for unit in ['B', 'KB', 'MB', 'GB', 'TB']:
if size < 1024.0:
return f"{size:.2f} {unit}"
size /= 1024.0
return f"{size:.2f} PB"
def main():
parser = argparse.ArgumentParser(
description='Database Management Utility for encoderPro',
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
%(prog)s stats # Show statistics
%(prog)s list -s completed -n 10 # List 10 most recent completed files
%(prog)s failed # Show failed files with errors
%(prog)s reset -f failed -t pending # Reset all failed files to pending
%(prog)s reset-file movie.mkv # Reset specific file to pending
%(prog)s search "Avatar" # Search for files with "Avatar" in path
%(prog)s vacuum # Optimize database
"""
)
parser.add_argument(
'-d', '--database',
type=Path,
default=Path('/var/lib/encoderpro/state.db'),
help='Path to database file (default: /var/lib/encoderpro/state.db)'
)
subparsers = parser.add_subparsers(dest='command', help='Command to run')
# Stats command
subparsers.add_parser('stats', help='Show database statistics')
# List command
list_parser = subparsers.add_parser('list', help='List files')
list_parser.add_argument('-s', '--state', help='Filter by state')
list_parser.add_argument('-n', '--limit', type=int, help='Limit number of results')
# Failed command
subparsers.add_parser('failed', help='Show failed files with errors')
# Reset command
reset_parser = subparsers.add_parser('reset', help='Reset files from one state to another')
reset_parser.add_argument('-f', '--from-state', required=True, help='Source state')
reset_parser.add_argument('-t', '--to-state', default='pending', help='Target state (default: pending)')
# Reset file command
reset_file_parser = subparsers.add_parser('reset-file', help='Reset a specific file')
reset_file_parser.add_argument('filepath', help='File path (absolute or relative)')
reset_file_parser.add_argument('-t', '--to-state', default='pending', help='Target state (default: pending)')
# Search command
search_parser = subparsers.add_parser('search', help='Search for files by path pattern')
search_parser.add_argument('pattern', help='Search pattern (case-insensitive)')
# Vacuum command
subparsers.add_parser('vacuum', help='Optimize database')
args = parser.parse_args()
if not args.command:
parser.print_help()
return 1
# Create manager
db = DatabaseManager(args.database)
try:
# Execute command
if args.command == 'stats':
db.stats()
elif args.command == 'list':
db.list_files(args.state, args.limit)
elif args.command == 'failed':
db.failed_files()
elif args.command == 'reset':
db.reset_state(args.from_state, args.to_state)
elif args.command == 'reset-file':
db.reset_file(args.filepath, args.to_state)
elif args.command == 'search':
db.search(args.pattern)
elif args.command == 'vacuum':
db.vacuum()
finally:
db.close()
return 0
if __name__ == '__main__':
sys.exit(main())

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#!/bin/bash
################################################################################
# encoderPro Automated Deployment Script for Dell R730 / Unraid
################################################################################
# This script automates the entire deployment process:
# - Clones/updates from private Git
# - Optimizes config for 48-thread R730
# - Builds Docker image
# - Pushes to private registry
# - Deploys container with optimal settings
################################################################################
set -e
# =============================================================================
# CONFIGURATION - CUSTOMIZE THESE VALUES
# =============================================================================
REGISTRY="your-registry.com" # Your private Docker registry
IMAGE_NAME="encoderpro" # Image name
IMAGE_TAG="latest" # Image tag
GIT_REPO="https://your-private-git/encoderpro.git" # Your private Git repo
APP_DIR="/mnt/user/appdata/encoderpro" # Installation directory
# R730 Optimizations
MAX_WORKERS=8 # Concurrent encodes (48 threads / 6)
CPU_SLOTS=24 # CPU slots (48 threads / 2)
CPU_LIMIT="46" # Docker CPU limit (leave 2 for system)
MEMORY_LIMIT="64g" # Docker memory limit
# =============================================================================
# SCRIPT START
# =============================================================================
echo "=========================================="
echo "encoderPro Deployment Script"
echo "Target: Dell R730 (48 threads)"
echo "=========================================="
echo ""
# =============================================================================
# Step 1: Clone or Update Repository
# =============================================================================
echo "[1/9] Checking repository..."
if [ -d "$APP_DIR/.git" ]; then
echo "Repository exists, updating..."
cd "$APP_DIR"
git pull
echo "✓ Repository updated"
else
echo "Cloning repository..."
mkdir -p /mnt/user/appdata
cd /mnt/user/appdata
git clone "$GIT_REPO" encoderpro
cd encoderpro
echo "✓ Repository cloned"
fi
echo ""
# =============================================================================
# Step 2: Create Required Directories
# =============================================================================
echo "[2/9] Creating directories..."
mkdir -p "$APP_DIR/db"
mkdir -p "$APP_DIR/logs"
mkdir -p /mnt/user/temp/encoderpro-work
mkdir -p /mnt/user/archive/movies
echo "✓ Directories created:"
echo " - $APP_DIR/db"
echo " - $APP_DIR/logs"
echo " - /mnt/user/temp/encoderpro-work"
echo " - /mnt/user/archive/movies"
echo ""
# =============================================================================
# Step 3: Setup Configuration
# =============================================================================
echo "[3/9] Configuring for R730..."
if [ ! -f "$APP_DIR/config.yaml" ]; then
echo "Creating optimized config.yaml..."
cp config-cpu.yaml config.yaml
# Optimize for R730 (48 threads)
sed -i "s/max_workers: 2/max_workers: $MAX_WORKERS/" config.yaml
sed -i "s/cpu_slots: 4/cpu_slots: $CPU_SLOTS/" config.yaml
echo "✓ Config created with R730 optimizations:"
echo " - max_workers: $MAX_WORKERS (8 concurrent encodes)"
echo " - cpu_slots: $CPU_SLOTS (utilizing 48 threads)"
else
echo "Config already exists (skipping)"
fi
echo ""
# =============================================================================
# Step 4: Build Docker Image
# =============================================================================
echo "[4/9] Building Docker image..."
echo "This may take a few minutes..."
docker build -t "${REGISTRY}/${IMAGE_NAME}:${IMAGE_TAG}" .
echo "✓ Image built: ${REGISTRY}/${IMAGE_NAME}:${IMAGE_TAG}"
echo ""
# =============================================================================
# Step 5: Push to Private Registry
# =============================================================================
echo "[5/9] Pushing to private registry..."
docker push "${REGISTRY}/${IMAGE_NAME}:${IMAGE_TAG}"
echo "✓ Image pushed to registry"
echo ""
# =============================================================================
# Step 6: Stop Existing Container
# =============================================================================
echo "[6/9] Checking for existing container..."
if [ "$(docker ps -aq -f name=encoderpro)" ]; then
echo "Stopping existing container..."
docker stop encoderpro 2>/dev/null || true
docker rm encoderpro 2>/dev/null || true
echo "✓ Existing container removed"
else
echo "No existing container found"
fi
echo ""
# =============================================================================
# Step 7: Deploy New Container
# =============================================================================
echo "[7/9] Deploying container..."
docker run -d \
--name encoderpro \
-e DASHBOARD_HOST=0.0.0.0 \
-e DASHBOARD_PORT=5000 \
-p 5000:5000 \
-v /mnt/user/movies:/movies:ro \
-v /mnt/user/archive/movies:/archive \
-v /mnt/user/temp/encoderpro-work:/work \
-v "$APP_DIR/config.yaml:/config/config.yaml:ro" \
-v "$APP_DIR/db:/db" \
-v "$APP_DIR/logs:/logs" \
--cpus="$CPU_LIMIT" \
--memory="$MEMORY_LIMIT" \
--restart unless-stopped \
"${REGISTRY}/${IMAGE_NAME}:${IMAGE_TAG}"
echo "✓ Container deployed with:"
echo " - CPU limit: $CPU_LIMIT cores"
echo " - Memory limit: $MEMORY_LIMIT"
echo " - Restart policy: unless-stopped"
echo ""
# =============================================================================
# Step 8: Verify Deployment
# =============================================================================
echo "[8/9] Verifying deployment..."
sleep 5
if [ "$(docker ps -q -f name=encoderpro)" ]; then
echo "✓ Container is running"
else
echo "✗ ERROR: Container failed to start!"
echo "Check logs: docker logs encoderpro"
exit 1
fi
echo ""
# =============================================================================
# Step 9: Display Summary
# =============================================================================
echo "[9/9] Deployment Summary"
echo "=========================================="
echo ""
echo "✓ encoderPro deployed successfully!"
echo ""
echo "Configuration:"
echo " - Container: encoderpro"
echo " - Image: ${REGISTRY}/${IMAGE_NAME}:${IMAGE_TAG}"
echo " - CPU Workers: $MAX_WORKERS concurrent encodes"
echo " - CPU Utilization: Up to $CPU_LIMIT cores"
echo " - Memory Limit: $MEMORY_LIMIT"
echo ""
echo "Access Points:"
echo " - Dashboard: http://$(hostname -I | awk '{print $1}'):5000"
echo " - Logs: docker logs -f encoderpro"
echo " - Log File: $APP_DIR/logs/encoderpro.log"
echo ""
echo "Useful Commands:"
echo " - View stats: docker exec encoderpro python3 /app/reencode.py -c /config/config.yaml --stats"
echo " - Scan library: docker exec encoderpro python3 /app/reencode.py -c /config/config.yaml --scan-only"
echo " - Start encode: docker exec encoderpro python3 /app/reencode.py -c /config/config.yaml"
echo " - View logs: tail -f $APP_DIR/logs/encoderpro.log"
echo ""
echo "Container Status:"
docker ps -f name=encoderpro --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}"
echo ""
echo "=========================================="
echo "Deployment complete! 🚀"
echo "=========================================="

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version: '3.8'
services:
# Web Dashboard Service
dashboard:
image: encoderpro:latest
container_name: encoderpro-dashboard
# GPU support (NVIDIA)
runtime: nvidia
environment:
- NVIDIA_VISIBLE_DEVICES=all
- NVIDIA_DRIVER_CAPABILITIES=compute,video,utility
- DASHBOARD_HOST=0.0.0.0
- DASHBOARD_PORT=5000
# Volume mounts
volumes:
# Media directories
- /mnt/user/movies:/movies:ro
- /mnt/user/archive/movies:/archive:ro
- /mnt/user/temp/encoderpro-work:/work
# Configuration and data
- ./config.yaml:/config/config.yaml:ro
- ./data/db:/db
- ./data/logs:/logs
# Ports
ports:
- "5000:5000"
# Resource limits
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
limits:
cpus: '2'
memory: 4G
# Restart policy
restart: unless-stopped
# Run dashboard by default
command: ["dashboard"]
# Logging
logging:
driver: "json-file"
options:
max-size: "10m"
max-file: "3"
# Health check
healthcheck:
test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:5000/api/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 10s
# One-shot Processing Service (for cron/scheduled runs)
processor:
image: encoderpro:latest
container_name: encoderpro-processor
profiles: ["process"] # Enable with: docker-compose --profile process up
runtime: nvidia
environment:
- NVIDIA_VISIBLE_DEVICES=all
- NVIDIA_DRIVER_CAPABILITIES=compute,video,utility
volumes:
- /mnt/user/movies:/movies
- /mnt/user/archive/movies:/archive
- /mnt/user/temp/encoderpro-work:/work
- ./config.yaml:/config/config.yaml:ro
- ./data/db:/db
- ./data/logs:/logs
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
limits:
cpus: '8'
memory: 16G
# Run once and exit
command: ["process"]
logging:
driver: "json-file"
options:
max-size: "10m"
max-file: "3"
# Intel Arc GPU Dashboard
dashboard-intel:
image: encoderpro-intel:latest
container_name: encoderpro-dashboard-intel
profiles: ["intel"] # Enable with: docker-compose --profile intel up
environment:
- DASHBOARD_HOST=0.0.0.0
- DASHBOARD_PORT=5000
- GPU_TYPE=intel
# Intel GPU device access
devices:
- /dev/dri:/dev/dri
volumes:
- /mnt/user/movies:/movies:ro
- /mnt/user/archive/movies:/archive:ro
- /mnt/user/temp/encoderpro-work:/work
- ./config-intel.yaml:/config/config.yaml:ro
- ./data/db:/db
- ./data/logs:/logs
ports:
- "5000:5000"
deploy:
resources:
limits:
cpus: '2'
memory: 4G
restart: unless-stopped
command: ["dashboard"]
logging:
driver: "json-file"
options:
max-size: "10m"
max-file: "3"
healthcheck:
test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:5000/api/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 10s
# Intel Arc Processor (for scheduled runs)
processor-intel:
image: encoderpro-intel:latest
container_name: encoderpro-processor-intel
profiles: ["intel-process"]
environment:
- GPU_TYPE=intel
devices:
- /dev/dri:/dev/dri
volumes:
- /mnt/user/movies:/movies
- /mnt/user/archive/movies:/archive
- /mnt/user/temp/encoderpro-work:/work
- ./config-intel.yaml:/config/config.yaml:ro
- ./data/db:/db
- ./data/logs:/logs
deploy:
resources:
limits:
cpus: '8'
memory: 16G
command: ["process"]
logging:
driver: "json-file"
options:
max-size: "10m"
max-file: "3"
# CPU-only Dashboard (no GPU)
dashboard-cpu:
image: encoderpro:latest
container_name: encoderpro-dashboard-cpu
profiles: ["cpu"] # Enable with: docker-compose --profile cpu up
environment:
- DASHBOARD_HOST=0.0.0.0
- DASHBOARD_PORT=5000
volumes:
- /mnt/user/movies:/movies:ro
- /mnt/user/archive/movies:/archive:ro
- /mnt/user/temp/encoderpro-work:/work
- ./config-cpu.yaml:/config/config.yaml:ro
- ./data/db:/db
- ./data/logs:/logs
ports:
- "5000:5000"
deploy:
resources:
limits:
cpus: '2'
memory: 4G
restart: unless-stopped
command: ["dashboard"]
logging:
driver: "json-file"
options:
max-size: "10m"
max-file: "3"

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#!/bin/bash
set -e
# Docker entrypoint script for encoderPro container
# Supports multiple modes: dashboard, process, or custom commands
MODE="${1:-dashboard}"
case "$MODE" in
dashboard)
echo "Starting Web Dashboard..."
exec python3 /app/dashboard.py
;;
process)
echo "Starting Encoding Process..."
shift
exec python3 /app/reencode.py -c /config/config.yaml "$@"
;;
scan)
echo "Scanning Library..."
exec python3 /app/reencode.py -c /config/config.yaml --scan-only
;;
stats)
echo "Displaying Statistics..."
exec python3 /app/reencode.py -c /config/config.yaml --stats
;;
bash|shell)
echo "Starting Interactive Shell..."
exec /bin/bash
;;
*)
echo "Running custom command: $@"
exec "$@"
;;
esac

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/usr/bin/bash: line 1: ffmpeg: command not found

271
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#!/usr/bin/env python3
"""
Example Usage: Quality Checker Module
======================================
Demonstrates how to use the quality_checker module to analyze video quality
and detect potential quality degradation before encoding.
"""
import logging
import sys
from pathlib import Path
from quality_checker import QualityChecker
# Setup logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
def example_analyze_quality(video_path: Path):
"""Example: Analyze video quality"""
print("\n" + "="*80)
print("EXAMPLE 1: Analyze Video Quality")
print("="*80)
checker = QualityChecker(logger)
quality = checker.analyze_quality(video_path)
if quality:
print(f"\nVideo Quality Analysis for: {video_path.name}")
print("-" * 80)
print(f" Resolution: {quality.resolution[0]}x{quality.resolution[1]}")
print(f" Bitrate: {quality.bitrate/1000000:.2f} Mbps")
print(f" Codec: {quality.codec}")
print(f" FPS: {quality.fps:.2f}")
print(f" HDR: {'Yes' if quality.is_hdr else 'No'}")
print(f" Quality Score: {quality.quality_score}/100")
print()
# Quality interpretation
if quality.quality_score >= 95:
print(" Rating: ⭐⭐⭐⭐⭐ Near-lossless / Exceptional")
elif quality.quality_score >= 85:
print(" Rating: ⭐⭐⭐⭐ Excellent / Archival quality")
elif quality.quality_score >= 70:
print(" Rating: ⭐⭐⭐ Good / Transparent quality")
elif quality.quality_score >= 50:
print(" Rating: ⭐⭐ Acceptable / Visible compression")
else:
print(" Rating: ⭐ Poor / Heavy compression artifacts")
else:
print(f"❌ Failed to analyze {video_path}")
def example_check_degradation(video_path: Path):
"""Example: Check if encoding will degrade quality"""
print("\n" + "="*80)
print("EXAMPLE 2: Check Quality Degradation")
print("="*80)
checker = QualityChecker(logger)
quality = checker.analyze_quality(video_path)
if not quality:
print(f"❌ Failed to analyze {video_path}")
return
# Example encoding profiles
profiles = [
{
'name': 'Sweet Spot (CRF 21)',
'encoder': 'nvidia_nvenc_h265',
'quality': 21,
'bitrate': None # Will be estimated
},
{
'name': 'Balanced (CRF 23)',
'encoder': 'nvidia_nvenc_h265',
'quality': 23,
'bitrate': None
},
{
'name': 'Fast (CRF 26)',
'encoder': 'nvidia_nvenc_h264',
'quality': 26,
'bitrate': None
}
]
print(f"\nQuality Degradation Check for: {video_path.name}")
print(f"Source Quality Score: {quality.quality_score}/100")
print("-" * 80)
for profile in profiles:
# Estimate target bitrate based on profile
target_bitrate = checker.estimate_target_bitrate(
profile,
quality.resolution,
quality.fps
)
# Check if encoding will degrade quality
will_degrade, reason = checker.will_degrade_quality(
quality,
target_bitrate,
profile['encoder'],
threshold=10.0
)
target_score = checker._calculate_quality_score(
target_bitrate,
quality.resolution[0],
quality.resolution[1],
profile['encoder'],
quality.fps
)
status = "⚠️ WARNING" if will_degrade else "✅ OK"
print(f"\n{status} - {profile['name']}")
print(f" Target Bitrate: {target_bitrate/1000000:.2f} Mbps")
print(f" Target Score: {target_score:.1f}/100")
print(f" Quality Drop: {quality.quality_score - target_score:.1f} points")
if will_degrade:
print(f" {reason}")
def example_comprehensive_check(video_path: Path):
"""Example: Comprehensive pre-encode quality check"""
print("\n" + "="*80)
print("EXAMPLE 3: Comprehensive Pre-Encode Check")
print("="*80)
checker = QualityChecker(logger)
# Define encoding profile
profile = {
'encoder': 'nvidia_nvenc_h265',
'quality': 21, # CRF value
'hdr_handling': 'preserve'
}
# Perform comprehensive check
result = checker.check_before_encode(
video_path,
profile,
warn_threshold=10.0,
error_threshold=20.0
)
print(f"\nPre-Encode Quality Check for: {video_path.name}")
print("-" * 80)
if result['source_quality']:
q = result['source_quality']
print(f"Source Quality:")
print(f" Resolution: {q.resolution[0]}x{q.resolution[1]}")
print(f" Bitrate: {q.bitrate/1000000:.2f} Mbps")
print(f" Quality Score: {q.quality_score}/100")
print(f" HDR: {'Yes' if q.is_hdr else 'No'}")
print()
print(f"Target Settings:")
print(f" Encoder: {profile['encoder']}")
print(f" CRF: {profile['quality']}")
print(f" Est. Bitrate: {result['estimated_target_bitrate']/1000000:.2f} Mbps")
print()
if result['ok']:
print("✅ Status: OK - Encoding can proceed")
elif result['error']:
print("❌ Status: ERROR - Quality degradation too severe")
print(f" Quality drop: {result['quality_drop']:.1f} points")
elif result['warning']:
print("⚠️ Status: WARNING - Quality may be degraded")
print(f" Quality drop: {result['quality_drop']:.1f} points")
if result['message']:
print(f"\n{result['message']}")
def example_batch_analysis(directory: Path):
"""Example: Batch analyze multiple videos"""
print("\n" + "="*80)
print("EXAMPLE 4: Batch Video Analysis")
print("="*80)
checker = QualityChecker(logger)
# Find all video files
video_extensions = {'.mkv', '.mp4', '.avi', '.m4v', '.ts', '.m2ts'}
video_files = [
f for f in directory.rglob('*')
if f.suffix.lower() in video_extensions
]
if not video_files:
print(f"No video files found in {directory}")
return
print(f"\nAnalyzing {len(video_files)} video files...")
print("-" * 80)
results = []
for video_file in video_files[:10]: # Limit to first 10 for demo
quality = checker.analyze_quality(video_file)
if quality:
results.append({
'file': video_file.name,
'quality': quality
})
# Sort by quality score
results.sort(key=lambda x: x['quality'].quality_score, reverse=True)
print(f"\n{'File':<40} {'Resolution':<12} {'Bitrate':>10} {'Score':>6}")
print("-" * 80)
for r in results:
q = r['quality']
print(
f"{r['file'][:40]:<40} "
f"{q.resolution[0]}x{q.resolution[1]:<12} "
f"{q.bitrate/1000000:>8.1f} M "
f"{q.quality_score:>6.1f}"
)
# Statistics
if results:
avg_score = sum(r['quality'].quality_score for r in results) / len(results)
avg_bitrate = sum(r['quality'].bitrate for r in results) / len(results)
print("-" * 80)
print(f"{'Average':<40} {'':<12} {avg_bitrate/1000000:>8.1f} M {avg_score:>6.1f}")
def main():
if len(sys.argv) < 2:
print("Usage: python3 example_quality_check.py <video_file_or_directory>")
print()
print("Examples:")
print(" # Analyze single video")
print(" python3 example_quality_check.py /movies/example.mkv")
print()
print(" # Batch analyze directory")
print(" python3 example_quality_check.py /movies/")
sys.exit(1)
path = Path(sys.argv[1])
if not path.exists():
print(f"❌ Error: {path} does not exist")
sys.exit(1)
if path.is_file():
# Single file analysis
example_analyze_quality(path)
example_check_degradation(path)
example_comprehensive_check(path)
elif path.is_dir():
# Batch analysis
example_batch_analysis(path)
else:
print(f"❌ Error: {path} is neither a file nor directory")
sys.exit(1)
if __name__ == '__main__':
main()

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#!/usr/bin/env python3
"""
Initialize encoderPro Database
================================
Creates the database schema if it doesn't exist.
Run this before starting the dashboard if the database doesn't exist.
"""
import os
import sqlite3
from pathlib import Path
def init_database(db_path: Path):
"""Initialize database with schema"""
print(f"Initializing database at: {db_path}")
# Create directory if needed
db_path.parent.mkdir(parents=True, exist_ok=True)
# Connect to database (creates file if doesn't exist)
conn = sqlite3.connect(str(db_path))
cursor = conn.cursor()
# Create files table
cursor.execute("""
CREATE TABLE IF NOT EXISTS files (
id INTEGER PRIMARY KEY AUTOINCREMENT,
filepath TEXT UNIQUE NOT NULL,
relative_path TEXT NOT NULL,
state TEXT NOT NULL,
has_subtitles BOOLEAN,
original_size INTEGER,
encoded_size INTEGER,
subtitle_count INTEGER,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
started_at TIMESTAMP,
completed_at TIMESTAMP,
error_message TEXT,
profile_name TEXT,
encoder_used TEXT,
encode_time_seconds REAL,
fps REAL
)
""")
# Create processing_history table
cursor.execute("""
CREATE TABLE IF NOT EXISTS processing_history (
id INTEGER PRIMARY KEY AUTOINCREMENT,
file_id INTEGER NOT NULL,
profile_name TEXT,
encoder_used TEXT,
started_at TIMESTAMP,
completed_at TIMESTAMP,
success BOOLEAN,
error_message TEXT,
original_size INTEGER,
encoded_size INTEGER,
encode_time_seconds REAL,
fps REAL,
FOREIGN KEY (file_id) REFERENCES files (id)
)
""")
# Create indices
cursor.execute("CREATE INDEX IF NOT EXISTS idx_state ON files(state)")
cursor.execute("CREATE INDEX IF NOT EXISTS idx_filepath ON files(filepath)")
cursor.execute("CREATE INDEX IF NOT EXISTS idx_profile ON files(profile_name)")
conn.commit()
conn.close()
print(f"✅ Database initialized successfully at: {db_path}")
def main():
"""Main entry point"""
# Get database path from environment or use default
db_path = Path(os.getenv('STATE_DB', '/db/state.db'))
if db_path.exists():
print(f"Database already exists at: {db_path}")
print("Run 'python3 reencode-v3.py -c config.yaml --scan-only' to populate it.")
else:
init_database(db_path)
print()
print("Next steps:")
print("1. Run: python3 reencode-v3.py -c /config/config.yaml --scan-only")
print("2. Start dashboard: python3 dashboard.py")
if __name__ == '__main__':
main()

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install-r730.sh Normal file
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#!/bin/bash
################################################################################
# encoderPro One-Liner Installer for Dell R730
# Usage: curl -fsSL https://your-git/encoderpro/raw/main/install-r730.sh | bash
################################################################################
# Configuration
REGISTRY="${ENCODERPRO_REGISTRY:-your-registry.com}"
GIT_REPO="${ENCODERPRO_GIT:-https://your-private-git/encoderpro.git}"
echo "Installing encoderPro for Dell R730..."
# Clone repository
cd /mnt/user/appdata
git clone "$GIT_REPO" encoderpro
cd encoderpro
# Make deploy script executable
chmod +x deploy-r730.sh
# Run deployment
./deploy-r730.sh

89
logs/reencode.log Normal file
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2025-12-24 12:15:12 - ReencodeApp - INFO - ============================================================
2025-12-24 12:15:12 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:15:12 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:15:12 - ReencodeApp - INFO - ============================================================
2025-12-24 12:16:59 - ReencodeApp - INFO - ============================================================
2025-12-24 12:16:59 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:16:59 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:16:59 - ReencodeApp - INFO - ============================================================
2025-12-24 12:31:12 - ReencodeApp - INFO - ============================================================
2025-12-24 12:31:12 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:31:12 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:31:12 - ReencodeApp - INFO - ============================================================
2025-12-24 12:35:29 - ReencodeApp - INFO - ============================================================
2025-12-24 12:35:29 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:35:29 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:35:29 - ReencodeApp - INFO - ============================================================
2025-12-24 12:35:29 - ReencodeApp - INFO - Cleaning up stale work files...
2025-12-24 12:35:29 - LibraryScanner - INFO - Scanning: C:\Users\ckoch\Videos\test-movies
2025-12-24 12:35:29 - LibraryScanner - INFO - Scan complete: 2 files found, 2 added/updated
2025-12-24 12:35:29 - ReencodeApp - INFO - Scan-only mode: exiting
2025-12-24 12:39:23 - ReencodeApp - INFO - ============================================================
2025-12-24 12:39:23 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:39:23 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:39:23 - ReencodeApp - INFO - ============================================================
2025-12-24 12:39:23 - ReencodeApp - INFO - Cleaning up stale work files...
2025-12-24 12:39:23 - LibraryScanner - INFO - Scanning: C:\Users\ckoch\Videos\test-movies
2025-12-24 12:39:23 - LibraryScanner - INFO - Scan complete: 3 files found, 1 added/updated
2025-12-24 12:39:23 - ReencodeApp - INFO - Scan-only mode: exiting
2025-12-24 12:40:45 - ReencodeApp - INFO - ============================================================
2025-12-24 12:40:45 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:40:45 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:40:45 - ReencodeApp - INFO - ============================================================
2025-12-24 12:40:45 - ReencodeApp - INFO - Cleaning up stale work files...
2025-12-24 12:40:45 - LibraryScanner - INFO - Scanning: C:\Users\ckoch\Videos\test-movies
2025-12-24 12:40:45 - LibraryScanner - INFO - Scan complete: 4 files found, 1 added/updated
2025-12-24 12:40:45 - ReencodeApp - INFO - Scan-only mode: exiting
2025-12-24 12:41:10 - ReencodeApp - INFO - ============================================================
2025-12-24 12:41:10 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:41:10 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:41:10 - ReencodeApp - INFO - ============================================================
2025-12-24 12:41:10 - ReencodeApp - INFO - Cleaning up stale work files...
2025-12-24 12:41:10 - LibraryScanner - INFO - Scanning: C:\Users\ckoch\Videos\test-movies
2025-12-24 12:41:10 - LibraryScanner - INFO - Scan complete: 4 files found, 1 added/updated
2025-12-24 12:41:10 - ReencodeApp - INFO - No files to process!
2025-12-24 12:46:35 - ReencodeApp - INFO - ============================================================
2025-12-24 12:46:35 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:46:35 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:46:35 - ReencodeApp - INFO - ============================================================
2025-12-24 12:46:35 - ReencodeApp - INFO - Cleaning up stale work files...
2025-12-24 12:46:35 - LibraryScanner - INFO - Scanning: C:\Users\ckoch\Videos\test-movies
2025-12-24 12:46:35 - LibraryScanner - INFO - Scan complete: 4 files found, 1 added/updated
2025-12-24 12:46:35 - ReencodeApp - INFO - No files to process!
2025-12-24 12:48:06 - ReencodeApp - INFO - ============================================================
2025-12-24 12:48:06 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:48:06 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:48:06 - ReencodeApp - INFO - ============================================================
2025-12-24 12:48:06 - ReencodeApp - INFO - Cleaning up stale work files...
2025-12-24 12:48:06 - LibraryScanner - INFO - Scanning: C:\Users\ckoch\Videos\test-movies
2025-12-24 12:48:06 - LibraryScanner - INFO - Scan complete: 4 files found, 1 added/updated
2025-12-24 12:48:06 - ReencodeApp - INFO - No files to process!
2025-12-24 12:52:46 - ReencodeApp - INFO - ============================================================
2025-12-24 12:52:46 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:52:46 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:52:46 - ReencodeApp - INFO - ============================================================
2025-12-24 12:52:46 - ReencodeApp - INFO - Cleaning up stale work files...
2025-12-24 12:52:46 - LibraryScanner - INFO - Scanning: C:\Users\ckoch\Videos\test-movies
2025-12-24 12:52:46 - LibraryScanner - INFO - Scan complete: 4 files found, 1 added/updated
2025-12-24 12:52:46 - ReencodeApp - INFO - No files to process!
2025-12-24 12:56:08 - ReencodeApp - INFO - ============================================================
2025-12-24 12:56:08 - ReencodeApp - INFO - ENCODERPRO - PHASE 3
2025-12-24 12:56:08 - ReencodeApp - INFO - Version: 3.0.0
2025-12-24 12:56:08 - ReencodeApp - INFO - ============================================================
2025-12-24 12:56:08 - ReencodeApp - INFO - Cleaning up stale work files...
2025-12-24 12:56:08 - ReencodeApp - INFO - Skipping library scan (--no-scan mode)
2025-12-24 12:56:08 - ReencodeApp - INFO - Processing 1 file(s)...
2025-12-24 12:56:08 - FileProcessor-2139710071072 - INFO - Processing: Pitch Perfect 3.mp4
2025-12-24 12:56:08 - ReencodeApp - ERROR - Fatal error: Encoder INTEL_QSV_H265 not available and no fallback found
Traceback (most recent call last):
File "C:\Users\ckoch\OneDrive\Documents\development\encoderPro\reencode.py", line 1201, in main
app.run(scan_only=args.scan_only, no_scan=args.no_scan, profile_name=args.profile)
~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ckoch\OneDrive\Documents\development\encoderPro\reencode.py", line 1131, in run
processor.process_file(Path(file_record['filepath']), profile_name)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ckoch\OneDrive\Documents\development\encoderPro\reencode.py", line 579, in process_file
encoder = self._select_encoder(profile.encoder)
File "C:\Users\ckoch\OneDrive\Documents\development\encoderPro\reencode.py", line 676, in _select_encoder
raise RuntimeError(f"Encoder {preferred.name} not available and no fallback found")
RuntimeError: Encoder INTEL_QSV_H265 not available and no fallback found

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// PAGINATION REPLACEMENT CODE FOR dashboard.html
// Replace lines 1439-1625 with this code
// File Quality Analysis with Pagination
let selectedFiles = new Set();
let currentAttributeFilter = null; // Track the current attribute filter
let currentStatusFilter = 'all'; // Track the current status filter
let currentPage = 1;
let totalPages = 1;
let filesPerPage = 100;
let totalFiles = 0;
async function loadFileQuality() {
const tbody = document.getElementById('qualityTableBody');
// Clear selections when reloading (not when paginating)
// selectedFiles stays intact during pagination
try {
// Calculate offset from current page
const offset = (currentPage - 1) * filesPerPage;
// Build URL with state filter and attribute filter
let url = `/api/files?limit=${filesPerPage}&offset=${offset}`;
if (currentStatusFilter !== 'all') {
url += `&state=${currentStatusFilter}`;
}
if (currentAttributeFilter && currentAttributeFilter !== 'all') {
url += `&filter=${currentAttributeFilter}`;
}
const response = await fetch(url);
const result = await response.json();
if (result.success) {
const files = result.data;
let html = '';
if (files.length === 0 && currentPage === 1) {
html = '<tr><td colspan="8" style="text-align: center; padding: 40px; color: #64748b;">No files found</td></tr>';
} else {
files.forEach(file => {
const savings = file.original_size && file.encoded_size ?
((file.original_size - file.encoded_size) / file.original_size * 100).toFixed(1) : '-';
const stateBadgeColors = {
'discovered': '#8b5cf6',
'pending': '#fbbf24',
'processing': '#3b82f6',
'completed': '#10b981',
'failed': '#ef4444',
'skipped': '#94a3b8'
};
const statusIcon = file.state === 'completed' ? '✅' :
file.state === 'failed' ? '❌' :
file.state === 'processing' ? '⏳' :
file.state === 'skipped' ? '⏭️' : '⏸️';
const disableCheckbox = file.state === 'processing' ? 'disabled' : '';
// Create tooltip for state badge
let stateTooltip = '';
if (file.state === 'discovered') {
stateTooltip = 'Discovered - select to encode';
} else if (file.state === 'pending') {
stateTooltip = 'Selected - will encode when you click Encode Selected';
} else if (file.state === 'processing') {
stateTooltip = 'Currently encoding...';
} else if (file.state === 'completed' && file.encoder_used) {
stateTooltip = `Completed with ${file.encoder_used}`;
} else if (file.state === 'failed' && file.error_message) {
stateTooltip = `Failed: ${file.error_message}`;
} else if (file.state === 'skipped' && file.error_message) {
stateTooltip = `Skipped: ${file.error_message}`;
}
const rowClass = file.state === 'processing' ? 'row-processing' : '';
// Escape all user-controlled content
const escapedPath = escapeHtml(file.relative_path);
const escapedPathAttr = escapeAttr(file.relative_path);
const escapedState = escapeHtml(file.state);
const escapedTooltip = escapeAttr(stateTooltip);
// Check if this file is selected
const isChecked = selectedFiles.has(file.id) ? 'checked' : '';
html += `
<tr class="${rowClass}" data-file-id="${file.id}" style="border-bottom: 1px solid #334155; transition: all 0.2s ease;">
<td style="padding: 12px; text-align: center;">
<input type="checkbox" class="file-checkbox" data-file-id="${file.id}"
onchange="toggleFileSelection(${file.id})"
${disableCheckbox} ${isChecked}
style="cursor: ${file.state === 'processing' ? 'not-allowed' : 'pointer'}; transform: scale(1.2);">
</td>
<td style="padding: 12px; max-width: 400px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap;" title="${escapedPathAttr}">
<span style="font-weight: 500;">${escapedPath}</span>
</td>
<td style="padding: 12px;">
<span style="background: ${stateBadgeColors[file.state] || '#64748b'}; padding: 6px 12px; border-radius: 6px; font-size: 0.85em; cursor: help; font-weight: 600;"
title="${escapedTooltip}">
${escapedState}
</span>
</td>
<td style="padding: 12px; color: #94a3b8;">-</td>
<td style="padding: 12px; font-weight: 500;">${file.original_size ? formatBytes(file.original_size) : '-'}</td>
<td style="padding: 12px; font-weight: 500;">${file.encoded_size ? formatBytes(file.encoded_size) : '-'}</td>
<td style="padding: 12px; font-weight: 600; color: ${savings !== '-' && parseFloat(savings) > 0 ? '#10b981' : '#64748b'};">
${savings !== '-' ? escapeHtml(savings) + '%' : '-'}
</td>
<td style="padding: 12px; font-size: 1.2em;">${statusIcon}</td>
</tr>
`;
});
}
tbody.innerHTML = html;
// Update pagination controls
// Calculate total pages (estimate based on if we got less than limit)
if (files.length < filesPerPage) {
totalPages = currentPage;
} else {
// We don't know total, just enable next
totalPages = currentPage + 1;
}
updatePaginationControls();
document.getElementById('selectAll').checked = false;
}
} catch (error) {
console.error('Failed to load file quality:', error);
tbody.innerHTML = '<tr><td colspan="8" style="text-align: center; padding: 40px; color: #ef4444;">Error loading files</td></tr>';
}
}
function updatePaginationControls() {
const paginationContainer = document.getElementById('paginationControls');
if (!paginationContainer) return;
const startFile = (currentPage - 1) * filesPerPage + 1;
const endFile = currentPage * filesPerPage;
let html = `
<div style="display: flex; align-items: center; justify-content: center; gap: 15px; padding: 20px;">
<button class="btn" onclick="goToPage(${currentPage - 1})" ${currentPage === 1 ? 'disabled' : ''}
style="background: #3b82f6; color: white; ${currentPage === 1 ? 'opacity: 0.5; cursor: not-allowed;' : ''}">
← Previous
</button>
<div style="color: #94a3b8; font-weight: 500;">
Page ${currentPage} | Showing ${startFile}-${endFile}
</div>
<button class="btn" onclick="goToPage(${currentPage + 1})"
style="background: #3b82f6; color: white;">
Next →
</button>
<div style="display: flex; align-items: center; gap: 8px;">
<label style="color: #94a3b8;">Go to page:</label>
<input type="number" id="pageInput" min="1" value="${currentPage}"
style="width: 80px; padding: 8px; border-radius: 6px; background: #1e293b; color: #e2e8f0; border: 1px solid #334155; text-align: center;"
onkeypress="if(event.key==='Enter') goToPageInput()">
<button class="btn" onclick="goToPageInput()" style="background: #10b981; color: white;">Go</button>
</div>
</div>
`;
paginationContainer.innerHTML = html;
}
function goToPage(page) {
if (page < 1) return;
currentPage = page;
loadFileQuality();
}
function goToPageInput() {
const input = document.getElementById('pageInput');
const page = parseInt(input.value);
if (page && page > 0) {
goToPage(page);
}
}
function changeStatusFilter(status) {
currentStatusFilter = status;
currentPage = 1; // Reset to first page
selectedFiles.clear(); // Clear selections when changing filter
loadFileQuality();
}
function toggleFileSelection(fileId) {
if (selectedFiles.has(fileId)) {
selectedFiles.delete(fileId);
} else {
selectedFiles.add(fileId);
}
updateSelectedCount();
}

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quality_checker.py Normal file
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#!/usr/bin/env python3
"""
Quality Checker Module for encoderPro
======================================
Detects source video quality and warns if encoding will degrade quality.
"""
import json
import logging
import re
import subprocess
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Dict, Tuple
@dataclass
class VideoQuality:
"""Video quality metrics"""
bitrate: int # bits per second
resolution: Tuple[int, int] # (width, height)
codec: str
fps: float
is_hdr: bool
quality_score: float # 0-100, estimated quality
def to_dict(self) -> Dict:
return {
'bitrate': self.bitrate,
'resolution': f"{self.resolution[0]}x{self.resolution[1]}",
'codec': self.codec,
'fps': self.fps,
'is_hdr': self.is_hdr,
'quality_score': self.quality_score
}
class QualityChecker:
"""Analyzes video quality before encoding"""
def __init__(self, logger: Optional[logging.Logger] = None):
self.logger = logger or logging.getLogger(__name__)
def get_video_info(self, filepath: Path) -> Optional[Dict]:
"""Extract detailed video information using ffprobe"""
try:
cmd = [
'ffprobe',
'-v', 'quiet',
'-print_format', 'json',
'-show_format',
'-show_streams',
'-select_streams', 'v:0',
str(filepath)
]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
if result.returncode != 0:
self.logger.error(f"ffprobe failed for {filepath}: {result.stderr}")
return None
data = json.loads(result.stdout)
return data
except subprocess.TimeoutExpired:
self.logger.error(f"ffprobe timeout for {filepath}")
return None
except json.JSONDecodeError as e:
self.logger.error(f"Failed to parse ffprobe output: {e}")
return None
except Exception as e:
self.logger.error(f"Error getting video info: {e}")
return None
def analyze_quality(self, filepath: Path) -> Optional[VideoQuality]:
"""Analyze video quality metrics"""
info = self.get_video_info(filepath)
if not info:
return None
try:
# Get video stream
video_stream = None
if 'streams' in info and len(info['streams']) > 0:
video_stream = info['streams'][0]
if not video_stream:
self.logger.error(f"No video stream found in {filepath}")
return None
# Extract metrics
width = int(video_stream.get('width', 0))
height = int(video_stream.get('height', 0))
codec = video_stream.get('codec_name', 'unknown')
# Get bitrate
bitrate = 0
if 'bit_rate' in video_stream:
bitrate = int(video_stream['bit_rate'])
elif 'format' in info and 'bit_rate' in info['format']:
bitrate = int(info['format']['bit_rate'])
# Get FPS
fps_str = video_stream.get('r_frame_rate', '0/1')
try:
num, den = fps_str.split('/')
fps = float(num) / float(den) if float(den) != 0 else 0
except:
fps = 0
# Detect HDR
is_hdr = self._detect_hdr(video_stream)
# Calculate quality score
quality_score = self._calculate_quality_score(
bitrate, width, height, codec, fps
)
return VideoQuality(
bitrate=bitrate,
resolution=(width, height),
codec=codec,
fps=fps,
is_hdr=is_hdr,
quality_score=quality_score
)
except Exception as e:
self.logger.error(f"Error analyzing quality: {e}")
return None
def _detect_hdr(self, stream: Dict) -> bool:
"""Detect if video has HDR"""
# Check for HDR transfer characteristics
transfer = stream.get('color_transfer', '').lower()
if 'smpte2084' in transfer or 'arib-std-b67' in transfer:
return True
# Check for HDR color primaries
primaries = stream.get('color_primaries', '').lower()
if 'bt2020' in primaries:
return True
# Check tags
tags = stream.get('tags', {})
if any('hdr' in str(v).lower() for v in tags.values()):
return True
return False
def _calculate_quality_score(self, bitrate: int, width: int, height: int,
codec: str, fps: float) -> float:
"""
Calculate a quality score (0-100) based on video metrics
This is a heuristic score based on:
- Bitrate per pixel
- Resolution
- Codec efficiency
- Frame rate
"""
if width == 0 or height == 0:
return 0
pixels = width * height
# Bitrate per pixel per frame
if fps > 0:
bits_per_pixel = bitrate / (pixels * fps)
else:
bits_per_pixel = bitrate / pixels
# Codec efficiency multiplier
codec_multiplier = {
'hevc': 1.5, # H.265 is more efficient
'h265': 1.5,
'av1': 1.8, # AV1 is very efficient
'h264': 1.0, # Baseline
'avc': 1.0,
'mpeg2': 0.5, # Less efficient
'mpeg4': 0.7,
}.get(codec.lower(), 1.0)
# Effective bits per pixel (adjusted for codec)
effective_bpp = bits_per_pixel * codec_multiplier
# Quality score based on bits per pixel
# Typical ranges:
# < 0.1 bpp: Poor quality (score < 50)
# 0.1-0.2 bpp: Acceptable (score 50-70)
# 0.2-0.3 bpp: Good (score 70-85)
# 0.3-0.5 bpp: Excellent (score 85-95)
# > 0.5 bpp: Near lossless (score 95-100)
if effective_bpp >= 0.5:
score = 95 + min(effective_bpp * 10, 5)
elif effective_bpp >= 0.3:
score = 85 + (effective_bpp - 0.3) * 50
elif effective_bpp >= 0.2:
score = 70 + (effective_bpp - 0.2) * 150
elif effective_bpp >= 0.1:
score = 50 + (effective_bpp - 0.1) * 200
else:
score = min(effective_bpp * 500, 50)
return min(round(score, 1), 100)
def will_degrade_quality(self, source_quality: VideoQuality,
target_bitrate: int,
target_codec: str,
threshold: float = 10.0) -> Tuple[bool, str]:
"""
Check if encoding will significantly degrade quality
Args:
source_quality: Source video quality metrics
target_bitrate: Target encoding bitrate
target_codec: Target codec
threshold: Quality score drop threshold (default 10 points)
Returns:
(will_degrade: bool, reason: str)
"""
# Calculate target quality score
target_quality_score = self._calculate_quality_score(
target_bitrate,
source_quality.resolution[0],
source_quality.resolution[1],
target_codec,
source_quality.fps
)
quality_drop = source_quality.quality_score - target_quality_score
if quality_drop > threshold:
reason = (
f"Encoding will degrade quality by {quality_drop:.1f} points "
f"(from {source_quality.quality_score:.1f} to {target_quality_score:.1f}). "
f"Source bitrate: {source_quality.bitrate/1000000:.1f} Mbps, "
f"Target bitrate: {target_bitrate/1000000:.1f} Mbps"
)
return True, reason
return False, ""
def estimate_target_bitrate(self, profile: Dict, resolution: Tuple[int, int],
fps: float) -> int:
"""
Estimate target bitrate based on profile settings
This uses common CRF-to-bitrate approximations for different codecs
"""
width, height = resolution
pixels = width * height
crf = profile.get('quality', 23)
codec = profile.get('encoder', 'cpu_x265')
# Determine codec type
if 'x265' in codec or 'h265' in codec or 'hevc' in codec:
codec_type = 'h265'
elif 'av1' in codec:
codec_type = 'av1'
else:
codec_type = 'h264'
# Base bitrate estimation (Mbps per megapixel)
# These are approximations for CRF encoding
base_bitrates = {
'h264': {
18: 0.20, # Near lossless
21: 0.15,
23: 0.10, # Good quality
26: 0.06,
28: 0.04
},
'h265': {
18: 0.10, # H.265 is ~50% more efficient
21: 0.075,
23: 0.05,
26: 0.03,
28: 0.02
},
'av1': {
18: 0.07, # AV1 is ~70% more efficient
21: 0.05,
23: 0.035,
26: 0.02,
28: 0.015
}
}
# Get closest CRF value
crf_values = sorted(base_bitrates[codec_type].keys())
closest_crf = min(crf_values, key=lambda x: abs(x - crf))
mbps_per_megapixel = base_bitrates[codec_type][closest_crf]
# Calculate target bitrate
megapixels = pixels / 1000000
target_mbps = mbps_per_megapixel * megapixels * (fps / 24) # Normalize to 24fps
target_bitrate = int(target_mbps * 1000000) # Convert to bps
return target_bitrate
def check_before_encode(self, filepath: Path, profile: Dict,
warn_threshold: float = 10.0,
error_threshold: float = 20.0) -> Dict:
"""
Comprehensive quality check before encoding
Returns:
{
'ok': bool,
'warning': bool,
'error': bool,
'message': str,
'source_quality': VideoQuality,
'estimated_target_bitrate': int,
'quality_drop': float
}
"""
result = {
'ok': True,
'warning': False,
'error': False,
'message': '',
'source_quality': None,
'estimated_target_bitrate': 0,
'quality_drop': 0
}
# Analyze source quality
source_quality = self.analyze_quality(filepath)
if not source_quality:
result['ok'] = False
result['error'] = True
result['message'] = "Failed to analyze source video quality"
return result
result['source_quality'] = source_quality
# Estimate target bitrate
target_bitrate = self.estimate_target_bitrate(
profile,
source_quality.resolution,
source_quality.fps
)
result['estimated_target_bitrate'] = target_bitrate
# Check for quality degradation
target_codec = profile.get('encoder', 'cpu_x265')
will_degrade, reason = self.will_degrade_quality(
source_quality,
target_bitrate,
target_codec,
warn_threshold
)
if will_degrade:
quality_drop = source_quality.quality_score - self._calculate_quality_score(
target_bitrate,
source_quality.resolution[0],
source_quality.resolution[1],
target_codec,
source_quality.fps
)
result['quality_drop'] = quality_drop
if quality_drop >= error_threshold:
result['ok'] = False
result['error'] = True
result['message'] = f"⚠️ CRITICAL: {reason}"
else:
result['warning'] = True
result['message'] = f"⚠️ WARNING: {reason}"
# Check for HDR content
if source_quality.is_hdr and profile.get('hdr_handling') != 'preserve':
result['warning'] = True
result['message'] += "\n⚠️ HDR content detected but HDR handling is not set to 'preserve'"
return result
if __name__ == '__main__':
# Test module
import sys
logging.basicConfig(level=logging.INFO)
checker = QualityChecker()
if len(sys.argv) > 1:
filepath = Path(sys.argv[1])
quality = checker.analyze_quality(filepath)
if quality:
print(f"\nVideo Quality Analysis:")
print(f" Resolution: {quality.resolution[0]}x{quality.resolution[1]}")
print(f" Bitrate: {quality.bitrate/1000000:.2f} Mbps")
print(f" Codec: {quality.codec}")
print(f" FPS: {quality.fps:.2f}")
print(f" HDR: {quality.is_hdr}")
print(f" Quality Score: {quality.quality_score}/100")
else:
print("Usage: python quality_checker.py <video_file>")

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#!/bin/bash
################################################################################
# ENCODERPRO SCRIPT - PHASE 1
################################################################################
# Purpose: Remove subtitle streams from movie files and re-encode safely
# Features:
# - Recursive directory scanning
# - Safe file replacement using staging
# - Original file archiving
# - Restart-safe operation
# - Detailed logging
################################################################################
set -euo pipefail # Exit on error, undefined vars, pipe failures
################################################################################
# CONFIGURATION
################################################################################
# Directories (CUSTOMIZE THESE)
MOVIES_DIR="${MOVIES_DIR:-/mnt/user/movies}"
ARCHIVE_DIR="${ARCHIVE_DIR:-/mnt/user/archive/movies}"
WORK_DIR="${WORK_DIR:-/mnt/user/temp/encoderpro-work}"
LOG_FILE="${LOG_FILE:-/var/log/encoderpro-movies.log}"
# Encoding settings
VIDEO_CODEC="${VIDEO_CODEC:-libx265}"
VIDEO_PRESET="${VIDEO_PRESET:-medium}"
VIDEO_CRF="${VIDEO_CRF:-23}"
# File extensions to process
FILE_EXTENSIONS=("mkv" "mp4" "avi" "m4v")
# Dry run mode (set to 1 to test without encoding)
DRY_RUN="${DRY_RUN:-0}"
################################################################################
# LOGGING FUNCTIONS
################################################################################
log() {
local timestamp=$(date '+%Y-%m-%d %H:%M:%S')
echo "[$timestamp] $*" | tee -a "$LOG_FILE"
}
log_error() {
local timestamp=$(date '+%Y-%m-%d %H:%M:%S')
echo "[$timestamp] ERROR: $*" | tee -a "$LOG_FILE" >&2
}
log_success() {
local timestamp=$(date '+%Y-%m-%d %H:%M:%S')
echo "[$timestamp] SUCCESS: $*" | tee -a "$LOG_FILE"
}
################################################################################
# VALIDATION FUNCTIONS
################################################################################
validate_dependencies() {
local missing_deps=0
for cmd in ffmpeg ffprobe; do
if ! command -v "$cmd" &> /dev/null; then
log_error "Required command '$cmd' not found"
missing_deps=1
fi
done
if [[ $missing_deps -eq 1 ]]; then
log_error "Missing dependencies. Please install ffmpeg."
exit 1
fi
log "All dependencies satisfied"
}
validate_directories() {
if [[ ! -d "$MOVIES_DIR" ]]; then
log_error "Movies directory does not exist: $MOVIES_DIR"
exit 1
fi
# Create necessary directories
mkdir -p "$ARCHIVE_DIR" "$WORK_DIR"
# Ensure log directory exists
local log_dir=$(dirname "$LOG_FILE")
mkdir -p "$log_dir"
log "Directory validation complete"
log " Movies: $MOVIES_DIR"
log " Archive: $ARCHIVE_DIR"
log " Work: $WORK_DIR"
log " Log: $LOG_FILE"
}
################################################################################
# FILE PROCESSING FUNCTIONS
################################################################################
build_file_list() {
local pattern=""
for ext in "${FILE_EXTENSIONS[@]}"; do
pattern="${pattern} -o -name *.${ext}"
done
pattern="${pattern# -o }" # Remove leading " -o "
find "$MOVIES_DIR" -type f \( $pattern \) | sort
}
get_relative_path() {
local file="$1"
echo "${file#$MOVIES_DIR/}"
}
create_archive_path() {
local relative_path="$1"
echo "$ARCHIVE_DIR/$relative_path"
}
create_work_path() {
local relative_path="$1"
local filename=$(basename "$relative_path")
local work_subdir="$WORK_DIR/$(dirname "$relative_path")"
mkdir -p "$work_subdir"
echo "$work_subdir/$filename"
}
reencode_file() {
local input_file="$1"
local output_file="$2"
log "Encoding: $input_file"
log " Output: $output_file"
log " Codec: $VIDEO_CODEC, Preset: $VIDEO_PRESET, CRF: $VIDEO_CRF"
# FFmpeg command:
# -i: input file
# -map 0:v: map all video streams
# -map 0:a: map all audio streams
# -c:v: video codec
# -preset: encoding preset
# -crf: quality setting (lower = better)
# -c:a copy: copy audio without re-encoding
# -sn: strip all subtitle streams
# -movflags +faststart: optimize for streaming (mp4)
# -n: never overwrite output file
ffmpeg -hide_banner -loglevel warning -stats \
-i "$input_file" \
-map 0:v -map 0:a \
-c:v "$VIDEO_CODEC" \
-preset "$VIDEO_PRESET" \
-crf "$VIDEO_CRF" \
-c:a copy \
-sn \
-movflags +faststart \
-n \
"$output_file" 2>&1 | tee -a "$LOG_FILE"
return ${PIPESTATUS[0]}
}
verify_output() {
local output_file="$1"
if [[ ! -f "$output_file" ]]; then
log_error "Output file does not exist: $output_file"
return 1
fi
local filesize=$(stat -c%s "$output_file" 2>/dev/null || stat -f%z "$output_file" 2>/dev/null)
if [[ $filesize -lt 1024 ]]; then
log_error "Output file is suspiciously small: $output_file ($filesize bytes)"
return 1
fi
# Verify with ffprobe
if ! ffprobe -v error "$output_file" &>/dev/null; then
log_error "Output file failed ffprobe validation: $output_file"
return 1
fi
log "Output file verified: $output_file ($filesize bytes)"
return 0
}
process_file() {
local input_file="$1"
local relative_path=$(get_relative_path "$input_file")
local archive_path=$(create_archive_path "$relative_path")
local work_path=$(create_work_path "$relative_path")
log "=========================================="
log "Processing: $relative_path"
if [[ $DRY_RUN -eq 1 ]]; then
log "[DRY RUN] Would encode: $input_file -> $work_path"
log "[DRY RUN] Would archive: $input_file -> $archive_path"
log "[DRY RUN] Would move: $work_path -> $input_file"
return 0
fi
# Step 1: Re-encode to work directory
if ! reencode_file "$input_file" "$work_path"; then
log_error "Encoding failed for: $input_file"
rm -f "$work_path"
return 1
fi
# Step 2: Verify output
if ! verify_output "$work_path"; then
log_error "Verification failed for: $work_path"
rm -f "$work_path"
return 1
fi
# Step 3: Archive original
local archive_dir=$(dirname "$archive_path")
mkdir -p "$archive_dir"
if ! mv "$input_file" "$archive_path"; then
log_error "Failed to archive original: $input_file"
rm -f "$work_path"
return 1
fi
log "Archived original: $archive_path"
# Step 4: Move new file into place
if ! mv "$work_path" "$input_file"; then
log_error "Failed to move encoded file into place: $work_path -> $input_file"
log_error "Original is in archive: $archive_path"
return 1
fi
log_success "Completed: $relative_path"
return 0
}
################################################################################
# MAIN EXECUTION
################################################################################
cleanup() {
log "Script interrupted or completed"
}
trap cleanup EXIT
main() {
log "=========================================="
log "ENCODERPRO - PHASE 1"
log "=========================================="
log "Started at: $(date)"
if [[ $DRY_RUN -eq 1 ]]; then
log "DRY RUN MODE ENABLED"
fi
# Validate environment
validate_dependencies
validate_directories
# Build file list
log "Scanning for media files..."
local file_count=0
local success_count=0
local failure_count=0
while IFS= read -r file; do
((file_count++)) || true
if process_file "$file"; then
((success_count++)) || true
else
((failure_count++)) || true
log_error "Failed to process: $file"
fi
done < <(build_file_list)
# Summary
log "=========================================="
log "PROCESSING COMPLETE"
log "=========================================="
log "Total files found: $file_count"
log "Successfully processed: $success_count"
log "Failed: $failure_count"
log "Completed at: $(date)"
}
# Run main function
main "$@"

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requirements-complete.txt Normal file
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# Python Requirements for Complete System (Dashboard + Processing)
# Install with: pip3 install -r requirements-complete.txt
# Core dependencies
pyyaml>=6.0
# Dashboard dependencies
flask>=2.3.0
flask-cors>=4.0.0
# Optional but recommended
# For better performance and security
gunicorn>=21.0.0 # Production WSGI server (instead of Flask dev server)
gevent>=23.0.0 # Async support for better performance
# Development dependencies (optional)
# pytest>=7.4.0 # For testing
# black>=23.0.0 # Code formatting
# flake8>=6.0.0 # Linting

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requirements.txt Normal file
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# Python Requirements for encoderPro - Phase 2
# Install with: pip3 install -r requirements.txt
# YAML configuration support (recommended but optional)
pyyaml>=6.0
# Note: The following are included in Python 3.7+ standard library:
# - sqlite3 (database)
# - pathlib (path handling)
# - logging (logging and rotation)
# - subprocess (FFmpeg execution)
# - argparse (command-line arguments)
# External dependencies (must be installed separately):
# - FFmpeg (with libx264/libx265 support)
# - ffprobe (usually comes with FFmpeg)

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run-local.ps1 Normal file
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# Local Development Startup Script
# Set all environment variables for local development
$env:CONFIG_FILE = "C:/Users/ckoch/OneDrive/Documents/development/encoderPro/config-local.yaml"
$env:DASHBOARD_DEBUG = "true"
$env:DASHBOARD_HOST = "127.0.0.1"
$env:DASHBOARD_PORT = "5000"
$env:SECRET_KEY = "dev-secret-key-change-in-production-12345678"
# Override Docker-specific paths with local Windows paths
$env:STATE_DB = "C:/Users/ckoch/OneDrive/Documents/development/encoderPro/data/state.db"
$env:LOG_DIR = "C:/Users/ckoch/OneDrive/Documents/development/encoderPro/logs"
$env:MOVIES_DIR = "C:/Users/ckoch/Videos/test-movies"
$env:ARCHIVE_DIR = "C:/Users/ckoch/Videos/archive"
$env:WORK_DIR = "C:/Users/ckoch/Videos/work"
$env:REENCODE_SCRIPT = "C:/Users/ckoch/OneDrive/Documents/development/encoderPro/reencode.py"
Write-Host "=====================================" -ForegroundColor Cyan
Write-Host " encoderPro Local Development" -ForegroundColor Cyan
Write-Host "=====================================" -ForegroundColor Cyan
Write-Host ""
Write-Host "Dashboard will be available at: http://localhost:5000" -ForegroundColor Green
Write-Host ""
Write-Host "Press Ctrl+C to stop the server" -ForegroundColor Yellow
Write-Host ""
# Run the dashboard
python dashboard.py

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#!/bin/bash
################################################################################
# SUBTITLE SCANNER UTILITY
################################################################################
# Scans a directory tree and reports which files have subtitle streams
# Useful for:
# - Planning which files need processing
# - Verifying results after re-encoding
# - Estimating workload
################################################################################
set -euo pipefail
SCAN_DIR="${1:-/mnt/user/movies}"
OUTPUT_MODE="${OUTPUT_MODE:-summary}" # summary, detailed, or csv
################################################################################
# Functions
################################################################################
check_file() {
local file="$1"
local subtitle_count=$(ffprobe -v error -select_streams s -show_entries stream=codec_name -of default=noprint_wrappers=1:nokey=1 "$file" 2>/dev/null | wc -l)
echo "$subtitle_count"
}
human_readable_size() {
local size=$1
if [[ $size -lt 1024 ]]; then
echo "${size}B"
elif [[ $size -lt 1048576 ]]; then
echo "$(( size / 1024 ))KB"
elif [[ $size -lt 1073741824 ]]; then
echo "$(( size / 1048576 ))MB"
else
echo "$(( size / 1073741824 ))GB"
fi
}
################################################################################
# Main
################################################################################
if [[ ! -d "$SCAN_DIR" ]]; then
echo "ERROR: Directory not found: $SCAN_DIR" >&2
exit 1
fi
echo "=========================================="
echo "Scanning: $SCAN_DIR"
echo "=========================================="
echo ""
total_files=0
files_with_subs=0
files_without_subs=0
total_size=0
size_with_subs=0
# CSV header if needed
if [[ "$OUTPUT_MODE" == "csv" ]]; then
echo "filepath,subtitle_count,size_bytes"
fi
while IFS= read -r file; do
((total_files++)) || true
subtitle_count=$(check_file "$file")
file_size=$(stat -c%s "$file" 2>/dev/null || stat -f%z "$file" 2>/dev/null)
total_size=$((total_size + file_size))
if [[ $subtitle_count -gt 0 ]]; then
((files_with_subs++)) || true
size_with_subs=$((size_with_subs + file_size))
case "$OUTPUT_MODE" in
detailed)
echo "[HAS SUBS] $file ($subtitle_count subtitle stream(s), $(human_readable_size $file_size))"
;;
csv)
echo "$file,$subtitle_count,$file_size"
;;
esac
else
((files_without_subs++)) || true
case "$OUTPUT_MODE" in
detailed)
echo "[NO SUBS] $file ($(human_readable_size $file_size))"
;;
csv)
echo "$file,0,$file_size"
;;
esac
fi
# Progress indicator for summary mode
if [[ "$OUTPUT_MODE" == "summary" ]] && [[ $((total_files % 10)) -eq 0 ]]; then
echo -ne "Scanned $total_files files...\r"
fi
done < <(find "$SCAN_DIR" -type f \( -name "*.mkv" -o -name "*.mp4" -o -name "*.avi" -o -name "*.m4v" \) | sort)
# Summary
if [[ "$OUTPUT_MODE" != "csv" ]]; then
echo ""
echo "=========================================="
echo "SUMMARY"
echo "=========================================="
echo "Total files scanned: $total_files"
echo "Files with subtitles: $files_with_subs"
echo "Files without subtitles: $files_without_subs"
echo ""
echo "Total library size: $(human_readable_size $total_size)"
echo "Size needing processing: $(human_readable_size $size_with_subs)"
if [[ $files_with_subs -gt 0 ]]; then
avg_size=$((size_with_subs / files_with_subs))
echo "Average file size: $(human_readable_size $avg_size)"
# Rough time estimate (assuming 2-hour movies at 1 fps average)
estimated_hours=$((files_with_subs * 2))
echo ""
echo "Estimated processing time: ~$estimated_hours hours (at 1 fps average)"
echo " (Actual time varies greatly based on hardware and settings)"
fi
fi
echo ""
echo "=========================================="
# Usage examples
if [[ "$OUTPUT_MODE" == "summary" ]]; then
echo ""
echo "TIP: For more detail, run with:"
echo " OUTPUT_MODE=detailed $0 $SCAN_DIR"
echo ""
echo "Or generate a CSV:"
echo " OUTPUT_MODE=csv $0 $SCAN_DIR > subtitles.csv"
fi

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#!/bin/bash
################################################################################
# TEST ENVIRONMENT SETUP
################################################################################
# This script creates a test environment with sample files to verify
# the re-encoding system works correctly before running on real data
################################################################################
set -euo pipefail
echo "=========================================="
echo "Setting up test environment"
echo "=========================================="
# Test directories
TEST_ROOT="/tmp/reencode-test"
TEST_MOVIES="$TEST_ROOT/movies"
TEST_ARCHIVE="$TEST_ROOT/archive"
TEST_WORK="$TEST_ROOT/work"
TEST_LOG="$TEST_ROOT/test.log"
# Clean up any existing test environment
if [[ -d "$TEST_ROOT" ]]; then
echo "Cleaning up existing test environment..."
rm -rf "$TEST_ROOT"
fi
# Create directory structure
echo "Creating test directories..."
mkdir -p "$TEST_MOVIES/Movie1 (2020)"
mkdir -p "$TEST_MOVIES/Movie2 (2021)/Special Features"
mkdir -p "$TEST_ARCHIVE"
mkdir -p "$TEST_WORK"
# Check if FFmpeg is available
if ! command -v ffmpeg &> /dev/null; then
echo "ERROR: FFmpeg not found. Please install FFmpeg first."
exit 1
fi
echo "Generating test video files..."
# Generate test video 1 (with subtitle track)
echo " Creating Movie1/movie.mkv (with subtitle track)..."
ffmpeg -f lavfi -i testsrc=duration=10:size=1280x720:rate=30 \
-f lavfi -i sine=frequency=1000:duration=10 \
-c:v libx264 -preset ultrafast -crf 23 \
-c:a aac -b:a 128k \
-metadata:s:s:0 language=eng \
-f srt -i <(cat <<'EOF'
1
00:00:00,000 --> 00:00:05,000
This is a test subtitle
2
00:00:05,000 --> 00:00:10,000
Second subtitle line
EOF
) \
-map 0:v -map 1:a -map 2:s \
-y "$TEST_MOVIES/Movie1 (2020)/movie.mkv" 2>/dev/null
# Generate test video 2 (MP4 format, with subtitle)
echo " Creating Movie2/movie.mp4 (with subtitle track)..."
ffmpeg -f lavfi -i testsrc=duration=5:size=1920x1080:rate=30 \
-f lavfi -i sine=frequency=500:duration=5 \
-c:v libx264 -preset ultrafast -crf 23 \
-c:a aac -b:a 128k \
-metadata:s:s:0 language=spa \
-f srt -i <(cat <<'EOF'
1
00:00:00,000 --> 00:00:03,000
Spanish subtitle test
2
00:00:03,000 --> 00:00:05,000
Another line
EOF
) \
-map 0:v -map 1:a -map 2:s \
-y "$TEST_MOVIES/Movie2 (2021)/movie.mp4" 2>/dev/null
# Generate test video 3 (bonus feature, no subtitles)
echo " Creating Movie2/Special Features/bonus.mkv (no subtitles)..."
ffmpeg -f lavfi -i testsrc=duration=3:size=640x480:rate=30 \
-f lavfi -i sine=frequency=800:duration=3 \
-c:v libx264 -preset ultrafast -crf 23 \
-c:a aac -b:a 128k \
-y "$TEST_MOVIES/Movie2 (2021)/Special Features/bonus.mkv" 2>/dev/null
echo ""
echo "Test environment created successfully!"
echo ""
echo "=========================================="
echo "Test Directory Structure:"
echo "=========================================="
tree "$TEST_MOVIES" 2>/dev/null || find "$TEST_MOVIES" -type f
echo ""
echo "=========================================="
echo "Verifying test files with ffprobe:"
echo "=========================================="
for file in $(find "$TEST_MOVIES" -type f -name "*.mkv" -o -name "*.mp4"); do
echo ""
echo "File: $file"
echo " Video streams: $(ffprobe -v error -select_streams v -show_entries stream=codec_name -of default=noprint_wrappers=1:nokey=1 "$file" | wc -l)"
echo " Audio streams: $(ffprobe -v error -select_streams a -show_entries stream=codec_name -of default=noprint_wrappers=1:nokey=1 "$file" | wc -l)"
echo " Subtitle streams: $(ffprobe -v error -select_streams s -show_entries stream=codec_name -of default=noprint_wrappers=1:nokey=1 "$file" | wc -l)"
done
echo ""
echo "=========================================="
echo "How to run the test:"
echo "=========================================="
cat <<'EOF'
# 1. First run in DRY RUN mode to verify:
MOVIES_DIR="/tmp/reencode-test/movies" \
ARCHIVE_DIR="/tmp/reencode-test/archive" \
WORK_DIR="/tmp/reencode-test/work" \
LOG_FILE="/tmp/reencode-test/test.log" \
DRY_RUN=1 \
./reencode-movies.sh
# 2. Then run for real:
MOVIES_DIR="/tmp/reencode-test/movies" \
ARCHIVE_DIR="/tmp/reencode-test/archive" \
WORK_DIR="/tmp/reencode-test/work" \
LOG_FILE="/tmp/reencode-test/test.log" \
./reencode-movies.sh
# 3. Verify results:
# Check that subtitles were removed:
for file in /tmp/reencode-test/movies/**/*.{mkv,mp4}; do
echo "Checking: $file"
ffprobe -v error -select_streams s -show_entries stream=codec_name "$file" 2>&1 | grep -q "Stream #" && echo " HAS SUBTITLES (BAD)" || echo " No subtitles (GOOD)"
done
# Check that originals are in archive:
ls -lR /tmp/reencode-test/archive/
# Review the log:
cat /tmp/reencode-test/test.log
# 4. Clean up when done:
rm -rf /tmp/reencode-test/
EOF
echo "=========================================="
echo "Test environment ready!"
echo "=========================================="

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* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
background: #0f172a;
color: #e2e8f0;
line-height: 1.6;
}
.container {
max-width: 1400px;
margin: 0 auto;
padding: 20px;
}
.header {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
padding: 30px;
border-radius: 12px;
margin-bottom: 30px;
box-shadow: 0 10px 30px rgba(0, 0, 0, 0.3);
}
.header h1 {
font-size: 2.5em;
margin-bottom: 10px;
}
.tab-nav {
background: #1e293b;
border-radius: 12px;
padding: 10px;
margin-bottom: 30px;
display: flex;
gap: 10px;
}
.tab-button {
padding: 12px 24px;
border: none;
background: transparent;
color: #94a3b8;
font-size: 1em;
font-weight: 600;
cursor: pointer;
border-radius: 8px;
transition: all 0.3s;
}
.tab-button:hover {
background: #0f172a;
color: #e2e8f0;
}
.tab-button.active {
background: #667eea;
color: white;
}
.tab-content {
display: none;
}
.tab-content.active {
display: block;
}
.controls {
background: #1e293b;
padding: 20px;
border-radius: 12px;
margin-bottom: 30px;
display: flex;
gap: 15px;
align-items: center;
flex-wrap: wrap;
}
.btn {
padding: 12px 24px;
border: none;
border-radius: 8px;
font-size: 1em;
font-weight: 600;
cursor: pointer;
transition: all 0.3s;
}
.btn-primary {
background: #10b981;
color: white;
}
.btn-secondary {
background: #3b82f6;
color: white;
}
.btn-danger {
background: #ef4444;
color: white;
}
.btn:hover {
transform: translateY(-2px);
}
.btn:disabled {
opacity: 0.5;
cursor: not-allowed;
}
.status-badge {
padding: 6px 12px;
border-radius: 20px;
font-size: 0.9em;
font-weight: 600;
margin-left: auto;
}
.status-active {
background: #10b981;
}
.status-idle {
background: #64748b;
}
select, input[type="text"], input[type="number"] {
padding: 10px 15px;
background: #0f172a;
color: #e2e8f0;
border: 1px solid #334155;
border-radius: 8px;
font-size: 1em;
}
.checkbox-container {
display: flex;
align-items: center;
gap: 10px;
}
.grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
gap: 20px;
margin-bottom: 30px;
}
.card {
background: #1e293b;
border-radius: 12px;
padding: 24px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.card-title {
font-size: 0.9em;
color: #94a3b8;
text-transform: uppercase;
margin-bottom: 12px;
}
.card-value {
font-size: 2.5em;
font-weight: 700;
}
.card-pending {
border-left: 4px solid #fbbf24;
}
.card-processing {
border-left: 4px solid #3b82f6;
}
.card-completed {
border-left: 4px solid #10b981;
}
.card-failed {
border-left: 4px solid #ef4444;
}
.card-skipped {
border-left: 4px solid #64748b;
}
.progress-section {
background: #1e293b;
border-radius: 12px;
padding: 24px;
margin-bottom: 30px;
}
.progress-bar {
width: 100%;
height: 30px;
background: #0f172a;
border-radius: 15px;
overflow: hidden;
margin: 20px 0;
}
.progress-fill {
height: 100%;
background: linear-gradient(90deg, #10b981, #059669);
transition: width 0.5s ease;
}
.stats-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 20px;
}
.system-stats {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
gap: 20px;
margin-bottom: 30px;
}
.activity-list, .logs-panel {
background: #1e293b;
border-radius: 12px;
padding: 24px;
margin-bottom: 30px;
}
.activity-item {
padding: 12px;
margin-bottom: 8px;
background: #0f172a;
border-radius: 8px;
border-left: 3px solid #10b981;
}
.logs-container {
background: #0f172a;
border-radius: 12px;
padding: 20px;
font-family: 'Courier New', monospace;
font-size: 0.85em;
max-height: 400px;
overflow-y: auto;
}
.settings-panel {
background: #1e293b;
border-radius: 12px;
padding: 30px;
}
.settings-section {
margin-bottom: 30px;
}
.settings-section h3 {
font-size: 1.3em;
margin-bottom: 15px;
color: #667eea;
}
.section-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 15px;
}
.form-group {
margin-bottom: 20px;
}
.form-group label {
display: block;
margin-bottom: 8px;
font-weight: 600;
color: #94a3b8;
}
.form-group input, .form-group select {
width: 100%;
}
.form-group small {
display: block;
margin-top: 5px;
color: #64748b;
font-size: 0.9em;
}
.form-row {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
gap: 20px;
}
.button-row {
display: flex;
gap: 15px;
margin-top: 30px;
}
.validation-message {
padding: 12px;
border-radius: 8px;
margin-bottom: 20px;
}
.validation-success {
background: rgba(16, 185, 129, 0.1);
border: 1px solid #10b981;
color: #10b981;
}
.validation-error {
background: rgba(239, 68, 68, 0.1);
border: 1px solid #ef4444;
color: #ef4444;
}
.validation-warning {
background: rgba(245, 158, 11, 0.1);
border: 1px solid #f59e0b;
color: #f59e0b;
}
.profile-editor {
background: #0f172a;
padding: 20px;
border-radius: 8px;
margin-bottom: 15px;
}
.profile-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 15px;
}
.modal {
display: none;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
background: rgba(0, 0, 0, 0.7);
z-index: 1000;
justify-content: center;
align-items: center;
}
.modal.active {
display: flex;
}
.modal-content {
background: #1e293b;
border-radius: 12px;
padding: 30px;
max-width: 600px;
width: 90%;
max-height: 80vh;
overflow-y: auto;
}
.modal-header {
display: flex;
justify-content: space-between;
margin-bottom: 20px;
}
.modal-close {
background: none;
border: none;
color: #94a3b8;
font-size: 1.5em;
cursor: pointer;
}

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// Dashboard State
let isProcessing = false;
let currentConfig = {};
let currentProfiles = {};
let editingProfileName = null;
// Initialize
document.addEventListener('DOMContentLoaded', () => {
loadConfiguration();
loadProfiles();
refreshData();
setInterval(refreshData, 5000);
});
// Tab Management
function switchTab(tabName) {
document.querySelectorAll('.tab-button').forEach(btn => btn.classList.remove('active'));
event.target.classList.add('active');
document.querySelectorAll('.tab-content').forEach(content => content.classList.remove('active'));
document.getElementById(tabName + '-tab').classList.add('active');
if (tabName === 'settings') loadConfiguration();
if (tabName === 'profiles') loadProfiles();
}
// Data Refresh
async function refreshData() {
await Promise.all([
updateStats(),
updateSystemStats(),
updateActivity(),
updateLogs(),
checkProcessingStatus()
]);
}
// Configuration Management
async function loadConfiguration() {
try {
const res = await fetch('/api/config');
const result = await res.json();
if (result.success) {
currentConfig = result.data;
document.getElementById('movies_dir').value = result.data.movies_dir || '';
document.getElementById('archive_dir').value = result.data.archive_dir || '';
document.getElementById('work_dir').value = result.data.work_dir || '';
if (result.data.parallel) {
document.getElementById('max_workers').value = result.data.parallel.max_workers || 2;
document.getElementById('gpu_slots').value = result.data.parallel.gpu_slots || 1;
document.getElementById('cpu_slots').value = result.data.parallel.cpu_slots || 4;
}
if (result.data.processing) {
document.getElementById('skip_without_subtitles').checked = result.data.processing.skip_without_subtitles !== false;
document.getElementById('cleanup_stale_work').checked = result.data.processing.cleanup_stale_work !== false;
}
if (result.data.advanced) {
document.getElementById('prefer_gpu').checked = result.data.advanced.prefer_gpu !== false;
document.getElementById('fallback_to_cpu').checked = result.data.advanced.fallback_to_cpu !== false;
}
}
} catch (e) {
console.error('Load config failed:', e);
}
}
async function validateConfiguration() {
const config = gatherConfig();
try {
const res = await fetch('/api/config/validate', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify(config)
});
const result = await res.json();
if (result.success) displayValidation(result.data);
} catch (e) {
alert('Validation failed: ' + e.message);
}
}
async function saveConfiguration() {
const config = gatherConfig();
try {
const res = await fetch('/api/config', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify(config)
});
const result = await res.json();
if (result.success) {
alert('✓ Configuration saved!');
currentConfig = result.data;
} else {
alert('Failed: ' + result.error);
}
} catch (e) {
alert('Save failed: ' + e.message);
}
}
function gatherConfig() {
return {
movies_dir: document.getElementById('movies_dir').value,
archive_dir: document.getElementById('archive_dir').value,
work_dir: document.getElementById('work_dir').value,
state_db: currentConfig.state_db || '/var/lib/reencode/state.db',
log_dir: currentConfig.log_dir || '/var/log/reencode',
parallel: {
max_workers: parseInt(document.getElementById('max_workers').value),
gpu_slots: parseInt(document.getElementById('gpu_slots').value),
cpu_slots: parseInt(document.getElementById('cpu_slots').value)
},
processing: {
file_extensions: currentConfig.processing?.file_extensions || ['mkv', 'mp4', 'avi', 'm4v'],
skip_without_subtitles: document.getElementById('skip_without_subtitles').checked,
cleanup_stale_work: document.getElementById('cleanup_stale_work').checked
},
advanced: {
auto_detect_encoders: true,
prefer_gpu: document.getElementById('prefer_gpu').checked,
fallback_to_cpu: document.getElementById('fallback_to_cpu').checked,
progress_interval: 10
},
profiles: currentConfig.profiles || {}
};
}
function displayValidation(data) {
let html = '';
if (data.errors.length === 0 && data.warnings.length === 0) {
html = '<div class="validation-message validation-success">✓ Configuration is valid!</div>';
} else {
if (data.errors.length > 0) {
html += '<div class="validation-message validation-error"><strong>Errors:</strong><ul>';
data.errors.forEach(e => html += `<li>${e}</li>`);
html += '</ul></div>';
}
if (data.warnings.length > 0) {
html += '<div class="validation-message validation-warning"><strong>Warnings:</strong><ul>';
data.warnings.forEach(w => html += `<li>${w}</li>`);
html += '</ul></div>';
}
}
document.getElementById('validationMessages').innerHTML = html;
}
// Profile Management
async function loadProfiles() {
try {
const res = await fetch('/api/profiles');
const result = await res.json();
if (result.success) {
currentProfiles = result.data.profiles;
renderProfiles();
populateProfileSelects(result.data.default);
}
} catch (e) {
console.error('Load profiles failed:', e);
}
}
function renderProfiles() {
let html = '';
for (const [name, profile] of Object.entries(currentProfiles)) {
html += `<div class="profile-editor">
<div class="profile-header">
<strong>${name}</strong>
<div>
<button class="btn btn-secondary" style="padding: 6px 12px;" onclick="editProfile('${name}')">✏️</button>
<button class="btn btn-danger" style="padding: 6px 12px;" onclick="deleteProfile('${name}')">🗑️</button>
</div>
</div>
<div style="color: #94a3b8; font-size: 0.9em;">
Encoder: ${profile.encoder} | Preset: ${profile.preset} | Quality: ${profile.quality}
</div>
</div>`;
}
document.getElementById('profilesList').innerHTML = html;
}
function populateProfileSelects(defaultProfile) {
let opts = '<option value="">Default</option>';
for (const name of Object.keys(currentProfiles)) {
opts += `<option value="${name}">${name}</option>`;
}
document.getElementById('profileSelect').innerHTML = opts;
document.getElementById('default_profile').innerHTML = opts.replace('<option value="">Default</option>', '');
if (defaultProfile) document.getElementById('default_profile').value = defaultProfile;
}
function addNewProfile() {
editingProfileName = null;
document.getElementById('modalTitle').textContent = 'Add Profile';
document.getElementById('modal_profile_name').value = '';
document.getElementById('modal_profile_name').disabled = false;
document.getElementById('modal_encoder').value = 'nvidia_nvenc_h265';
document.getElementById('modal_preset').value = 'medium';
document.getElementById('modal_quality').value = '23';
document.getElementById('modal_audio_codec').value = 'copy';
document.getElementById('profileModal').classList.add('active');
}
function editProfile(name) {
editingProfileName = name;
const profile = currentProfiles[name];
document.getElementById('modalTitle').textContent = 'Edit: ' + name;
document.getElementById('modal_profile_name').value = name;
document.getElementById('modal_profile_name').disabled = true;
document.getElementById('modal_encoder').value = profile.encoder;
document.getElementById('modal_preset').value = profile.preset;
document.getElementById('modal_quality').value = profile.quality;
document.getElementById('modal_audio_codec').value = profile.audio_codec || 'copy';
document.getElementById('profileModal').classList.add('active');
}
function deleteProfile(name) {
if (confirm(`Delete "${name}"?`)) {
delete currentProfiles[name];
renderProfiles();
}
}
function closeProfileModal() {
document.getElementById('profileModal').classList.remove('active');
}
function saveProfileFromModal() {
const name = document.getElementById('modal_profile_name').value.trim();
if (!name) return alert('Name required');
currentProfiles[name] = {
encoder: document.getElementById('modal_encoder').value,
preset: document.getElementById('modal_preset').value,
quality: parseInt(document.getElementById('modal_quality').value),
audio_codec: document.getElementById('modal_audio_codec').value
};
renderProfiles();
populateProfileSelects(currentConfig.profiles?.default);
closeProfileModal();
}
async function saveProfiles() {
const config = gatherConfig();
config.profiles = {
default: document.getElementById('default_profile').value,
definitions: currentProfiles
};
try {
const res = await fetch('/api/config', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify(config)
});
const result = await res.json();
if (result.success) {
alert('✓ Profiles saved!');
currentConfig = result.data;
} else {
alert('Failed: ' + result.error);
}
} catch (e) {
alert('Save failed: ' + e.message);
}
}
// Job Control
async function startProcessing() {
const profile = document.getElementById('profileSelect').value;
const dryRun = document.getElementById('dryRunCheckbox').checked;
try {
const res = await fetch('/api/jobs/start', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({profile: profile || null, dry_run: dryRun})
});
const result = await res.json();
if (result.success) {
isProcessing = true;
updateUIState();
setTimeout(refreshData, 1000);
} else {
alert('Failed: ' + result.message);
}
} catch (e) {
alert('Error: ' + e.message);
}
}
async function stopProcessing() {
if (!confirm('Stop processing?')) return;
try {
const res = await fetch('/api/jobs/stop', {method: 'POST'});
const result = await res.json();
if (result.success) {
isProcessing = false;
updateUIState();
setTimeout(refreshData, 1000);
}
} catch (e) {
alert('Error: ' + e.message);
}
}
async function checkProcessingStatus() {
try {
const res = await fetch('/api/processing');
const result = await res.json();
if (result.success) {
isProcessing = result.data.active;
updateUIState();
}
} catch (e) {}
}
function updateUIState() {
document.getElementById('btnStart').disabled = isProcessing;
document.getElementById('btnStop').disabled = !isProcessing;
const badge = document.getElementById('statusBadge');
if (isProcessing) {
badge.textContent = 'Processing';
badge.className = 'status-badge status-active';
} else {
badge.textContent = 'Idle';
badge.className = 'status-badge status-idle';
}
}
// Stats Updates
async function updateStats() {
try {
const res = await fetch('/api/stats');
const result = await res.json();
if (result.success) {
const d = result.data;
document.getElementById('statPending').textContent = d.pending || 0;
document.getElementById('statProcessing').textContent = d.processing || 0;
document.getElementById('statCompleted').textContent = d.completed || 0;
document.getElementById('statFailed').textContent = d.failed || 0;
document.getElementById('statSkipped').textContent = d.skipped || 0;
const total = (d.completed || 0) + (d.failed || 0) + (d.pending || 0) + (d.processing || 0);
const done = (d.completed || 0) + (d.failed || 0);
const prog = total > 0 ? (done / total * 100) : 0;
document.getElementById('progressBar').style.width = prog + '%';
document.getElementById('originalSize').textContent = formatBytes(d.original_size);
document.getElementById('encodedSize').textContent = formatBytes(d.encoded_size);
document.getElementById('spaceSaved').textContent = formatBytes(d.space_saved) + ' (' + d.space_saved_percent + '%)';
document.getElementById('avgFps').textContent = (d.avg_fps || 0).toFixed(1) + ' fps';
}
} catch (e) {}
}
async function updateSystemStats() {
try {
const res = await fetch('/api/system');
const result = await res.json();
if (result.success) {
let html = '';
if (result.data.gpu && result.data.gpu.length > 0) {
result.data.gpu.forEach(gpu => {
html += `<div class="card" style="background: linear-gradient(135deg, #667eea, #764ba2); color: white;">
<h3>🎮 ${gpu.name}</h3>
<div style="margin-top: 10px;">
GPU: ${gpu.utilization}% | Memory: ${gpu.memory_used}/${gpu.memory_total}MB | Temp: ${gpu.temperature}°C
</div>
</div>`;
});
}
document.getElementById('systemStats').innerHTML = html;
}
} catch (e) {}
}
async function updateActivity() {
try {
const res = await fetch('/api/activity?limit=10');
const result = await res.json();
if (result.success) {
let html = '';
result.data.forEach(item => {
const icon = item.state === 'completed' ? '✓' : '✗';
html += `<div class="activity-item">${icon} ${item.relative_path}</div>`;
});
document.getElementById('activityList').innerHTML = html || '<p>No activity</p>';
}
} catch (e) {}
}
async function updateLogs() {
try {
const res = await fetch('/api/logs?lines=50');
const result = await res.json();
if (result.success) {
const html = result.data.map(line => `<div>${escapeHtml(line)}</div>`).join('');
document.getElementById('logsContainer').innerHTML = html;
}
} catch (e) {}
}
// Utilities
function formatBytes(bytes) {
if (!bytes) return '0 B';
const k = 1024;
const sizes = ['B', 'KB', 'MB', 'GB', 'TB'];
const i = Math.floor(Math.log(bytes) / Math.log(k));
return (bytes / Math.pow(k, i)).toFixed(2) + ' ' + sizes[i];
}
function escapeHtml(text) {
const div = document.createElement('div');
div.textContent = text;
return div.innerHTML;
}

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test-av1-support.sh Normal file
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#!/bin/bash
# Test script to check AV1 encoder availability in Docker container
echo "============================================"
echo "TESTING AV1 ENCODER SUPPORT"
echo "============================================"
echo ""
echo "Checking FFmpeg encoders for AV1..."
echo ""
echo "Intel QSV AV1:"
ffmpeg -hide_banner -encoders 2>&1 | grep -i "av1_qsv" && echo " [FOUND]" || echo " [NOT FOUND]"
echo ""
echo "NVIDIA NVENC AV1:"
ffmpeg -hide_banner -encoders 2>&1 | grep -i "av1_nvenc" && echo " [FOUND]" || echo " [NOT FOUND]"
echo ""
echo "CPU AV1 (SVT-AV1):"
ffmpeg -hide_banner -encoders 2>&1 | grep -i "libsvtav1" && echo " [FOUND]" || echo " [NOT FOUND]"
echo ""
echo "CPU AV1 (libaom):"
ffmpeg -hide_banner -encoders 2>&1 | grep -i "libaom-av1" && echo " [FOUND]" || echo " [NOT FOUND]"
echo ""
echo "============================================"
echo "Checking /dev/dri access..."
ls -la /dev/dri/ 2>/dev/null || echo " [NOT ACCESSIBLE]"
echo ""
echo "============================================"
echo "Full encoder list:"
echo "============================================"
ffmpeg -hide_banner -encoders 2>&1 | grep -E "(qsv|nvenc|vaapi|x265|x264|av1)"

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test-dashboard.py Normal file
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#!/usr/bin/env python3
"""Test if dashboard can be imported and routes are registered"""
import sys
import os
# Set environment variables
os.environ['CONFIG_FILE'] = 'C:/Users/ckoch/OneDrive/Documents/development/encoderPro/config-local.yaml'
os.environ['STATE_DB'] = 'C:/Users/ckoch/OneDrive/Documents/development/encoderPro/data/state.db'
os.environ['LOG_DIR'] = 'C:/Users/ckoch/OneDrive/Documents/development/encoderPro/logs'
os.environ['REENCODE_SCRIPT'] = 'C:/Users/ckoch/OneDrive/Documents/development/encoderPro/reencode.py'
try:
print("Importing dashboard module...")
import dashboard
print("Dashboard module imported successfully")
print("\nChecking Flask app...")
print(f"App: {dashboard.app}")
print("\nRegistered routes:")
for rule in dashboard.app.url_map.iter_rules():
print(f" {rule.endpoint:30s} {rule.rule}")
print(f"\nTotal routes: {len(list(dashboard.app.url_map.iter_rules()))}")
except Exception as e:
print(f"Error: {e}")
import traceback
traceback.print_exc()

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unraid-template.xml Normal file
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<?xml version="1.0"?>
<Container version="2">
<Name>encoderPro</Name>
<Repository>your-registry.com/encoderpro:latest</Repository>
<Registry>https://your-registry.com</Registry>
<Network>bridge</Network>
<MyIP/>
<Shell>bash</Shell>
<Privileged>false</Privileged>
<Support>https://your-git/encoderpro</Support>
<Project>https://your-git/encoderpro</Project>
<Overview>GPU-accelerated media encoding with subtitle removal. Optimized for Dell R730 with 48 threads. Automatically archives originals and provides web dashboard for monitoring.</Overview>
<Category>MediaApp:Video Status:Stable</Category>
<WebUI>http://[IP]:[PORT:5000]</WebUI>
<TemplateURL/>
<Icon>https://raw.githubusercontent.com/your-repo/encoderpro/main/icon.png</Icon>
<ExtraParams>--cpus="46" --memory="64g"</ExtraParams>
<PostArgs/>
<CPUset/>
<DateInstalled></DateInstalled>
<DonateText/>
<DonateLink/>
<Description>GPU-accelerated media encoding with subtitle removal. Optimized for Dell R730 with 48 threads. Automatically archives originals and provides web dashboard for monitoring.</Description>
<Networking>
<Mode>bridge</Mode>
<Publish>
<Port>
<HostPort>5000</HostPort>
<ContainerPort>5000</ContainerPort>
<Protocol>tcp</Protocol>
</Port>
</Publish>
</Networking>
<Data>
<Volume>
<HostDir>/mnt/user/movies</HostDir>
<ContainerDir>/movies</ContainerDir>
<Mode>ro</Mode>
</Volume>
<Volume>
<HostDir>/mnt/user/archive/movies</HostDir>
<ContainerDir>/archive</ContainerDir>
<Mode>rw</Mode>
</Volume>
<Volume>
<HostDir>/mnt/user/temp/encoderpro-work</HostDir>
<ContainerDir>/work</ContainerDir>
<Mode>rw</Mode>
</Volume>
<Volume>
<HostDir>/mnt/user/appdata/encoderpro/config.yaml</HostDir>
<ContainerDir>/config/config.yaml</ContainerDir>
<Mode>ro</Mode>
</Volume>
<Volume>
<HostDir>/mnt/user/appdata/encoderpro/db</HostDir>
<ContainerDir>/db</ContainerDir>
<Mode>rw</Mode>
</Volume>
<Volume>
<HostDir>/mnt/user/appdata/encoderpro/logs</HostDir>
<ContainerDir>/logs</ContainerDir>
<Mode>rw</Mode>
</Volume>
</Data>
<Environment>
<Variable>
<Value>0.0.0.0</Value>
<Name>DASHBOARD_HOST</Name>
<Mode/>
</Variable>
<Variable>
<Value>5000</Value>
<Name>DASHBOARD_PORT</Name>
<Mode/>
</Variable>
</Environment>
<Labels/>
<Config Name="WebUI Port" Target="5000" Default="5000" Mode="tcp" Description="Web Dashboard Port" Type="Port" Display="always" Required="true" Mask="false">5000</Config>
<Config Name="Movies Directory" Target="/movies" Default="/mnt/user/movies" Mode="ro" Description="Your movie library (read-only)" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/movies</Config>
<Config Name="Archive Directory" Target="/archive" Default="/mnt/user/archive/movies" Mode="rw" Description="Where original files are archived" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/archive/movies</Config>
<Config Name="Work Directory" Target="/work" Default="/mnt/user/temp/encoderpro-work" Mode="rw" Description="Temporary encoding workspace (use SSD if available)" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/temp/encoderpro-work</Config>
<Config Name="Config File" Target="/config/config.yaml" Default="/mnt/user/appdata/encoderpro/config.yaml" Mode="ro" Description="Configuration file" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/appdata/encoderpro/config.yaml</Config>
<Config Name="Database Directory" Target="/db" Default="/mnt/user/appdata/encoderpro/db" Mode="rw" Description="SQLite database directory" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/appdata/encoderpro/db</Config>
<Config Name="Logs Directory" Target="/logs" Default="/mnt/user/appdata/encoderpro/logs" Mode="rw" Description="Log files directory" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/appdata/encoderpro/logs</Config>
<Config Name="Dashboard Host" Target="DASHBOARD_HOST" Default="0.0.0.0" Mode="" Description="Dashboard bind address (leave as 0.0.0.0)" Type="Variable" Display="advanced" Required="false" Mask="false">0.0.0.0</Config>
<Config Name="Dashboard Port" Target="DASHBOARD_PORT" Default="5000" Mode="" Description="Dashboard port inside container" Type="Variable" Display="advanced" Required="false" Mask="false">5000</Config>
</Container>

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update-database.py Normal file
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#!/usr/bin/env python3
"""
Database Update Script
Populates container_format for existing records in the database
"""
import sqlite3
import json
import subprocess
from pathlib import Path
import sys
def get_container_format(filepath):
"""Get container format using ffprobe"""
try:
result = subprocess.run(
['ffprobe', '-v', 'quiet', '-print_format', 'json', '-show_format', str(filepath)],
capture_output=True,
text=True,
timeout=30
)
if result.returncode == 0:
info = json.loads(result.stdout)
if 'format' in info:
format_name = info['format'].get('format_name', '')
return format_name.split(',')[0] if format_name else None
except Exception as e:
print(f"Error getting container format for {filepath}: {e}")
return None
def update_database(db_path):
"""Update database with container format for existing records"""
print(f"Opening database: {db_path}")
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
# Get all files without container_format
cursor.execute("""
SELECT id, filepath, container_format
FROM files
WHERE container_format IS NULL OR container_format = ''
""")
files = cursor.fetchall()
total = len(files)
if total == 0:
print("No files need updating!")
conn.close()
return
print(f"Found {total} files to update")
updated = 0
failed = 0
for i, file in enumerate(files, 1):
filepath = Path(file['filepath'])
# Check if file exists
if not filepath.exists():
print(f"[{i}/{total}] Skipping (file not found): {filepath.name}")
failed += 1
continue
# Get container format
container = get_container_format(filepath)
if container:
cursor.execute("""
UPDATE files
SET container_format = ?
WHERE id = ?
""", (container, file['id']))
print(f"[{i}/{total}] Updated: {filepath.name} -> {container}")
updated += 1
else:
print(f"[{i}/{total}] Failed to get format: {filepath.name}")
failed += 1
# Commit every 10 files
if i % 10 == 0:
conn.commit()
# Final commit
conn.commit()
conn.close()
print(f"\nUpdate complete!")
print(f" Updated: {updated}")
print(f" Failed: {failed}")
print(f" Total: {total}")
if __name__ == '__main__':
# Check if ffprobe is available
try:
subprocess.run(['ffprobe', '-version'], capture_output=True, check=True)
except (subprocess.CalledProcessError, FileNotFoundError):
print("Error: ffprobe not found. Please install ffmpeg.")
sys.exit(1)
# Get database path from command line or use default
if len(sys.argv) > 1:
db_path = sys.argv[1]
else:
# Try to find database in common locations
possible_paths = [
Path('data/state.db'),
Path('C:/Users/ckoch/OneDrive/Documents/development/encoderPro/data/state.db'),
]
db_path = None
for p in possible_paths:
if p.exists():
db_path = p
break
if not db_path:
print("Error: Could not find database file.")
print("Usage: python update-database.py [path/to/state.db]")
sys.exit(1)
update_database(db_path)