# Copy to .env and adjust values for your deployment

# Paths
OUTPUT_DIR=/opt/jetson-counter
DB_PATH=/opt/jetson-counter/jetson_counter.db
STATE_FILE=/opt/jetson-counter/current_batch.json

# Camera stream (rtsp://, http://, or file path for offline testing)
SOURCE=rtsp://user:pass@192.168.0.100:554/stream1

# RKNN model
MODEL_PATH=/opt/jetson-counter/yolo9t.rknn

# Identity
CAMERA_NAME=CC1
OBJECT_LABEL=ayam-potong
CLASS_AYAM=ayam
CLASS_TALENAN=talenan

# Counting line: pixel position (empty = auto from LINE_X_FRAC)
LINE_X=
LINE_X_FRAC=0.5
CROSS_DIRECTION=rtl

# Model input size
IMGSZ=320

# Inference (ignored by RKNN scripts; used by Jetson/TensorRT variant)
HALF=false

# Confidence threshold for detections
CONF=0.3

# TensorRT device index (ignored by RKNN scripts; used by Jetson variant)
DEVICE=0

# RKNN NPU core mask: 1=core0, 2=core1, 3=dual, 7=all
CORE_MASK=1

# YOLO decoder: number of classes
NUM_CLASSES=2

# Set to true if model outputs raw logits instead of sigmoid probabilities
SCORE_SIGMOID=false

# ByteTrack parameters (counter_live_rknn_bytetrack.py only)
TRACK_HIGH_THRESH=0.5
TRACK_LOW_THRESH=0.1
TRACK_MATCH_THRESH=0.8
TRACK_BUFFER=30
TRACK_MIN_HITS=3

# Batch / cutoff
DAILY_CUTOFF_TIME=20:00
BATCH_TIMEOUT_SECONDS=300
IGNORE_BATCH_LABEL_TIMEOUT_SECONDS=30
MIN_OBJECT_PER_BATCH=60
MIN_DURATION_PER_BATCH=60

# CSV export
EXPORT_CSV=true
CROSS_CSV=/opt/jetson-counter/batch_crossings.csv

# Stream connection
WARMUP_FRAMES=30
RECONNECT_DELAY_SEC=3
MAX_RECONNECT_ATTEMPTS=0

# Health logging interval
FLUSH_EVERY_N_FRAMES=100

# Stale track pruning (seconds)
TRACKED_PRUNE_SEC=300

# Video recording
RECORD_VIDEO=false
VIDEO_SEGMENT_SEC=3600
OUTPUT_FPS=15

# Live stream (writes JPEG snapshot to disk for nginx)
LIVE_STREAM_ENABLED=false
LIVE_STREAM_FRAME_PATH=/dev/shm/jetson-counter/live_frame.jpg
LIVE_STREAM_QUALITY=75
LIVE_STREAM_EVERY_N=2

# OpenCV FFmpeg backend options (semicolon/pipe separated)
OPENCV_FFMPEG_CAPTURE_OPTIONS=rtsp_transport;tcp|fflags;nobuffer|flags;low_delay