"""Expression analysis: facial expression tags from pose + WD14 tags.

Extends the basic open_mouth/closed_eyes detection in wholebody_pose.py
with additional heuristics. Primary source is WD14 tag detection since
anime facial expression recognition via landmarks alone is unreliable.
"""
from __future__ import annotations

import numpy as np

from src.wholebody_pose import _vis


# WD14 tag → Danbooru canon mapping for expression tags
_WD_EXPRESSION_MAP = {
    "smile": "smile",
    "happy": "happy",
    "laughing": "laughing",
    "frown": "frown",
    "sad": "sad",
    "crying": "crying",
    "tears": "tears",
    "angry": "angry",
    "serious": "serious",
    "surprised": "surprised",
    "blush": "blush",
    "blushing": "blush",
    "red face": "blush",
    "wink": "wink",
    "confused": "confused",
    "expressionless": "expressionless",
    "poker face": "expressionless",
    "ahegao": "ahegao_face",
    "ahegao_face": "ahegao_face",
    # Note: open_mouth, closed_eyes, looking_*, wide_eyes are handled by pose
    # keypoints and should not be duplicated in expression tags
}


def _safe_dist(a: np.ndarray, b: np.ndarray) -> float:
    if not (_vis(a) and _vis(b)):
        return 0.0
    return float(np.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2))


def _vis_kpt(kp_arr: np.ndarray, idx: int, thresh: float = 0.15) -> bool:
    """Check if keypoint at index ``idx`` in ``kp_arr`` is visible."""
    if kp_arr.ndim != 2 or kp_arr.shape[0] <= idx:
        return False
    return kp_arr[idx, 2] >= thresh


def analyze_expression(face_kpts: np.ndarray,
                       body_kpts: np.ndarray,
                       wd14_tags: list[str]) -> list[str]:
    """Analyze facial expression from face landmarks + WD14 tags.

    Args:
        face_kpts: 68 face landmarks array (or full 133-kpt array).
        body_kpts: Body keypoints (for nose reference, first 17).
        wd14_tags: General tags from WD14 ensemble.

    Returns:
        List of Danbooru-style expression tags.
    """
    tags: set[str] = set()

    # --- Pass-through from WD14 (most reliable for anime expressions) ---
    for tag in wd14_tags:
        canon = _WD_EXPRESSION_MAP.get(tag)
        if canon:
            tags.add(canon)

    # --- Face landmark heuristics (supplement WD14) ---
    # Note: open_mouth, closed_eyes, and looking direction are also detected
    # by wholebody_pose._face_tags(). The expression analyzer supplements these
    # with WD14-derived tags (smile, blush, etc.) since landmark-based smile
    # detection is unreliable for anime art.
    if face_kpts is not None:
        # Normalize face array: if full 133-kpt array, extract face slice
        if face_kpts.shape[0] >= 91:
            face = face_kpts[23:91]  # 68 face landmarks
            body = face_kpts[:17]
        else:
            face = face_kpts[:68]
            body = body_kpts[:17] if body_kpts is not None and body_kpts.shape[0] >= 17 else None

        if face.shape[0] >= 68:
            # Mouth analysis
            if _check_open_mouth(face):
                tags.add("open_mouth")

            # Eye openness
            eye_tags = _check_eyes(face)
            tags.update(eye_tags)

            # Looking direction
            look_tags = _check_looking_direction(face, body)
            tags.update(look_tags)

    return sorted(tags) if tags else []


def _check_open_mouth(face: np.ndarray) -> bool:
    """Check if mouth is open using lip landmark distances."""
    # iBug 68-point face indices:
    # 48-54: outer lip contour (48 = left corner, 54 = right corner)
    # 55-59: inner lip upper
    # 60-64: inner lip lower
    # 65-67: mouth interior

    # Upper lip center (points 51-53) and lower lip center (points 57-59)
    ul = None
    ll = None

    if all(_vis_kpt(face, i, 0.15) for i in [51, 52, 53]):
        ul = np.mean(face[51:54], axis=0)
    if all(_vis_kpt(face, i, 0.15) for i in [57, 58, 59]):
        ll = np.mean(face[57:60], axis=0)

    if ul is not None and ll is not None:
        mouth_open_dist = _safe_dist(ul, ll)
        # Normalize by inter-eye distance
        leye_c = np.mean(face[36:42], axis=0) if all(_vis_kpt(face, i, 0.15) for i in range(36, 42)) else None
        reye_c = np.mean(face[42:48], axis=0) if all(_vis_kpt(face, i, 0.15) for i in range(42, 48)) else None
        if leye_c is not None and reye_c is not None:
            eye_dist = _safe_dist(leye_c, reye_c)
            if eye_dist > 5 and mouth_open_dist > 0.25 * eye_dist:
                return True
    return False


def _check_eyes(face: np.ndarray) -> set[str]:
    """Check eye openness using Eye Aspect Ratio (EAR)."""
    tags: set[str] = set()

    def _eye_ear(indices):
        if not all(_vis_kpt(face, i, 0.15) for i in indices):
            return None
        p = face[indices]
        v1 = _safe_dist(p[1], p[5])
        v2 = _safe_dist(p[2], p[4])
        h = _safe_dist(p[0], p[3])
        if h < 1e-3:
            return None
        return (v1 + v2) / (2 * h)

    # Left eye: 36-41, Right eye: 42-47
    l_ear = _eye_ear([36, 37, 38, 39, 40, 41])
    r_ear = _eye_ear([42, 43, 44, 45, 46, 47])

    if l_ear is not None and r_ear is not None:
        avg = (l_ear + r_ear) / 2
        if avg < 0.2:
            tags.add("closed_eyes")
        elif avg > 0.4:
            tags.add("wide_eyes")

    return tags


def _check_looking_direction(face: np.ndarray, body: np.ndarray) -> set[str]:
    """Detect looking direction from eye-to-nose offset."""
    tags: set[str] = set()

    if body is None or not _vis(body[0], 0.15):
        return tags

    nose_tip = body[0]
    leye_c = np.mean(face[36:42], axis=0) if all(_vis_kpt(face, i, 0.15) for i in range(36, 42)) else None
    reye_c = np.mean(face[42:48], axis=0) if all(_vis_kpt(face, i, 0.15) for i in range(42, 48)) else None

    if leye_c is not None and reye_c is not None:
        eye_mid_x = (leye_c[0] + reye_c[0]) / 2
        eye_mid_y = (leye_c[1] + reye_c[1]) / 2
        inter_eye = _safe_dist(leye_c, reye_c)
        if inter_eye > 5:
            nx = (nose_tip[0] - eye_mid_x) / inter_eye
            ny = (nose_tip[1] - eye_mid_y) / inter_eye

            if nx > 0.35:
                tags.add("looking_right")
            elif nx < -0.35:
                tags.add("looking_left")

            if ny > 0.35:
                tags.add("looking_up")
            elif ny < -0.35:
                tags.add("looking_down")

    return tags