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。
350 lines
17 KiB
Python
350 lines
17 KiB
Python
import cv2
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import numpy as np
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import torch
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import json
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import time
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import re
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import os
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from process_modules import Get_Landmark, Process_Data
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from utils import landmark_processor
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class GenderClassifyProcessor(object):
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def __init__(self, gpu_id=0):
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model_path = "./weights/gender_models"
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self.output_img_size = 128
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if not os.path.exists(model_path):
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print("GenderClassifyProcessor don't have model!")
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if gpu_id == 'cpu':
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self.device = torch.device(gpu_id)
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else:
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self.device = torch.device('cuda:{0}'.format(gpu_id))
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self.gender_model = cv2.dnn.readNetFromCaffe(os.path.join(model_path, "gender.prototxt"), os.path.join(model_path, "gender.caffemodel"))
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def forward(self, img, landmark_137):
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image_to_face_mat = landmark_processor.get_transform_mat_sex(landmark_137, self.output_img_size)
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gender_img = cv2.warpAffine(img, image_to_face_mat, (self.output_img_size, self.output_img_size), cv2.INTER_LANCZOS4)
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inpBlob = cv2.dnn.blobFromImage(gender_img, 1.0, (self.output_img_size, self.output_img_size), (0, 0, 0), swapRB=False,
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crop=False)
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self.gender_model.setInput(inpBlob)
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output = self.gender_model.forward()
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is_female = True
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if output[0][0] > output[0][1]:
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is_female = False
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return is_female
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class Prepare_Ref_HairStyle_Data(object):
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def __init__(self, gpu, device_id):
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if gpu and torch.cuda.is_available():
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self.device = torch.device("cuda:%d" % device_id if device_id >= 0 else "cpu")
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self.cuda = True
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else:
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self.device = torch.device("cpu")
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self.cuda = False
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self.process_data = Process_Data(gpu, device_id)
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self.get_landmark = Get_Landmark(gpu_id=device_id)
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self.gender_classify = GenderClassifyProcessor(gpu_id=device_id)
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def get_prepare_ref_768_color_data(self, ref_rgb_8uc3_orisize):
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ref_landmark_1k2_f_orisize = self.get_landmark.forward(ref_rgb_8uc3_orisize)
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ref_rgb_8uc3_color_768, ref_matting_8uc3_color_768, ref_baldseg_8uc3_color_768, ref_landmark_f1k2_color_768 = \
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self.process_data.get_prepare_ref_768_bald_data(ref_rgb_8uc3_orisize, ref_landmark_1k2_f_orisize)
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return ref_rgb_8uc3_color_768, ref_matting_8uc3_color_768, ref_baldseg_8uc3_color_768, ref_landmark_f1k2_color_768
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def get_prepare_ref_768_color_data_landmark1k(self, ref_rgb_8uc3_orisize,ref_landmark_1k2_f_orisize):
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# ref_landmark_1k2_f_orisize = self.get_landmark.inference(ref_rgb_8uc3_orisize)
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ref_rgb_8uc3_color_768, ref_matting_8uc3_color_768, ref_baldseg_8uc3_color_768, ref_landmark_f1k2_color_768 = \
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self.process_data.get_prepare_ref_768_bald_data(ref_rgb_8uc3_orisize, ref_landmark_1k2_f_orisize)
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return ref_rgb_8uc3_color_768, ref_matting_8uc3_color_768, ref_baldseg_8uc3_color_768, ref_landmark_f1k2_color_768
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def calculate_hair_ratio_after_align(self, hair_mask, origin_landmark1k, img_size=768):
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image_to_face_mat = landmark_processor.get_transform_mat_hair_ratio_v1(origin_landmark1k, 768, ratio=0.35, h_offset=0.32)
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hair_mask_align = cv2.warpAffine(hair_mask, image_to_face_mat, (img_size, img_size))
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hair_rect = cv2.boundingRect(hair_mask_align[:, :, :1])
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hair_mask_ratio = hair_rect[2] * hair_rect[3] / (img_size * img_size)
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return hair_mask_ratio
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def check_female_hair_ratio(self, origin_img_8uc3, landmark_1k2_f_orisize):
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ref_matte_fg_8uc3_orisize, ref_matte_pred_8uc1_orisize = self.process_data.generator_matte.matte_inference(origin_img_8uc3, landmark_1k2_f_orisize)
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ref_matte_pred_8uc3_orisize = np.repeat(ref_matte_pred_8uc1_orisize[:, :, np.newaxis], 3, axis=2)
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hairstyle_M = self.process_data.get_hair_M_girl_v1(landmark_1k2_f_orisize)
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ref_rgb_8uc3_768 = cv2.warpAffine(ref_matte_pred_8uc3_orisize, hairstyle_M, (768, 768))
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# cv2.imshow("ref_rgb_8uc3_768", ref_rgb_8uc3_768)
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# cv2.waitKey()
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edge_width = 5
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if (ref_rgb_8uc3_768[-edge_width:, :, :]).max() > 0 or (ref_rgb_8uc3_768[:, -edge_width:, :]).max() > 0 or (ref_rgb_8uc3_768[:, :edge_width, :]).max() > 0:
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return False
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else:
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return True
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def get_prepare_ref_768_data(self, ref_rgb_8uc3_orisize, long_flag=False):
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ref_landmark_1k2_f_orisize = self.get_landmark.forward(ref_rgb_8uc3_orisize)
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# ref_matte_fg_8uc3_orisize, ref_matte_pred_8uc1_orisize = self.process_data.generator_matte.matte_inference(
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# ref_rgb_8uc3_orisize, ref_landmark_1k2_f_orisize)
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ref_landmark_137kpts_f_orisize = landmark_processor.pts_1k_to_137(ref_landmark_1k2_f_orisize)
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gender_res = self.gender_classify.forward(ref_rgb_8uc3_orisize, ref_landmark_137kpts_f_orisize)
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# hair_ratio = self.calculate_hair_ratio_after_align(ref_rgb_8uc3_orisize, ref_landmark_1k2_f_orisize)
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# if hair_ratio > 0.3:
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# ratio = 2
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# else:
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# if gender_res:
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# ratio = 1
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# else:
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# ratio = 0
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if not gender_res:
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ratio = 0
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else:
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check_res = self.check_female_hair_ratio(ref_rgb_8uc3_orisize, ref_landmark_137kpts_f_orisize)
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if check_res:
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ratio = 1
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else:
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ratio = 2
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if gender_res:
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gender = "girl"
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else:
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gender = "boy"
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print("gender: ", gender, " ratio: ", ratio)
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# show_concat = np.concatenate((ref_rgb_8uc3_orisize, ref_matte_fg_8uc3_orisize), axis=1)
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# resize_ratio = 1024. / max(show_concat.shape[:2])
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# show_concat = cv2.resize(show_concat, (0, 0), fx=resize_ratio, fy=resize_ratio)
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# print("gender_res: ", gender_res, " hair_ratio: ", hair_ratio)
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# cv2.imshow("show_concat", show_concat)
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# cv2.imshow("ref_matte_pred_8uc1_orisize", ref_matte_pred_8uc1_orisize)
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# cv2.waitKey()
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ref_rgb_8uc3_768, ref_matting_fg_8uc3_768, ref_matting_8uc3_768, ref_baldseg_8uc3_768, ref_landmark_f1k2_768 = \
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self.process_data.get_prepare_ref_768_data(ref_rgb_8uc3_orisize, ref_landmark_1k2_f_orisize, ratio, long=long_flag)
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# cv2.imshow("ref_rgb_8uc3_768", ref_rgb_8uc3_768)
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# cv2.waitKey()
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return ref_rgb_8uc3_768, ref_matting_fg_8uc3_768, ref_matting_8uc3_768, ref_baldseg_8uc3_768, ref_landmark_f1k2_768, gender, ratio
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def Generator_reftensor(self, ref_rgb_8uc3_768, ref_matting_8uc3_768, ref_baldseg_8uc3_768,
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ref_landmark_f1k2_768):
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"""
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input：
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图像尺寸基于： 人脸 512， 图像均为3通道
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ref_rgb_8uc3_512: 参考图， size 512， uint8 （0-255）
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ref_matting_8uc3_512：参考图 matting alpha 值， uint8 （0-255）
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ref_baldseg_8uc3_512: 参考图 光头分割， uint8 （0-255）
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ref_landmark_f1k2_512： 参考图 关键点 1k*2 float32
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output：
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input_another_pose_hair_image: 参考图 条件图， float32 （0-255）
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"""
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# 8 ********************** another pose hair_image **********************
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another_pose_image = ref_rgb_8uc3_768.copy()
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another_nohair_pose_mask = ref_baldseg_8uc3_768.copy()
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# cv2.imshow("another_nohair_pose_mask", another_nohair_pose_mask)
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# cv2.waitKey()
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another_nohair_pose_mask[(np.abs(another_nohair_pose_mask - [0, 0, 255]) < 50).all(axis=2)] = [0, 255, 255]
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another_nohair_pose_mask[(another_nohair_pose_mask == [0, 0, 0]).all(axis=2)] = [255, 255, 255]
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another_pose_pts137 = landmark_processor.pts_1k_to_137(ref_landmark_f1k2_768).astype(np.int32)
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cv2.fillPoly(another_nohair_pose_mask,
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np.concatenate((another_pose_pts137[47:35:-1], another_pose_pts137[56:64],
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[another_pose_pts137[48], another_pose_pts137[22]]))[
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np.newaxis, :, :], (255, 255, 0))
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cv2.fillPoly(another_nohair_pose_mask,
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np.concatenate((another_pose_pts137[22:37], another_pose_pts137[56:47:-1]))[np.newaxis, :, :],
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(255, 0, 128))
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cv2.fillPoly(another_nohair_pose_mask, another_pose_pts137[88:104][np.newaxis, :, :], (255, 0, 255)) # eye
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cv2.fillPoly(another_nohair_pose_mask, another_pose_pts137[105:121][np.newaxis, :, :], (255, 0, 255)) # eye
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# Label nose
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cv2.fillPoly(another_nohair_pose_mask, another_pose_pts137[64:79][np.newaxis, :, :], (255, 255, 255))
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# Label eyebrow
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cv2.fillPoly(another_nohair_pose_mask, another_pose_pts137[121:129][np.newaxis, :, :], (255, 128, 0))
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cv2.fillPoly(another_nohair_pose_mask, another_pose_pts137[129:137][np.newaxis, :, :], (255, 128, 0))
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another_pose_hair_alpha = ref_matting_8uc3_768.copy() / 255.
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another_pose_hair_image = (another_pose_image * another_pose_hair_alpha + another_nohair_pose_mask * (
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1 - another_pose_hair_alpha)).astype(np.uint8)
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input_another_pose_hair_image = another_pose_hair_image.astype(np.float32) / 255
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return input_another_pose_hair_image
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def prepare_single(img_path=None, dst_dir=None, long_flag=False):
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# img_path = "/home/yangchaojie/Desktop/hairstyle/test_data/1123/pics/test3.jpg"
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# img_path = "./test_data/ref_imgs/female1.jpg"
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# dst_dir = "./data/ref_hairstyle/tmp"
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# img_path = "/home/szlc/Downloads/111/111/ddbc6cb6-766c-4f08-b1fd-22c6bab86432.jpg"
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# dst_dir = "/home/szlc/Downloads/111/res"
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if not os.path.exists(dst_dir):
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os.makedirs(dst_dir)
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prepare_data_process = Prepare_Ref_HairStyle_Data(True, device_id=0)
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ref_rgb_8uc3_orisize = cv2.imread(img_path)
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# debug = True
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debug = False
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version = "20221206"
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start_time = time.time()
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ref_rgb_8uc3_768, ref_matting_fg_8uc3_768, ref_matting_8uc3_768, ref_baldseg_8uc3_768, ref_landmark_f1k2_768, gender, ratio = \
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prepare_data_process.get_prepare_ref_768_data(ref_rgb_8uc3_orisize, long_flag)
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if not debug:
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cv2.imwrite(os.path.join(dst_dir, "ref_rgb_8uc3_768.png"), ref_rgb_8uc3_768)
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cv2.imwrite(os.path.join(dst_dir, "ref_matting_fg_8uc3_768.png"), ref_matting_fg_8uc3_768)
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cv2.imwrite(os.path.join(dst_dir, "ref_matting_8uc3_768.png"), ref_matting_8uc3_768)
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cv2.imwrite(os.path.join(dst_dir, "ref_baldseg_8uc3_768.png"), ref_baldseg_8uc3_768)
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np.savetxt(os.path.join(dst_dir, "ref_landmark_f1k2_768.txt"), ref_landmark_f1k2_768)
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input_another_pose_hair_image = prepare_data_process.Generator_reftensor(ref_rgb_8uc3_768, ref_matting_8uc3_768,
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ref_baldseg_8uc3_768,
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ref_landmark_f1k2_768)
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print("cost time: ", time.time() - start_time)
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if debug:
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print("gender: ", gender, " ratio: ", ratio)
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show_concat = np.concatenate((ref_rgb_8uc3_768, ref_matting_fg_8uc3_768, ref_matting_8uc3_768, ref_baldseg_8uc3_768), axis=1)
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# cv2.imshow("show_concat", cv2.resize(show_concat, (0, 0), fx=0.6, fy=0.6))
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# cv2.imshow("input_another_pose_hair_image", input_another_pose_hair_image)
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# cv2.waitKey()
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if not debug:
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np.save(os.path.join(dst_dir, "input_another_pose_hair_image.npy"), input_another_pose_hair_image)
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config_dict = {}
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config_dict['gender'] = gender
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config_dict['version'] = version
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config_dict['ratio'] = str(ratio)
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with open(os.path.join(dst_dir, "config.json"), "w") as f:
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json.dump(config_dict, f)
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print("写入文件完成...", dst_dir)
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def prepare_single_color(img_path=None, dst_dir=None, long_flag=False):
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if not os.path.exists(dst_dir):
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os.makedirs(dst_dir)
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prepare_data_process = Prepare_Ref_HairStyle_Data(True, device_id=0)
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ref_rgb_8uc3_orisize = cv2.imread(img_path)
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# debug = True
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debug = False
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version = "20221206"
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start_time = time.time()
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ref_rgb_8uc3_768, ref_matting_fg_8uc3_768, ref_matting_8uc3_768, ref_baldseg_8uc3_768, ref_landmark_f1k2_768, gender, ratio = \
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prepare_data_process.get_prepare_ref_768_data(ref_rgb_8uc3_orisize, long_flag)
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if not debug:
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cv2.imwrite(os.path.join(dst_dir, "ref_rgb_8uc3_color_768.png"), ref_rgb_8uc3_768)
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cv2.imwrite(os.path.join(dst_dir, "ref_matting_fg_8uc3_color_768.png"), ref_matting_fg_8uc3_768)
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cv2.imwrite(os.path.join(dst_dir, "ref_matting_8uc3_color_768.png"), ref_matting_8uc3_768)
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cv2.imwrite(os.path.join(dst_dir, "ref_baldseg_8uc3_color_768.png"), ref_baldseg_8uc3_768)
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np.savetxt(os.path.join(dst_dir, "ref_landmark_f1k2_color_768.txt"), ref_landmark_f1k2_768)
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input_another_pose_hair_image = prepare_data_process.Generator_reftensor(ref_rgb_8uc3_768, ref_matting_8uc3_768,
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ref_baldseg_8uc3_768,
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ref_landmark_f1k2_768)
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print("cost time: ", time.time() - start_time)
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if debug:
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print("gender: ", gender, " ratio: ", ratio)
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show_concat = np.concatenate((ref_rgb_8uc3_768, ref_matting_fg_8uc3_768, ref_matting_8uc3_768, ref_baldseg_8uc3_768), axis=1)
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# cv2.imshow("show_concat", cv2.resize(show_concat, (0, 0), fx=0.6, fy=0.6))
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# cv2.imshow("input_another_pose_hair_image", input_another_pose_hair_image)
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# cv2.waitKey()
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if not debug:
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np.save(os.path.join(dst_dir, "input_another_pose_hair_image.npy"), input_another_pose_hair_image)
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config_dict = {}
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config_dict['gender'] = gender
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config_dict['version'] = version
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config_dict['ratio'] = str(ratio)
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with open(os.path.join(dst_dir, "config.json"), "w") as f:
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json.dump(config_dict, f)
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print("写入文件完成...", dst_dir)
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def prepare_multi():
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img_dir = "/home/yangchaojie/Desktop/hairstyle/test_data/testdata_tj_0309/raw_pics"
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save_dir = "/home/yangchaojie/Desktop/hairstyle/test_data/testdata_tj_0309/ref_haircolor"
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# pattern = re.compile(r'^[^\.].*\.jpg$')
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pattern = re.compile(r'^[^\.].*\.(jpg|JPG)$')
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for dirpath, dirnames, filenames in os.walk(img_dir, followlinks=True):
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for filename in filenames:
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if not pattern.match(filename): continue
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img_path = os.path.join(dirpath, filename)
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dst_dir = os.path.join(save_dir, filename[:-4])
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if not os.path.exists(dst_dir):
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os.makedirs(dst_dir)
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prepare_data_process = Prepare_Ref_HairStyle_Data(True, device_id=0)
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ref_rgb_8uc3_orisize = cv2.imread(img_path)
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# debug = True
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debug = False
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version = "20230309"
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start_time = time.time()
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ref_rgb_8uc3_768, ref_matting_fg_8uc3_768, ref_matting_8uc3_768, ref_baldseg_8uc3_768, ref_landmark_f1k2_768, gender, ratio = \
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prepare_data_process.get_prepare_ref_768_data(ref_rgb_8uc3_orisize)
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if not debug:
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cv2.imwrite(os.path.join(dst_dir, "ref_rgb_8uc3_768.png"), ref_rgb_8uc3_768)
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cv2.imwrite(os.path.join(dst_dir, "ref_matting_fg_8uc3_768.png"), ref_matting_fg_8uc3_768)
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cv2.imwrite(os.path.join(dst_dir, "ref_matting_8uc3_768.png"), ref_matting_8uc3_768)
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cv2.imwrite(os.path.join(dst_dir, "ref_baldseg_8uc3_768.png"), ref_baldseg_8uc3_768)
|
||
np.savetxt(os.path.join(dst_dir, "ref_landmark_f1k2_768.txt"), ref_landmark_f1k2_768)
|
||
|
||
input_another_pose_hair_image = prepare_data_process.Generator_reftensor(ref_rgb_8uc3_768, ref_matting_8uc3_768,
|
||
ref_baldseg_8uc3_768,
|
||
ref_landmark_f1k2_768)
|
||
print("cost time: ", time.time() - start_time)
|
||
if debug:
|
||
print("gender: ", gender, " ratio: ", ratio)
|
||
show_concat = np.concatenate((ref_rgb_8uc3_768, ref_matting_fg_8uc3_768, ref_matting_8uc3_768, ref_baldseg_8uc3_768), axis=1)
|
||
cv2.imshow("show_concat", cv2.resize(show_concat, (0, 0), fx=0.6, fy=0.6))
|
||
cv2.imshow("input_another_pose_hair_image", input_another_pose_hair_image)
|
||
cv2.waitKey()
|
||
if not debug:
|
||
np.save(os.path.join(dst_dir, "input_another_pose_hair_image.npy"), input_another_pose_hair_image)
|
||
|
||
config_dict = {}
|
||
|
||
config_dict['gender'] = gender
|
||
config_dict['version'] = version
|
||
config_dict['ratio'] = str(ratio)
|
||
|
||
with open(os.path.join(dst_dir, "config.json"), "w") as f:
|
||
json.dump(config_dict, f)
|
||
print("写入文件完成...", dst_dir)
|
||
|
||
if __name__ == '__main__':
|
||
prepare_single()
|
||
# prepare_multi() |