feat: 印花逻辑修改 默认不处理除overall以外所有印花类型
fix:
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@@ -1,235 +0,0 @@
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import os
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import pika
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from dotenv import load_dotenv
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from pydantic import BaseSettings
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BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))
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load_dotenv(os.path.join(BASE_DIR, '.env'))
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class Settings(BaseSettings):
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PROJECT_NAME: str = 'FASTAPI BASE'
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SECRET_KEY: str = ''
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API_PREFIX: str = ''
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BACKEND_CORS_ORIGINS: list[str] = ['*']
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DATABASE_URL: str = ''
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ACCESS_TOKEN_EXPIRE_SECONDS: int = 60 * 60 * 24 * 7 # Token expired after 7 days
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SECURITY_ALGORITHM: str = 'HS256'
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LOGGING_CONFIG_FILE: str = os.path.join(BASE_DIR, 'logging_env.py')
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OSS = "minio"
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DEBUG = False
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if DEBUG:
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LOGS_PATH = "logs/"
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CATEGORY_PATH = "service/attribute/config/descriptor/category/category_dis.csv"
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SEG_CACHE_PATH = "../seg_cache/"
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POSE_TRANSFORM_VIDEO_PATH = "../pose_transform_video/"
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RECOMMEND_PATH_PREFIX = "service/recommend/"
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CHROMADB_PATH = "./chromadb/"
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else:
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LOGS_PATH = "app/logs/"
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CATEGORY_PATH = "app/service/attribute/config/descriptor/category/category_dis.csv"
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SEG_CACHE_PATH = "/seg_cache/"
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POSE_TRANSFORM_VIDEO_PATH = "/pose_transform_video/"
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RECOMMEND_PATH_PREFIX = "app/service/recommend/"
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CHROMADB_PATH = "/chromadb/"
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# RABBITMQ_ENV = "" # 生产环境
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RABBITMQ_ENV = os.getenv("RABBITMQ_ENV", "-dev")
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# RABBITMQ_ENV = "-local" # 本地测试环境
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if RABBITMQ_ENV == "-dev":
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JAVA_STREAM_API_URL = f"https://develop.api.aida.com.hk/api/third/party/receiveDesignResults"
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elif RABBITMQ_ENV == "-prod":
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JAVA_STREAM_API_URL = f"https://api.aida.com.hk/api/third/party/receiveDesignResults"
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settings = Settings()
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# minio 配置
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MINIO_URL = "www.minio-api.aida.com.hk"
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MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB'
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MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR'
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MINIO_SECURE = True
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# S3 配置
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S3_ACCESS_KEY = "AKIAVD3OJIMF6UJFLSHZ"
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S3_AWS_SECRET_ACCESS_KEY = "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"
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S3_REGION_NAME = "ap-east-1"
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# redis 配置
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REDIS_HOST = "10.1.1.240"
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REDIS_PORT = "6379"
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REDIS_DB = "2"
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# rabbitmq config
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RABBITMQ_PARAMS = {
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"host": "18.167.251.121",
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"port": 5672,
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"credentials": pika.credentials.PlainCredentials(username='rabbit', password='123456'),
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"virtual_host": "/"
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}
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# milvus 配置
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MILVUS_URL = "http://10.1.1.240:19530"
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MILVUS_TOKEN = "root:Milvus"
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MILVUS_ALIAS = "default"
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MILVUS_TABLE_KEYPOINT = "keypoint_cache_2"
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MILVUS_TABLE_SEG = "seg_cache"
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# Mysql 配置
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DB_HOST = '18.167.251.121' # 数据库主机地址
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# DB_PORT = int( 33006)
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DB_PORT = 33008 # 数据库端口
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DB_USERNAME = 'aida_con_python' # 数据库用户名
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DB_PASSWORD = '123456' # 数据库密码
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DB_NAME = 'aida' # 数据库库名
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# openai
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os.environ['SERPAPI_API_KEY'] = "a793513017b0718db7966207c31703d280d12435c982f1e67bbcbffa52e7632c"
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OPENAI_STREAM = True
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BUFFER_THRESHOLD = 6 # must be even number
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SINGLE_TOKEN_THRESHOLD = 200
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TOKEN_THRESHOLD = 600
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OPENAI_TEMPERATURE = 0
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# OPENAI_API_KEY = "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"
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OPENAI_API_KEY = "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"
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OPENAI_MODEL = "gpt-3.5-turbo-0613"
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OPENAI_MODEL_LIST = {"gpt-3.5-turbo-0613",
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"gpt-3.5-turbo-16k-0613",
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"gpt-4-0314",
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"gpt-4-32k-0314",
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"gpt-4-0613",
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"gpt-4-32k-0613", }
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# SR service config
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SR_MODEL_NAME = "super_resolution"
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SR_TRITON_URL = "10.1.1.240:10031"
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SR_MINIO_BUCKET = "aida-users"
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SR_RABBITMQ_QUEUES = f"SuperResolution{RABBITMQ_ENV}"
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# GenerateImage service config
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FAST_GI_MODEL_URL = '10.1.1.243:10011'
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FAST_GI_MODEL_NAME = 'stable_diffusion_xl'
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GI_MODEL_URL = '10.1.1.240:10061'
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GI_MODEL_NAME = 'flux'
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GMV_MODEL_URL = '10.1.1.243:10081'
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GMV_MODEL_NAME = 'multi_view'
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GMV_RABBITMQ_QUEUES = f"GenerateMultiView{RABBITMQ_ENV}"
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GI_MINIO_BUCKET = "aida-users"
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GI_RABBITMQ_QUEUES = f"GenerateImage{RABBITMQ_ENV}"
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GI_SYS_IMAGE_URL = "aida-sys-image/generate_image/white_image.jpg"
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# SLOGAN service config
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SLOGAN_RABBITMQ_QUEUES = f"Slogan{RABBITMQ_ENV}"
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# Generate Single Logo service config
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GSL_MODEL_URL = '10.1.1.243:10041'
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GSL_MINIO_BUCKET = "aida-users"
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GSL_MODEL_NAME = 'stable_diffusion_xl_transparent'
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GEN_SINGLE_LOGO_RABBITMQ_QUEUES = f"GenSingleLogo{RABBITMQ_ENV}"
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# Generate Product service config
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# GPI_RABBITMQ_QUEUES = os.getenv("GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES", f"ToProductImage{RABBITMQ_ENV}")
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# GPI_MODEL_NAME_OVERALL = 'sdxl_ensemble_all'
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# GPI_MODEL_URL = '10.1.1.243:10051'
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# Generate Product service config 旧版product img 模型
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GPI_RABBITMQ_QUEUES = f"ToProductImage{RABBITMQ_ENV}"
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BATCH_GPI_RABBITMQ_QUEUES = f"BatchToProductImage{RABBITMQ_ENV}"
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GPI_MODEL_NAME_OVERALL = 'diffusion_ensemble_all'
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GPI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_cnet'
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GPI_MODEL_URL = '10.1.1.243:10051'
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# Generate Single Logo service config
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GRI_RABBITMQ_QUEUES = f"Relight{RABBITMQ_ENV}"
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BATCH_GRI_RABBITMQ_QUEUES = f"BatchRelight{RABBITMQ_ENV}"
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GRI_MODEL_NAME_OVERALL = 'diffusion_relight_ensemble'
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GRI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_relight'
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GRI_MODEL_URL = '10.1.1.240:10051'
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# Pose Transform service config
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PS_RABBITMQ_QUEUES = f"PoseTransform{RABBITMQ_ENV}"
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BATCH_PS_RABBITMQ_QUEUES = f"BatchPoseTransform{RABBITMQ_ENV}"
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PT_MODEL_URL = '10.1.1.243:10061'
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# SEG service config
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SEGMENTATION = {
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"new_model_name": "seg_knet",
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"name": "seg_ocrnet_hr18",
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"input": "seg_input__0",
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"output": "seg_output__0",
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}
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# ollama config
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OLLAMA_URL = "http://10.1.1.240:11434/api/embeddings"
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# design batch
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BATCH_DESIGN_RABBITMQ_QUEUES = f"DesignBatch{RABBITMQ_ENV}"
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# DESIGN config
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DESIGN_MODEL_URL = '10.1.1.240:10000'
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AIDA_CLOTHING = "aida-clothing"
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KEYPOINT_RESULT_TABLE_FIELD_SET = ('neckline_left', 'neckline_right', 'shoulder_left', 'shoulder_right', 'armpit_left', 'armpit_right',
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'cuff_left_in', 'cuff_left_out', 'cuff_right_in', 'cuff_right_out', 'waistband_left', 'waistband_right')
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# DESIGN 预处理
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IF_DEBUG_SHOW = False
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# 优先级
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PRIORITY_DICT = {
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'earring_front': 99,
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'bag_front': 98,
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'hairstyle_front': 97,
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'outwear_front': 20,
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'tops_front': 19,
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'dress_front': 18,
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'blouse_front': 17,
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'skirt_front': 16,
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'trousers_front': 15,
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'bottoms_front': 14,
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'shoes_right': 1,
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'shoes_left': 1,
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'body': 0,
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'bottoms_back': -14,
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'trousers_back': -15,
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'skirt_back': -16,
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'blouse_back': -17,
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'dress_back': -18,
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'tops_back': -19,
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'outwear_back': -20,
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'hairstyle_back': -97,
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'bag_back': -98,
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'earring_back': -99,
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}
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QWEN_API_KEY = "sk-f31c29e61ac2498ba5e307aaa6dc10e0"
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DB_CONFIG = {
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"host": "18.167.251.121",
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"port": 3306,
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"user": "root",
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"password": "QWa998345",
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"database": "aida",
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"charset": "utf8mb4"
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}
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TABLE_CATEGORIES = {
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"female_dress": "female/dress",
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"female_outwear": "female/outwear",
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"female_trousers": "female/trousers",
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"female_skirt": "female/skirt",
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"female_blouse": "female/blouse",
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"male_tops": "male/tops",
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"male_bottoms": "male/bottoms",
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"male_outwear": "male/outwear"
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}
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# --- ComfyUI 配置信息 ---
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COMFYUI_SERVER_ADDRESS = "10.1.2.227:8080" # 替换为您的 ComfyUI 服务器地址
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@@ -12,9 +12,13 @@ class NoSegPrintPainting:
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self.minio_client = minio_client
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def __call__(self, result):
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single_print = result['print']['single']
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# single_print = [result['print']['single']]
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overall_print = result['print']['overall']
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element_print = result['print']['element']
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# element_print = result['print']['element'
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single_print = None
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element_print = None
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result['single_image'] = None
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result['print_image'] = None
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@@ -25,7 +29,7 @@ class NoSegPrintPainting:
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result['no_seg_sketch_overall'] = result['no_seg_sketch_print'] = self.printpaint(result, painting_dict, print_=True)
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result['pattern_image'] = result['no_seg_sketch_overall']
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if single_print['print_path_list']:
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if single_print:
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print_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
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mask_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
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for i in range(len(single_print['print_path_list'])):
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@@ -65,7 +69,7 @@ class NoSegPrintPainting:
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single_image = cv2.add(tmp1, tmp2)
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result['no_seg_sketch_print'] = single_image
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if element_print['element_path_list']:
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if element_print:
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print_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
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mask_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
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for i in range(len(element_print['element_path_list'])):
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@@ -12,10 +12,14 @@ class PrintPainting:
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self.minio_client = minio_client
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def __call__(self, result):
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single_print = result['print']['single']
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# single_print = result['print']['single']
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overall_print = result['print']['overall']
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element_print = result['print']['element']
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partial_path = result['print']['partial'] if 'partial' in result['print'] else None
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# element_print = result['print']['element']
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# partial_path = result['print']['partial'] if 'partial' in result['print'] else None
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single_print = None
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element_print = None
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partial_path = None
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result['single_image'] = None
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result['print_image'] = None
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# TODO 给result['pattern_image'] resize 到resize_scale的大小
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@@ -43,7 +47,7 @@ class PrintPainting:
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result['print_image'] = self.printpaint(result, painting_dict, print_=True)
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result['single_image'] = result['final_image'] = result['pattern_image'] = result['print_image']
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if single_print['print_path_list']:
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if single_print:
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# 2025-9-19 印花调整 印花坐标按照sketch的缩放比调整
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sketch_resize_scale = result['resize_scale']
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print_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
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@@ -84,7 +88,7 @@ class PrintPainting:
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tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8)
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result['single_image'] = cv2.add(tmp1, tmp2)
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if element_print['element_path_list']:
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if element_print:
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# 2025-9-19 印花调整 印花坐标按照sketch的缩放比调整
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sketch_resize_scale = result['resize_scale']
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print_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
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