aidlab/AiDA_Python
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Merge branch 'refs/heads/local' into develop

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This commit is contained in:
zhouchengrong
2024-06-27 15:05:12 +08:00
parent 35a30f4ed6 ae2be27173
commit a63e6d21e4
28 changed files with 1197 additions and 1077 deletions
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31
app/api/api_attribute_retrieve.py
View File

@@ -1,5 +1,6 @@
import json
import logging
from fastapi import APIRouter, HTTPException
from app.core.config import DEBUG
@@ -16,6 +17,22 @@ logger = logging.getLogger()
# 属性识别
@router.post("/attribute_recognition", response_model=ResponseModel)
def attribute_recognition(request_item: list[AttributeRecognitionModel]):
"""
获取sketch的属性，collar sleeve_length 等等
创建一个具有以下参数的请求体:
- **category**: sketch的类别 ，Dress
- **colony**: 服装类别，男装或女装
- **sketch_img_url**: 被提取属性的S3或minio url地址
示例参数：
[
{
"category": "Dress",
"colony": "Female",
"sketch_img_url": "aida-users/89/sketchboard/female/Dress/ae976103-d7ec-4eed-b5d1-3e5f04d8be26.jpg"
}
]
"""
try:
logger.info(f"attribute_recognition request item is : @@@@@@:{request_item}")
if DEBUG:
@@ -33,6 +50,20 @@ def attribute_recognition(request_item: list[AttributeRecognitionModel]):
# 类别识别
@router.post("/category_recognition")
def category_recognition(request_item: list[CategoryRecognitionModel]):
"""
获取sketch的类别，dress blouse 等等
创建一个具有以下参数的请求体:
- **colony**: 服装类别，male或Female
- **sketch_img_url**: 被提取sketch类别的S3或minio url地址
示例参数：
[
{
"colony": "Female",
"sketch_img_url": "aida-users/89/sketchboard/female/Dress/ae976103-d7ec-4eed-b5d1-3e5f04d8be26.jpg"
}
]
"""
try:
logger.info(f"category_recognition request item is : @@@@@@:{request_item}")
service = CategoryRecognition(request_data=request_item)

18
app/api/api_chat_robot.py
View File

@@ -1,6 +1,6 @@
import json
import logging
import time
from fastapi import APIRouter, HTTPException
from app.schemas.chat_robot import ChatRobotModel
@@ -13,6 +13,22 @@ logger = logging.getLogger()
@router.post("/chat_robot")
def chat_robot(request_data: ChatRobotModel):
"""
对话机器人
创建一个具有以下参数的请求体:
- **gender**: 服装类别
- **message**: 消息
- **session_id**: 会话id
- **user_id**: 用户id
示例参数：
{
"gender": "male",
"message": "你好",
"session_id": "string-89",
"user_id": 89
}
"""
try:
logger.info(f"chat_robot request item is : @@@@@@:{request_data}")
data = chat(post_data=request_data)

106
app/api/api_design.py
View File

@@ -15,6 +15,92 @@ logger = logging.getLogger()
@router.post("/design")
def design(request_data: DesignModel):
"""
创建一个具有以下参数的请求体:
示例参数：
{
"objects": [
{
"basic": {
"body_point_test": {
"waistband_right": [
203,
249
],
"hand_point_right": [
229,
343
],
"waistband_left": [
119,
248
],
"hand_point_left": [
97,
343
],
"shoulder_left": [
108,
107
],
"shoulder_right": [
212,
107
]
},
"layer_order": true,
"scale_bag": 0.7,
"scale_earrings": 0.16,
"self_template": true,
"single_overall": "overall",
"switch_category": ""
},
"items": [
{
"businessId": 255303,
"color": "139 148 156",
"image_id": 95159,
"offset": [
0,
0
],
"path": "aida-users/89/sketch/c89d75f3-581f-4edd-9f8e-b08e84a2cbe7-3-89.png",
"print": {
"IfSingle": false,
"location": [
[
512.0,
512.0
]
],
"print_angle_list": [
0.0
],
"print_path_list": [
"aida-users/89/print/468643b4-bc2d-41b2-9a16-79766606a2db-3-89.png"
],
"print_scale_list": [
1.0
]
},
"priority": 10,
"resize_scale": [
1.0,
1.0
],
"type": "Dress"
},
{
"body_path": "aida-sys-image/models/female/2e4815b9-1191-419d-94ed-5771239ca4a5.png",
"image_id": 67277,
"type": "Body"
}
]
}
],
"process_id": "89"
}
"""
try:
logger.info(f"design request item is : @@@@@@:{request_data.dict()}")
data = generate(request_data=request_data)
@@ -27,6 +113,16 @@ def design(request_data: DesignModel):
@router.post('/get_progress')
def get_progress(request_data: DesignProgressModel):
"""
获取design 进度
创建一个具有以下参数的请求体:
- **process_id**: 进度id
示例参数：
{
"process_id": "6878547032381675"
}
"""
try:
logger.info(f"get_progress request item is : @@@@@@:{request_data.dict()}")
process_id = request_data.process_id
@@ -43,6 +139,16 @@ def get_progress(request_data: DesignProgressModel):
@router.post('/model_process')
def model_process(request_data: ModelProgressModel):
"""
获取模特图片预处理
创建一个具有以下参数的请求体:
- **model_path**: 模特图片的minio或s3 url地址
示例参数：
{
"model_path": "aida-users/10/models/female/9c788f5b-b8c7-424c-b149-025aeb0bda51model.jpg"
}
"""
try:
logger.info(f"model_process request item is : @@@@@@:{request_data.dict()}")

18
app/api/api_design_pre_processing.py
View File

@@ -1,6 +1,8 @@
import json
import logging
from fastapi import APIRouter, HTTPException
from app.schemas.pre_processing import DesignPreProcessingModel
from app.schemas.response_template import ResponseModel
from app.service.design_pre_processing.service import DesignPreprocessing
@@ -11,6 +13,22 @@ logger = logging.getLogger()
@router.post("/design_pre_processing")
def design_pre_processing(request_data: DesignPreProcessingModel):
"""
design 预处理 获取sketch的基本信息
创建一个具有以下参数的请求体:
- **sketches**: sketch url等信息
示例参数：
{
"sketches": [
{
"image_category": "dress",
"image_id": "107903",
"image_url": "aida-sys-image/images/female/dress/0628000000.jpg"
}
]
}
"""
try:
logger.info(f"design_pre_processing request item is : @@@@@@:{request_data}")
server = DesignPreprocessing()

58
app/api/api_generate_image.py
View File

@@ -18,6 +18,25 @@ logger = logging.getLogger()
@router.post("/generate_image")
def generate_image(request_item: GenerateImageModel, background_tasks: BackgroundTasks):
"""
创建一个具有以下参数的请求体:
- **tasks_id**: 任务id 用于取消生成任务和获取生成结果
- **prompt**: 想要生成图片的描述词
- **image_url**: 图生图的输入，minio或S3 url 地址
- **mode**: 生成模式，img2img或者txt2img
- **category**: 生成图片的类别，sketch print 等等
- **gender**: 生成sketch专用，服装类别
示例参数：
{
"tasks_id": "123-89",
"prompt": "skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic",
"image_url": "aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg",
"mode": "img2img",
"category": "sketch",
"gender": "male"
}
"""
try:
logger.info(f"generate_image request item is : @@@@@@:{request_item}")
service = GenerateImage(request_item)
@@ -45,6 +64,19 @@ def generate_image(tasks_id: str):
@router.post("/generate_single_logo")
def generate_single_logo(request_item: GenerateSingleLogoImageModel, background_tasks: BackgroundTasks):
"""
创建一个具有以下参数的请求体:
- **tasks_id**: 任务id 用于取消生成任务和获取生成结果
- **prompt**: 想要生成图片的描述词
- **seed**: 固定的prompt和固定的seed 每次的生成结果都是一样的
示例参数：
{
"tasks_id": "123-89",
"prompt": "an apple",
"seed": "2"
}
"""
try:
logger.info(f"generate_single_logo request item is : @@@@@@:{request_item}")
service = GenerateSingleLogoImage(request_item)
@@ -72,6 +104,19 @@ def generate_single_logo_image(tasks_id: str):
@router.post("/generate_product_image")
def generate_product_image(request_item: GenerateProductImageModel, background_tasks: BackgroundTasks):
"""
创建一个具有以下参数的请求体:
- **tasks_id**: 任务id 用于取消生成任务和获取生成结果
- **prompt**: 想要生成图片的描述词
- **image_url**: 被生成图片的S3或minio url地址
示例参数：
{
"tasks_id": "123-89",
"prompt": "the best quality, masterpiece. detailed, high-res, simple background, studio photography, extremely detailed, updo, detailed face, face, close-up, HDR, UHD, 8K realistic, Highly detailed, simple background, Studio lighting",
"image_url": "aida-results/result_00097282-ebb2-11ee-a822-b48351119060.png"
}
"""
try:
logger.info(f"generate_product_image request item is : @@@@@@:{request_item}")
service = GenerateProductImage(request_item)
@@ -99,6 +144,19 @@ def generate_product_image(tasks_id: str):
@router.post("/generate_relight_image")
def generate_relight_image(request_item: GenerateProductImageModel, background_tasks: BackgroundTasks):
"""
创建一个具有以下参数的请求体:
- **tasks_id**: 任务id 用于取消生成任务和获取生成结果
- **prompt**: 想要生成图片的描述词
- **image_url**: 被生成图片的S3或minio url地址
示例参数：
{
"tasks_id": "123-89",
"prompt": "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
"image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png"
}
"""
try:
logger.info(f"generate_relight_image request item is : @@@@@@:{request_item}")
service = GenerateRelightImage(request_item)

10
app/api/api_prompt_generation.py
View File

@@ -14,6 +14,16 @@ logger = logging.getLogger()
@router.post("/translateToEN")
def prompt_generation(request_data: PromptGenerationImageModel):
"""
翻译prompt接口
创建一个具有以下参数的请求体:
- **text**: 待翻译语句
示例参数：
{
"text": "你好"
}
"""
try:
logger.info(f"prompt_generation request item is : @@@@@@:{request_data}")
data = translate_to_en(request_data.text)

13
app/api/api_super_resolution.py
View File

@@ -13,6 +13,19 @@ logger = logging.getLogger()
@router.post("/super_resolution")
def super_resolution(request_item: SuperResolutionModel, background_tasks: BackgroundTasks):
"""
创建一个具有以下参数的请求体:
- **sr_image_url**: 超分图片的minio或s3 url地址
- **sr_xn**: 超分的倍数，只接受2或4
- **sr_tasks_id**: 任务id 用于取消超分任务和获取超分结果
示例参数：
{
"sr_image_url": "aida-sys-image/images/female/blouse/0628000098.jpg",
"sr_xn": 2,
"sr_tasks_id": "12341556-89"
}
"""
try:
logger.info(f"super_resolution request item is : @@@@@@:{request_item}")
service = SuperResolution(request_item)

2
app/core/config.py
View File

@@ -19,7 +19,7 @@ class Settings(BaseSettings):
LOGGING_CONFIG_FILE = os.path.join(BASE_DIR, 'logging_env.py')
OSS = "minio"
OSS = "S3"
DEBUG = False
if DEBUG:
LOGS_PATH = "logs/"

101
app/service/design/design_request.json
View File

@@ -1,101 +0,0 @@
{
"objects": [
{
"basic": {
"body_point": {
"waistband_right": [
1081,
1318
],
"hand_point_right": [
1200,
1857
],
"waistband_left": [
639,
1315
],
"hand_point_left": [
493,
1808
],
"shoulder_left": [
556,
582
],
"shoulder_right": [
1130,
576
]
},
"layer_order": false,
"scale_bag": 0.7,
"scale_earrings": 0.16,
"self_template": true,
"single_overall": "overall",
"switch_category": ""
},
"items": [
{
"color": "151 78 78",
"icon": "none",
"image_id": 67315,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/trousers/0628000325.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Trousers"
},
{
"color": "151 78 78",
"icon": "none",
"image_id": 92912,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/blouse/0825001943.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Blouse"
},
{
"color": "151 78 78",
"icon": "none",
"image_id": 91430,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/outwear/0825000856.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Outwear"
},
{
"body_path": "aida-users/89/models/female/5d39394e-9809-43c2-80b8-4e96497b1974.png",
"image_id": 69331,
"offset": [
1,
1
],
"resize_scale": 1.0,
"type": "Body"
}
]
}
],
"process_id": "7296013643475027"
}

684
app/service/design/design_request_2.json
View File

@@ -1,684 +0,0 @@
{
"objects": [
{
"basic": {
"body_point_test": {
"waistband_right": [
1081,
1318
],
"hand_point_right": [
1200,
1857
],
"waistband_left": [
639,
1315
],
"hand_point_left": [
493,
1808
],
"shoulder_left": [
556,
582
],
"shoulder_right": [
1130,
576
]
},
"layer_order": false,
"scale_bag": 0.7,
"scale_earrings": 0.16,
"self_template": true,
"single_overall": "overall",
"switch_category": ""
},
"items": [
{
"color": "151 78 78",
"icon": "none",
"image_id": 67315,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/trousers/0628000325.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Trousers"
},
{
"color": "151 78 78",
"icon": "none",
"image_id": 92912,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/blouse/0825001943.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Blouse"
},
{
"color": "151 78 78",
"icon": "none",
"image_id": 91430,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/outwear/0825000856.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Outwear"
},
{
"body_path": "aida-users/89/models/female/5d39394e-9809-43c2-80b8-4e96497b1974.png",
"image_id": 69331,
"offset": [
1,
1
],
"resize_scale": 1.0,
"type": "Body"
}
]
}
,
{
"basic": {
"body_point_test": {
"waistband_right": [
1081,
1318
],
"hand_point_right": [
1200,
1857
],
"waistband_left": [
639,
1315
],
"hand_point_left": [
493,
1808
],
"shoulder_left": [
556,
582
],
"shoulder_right": [
1130,
576
]
},
"layer_order": false,
"scale_bag": 0.7,
"scale_earrings": 0.16,
"self_template": true,
"single_overall": "overall",
"switch_category": ""
},
"items": [
{
"color": "151 78 78",
"icon": "none",
"image_id": 92913,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/dress/826000033.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Dress"
},
{
"body_path": "aida-users/89/models/female/5d39394e-9809-43c2-80b8-4e96497b1974.png",
"image_id": 69331,
"offset": [
1,
1
],
"resize_scale": 1.0,
"type": "Body"
}
]
}
,
{
"basic": {
"body_point_test": {
"waistband_right": [
1081,
1318
],
"hand_point_right": [
1200,
1857
],
"waistband_left": [
639,
1315
],
"hand_point_left": [
493,
1808
],
"shoulder_left": [
556,
582
],
"shoulder_right": [
1130,
576
]
},
"layer_order": false,
"scale_bag": 0.7,
"scale_earrings": 0.16,
"self_template": true,
"single_overall": "overall",
"switch_category": ""
},
"items": [
{
"color": "151 78 78",
"icon": "none",
"image_id": 92914,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/skirt/0902001788.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Skirt"
},
{
"color": "151 78 78",
"icon": "none",
"image_id": 92915,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/blouse/0902003817.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Blouse"
},
{
"body_path": "aida-users/89/models/female/5d39394e-9809-43c2-80b8-4e96497b1974.png",
"image_id": 69331,
"offset": [
1,
1
],
"resize_scale": 1.0,
"type": "Body"
}
]
}
,
{
"basic": {
"body_point_test": {
"waistband_right": [
1081,
1318
],
"hand_point_right": [
1200,
1857
],
"waistband_left": [
639,
1315
],
"hand_point_left": [
493,
1808
],
"shoulder_left": [
556,
582
],
"shoulder_right": [
1130,
576
]
},
"layer_order": false,
"scale_bag": 0.7,
"scale_earrings": 0.16,
"self_template": true,
"single_overall": "overall",
"switch_category": ""
},
"items": [
{
"color": "151 78 78",
"icon": "none",
"image_id": 92916,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/skirt/skirt_p4_838.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Skirt"
},
{
"color": "151 78 78",
"icon": "none",
"image_id": 84210,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/blouse/0916000703.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Blouse"
},
{
"body_path": "aida-users/89/models/female/5d39394e-9809-43c2-80b8-4e96497b1974.png",
"image_id": 69331,
"offset": [
1,
1
],
"resize_scale": 1.0,
"type": "Body"
}
]
}
,
{
"basic": {
"body_point_test": {
"waistband_right": [
1081,
1318
],
"hand_point_right": [
1200,
1857
],
"waistband_left": [
639,
1315
],
"hand_point_left": [
493,
1808
],
"shoulder_left": [
556,
582
],
"shoulder_right": [
1130,
576
]
},
"layer_order": false,
"scale_bag": 0.7,
"scale_earrings": 0.16,
"self_template": true,
"single_overall": "overall",
"switch_category": ""
},
"items": [
{
"color": "151 78 78",
"icon": "none",
"image_id": 62041,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/outwear/0902000232.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Outwear"
},
{
"color": "151 78 78",
"icon": "none",
"image_id": 67039,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/blouse/0902002591.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Blouse"
},
{
"color": "151 78 78",
"icon": "none",
"image_id": 78016,
"offset": [
1,
1
],
"path": "aida-sys-image/images/female/trousers/trousers_p4_302.jpg",
"print": {
"IfSingle": false,
"print_path_list": []
},
"resize_scale": 1.0,
"type": "Trousers"
},
{
"body_path": "aida-users/89/models/female/5d39394e-9809-43c2-80b8-4e96497b1974.png",
"image_id": 69331,
"offset": [
1,
1
],
"resize_scale": 1.0,
"type": "Body"
}
]
}
,
{
"basic": {
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836
app/service/design/fastapi_request.json
View File

@@ -1,69 +1,771 @@
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}
],
"process_id": "6878547032381675"
}

3
app/service/design/items/bag.py
View File

@@ -1,6 +1,7 @@
import random
from .builder import ITEMS
from .clothing import Clothing
import random
@ITEMS.register_module()

1
app/service/design/items/body.py
View File

@@ -1,4 +1,5 @@
import cv2
from .builder import ITEMS
from .pipelines import Compose

7
app/service/design/items/pipelines/contour_detection.py
View File

@@ -1,9 +1,8 @@
import logging
from ..builder import PIPELINES
import cv2
import numpy as np
from ..builder import PIPELINES
@PIPELINES.register_module()
class ContourDetection(object):
@@ -11,7 +10,7 @@ class ContourDetection(object):
# logging.info("ContourDetection run ")
pass
#@ RunTime
# @ RunTime
def __call__(self, result):
# shoe diff
if result['name'] == 'shoes':

21
app/service/design/items/pipelines/loading.py
View File

@@ -1,12 +1,5 @@
import io
import logging
import cv2
import numpy as np
from PIL import Image
from minio import Minio
from app.core.config import *
from app.service.utils.oss_client import oss_get_image
from ..builder import PIPELINES
@@ -17,11 +10,7 @@ class LoadImageFromFile(object):
self.path = path
self.color = color
self.print_dict = print_dict
self.minio_client = Minio(
f"{MINIO_URL}",
access_key=MINIO_ACCESS,
secret_key=MINIO_SECRET,
secure=MINIO_SECURE)
# self.minio_client = Minio(f"{MINIO_URL}", access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
def __call__(self, result):
result['image'], result['pre_mask'] = self.read_image(self.path)
@@ -53,11 +42,13 @@ class LoadImageFromFile(object):
f"bag, shoes, hairstyle, earring.")
return keypoint
def read_image(self, image_path):
@staticmethod
def read_image(image_path):
image_mask = None
file = self.minio_client.get_object(image_path.split("/", 1)[0], image_path.split("/", 1)[1]).data
image = cv2.imdecode(np.frombuffer(file, np.uint8), 1)
# file = self.minio_client.get_object(image_path.split("/", 1)[0], image_path.split("/", 1)[1]).data
# image = cv2.imdecode(np.frombuffer(file, np.uint8), 1)
image = oss_get_image(bucket=image_path.split("/", 1)[0], object_name=image_path.split("/", 1)[1], data_type="cv2")
if len(image.shape) == 2:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
if image.shape[2] == 4: # 如果是四通道 mask

126
app/service/design/items/pipelines/painting.py
View File

@@ -1,6 +1,5 @@
import random
# import boto3
import cv2
import numpy as np
from PIL import Image
@@ -9,12 +8,6 @@ from app.service.utils.oss_client import oss_get_image
from ..builder import PIPELINES
# minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
# s3 = boto3.client('s3', aws_access_key_id=S3_ACCESS_KEY, aws_secret_access_key=S3_AWS_SECRET_ACCESS_KEY, region_name=S3_REGION_NAME)
@PIPELINES.register_module()
class Painting(object):
def __init__(self, painting_flag=True):
@@ -65,6 +58,8 @@ class Painting(object):
# 使用OpenCV解码图像数组
# image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
image = oss_get_image(bucket=bucket_name, object_name=object_name, data_type="cv2")
if image.shape[2] == 4:
image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
return image
@staticmethod
@@ -211,7 +206,16 @@ class PrintPainting(object):
result['print_image'] = result['pattern_image']
# print
else:
painting_dict = self.painting_collection(painting_dict, result, print_trigger=True)
if "print_angle_list" in result['print'].keys() and result['print']['print_angle_list'][0] != 0:
painting_dict = self.painting_collection(painting_dict, result, print_trigger=True)
painting_dict['tile_print'] = self.rotate_crop_image(img=painting_dict['tile_print'], angle=-result['print']['print_angle_list'][0], crop=True)
painting_dict['mask_inv_print'] = self.rotate_crop_image(img=painting_dict['mask_inv_print'], angle=-result['print']['print_angle_list'][0], crop=True)
# resize 到sketch大小
painting_dict['tile_print'] = self.resize_and_crop(img=painting_dict['tile_print'], target_width=painting_dict['dim_image_w'], target_height=painting_dict['dim_image_h'])
painting_dict['mask_inv_print'] = self.resize_and_crop(img=painting_dict['mask_inv_print'], target_width=painting_dict['dim_image_w'], target_height=painting_dict['dim_image_h'])
else:
painting_dict = self.painting_collection(painting_dict, result, print_trigger=True)
result['print_image'] = self.printpaint(result, painting_dict, print_=True)
result['final_image'] = result['print_image']
canvas = np.full_like(result['final_image'], 255)
@@ -358,8 +362,13 @@ class PrintPainting(object):
dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5))
if not print_['IfSingle']:
self.random_seed = random.randint(0, 1000)
painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True)
painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True)
# 如果print 模式为overall 且 有角度的话 ， 组合的print为正方形，方便裁剪
if "print_angle_list" in result['print'].keys() and result['print']['print_angle_list'][0] != 0:
painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], dim_max, dim_max, painting_dict['location'], trigger=True)
painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], dim_max, dim_max, painting_dict['location'], trigger=True)
else:
painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True)
painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True)
else:
painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'])
painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'])
@@ -540,6 +549,52 @@ class PrintPainting(object):
return rotated_img, ((rotated_img.shape[1] - image.shape[1] * scale) // 2, (rotated_img.shape[0] - image.shape[0] * scale) // 2)
# return rotated_img, (0, 0)
@staticmethod
def rotate_crop_image(img, angle, crop):
"""
angle: 旋转的角度
crop: 是否需要进行裁剪，布尔向量
"""
crop_image = lambda img, x0, y0, w, h: img[y0:y0 + h, x0:x0 + w]
w, h = img.shape[:2]
# 旋转角度的周期是360°
angle %= 360
# 计算仿射变换矩阵
M_rotation = cv2.getRotationMatrix2D((w / 2, h / 2), angle, 1)
# 得到旋转后的图像
img_rotated = cv2.warpAffine(img, M_rotation, (w, h))
# 如果需要去除黑边
if crop:
# 裁剪角度的等效周期是180°
angle_crop = angle % 180
if angle > 90:
angle_crop = 180 - angle_crop
# 转化角度为弧度
theta = angle_crop * np.pi / 180
# 计算高宽比
hw_ratio = float(h) / float(w)
# 计算裁剪边长系数的分子项
tan_theta = np.tan(theta)
numerator = np.cos(theta) + np.sin(theta) * np.tan(theta)
# 计算分母中和高宽比相关的项
r = hw_ratio if h > w else 1 / hw_ratio
# 计算分母项
denominator = r * tan_theta + 1
# 最终的边长系数
crop_mult = numerator / denominator
# 得到裁剪区域
w_crop = int(crop_mult * w)
h_crop = int(crop_mult * h)
x0 = int((w - w_crop) / 2)
y0 = int((h - h_crop) / 2)
img_rotated = crop_image(img_rotated, x0, y0, w_crop, h_crop)
return img_rotated
@staticmethod
def read_image(image_url):
image = oss_get_image(bucket=image_url.split("/", 1)[0], object_name=image_url.split("/", 1)[1], data_type="cv2")
@@ -551,26 +606,33 @@ class PrintPainting(object):
image_mode = "RGB"
return image, image_mode
# data = minio_client.get_object(image_url.split("/", 1)[0], image_url.split("/", 1)[1])
# # data = s3.get_object(Bucket=image_url.split("/", 1)[0], Key=image_url.split("/", 1)[1])['Body']
#
# data_bytes = BytesIO(data.read())
# image = Image.open(data_bytes)
# image_mode = image.mode
# # 判断图片格式，如果是RGBA 则贴在一张纯白图片上 防止透明转黑
# if image_mode == "RGBA":
# # new_background = Image.new('RGB', image.size, (255, 255, 255))
# # new_background.paste(image, mask=image.split()[3])
# # image = new_background
# return image, image_mode
# image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
# return image, "RGB"
@staticmethod
def resize_and_crop(img, target_width, target_height):
# 获取原始图像的尺寸
original_height, original_width = img.shape[:2]
# @staticmethod
# def read_image(image_url):
# response = requests.get(image_url)
# image_data = np.frombuffer(response.content, np.uint8)
#
# # 解码图像
# image = cv2.imdecode(image_data, 3)
# return image
# 计算目标尺寸的宽高比
target_ratio = target_width / target_height
# 计算原始图像的宽高比
original_ratio = original_width / original_height
# 调整尺寸
if original_ratio > target_ratio:
# 原始图像更宽，按高度resize，然后裁剪宽度
new_height = target_height
new_width = int(original_width * (target_height / original_height))
resized_img = cv2.resize(img, (new_width, new_height))
# 裁剪宽度
start_x = (new_width - target_width) // 2
cropped_img = resized_img[:, start_x:start_x + target_width]
else:
# 原始图像更高，按宽度resize，然后裁剪高度
new_width = target_width
new_height = int(original_height * (target_width / original_width))
resized_img = cv2.resize(img, (new_width, new_height))
# 裁剪高度
start_y = (new_height - target_height) // 2
cropped_img = resized_img[start_y:start_y + target_height, :]
return cropped_img

4
app/service/design/items/pipelines/scale.py
View File

@@ -1,7 +1,9 @@
from ..builder import PIPELINES
import math
import cv2
from ..builder import PIPELINES
@PIPELINES.register_module()
class Scaling(object):

5
app/service/design/items/pipelines/split.py
View File

@@ -1,11 +1,12 @@
import logging
import cv2
import numpy as np
from PIL import Image
from cv2 import cvtColor, COLOR_BGR2RGBA
from app.service.utils.generate_uuid import generate_uuid
from ..builder import PIPELINES
from PIL import Image
from ...utils.conversion_image import rgb_to_rgba
from ...utils.upload_image import upload_png_mask
@@ -48,7 +49,6 @@ class Split(object):
result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
front_new_size = (int(result_front_image_pil.width * result["scale"] * result["resize_scale"][0]), int(result_front_image_pil.height * result["scale"] * result["resize_scale"][1]))
result_front_image_pil = result_front_image_pil.resize(front_new_size, Image.LANCZOS)
# TODO 多线程外部上传图片到minio
# result['front_mask_image'] = cv2.resize(front_mask, front_new_size)
# result['front_image'] = result_front_image_pil
front_mask = cv2.resize(front_mask, front_new_size)
@@ -61,7 +61,6 @@ class Split(object):
result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
back_new_size = (int(result_back_image_pil.width * result["scale"] * result["resize_scale"][0]), int(result_back_image_pil.height * result["scale"] * result["resize_scale"][1]))
result_back_image_pil = result_back_image_pil.resize(back_new_size, Image.LANCZOS)
# TODO 多线程外部上传图片到minio
# result['back_mask_image'] = cv2.resize(back_mask, back_new_size)
# result['back_image'] = result_back_image_pil

7
app/service/design/items/shoes.py
View File

@@ -1,14 +1,9 @@
import io
import logging
import time
import cv2
import numpy as np
from PIL import Image
from .builder import ITEMS
from .clothing import Clothing
from PIL import Image
from ..utils.conversion_image import rgb_to_rgba
from ..utils.upload_image import upload_png_mask
from ...utils.generate_uuid import generate_uuid

3
app/service/design/utils/conversion_image.py
View File

@@ -19,5 +19,6 @@ def rgb_to_rgba(rgb_size, rgb_image, mask):
rgba_image[:, :, 3] = alpha_channel
return rgba_image
if __name__ == '__main__':
image = open("")
image = open("")

4
app/service/design/utils/design_ensemble.py
View File

@@ -8,12 +8,14 @@
@detail ：发起请求 获取推理结果
"""
import logging
import cv2
import mmcv
import numpy as np
import tritonclient.http as httpclient
import torch
import torch.nn.functional as F
import tritonclient.http as httpclient
from app.core.config import *
"""

34
app/service/design/utils/synthesis_item.py
View File

@@ -9,30 +9,13 @@
"""
import io
import logging
import time
# import boto3
import cv2
import numpy as np
from PIL import Image
from minio import Minio
from app.core.config import *
from app.service.utils.decorator import RunTime
from app.service.utils.generate_uuid import generate_uuid
minio_client = Minio(
MINIO_URL,
access_key=MINIO_ACCESS,
secret_key=MINIO_SECRET,
secure=MINIO_SECURE)
# s3 = boto3.client(
# 's3',
# aws_access_key_id=S3_ACCESS_KEY,
# aws_secret_access_key=S3_AWS_SECRET_ACCESS_KEY,
# region_name=S3_REGION_NAME
# )
from app.service.utils.oss_client import oss_upload_image
def positioning(all_mask_shape, mask_shape, offset):
@@ -134,8 +117,14 @@ def synthesis(data, size):
image_data = io.BytesIO()
result_image.save(image_data, format='PNG')
image_data.seek(0)
# oss upload
image_bytes = image_data.read()
return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
bucket_name = 'aida-results'
object_name = f'result_{generate_uuid()}.png'
req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
return f"{bucket_name}/{object_name}"
# return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
# object_name = f'result_{generate_uuid()}.png'
# response = s3.put_object(Bucket="aida-results", Key=object_name, Body=data, ContentType='image/png')
@@ -170,4 +159,9 @@ def synthesis_single(front_image, back_image):
result_image.save(image_data, format='PNG')
image_data.seek(0)
image_bytes = image_data.read()
return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
# return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
# oss upload
bucket_name = 'aida-results'
object_name = f'result_{generate_uuid()}.png'
req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
return f"{bucket_name}/{object_name}"

133
app/service/design/utils/upload_image.py
View File

@@ -9,99 +9,15 @@
"""
import io
import logging
import time
# import boto3
import cv2
from minio import Minio
from app.core.config import *
from app.service.utils.decorator import RunTime
minio_client = Minio(
f"{MINIO_URL}",
access_key=MINIO_ACCESS,
secret_key=MINIO_SECRET,
secure=MINIO_SECURE)
"""S3 上传"""
# s3 = boto3.client('s3', aws_access_key_id=S3_ACCESS_KEY, aws_secret_access_key=S3_AWS_SECRET_ACCESS_KEY, region_name=S3_REGION_NAME)
#
# @RunTime
# def upload_png_mask(front_image, object_name, mask=None):
# mask_url = None
# if mask is not None:
# # 反转掩模
# mask_inverted = cv2.bitwise_not(mask)
# # 将掩模转换为 RGBA 格式
# rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
# rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
# # 将图像数据保存到内存中的 BytesIO 对象中
# image_bytes = io.BytesIO()
# image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
# image_bytes.seek(0)
# try:
# key = f"mask/mask_{object_name}.png"
# mask_url = f"{AIDA_CLOTHING}/{key}"
# s3.put_object(Bucket=AIDA_CLOTHING, Key=key, Body=image_bytes, ContentType='image/png')
# except Exception as e:
# print(f'上传到 S3 失败: {e}')
# with io.BytesIO() as output:
# front_image.save(output, format='PNG')
# data = output.getvalue()
# # 创建一个 S3 客户端
# try:
# key = f"image/image_{object_name}.png"
# image_url = f"{AIDA_CLOTHING}/{key}"
# s3.put_object(Bucket=AIDA_CLOTHING, Key=key, Body=data, ContentType='image/png')
# return front_image, image_url, mask_url
# except Exception as e:
# print(f'上传到 S3 失败: {e}')
#
#
# @RunTime
# def upload_layer_image(image, object_name):
# with io.BytesIO() as output:
# image.save(output, format='PNG')
# data = output.getvalue()
# # 创建一个 S3 客户端
# try:
# key = f"image/image_{object_name}.png"
# image_url = f"{AIDA_CLOTHING}/{key}"
# s3.put_object(Bucket=AIDA_CLOTHING, Key=key, Body=data, ContentType='image/png')
# return image_url
# except Exception as e:
# print(f'上传到 S3 失败: {e}')
#
#
# @RunTime
# def upload_mask_image(mask, object_name):
# # 反转掩模
# mask_inverted = cv2.bitwise_not(mask)
# # 将掩模转换为 RGBA 格式
# rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
# rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
# # 将图像数据保存到内存中的 BytesIO 对象中
# image_bytes = io.BytesIO()
# image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
# image_bytes.seek(0)
# try:
# key = f"mask/mask_{object_name}.png"
# mask_url = f"{AIDA_CLOTHING}/{key}"
# s3.put_object(Bucket=AIDA_CLOTHING, Key=key, Body=image_bytes, ContentType='image/png')
# return mask_url
# except Exception as e:
# print(f'上传到 S3 失败: {e}')
"""minio 上传"""
from app.service.utils.oss_client import oss_upload_image
# @RunTime
def upload_png_mask(front_image, object_name, mask=None):
start_time = time.time()
try:
mask_url = None
if mask is not None:
@@ -109,48 +25,21 @@ def upload_png_mask(front_image, object_name, mask=None):
# 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明
rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
image_bytes = io.BytesIO()
image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
image_bytes.seek(0)
mask_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'mask/mask_{object_name}.png', image_bytes, len(image_bytes.getvalue()), content_type='image/png').object_name}"
# image_bytes = io.BytesIO()
# image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
# image_bytes.seek(0)
# mask_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'mask/mask_{object_name}.png', image_bytes, len(image_bytes.getvalue()), content_type='image/png').object_name}"
# oss upload ####################
req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"mask/mask_{object_name}.png", image_bytes=cv2.imencode('.png', rgba_image)[1])
mask_url = f"{AIDA_CLOTHING}/mask/mask_{object_name}.png"
image_data = io.BytesIO()
front_image.save(image_data, format='PNG')
image_data.seek(0)
image_bytes = image_data.read()
image_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'image/image_{object_name}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
# print(f"upload_png_mask {object_name} = {time.time() - start_time}")
# image_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'image/image_{object_name}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"image/image_{object_name}.png", image_bytes=image_bytes)
image_url = f"{AIDA_CLOTHING}/image/image_{object_name}.png"
return front_image, image_url, mask_url
except Exception as e:
logging.warning(f"upload_png_mask runtime exception : {e}")
@RunTime
def upload_layer_image(image, object_name):
try:
image_data = io.BytesIO()
image.save(image_data, format='PNG')
image_data.seek(0)
image_bytes = image_data.read()
image_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'image/image_{object_name}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
return image_url
except Exception as e:
logging.warning(f"upload_png_mask runtime exception : {e}")
@RunTime
def upload_mask_image(mask, object_name):
try:
mask_inverted = cv2.bitwise_not(mask)
# 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明
rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
image_bytes = io.BytesIO()
image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
image_bytes.seek(0)
mask_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'mask/mask_{object_name}.png', image_bytes, len(image_bytes.getvalue()), content_type='image/png').object_name}"
return mask_url
except Exception as e:
logging.warning(f"upload_png_mask runtime exception : {e}")

17
app/service/design_pre_processing/service.py
View File

@@ -8,6 +8,7 @@ import tritonclient.grpc as grpcclient
from urllib3.exceptions import ResponseError
from app.core.config import *
from app.schemas.pre_processing import DesignPreProcessingModel
from app.service.design.utils.design_ensemble import get_keypoint_result
from app.service.utils.oss_client import oss_get_image, oss_upload_image
@@ -355,3 +356,19 @@ class DesignPreprocessing:
except Exception as e:
logging.info(f"save keypoint cache milvus error : {e}")
return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
if __name__ == '__main__':
data = {
"sketches": [
{
"image_category": "dress",
"image_id": "107903",
"image_url": "aida-sys-image/images/female/dress/0628000000.jpg"
}
]
}
request_data = DesignPreProcessingModel(sketches=data["sketches"])
server = DesignPreprocessing()
data = server.pipeline(image_list=request_data.sketches)
print(data)

19
app/service/generate_image/service_generate_image.py
View File

@@ -10,15 +10,17 @@
import json
import logging
import time
import cv2
import minio
import numpy as np
import redis
import tritonclient.grpc as grpcclient
import numpy as np
from tritonclient.utils import np_to_triton_dtype
from app.core.config import *
from app.schemas.generate_image import GenerateImageModel
from app.service.generate_image.utils.image_processing import remove_background, stain_detection, generate_category_recognition, autoLevels, luminance_adjust, face_detect_pic
from app.service.generate_image.utils.image_processing import remove_background, stain_detection, generate_category_recognition, autoLevels, luminance_adjust
from app.service.generate_image.utils.upload_sd_image import upload_png_sd
from app.service.utils.oss_client import oss_get_image
@@ -120,13 +122,6 @@ class GenerateImage:
status_data = self.redis_client.get(self.tasks_id)
return json.loads(status_data), status_data
def infer(self, inputs):
return self.grpc_client.async_infer(
model_name=GI_MODEL_NAME,
inputs=inputs,
callback=self.callback
)
def get_result(self):
try:
prompts = [self.prompt] * self.batch_size
@@ -146,7 +141,7 @@ class GenerateImage:
input_mode.set_data_from_numpy(mode_obj)
inputs = [input_text, input_image, input_mode]
ctx = self.infer(inputs)
ctx = self.grpc_client.async_infer(model_name=GI_MODEL_NAME, inputs=inputs, callback=self.callback)
time_out = 600
generate_data = None
while time_out > 0:
@@ -186,10 +181,10 @@ if __name__ == '__main__':
rd = GenerateImageModel(
tasks_id="123-89",
prompt='skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic',
image_url="",
image_url="aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg",
mode='txt2img',
category="test",
gender="male"
)
server = GenerateImage(rd)
print(server.get_result())
print(server.get_result())

2
app/service/generate_image/service_generate_relight_image.py
View File

@@ -137,7 +137,7 @@ if __name__ == '__main__':
tasks_id="123-89",
# prompt="beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
prompt="Colorful black",
image_url='aida-users/89/product_image/123-89.png'
image_url='aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png'
)
server = GenerateRelightImage(rd)
print(server.get_result())

1
app/service/super_resolution/service.py
View File

@@ -64,7 +64,6 @@ class SuperResolution:
if self.sr_xn == 2:
new_shape = (sample.shape[0] // self.sr_xn, sample.shape[1] // self.sr_xn)
sample = cv2.resize(sample, new_shape)
print(new_shape)
sample = np.transpose(sample if sample.shape[2] == 1 else sample[:, :, [2, 1, 0]], (2, 0, 1))
sample = torch.from_numpy(sample).float().unsqueeze(0).numpy()
inputs = [

10
app/service/utils/oss_client.py
View File

@@ -44,7 +44,7 @@ def oss_upload_image(bucket, object_name, image_bytes):
req = oss_client.put_object(bucket_name=bucket, object_name=object_name, data=io.BytesIO(image_bytes), length=len(image_bytes), content_type='image/png')
else:
oss_client = boto3.client('s3', aws_access_key_id=S3_ACCESS_KEY, aws_secret_access_key=S3_AWS_SECRET_ACCESS_KEY, region_name=S3_REGION_NAME)
req = oss_client.put_object(Bucket=AIDA_CLOTHING, Key=object_name, Body=image_bytes, ContentType='image/png')
req = oss_client.put_object(Bucket=bucket, Key=object_name, Body=io.BytesIO(image_bytes), ContentType='image/png')
except Exception as e:
logger.warning(f"{OSS} | 上传图片出现异常 ######: {e}")
return req
@@ -55,12 +55,16 @@ if __name__ == '__main__':
# url = "aida-collection-element/11523/Moodboard/f60af0d2-94c2-48f9-90ff-74b8e8a481b5.jpg"
# url = "aida-sys-image/images/female/outwear/0628000054.jpg"
# url = "aida-users/89/product_image/string-89.png"
url = "test/845046c7-4f62-4f54-a4a9-c26d49c6969335b5b3a9-d335-4871-a46c-3cc3caf07da259629dfd1f1f555a2e2a9def7e719366.png"
# url = "test/845046c7-4f62-4f54-a4a9-c26d49c6969335b5b3a9-d335-4871-a46c-3cc3caf07da259629dfd1f1f555a2e2a9def7e719366.png"
# url = 'aida-users/89/relight_image/123-89.png'
# url = 'aida-users/89/relight_image/123-89.png'
# url = 'aida-users/89/relight_image/123-89.png'
# url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg"
read_type = "PIL "
# url = "aida-users/87/print/956614a2-7e75-4fbe-9ed0-c1831e37a2c9-4-87.png"
# url = "aida-users/89/single_logo/123-89.png"
# url = "aida-users/89/product_image/string-89.png"
url = "aida-results/result_c6520ce7-33a1-11ef-a8d3-b0dcefbff887.png"
read_type = "PIL"
if read_type == "cv2":
img = oss_get_image(bucket=url.split('/')[0], object_name=url[url.find('/') + 1:], data_type=read_type)
cv2.imshow("", img)