初始化：换发型/换发色/训练发型服务

包含：
- hair_service_sd: 主服务（换发型/换发色/生发，端口8801）
- photo_service: LoRA调度+训练（端口32678）
- hair_grow_service: 调试测试页（端口8888，含4个测试页）
- 批量训练脚本（batch_train_hairstyles.py）
- 发际线mask自动识别（hairline_mask.py，4种方案）
- 手绘mask换发型（hair_swap_manual.py）
- 文档：README.md + LARGE_FILES.md + docs/

大文件（模型权重200G、训练数据123G）已排除，见 LARGE_FILES.md
OSS/COS密钥已脱敏为环境变量，原文件备份在本地

project/hair_service_sd/scripts/tools.py

@@ -0,0 +1,70 @@
+import time
+import cv2
+import math
+import numpy as np
+
+
+def draw_protect_mask(sourceImage, landmark_137):
+    h, w, _ = sourceImage.shape
+    inpaint_mask = np.zeros((h, w), dtype=np.uint8)
+    cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[129:137]), (255,))
+    cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[121:129]), (255,))
+    cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[22:48]), (255,))
+    cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[88:104]), (255,))
+    cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[105:121]), (255,))
+    return inpaint_mask
+
+
+def localtranslationwarpfastwithstrength(srcimg, kpt137, startx, starty, endx, endy, radius, strength):
+    ddradius = float(radius * radius)
+    mask_keep = draw_protect_mask(srcimg, kpt137)
+    # mask_keep = cv2.imread('/home/colo/Pictures/test/102.png')
+    # mask_keep = cv2.cvtColor(mask_keep, cv2.COLOR_BGR2GRAY)
+    # copyimg = np.zeros(srcimg.shape, np.uint8)
+    # copyimg = srcimg.copy()
+    maskimg = np.zeros(srcimg.shape[:2], np.uint8)
+    cv2.circle(maskimg, (startx, starty), math.ceil(radius), (255, 255, 255), -1)
+    # cv2.imshow('maskimg_before', maskimg)
+    # cv2.imshow('maskimg', maskimg)
+    # cv2.imshow('mask_keep', mask_keep)
+    # cv2.waitKey()
+    maskimg = maskimg * (1-mask_keep/255).astype(np.uint8)
+
+    k0 = 100 / strength  # 计算公式中的|m-c|^2
+    ddmc_x = (endx - startx) * (endx - startx)
+    ddmc_y = (endy - starty) * (endy - starty)
+    h, w, c = srcimg.shape
+    mapx = np.vstack([np.arange(w).astype(np.float32).reshape(1, -1)] * h)
+    mapy = np.hstack([np.arange(h).astype(np.float32).reshape(-1, 1)] * w)
+    distance_x = (mapx - startx) * (mapx - startx)
+    distance_y = (mapy - starty) * (mapy - starty)
+    distance = distance_x + distance_y
+    k1 = np.sqrt(distance)
+    ratio_x = (ddradius - distance_x) / (ddradius - distance_x + k0 * ddmc_x)
+    ratio_y = (ddradius - distance_y) / (ddradius - distance_y + k0 * ddmc_y)
+    ratio_x = ratio_x * ratio_x
+    ratio_y = ratio_y * ratio_y
+    ux = mapx - ratio_x * (endx - startx) * (1 - k1/radius)
+    uy = mapy - ratio_y * (endy - starty) * (1 - k1/radius)
+    np.copyto(ux, mapx, where=maskimg == 0)
+    np.copyto(uy, mapy, where=maskimg == 0)
+    ux = ux.astype(np.float32)
+    uy = uy.astype(np.float32)
+    copyimg = cv2.remap(srcimg, ux, uy, interpolation=cv2.INTER_LINEAR)
+    return copyimg
+
+image = cv2.imread("/home/colo/Pictures/for_hn/102.jpg")
+processed_image = image.copy()
+startx_left, starty_left, endx_left, endy_left = 170, 123, 190, 74
+# startx_right, starty_right, endx_right, endy_right = 287, 275, 192, 233
+radius = 60
+strength = 100  # 瘦左边脸
+t0 = time.time()
+processed_image = localtranslationwarpfastwithstrength(processed_image, startx_left, starty_left, endx_left, endy_left, radius, strength)  # 瘦右边脸
+# processed_image = localtranslationwarpfastwithstrength(processed_image, startx_right, starty_right, endx_right, endy_right, radius, strength)
+# cv2.imwrite("thin.jpg", processed_image)
+print('costs', time.time() - t0)
+# cv2.imshow('image', image)
+
+# cv2.imshow('processed_image', processed_image)
+# cv2.waitKey()