# YOLO + SAM2 Video Processing Configuration
# This file serves as a complete reference for all available settings.

input:
  # Full path to the input video file.
  video_path: "/path/to/input/video.mp4"
  
output:
  # Directory where all output files and segments will be stored.
  directory: "/path/to/output/"
  # Filename for the final assembled video.
  filename: "processed_video.mp4"
  
processing:
  # Duration of each video segment in seconds. Shorter segments use less memory.
  segment_duration: 5
  
  # Scale factor for SAM2 inference (e.g., 0.5 = half resolution).
  # Lower values are faster but may reduce mask quality.
  inference_scale: 0.5
  
  # YOLO detection confidence threshold (0.0 to 1.0).
  yolo_confidence: 0.6
  
  # Which segments to run YOLO detection on.
  # Options: "all", a list of specific segment indices (e.g., [0, 10, 20]), or [] for default ("all").
  detect_segments: "all"
  
  # --- VR180 Stereo Processing ---
  # Enables special logic for VR180 SBS video. When false, video is treated as a single view.
  separate_eye_processing: false
  
  # Threshold for stereo mask agreement (Intersection over Union).
  # A value of 0.5 means masks must overlap by 50% to be considered a pair.
  stereo_iou_threshold: 0.5
  
  # Factor to reduce YOLO confidence by if no stereo pairs are found on the first try (e.g., 0.8 = 20% reduction).
  confidence_reduction_factor: 0.8
  
  # If no humans are detected in a segment, create a full green screen video.
  # Only used when separate_eye_processing is true.
  enable_greenscreen_fallback: true
  
  # Pixel overlap between left/right eyes for smoother blending at the center seam.
  eye_overlap_pixels: 0
  
models:
  # YOLO mode: "detection" (for bounding boxes) or "segmentation" (for direct masks).
  # "segmentation" is generally recommended as it provides initial masks to SAM2.
  yolo_mode: "segmentation"
  
  # Path to the YOLO model for "detection" mode.
  yolo_detection_model: "models/yolo/yolo11l.pt"
  # Path to the YOLO model for "segmentation" mode.
  yolo_segmentation_model: "models/yolo/yolo11x-seg.pt"
  
  # --- SAM2 Model Configuration ---
  sam2_checkpoint: "models/sam2/checkpoints/sam2.1_hiera_small.pt"
  sam2_config: "models/sam2/configs/sam2.1/sam2.1_hiera_s.yaml"
  # (Experimental) Use optimized VOS predictor for a significant speedup. Requires PyTorch 2.5.1+.
  sam2_vos_optimized: false

video:
  # Use NVIDIA's NVENC for hardware-accelerated video encoding.
  use_nvenc: true
  
  # Bitrate for the output video (e.g., "25M", "50M").
  output_bitrate: "50M"
  
  # If true, the audio track from the input video will be copied to the final output.
  preserve_audio: true
  
  # Force keyframes at the start of each segment for clean cuts. Recommended to keep true.
  force_keyframes: true
  
advanced:
  # RGB color for the green screen background.
  green_color: [0, 255, 0]
  
  # RGB color for the second object's mask (typically the right eye in VR180).
  blue_color: [255, 0, 0]
  
  # The class ID for humans in the YOLO model (COCO default is 0 for "person").
  human_class_id: 0
  
  # If true, deletes intermediate files like segment videos after processing.
  cleanup_intermediate_files: true
  
  # Logging level: DEBUG, INFO, WARNING, ERROR.
  log_level: "INFO"

  # If true, saves debug images for YOLO detections.
  save_yolo_debug_frames: true
  
  # --- Mid-Segment Re-detection ---
  # Re-run YOLO at intervals within a segment to correct tracking drift.
  enable_mid_segment_detection: false
  redetection_interval: 30 # Frames between re-detections.
  max_redetections_per_segment: 10

  # --- Parallel Processing Optimizations ---
  # (Experimental) Generate low-res videos for upcoming segments in the background.
  enable_background_lowres_generation: false
  max_concurrent_lowres: 2 # Max parallel FFmpeg processes.
  lowres_segments_ahead: 2 # How many segments to prepare in advance.
  use_ffmpeg_lowres: true # Use FFmpeg (faster) instead of OpenCV for low-res creation.

# --- Mask Quality Enhancement Settings ---
# These settings allow fine-tuning of the final mask appearance.
# Enabling these may increase processing time.
mask_processing:
  # Edge feathering and blurring for smoother transitions.
  enable_edge_blur: true
  edge_blur_radius: 3
  edge_blur_sigma: 0.5
  
  # Temporal smoothing to reduce mask flickering between frames.
  enable_temporal_smoothing: false
  temporal_blend_weight: 0.2
  temporal_history_frames: 2
  
  # Clean up small noise and holes in the mask.
  # Generally not needed when using SAM2, as its masks are high quality.
  enable_morphological_cleaning: false
  morphology_kernel_size: 5
  min_component_size: 500
  
  # Method for blending the mask edge with the background.
  # Options: "linear" (fastest), "gaussian", "sigmoid".
  alpha_blending_mode: "linear"
  alpha_transition_width: 1
  
  # Advanced edge-preserving smoothing filter. Slower but can produce higher quality edges.
  enable_bilateral_filter: false
  bilateral_d: 9
  bilateral_sigma_color: 75
  bilateral_sigma_space: 75