from __future__ import annotations

from dataclasses import dataclass, field
from pathlib import Path


@dataclass
class YoloConfig:
    model_path: str = "yolo11n.pt"
    tracker: str = "botsort.yaml"
    conf: float = 0.35
    iou: float = 0.5
    imgsz: int = 1280
    device: str = "cuda:0"


@dataclass
class CountingConfig:
    line: tuple[float, float, float, float] = (0.1, 0.55, 0.9, 0.55)
    line_mode: str = "normalized"
    crossing_tolerance: float = 12.0

    def to_pixel_line(
        self, width: int, height: int
    ) -> tuple[float, float, float, float]:
        x1, y1, x2, y2 = self.line
        if self.line_mode == "pixel":
            return x1, y1, x2, y2
        return x1 * width, y1 * height, x2 * width, y2 * height


@dataclass
class AttributeConfig:
    enabled: bool = False
    sample_every_n_frames: int = 12
    max_samples_per_track: int = 5
    min_person_box_width: int = 80
    min_person_box_height: int = 160
    person_crop_padding: float = 0.15
    detector_backend: str = "retinaface"
    enforce_detection: bool = False


@dataclass
class OutputConfig:
    save_video: bool = True
    save_json: bool = True
    save_csv: bool = True
    draw_boxes: bool = True
    draw_labels: bool = True


@dataclass
class RtspConfig:
    sample_interval_seconds: float = 1.0
    window_seconds: int = 1800
    reconnect_delay_seconds: float = 5.0
    stream_open_timeout_seconds: float = 10.0
    idle_sleep_seconds: float = 0.05
    output_subdir: str = "rtsp_stream"


@dataclass
class QueueConfig:
    enabled: bool = True
    area: tuple[float, float, float, float] = (0.0, 0.0, 1.0, 1.0)
    area_mode: str = "normalized"
    queue_time_threshold_seconds: int = 300
    crowded_count_threshold: int = 5
    normal_count_threshold: int = 2
    pause_timeout_seconds: int = 5
    source_id: str = "people_flow_queue"

    def to_pixel_area(
        self, width: int, height: int
    ) -> tuple[float, float, float, float]:
        x1, y1, x2, y2 = self.area
        if self.area_mode == "pixel":
            return x1, y1, x2, y2
        return x1 * width, y1 * height, x2 * width, y2 * height


@dataclass
class WebhookConfig:
    url: str = ""
    timeout_seconds: float = 5.0
    event_log_path: str = "outputs/rtsp_stream/webhook_events.jsonl"


@dataclass
class RuntimeConfig:
    rtsp_url: str = "rtsp://user:password@camera-ip:554/h264/ch1/main/av_stream"
    output_dir: str = "outputs"


@dataclass
class AppConfig:
    yolo: YoloConfig = field(default_factory=YoloConfig)
    counting: CountingConfig = field(default_factory=CountingConfig)
    attributes: AttributeConfig = field(default_factory=AttributeConfig)
    output: OutputConfig = field(default_factory=OutputConfig)
    rtsp: RtspConfig = field(default_factory=RtspConfig)
    queue: QueueConfig = field(default_factory=QueueConfig)
    webhook: WebhookConfig = field(default_factory=WebhookConfig)
    runtime: RuntimeConfig = field(default_factory=RuntimeConfig)
    config_path: Path | None = None


@dataclass
class TrackObservation:
    track_id: int
    bbox: tuple[int, int, int, int]
    confidence: float
    center: tuple[float, float]


@dataclass
class CrossingEvent:
    track_id: int
    direction: str


@dataclass
class AttributeVote:
    age: int
    age_bucket: str
    gender: str


@dataclass
class TrackAttributeSummary:
    track_id: int
    age: int
    age_bucket: str
    gender: str
    samples_used: int