106 lines
2.5 KiB
YAML
106 lines
2.5 KiB
YAML
# Frigate-Mini Configuration - CPU Only (ONNX)
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#
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# This configuration uses ONNX Runtime for CPU-only inference.
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# No special hardware required - works on any x86 or ARM system.
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#
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# Usage:
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# python scripts/frigate_mini.py --config configs/frigate_mini_cpu.yaml
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# Global settings
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debug: true # Enable debug mode
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log_level: "info" # debug, info, warning, error
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# Detector configuration - ONNX CPU Mode
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detector:
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type: "pt" # Use PYTORCH Runtime (CPU)
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model_path: "models/krg_masuk_yolov9t_best.pt" # PT model file
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input_size: [640, 640] # Model input resolution [width, height]
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conf_threshold: 0.25 # Detection confidence threshold
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nms_threshold: 0.45 # NMS IoU threshold
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# ONNX specific settings
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onnx:
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device: "cpu" # cpu or cuda
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num_threads: 0 # CPU threads (0 = auto, uses all cores)
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optimization_level: "all" # none, basic, extended, all
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# No fallback needed for ONNX (it's already the most compatible)
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fallback:
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enabled: false
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# Video sources (cameras)
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cameras:
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default:
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enabled: true
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source: "input/video.mp4" # MP4 file path
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fps: 5 # Processing FPS (adjust based on CPU speed)
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loop: true # Loop video playback
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detect:
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enabled: true
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width: 1280 # Processing resolution
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height: 720
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objects:
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track:
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- person
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- car
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- dog
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- cat
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filters:
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person:
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min_area: 1000
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min_score: 0.4
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car:
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min_area: 2000
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min_score: 0.35
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# Snapshot settings
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snapshots:
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enabled: true
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output_dir: "output/snapshots"
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trigger:
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objects:
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- person
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- car
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min_score: 0.5
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cooldown: 2.0
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format: "jpg"
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quality: 95
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clean: true # No annotations on snapshot
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# Annotation export
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annotations:
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enabled: true
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output_dir: "output/labels"
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format: "yolo"
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pair_with_snapshots: true
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min_score: 0.3
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# Debug output
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debug_output:
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enabled: true
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output_dir: "output/debug"
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object_list:
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enabled: true
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show_confidence: true
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show_class: true
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show_bbox: true
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visualization:
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enabled: true
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draw_boxes: true
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draw_labels: true
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save_interval: 10 # Save every 10 frames
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stats:
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show_fps: true
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log_interval: 100
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# Class names (COCO)
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class_names:
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0: karung
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