"""V4L2 MJPEG capture with face detection via mmap buffers.

Opens /dev/video0 and uses memory-mapped V4L2 buffers for efficient frame
capture. Faces are detected on each frame using OpenCV haar cascades
(downscaled for speed) and drawn as rectangles.
"""

import fcntl
import os
import time
import threading
import mmap

import cv2
import numpy as np

import v4l2

# Fourcc for Motion JPEG
MJPG = v4l2.v4l2_fourcc("M", "J", "P", "G")

# Face detection scale — detects at this fraction of original resolution
# to keep CPU usage low. 0.3 ≈ 384x216 for 1280x720 input → ~150 FPS detection.
FACE_DETECT_SCALE = 0.3
FACE_MIN_NEIGHBORS = 4
FACE_MIN_SIZE = 20

# Global camera singleton
_camera: "Capture | None" = None
_camera_lock = threading.Lock()


def _ioctl(fd, request, arg):
    """Wrapper for fcntl.ioctl with v4l2 structs."""
    return fcntl.ioctl(fd, request, arg)


class Capture:
    """Captures MJPEG frames with face detection via V4L2 mmap buffers."""

    def __init__(
        self,
        device: str = "/dev/video0",
        width: int = 1280,
        height: int = 720,
        detect_faces: bool = True,
    ) -> None:
        self._device = device
        self._width = width
        self._height = height
        self._detect_faces = detect_faces
        self._fd = -1
        self._buffers: list[dict] = []
        self._lock = threading.Lock()
        self._last_frame: bytes | None = None
        self._capture_thread: threading.Thread | None = None
        self._running = False
        self._face_cascade: cv2.CascadeClassifier | None = None

    @classmethod
    def get(
        cls,
        device: str = "/dev/video0",
        width: int = 1280,
        height: int = 720,
        detect_faces: bool = True,
    ) -> "Capture":
        """Return the global singleton, creating it if needed."""
        global _camera
        with _camera_lock:
            if _camera is None:
                _camera = cls(device, width, height, detect_faces)
            return _camera

    def start(self) -> None:
        """Open the device, set up buffers, and begin capturing."""
        with self._lock:
            if self._running:
                return
            if self._detect_faces:
                self._load_face_cascade()
            self._open_device()
            self._setup_format()
            self._allocate_buffers()
            self._queue_buffers()
            self._start_streaming()
            self._running = True
            self._capture_thread = threading.Thread(
                target=self._capture_loop, daemon=True, name="camera-capture"
            )
            self._capture_thread.start()

    def stop(self) -> None:
        """Stop streaming and release resources."""
        with self._lock:
            self._running = False
            if self._capture_thread:
                self._capture_thread.join(timeout=3)
            self._release()

    def _load_face_cascade(self) -> None:
        """Load the haar cascade XML for face detection."""
        cascade_path = os.path.join(
            os.path.dirname(os.path.abspath(__file__)),
            "haarcascades",
            "haarcascade_frontalface_default.xml",
        )
        self._face_cascade = cv2.CascadeClassifier()
        self._face_cascade.load(cascade_path)
        print("Face cascade loaded.")

    def _open_device(self) -> None:
        self._fd = os.open(self._device, os.O_RDWR)
        cap = v4l2.v4l2_capability()
        _ioctl(self._fd, v4l2.VIDIOC_QUERYCAP, cap)
        print(f"Camera: {cap.card.decode('utf-8', errors='replace')[:40]}")

    def _setup_format(self) -> None:
        fmt = v4l2.v4l2_format()
        fmt.type = v4l2.V4L2_BUF_TYPE_VIDEO_CAPTURE
        fmt.fmt.pix.width = self._width
        fmt.fmt.pix.height = self._height
        fmt.fmt.pix.pixelformat = MJPG
        fmt.fmt.pix.field = v4l2.V4L2_FIELD_ANY
        _ioctl(self._fd, v4l2.VIDIOC_S_FMT, fmt)
        print(f"Format: {fmt.fmt.pix.width}x{fmt.fmt.pix.height} {v4l2.v4l2_fourcc2str(fmt.fmt.pix.pixelformat)}")

    def _allocate_buffers(self) -> None:
        req = v4l2.v4l2_requestbuffers()
        req.count = 4
        req.type = v4l2.V4L2_BUF_TYPE_VIDEO_CAPTURE
        req.memory = v4l2.V4L2_MEMORY_MMAP
        _ioctl(self._fd, v4l2.VIDIOC_REQBUFS, req)

        self._buffers = []
        for i in range(req.count):
            buf = v4l2.v4l2_buffer()
            buf.type = v4l2.V4L2_BUF_TYPE_VIDEO_CAPTURE
            buf.memory = v4l2.V4L2_MEMORY_MMAP
            buf.index = i
            _ioctl(self._fd, v4l2.VIDIOC_QUERYBUF, buf)

            length = buf.length
            offset = buf.m.offset
            data = mmap.mmap(self._fd, length, mmap.MAP_SHARED, mmap.PROT_READ, 0, offset)
            self._buffers.append({
                "index": i,
                "length": length,
                "mmap": data,
            })

    def _queue_buffers(self) -> None:
        for info in self._buffers:
            buf = v4l2.v4l2_buffer()
            buf.type = v4l2.V4L2_BUF_TYPE_VIDEO_CAPTURE
            buf.memory = v4l2.V4L2_MEMORY_MMAP
            buf.index = info["index"]
            _ioctl(self._fd, v4l2.VIDIOC_QBUF, buf)

    def _start_streaming(self) -> None:
        typ = v4l2.v4l2_buf_type(v4l2.V4L2_BUF_TYPE_VIDEO_CAPTURE)
        _ioctl(self._fd, v4l2.VIDIOC_STREAMON, typ)

    def _stop_streaming(self) -> None:
        typ = v4l2.v4l2_buf_type(v4l2.V4L2_BUF_TYPE_VIDEO_CAPTURE)
        try:
            _ioctl(self._fd, v4l2.VIDIOC_STREAMOFF, typ)
        except Exception:
            pass

    def _release(self) -> None:
        self._stop_streaming()
        for info in self._buffers:
            info["mmap"].close()
        self._buffers = []
        if self._fd >= 0:
            os.close(self._fd)
            self._fd = -1

    def _capture_loop(self) -> None:
        """Background loop: dequeue, detect faces, copy, requeue buffers."""
        while self._running:
            try:
                self._capture_one()
            except BlockingIOError:
                time.sleep(0.001)
            except Exception:
                time.sleep(0.01)

    def _capture_one(self) -> None:
        """Dequeue one frame, detect faces, copy it, and requeue the buffer."""
        buf = v4l2.v4l2_buffer()
        buf.type = v4l2.V4L2_BUF_TYPE_VIDEO_CAPTURE
        buf.memory = v4l2.V4L2_MEMORY_MMAP
        _ioctl(self._fd, v4l2.VIDIOC_DQBUF, buf)

        info = self._buffers[buf.index]
        raw_data = bytes(info["mmap"][:buf.bytesused])

        # Process frame with face detection if enabled
        if self._detect_faces and self._face_cascade is not None:
            frame_data = self._draw_faces(raw_data)
        else:
            frame_data = raw_data

        self._last_frame = frame_data

        # Requeue buffer
        buf.index = info["index"]
        buf.memory = v4l2.V4L2_MEMORY_MMAP
        buf.type = v4l2.V4L2_BUF_TYPE_VIDEO_CAPTURE
        _ioctl(self._fd, v4l2.VIDIOC_QBUF, buf)

    def _draw_faces(self, jpeg_bytes: bytes) -> bytes:
        """Decode JPEG, detect faces, draw rectangles, re-encode."""
        # Decode the JPEG frame from V4L2
        frame = cv2.imdecode(np.frombuffer(jpeg_bytes, dtype=np.uint8), cv2.IMREAD_COLOR)
        if frame is None:
            return jpeg_bytes

        h, w = frame.shape[:2]

        # Scale down for fast detection
        sw, sh = int(w * FACE_DETECT_SCALE), int(h * FACE_DETECT_SCALE)
        small = cv2.resize(frame, (sw, sh), interpolation=cv2.INTER_AREA)
        gray = cv2.cvtColor(small, cv2.COLOR_BGR2GRAY)

        # Detect faces
        faces = self._face_cascade.detectMultiScale(
            gray,
            scaleFactor=1.1,
            minNeighbors=FACE_MIN_NEIGHBORS,
            minSize=(int(FACE_MIN_SIZE / FACE_DETECT_SCALE), int(FACE_MIN_SIZE / FACE_DETECT_SCALE)),
        )

        # Draw rectangles at original scale
        for (x, y, fw, fh) in faces:
            x_orig = int(x / FACE_DETECT_SCALE)
            y_orig = int(y / FACE_DETECT_SCALE)
            fw_orig = int(fw / FACE_DETECT_SCALE)
            fh_orig = int(fh / FACE_DETECT_SCALE)
            cv2.rectangle(frame, (x_orig, y_orig), (x_orig + fw_orig, y_orig + fh_orig), (0, 255, 0), 2)

        # Re-encode as JPEG (quality 85 is a good balance)
        _, encoded = cv2.imencode(".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
        return bytes(encoded.tobytes())

    def frame(self) -> bytes | None:
        """Return the most recently captured frame (with face boxes if enabled)."""
        return self._last_frame


def get_camera(
    device: str = "/dev/video0",
    width: int = 1280,
    height: int = 720,
    detect_faces: bool = True,
) -> Capture:
    """Convenience accessor for the global camera singleton."""
    return Capture.get(device, width, height, detect_faces)