"""
Centralized configuration for the person detection dataset pipeline.
All tunable parameters are defined here.
"""
import os

# ──────────────────────────────────────────────
# Paths
# ──────────────────────────────────────────────
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
VIDEO_DIR = os.path.join(BASE_DIR, "video_data")
DATASET_DIR = os.path.join(BASE_DIR, "dataset")
CHECKPOINT_DIR = os.path.join(BASE_DIR, "checkpoints")
LOG_DIR = os.path.join(BASE_DIR, "logs")

# Model paths
DETECTOR_MODEL = os.path.join(BASE_DIR, "yolo26x.pt")   # Large model for auto-labeling
TRAIN_MODEL = "yolo26n.pt"                                # Nano model to train (auto-downloads)

# ──────────────────────────────────────────────
# Dataset Extraction
# ──────────────────────────────────────────────
MAX_DATASET_SIZE_GB = 50        # Stop extraction if dataset exceeds this
JPEG_QUALITY = 85               # JPEG save quality (1-100)
DETECTION_CONF = 0.35           # Min confidence for person detection
DETECTION_IOU = 0.45            # NMS IoU threshold
BATCH_SIZE = 16                 # Frames per YOLO inference batch
PERSON_CLASS_ID = 0             # YOLO class ID for "person"

# ──────────────────────────────────────────────
# Adaptive Sampling
# ──────────────────────────────────────────────
BASE_FPS = 1.0                  # Default: 1 frame per second
HIGH_FPS = 3.0                  # When person detected: 3 frames per second
LOW_FPS = 0.5                   # When idle (no person): 0.5 frames per second
HIGH_FPS_DURATION = 5           # Seconds to stay at high FPS after person detected
LOW_FPS_THRESHOLD = 10          # Seconds without person before dropping to low FPS

# ──────────────────────────────────────────────
# Train/Test Split (Camera-Level)
# ──────────────────────────────────────────────
TEST_CAMERAS = 4                # Number of cameras to hold out for testing
RANDOM_SEED = 42                # For reproducible camera selection

# ──────────────────────────────────────────────
# Training
# ──────────────────────────────────────────────
TRAIN_EPOCHS = 100
TRAIN_BATCH = 16                # Batch size for training (adjust for VRAM)
TRAIN_IMGSZ = 640               # Training image size
EARLY_STOP_PATIENCE = 15        # Stop if no improvement for N epochs
TRAIN_WORKERS = 8               # DataLoader workers
TRAIN_PROJECT = os.path.join(BASE_DIR, "runs", "detect")
TRAIN_NAME = "person_detection"

# ──────────────────────────────────────────────
# Video file extensions to process
# ──────────────────────────────────────────────
VIDEO_EXTENSIONS = {".mp4", ".avi", ".mkv", ".mov", ".wmv", ".flv"}