Ryan Malloy
840bd34f29
Professional video processing pipeline with AI analysis, 360° processing, and adaptive streaming capabilities. ✨ Core Features: • AI-powered content analysis with scene detection and quality assessment • Next-generation codec support (AV1, HEVC, HDR10) • Adaptive streaming (HLS/DASH) with smart bitrate ladders • Complete 360° video processing with multiple projection support • Spatial audio processing (Ambisonic, binaural, object-based) • Viewport-adaptive streaming with up to 75% bandwidth savings • Professional testing framework with video-themed HTML dashboards 🏗️ Architecture: • Modern Python 3.11+ with full type hints • Pydantic-based configuration with validation • Async processing with Procrastinate task queue • Comprehensive test coverage with 11 detailed examples • Professional documentation structure 🚀 Production Ready: • MIT License for open source use • PyPI-ready package metadata • Docker support for scalable deployment • Quality assurance with ruff, mypy, and pytest • Comprehensive example library From simple encoding to immersive experiences - complete multimedia processing platform for modern applications.
264 lines
8.4 KiB
Python
264 lines
8.4 KiB
Python
#!/usr/bin/env python3
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"""
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Simple web demo interface for Video Processor.
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This provides a basic Flask web interface to demonstrate video processing
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capabilities in a browser-friendly format.
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"""
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import asyncio
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import os
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import tempfile
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from pathlib import Path
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try:
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from flask import Flask, jsonify, render_template_string, request
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except ImportError:
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print("Flask not installed. Install with: uv add flask")
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exit(1)
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from video_processor import ProcessorConfig, VideoProcessor
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from video_processor.tasks import setup_procrastinate
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from video_processor.tasks.compat import get_version_info
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# Simple HTML template
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HTML_TEMPLATE = """
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<!DOCTYPE html>
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<html>
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<head>
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<title>Video Processor Demo</title>
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<style>
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body { font-family: Arial, sans-serif; margin: 40px; }
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.container { max-width: 800px; margin: 0 auto; }
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.status { padding: 10px; margin: 10px 0; border-radius: 5px; }
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.success { background: #d4edda; color: #155724; }
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.error { background: #f8d7da; color: #721c24; }
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.info { background: #d1ecf1; color: #0c5460; }
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pre { background: #f8f9fa; padding: 10px; border-radius: 5px; }
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button { background: #007bff; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; }
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button:hover { background: #0056b3; }
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</style>
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</head>
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<body>
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<div class="container">
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<h1>🎬 Video Processor Demo</h1>
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<div class="status info">
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<strong>System Information:</strong><br>
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Version: {{ version_info.version }}<br>
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Procrastinate: {{ version_info.procrastinate_version }}<br>
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Features: {{ version_info.features }}
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</div>
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<h2>Test Video Processing</h2>
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<button onclick="processTestVideo()">Create & Process Test Video</button>
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<button onclick="submitAsyncJob()">Submit Async Processing Job</button>
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<button onclick="getSystemInfo()">Refresh System Info</button>
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<div id="results"></div>
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<h2>Processing Logs</h2>
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<pre id="logs">Ready...</pre>
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</div>
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<script>
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function log(message) {
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const logs = document.getElementById('logs');
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logs.textContent += new Date().toLocaleTimeString() + ': ' + message + '\\n';
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logs.scrollTop = logs.scrollHeight;
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}
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function showResult(data, isError = false) {
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const results = document.getElementById('results');
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const className = isError ? 'error' : 'success';
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results.innerHTML = '<div class="status ' + className + '"><pre>' + JSON.stringify(data, null, 2) + '</pre></div>';
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}
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async function processTestVideo() {
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log('Starting test video processing...');
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try {
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const response = await fetch('/api/process-test', { method: 'POST' });
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const data = await response.json();
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if (response.ok) {
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log('Test video processing completed successfully');
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showResult(data);
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} else {
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log('Test video processing failed: ' + data.error);
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showResult(data, true);
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}
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} catch (error) {
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log('Request failed: ' + error);
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showResult({error: error.message}, true);
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}
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}
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async function submitAsyncJob() {
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log('Submitting async processing job...');
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try {
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const response = await fetch('/api/async-job', { method: 'POST' });
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const data = await response.json();
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if (response.ok) {
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log('Async job submitted with ID: ' + data.job_id);
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showResult(data);
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} else {
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log('Async job submission failed: ' + data.error);
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showResult(data, true);
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}
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} catch (error) {
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log('Request failed: ' + error);
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showResult({error: error.message}, true);
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}
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}
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async function getSystemInfo() {
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log('Refreshing system information...');
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try {
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const response = await fetch('/api/info');
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const data = await response.json();
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showResult(data);
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log('System info refreshed');
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} catch (error) {
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log('Failed to get system info: ' + error);
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showResult({error: error.message}, true);
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}
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}
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</script>
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</body>
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</html>
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"""
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app = Flask(__name__)
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async def create_test_video(output_dir: Path) -> Path:
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"""Create a simple test video for processing."""
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import subprocess
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video_file = output_dir / "web_demo_test.mp4"
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cmd = [
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"ffmpeg",
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"-y",
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"-f",
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"lavfi",
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"-i",
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"testsrc=duration=5:size=320x240:rate=15",
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"-c:v",
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"libx264",
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"-preset",
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"ultrafast",
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"-crf",
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"30",
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str(video_file),
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]
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try:
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode != 0:
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raise RuntimeError(f"FFmpeg failed: {result.stderr}")
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return video_file
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except FileNotFoundError:
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raise RuntimeError("FFmpeg not found. Please install FFmpeg.")
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@app.route("/")
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def index():
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"""Serve the demo web interface."""
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version_info = get_version_info()
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return render_template_string(HTML_TEMPLATE, version_info=version_info)
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@app.route("/api/info")
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def api_info():
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"""Get system information."""
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return jsonify(get_version_info())
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@app.route("/api/process-test", methods=["POST"])
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def api_process_test():
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"""Process a test video synchronously."""
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try:
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with tempfile.TemporaryDirectory() as temp_dir:
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temp_path = Path(temp_dir)
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# Create test video
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test_video = asyncio.run(create_test_video(temp_path))
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# Configure processor for fast processing
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config = ProcessorConfig(
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output_dir=temp_path / "outputs",
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output_formats=["mp4"],
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quality_preset="ultrafast",
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generate_thumbnails=True,
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generate_sprites=False, # Skip sprites for faster demo
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enable_360_processing=False, # Skip 360 for faster demo
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)
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# Process video
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processor = VideoProcessor(config)
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result = processor.process_video(test_video)
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return jsonify(
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{
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"status": "success",
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"video_id": result.video_id,
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"encoded_files": len(result.encoded_files),
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"thumbnails": len(result.thumbnails),
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"processing_time": "< 30s (estimated)",
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"message": "Test video processed successfully!",
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}
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)
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@app.route("/api/async-job", methods=["POST"])
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def api_async_job():
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"""Submit an async processing job."""
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try:
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database_url = os.environ.get(
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"PROCRASTINATE_DATABASE_URL",
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"postgresql://video_user:video_password@postgres:5432/video_processor",
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)
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# Set up Procrastinate
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app_context = setup_procrastinate(database_url)
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# In a real application, you would:
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# 1. Accept file uploads
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# 2. Store them temporarily
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# 3. Submit processing jobs
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# 4. Return job IDs for status tracking
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# For demo, we'll just simulate job submission
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job_id = f"demo-job-{os.urandom(4).hex()}"
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return jsonify(
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{
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"status": "submitted",
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"job_id": job_id,
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"queue": "video_processing",
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"message": "Job submitted to background worker",
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"note": "In production, this would submit a real Procrastinate job",
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}
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)
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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def main():
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"""Run the web demo server."""
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port = int(os.environ.get("PORT", 8080))
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debug = os.environ.get("FLASK_ENV") == "development"
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print(f"🌐 Starting Video Processor Web Demo on port {port}")
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print(f"📖 Open http://localhost:{port} in your browser")
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app.run(host="0.0.0.0", port=port, debug=debug)
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if __name__ == "__main__":
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main()
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