colomi
0eb61f3e60
包含： - hair_service_sd: 换发型/换发色算法服务 (端口 8801) - photo_service: LoRA 训练调度服务 (端口 32678) - stable-diffusion-webui: SD WebUI 推理服务 (端口 57860) - kohya_ss_home: 训练环境代码 - meidaojia: 监控测试脚本 - setup.sh: 一键部署脚本 (conda环境恢复 + 配置生成 + 完整性检查) - start_all_services.sh: 启动3个服务 - configure.ini.template: 路径模板化 (BASE_DIR自动推导) - conda_envs/py310.yml: py310 环境定义 大文件 (weights/, models/, data/, conda_envs/*.tar.gz 等) 通过 .gitignore 排除， 由网盘单独上传。
71 lines
3.1 KiB
Python
71 lines
3.1 KiB
Python
import time
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import cv2
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import math
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import numpy as np
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def draw_protect_mask(sourceImage, landmark_137):
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h, w, _ = sourceImage.shape
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inpaint_mask = np.zeros((h, w), dtype=np.uint8)
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cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[129:137]), (255,))
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cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[121:129]), (255,))
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cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[22:48]), (255,))
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cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[88:104]), (255,))
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cv2.fillConvexPoly(inpaint_mask, cv2.convexHull(landmark_137[105:121]), (255,))
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return inpaint_mask
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def localtranslationwarpfastwithstrength(srcimg, kpt137, startx, starty, endx, endy, radius, strength):
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ddradius = float(radius * radius)
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mask_keep = draw_protect_mask(srcimg, kpt137)
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# mask_keep = cv2.imread('/home/colo/Pictures/test/102.png')
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# mask_keep = cv2.cvtColor(mask_keep, cv2.COLOR_BGR2GRAY)
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# copyimg = np.zeros(srcimg.shape, np.uint8)
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# copyimg = srcimg.copy()
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maskimg = np.zeros(srcimg.shape[:2], np.uint8)
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cv2.circle(maskimg, (startx, starty), math.ceil(radius), (255, 255, 255), -1)
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# cv2.imshow('maskimg_before', maskimg)
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# cv2.imshow('maskimg', maskimg)
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# cv2.imshow('mask_keep', mask_keep)
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# cv2.waitKey()
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maskimg = maskimg * (1-mask_keep/255).astype(np.uint8)
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k0 = 100 / strength # 计算公式中的|m-c|^2
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ddmc_x = (endx - startx) * (endx - startx)
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ddmc_y = (endy - starty) * (endy - starty)
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h, w, c = srcimg.shape
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mapx = np.vstack([np.arange(w).astype(np.float32).reshape(1, -1)] * h)
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mapy = np.hstack([np.arange(h).astype(np.float32).reshape(-1, 1)] * w)
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distance_x = (mapx - startx) * (mapx - startx)
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distance_y = (mapy - starty) * (mapy - starty)
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distance = distance_x + distance_y
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k1 = np.sqrt(distance)
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ratio_x = (ddradius - distance_x) / (ddradius - distance_x + k0 * ddmc_x)
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ratio_y = (ddradius - distance_y) / (ddradius - distance_y + k0 * ddmc_y)
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ratio_x = ratio_x * ratio_x
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ratio_y = ratio_y * ratio_y
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ux = mapx - ratio_x * (endx - startx) * (1 - k1/radius)
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uy = mapy - ratio_y * (endy - starty) * (1 - k1/radius)
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np.copyto(ux, mapx, where=maskimg == 0)
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np.copyto(uy, mapy, where=maskimg == 0)
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ux = ux.astype(np.float32)
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uy = uy.astype(np.float32)
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copyimg = cv2.remap(srcimg, ux, uy, interpolation=cv2.INTER_LINEAR)
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return copyimg
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image = cv2.imread("/home/colo/Pictures/for_hn/102.jpg")
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processed_image = image.copy()
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startx_left, starty_left, endx_left, endy_left = 170, 123, 190, 74
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# startx_right, starty_right, endx_right, endy_right = 287, 275, 192, 233
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radius = 60
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strength = 100 # 瘦左边脸
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t0 = time.time()
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processed_image = localtranslationwarpfastwithstrength(processed_image, startx_left, starty_left, endx_left, endy_left, radius, strength) # 瘦右边脸
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# processed_image = localtranslationwarpfastwithstrength(processed_image, startx_right, starty_right, endx_right, endy_right, radius, strength)
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# cv2.imwrite("thin.jpg", processed_image)
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print('costs', time.time() - t0)
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# cv2.imshow('image', image)
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# cv2.imshow('processed_image', processed_image)
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# cv2.waitKey()
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