"""OpenCV-based visualizer for demo pipeline.

Provides real-time visualization of detection, segmentation, and classification results
with interactive mode switching for mask display.
"""

from __future__ import annotations

import logging
from typing import cast

import cv2
import numpy as np
from numpy.typing import NDArray

from .preprocess import BBoxXYXY

logger = logging.getLogger(__name__)


# Window names
MAIN_WINDOW = "Scoliosis Detection"
SEG_WINDOW = "Normalized Silhouette"
RAW_WINDOW = "Raw Mask"
WINDOW_SEG_INPUT = "Segmentation Input"

# Silhouette dimensions (from preprocess.py)
SIL_HEIGHT = 64
SIL_WIDTH = 44

# Display dimensions for upscaled silhouette
DISPLAY_HEIGHT = 256
DISPLAY_WIDTH = 176
RAW_STATS_PAD = 54
MODE_LABEL_PAD = 26

# Colors (BGR)
COLOR_GREEN = (0, 255, 0)
COLOR_WHITE = (255, 255, 255)
COLOR_BLACK = (0, 0, 0)
COLOR_DARK_GRAY = (56, 56, 56)
COLOR_RED = (0, 0, 255)
COLOR_YELLOW = (0, 255, 255)
# Type alias for image arrays (NDArray or cv2.Mat)
COLOR_CYAN = (255, 255, 0)
COLOR_ORANGE = (0, 165, 255)
COLOR_MAGENTA = (255, 0, 255)
ImageArray = NDArray[np.uint8]

# COCO-format skeleton connections (17 keypoints)
# Connections are pairs of keypoint indices
SKELETON_CONNECTIONS: list[tuple[int, int]] = [
    (0, 1),  # nose -> left_eye
    (0, 2),  # nose -> right_eye
    (1, 3),  # left_eye -> left_ear
    (2, 4),  # right_eye -> right_ear
    (5, 6),  # left_shoulder -> right_shoulder
    (5, 7),  # left_shoulder -> left_elbow
    (7, 9),  # left_elbow -> left_wrist
    (6, 8),  # right_shoulder -> right_elbow
    (8, 10),  # right_elbow -> right_wrist
    (11, 12),  # left_hip -> right_hip
    (5, 11),  # left_shoulder -> left_hip
    (6, 12),  # right_shoulder -> right_hip
    (11, 13),  # left_hip -> left_knee
    (13, 15),  # left_knee -> left_ankle
    (12, 14),  # right_hip -> right_knee
    (14, 16),  # right_knee -> right_ankle
]


class OpenCVVisualizer:
    def __init__(self) -> None:
        self.show_raw_window: bool = False
        self.show_raw_debug: bool = False
        self._windows_created: bool = False
        self._raw_window_created: bool = False

    def _ensure_windows(self) -> None:
        if not self._windows_created:
            cv2.namedWindow(MAIN_WINDOW, cv2.WINDOW_NORMAL)
            cv2.namedWindow(SEG_WINDOW, cv2.WINDOW_NORMAL)
            cv2.namedWindow(WINDOW_SEG_INPUT, cv2.WINDOW_NORMAL)
            self._windows_created = True

    def _ensure_raw_window(self) -> None:
        if not self._raw_window_created:
            cv2.namedWindow(RAW_WINDOW, cv2.WINDOW_NORMAL)
            self._raw_window_created = True

    def _hide_raw_window(self) -> None:
        if self._raw_window_created:
            cv2.destroyWindow(RAW_WINDOW)
            self._raw_window_created = False

    def _draw_bbox(
        self,
        frame: ImageArray,
        bbox: BBoxXYXY | None,
    ) -> None:
        """Draw bounding box on frame if present.

        Args:
            frame: Input frame (H, W, 3) uint8 - modified in place
            bbox: Bounding box in XYXY format as (x1, y1, x2, y2) or None
        """
        if bbox is None:
            return

        x1, y1, x2, y2 = bbox
        # Draw rectangle with green color, thickness 2
        _ = cv2.rectangle(frame, (x1, y1), (x2, y2), COLOR_GREEN, 2)

    def _draw_text_overlay(
        self,
        frame: ImageArray,
        track_id: int,
        fps: float,
        label: str | None,
        confidence: float | None,
    ) -> None:
        """Draw text overlay with track info, FPS, label, and confidence.

        Args:
            frame: Input frame (H, W, 3) uint8 - modified in place
            track_id: Tracking ID
            fps: Current FPS
            label: Classification label or None
            confidence: Classification confidence or None
        """
        # Prepare text lines
        lines: list[str] = []
        lines.append(f"ID: {track_id}")
        lines.append(f"FPS: {fps:.1f}")

        if label is not None:
            if confidence is not None:
                lines.append(f"{label}: {confidence:.2%}")
            else:
                lines.append(label)

        # Draw text with background for readability
        font = cv2.FONT_HERSHEY_SIMPLEX
        font_scale = 0.6
        thickness = 1
        line_height = 25
        margin = 10

        for i, text in enumerate(lines):
            y_pos = margin + (i + 1) * line_height

            # Draw background rectangle
            (text_width, text_height), _ = cv2.getTextSize(
                text, font, font_scale, thickness
            )
            _ = cv2.rectangle(
                frame,
                (margin, y_pos - text_height - 5),
                (margin + text_width + 10, y_pos + 5),
                COLOR_BLACK,
                -1,
            )

            # Draw text
            _ = cv2.putText(
                frame,
                text,
                (margin + 5, y_pos),
                font,
                font_scale,
                COLOR_WHITE,
                thickness,
            )

    def _draw_pose_skeleton(
        self,
        frame: ImageArray,
        pose_data: dict[str, object] | None,
    ) -> None:
        """Draw pose skeleton on frame.

        Args:
            frame: Input frame (H, W, 3) uint8 - modified in place
            pose_data: Pose data dictionary from Sports2D or similar
                     Expected format: {'keypoints': [[x1, y1], [x2, y2], ...],
                                      'confidence': [c1, c2, ...],
                                      'angles': {'joint_name': angle, ...}}
        """
        if pose_data is None:
            return

        keypoints_obj = pose_data.get("keypoints")
        if keypoints_obj is None:
            return

        # Convert keypoints to numpy array
        keypoints = np.asarray(keypoints_obj, dtype=np.float32)
        if keypoints.size == 0:
            return

        h, w = frame.shape[:2]

        # Get confidence scores if available
        confidence_obj = pose_data.get("confidence")
        confidences = (
            np.asarray(confidence_obj, dtype=np.float32)
            if confidence_obj is not None
            else np.ones(len(keypoints), dtype=np.float32)
        )

        # Draw skeleton connections
        for connection in SKELETON_CONNECTIONS:
            idx1, idx2 = connection
            if idx1 < len(keypoints) and idx2 < len(keypoints):
                # Check confidence threshold (0.3)
                if confidences[idx1] > 0.3 and confidences[idx2] > 0.3:
                    pt1 = (int(keypoints[idx1][0]), int(keypoints[idx1][1]))
                    pt2 = (int(keypoints[idx2][0]), int(keypoints[idx2][1]))
                    # Clip to frame bounds
                    pt1 = (max(0, min(w - 1, pt1[0])), max(0, min(h - 1, pt1[1])))
                    pt2 = (max(0, min(w - 1, pt2[0])), max(0, min(h - 1, pt2[1])))
                    _ = cv2.line(frame, pt1, pt2, COLOR_CYAN, 2)

        # Draw keypoints
        for i, (kp, conf) in enumerate(zip(keypoints, confidences)):
            if conf > 0.3 and i < len(keypoints):
                x, y = int(kp[0]), int(kp[1])
                # Clip to frame bounds
                x = max(0, min(w - 1, x))
                y = max(0, min(h - 1, y))
                # Draw keypoint as circle
                _ = cv2.circle(frame, (x, y), 4, COLOR_MAGENTA, -1)
                _ = cv2.circle(frame, (x, y), 4, COLOR_WHITE, 1)

    def _draw_pose_angles(
        self,
        frame: ImageArray,
        pose_data: dict[str, object] | None,
    ) -> None:
        """Draw pose angles as text overlay.

        Args:
            frame: Input frame (H, W, 3) uint8 - modified in place
            pose_data: Pose data dictionary with 'angles' key
        """
        if pose_data is None:
            return

        angles_obj = pose_data.get("angles")
        if angles_obj is None:
            return

        angles = cast(dict[str, float], angles_obj)
        if not angles:
            return

        # Draw angles in top-right corner
        font = cv2.FONT_HERSHEY_SIMPLEX
        font_scale = 0.45
        thickness = 1
        line_height = 20
        margin = 10
        h, w = frame.shape[:2]

        # Filter and format angles
        angle_texts: list[tuple[str, float]] = []
        for name, angle in angles.items():
            # Only show angles that are reasonable (0-180 degrees)
            if 0 <= angle <= 180:
                angle_texts.append((str(name), float(angle)))

        # Sort by name for consistent display
        angle_texts.sort(key=lambda x: x[0])

        # Draw from top-right
        for i, (name, angle) in enumerate(angle_texts[:8]):  # Limit to 8 angles
            text = f"{name}: {angle:.1f}"
            (text_width, text_height), _ = cv2.getTextSize(
                text, font, font_scale, thickness
            )
            x_pos = w - margin - text_width - 10
            y_pos = margin + (i + 1) * line_height

            # Draw background rectangle
            _ = cv2.rectangle(
                frame,
                (x_pos - 4, y_pos - text_height - 4),
                (x_pos + text_width + 4, y_pos + 4),
                COLOR_BLACK,
                -1,
            )
            # Draw text in orange
            _ = cv2.putText(
                frame,
                text,
                (x_pos, y_pos),
                font,
                font_scale,
                COLOR_ORANGE,
                thickness,
            )

    def _prepare_main_frame(
        self,
        frame: ImageArray,
        bbox: BBoxXYXY | None,
        track_id: int,
        fps: float,
        label: str | None,
        confidence: float | None,
        pose_data: dict[str, object] | None = None,
    ) -> ImageArray:
        """Prepare main display frame with bbox and text overlay.

        Args:
            frame: Input frame (H, W, C) uint8
            bbox: Bounding box in XYXY format (x1, y1, x2, y2) or None
            track_id: Tracking ID
            fps: Current FPS
            label: Classification label or None
            confidence: Classification confidence or None
            pose_data: Pose data dictionary or None

        Returns:
            Processed frame ready for display
        """
        # Ensure BGR format (convert grayscale if needed)
        if len(frame.shape) == 2:
            display_frame = cast(ImageArray, cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR))
        elif frame.shape[2] == 1:
            display_frame = cast(ImageArray, cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR))
        elif frame.shape[2] == 3:
            display_frame = frame.copy()
        elif frame.shape[2] == 4:
            display_frame = cast(ImageArray, cv2.cvtColor(frame, cv2.COLOR_BGRA2BGR))
        else:
            display_frame = frame.copy()

        # Draw bbox and text (modifies in place)
        self._draw_bbox(display_frame, bbox)
        self._draw_text_overlay(display_frame, track_id, fps, label, confidence)

        # Draw pose skeleton and angles if available
        self._draw_pose_skeleton(display_frame, pose_data)
        self._draw_pose_angles(display_frame, pose_data)

        return display_frame

    def _upscale_silhouette(
        self,
        silhouette: NDArray[np.float32] | NDArray[np.uint8],
    ) -> ImageArray:
        """Upscale silhouette to display size.

        Args:
            silhouette: Input silhouette (64, 44) float32 [0,1] or uint8 [0,255]

        Returns:
            Upscaled silhouette (256, 176) uint8
        """
        # Normalize to uint8 if needed
        if silhouette.dtype == np.float32 or silhouette.dtype == np.float64:
            sil_u8 = (silhouette * 255).astype(np.uint8)
        else:
            sil_u8 = silhouette.astype(np.uint8)

        # Upscale using nearest neighbor to preserve pixelation
        upscaled = cast(
            ImageArray,
            cv2.resize(
                sil_u8,
                (DISPLAY_WIDTH, DISPLAY_HEIGHT),
                interpolation=cv2.INTER_NEAREST,
            ),
        )

        return upscaled

    def _normalize_mask_for_display(self, mask: NDArray[np.generic]) -> ImageArray:
        mask_array = np.asarray(mask)
        if mask_array.dtype == np.bool_:
            bool_scaled = np.where(mask_array, np.uint8(255), np.uint8(0)).astype(
                np.uint8
            )
            return cast(ImageArray, bool_scaled)

        if mask_array.dtype == np.uint8:
            mask_array = cast(ImageArray, mask_array)
            max_u8 = int(np.max(mask_array)) if mask_array.size > 0 else 0
            if max_u8 <= 1:
                scaled_u8 = np.where(mask_array > 0, np.uint8(255), np.uint8(0)).astype(
                    np.uint8
                )
                return cast(ImageArray, scaled_u8)
            return cast(ImageArray, mask_array)

        if np.issubdtype(mask_array.dtype, np.integer):
            max_int = float(np.max(mask_array)) if mask_array.size > 0 else 0.0
            if max_int <= 1.0:
                return cast(
                    ImageArray, (mask_array.astype(np.float32) * 255.0).astype(np.uint8)
                )
            clipped = np.clip(mask_array, 0, 255).astype(np.uint8)
            return cast(ImageArray, clipped)

        mask_float = np.asarray(mask_array, dtype=np.float32)
        max_val = float(np.max(mask_float)) if mask_float.size > 0 else 0.0
        if max_val <= 0.0:
            return np.zeros(mask_float.shape, dtype=np.uint8)

        normalized = np.clip((mask_float / max_val) * 255.0, 0.0, 255.0).astype(
            np.uint8
        )
        return cast(ImageArray, normalized)

    def _draw_raw_stats(self, image: ImageArray, mask_raw: ImageArray | None) -> None:
        if mask_raw is None:
            return

        mask = np.asarray(mask_raw)
        if mask.size == 0:
            return

        stats = [
            f"raw: {mask.dtype}",
            f"min/max: {float(mask.min()):.3f}/{float(mask.max()):.3f}",
            f"nnz: {int(np.count_nonzero(mask))}",
        ]

        font = cv2.FONT_HERSHEY_SIMPLEX
        font_scale = 0.45
        thickness = 1
        line_h = 18
        x0 = 8
        y0 = 20

        for i, txt in enumerate(stats):
            y = y0 + i * line_h
            (tw, th), _ = cv2.getTextSize(txt, font, font_scale, thickness)
            _ = cv2.rectangle(
                image, (x0 - 4, y - th - 4), (x0 + tw + 4, y + 4), COLOR_BLACK, -1
            )
            _ = cv2.putText(
                image, txt, (x0, y), font, font_scale, COLOR_YELLOW, thickness
            )

    def _prepare_segmentation_view(
        self,
        _mask_raw: ImageArray | None,
        silhouette: NDArray[np.float32] | None,
        _bbox: BBoxXYXY | None,
    ) -> ImageArray:
        return self._prepare_normalized_view(silhouette)

    def _fit_gray_to_display(
        self,
        gray: ImageArray,
        out_h: int = DISPLAY_HEIGHT,
        out_w: int = DISPLAY_WIDTH,
    ) -> ImageArray:
        src_h, src_w = gray.shape[:2]
        if src_h <= 0 or src_w <= 0:
            return np.zeros((out_h, out_w), dtype=np.uint8)

        scale = min(out_w / src_w, out_h / src_h)
        new_w = max(1, int(round(src_w * scale)))
        new_h = max(1, int(round(src_h * scale)))

        resized = cast(
            ImageArray,
            cv2.resize(gray, (new_w, new_h), interpolation=cv2.INTER_NEAREST),
        )
        canvas = np.zeros((out_h, out_w), dtype=np.uint8)
        x0 = (out_w - new_w) // 2
        y0 = (out_h - new_h) // 2
        canvas[y0 : y0 + new_h, x0 : x0 + new_w] = resized
        return cast(ImageArray, canvas)

    def _crop_mask_to_bbox(
        self,
        mask_gray: ImageArray,
        bbox: BBoxXYXY | None,
    ) -> ImageArray:
        if bbox is None:
            return mask_gray

        h, w = mask_gray.shape[:2]
        x1, y1, x2, y2 = bbox
        x1c = max(0, min(w, int(x1)))
        x2c = max(0, min(w, int(x2)))
        y1c = max(0, min(h, int(y1)))
        y2c = max(0, min(h, int(y2)))

        if x2c <= x1c or y2c <= y1c:
            return mask_gray

        cropped = mask_gray[y1c:y2c, x1c:x2c]
        if cropped.size == 0:
            return mask_gray
        return cast(ImageArray, cropped)

    def _prepare_segmentation_input_view(
        self,
        silhouettes: NDArray[np.float32] | None,
    ) -> ImageArray:
        if silhouettes is None or silhouettes.size == 0:
            placeholder = np.zeros((DISPLAY_HEIGHT, DISPLAY_WIDTH, 3), dtype=np.uint8)
            self._draw_mode_indicator(placeholder, "Input Silhouettes (No Data)")
            return placeholder

        n_frames = int(silhouettes.shape[0])
        tiles_per_row = int(np.ceil(np.sqrt(n_frames)))
        rows = int(np.ceil(n_frames / tiles_per_row))

        tile_h = DISPLAY_HEIGHT
        tile_w = DISPLAY_WIDTH
        grid = np.zeros((rows * tile_h, tiles_per_row * tile_w), dtype=np.uint8)

        for idx in range(n_frames):
            sil = silhouettes[idx]
            tile = self._upscale_silhouette(sil)
            r = idx // tiles_per_row
            c = idx % tiles_per_row
            y0, y1 = r * tile_h, (r + 1) * tile_h
            x0, x1 = c * tile_w, (c + 1) * tile_w
            grid[y0:y1, x0:x1] = tile

        grid_bgr = cast(ImageArray, cv2.cvtColor(grid, cv2.COLOR_GRAY2BGR))

        for idx in range(n_frames):
            r = idx // tiles_per_row
            c = idx % tiles_per_row
            y0 = r * tile_h
            x0 = c * tile_w
            cv2.putText(
                grid_bgr,
                str(idx),
                (x0 + 8, y0 + 22),
                cv2.FONT_HERSHEY_SIMPLEX,
                0.6,
                (0, 255, 255),
                2,
                cv2.LINE_AA,
            )

        return grid_bgr

    def _prepare_raw_view(
        self,
        mask_raw: ImageArray | None,
        bbox: BBoxXYXY | None = None,
    ) -> ImageArray:
        """Prepare raw mask view.

        Args:
            mask_raw: Raw binary mask or None

        Returns:
            Displayable image with mode indicator
        """
        if mask_raw is None:
            # Create placeholder
            placeholder = np.zeros((DISPLAY_HEIGHT, DISPLAY_WIDTH, 3), dtype=np.uint8)
            self._draw_mode_indicator(placeholder, "Raw Mask (No Data)")
            return placeholder

        # Ensure single channel
        if len(mask_raw.shape) == 3:
            mask_gray = cast(ImageArray, cv2.cvtColor(mask_raw, cv2.COLOR_BGR2GRAY))
        else:
            mask_gray = cast(ImageArray, mask_raw)

        mask_gray = self._normalize_mask_for_display(mask_gray)
        mask_gray = self._crop_mask_to_bbox(mask_gray, bbox)

        debug_pad = RAW_STATS_PAD if self.show_raw_debug else 0
        content_h = max(1, DISPLAY_HEIGHT - debug_pad - MODE_LABEL_PAD)
        mask_resized = self._fit_gray_to_display(
            mask_gray, out_h=content_h, out_w=DISPLAY_WIDTH
        )
        full_mask = np.zeros((DISPLAY_HEIGHT, DISPLAY_WIDTH), dtype=np.uint8)
        full_mask[debug_pad : debug_pad + content_h, :] = mask_resized

        # Convert to BGR for display
        mask_bgr = cast(ImageArray, cv2.cvtColor(full_mask, cv2.COLOR_GRAY2BGR))
        if self.show_raw_debug:
            self._draw_raw_stats(mask_bgr, mask_raw)
        self._draw_mode_indicator(mask_bgr, "Raw Mask")

        return mask_bgr

    def _prepare_normalized_view(
        self,
        silhouette: NDArray[np.float32] | None,
    ) -> ImageArray:
        """Prepare normalized silhouette view.

        Args:
            silhouette: Normalized silhouette (64, 44) or None

        Returns:
            Displayable image with mode indicator
        """
        if silhouette is None:
            # Create placeholder
            placeholder = np.zeros((DISPLAY_HEIGHT, DISPLAY_WIDTH, 3), dtype=np.uint8)
            self._draw_mode_indicator(placeholder, "Normalized (No Data)")
            return placeholder

        # Upscale and convert
        upscaled = self._upscale_silhouette(silhouette)
        content_h = max(1, DISPLAY_HEIGHT - MODE_LABEL_PAD)
        sil_compact = self._fit_gray_to_display(
            upscaled, out_h=content_h, out_w=DISPLAY_WIDTH
        )
        sil_canvas = np.zeros((DISPLAY_HEIGHT, DISPLAY_WIDTH), dtype=np.uint8)
        sil_canvas[:content_h, :] = sil_compact
        sil_bgr = cast(ImageArray, cv2.cvtColor(sil_canvas, cv2.COLOR_GRAY2BGR))
        self._draw_mode_indicator(sil_bgr, "Normalized")

        return sil_bgr

    def _draw_mode_indicator(self, image: ImageArray, label: str) -> None:
        h, w = image.shape[:2]

        mode_text = label

        font = cv2.FONT_HERSHEY_SIMPLEX
        font_scale = 0.5
        thickness = 1

        # Get text size for background
        (text_width, text_height), _ = cv2.getTextSize(
            mode_text, font, font_scale, thickness
        )

        x_pos = 14
        y_pos = h - 8
        y_top = max(0, h - MODE_LABEL_PAD)

        _ = cv2.rectangle(image, (0, y_top), (w, h), COLOR_DARK_GRAY, -1)

        # Draw text
        _ = cv2.putText(
            image,
            mode_text,
            (x_pos, y_pos),
            font,
            font_scale,
            COLOR_YELLOW,
            thickness,
        )

    def update(
        self,
        frame: ImageArray,
        bbox: BBoxXYXY | None,
        bbox_mask: BBoxXYXY | None,
        track_id: int,
        mask_raw: ImageArray | None,
        silhouette: NDArray[np.float32] | None,
        segmentation_input: NDArray[np.float32] | None,
        label: str | None,
        confidence: float | None,
        fps: float,
        pose_data: dict[str, object] | None = None,
    ) -> bool:
        """Update visualization with new frame data.

        Args:
            frame: Input frame (H, W, C) uint8
            bbox: Bounding box in XYXY format (x1, y1, x2, y2) or None
            bbox_mask: Bounding box in mask coordinates (x1, y1, x2, y2) or None
            track_id: Tracking ID
            mask_raw: Raw binary mask (H, W) uint8 or None
            silhouette: Normalized silhouette (64, 44) float32 [0,1] or None
            segmentation_input: Windowed silhouette stack for model input visualization
            label: Classification label or None
            confidence: Classification confidence [0,1] or None
            fps: Current FPS
            pose_data: Pose data dictionary or None

        Returns:
            False if user requested quit (pressed 'q'), True otherwise
        """
        self._ensure_windows()

        # Prepare and show main window
        main_display = self._prepare_main_frame(
            frame, bbox, track_id, fps, label, confidence, pose_data
        )
        cv2.imshow(MAIN_WINDOW, main_display)

        # Prepare and show segmentation window
        seg_display = self._prepare_segmentation_view(mask_raw, silhouette, bbox)
        cv2.imshow(SEG_WINDOW, seg_display)

        if self.show_raw_window:
            self._ensure_raw_window()
            raw_display = self._prepare_raw_view(mask_raw, bbox_mask)
            cv2.imshow(RAW_WINDOW, raw_display)

        seg_input_display = self._prepare_segmentation_input_view(segmentation_input)
        cv2.imshow(WINDOW_SEG_INPUT, seg_input_display)

        # Handle keyboard input
        key = cv2.waitKey(1) & 0xFF

        if key == ord("q"):
            return False
        elif key == ord("r"):
            self.show_raw_window = not self.show_raw_window
            if self.show_raw_window:
                self._ensure_raw_window()
                logger.debug("Raw mask window enabled")
            else:
                self._hide_raw_window()
                logger.debug("Raw mask window disabled")
        elif key == ord("d"):
            self.show_raw_debug = not self.show_raw_debug
            logger.debug(
                "Raw mask debug overlay %s",
                "enabled" if self.show_raw_debug else "disabled",
            )

        return True

    def close(self) -> None:
        if self._windows_created:
            self._hide_raw_window()
            cv2.destroyAllWindows()
            self._windows_created = False
            self._raw_window_created = False