"""接口99：人脸特殊点检测。

返回 MediaPipe Face Mesh 关键点中一组固定编号的像素坐标（英文组名_点ID 为字段名），
外加 5 个发际线特殊点：从指定关键点沿指定方向射线步进，与 BiSeNet 头发分割 mask
首次相交处即发际线点（参考接口1 方案B 的发际线定位方式，见 hair_segmenter.py）。

组与编号（需求方给定）：
- eyebrow       107 66 105 63 70 336 296 334 293 300
- eye           189 222 224 113 226 413 442 444 342 446
- mouth         0 37 40 67 91 84 17 405 375 409 269
- nose_midline  9 168 197 5 4 194 278 48 437 217
- cheek         101 117 123 187 205 50 330 346 352 280 425
- face_edge     162 234 58 136 149 152 378 365 288 401 366 389 251

发际线点（字段名需求方给定；A/B 两射线时取中点，仅一条命中则返回该点，均未命中为 null）：
- hair_line_center       151 → 10 方向与发际线交点
- hair_line_right        mid(108→109 交点A, 69→67 交点B)
- hair_line_right_right  mid(104→103 交点A, 68→54 交点B)
- hair_line_left         mid(337→338 交点A, 299→297 交点B)
- hair_line_left_left    mid(333→332 交点A, 298→284 交点B)  # 与 right_right 镜像对称
"""
import math

import numpy as np

# 固定关键点组：组英文名 → MediaPipe 点 ID 列表（顺序即需求方给定顺序）
GROUPS = {
    "eyebrow": [107, 66, 105, 63, 70, 336, 296, 334, 293, 300],
    "eye": [189, 222, 224, 113, 226, 413, 442, 444, 342, 446],
    "mouth": [0, 37, 40, 67, 91, 84, 17, 405, 375, 409, 269],
    "nose_midline": [9, 168, 197, 5, 4, 194, 278, 48, 437, 217],
    "cheek": [101, 117, 123, 187, 205, 50, 330, 346, 352, 280, 425],
    "face_edge": [162, 234, 58, 136, 149, 152, 378, 365, 288, 401, 366, 389, 251],
}

# 发际线点定义：字段名 → 参与计算的 (起点ID, 方向点ID) 射线组；单射线直接取交点，
# 双射线取两交点中点。
HAIRLINE_RAYS = {
    "hair_line_center": [(151, 10)],
    "hair_line_right": [(108, 109), (69, 67)],
    "hair_line_right_right": [(104, 103), (68, 54)],
    "hair_line_left": [(337, 338), (299, 297)],
    "hair_line_left_left": [(333, 332), (298, 284)],
}


class NoFaceError(Exception):
    """未检出人脸。"""


class NonFrontalError(Exception):
    """非正面照（复用接口1 姿态阈值）。"""


def ray_hairline_intersection(p1, p2, hair_mask):
    """从 p1 沿 p1→p2 方向逐像素步进，返回首次进入头发 mask 的整数像素坐标。

    p1/p2: (x, y) 像素坐标（float）。起点本身已在头发内时直接返回起点（已到发际线）。
    步进直到出图或步数上限（3×图对角线，防极端死循环）；未命中返回 None。
    """
    mask = np.asarray(hair_mask).astype(bool)
    if mask.size == 0:
        return None
    h, w = mask.shape
    x0, y0 = float(p1[0]), float(p1[1])
    dx, dy = float(p2[0]) - x0, float(p2[1]) - y0
    length = math.hypot(dx, dy)
    if length < 1e-6:
        return None
    ux, uy = dx / length, dy / length
    max_steps = int(3 * math.hypot(h, w))

    def _hit(x, y):
        xi, yi = int(round(x)), int(round(y))
        if 0 <= xi < w and 0 <= yi < h:
            return mask[yi, xi]
        return False

    x, y = x0, y0
    for _ in range(max_steps):
        if _hit(x, y):
            return int(round(x)), int(round(y))
        x += ux
        y += uy
        if not (0 <= int(round(x)) < w and 0 <= int(round(y)) < h):
            return None   # 已步出图像，方向上无头发
    return None


def detect_special_points(image_bgr):
    """主入口：图片 → {字段名: {"x","y"} 或 None} + 元信息。

    Returns: {"points": {...}, "image_size": {"width","height"},
              "hairline_source": "segmentation"|"unavailable"}
    无人脸抛 NoFaceError；非正面抛 NonFrontalError。
    """
    from face_analysis.detector import detector
    from face_analysis.calibration import normalized_to_pixel
    from face_analysis.pose import check_frontal_face

    h, w = image_bgr.shape[:2]
    landmarks = detector.detect(image_bgr)
    if landmarks is None:
        raise NoFaceError("未检出人脸")
    if not check_frontal_face(landmarks, w, h):
        raise NonFrontalError("非正面照")

    lm = landmarks.landmark
    px = {i: normalized_to_pixel(lm[i], w, h) for i in range(len(lm))}

    points = {}
    for group, ids in GROUPS.items():
        for idx in ids:
            x, y = px[idx]
            points[f"{group}_{idx}"] = {"x": int(round(x)), "y": int(round(y))}

    # 头发分割（与接口1 相同：按人脸框裁剪后再分割，脸外背景不干扰）
    hair_mask = None
    try:
        from face_analysis.hair_segmenter import get_segmenter
        face_box = (min(p[0] for p in px.values()), min(p[1] for p in px.values()),
                    max(p[0] for p in px.values()), max(p[1] for p in px.values()))
        hair_mask, _ = get_segmenter().segment_hair_and_ears(
            image_bgr, face_box=face_box)
    except Exception:  # noqa: BLE001  分割不可用 → 发际线点置 null，不阻塞关键点
        hair_mask = None

    for name, rays in HAIRLINE_RAYS.items():
        hits = []
        if hair_mask is not None:
            for a, b in rays:
                hits.append(ray_hairline_intersection(px[a], px[b], hair_mask))
        found = [p for p in hits if p is not None]
        if not found:
            points[name] = None
        elif len(found) == 1:
            points[name] = {"x": found[0][0], "y": found[0][1]}
        else:
            points[name] = {
                "x": int(round(sum(p[0] for p in found) / len(found))),
                "y": int(round(sum(p[1] for p in found) / len(found))),
            }

    return {
        "points": points,
        "image_size": {"width": w, "height": h},
        "hairline_source": "segmentation" if hair_mask is not None else "unavailable",
    }


if __name__ == "__main__":
    import sys
    import cv2
    import json

    path = sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg"
    img = cv2.imread(path)
    if img is None:
        print(f"无法读取图片: {path}")
        sys.exit(1)
    try:
        out = detect_special_points(img)
    except (NoFaceError, NonFrontalError) as e:
        print(f"检测失败: {e}")
        sys.exit(1)
    print(json.dumps(out, ensure_ascii=False, indent=2))