xslandClaude Opus 4.8
891bc0da8b
- 5 条横线的线名(头顶/发际线/眉心/鼻翼下缘/下巴尖)从左侧移到右侧 - 四庭 cm(顶庭/上庭/中庭/下庭)留在左侧，右对齐贴脸盒左缘 - 横线只覆盖脸宽(左右脸颊)、竖线只覆盖脸高(头顶→下巴)，渐变消失 - 七眼段宽标注移到脸盒上端/下端(配合新线长)，相邻段上下错行防重叠 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
224 lines
8.7 KiB
Python
224 lines
8.7 KiB
Python
"""标注图层生成（透明底 RGBA PNG，仅标注、不含人物）。
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规格（技术方案 §6）：线/字色 #FFFFFF、字体 10pt、线宽 1pt、透明底。
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- 四庭水平分界线：numpy 向量化渐变消失（中间亮、两侧渐隐）。
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- 四庭 cm 数值：图片左侧。
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- 七眼标注：眼宽/两眼间距/脸宽，虚线带箭头，标签上下穿插。
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中文字体用打包的思源黑体绝对路径加载，缺字体直接抛错（不静默降级成方块）。
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"""
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import os
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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FONT_PATH = os.path.join(os.path.dirname(__file__), "fonts", "NotoSansCJKsc-Regular.otf")
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FONT_SIZE = 10
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LINE_COLOR = (255, 255, 255, 255) # #FFFFFF 100%
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LINE_WIDTH = 1
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def _load_font():
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if not os.path.isfile(FONT_PATH):
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raise FileNotFoundError(f"中文字体缺失：{FONT_PATH}（请按 OFFLINE_ASSETS.md 放置）")
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return ImageFont.truetype(FONT_PATH, FONT_SIZE)
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def draw_gradient_horizontal_line(buf, cx, cy, color=LINE_COLOR, half_length=None):
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"""在 RGBA numpy 缓冲 buf 上，以 (cx,cy) 为中心画向两侧渐变消失的水平线。
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numpy 向量化：一次性算整行 alpha，避免逐像素 draw.point。
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"""
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h, w = buf.shape[:2]
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cy = int(round(cy)); cx = int(round(cx))
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if not (0 <= cy < h):
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return
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half = half_length or (w // 3)
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xs = np.arange(w)
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dist = np.abs(xs - cx)
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alpha = np.clip(1.0 - dist / half, 0.0, 1.0) * color[3]
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mask = alpha > 0
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row = buf[cy]
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row[mask, 0] = color[0]
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row[mask, 1] = color[1]
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row[mask, 2] = color[2]
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row[mask, 3] = np.maximum(row[mask, 3], alpha[mask].astype(np.uint8))
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def draw_gradient_vertical_line(buf, cx, y0, y1, color=LINE_COLOR, fade=None):
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"""在 RGBA numpy 缓冲 buf 上画一条竖线，两端渐变消失（中间实、上下淡）。"""
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h, w = buf.shape[:2]
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cx = int(round(cx))
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if not (0 <= cx < w):
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return
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y0, y1 = int(round(y0)), int(round(y1))
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y0, y1 = max(0, min(y0, y1)), min(h - 1, max(y0, y1))
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if y1 <= y0:
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return
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ys = np.arange(y0, y1 + 1)
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span = y1 - y0
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fade = fade or max(1, span // 5) # 仅两端 ~1/5 段渐隐
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d = np.minimum(ys - y0, y1 - ys) # 到最近端点的距离
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alpha = np.clip(d / fade, 0.0, 1.0) * color[3]
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col = buf[y0:y1 + 1, cx]
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m = alpha > 0
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col[m, 0] = color[0]
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col[m, 1] = color[1]
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col[m, 2] = color[2]
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col[m, 3] = np.maximum(col[m, 3], alpha[m].astype(np.uint8))
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def draw_dashed_line_with_arrows(draw, x1, y1, x2, y2, color=LINE_COLOR,
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dash_len=6, gap_len=4, arrow_size=5):
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"""两点间画虚线，两端带箭头（等腰三角）。"""
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total = ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
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if total == 0:
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return
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dx = (x2 - x1) / total
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dy = (y2 - y1) / total
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pos = 0.0
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while pos < total:
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seg_end = min(pos + dash_len, total)
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draw.line([(x1 + dx * pos, y1 + dy * pos),
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(x1 + dx * seg_end, y1 + dy * seg_end)], fill=color, width=LINE_WIDTH)
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pos += dash_len + gap_len
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# 法向量（用于箭头两翼张开）
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nx, ny = -dy, dx
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for (ex, ey, sdx, sdy) in [(x1, y1, dx, dy), (x2, y2, -dx, -dy)]:
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p1 = (ex + sdx * arrow_size + nx * arrow_size * 0.6,
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ey + sdy * arrow_size + ny * arrow_size * 0.6)
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p2 = (ex + sdx * arrow_size - nx * arrow_size * 0.6,
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ey + sdy * arrow_size - ny * arrow_size * 0.6)
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draw.line([p1, (ex, ey)], fill=color, width=LINE_WIDTH)
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draw.line([p2, (ex, ey)], fill=color, width=LINE_WIDTH)
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def _text_size(draw, text, font):
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bbox = draw.textbbox((0, 0), text, font=font)
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return bbox[2] - bbox[0], bbox[3] - bbox[1]
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_LINE_NAMES = {
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"hair_top": "头顶",
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"hairline": "发际线",
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"brow_center": "眉心",
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"nose_bottom": "鼻翼下缘",
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"chin_tip": "下巴尖",
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}
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def create_annotated_image(image_bgr, measure_result):
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"""生成标注图层 PNG（透明底 RGBA，尺寸同原图）。返回 PIL.Image。
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布局：
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- 线条只覆盖人脸范围（横线=脸宽，竖线=脸高），渐变消失。
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- 横向 5 条分界线：头顶/发际线/眉心/鼻翼下缘/下巴尖，**线名在右侧**；
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四庭 cm 数值（顶庭/上庭/中庭/下庭）在左侧各段中点。
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- 纵向 6 条线：左脸颊/左眼外角/左眼内角/右眼内角/右眼外角/右脸颊，把脸宽切 5 段；
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每段宽度在脸的**上端和下端**各标一次（只标 `X.XXcm`，不写名）。
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"""
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h, w = image_bgr.shape[:2]
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v = measure_result.vertical
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pc = measure_result.px_per_cm
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buf = np.zeros((h, w, 4), dtype=np.uint8)
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order = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
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ys = [v[name][1] for name in order]
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pts = measure_result.eyes["points"]
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seven_keys = ["left_cheek", "left_outer", "left_inner",
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"right_inner", "right_outer", "right_cheek"]
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xs = sorted(pts[k][0] for k in seven_keys) # 自左向右
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# 人脸包围盒：x 为脸宽(左右脸颊)，y 为脸高(头顶→下巴)
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fx0, fx1 = xs[0], xs[-1]
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fy0, fy1 = ys[0], ys[-1]
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face_cx = (fx0 + fx1) / 2
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face_half = (fx1 - fx0) / 2 * 1.08 # 略放大确保横线覆盖到脸颊
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# --- 1. 横向 5 条分界线（渐变，覆盖脸宽） ---
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for cy in ys:
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draw_gradient_horizontal_line(buf, face_cx, cy, half_length=face_half)
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# --- 2. 纵向 6 条线（渐变，覆盖脸高 头顶→下巴） ---
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for vx in xs:
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draw_gradient_vertical_line(buf, vx, fy0, fy1)
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canvas = Image.fromarray(buf, mode="RGBA")
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draw = ImageDraw.Draw(canvas)
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font = _load_font()
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# --- 3a. 横线右侧：线名（头顶/发际线/眉心/鼻翼下缘/下巴尖） ---
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name_x = fx1 + 8
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for i, name in enumerate(order):
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text = _LINE_NAMES[name]
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tw, _ = _text_size(draw, text, font)
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x = min(name_x, w - 2 - tw) # 右侧越界时回收
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draw.text((x, ys[i] - FONT_SIZE / 2), text, fill=LINE_COLOR, font=font)
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# --- 3b. 横线左侧：四庭 cm 数值（各段中点，右对齐到脸盒左缘） ---
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court_cm = [measure_result.top_cm, measure_result.upper_cm,
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measure_result.middle_cm, measure_result.lower_cm]
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court_name = ["顶庭", "上庭", "中庭", "下庭"]
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for i in range(4):
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text = f"{court_name[i]} {court_cm[i]:.2f}cm"
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tw, _ = _text_size(draw, text, font)
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x = max(2, fx0 - 8 - tw) # 贴脸盒左缘，右对齐
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y_mid = (ys[i] + ys[i + 1]) / 2 - FONT_SIZE / 2
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draw.text((x, y_mid), text, fill=LINE_COLOR, font=font)
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# --- 4. 七眼每段宽度：脸的上端 + 下端各标一次（相邻段上下错行防重叠） ---
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row_h = FONT_SIZE + 2
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y_top_a = max(1, fy0 - row_h - 2) # 上端：头顶线上方两行
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y_top_b = max(1, fy0 - 2 * row_h - 2)
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y_bot_a = min(h - FONT_SIZE - 1, fy1 + 2) # 下端：下巴线下方两行
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y_bot_b = min(h - FONT_SIZE - 1, fy1 + row_h + 2)
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for i in range(len(xs) - 1):
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seg_cm = (xs[i + 1] - xs[i]) / pc
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cx_seg = (xs[i] + xs[i + 1]) / 2
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text = f"{seg_cm:.2f}cm"
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tw, _ = _text_size(draw, text, font)
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ty_top = y_top_a if i % 2 == 0 else y_top_b
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ty_bot = y_bot_a if i % 2 == 0 else y_bot_b
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draw.text((cx_seg - tw / 2, ty_top), text, fill=LINE_COLOR, font=font)
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draw.text((cx_seg - tw / 2, ty_bot), text, fill=LINE_COLOR, font=font)
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return canvas
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if __name__ == "__main__":
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import sys
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import time
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import cv2
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from face_analysis.detector import detector
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from face_analysis.measure import measure_face
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path = sys.argv[1] if len(sys.argv) > 1 else "tests/fixtures/frontal.jpg"
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out = sys.argv[2] if len(sys.argv) > 2 else "tests/output/annotated.png"
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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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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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result = measure_face(lms, mask, w, h)
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t0 = time.time()
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canvas = create_annotated_image(img, result)
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dt = time.time() - t0
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os.makedirs(os.path.dirname(out), exist_ok=True)
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canvas.save(out)
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arr = np.asarray(canvas)
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print(f"saved {out} mode={canvas.mode} size={canvas.size} "
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f"transparent={bool((arr[:,:,3]==0).any())} opaque={bool((arr[:,:,3]>0).any())} "
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f"elapsed={dt*1000:.1f}ms")
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