add sam2 yolo auto annotation
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# Frigate-Mini-RKNN Configuration
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# Minimal Frigate fork for RKNN inference with MP4 input
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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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# Model / Detector configuration
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detector:
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type: "rknn" # rknn, onnx, or yolo
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model_path: "models/yolov9t.rknn" # Path to 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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# RKNN specific settings
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rknn:
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target_platform: "rk3588" # rk3588, rk3568, rk3566, rk3562, rv1106, rv1103
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core_mask: 7 # NPU core mask (RK3588: 7=all 3 cores, 1/2/4=single core)
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async_mode: false # Async inference mode
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# Fallback settings (when RKNN not available)
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fallback:
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enabled: true # Fall back to ONNX/YOLO if RKNN fails
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type: "yolo" # onnx or yolo
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device: "cpu" # cpu or cuda
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# Video sources (cameras)
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cameras:
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# Example camera 1: Front door
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front_door:
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enabled: true
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source: "input/front_door.mp4" # MP4 file path (acts as camera feed)
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fps: 5 # Max processing FPS
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loop: true # Loop video when finished
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# Detection settings
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detect:
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enabled: true
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width: 1280 # Processing resolution width
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height: 720 # Processing resolution height
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# Object filtering per camera
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objects:
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track: # Objects to detect
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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 # Minimum bbox area in pixels
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max_area: 500000 # Maximum bbox area
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min_score: 0.4 # Minimum confidence
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car:
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min_area: 2000
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min_score: 0.35
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# Example camera 2: Backyard
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backyard:
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enabled: false # Disabled by default
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source: "input/backyard.mp4"
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fps: 5
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loop: true
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detect:
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enabled: true
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width: 1280
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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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- dog
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- cat
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- bird
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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 settings
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trigger:
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objects: # Objects that trigger snapshot
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- person
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- car
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min_score: 0.5 # Minimum score to trigger snapshot
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cooldown: 2.0 # Seconds between snapshots per object type
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# Output settings
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format: "jpg" # jpg or png
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quality: 95 # JPEG quality (1-100)
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clean: true # Save clean snapshots (no bboxes drawn)
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crop: false # Crop to detected object bbox
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retain_days: 7 # Days to keep snapshots (0 = forever)
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# Annotation export settings
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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" # YOLO format (class x_center y_center w h)
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# Pairing with snapshots
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pair_with_snapshots: true # Create snapshot-label pairs
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# Filtering
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min_score: 0.3 # Minimum score to include in annotation
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classes: null # null = all classes, or list like [0, 2]
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# Debug output settings
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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 display (console output)
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object_list:
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enabled: true
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show_confidence: true # Show confidence scores
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show_class: true # Show class names
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show_bbox: true # Show bbox coordinates
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show_track_id: false # Show tracking ID (if tracking enabled)
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# Visualization (debug images)
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visualization:
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enabled: true
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draw_boxes: true # Draw bounding boxes
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draw_labels: true # Draw class labels
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draw_confidence: true # Draw confidence scores
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box_color: [0, 255, 0] # BGR color for boxes
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box_thickness: 2 # Line thickness
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font_scale: 0.5 # Font scale for labels
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save_interval: 10 # Save every N frames (0 = save all)
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# Statistics
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stats:
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show_fps: true # Show FPS counter
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show_detection_count: true # Show total detections
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log_interval: 100 # Log stats every N frames
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# Class name mapping (same as annotator for consistency)
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class_names:
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0: person
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1: bicycle
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2: car
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3: motorcycle
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4: airplane
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5: bus
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6: train
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7: truck
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8: boat
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9: traffic light
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10: fire hydrant
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11: stop sign
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12: parking meter
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13: bench
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14: bird
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15: cat
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16: dog
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17: horse
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18: sheep
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19: cow
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20: elephant
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21: bear
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22: zebra
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23: giraffe
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24: backpack
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25: umbrella
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26: handbag
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27: tie
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28: suitcase
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29: frisbee
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30: skis
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31: snowboard
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32: sports ball
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33: kite
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34: baseball bat
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35: baseball glove
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36: skateboard
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37: surfboard
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38: tennis racket
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39: bottle
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40: wine glass
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41: cup
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42: fork
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43: knife
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44: spoon
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45: bowl
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46: banana
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47: apple
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48: sandwich
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49: orange
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50: broccoli
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51: carrot
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52: hot dog
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53: pizza
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54: donut
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55: cake
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56: chair
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57: couch
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58: potted plant
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59: bed
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60: dining table
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61: toilet
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62: tv
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63: laptop
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64: mouse
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65: remote
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66: keyboard
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67: cell phone
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68: microwave
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69: oven
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70: toaster
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71: sink
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72: refrigerator
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73: book
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74: clock
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75: vase
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76: scissors
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77: teddy bear
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78: hair drier
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79: toothbrush
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