# =============================================================================
# Edge RK3588 production counter + dashboard
# Shared config for: counter_live_rknn_bytetrack.py + counter_dashboard.py
# Copy to .env on device:  cp config.env.example .env && nano .env
# =============================================================================

# --- Core paths ---
# Root output directory (logs, DB, video, CSV)
OUTPUT_DIR=/opt/zenai-kpc-bt-counter
# SQLite database path for daily counter records & crossing logs
DB_PATH=/tmp/counter.db
# JSON file persisting the current active counting day state
STATE_FILE=/tmp/current_counter.json

# --- Input source ---
# RTSP / HTTP live stream, or a local video file path
#SOURCE=rtsp://user:pass@192.168.0.100:554/stream1
SOURCE=rtsp://10.38.30.64:8554/my_stream
# FFmpeg capture options passed to cv2.VideoCapture (RTSP low-latency flags)
OPENCV_FFMPEG_CAPTURE_OPTIONS=rtsp_transport;tcp|fflags;nobuffer|flags;low_delay

# --- RKNN model ---
# Path to exported .rknn model (YOLO format, e.g. yolo11n.rknn)
MODEL_PATH=/opt/models/zenai_kac_sukawarna_20260702.rknn
# Input image size for the model (square, e.g. 320 → 320×320)
IMGSZ=320
# Use FP16 inference on NPU (true/false); currently unused in ByteTrack variant
HALF=false
# NPU core mask: 1=core0, 2=core1, 3=core0+core1, 7=all three
CORE_MASK=1
# Compute device index (reserved; not used at runtime)
DEVICE=0

# --- YOLO decoder ---
# Number of object classes the model outputs
NUM_CLASSES=4
# Apply sigmoid to raw class scores (true/false); set true if model head uses BCE logits
SCORE_SIGMOID=false

# --- Detection ---
# Confidence threshold – detections below this are discarded before NMS
CONF=0.5

# --- ByteTrack tracking ---
# Detections with score >= this get priority matching in the first association stage
TRACK_HIGH_THRESH=0.5
# Detections with score between this and TRACK_HIGH_THRESH are matched in the second stage
TRACK_LOW_THRESH=0.3
# IoU threshold for the first-stage association (0–1). Higher = stricter overlap required
TRACK_MATCH_THRESH=0.7
# Frames a track survives without a match before being permanently removed
TRACK_BUFFER=60
# Minimum consecutive (or total) hits needed before a track is considered confirmed
TRACK_MIN_HITS=3

# --- Display ---
# Site name shown on the dashboard header (top-right)
SITE_NAME=ZenAi

# --- Object class names ---
# Camera / location identifier shown in HUD and stored in DB
CAMERA_NAME=ZenAi
# Label used for batch grouping in the database
OBJECT_LABEL=karung
# Class name for the counted object (must match model class order)
CLASS_OBJECT=karung
# Model class ID for the object being counted (default 0)
OBJECT_CLASS_ID=0

# --- Line crossing ---
# Two horizontal counting lines with sequence logic:
#   Line 1 (IN):  top→down counts IN  (OUT→IN sequence, or IN-only)
#   Line 2 (OUT): bottom→up counts OUT (IN→OUT sequence, or OUT-only)
# Passing the opposite line first only arms; the destination line still counts
# even if the opposite line was never crossed.
# Fixed y-coordinate for line 1/IN (overrides LINE_Y1_FRAC if set)
LINE_Y1=
# Fraction of frame height for line 1 (default 0.33)
LINE_Y1_FRAC=0.70
# Fixed y-coordinate for line 2 (overrides LINE_Y2_FRAC if set)
LINE_Y2=
# Fraction of frame height for line 2 (default 0.66)
LINE_Y2_FRAC=0.30

# --- Counting day management ---
# Daily cutoff time (HH:MM) – a new counting day starts after this time and the
# previous day's counter_in / counter_out totals are finalized in the database.
# CUTOFF_TIME is an alias used by the dashboard; DAILY_CUTOFF_TIME takes priority in counter_live_rknn.py.
DAILY_CUTOFF_TIME=20:00
CUTOFF_TIME=20:00

# --- CSV export ---
# Write per-crossing events to a CSV file (true/false)
EXPORT_CSV=false
# Path where the crossing CSV is written
CROSS_CSV=/tmp/crossings.csv

# --- Rate / performance ---
# Enable motion detection pre-filter: skip inference on frames with no movement
# (true/false, default: false). When enabled, frames below MOTION_THRESHOLD are
# skipped, saving NPU/CPU load.
MOTION_DETECTION_ENABLED=true
# Mean absolute pixel difference threshold (0–255) to consider a frame as having
# motion. Lower = more sensitive. Default 5.0.
MOTION_THRESHOLD=5.0
# Sliding window in seconds for computing the crossing rate (objects/minute)
RATE_WINDOW_SEC=60
# Number of frames to discard at startup to let the stream buffer stabilise
WARMUP_FRAMES=30
# Delay in seconds between stream reconnection attempts
RECONNECT_DELAY_SEC=3
# Maximum reconnection attempts (0 = infinite)
MAX_RECONNECT_ATTEMPTS=0
# Seconds after which a tracked but unseen object is pruned from the active set
TRACKED_PRUNE_SEC=300

# --- Video recording ---
# Save annotated frames to segmented MP4 files (true/false)
RECORD_VIDEO=false
# Duration in seconds of each video segment file
VIDEO_SEGMENT_SEC=3600
# Output video FPS (fallback if source FPS is unknown or ≤ 1)
OUTPUT_FPS=15

# --- Live stream snapshot ---
# Periodically write the latest annotated frame as JPEG for an external web server
LIVE_STREAM_ENABLED=true
# Path to the shared-memory snapshot file (served by nginx / lighttpd)
LIVE_STREAM_FRAME_PATH=/dev/shm/byetrack-counter/live_frame.jpg
# JPEG quality (1–100)
LIVE_STREAM_QUALITY=75
# Write the snapshot every N frames (lower = more frequent updates)
LIVE_STREAM_EVERY_N=2

# --- Dashboard (counter_dashboard.py) ---
# Flask secret key for session/cookie signing — change in production!
SECRET_KEY=change-me-in-production
# Bind address for the Flask web server
DASHBOARD_HOST=0.0.0.0
# Listen port for the dashboard web UI
DASHBOARD_PORT=5000
# Enable Flask debug mode (true/false) — auto-reloads on code changes; disable in production
FLASK_DEBUG=false
# Fallback name for the active counting-day JSON state file used by the dashboard
CURRENT_COUNTER_PATH=/tmp/bytetrack_current_counter.json

SAVE_CROSS_SNAPSHOT=true
CROSS_SNAPSHOT_DIR=/opt/zenai-kpc-snaps/snapshots
CROSS_SNAPSHOT_CLEANUP_SEC=3600
CROSS_SNAPSHOT_MAX_AGE_DAYS=3

DEDUP_PX=-1