# Evaluation Configuration
# This configuration file maps to command-line arguments in eval.py
# All settings can be overridden by command-line arguments

# Dataset paths (--det-path, --gt-path)
# dataset:
#   det_path: "/data1/dongying/Mono3d/G1M3/inference_results/yolov5s-300w-newdata/evalset_roi0"  # Root directory containing case folders with txt_results
#   gt_path: "/data1/xdzhu/Testdata_0129"        # Root directory containing case folders with labels
#   path_depth: 1  # Directory depth: 1 = det_path/case/txt_results, 2 = det_path/level1/case/txt_results

# CNCAP gt_path: "/mnt/mono3d/xdzhu_data/Mono3d/Mono3d_4face_2m_g1m3/driving_png/G1M3_AFS1616"

dataset:
  det_path: "/data1/dongying/Mono3d/G1M3/inference_results/yolov5s-300w-newdata/evalset_roi0"  # Root directory containing case folders with txt_results
  gt_path: "/data1/xdzhu/Testdata_0129"        # Root directory containing case folders with labels
  path_depth: 1  # Directory depth: 1 = det_path/case/txt_results, 2 = det_path/level1/case/txt_results
  det_format: "json"   # Detection file format: "auto" (probe json_results/ then txt_results/), "json", or "txt"
  gt_format: "json"    # Ground truth file format: "auto" (probe labels_json/ then labels/), "json", or "txt"

# Image properties (--img-width, --img-height)
image:
  width: 1920   # Default: 1920
  height: 1080  # Default: 1080

# Model configuration for GT filtering
model:
  input_size: 704  # Model input width (used for GT min box size calculation)
  min_box_size_at_input_scale: 8  # Minimum box size at model input scale
  # 
  # GT filtering threshold is automatically calculated as:
  # min_box_size = min_box_size_at_input_scale * roi_width / model_input_size
  # 
  # Examples:
  # - ROI0 (1920->704): 8 * 1920 / 704 ≈ 21.8 pixels at original scale
  # - ROI1 (704->704):  8 * 704 / 704 = 8.0 pixels at original scale

# Performance settings
performance:
  num_workers: 32  # Number of parallel workers (null = auto-detect, 1 = single process)
                     # Command-line: --num-workers
                     # Recommended: 4-8 for typical workloads

# ROI Ground Truth Processing (NEW!)
roi_gt:
  enabled: true                              # Enable ROI processing for ground truth
  calib_root: "/data1/xdzhu/Testdata_0129"  # Root path to calibration files (camera4.json)
  roi_config: [1920, 960]                    # ROI configuration (matches training config)
  roi_bottom_offset: 0                       # Pixels to trim from ROI bottom edge (matches training roi_bottom_offset)
  # roi_config can be:
  #   - [width, height]: ROI size mode (center crop based on vanishing point)
  #   - [x1, y1, x2, y2]: ROI bounds mode (fixed bounds)
  #   - dict: {'mode': 'size', 'width': 1920, 'height': 960} or 
  #           {'mode': 'bounds', 'x1': 0, 'y1': 120, 'x2': 1920, 'y2': 1080}
  # 
  # IMPORTANT: This should match the ROI configuration used during training!
  # For ROI0: [1920, 960] with roi_bottom_offset matching training config
  # For ROI1: [704, 352] with roi_bottom_offset: 0

# Class definitions
classes:
  3d_classes: [0, 1, 2, 3, 4, 5, 6, 7, 8]  # vehicle, bus, truck, tanker, unknown, pedestrian, bicycle, motorcyclist, tricycle
  2d_classes: [9, 10, 11, 12]               # traffic_sign, wheel, plate, face
  class_names:
    0: "vehicle"
    1: "bus"
    2: "truck"
    3: "tanker"
    4: "unknown"
    5: "pedestrian"
    6: "bicycle"
    7: "motorcyclist"
    8: "tricycle"
    9: "traffic_sign"
    10: "wheel"
    11: "plate"
    12: "face"
# Matching parameters (--iou-threshold)
matching:
  iou_threshold: 0.5  # Default: 0.5, IoU threshold for 2D matching
  
# 2D metrics configuration (--eval-2d-only, --conf-threshold, --ap-method)
metrics_2d:
  enabled: true                # Set to false with --eval-3d-only
  conf_threshold: 0.4          # Default: 0.5, confidence threshold for precision/recall
  ap_method: "voc2010"         # Default: "voc2010", choices: ["voc2010", "coco"]
                               # voc2010: 11-point interpolation
                               # coco: all-point interpolation
  distance_ranges:             # Optional: distance-wise 2D evaluation (in metres, z3d).
                               # Only applies to 3D-capable classes (vehicle/pedestrian/bicycle/rider)
                               # that carry a ground-truth z3d depth value.
                               # Independent from metrics_3d.distance_ranges — use coarser bins here
                               # so the per-range GT counts are large enough to be statistically meaningful.
    - [0, 30]                  # Near range
    - [30, 60]                 # Mid range
    - [60, 100]                # Far range
    - [100, 999]               # Extreme range
  lateral_roi: [-15, 15]     # Optional: lateral ROI pre-filter (metres, x3d).
                               # When set, produces a second evaluation view that restricts all
                               # counts (TP/FP/FN/GT) to objects within this lateral range.
                               # e.g. [-15, 15] keeps only objects within 15 m of the centre line.
  
# 3D metrics configuration (--eval-3d-only)
metrics_3d:
  enabled: true                # Set to false with --eval-2d-only
  heading_tolerance: "both"  # Heading error calculation mode (--heading-tolerance)
                               # "strict": Standard calculation (default)
                               # "relaxed": Consider 180° symmetry for symmetric objects
                               # "both": Calculate and report both strict and relaxed metrics
                               # Relaxed mode: for errors close to 180°, use min(error, π - error)
  distance_ranges:             # Optional: distance-wise evaluation (in meters)
    - [0, 10]                  # Near range
    - [10, 20]                 
    - [20, 30]
    - [30, 40]                 # Medium range
    - [40, 50]                 # Far range
    - [50, 60]
    - [60, 70]                 # Far range
    - [70, 80]
    - [80, 90]
    - [90, 100]
    - [100, 999]               # Very far range
  lateral_distance_ranges:     # Optional: lateral distance-wise evaluation (in meters, x-axis)
    - [-50, -40]              # Far left
    - [-40, -30]                # Medium left
    - [-30, -20]                # Near left far
    - [-20, -10]                # Near left medium
    - [-10, 0]                  # Near left
    - [0, 10]                   # Near right
    - [10, 20]                  # Medium right
    - [20, 30]                # Near right far
    - [30, 40]                # Medium right far
    - [40, 50]                # Far right

# Output configuration (--output-dir)
output:
  save_path: "eval_results_multiprocess/yolov5s/{timestamp}"  # {timestamp} will be auto-replaced
  formats: ["json", "txt"]            # Available: json, txt, csv
  print_details: true                 # Print detailed per-class metrics
  per_case_reports: true              # Generate per-case detailed reports

# Command-line override examples:
# 1. Override paths:
#    python eval_tools/eval.py --config eval_config.yaml --det-path /new/path
# 
# 2. Override workers:
#    python eval_tools/eval.py --config eval_config.yaml --num-workers 8
#
# 3. 2D only evaluation:
#    python eval_tools/eval.py --config eval_config.yaml --eval-2d-only
#
# 4. Custom thresholds:
#    python eval_tools/eval.py --config eval_config.yaml --iou-threshold 0.7 --conf-threshold 0.3