118 lines
5.1 KiB
Python
118 lines
5.1 KiB
Python
"""接口52设计高度：目标上庭为眉心到下巴垂直距离的 45%。"""
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from __future__ import annotations
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import cv2
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import numpy as np
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from .mask import mask_from_curve
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def height_target(landmarks, width, height, reference=None):
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"""Use unrounded measurement coordinates; fall back to the same MP indices.
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Distances use image Y, matching face_analysis.measure's court heights.
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No rounded centimetre values or existing-hairline estimates enter the rule.
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"""
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scale = np.array([width, height], dtype=np.float64)
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if reference is None:
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brow, nose, chin = landmarks[[9, 94, 152], :2] * scale
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source = "hairline_landmarks"
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px_per_cm = None
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else:
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brow = np.asarray(reference["brow_center"], dtype=np.float64)
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nose = np.asarray(reference["nose_bottom"], dtype=np.float64)
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chin = np.asarray(reference["chin_tip"], dtype=np.float64)
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source = "face_measure"
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px_per_cm = reference.get("px_per_cm")
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if not np.isfinite([brow, nose, chin]).all() or not brow[1] < nose[1] < chin[1]:
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raise ValueError("眉心、鼻翼下缘与下巴坐标异常，无法计算目标上庭高度")
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middle, lower = float(nose[1] - brow[1]), float(chin[1] - nose[1])
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upper = (middle + lower) * 0.45
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target_y = float(brow[1] - upper)
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if not (0 <= brow[0] < width and 1 <= target_y < height - 1):
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raise ValueError("按中下庭高度计算的目标发际线超出图片范围，请使用完整正面照")
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def pt(p):
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return {"x": float(p[0]), "y": float(p[1])}
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return {
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"name": "court_average",
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"formula": "上庭 = (中庭 + 下庭) × 0.45",
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"measurement_source": source,
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"brow_center": pt(brow), "nose_bottom": pt(nose), "chin_tip": pt(chin),
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"middle_height_px": middle, "lower_height_px": lower,
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"target_upper_height_px": upper,
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"target_upper_height_cm": upper / px_per_cm if px_per_cm else None,
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"target_center": {"x": float(brow[0]), "y": target_y},
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}
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def clean_curve(overlay):
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"""Keep the white design line; discard opaque black texture-border pixels."""
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out = np.zeros_like(overlay, dtype=np.float32)
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out[:, :, :3] = 255
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out[:, :, 3] = overlay[:, :, 3] * (overlay[:, :, :3].mean(axis=2) > 128)
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return out
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def curve_center(overlay, center_x):
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"""Alpha-weighted centre of the line at the facial midline, in image pixels."""
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h, w = overlay.shape[:2]
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column = int(round(center_x))
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if not 0 <= column < w:
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raise ValueError("面部中轴超出图片范围")
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alpha = overlay[:, column, 3].astype(np.float64)
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alpha[alpha <= 1] = 0
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if not alpha.any():
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# Rasterized endpoints may leave a 1px gap, but distant sideburns
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# must never be silently substituted for a missing central curve.
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columns = np.flatnonzero((overlay[:, :, 3] > 1).any(axis=0))
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if not columns.size or np.min(np.abs(columns - column)) > 2:
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raise ValueError("发际线模板在面部中轴处没有有效曲线")
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column = int(columns[np.argmin(np.abs(columns - column))])
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alpha = overlay[:, column, 3].astype(np.float64)
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alpha[alpha <= 1] = 0
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return {"x": float(center_x), "y": float(np.dot(np.arange(h), alpha) / alpha.sum())}
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def place_curve(overlay, target, landmarks):
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"""Rigidly shift a rendered template along face-up to meet the target Y.
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Keeping the curve rigid preserves roll and the template's shape. Pixel Y
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remains the height convention, even for a rolled face. A short correction
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compensates for the curve's slope as the local-axis shift also changes X.
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"""
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curve = clean_curve(overlay)
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h, w = curve.shape[:2]
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center_x, target_y = target["x"], target["y"]
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before = curve_center(curve, center_x)
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up = (landmarks[10, :2] - landmarks[152, :2]) * [w, h]
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if abs(up[1]) < 1e-6:
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raise ValueError("无法确定人脸向上方向")
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x_per_y = float(np.clip(up[0] / up[1], -1.0, 1.0))
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dy = target_y - before["y"]
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moved = curve
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for _ in range(8):
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transform = np.array([[1, 0, dy * x_per_y], [0, 1, dy]], np.float32)
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moved = cv2.warpAffine(curve, transform, (w, h), flags=cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_CONSTANT, borderValue=0)
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center = curve_center(moved, center_x)
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residual = target_y - center["y"]
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if abs(residual) <= 0.15:
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break
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dy += residual
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if abs(center["y"] - target_y) > 1.0:
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raise ValueError("发际线中心无法对齐目标高度，请检查照片姿态与裁剪范围")
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return moved, {
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"old_center": before, "new_center": center,
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"shift_x_px": float(dy * x_per_y), "shift_y_px": float(dy),
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"target_error_px": float(abs(center["y"] - target_y)),
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}
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def inpaint_from_curve(photo, landmarks, parse_map, overlay):
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"""The preview line is also the exact boundary used for the black line/mask."""
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alpha = np.clip(overlay[:, :, 3:4] / 255.0, 0, 1)
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marked = np.clip(photo * (1.0 - alpha), 0, 255).astype(np.uint8)
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curve = (overlay[:, :, 3] > 40).astype(np.uint8) * 255
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mask = mask_from_curve(curve, landmarks, parse_map)
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return marked, mask
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