153 lines
7.3 KiB
YAML
153 lines
7.3 KiB
YAML
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# Evaluation Configuration
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# This configuration file maps to command-line arguments in eval.py
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# All settings can be overridden by command-line arguments
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# Dataset paths (--det-path, --gt-path)
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# dataset:
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# det_path: "/data1/dongying/Mono3d/G1M3/inference_results/yolov5s-300w-newdata/evalset_roi0" # Root directory containing case folders with txt_results
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# gt_path: "/data1/xdzhu/Testdata_0129" # Root directory containing case folders with labels
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# path_depth: 1 # Directory depth: 1 = det_path/case/txt_results, 2 = det_path/level1/case/txt_results
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# CNCAP gt_path: "/mnt/mono3d/xdzhu_data/Mono3d/Mono3d_4face_2m_g1m3/driving_png/G1M3_AFS1616"
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dataset:
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det_path: "/data1/dongying/Mono3d/G1M3/inference_results/yolov5s-300w-newdata/evalset_roi1" # Root directory containing case folders with txt_results
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gt_path: "/data1/xdzhu/Testdata_0129" # Root directory containing case folders with labels
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path_depth: 1 # Directory depth: 1 = det_path/case/txt_results, 2 = det_path/level1/case/txt_results
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det_format: "auto" # Detection file format: "auto" (probe json_results/ then txt_results/), "json", or "txt"
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gt_format: "auto" # Ground truth file format: "auto" (probe labels_json/ then labels/), "json", or "txt"
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# Image properties (--img-width, --img-height)
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image:
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width: 1920 # Default: 1920
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height: 1080 # Default: 1080
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# Model configuration for GT filtering
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model:
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input_size: 704 # Model input width (used for GT min box size calculation)
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min_box_size_at_input_scale: 8 # Minimum box size at model input scale
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#
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# GT filtering threshold is automatically calculated as:
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# min_box_size = min_box_size_at_input_scale * roi_width / model_input_size
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#
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# Examples:
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# - ROI0 (1920->704): 8 * 1920 / 704 ≈ 21.8 pixels at original scale
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# - ROI1 (704->704): 8 * 704 / 704 = 8.0 pixels at original scale
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# Performance settings
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performance:
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num_workers: 32 # Number of parallel workers (null = auto-detect, 1 = single process)
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# Command-line: --num-workers
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# Recommended: 4-8 for typical workloads
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# ROI Ground Truth Processing (NEW!)
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roi_gt:
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enabled: true # Enable ROI processing for ground truth
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calib_root: "/data1/xdzhu/Testdata_0129" # Root path to calibration files (camera4.json)
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roi_config: [704, 352] # ROI configuration (matches training config)
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roi_bottom_offset: 0 # Pixels to trim from ROI bottom edge (matches training roi_bottom_offset)
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# roi_config can be:
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# - [width, height]: ROI size mode (center crop based on vanishing point)
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# - [x1, y1, x2, y2]: ROI bounds mode (fixed bounds)
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# - dict: {'mode': 'size', 'width': 1920, 'height': 960} or
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# {'mode': 'bounds', 'x1': 0, 'y1': 120, 'x2': 1920, 'y2': 1080}
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#
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# IMPORTANT: This should match the ROI configuration used during training!
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# For ROI0: [1920, 960] with roi_bottom_offset matching training config
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# For ROI1: [704, 352] with roi_bottom_offset: 0
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# Class definitions
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classes:
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3d_classes: [0, 1, 2, 3, 4, 5, 6, 7, 8] # vehicle, bus, truck, tanker, unknown, pedestrian, bicycle, motorcyclist, tricycle
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2d_classes: [9, 10, 11, 12] # traffic_sign, wheel, plate, face
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class_names:
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0: "vehicle"
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1: "bus"
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2: "truck"
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3: "tanker"
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4: "unknown"
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5: "pedestrian"
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6: "bicycle"
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7: "motorcyclist"
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8: "tricycle"
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9: "traffic_sign"
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10: "wheel"
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11: "plate"
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12: "face"
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# Matching parameters (--iou-threshold)
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matching:
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iou_threshold: 0.5 # Default: 0.5, IoU threshold for 2D matching
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# 2D metrics configuration (--eval-2d-only, --conf-threshold, --ap-method)
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metrics_2d:
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enabled: true # Set to false with --eval-3d-only
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conf_threshold: 0.4 # Default: 0.5, confidence threshold for precision/recall
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ap_method: "voc2010" # Default: "voc2010", choices: ["voc2010", "coco"]
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# voc2010: 11-point interpolation
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# coco: all-point interpolation
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distance_ranges: # Optional: distance-wise 2D evaluation (in metres, z3d).
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# Only applies to 3D-capable classes (vehicle/pedestrian/bicycle/rider)
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# that carry a ground-truth z3d depth value.
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# Independent from metrics_3d.distance_ranges — use coarser bins here
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# so the per-range GT counts are large enough to be statistically meaningful.
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- [0, 30] # Near range
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- [30, 60] # Mid range
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- [60, 100] # Far range
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- [100, 999] # Extreme range
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lateral_roi: [-15, 15] # Optional: lateral ROI pre-filter (metres, x3d).
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# When set, produces a second evaluation view that restricts all
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# counts (TP/FP/FN/GT) to objects within this lateral range.
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# e.g. [-15, 15] keeps only objects within 15 m of the centre line.
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# 3D metrics configuration (--eval-3d-only)
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metrics_3d:
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enabled: true # Set to false with --eval-2d-only
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heading_tolerance: "both" # Heading error calculation mode (--heading-tolerance)
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# "strict": Standard calculation (default)
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# "relaxed": Consider 180° symmetry for symmetric objects
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# "both": Calculate and report both strict and relaxed metrics
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# Relaxed mode: for errors close to 180°, use min(error, π - error)
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distance_ranges: # Optional: distance-wise evaluation (in meters)
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- [0, 10] # Near range
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- [10, 20]
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- [20, 30]
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- [30, 40] # Medium range
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- [40, 50] # Far range
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- [50, 60]
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- [60, 70] # Far range
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- [70, 80]
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- [80, 90]
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- [90, 100]
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- [100, 999] # Very far range
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lateral_distance_ranges: # Optional: lateral distance-wise evaluation (in meters, x-axis)
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- [-50, -40] # Far left
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- [-40, -30] # Medium left
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- [-30, -20] # Near left far
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- [-20, -10] # Near left medium
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- [-10, 0] # Near left
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- [0, 10] # Near right
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- [10, 20] # Medium right
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- [20, 30] # Near right far
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- [30, 40] # Medium right far
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- [40, 50] # Far right
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# Output configuration (--output-dir)
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output:
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save_path: "eval_results_multiprocess/yolov5s/{timestamp}" # {timestamp} will be auto-replaced
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formats: ["json", "txt"] # Available: json, txt, csv
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print_details: true # Print detailed per-class metrics
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per_case_reports: true # Generate per-case detailed reports
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# Command-line override examples:
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# 1. Override paths:
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# python eval_tools/eval.py --config eval_config.yaml --det-path /new/path
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#
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# 2. Override workers:
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# python eval_tools/eval.py --config eval_config.yaml --num-workers 8
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#
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# 3. 2D only evaluation:
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# python eval_tools/eval.py --config eval_config.yaml --eval-2d-only
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#
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# 4. Custom thresholds:
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# python eval_tools/eval.py --config eval_config.yaml --iou-threshold 0.7 --conf-threshold 0.3
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