xslandClaude
63ee8444c0
顶庭(头顶→发际线)<0.7cm 时不再返回 null： - 上庭 = 0.95×中庭 反推发际线；若越过头顶则顶庭固定 0.7cm（发际线钉在头顶下方） - hairline_source 新增 "corrected"；to_response 始终输出完整四庭 - 标注图始终正常画发际线横线与庭段 - 接口6 三庭逻辑同步，去 discarded 分支 Co-Authored-By: Claude <noreply@anthropic.com>
300 lines
13 KiB
Python
300 lines
13 KiB
Python
"""四庭七眼测量核心：纵向定位（方案 B 主 / 方案 A 兜底）+ 七眼 + 厘米换算。
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整合：
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- estimate_vertical_landmarks：方案 A，按三庭比例推算上/顶庭（兜底）。
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- 决策逻辑：优先方案 B（分割发际线/头顶），合理性校验不过则回退方案 A。
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- measure_seven_eyes：眼宽/脸宽/两眼间距实测。
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- measure_face：主入口，产出结构化结果 MeasureResult（含 to_response）。
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详见技术方案 §4 / §5。本模块不依赖 torch，可在纯几何环境单独运行。
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"""
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from face_analysis.calibration import (
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estimate_scale_factor, normalized_to_pixel, pixel_distance, _lm_list,
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)
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from face_analysis.face_mesh_landmarks import (
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GLABELLA_9, NOSE_BOTTOM, CHIN_TIP,
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LEFT_EYE_OUTER, LEFT_EYE_INNER, RIGHT_EYE_INNER, RIGHT_EYE_OUTER,
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LEFT_CHEEK, RIGHT_CHEEK, LEFT_POSITION, RIGHT_POSITION,
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)
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from face_analysis.hair_segmenter import locate_hairline_by_segmentation
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# 方案 A 推算比例常量（顶:上:中:下 = 0.22:0.25:0.28:0.25），见技术方案 §4.2
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_UPPER_RATIO = 0.25 / 0.265 # 上庭 ÷ 中下庭均值
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_TOP_RATIO = 0.22 / 0.28 # 顶庭 ÷ 中庭（≈ 0.786）
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def _brow_center(lm, w, h):
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"""眉心 = 索引 9（眉间上点）。"""
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return normalized_to_pixel(lm[GLABELLA_9], w, h)
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def estimate_vertical_landmarks(landmarks, image_width, image_height):
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"""方案 A（兜底）：实测中/下庭，按比例推算上/顶庭。
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返回 5 个纵向点像素坐标 + 各段像素高度。注意其循环论证局限：
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上/顶庭为估算值，不反映真实脸型（详见技术方案 §4.1）。
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"""
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lm = _lm_list(landmarks)
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w, h = image_width, image_height
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brow_x, brow_y = _brow_center(lm, w, h)
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nose_bottom = normalized_to_pixel(lm[NOSE_BOTTOM], w, h)
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chin_tip = normalized_to_pixel(lm[CHIN_TIP], w, h)
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middle_court_px = abs(brow_y - nose_bottom[1]) # 眉心 → 鼻翼下缘
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lower_court_px = abs(nose_bottom[1] - chin_tip[1]) # 鼻翼下缘 → 下巴尖
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one_unit_px = (middle_court_px + lower_court_px) / 2 # 一等份 ≈ 中/下庭均值
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upper_court_px = one_unit_px * _UPPER_RATIO
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top_court_px = one_unit_px * _TOP_RATIO
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hairline_y = brow_y - upper_court_px
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hair_top_y = hairline_y - top_court_px
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return {
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"hair_top": (brow_x, hair_top_y),
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"hairline": (brow_x, hairline_y),
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"brow_center": (brow_x, brow_y),
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"nose_bottom": (nose_bottom[0], nose_bottom[1]),
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"chin_tip": (chin_tip[0], chin_tip[1]),
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"top_court_px": top_court_px,
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"upper_court_px": upper_court_px,
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"middle_court_px": middle_court_px,
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"lower_court_px": lower_court_px,
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}
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def _vertical_from_segmentation(lm, w, h, hair_mask):
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"""方案 B：用分割得到的发际线/头顶替换方案 A 的上/顶庭。
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成功且通过合理性校验返回 vertical dict，否则返回 None。
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"""
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res = locate_hairline_by_segmentation(hair_mask, _brow_center(lm, w, h)[0], h)
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if res is None:
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return None
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hairline_y, hair_top_y = res
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brow_x, brow_y = _brow_center(lm, w, h)
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nose_bottom = normalized_to_pixel(lm[NOSE_BOTTOM], w, h)
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chin_tip = normalized_to_pixel(lm[CHIN_TIP], w, h)
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middle_court_px = abs(brow_y - nose_bottom[1])
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lower_court_px = abs(nose_bottom[1] - chin_tip[1])
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upper_court_px = brow_y - hairline_y # 发际线 → 眉心
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top_court_px = hairline_y - hair_top_y # 头顶 → 发际线
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# 合理性校验：发际线在眉心上方、头顶在发际线上方、各庭为正
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if not (hair_top_y < hairline_y < brow_y):
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return None
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if upper_court_px <= 0 or top_court_px <= 0:
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return None
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if middle_court_px <= 0 or lower_court_px <= 0:
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return None
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return {
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"hair_top": (brow_x, float(hair_top_y)),
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"hairline": (brow_x, float(hairline_y)),
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"brow_center": (brow_x, brow_y),
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"nose_bottom": (nose_bottom[0], nose_bottom[1]),
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"chin_tip": (chin_tip[0], chin_tip[1]),
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"top_court_px": top_court_px,
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"upper_court_px": upper_court_px,
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"middle_court_px": middle_court_px,
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"lower_court_px": lower_court_px,
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}
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def decide_vertical(landmarks, image_width, image_height, hair_mask):
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"""纵向定位决策：方案 B 优先，失败回退方案 A。
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返回 (vertical_dict, hairline_source)，source ∈ {"segmentation","estimated"}。
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"""
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lm = _lm_list(landmarks)
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vb = _vertical_from_segmentation(lm, image_width, image_height, hair_mask)
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if vb is not None:
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return vb, "segmentation"
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return estimate_vertical_landmarks(landmarks, image_width, image_height), "estimated"
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def measure_seven_eyes(landmarks, image_width, image_height):
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"""七眼：眼宽（左右均值）、脸宽、两眼间距（像素）。"""
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lm = _lm_list(landmarks)
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w, h = image_width, image_height
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left_outer = normalized_to_pixel(lm[LEFT_EYE_OUTER], w, h)
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left_inner = normalized_to_pixel(lm[LEFT_EYE_INNER], w, h)
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right_inner = normalized_to_pixel(lm[RIGHT_EYE_INNER], w, h)
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right_outer = normalized_to_pixel(lm[RIGHT_EYE_OUTER], w, h)
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left_cheek = normalized_to_pixel(lm[LEFT_CHEEK], w, h)
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right_cheek = normalized_to_pixel(lm[RIGHT_CHEEK], w, h)
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left_eye = pixel_distance(left_outer, left_inner)
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right_eye = pixel_distance(right_inner, right_outer)
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return {
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"eye_width_px": (left_eye + right_eye) / 2,
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"face_width_px": pixel_distance(left_cheek, right_cheek),
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"inter_eye_distance_px": pixel_distance(left_inner, right_inner),
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# 标注图用的横向点像素坐标（不进 to_response）
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"points": {
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"left_outer": left_outer, "left_inner": left_inner,
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"right_inner": right_inner, "right_outer": right_outer,
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"left_cheek": left_cheek, "right_cheek": right_cheek,
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},
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}
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def pt_or_none(vertical, name):
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"""vertical dict 的点 → {"x","y"}，值为 None 时返回 None。"""
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v = vertical.get(name)
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if v is None:
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return None
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return {"x": int(round(v[0])), "y": int(round(v[1]))}
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class MeasureResult:
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"""测量结果，提供 to_response() 输出与接口文档同构的 data 字段。"""
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# 发际线修正阈值/固定值：发际线离头顶（顶庭）< 此值时判定分割不可靠，需修正发际线。
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# hairline 与 hair_top 几乎重合（如稀疏头发中轴漏检只剩一小撮）→ 上庭改取
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# 0.95×中庭反推发际线；若反推后越过头顶，则顶庭固定为 0.7cm（发际线钉在头顶下方
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# 0.7cm），上庭取眉心→发际线剩余距离。修正后始终返回完整四庭，不再置 null。
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HAIRLINE_CORRECT_TOP_CM = 0.7
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UPPER_TO_MIDDLE_RATIO = 0.95 # 贴近头顶时，上庭 = 中庭 × 此比例
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def __init__(self, vertical, eyes, px_per_cm, hairline_source, head_pose,
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landmarks=None, image_width=None, image_height=None):
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self.vertical = vertical
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self.eyes = eyes
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self.px_per_cm = px_per_cm
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self.hairline_source = hairline_source
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self.head_pose = head_pose # (yaw, pitch, roll) 或 None
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# 原始 mediapipe 点集 + 图像尺寸，供 to_response 输出 21/251 号定位点
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self.landmarks = landmarks
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self.w = image_width
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self.h = image_height
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# 发际线修正：顶庭（头顶→发际线）< 阈值视为发际线贴近头顶、不可靠。
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# 改写：上庭 = 0.95×中庭 → 由眉心反推发际线 y；若发际线越过头顶
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# （hairline_y < hair_top_y），顶庭固定为 0.7cm（发际线钉在头顶下方 0.7cm）。
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# 回写 vertical，使 to_response/标注图/v6 复用全部拿到修正后的发际线。
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self.detected_top_cm = vertical["top_court_px"] / px_per_cm
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self.hairline_corrected = self.detected_top_cm < self.HAIRLINE_CORRECT_TOP_CM
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if self.hairline_corrected:
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brow_x, brow_y = vertical["brow_center"]
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hair_top_y = vertical["hair_top"][1]
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new_upper_px = vertical["middle_court_px"] * self.UPPER_TO_MIDDLE_RATIO
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hairline_y = brow_y - new_upper_px
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if hairline_y < hair_top_y: # 反推发际线越过头顶 → 顶庭固定 0.7cm
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hairline_y = hair_top_y + self.HAIRLINE_CORRECT_TOP_CM * px_per_cm
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new_upper_px = brow_y - hairline_y
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vertical["hairline"] = (brow_x, float(hairline_y))
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vertical["upper_court_px"] = new_upper_px
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vertical["top_court_px"] = hairline_y - hair_top_y
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self.hairline_source = "corrected"
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# 各庭厘米（修正后）
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self.top_cm = vertical["top_court_px"] / px_per_cm
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self.upper_cm = vertical["upper_court_px"] / px_per_cm
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self.middle_cm = vertical["middle_court_px"] / px_per_cm
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self.lower_cm = vertical["lower_court_px"] / px_per_cm
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self.face_total_cm = (self.top_cm + self.upper_cm
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+ self.middle_cm + self.lower_cm)
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# 七眼厘米
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self.eye_width_cm = eyes["eye_width_px"] / px_per_cm
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self.face_width_cm = eyes["face_width_px"] / px_per_cm
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self.inter_eye_cm = eyes["inter_eye_distance_px"] / px_per_cm
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def to_response(self):
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# 四庭完整输出（发际线贴近头顶时已在 __init__ 修正 vertical，无 null 分支）。
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total_px = (self.vertical["top_court_px"] + self.vertical["upper_court_px"]
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+ self.vertical["middle_court_px"] + self.vertical["lower_court_px"])
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data = {
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"face_total_height_cm": round(self.face_total_cm, 2),
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"four_courts": {
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"top_court_cm": round(self.top_cm, 2),
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"upper_court_cm": round(self.upper_cm, 2),
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"middle_court_cm": round(self.middle_cm, 2),
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"lower_court_cm": round(self.lower_cm, 2),
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"ratios": {
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"top_court": round(self.vertical["top_court_px"] / total_px, 3),
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"upper_court": round(self.vertical["upper_court_px"] / total_px, 3),
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"middle_court": round(self.vertical["middle_court_px"] / total_px, 3),
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"lower_court": round(self.vertical["lower_court_px"] / total_px, 3),
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},
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},
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"seven_eyes": {
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"eye_width_cm": round(self.eye_width_cm, 2),
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"face_width_cm": round(self.face_width_cm, 2),
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"inter_eye_distance_cm": round(self.inter_eye_cm, 2),
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"ratios": {
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"eye_width": round(self.eyes["eye_width_px"] / self.eyes["face_width_px"], 3),
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"inter_eye_distance": round(self.eyes["inter_eye_distance_px"] / self.eyes["face_width_px"], 3),
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},
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},
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"landmarks": {
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"hair_top": pt_or_none(self.vertical, "hair_top"),
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"hairline": pt_or_none(self.vertical, "hairline"),
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"brow_center": pt_or_none(self.vertical, "brow_center"),
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"nose_bottom": pt_or_none(self.vertical, "nose_bottom"),
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"chin_tip": pt_or_none(self.vertical, "chin_tip"),
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},
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"hairline_source": self.hairline_source,
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}
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# left/right_position：mediapipe 21/251 号定位点（原图像素，与 landmarks 同坐标系）。
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# landmarks 缺省（如测试直构 MeasureResult）时不输出，保持向后兼容。
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if self.landmarks is not None and self.w and self.h:
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lm = _lm_list(self.landmarks)
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def _pt_lm(idx):
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px, py = normalized_to_pixel(lm[idx], self.w, self.h)
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return {"x": int(round(px)), "y": int(round(py))}
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data["left_position"] = _pt_lm(LEFT_POSITION)
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data["right_position"] = _pt_lm(RIGHT_POSITION)
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if self.head_pose is not None:
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yaw, pitch, roll = self.head_pose
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data["head_pose"] = {
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"yaw": round(yaw, 2), "pitch": round(pitch, 2), "roll": round(roll, 2),
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}
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return data
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def measure_face(landmarks, hair_mask, image_width, image_height, head_pose=None):
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"""主入口：纵向决策 + 七眼 + 尺度换算 → MeasureResult。"""
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vertical, source = decide_vertical(landmarks, image_width, image_height, hair_mask)
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eyes = measure_seven_eyes(landmarks, image_width, image_height)
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px_per_cm = estimate_scale_factor(landmarks, image_width, image_height)
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return MeasureResult(vertical, eyes, px_per_cm, source, head_pose,
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landmarks, image_width, image_height)
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if __name__ == "__main__":
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import sys
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import json
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import cv2
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from face_analysis.detector import detector
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from face_analysis.pose import estimate_head_pose
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path = sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg"
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img = cv2.imread(path)
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if img is None:
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print(f"无法读取图片: {path}")
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sys.exit(1)
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h, w = img.shape[:2]
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lms = detector.detect(img)
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if lms is None:
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print("未检出人脸")
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sys.exit(1)
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# 尝试分割（若 torch 不可用则走方案 A）
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mask = None
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try:
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from face_analysis.hair_segmenter import get_segmenter
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mask = get_segmenter().segment_hair(img)
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except Exception as e: # noqa: BLE001
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print(f"[warn] 分割不可用，回退方案 A：{e}")
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pose = estimate_head_pose(lms, w, h)
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result = measure_face(lms, mask, w, h, head_pose=pose)
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print(json.dumps(result.to_response(), ensure_ascii=False, indent=2))
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