191 lines
8.0 KiB
Bash
191 lines
8.0 KiB
Bash
# =============================================================================
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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/bytetrack-counter
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# SQLite database path for batch entries & crossing logs
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DB_PATH=/opt/bytetrack-counter/bytetrack_counter.db
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# JSON file persisting the current active batch state
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STATE_FILE=/tmp/bytetrack_current_batch.json
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# Shared memory directory used for reset signal (touch .reset to clear counter state)
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SHM_DIR=/dev/shm/bytetrack-counter
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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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# 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/yolo9t.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=7
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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 (e.g. 2 = ayam + talenan)
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NUM_CLASSES=2
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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=0.3
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# --- ByteTrack tracking ---
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# General for ayam, index 0
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# Detections with score ≥ this get priority matching in the first association stage
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TRACK_HIGH_THRESH_0=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=0.1
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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=0.8
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# Frames a track survives without a match before being permanently removed
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TRACK_BUFFER_0=30
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# Minimum consecutive (or total) hits needed before a track is considered confirmed
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TRACK_MIN_HITS_0=3
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# For talenan, index 1
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# Detections with score ≥ this get priority matching in the first association stage
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TRACK_HIGH_THRESH_1=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_1=0.1
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# IoU threshold for the first-stage association (0–1). Higher = stricter overlap required
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TRACK_MATCH_THRESH_1=0.6
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# Frames a track survives without a match before being permanently removed
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TRACK_BUFFER_1=30
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# Minimum consecutive (or total) hits needed before a track is considered confirmed
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TRACK_MIN_HITS_1=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=ayam-potong
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# Class name for the counted object (must match NUM_CLASSES order, index 0)
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CLASS_AYAM=ayam
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# Class name for the batch-closing trigger object (must match NUM_CLASSES order, index 1)
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CLASS_TALENAN=talenan
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# --- Line crossing ---
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# Direction for counting: rtl (right-to-left, default) | ltr (left-to-right) | both
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CROSS_DIRECTION=rtl
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# Fixed x-coordinate for the counting line (overrides LINE_X_FRAC if set)
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LINE_X=
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# Fraction of frame width where the counting line is drawn (default 0.5 = centre)
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LINE_X_FRAC=0.5
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# --- Batch management ---
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# Daily cutoff time (HH:MM) – a new day's batch numbering starts after this time.
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# CUTOFF_TIME is an alias used by the dashboard; DAILY_CUTOFF_TIME takes priority in counter_live_rknn_bytetrack.py.
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DAILY_CUTOFF_TIME=20:00
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CUTOFF_TIME=20:00
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# Reset frame counter and ByteTrack track-ID counter back to 0 when the daily cutoff is reached (true/false, default: true)
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RESET_COUNTERS_AT_CUTOFF=true
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# Seconds of inactivity after which the current batch is auto-closed
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BATCH_TIMEOUT_SECONDS=300
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# Seconds a newly-opened batch ignores the talenan label before accepting a close trigger
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IGNORE_BATCH_LABEL_TIMEOUT_SECONDS=30
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# Minimum number of objects required for a batch to be saved as valid
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MIN_OBJECT_PER_BATCH=60
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# Minimum duration in seconds a batch must be open to be saved as valid
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MIN_DURATION_PER_BATCH=60
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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=true
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# Path where the crossing CSV is written
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CROSS_CSV=/opt/batch-counter/batch_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=false
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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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# Print status log every N processed frames
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FLUSH_EVERY_N_FRAMES=100
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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=false
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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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# Template file: "dashboard.html" (default), "dashboard_lamborghini.html", or "dashboard_tesla.html"
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DASHBOARD_TEMPLATE=dashboard.html
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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-batch JSON state file used by the dashboard
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CURRENT_BATCH_PATH=/tmp/bytetrack_current_batch.json
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# --- Recounting Dashboard (recounting_dashboard.py) ---
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# Bind address and port
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RECOUNTING_DASHBOARD_PORT=5002
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# Base URL of the live counter dashboard API (same host)
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LIVE_API_URL=http://localhost:5000
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# Base URL of the recounting counter dashboard API (second node)
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RECOUNT_API_URL=http://localhost:5001
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# go2rtc base URL for consuming MJPEG stream (browser-facing)
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GO2RTC_URL=http://localhost:1984
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# RTSP port used by ffmpeg for pushing stream to go2rtc
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RTSP_PORT=8554
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# Stream name pushed to RTSP and consumed from go2rtc
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RECOUNT_STREAM_NAME=stream_1
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# --- Recounting Upload Dashboard (recounting_dashboard_upload.py) ---
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# Bind port for the upload-first recount dashboard (distinct from 5002)
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RECOUNTING_UPLOAD_PORT=5003
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# Directory where uploaded MP4 files are stored
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UPLOAD_DIR=/opt/bytetrack-counter/uploads
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# Command to launch the recounting counter process (placeholder, fill in real command).
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# {path} is substituted with the absolute path of the uploaded MP4 file.
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RECOUNT_CMD=bytetrack-counter config.env --source {path}
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# Template file for the upload dashboard: "recounting_upload.html" (default)
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RECOUNTING_UPLOAD_TEMPLATE=recounting_upload.html
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