deployment update

config.env.example

@@ -1,195 +1,105 @@
 # =============================================================================
-# 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
+# DEPRECATED — use env.example instead.
+#
+#   cp env.example .env && nano .env
+#
+# This file is kept for backward compatibility only. All new deployments should
+# use env.example as the single canonical template. See DEPLOY.md.
 # =============================================================================
 
 # --- Core paths ---
-# Root output directory (logs, DB, video, CSV)
-OUTPUT_DIR=/opt/bytetrack-counter
-# SQLite database path for daily counter records & crossing logs
-DB_PATH=/opt/bytetrack-counter/bytetrack_counter.db
-# JSON file persisting the current active counting day state
-STATE_FILE=/tmp/bytetrack_current_counter.json
+OUTPUT_DIR=/opt/zenai-kpc-counter
+DB_PATH=/opt/zenai-kpc-counter/counter.db
+STATE_FILE=/opt/zenai-kpc-counter/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
-# 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/yolo9t.rknn
-# Input image size for the model (square, e.g. 320 → 320×320)
+MODEL_PATH=/opt/models/your_model.rknn
 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=7
-# Compute device index (reserved; not used at runtime)
+CORE_MASK=1
 DEVICE=0
 
 # --- YOLO decoder ---
-# Number of object classes the model outputs
-NUM_CLASSES=2
-# Apply sigmoid to raw class scores (true/false); set true if model head uses BCE logits
+NUM_CLASSES=4
 SCORE_SIGMOID=false
 
 # --- Detection ---
-# Confidence threshold – detections below this are discarded before NMS
-CONF=0.3
+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=0.1
-# IoU threshold for the first-stage association (0–1). Higher = stricter overlap required
-TRACK_MATCH_THRESH=0.8
-# Frames a track survives without a match before being permanently removed
-TRACK_BUFFER=30
-# Minimum consecutive (or total) hits needed before a track is considered confirmed
+TRACK_LOW_THRESH=0.3
+TRACK_MATCH_THRESH=0.7
+TRACK_BUFFER=60
 TRACK_MIN_HITS=3
 
 # --- ID-switch counting guards ---
-# When a track's ID changes right at the counting line, one physical object can be
-# counted twice (two IDs cross) or missed (neither ID sees the full transition).
-# These two guards correct for that.
-#
-# Dedup guard (prevents double counting): after a crossing, a second crossing in
-# the SAME direction within DEDUP_FRAMES frames and DEDUP_PX horizontal pixels is
-# ignored (treated as the same object under a new ID).
 DEDUP_FRAMES=15
 DEDUP_PX=60
-# To DISABLE the dedup guard, set DEDUP_PX=-1 (distance check can never match).
-#
-# Inheritance guard (prevents missed counting): when a brand-new track appears, it
-# inherits the last position of a recently-seen nearby track (within INHERIT_SEC
-# seconds and INHERIT_PX horizontal pixels) so the crossing is still detected
-# across the ID switch.
 INHERIT_SEC=1.0
 INHERIT_PX=60
-# To DISABLE the inheritance guard, set INHERIT_PX=-1 (distance check can never match).
 
 # --- 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=object
-# Class name for the counted object (must match model class order)
-CLASS_OBJECT=object
-# Model class ID for the object being counted (default 0)
+OBJECT_LABEL=karung
+CLASS_OBJECT=karung
 OBJECT_CLASS_ID=0
 
 # --- Line crossing ---
-# Two horizontal counting lines:
-#   Line 1 (default ~33%): counts top-to-down (IN)
-#   Line 2 (default ~66%): counts bottom-to-up (OUT)
-# 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=0.33
-# Fixed y-coordinate for line 2 (overrides LINE_Y2_FRAC if set)
+LINE_Y1_FRAC=0.70
 LINE_Y2=
-# Fraction of frame height for line 2 (default 0.66)
-LINE_Y2_FRAC=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=true
-# Path where the crossing CSV is written
-CROSS_CSV=/opt/batch-counter/crossings.csv
+EXPORT_CSV=false
+CROSS_CSV=/opt/zenai-kpc-counter/crossings.csv
 
 # --- Crossing snapshots ---
-# Save an annotated frame image every time an object crosses a line and the
-# counter increases (true/false, default: false). Written to <DIR>/cross/
-# (filename: <YYYYmmdd_HHMMSS_mmm>_<in|out>_id<track>_f<frame>.jpg)
-SAVE_CROSS_SNAPSHOT=false
-# Also save one snapshot the first time each object is detected, before it crosses
-# (true/false, default: false). Written to <DIR>/detect/ with the same track id so
-# it can be correlated with the crossing snapshot
-# (filename: <YYYYmmdd_HHMMSS_mmm>_detect_id<track>_f<frame>.jpg)
+SAVE_CROSS_SNAPSHOT=true
 SAVE_DETECT_SNAPSHOT=false
-# Base directory for snapshots (detect/ and cross/ subfolders are created inside).
-# The dashboard reads this same path to display the snapshot gallery, so keep it
-# identical for both the counter and the dashboard.
-CROSS_SNAPSHOT_DIR=/opt/batch-counter/snapshots
-# JPEG quality for snapshots (1-100)
+CROSS_SNAPSHOT_DIR=/opt/zenai-kpc-counter/snapshots
 CROSS_SNAPSHOT_QUALITY=85
-# Retention: keep at most this many snapshot files (detect + cross combined);
-# oldest are deleted first (0 = unlimited)
 CROSS_SNAPSHOT_MAX_FILES=1000
-# Retention: delete snapshots older than this many days (0 = never by age)
-CROSS_SNAPSHOT_MAX_AGE_DAYS=7
-# Run the cleanup sweep at most once every N seconds
-CROSS_SNAPSHOT_CLEANUP_SEC=60
+CROSS_SNAPSHOT_MAX_AGE_DAYS=3
+CROSS_SNAPSHOT_CLEANUP_SEC=3600
 
 # --- Rate / performance ---
-# Enable motion detection pre-filter: skip inference on frames with no movement
-# (true/false, default: false), saving NPU/CPU load. Motion is measured by the
-# fraction of pixels that changed (localized-motion aware), NOT the whole-frame
-# average, so an object entering the edge of the frame is detected immediately.
-MOTION_DETECTION_ENABLED=false
-# Per-pixel intensity change (0-255) for a pixel to count as "moved". Lower = more
-# sensitive to subtle movement. Default 25.
+MOTION_DETECTION_ENABLED=true
 MOTION_PIXEL_DELTA=25
-# Fraction of frame pixels (0-1) that must change to trigger inference. Lower =
-# more sensitive / detects smaller or farther objects sooner. Default 0.002 (0.2%).
 MOTION_MIN_AREA_FRAC=0.002
-# Heartbeat: always run inference at least every N frames even with no detected
-# motion, so a slow or barely-moving object is never missed for long. Default 15.
 MOTION_HEARTBEAT_FRAMES=15
-# (Deprecated) old whole-frame mean-difference threshold; no longer used.
 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=false
-# 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_ENABLED=true
+LIVE_STREAM_FRAME_PATH=/dev/shm/zenai-kpc-counter/live_frame.jpg
 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