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.
238 lines
8.2 KiB
Python
238 lines
8.2 KiB
Python
"""Demo test showcasing the video processing testing framework capabilities."""
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import pytest
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import time
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from pathlib import Path
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@pytest.mark.smoke
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def test_framework_smoke_test(quality_tracker, video_test_config, video_assert):
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"""Quick smoke test to verify framework functionality."""
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# Record some basic assertions for quality tracking
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quality_tracker.record_assertion(True, "Framework initialization successful")
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quality_tracker.record_assertion(True, "Configuration loaded correctly")
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quality_tracker.record_assertion(True, "Quality tracker working")
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# Test basic configuration
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assert video_test_config.project_name == "Video Processor"
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assert video_test_config.parallel_workers >= 1
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# Test custom assertions
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video_assert.assert_video_quality(8.5, 7.0) # Should pass
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video_assert.assert_encoding_performance(15.0, 10.0) # Should pass
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print("✅ Framework smoke test completed successfully")
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@pytest.mark.unit
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def test_enhanced_fixtures(enhanced_temp_dir, video_config, test_video_scenarios):
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"""Test the enhanced fixtures provided by the framework."""
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# Test enhanced temp directory structure
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assert enhanced_temp_dir.exists()
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assert (enhanced_temp_dir / "input").exists()
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assert (enhanced_temp_dir / "output").exists()
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assert (enhanced_temp_dir / "thumbnails").exists()
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assert (enhanced_temp_dir / "sprites").exists()
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assert (enhanced_temp_dir / "logs").exists()
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# Test video configuration
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assert video_config.base_path == enhanced_temp_dir
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assert "mp4" in video_config.output_formats
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assert "webm" in video_config.output_formats
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# Test video scenarios
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assert "standard_hd" in test_video_scenarios
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assert "short_clip" in test_video_scenarios
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assert test_video_scenarios["standard_hd"]["resolution"] == "1920x1080"
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print("✅ Enhanced fixtures test completed")
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@pytest.mark.unit
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def test_quality_metrics_tracking(quality_tracker):
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"""Test quality metrics tracking functionality."""
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# Simulate some test activity
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quality_tracker.record_assertion(True, "Basic functionality works")
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quality_tracker.record_assertion(True, "Configuration is valid")
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quality_tracker.record_assertion(False, "This is an expected failure for testing")
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# Record a warning
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quality_tracker.record_warning("This is a test warning")
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# Simulate video processing
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quality_tracker.record_video_processing(
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input_size_mb=50.0,
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duration=2.5,
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output_quality=8.7
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)
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# The metrics will be finalized automatically by the framework
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print("✅ Quality metrics tracking test completed")
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@pytest.mark.integration
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def test_mock_ffmpeg_environment(mock_ffmpeg_environment, quality_tracker):
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"""Test the comprehensive FFmpeg mocking environment."""
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# Test that mocks are available
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assert "success" in mock_ffmpeg_environment
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assert "failure" in mock_ffmpeg_environment
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assert "probe" in mock_ffmpeg_environment
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# Record this as a successful integration test
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quality_tracker.record_assertion(True, "FFmpeg environment mocked successfully")
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quality_tracker.record_video_processing(
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input_size_mb=25.0,
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duration=1.2,
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output_quality=9.0
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)
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print("✅ FFmpeg environment test completed")
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@pytest.mark.performance
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def test_performance_benchmarking(performance_benchmarks, quality_tracker):
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"""Test performance benchmarking functionality."""
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# Simulate a performance test
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start_time = time.time()
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# Simulate some work
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time.sleep(0.1)
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duration = time.time() - start_time
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# Check against benchmarks
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h264_720p_target = performance_benchmarks["encoding"]["h264_720p"]
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assert h264_720p_target > 0
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# Record performance metrics
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simulated_fps = 20.0 # Simulated encoding FPS
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quality_tracker.record_video_processing(
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input_size_mb=30.0,
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duration=duration,
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output_quality=8.0
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)
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quality_tracker.record_assertion(
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simulated_fps >= 10.0,
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f"Encoding FPS {simulated_fps} meets minimum requirement"
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)
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print(f"✅ Performance test completed in {duration:.3f}s")
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@pytest.mark.video_360
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def test_360_video_fixtures(video_360_fixtures, quality_tracker):
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"""Test 360° video processing fixtures."""
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# Test equirectangular projection
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equirect = video_360_fixtures["equirectangular"]
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assert equirect["projection"] == "equirectangular"
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assert equirect["fov"] == 360
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assert equirect["resolution"] == "4096x2048"
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# Test cubemap projection
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cubemap = video_360_fixtures["cubemap"]
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assert cubemap["projection"] == "cubemap"
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assert cubemap["expected_faces"] == 6
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# Record 360° specific metrics
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quality_tracker.record_assertion(True, "360° fixtures loaded correctly")
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quality_tracker.record_video_processing(
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input_size_mb=150.0, # 360° videos are typically larger
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duration=5.0,
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output_quality=8.5
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)
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print("✅ 360° video fixtures test completed")
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@pytest.mark.ai_analysis
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def test_ai_analysis_fixtures(ai_analysis_fixtures, quality_tracker):
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"""Test AI analysis fixtures."""
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# Test scene detection configuration
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scene_detection = ai_analysis_fixtures["scene_detection"]
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assert scene_detection["min_scene_duration"] == 2.0
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assert scene_detection["confidence_threshold"] == 0.8
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assert len(scene_detection["expected_scenes"]) == 2
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# Test object tracking configuration
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object_tracking = ai_analysis_fixtures["object_tracking"]
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assert object_tracking["min_object_size"] == 50
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assert object_tracking["max_objects_per_frame"] == 10
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# Record AI analysis metrics
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quality_tracker.record_assertion(True, "AI analysis fixtures configured")
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quality_tracker.record_assertion(True, "Scene detection parameters valid")
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print("✅ AI analysis fixtures test completed")
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@pytest.mark.streaming
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def test_streaming_fixtures(streaming_fixtures, quality_tracker):
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"""Test streaming and adaptive bitrate fixtures."""
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# Test adaptive streaming configuration
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adaptive = streaming_fixtures["adaptive_streams"]
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assert "360p" in adaptive["resolutions"]
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assert "720p" in adaptive["resolutions"]
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assert "1080p" in adaptive["resolutions"]
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assert len(adaptive["bitrates"]) == 3
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# Test live streaming configuration
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live = streaming_fixtures["live_streaming"]
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assert live["latency_target"] == 3.0
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assert live["keyframe_interval"] == 2.0
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# Record streaming metrics
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quality_tracker.record_assertion(True, "Streaming fixtures configured")
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quality_tracker.record_video_processing(
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input_size_mb=100.0,
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duration=3.0,
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output_quality=7.8
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)
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print("✅ Streaming fixtures test completed")
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@pytest.mark.slow
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def test_comprehensive_framework_integration(
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enhanced_temp_dir,
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video_config,
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quality_tracker,
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test_artifacts_dir,
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video_assert
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):
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"""Comprehensive test demonstrating full framework integration."""
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# Test artifacts directory
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assert test_artifacts_dir.exists()
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assert test_artifacts_dir.name.startswith("test_comprehensive_framework_integration")
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# Create a test artifact
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test_artifact = test_artifacts_dir / "test_output.txt"
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test_artifact.write_text("This is a test artifact")
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assert test_artifact.exists()
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# Simulate comprehensive video processing workflow
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quality_tracker.record_assertion(True, "Test environment setup")
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quality_tracker.record_assertion(True, "Configuration validated")
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quality_tracker.record_assertion(True, "Input video loaded")
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# Simulate multiple processing steps
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for i in range(3):
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quality_tracker.record_video_processing(
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input_size_mb=40.0 + i * 10,
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duration=1.0 + i * 0.5,
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output_quality=8.0 + i * 0.2
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)
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# Test custom assertions
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video_assert.assert_duration_preserved(10.0, 10.1, 0.2) # Should pass
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video_assert.assert_file_size_reasonable(45.0, 100.0) # Should pass
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quality_tracker.record_assertion(True, "All processing steps completed")
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quality_tracker.record_assertion(True, "Output validation successful")
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print("✅ Comprehensive framework integration test completed")
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if __name__ == "__main__":
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# Allow running this test file directly for quick testing
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pytest.main([__file__, "-v"]) |