151 lines
5.7 KiB
Plaintext
151 lines
5.7 KiB
Plaintext
# =============================================================================
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# Edge RK3588 production counter + dashboard
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# Shared config for: counter_live_rknn_bytetrack.py + counter_dashboard.py
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# Copy to .env on device: cp config.env.example .env && nano .env
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# =============================================================================
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# --- Core paths ---
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# Root output directory (logs, DB, video, CSV)
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OUTPUT_DIR=/opt/zenai-kpc-bt-counter
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# SQLite database path for daily counter records & crossing logs
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DB_PATH=/tmp/counter.db
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# JSON file persisting the current active counting day state
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STATE_FILE=/tmp/current_counter.json
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# --- Input source ---
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# RTSP / HTTP live stream, or a local video file path
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#SOURCE=rtsp://user:pass@192.168.0.100:554/stream1
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SOURCE=rtsp://10.38.30.64:8554/my_stream
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# FFmpeg capture options passed to cv2.VideoCapture (RTSP low-latency flags)
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OPENCV_FFMPEG_CAPTURE_OPTIONS=rtsp_transport;tcp|fflags;nobuffer|flags;low_delay
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# --- RKNN model ---
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# Path to exported .rknn model (YOLO format, e.g. yolo11n.rknn)
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MODEL_PATH=/opt/models/zenai_kac_sukawarna_20260702.rknn
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# Input image size for the model (square, e.g. 320 → 320×320)
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IMGSZ=320
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# Use FP16 inference on NPU (true/false); currently unused in ByteTrack variant
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HALF=false
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# NPU core mask: 1=core0, 2=core1, 3=core0+core1, 7=all three
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CORE_MASK=1
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# Compute device index (reserved; not used at runtime)
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DEVICE=0
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# --- YOLO decoder ---
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# Number of object classes the model outputs
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NUM_CLASSES=4
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# Apply sigmoid to raw class scores (true/false); set true if model head uses BCE logits
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SCORE_SIGMOID=false
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# --- Detection ---
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# Confidence threshold – detections below this are discarded before NMS
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CONF=0.5
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# --- ByteTrack tracking ---
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# Detections with score >= this get priority matching in the first association stage
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TRACK_HIGH_THRESH=0.5
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# Detections with score between this and TRACK_HIGH_THRESH are matched in the second stage
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TRACK_LOW_THRESH=0.3
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# IoU threshold for the first-stage association (0–1). Higher = stricter overlap required
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TRACK_MATCH_THRESH=0.7
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# Frames a track survives without a match before being permanently removed
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TRACK_BUFFER=60
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# Minimum consecutive (or total) hits needed before a track is considered confirmed
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TRACK_MIN_HITS=3
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# --- Display ---
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# Site name shown on the dashboard header (top-right)
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SITE_NAME=ZenAi
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# --- Object class names ---
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# Camera / location identifier shown in HUD and stored in DB
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CAMERA_NAME=ZenAi
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# Label used for batch grouping in the database
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OBJECT_LABEL=karung
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# Class name for the counted object (must match model class order)
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CLASS_OBJECT=karung
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# Model class ID for the object being counted (default 0)
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OBJECT_CLASS_ID=0
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# --- Line crossing ---
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# Two horizontal counting lines:
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# Line 1 (default ~33%): counts top-to-down (IN)
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# Line 2 (default ~66%): counts bottom-to-up (OUT)
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# Fixed y-coordinate for line 1/IN (overrides LINE_Y1_FRAC if set)
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LINE_Y1=
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# Fraction of frame height for line 1 (default 0.33)
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LINE_Y1_FRAC=0.70
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# Fixed y-coordinate for line 2 (overrides LINE_Y2_FRAC if set)
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LINE_Y2=
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# Fraction of frame height for line 2 (default 0.66)
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LINE_Y2_FRAC=0.30
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# --- Counting day management ---
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# Daily cutoff time (HH:MM) – a new counting day starts after this time and the
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# previous day's counter_in / counter_out totals are finalized in the database.
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# CUTOFF_TIME is an alias used by the dashboard; DAILY_CUTOFF_TIME takes priority in counter_live_rknn.py.
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DAILY_CUTOFF_TIME=20:00
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CUTOFF_TIME=20:00
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# --- CSV export ---
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# Write per-crossing events to a CSV file (true/false)
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EXPORT_CSV=false
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# Path where the crossing CSV is written
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CROSS_CSV=/tmp/crossings.csv
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# --- Rate / performance ---
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# Enable motion detection pre-filter: skip inference on frames with no movement
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# (true/false, default: false). When enabled, frames below MOTION_THRESHOLD are
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# skipped, saving NPU/CPU load.
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MOTION_DETECTION_ENABLED=true
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# Mean absolute pixel difference threshold (0–255) to consider a frame as having
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# motion. Lower = more sensitive. Default 5.0.
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MOTION_THRESHOLD=5.0
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# Sliding window in seconds for computing the crossing rate (objects/minute)
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RATE_WINDOW_SEC=60
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# Number of frames to discard at startup to let the stream buffer stabilise
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WARMUP_FRAMES=30
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# Delay in seconds between stream reconnection attempts
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RECONNECT_DELAY_SEC=3
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# Maximum reconnection attempts (0 = infinite)
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MAX_RECONNECT_ATTEMPTS=0
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# Seconds after which a tracked but unseen object is pruned from the active set
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TRACKED_PRUNE_SEC=300
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# --- Video recording ---
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# Save annotated frames to segmented MP4 files (true/false)
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RECORD_VIDEO=false
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# Duration in seconds of each video segment file
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VIDEO_SEGMENT_SEC=3600
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# Output video FPS (fallback if source FPS is unknown or ≤ 1)
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OUTPUT_FPS=15
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# --- Live stream snapshot ---
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# Periodically write the latest annotated frame as JPEG for an external web server
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LIVE_STREAM_ENABLED=true
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# Path to the shared-memory snapshot file (served by nginx / lighttpd)
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LIVE_STREAM_FRAME_PATH=/dev/shm/byetrack-counter/live_frame.jpg
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# JPEG quality (1–100)
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LIVE_STREAM_QUALITY=75
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# Write the snapshot every N frames (lower = more frequent updates)
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LIVE_STREAM_EVERY_N=2
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# --- Dashboard (counter_dashboard.py) ---
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# Flask secret key for session/cookie signing — change in production!
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SECRET_KEY=change-me-in-production
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# Bind address for the Flask web server
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DASHBOARD_HOST=0.0.0.0
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# Listen port for the dashboard web UI
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DASHBOARD_PORT=5000
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# Enable Flask debug mode (true/false) — auto-reloads on code changes; disable in production
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FLASK_DEBUG=false
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# Fallback name for the active counting-day JSON state file used by the dashboard
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CURRENT_COUNTER_PATH=/tmp/bytetrack_current_counter.json
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SAVE_CROSS_SNAPSHOT=true
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CROSS_SNAPSHOT_DIR=/opt/zenai-kpc-snaps/snapshots
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CROSS_SNAPSHOT_CLEANUP_SEC=3600
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CROSS_SNAPSHOT_MAX_AGE_DAYS=3
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DEDUP_PX=-1
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