""" Configuration settings for the Video Event Detection and Preprocessing Pipeline. This file contains all configurable parameters that can be tweaked to control: - Keyframe extraction sensitivity - Event detection thresholds - Video quality settings - Output formats and paths """ import os from dataclasses import dataclass from typing import Dict, List, Optional, Tuple @dataclass class VideoProcessingConfig: """Main configuration class for video processing pipeline""" # ===== KEYFRAME EXTRACTION PARAMETERS ===== # Control how many keyframes are extracted # Base quality threshold (0.1-0.3): Lower = more keyframes, Higher = fewer but better quality base_quality_threshold: float = 0.15 # Motion detection threshold (0.005-0.02): Lower = more motion-sensitive, Higher = only significant motion motion_threshold: float = 0.008 # Burst sampling rate (1-10): Higher = more frames during high activity periods burst_sampling_rate: int = 3 # Frame sampling interval in seconds (0.5-3.0): Lower = more frequent sampling frame_sampling_interval: float = 1.0 # ===== EVENT DETECTION PARAMETERS ===== # Control how events are detected and prioritized # Event importance threshold (0.2-0.5): Lower = more events detected event_importance_threshold: float = 0.25 # Burst activity weight (1.5-3.0): Higher = burst frames get higher priority burst_weight: float = 2.5 # Temporal clustering window in seconds (10-30): Frames within this window are clustered temporal_clustering_window: float = 15.0 # Scene change detection threshold (0.01-0.05): Lower = more scene changes detected scene_change_threshold: float = 0.02 # ===== VIDEO SEGMENTATION PARAMETERS ===== # Control how video is divided into segments # Segment duration in seconds (30-60): Length of each temporal segment segment_duration: float = 45.0 # Keyframes per segment (3-8): How many keyframes to extract per segment keyframes_per_segment: int = 5 # ===== HIGHLIGHT REEL PARAMETERS ===== # Control the final summary video creation # Maximum summary duration in seconds (15-60): Total length of highlight reel max_summary_duration: float = 25.0 # Frame display duration in seconds (0.5-3.0): How long each frame is shown frame_display_duration: float = 1.5 # Maximum frames in summary (10-30): Total number of frames in highlight reel max_summary_frames: int = 18 # Summary video FPS (0.4-1.0): Playback speed of summary summary_fps: float = 0.6 # ===== DEDUPLICATION PARAMETERS ===== # Control duplicate frame removal # Similarity threshold (0.80-0.95): Higher = stricter deduplication similarity_threshold: float = 0.85 # Minimum time gap between frames in seconds (1-5): Prevents frames too close in time min_frame_gap: float = 2.0 # ===== COMPRESSION PARAMETERS ===== # Control video compression settings # Output resolution (720p, 1080p, or original) output_resolution: str = "720p" # Compression quality (18-28): Lower = better quality, larger files compression_crf: int = 23 # Compression preset (ultrafast, fast, medium, slow): Affects encoding speed vs efficiency compression_preset: str = "fast" # ===== ADAPTIVE ENHANCEMENT PARAMETERS ===== # Control image enhancement # Enable adaptive histogram equalization enable_clahe: bool = True # CLAHE clip limit (1.0-4.0): Higher = more contrast enhancement clahe_clip_limit: float = 2.0 # Enable denoising enable_denoising: bool = True # Denoising strength (3-10): Higher = more denoising denoise_strength: int = 5 # ===== OUTPUT SETTINGS ===== # Control output files and formats # Base output directory output_base_dir: str = "video_processing_outputs" # Enable various output formats generate_json_reports: bool = True generate_html_gallery: bool = True generate_compressed_video: bool = True generate_segments: bool = True generate_highlight_reels: bool = False # Disabled for security focus - saves processing time # Video output format (mp4, avi, mov) video_output_format: str = "mp4" # ===== ADVANCED PARAMETERS ===== # Fine-tuning for specific use cases # Enable GPU acceleration if available use_gpu_acceleration: bool = True # Enable face detection for human-centric events enable_face_detection: bool = False # Enable object detection for context-aware processing enable_object_detection: bool = False # Enable facial recognition for suspicious person tracking (FULL implementation with FAISS + MongoDB) enable_facial_recognition: bool = True # Face recognition confidence threshold (0.5-0.95) face_recognition_confidence: float = 0.7 # Face detection model to use (MTCNN for detection, FaceNet for embeddings) face_detection_model: str = "mtcnn" # Face recognition model to use (InceptionResnetV1 with FAISS similarity search) face_recognition_model: str = "facenet_faiss" # Enable suspicious person database and tracking suspicious_person_tracking: bool = True # Face database settings face_database_enabled: bool = True # ===== OBJECT DETECTION PARAMETERS ===== # Configuration for fire, knife, gun detection # Models directory path (relative to backend directory when running from project root) models_dir: str = os.path.join(os.path.dirname(__file__), "models") # Object detection confidence threshold (0.1-0.9) object_detection_confidence: float = 0.5 # Temporal window for grouping object detections into events (seconds) object_event_temporal_window: float = 5.0 # Enable annotation of detected objects on keyframes enable_object_annotation: bool = True # Object detection specific thresholds fire_detection_confidence: float = 0.7 # Lower threshold for fire (safety critical) weapon_detection_confidence: float = 0.7 # Higher threshold for weapons (reduce false positives) # Enable specific object types enable_fire_detection: bool = True enable_weapon_detection: bool = True # Object event importance multiplier object_event_importance_multiplier: float = 2.0 # ===== BEHAVIOR ANALYSIS PARAMETERS ===== # Configuration for behavior/action recognition (fighting, accidents, climbing) # Enable behavior analysis enable_behavior_analysis: bool = False # Behavior analysis models directory behavior_models_dir: str = os.path.join(os.path.dirname(__file__), "behavior_analysis") # Behavior detection confidence thresholds per action type (0.3-0.8) fighting_detection_confidence: float = 0.5 accident_detection_confidence: float = 0.6 climbing_detection_confidence: float = 0.7 # Temporal window for grouping behavior detections into events (seconds) behavior_event_temporal_window: float = 5.0 # Behavior event importance multiplier behavior_event_importance_multiplier: float = 2.5 # Enable specific behavior types enable_fighting_detection: bool = True enable_accident_detection: bool = True enable_climbing_detection: bool = True # ===== VIDEO CAPTIONING PARAMETERS ===== # Configuration for video frame captioning with vision-language models # Enable video captioning enable_video_captioning: bool = False # Vision model for caption generation captioning_vision_model: str = "Salesforce/blip-image-captioning-base" # Embedding model for semantic search captioning_embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2" # Device for captioning models (cpu or cuda) captioning_device: str = "cpu" # Batch size for captioning (increased for better throughput) captioning_batch_size: int = 8 # Database paths for caption storage captioning_db_path: str = None # Will use default if None captioning_vector_db_path: str = "./video_captioning_store" # Enable async processing for captioning captioning_async: bool = True # Parallel processing workers (1-8): More workers = faster but more memory num_workers: int = 4 def __post_init__(self): """Validate configuration parameters""" # Ensure output directory exists os.makedirs(self.output_base_dir, exist_ok=True) # Validate thresholds assert 0.1 <= self.base_quality_threshold <= 0.3, "Quality threshold must be between 0.1-0.3" assert 0.005 <= self.motion_threshold <= 0.02, "Motion threshold must be between 0.005-0.02" assert 0.8 <= self.similarity_threshold <= 0.95, "Similarity threshold must be between 0.8-0.95" # ===== PRESET CONFIGURATIONS ===== def get_high_recall_config() -> VideoProcessingConfig: """Configuration optimized for capturing more events (more keyframes)""" return VideoProcessingConfig( base_quality_threshold=0.12, # Lower quality threshold motion_threshold=0.005, # Very sensitive motion detection event_importance_threshold=0.20, # Lower event threshold max_summary_frames=25, # More frames in summary frame_sampling_interval=0.8, # More frequent sampling temporal_clustering_window=20.0, # Wider clustering window burst_weight=3.0, # Higher burst priority keyframes_per_segment=6 # More keyframes per segment ) def get_high_precision_config() -> VideoProcessingConfig: """Configuration optimized for quality over quantity (fewer but better keyframes)""" return VideoProcessingConfig( base_quality_threshold=0.20, # Higher quality threshold motion_threshold=0.015, # Less sensitive motion detection event_importance_threshold=0.35, # Higher event threshold max_summary_frames=12, # Fewer frames in summary frame_sampling_interval=1.5, # Less frequent sampling temporal_clustering_window=10.0, # Tighter clustering burst_weight=2.0, # Moderate burst priority keyframes_per_segment=4 # Fewer keyframes per segment ) def get_balanced_config() -> VideoProcessingConfig: """Balanced configuration for general use""" return VideoProcessingConfig() # Uses default values # Removed robbery detection config - using security_focused_config instead def get_security_focused_config() -> VideoProcessingConfig: """Configuration optimized specifically for security and threat detection""" return VideoProcessingConfig( base_quality_threshold=0.12, motion_threshold=0.005, # Very sensitive event_importance_threshold=0.20, burst_weight=3.0, # Highest priority for burst activity temporal_clustering_window=20.0, max_summary_frames=25, frame_display_duration=2.0, similarity_threshold=0.82, enable_clahe=True, clahe_clip_limit=3.0, # Enhanced object detection for security enable_object_detection=True, object_detection_confidence=0.4, # Lower threshold for better recall fire_detection_confidence=0.5, # Very sensitive for fire weapon_detection_confidence=0.7, # Higher threshold for weapons to reduce false positives object_event_temporal_window=8.0, # Longer window for complex events enable_object_annotation=True, object_event_importance_multiplier=3.0, # High importance for security events # Enhanced behavior analysis for security enable_behavior_analysis=True, fighting_detection_confidence=0.5, accident_detection_confidence=0.6, climbing_detection_confidence=0.7, behavior_event_temporal_window=8.0, # Longer window for complex events behavior_event_importance_multiplier=3.0, # High importance for security events # Video captioning for semantic search enable_video_captioning=True, captioning_device="cpu" # Change to "cuda" if GPU available ) # ===== PARAMETER ADJUSTMENT GUIDE ===== PARAMETER_GUIDE = { "More Keyframes": { "base_quality_threshold": "Decrease (0.10-0.12)", "motion_threshold": "Decrease (0.005-0.008)", "event_importance_threshold": "Decrease (0.20-0.25)", "max_summary_frames": "Increase (20-30)", "keyframes_per_segment": "Increase (6-8)", "frame_sampling_interval": "Decrease (0.5-1.0)" }, "Fewer Keyframes": { "base_quality_threshold": "Increase (0.18-0.25)", "motion_threshold": "Increase (0.012-0.020)", "event_importance_threshold": "Increase (0.30-0.40)", "max_summary_frames": "Decrease (8-15)", "keyframes_per_segment": "Decrease (3-4)", "frame_sampling_interval": "Increase (1.5-2.5)" }, "Better Quality": { "base_quality_threshold": "Increase (0.18-0.25)", "compression_crf": "Decrease (18-20)", "enable_clahe": "True", "enable_denoising": "True", "output_resolution": "'1080p'" }, "Faster Processing": { "compression_preset": "'ultrafast'", "num_workers": "Increase (6-8)", "enable_face_detection": "False", "enable_object_detection": "False", "keyframes_per_segment": "Decrease (3-4)" }, "More Sensitive Event Detection": { "motion_threshold": "Decrease (0.005-0.008)", "burst_weight": "Increase (2.5-3.0)", "event_importance_threshold": "Decrease (0.20-0.25)", "temporal_clustering_window": "Increase (15-25)" } } def print_parameter_guide(): """Print parameter adjustment guide""" print("šŸ”§ VIDEO PROCESSING PARAMETER ADJUSTMENT GUIDE") print("=" * 60) for goal, params in PARAMETER_GUIDE.items(): print(f"\nšŸŽÆ {goal}:") for param, adjustment in params.items(): print(f" ā€¢ {param}: {adjustment}") print(f"\nšŸ“ Available Preset Configurations:") print(f" ā€¢ get_high_recall_config() - More keyframes, sensitive detection") print(f" ā€¢ get_high_precision_config() - Fewer but higher quality keyframes") print(f" ā€¢ get_balanced_config() - General purpose settings") print(f" ā€¢ get_security_focused_config() - Optimized for security/threat detection") if __name__ == "__main__": print_parameter_guide()