xsl
9b7324cd26
将所有绝对路径从 /home/ubuntu 改回 /home/xsl，覆盖启动脚本、 configure.ini 及训练/测试脚本。纯路径替换，无功能性改动。 本分支(xsl5090)用于本机(RTX 5090)运行；master 维持 /home/ubuntu 供远程 ubuntu 机器使用。
345 lines
16 KiB
Python
345 lines
16 KiB
Python
# -*- coding: utf-8 -*-
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"""发际线区域 mask 自动识别（4 种方案）
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需求约束：
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- 只取额头部分的发际线（前面，不要侧面/鬓角）
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- 下面不超过眉毛（用眉毛峰值点作下界）
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- 左右不超过耳朵/太阳穴（用太阳穴点作左右界）
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- 上面不超过已有头发（用头发mask的上边界作上界）
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4 种方案：
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1. boundary_band: 头发mask边界带（现有方案，沿头发外轮廓的带）
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2. mediapipe: MediaPipe 478关键点 → 额头多边形
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3. landmark_1k: 现有1k模型关键点 → 几何裁剪额头区
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4. deeplab: 复用3分类DeepLab的skin/hair边界
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所有方案输入输出统一：
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输入: origin_matting(头发mask 0-255), landmarks_1k(np.array), img(BGR), band_width(int)
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输出: (mask_8u, info_str, debug_images_dict)
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mask_8u: 发际线mask, 0或255, 与img同尺寸
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info_str: 方案说明文字
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debug_images_dict: {label: ndarray} 用于前端展示中间产物
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"""
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import os
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import cv2
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import numpy as np
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# MediaPipe 模型路径
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MP_MODEL = "/home/xsl/change_hair/project/hair_service_sd/weights/mediapipe/face_landmarker.task"
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_mp_detector = None
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def detect_hairline(origin_matting, landmarks_1k, img, band_width=15, method="mediapipe",
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height_ratio=0.432, width_ratio=0.144, corner_ratio=0.25,
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vertical_offset=0.0):
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"""统一入口：按 method 调用对应方案。
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height_ratio/width_ratio/corner_ratio/vertical_offset 仅 landmark_1k 方案使用。
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vertical_offset: 重绘区上下平移比例（相对额头高度，正值下移，负值上移）
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返回 (mask_8u, info_str, debug_images)
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"""
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if method == "boundary_band":
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return _method_boundary_band(origin_matting, landmarks_1k, band_width)
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elif method == "mediapipe":
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return _method_mediapipe(img, band_width)
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elif method == "landmark_1k":
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return _method_landmark_1k(origin_matting, landmarks_1k, band_width,
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height_ratio, width_ratio, corner_ratio, vertical_offset)
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elif method == "deeplab":
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return _method_deeplab(img, origin_matting, landmarks_1k, band_width)
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else:
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raise ValueError(f"未知方法: {method}，可选: boundary_band/mediapipe/landmark_1k/deeplab")
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# ============================================================
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# 方案1: 边界带法（现有方案）
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# ============================================================
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def _method_boundary_band(origin_matting, landmarks_1k, band_width):
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"""头发mask的形态学边界带（沿整个头发外轮廓的带，不限于额头）。
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注意：这是最宽的方案，会包含鬓角/发尾的边界。
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"""
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bw = max(1, int(band_width))
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kernel = np.ones((bw, bw), np.uint8)
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mask = _boundary_band(origin_matting, kernel)
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info = f"边界带法(band_width={bw})：沿整个头发外轮廓的带，带宽≈{2*bw}px。注意：会包含鬓角/侧面边界"
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debug = {"原头发mask": origin_matting, "边界带(重绘区)": mask}
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return mask, info, debug
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# ============================================================
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# 方案2: MediaPipe 478关键点 → 额头多边形
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# ============================================================
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def _method_mediapipe(img, band_width):
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"""用 MediaPipe Face Mesh 的额头/太阳穴/眉毛关键点构造额头多边形。
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额头多边形顶点（MediaPipe索引）：
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发际线侧：10(顶), 151(上沿), 104(右太阳穴上), 333(左太阳穴上), 346(右发际侧)
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眉毛峰值（下界）：70(左眉峰), 300(右眉峰)
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眉间（下界中）：8(眉间上)
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多边形 = [左太阳穴上 → 顶 → 右太阳穴上 → 右发际侧 → 右眉峰 → 眉间 → 左眉峰 → 左太阳穴下]
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然后膨胀成带（band_width）。
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"""
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global _mp_detector
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try:
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if _mp_detector is None:
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import mediapipe as mp
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from mediapipe.tasks import python
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from mediapipe.tasks.python import vision
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os.environ.setdefault("GLOG_minloglevel", "3") # 抑制 mediapipe 日志
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base = python.BaseOptions(model_asset_path=MP_MODEL)
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opts = vision.FaceLandmarkerOptions(base_options=base, num_faces=1)
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_mp_detector = vision.FaceLandmarker.create_from_options(opts)
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import mediapipe as mp
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h, w = img.shape[:2]
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mp_img = mp.Image(image_format=mp.ImageFormat.SRGB, data=cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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res = _mp_detector.detect(mp_img)
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if not res.face_landmarks:
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return _fallback_empty("MediaPipe未检测到人脸")
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lm = res.face_landmarks[0]
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# 额头多边形关键点索引（顺时针）
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# 左太阳穴上(104) → 顶(10) → 右太阳穴上(333) → 右发际侧(346)
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# → 右眉峰(300) → 眉间(8) → 左眉峰(70) → 回到左太阳穴
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poly_indices = [104, 103, 67, 109, 10, 337, 332, 333, 299, 346, 300, 293, 8, 63, 105, 107, 70, 63]
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poly_indices = [104, 109, 10, 338, 333, 346, 300, 8, 70] # 精简版
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pts = []
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for idx in poly_indices:
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if idx < len(lm):
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p = lm[idx]
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pts.append((int(p.x * w), int(p.y * h)))
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if len(pts) < 3:
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return _fallback_empty("MediaPipe关键点不足")
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# 画多边形
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mask = np.zeros((h, w), dtype=np.uint8)
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pts_arr = np.array(pts, dtype=np.int32)
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cv2.fillPoly(mask, [pts_arr], 255)
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# 关键点可视化（用于debug图）
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vis = img.copy()
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for i, idx in enumerate(poly_indices):
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if idx < len(lm):
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p = lm[idx]
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x, y = int(p.x * w), int(p.y * h)
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cv2.circle(vis, (x, y), 4, (0, 255, 255), -1)
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cv2.putText(vis, str(idx), (x+4, y-4), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 255), 1)
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cv2.polylines(vis, [pts_arr], True, (0, 255, 0), 2)
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# 膨胀成带（让边缘羽化）
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bw = max(1, int(band_width))
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mask_band = cv2.dilate(mask, np.ones((bw, bw), np.uint8))
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info = f"MediaPipe法(478点)：额头多边形(顶点10/104/333等)+眉毛峰70/300作下界，膨胀带={bw}px。约束：下不超眉毛，左右不超太阳穴"
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debug = {"MediaPipe关键点+多边形": vis, "额头多边形": mask, "膨胀后(重绘区)": mask_band}
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return mask_band, info, debug
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except Exception as e:
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return _fallback_empty(f"MediaPipe失败: {e}")
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# ============================================================
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# 方案3: 现有1k关键点 → 圆角矩形额头区
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# ============================================================
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def _method_landmark_1k(origin_matting, landmarks_1k, band_width,
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height_ratio=0.432, width_ratio=0.144, corner_ratio=0.25,
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vertical_offset=0.0):
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"""用现有1k模型的关键点（眉毛/眼睛/脸轮廓）构造【圆角矩形】额头区。
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形状参数（可调）：
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- height_ratio: 高度缩放比（相对原矩形高度，越小越窄）
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- width_ratio: 左右各扩展比例（相对眉宽，越大越宽）
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- corner_ratio: 圆角半径比例（相对短边）
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- vertical_offset: 上下平移比例（相对额头高度，正值下移）
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下界：眉毛最高点（137索引 121:129 右眉, 129:137 左眉）
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左右界：脸轮廓太阳穴（向左右扩展）
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上界：头发mask的上边界
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"""
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from utils.landmark_processor import pts_1k_to_137
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try:
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lm137 = pts_1k_to_137(np.asarray(landmarks_1k))
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h, w = origin_matting.shape[:2]
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# 下界：眉毛峰值（137索引 121:129 右眉, 129:137 左眉）
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brow_y = []
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for seg in [lm137[121:129], lm137[129:137]]:
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if len(seg) > 0:
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brow_y.append(int(seg[:, 1].min())) # 眉毛最高点(y最小)
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if not brow_y:
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return _fallback_empty("1k关键点无眉毛点")
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brow_top = min(brow_y) # 取最高的眉毛点
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# 左右界：眉尾向外扩展（左右长一点）
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left_brow_x = int(lm137[129:137][:, 0].min()) # 左眉最左
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right_brow_x = int(lm137[121:129][:, 0].max()) # 右眉最右
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brow_width = right_brow_x - left_brow_x
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# 左右各扩展 width_ratio（向两侧扩，默认0.144）
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extend = int(brow_width * width_ratio)
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left = max(0, left_brow_x - extend)
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right = min(w, right_brow_x + extend)
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# 上界：头发mask在左右界范围内每列的最上像素
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sub_hair = origin_matting[:, left:right+1] if right > left else origin_matting
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hair_rows = np.where(sub_hair.max(axis=1) > 30)[0]
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hair_top = int(hair_rows.min()) if len(hair_rows) > 0 else 0
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# 基础矩形
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raw_top = max(0, hair_top)
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raw_bottom = min(h, brow_top)
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raw_left = max(0, left)
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raw_right = min(w, right)
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if raw_bottom <= raw_top or raw_right <= raw_left:
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return _fallback_empty("额头矩形无效")
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# ★ 上下短一点：以矩形中心为基准，高度缩到 height_ratio
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cy = (raw_top + raw_bottom) / 2.0
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half_h = (raw_bottom - raw_top) / 2.0 * height_ratio
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# ★ vertical_offset 上下平移（正值下移，相对额头高度）
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forehead_h = raw_bottom - raw_top
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offset_px = forehead_h * vertical_offset
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top = int(max(0, cy - half_h + offset_px))
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bottom = int(min(h, cy + half_h + offset_px))
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# ★ 圆角矩形
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corner_radius = int(min(bottom - top, raw_right - raw_left) * corner_ratio)
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mask = _rounded_rect((h, w), raw_left, top, raw_right, bottom, corner_radius)
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# 可视化关键点 + 圆角矩形描边
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vis = np.zeros((h, w, 3), dtype=np.uint8)
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vis[:, :, 1] = origin_matting # 头发mask用绿色显示
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cv2.rectangle(vis, (raw_left, raw_top), (raw_right, raw_bottom), (60, 60, 60), 1) # 原矩形(灰)
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# 描圆角矩形边
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cv2.drawContours(vis, contours, -1, (0, 0, 255), 2) # 圆角矩形(红)
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for seg, color in [(lm137[121:129], (255, 255, 0)), (lm137[129:137], (255, 255, 0))]:
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for p in seg:
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cv2.circle(vis, (int(p[0]), int(p[1])), 3, color, -1)
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# 膨胀
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bw = max(1, int(band_width))
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mask_band = cv2.dilate(mask, np.ones((bw, bw), np.uint8))
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info = (f"1k关键点法(圆角矩形)：原矩形[{raw_left},{raw_top},{raw_right},{raw_bottom}] → "
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f"高度×{height_ratio}(上下短) 宽度+{width_ratio*2*100:.0f}%(左右长) 圆角r={corner_radius} "
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f"下移{offset_px:.0f}px(offset={vertical_offset})。"
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f"下界=眉毛峰 左右界=眉尾扩展 上界=头发顶")
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debug = {"1k关键点+圆角矩形(灰=原矩形/红=优化后)": vis,
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"圆角矩形mask": mask, "膨胀后(重绘区)": mask_band}
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return mask_band, info, debug
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except Exception as e:
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return _fallback_empty(f"1k关键点失败: {e}")
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def _rounded_rect(size, x1, y1, x2, y2, r):
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"""画圆角矩形 mask（白色填充）。r=圆角半径。"""
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h, w = size
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mask = np.zeros((h, w), dtype=np.uint8)
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r = max(1, min(r, (x2 - x1) // 2, (y2 - y1) // 2))
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# 中间矩形（去掉四角的圆角区）
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cv2.rectangle(mask, (x1 + r, y1), (x2 - r, y2), 255, -1)
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cv2.rectangle(mask, (x1, y1 + r), (x2, y2 - r), 255, -1)
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# 四个角的扇形
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cv2.ellipse(mask, (x1 + r, y1 + r), (r, r), 180, 0, 90, 255, -1) # 左上
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cv2.ellipse(mask, (x2 - r, y1 + r), (r, r), 270, 0, 90, 255, -1) # 右上
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cv2.ellipse(mask, (x1 + r, y2 - r), (r, r), 90, 0, 90, 255, -1) # 左下
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cv2.ellipse(mask, (x2 - r, y2 - r), (r, r), 0, 0, 90, 255, -1) # 右下
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return mask
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# ============================================================
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# 方案4: 复用3分类DeepLab（skin/hair边界）
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# ============================================================
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def _method_deeplab(img, origin_matting, landmarks_1k, band_width):
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"""用项目现有的3分类DeepLab（bg/skin/hair）找skin与hair的交界线。
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发际线 = skin区域中紧邻hair的像素带。
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用 Evaluator(gpu, nclass=3).eval(image, landmark1k) 得到分割图。
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"""
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_deeplab_model = getattr(_method_deeplab, "_model", None)
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try:
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import torch
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from core.seg.hairseg_single_model import Evaluator
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if _deeplab_model is None:
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model_root = os.path.join(os.path.dirname(__file__), "weights")
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model_path = os.path.join(model_root, "deeplabv3_hair512_360_0520_wl.pth")
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_deeplab_model = Evaluator(gpu_id=0, output_img_size=512,
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nclass=3, seg_model_path=model_path)
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_method_deeplab._model = _deeplab_model # 缓存
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with torch.no_grad():
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seg = _deeplab_model.eval(img, np.asarray(landmarks_1k)) # 0=bg, 128=skin, 255=hair
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if seg is None:
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return _fallback_empty("DeepLab分割失败")
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skin = ((seg > 60) & (seg < 200)).astype(np.uint8) * 255 # skin
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hair = (seg > 200).astype(np.uint8) * 255 # hair
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# 发际线带 = skin侧紧邻hair的带：skin膨胀后 ∩ hair的边界带
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bw = max(2, int(band_width))
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kernel = np.ones((bw, bw), np.uint8)
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hair_band = _boundary_band(hair, kernel) # hair边界带
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skin_dilate = cv2.dilate(skin, kernel) # skin膨胀
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hairline = cv2.bitwise_and(hair_band, skin_dilate) # 二者交集≈发际线
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# 用眉毛点约束下界（不跑到眉毛以下）
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from utils.landmark_processor import pts_1k_to_137
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lm137 = pts_1k_to_137(np.asarray(landmarks_1k))
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brow_y = []
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for s in [lm137[121:129], lm137[129:137]]:
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if len(s) > 0:
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brow_y.append(int(s[:, 1].min()))
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if brow_y:
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brow_top = min(brow_y)
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hairline[brow_top:, :] = 0
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# 可视化分割结果（伪彩色）
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seg_vis = cv2.applyColorMap(seg, cv2.COLORMAP_JET)
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info = f"DeepLab法(3分类)：skin/hair交界线带(band={bw}px)，下界=眉毛峰。复用现有triseg模型，无需新依赖"
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debug = {"3分类(蓝=bg/绿=skin/红=hair)": seg_vis, "skin/hair交界带(发际线)": hairline}
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return hairline, info, debug
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except ImportError as e:
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return _fallback_empty(f"DeepLab模块未找到: {e}")
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except Exception as e:
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return _fallback_empty(f"DeepLab失败: {e}")
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# ============================================================
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# 工具函数
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# ============================================================
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def _boundary_band(mask, kernel):
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"""形态学梯度边界带：dilate - erode"""
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binary = (mask > 10).astype(np.uint8) * 255
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dilated = cv2.dilate(binary, kernel)
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eroded = cv2.erode(binary, kernel)
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return cv2.subtract(dilated, eroded)
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def _fallback_empty(reason):
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"""方案失败时的空mask回退"""
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return None, f"⚠️ {reason}", {}
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def make_overlay(img, mask, alpha=0.45, color=(0, 0, 255)):
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"""把 mask 半透明叠加到 img 上（红色高亮重绘区域）。
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参数:
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||
img: BGR ndarray
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mask: 灰度mask (0/255 或 0-255)
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||
alpha: 透明度 0~1（mask区域的颜色强度）
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||
color: 高亮颜色 BGR，默认红色
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||
返回: 叠加后的 BGR ndarray
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||
"""
|
||
if mask is None:
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||
return img.copy()
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||
m = (mask > 10).astype(np.float32)
|
||
if m.ndim == 3:
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||
m = m[:, :, 0]
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||
overlay = img.copy().astype(np.float32)
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||
# mask区域混入颜色
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||
for c in range(3):
|
||
overlay[:, :, c] = overlay[:, :, c] * (1 - m * alpha) + color[c] * (m * alpha)
|
||
# mask边界描边（更清晰）
|
||
m_u8 = (m * 255).astype(np.uint8)
|
||
contours, _ = cv2.findContours(m_u8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||
cv2.drawContours(overlay.astype(np.uint8), contours, -1, color, 1)
|
||
return np.clip(overlay, 0, 255).astype(np.uint8)
|