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design batch 代码整理

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alab
2024-09-26 06:09:05 +00:00
parent a539923207
commit 04b15aa200
33 changed files with 585 additions and 61 deletions
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13
app/api/api_design.py
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@@ -6,8 +6,9 @@ from fastapi import APIRouter, HTTPException, UploadFile, File, Form
from app.schemas.design import DesignModel, DesignProgressModel, ModelProgressModel, DBGConfigModel
from app.schemas.response_template import ResponseModel
from app.service.design.model_process_service import model_transpose
from app.service.design.service_design_batch_generate import start_design_batch_generate
from app.service.design_batch.service import start_design_batch_generate
# from app.service.design.model_process_service import model_transpose
# from app.service.design.service_design_batch_generate import start_design_batch_generate
from app.service.design_fast.design_generate import design_generate
from app.service.design_fast.utils.redis_utils import Redis
@@ -236,7 +237,7 @@ def model_process(request_data: ModelProgressModel):
try:
logger.info(f"model_process request item is : @@@@@@:{json.dumps(request_data.dict())}")
data = model_transpose(image_path=request_data.model_path)
# data = model_transpose(image_path=request_data.model_path)
logger.info(f"model_process response @@@@@@:{json.dumps(data)}")
except Exception as e:
logger.warning(f"model_process Run Exception @@@@@@:{e}")
@@ -251,20 +252,18 @@ def model_process(request_data: ModelProgressModel):
async def design(file: UploadFile = File(...),
tasks_id: str = Form(...),
user_id: str = Form(...),
priority: int = Form(...),
file_name: str = Form(...),
total: int = Form(...)
):
# file_content = await file.read()
dbg_config = DBGConfigModel(
tasks_id=tasks_id,
user_id=user_id,
priority=priority,
file_name=file_name,
total=total
)
contents = await file.read()
file_name = file.filename
await save_request_file(contents, file_name)
return await start_design_batch_generate(dbg_config, contents)

2
app/schemas/design.py
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@@ -17,5 +17,5 @@ class ModelProgressModel(BaseModel):
class DBGConfigModel(BaseModel):
tasks_id: str
user_id: str
priority: int
file_name: str
total: int

0
app/service/design/design_batch/items/__init__.py
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0
app/service/design/design_batch/items/utils/__init__.py
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0
app/service/design/utils/__init__.py
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126
app/service/design_batch/design_batch_celery.py Normal file
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@@ -0,0 +1,126 @@
import logging
import threading
from celery import Celery
from minio import Minio
from app.core.config import *
from app.service.design_batch.item import BodyItem, TopItem, BottomItem
from app.service.design_batch.utils.MQ import publish_status
from app.service.design_batch.utils.organize import organize_body, organize_clothing
from app.service.design_batch.utils.save_json import oss_upload_json
from app.service.design_batch.utils.synthesis_item import update_base_size_priority, synthesis, synthesis_single
id_lock = threading.Lock()
celery_app = Celery('tasks', broker='amqp://guest:guest@10.1.2.213:5672//', backend='rpc://')
celery_app.conf.worker_log_format = '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s'
celery_app.conf.worker_hijack_root_logger = False
logging.getLogger('pika').setLevel(logging.WARNING)
logger = logging.getLogger()
minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
def process_item(item, basic):
# 处理project中单个item
if item['type'] == "Body":
body_server = BodyItem(data=item, basic=basic, minio_client=minio_client)
item_data = body_server.process()
elif item['type'].lower() in ['blouse', 'outwear', 'dress', 'tops']:
top_server = TopItem(data=item, basic=basic, minio_client=minio_client)
item_data = top_server.process()
else:
bottom_server = BottomItem(data=item, basic=basic, minio_client=minio_client)
item_data = bottom_server.process()
return item_data
def process_layer(item, layers):
# item处理结束后 对图层数据组装
if item['name'] == "mannequin":
body_layer = organize_body(item)
layers.append(body_layer)
return item['body_image'].size
else:
front_layer, back_layer = organize_clothing(item)
layers.append(front_layer)
layers.append(back_layer)
@celery_app.task
def batch_design(objects_data, tasks_id, json_name):
object_response = []
threads = []
active_threads = 0
lock = threading.Lock()
def process_object(step, object):
nonlocal active_threads
basic = object['basic']
items_response = {'layers': []}
if basic['single_overall'] == "overall":
item_results = []
for item in object['items']:
item_results.append(process_item(item, basic))
layers = []
body_size = None
for item in item_results:
body_size = process_layer(item, layers)
layers = sorted(layers, key=lambda s: s.get("priority", float('inf')))
layers, new_size = update_base_size_priority(layers, body_size)
for lay in layers:
items_response['layers'].append({
'image_category': lay['name'],
'position': lay['position'],
'priority': lay.get("priority", None),
'resize_scale': lay['resize_scale'] if "resize_scale" in lay.keys() else None,
'image_size': lay['image'] if lay['image'] is None else lay['image'].size,
'gradient_string': lay['gradient_string'] if 'gradient_string' in lay.keys() else "",
'mask_url': lay['mask_url'],
'image_url': lay['image_url'] if 'image_url' in lay.keys() else None,
'pattern_image_url': lay['pattern_image_url'] if 'pattern_image_url' in lay.keys() else None,
})
items_response['synthesis_url'] = synthesis(layers, new_size, basic)
else:
item_result = process_item(object['items'][0], basic)
items_response['layers'].append({
'image_category': f"{item_result['name']}_front",
'image_size': item_result['back_image'].size if item_result['back_image'] else None,
'position': None,
'priority': 0,
'image_url': item_result['front_image_url'],
'mask_url': item_result['mask_url'],
"gradient_string": item_result['gradient_string'] if 'gradient_string' in item_result.keys() else "",
'pattern_image_url': item_result['pattern_image_url'] if 'pattern_image_url' in item_result.keys() else None,
})
items_response['layers'].append({
'image_category': f"{item_result['name']}_back",
'image_size': item_result['front_image'].size if item_result['front_image'] else None,
'position': None,
'priority': 0,
'image_url': item_result['back_image_url'],
'mask_url': item_result['mask_url'],
"gradient_string": item_result['gradient_string'] if 'gradient_string' in item_result.keys() else "",
'pattern_image_url': item_result['pattern_image_url'] if 'pattern_image_url' in item_result.keys() else None,
})
items_response['synthesis_url'] = synthesis_single(item_result['front_image'], item_result['back_image'])
with lock:
object_response.append(items_response)
publish_status(tasks_id, step + 1, items_response)
active_threads -= 1
for step, object in enumerate(objects_data):
t = threading.Thread(target=process_object, args=(step, object))
threads.append(t)
t.start()
with lock:
active_threads += 1
for t in threads:
t.join()
oss_upload_json(minio_client, object_response, json_name)
publish_status(tasks_id, "ok", json_name)
return object_response

61
app/service/design_batch/item.py Normal file
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@@ -0,0 +1,61 @@
from app.service.design_batch.pipeline import *
class BaseItem:
def __init__(self, data, basic):
self.result = data.copy()
self.result['name'] = data['type'].lower()
self.result.pop("type")
self.result.update(basic)
class TopItem(BaseItem):
def __init__(self, data, basic, minio_client):
super().__init__(data, basic)
self.top_pipeline = [
LoadImage(minio_client),
KeyPoint(),
Segmentation(minio_client),
Color(minio_client),
PrintPainting(minio_client),
Scaling(),
Split(minio_client)
]
def process(self):
for item in self.top_pipeline:
self.result = item(self.result)
return self.result
class BottomItem(BaseItem):
def __init__(self, data, basic, minio_client):
super().__init__(data, basic)
self.bottom_pipeline = [
LoadImage(minio_client),
KeyPoint(),
ContourDetection(),
# Segmentation(),
Color(minio_client),
PrintPainting(minio_client),
Scaling(),
Split(minio_client)
]
def process(self):
for item in self.bottom_pipeline:
self.result = item(self.result)
return self.result
class BodyItem(BaseItem):
def __init__(self, data, basic, minio_client):
super().__init__(data, basic)
self.top_pipeline = [
LoadBodyImage(minio_client),
]
def process(self):
for item in self.top_pipeline:
self.result = item(self.result)
return self.result

0
app/service/design/design_batch/items/pipeline/__init__.py → app/service/design_batch/pipeline/__init__.py
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10
app/service/design/design_batch/items/pipeline/color.py → app/service/design_batch/pipeline/color.py
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@@ -3,12 +3,15 @@ import logging
import cv2
import numpy as np
from app.service.utils.oss_client import oss_get_image
from app.service.utils.new_oss_client import oss_get_image
logger = logging.getLogger()
class Color:
def __init__(self, minio_client):
self.minio_client = minio_client
def __call__(self, result):
dim_image_h, dim_image_w = result['image'].shape[0:2]
if "gradient" in result.keys() and result['gradient'] != "":
@@ -33,10 +36,9 @@ class Color:
result['alpha'] = 100 / 255.0
return result
@staticmethod
def get_gradient(bucket_name, object_name):
def get_gradient(self, bucket_name, object_name):
# 获取渐变色图案
image = oss_get_image(bucket=bucket_name, object_name=object_name, data_type="cv2")
image = oss_get_image(oss_client=self.minio_client, bucket=bucket_name, object_name=object_name, data_type="cv2")
if image.shape[2] == 4:
image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
return image

0
app/service/design/design_batch/items/pipeline/contour_detection.py → app/service/design_batch/pipeline/contour_detection.py
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2
app/service/design/design_batch/items/pipeline/keypoint.py → app/service/design_batch/pipeline/keypoint.py
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@@ -4,7 +4,7 @@ import numpy as np
from pymilvus import MilvusClient
from app.core.config import *
from app.service.design.utils.design_ensemble import get_keypoint_result
from app.service.design_batch.utils.design_ensemble import get_keypoint_result
logger = logging.getLogger(__name__)

24
app/service/design/design_batch/items/pipeline/loading.py → app/service/design_batch/pipeline/loading.py
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@@ -1,24 +1,37 @@
import cv2
import io
import logging
from app.service.utils.oss_client import oss_get_image
import cv2
import numpy as np
from PIL import Image
from app.service.utils.new_oss_client import oss_get_image
logger = logging.getLogger()
class LoadBodyImage:
name = "LoadBodyImage"
def __init__(self, minio_client):
self.minio_client = minio_client
@classmethod
def get_name(cls):
return cls.name
def __call__(self, result):
result["name"] = "mannequin"
result['body_image'] = oss_get_image(bucket=result['body_path'].split("/", 1)[0], object_name=result['body_path'].split("/", 1)[1], data_type="PIL")
result['body_image'] = oss_get_image(oss_client=self.minio_client, bucket=result['body_path'].split("/", 1)[0], object_name=result['body_path'].split("/", 1)[1], data_type="PIL")
return result
class LoadImage:
name = "LoadImage"
def __init__(self, minio_client):
self.minio_client = minio_client
@classmethod
def get_name(cls):
return cls.name
@@ -31,10 +44,9 @@ class LoadImage:
result['ori_shape'] = result['image'].shape
return result
@staticmethod
def read_image(image_path):
def read_image(self, image_path):
image_mask = None
image = oss_get_image(bucket=image_path.split("/", 1)[0], object_name=image_path.split("/", 1)[1], data_type="cv2")
image = oss_get_image(oss_client=self.minio_client, 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

15
app/service/design/design_batch/items/pipeline/print_painting.py → app/service/design_batch/pipeline/print_painting.py
View File

@@ -4,10 +4,13 @@ import cv2
import numpy as np
from PIL import Image
from app.service.utils.oss_client import oss_get_image
from app.service.utils.new_oss_client import oss_get_image
class PrintPainting:
def __init__(self, minio_client):
self.minio_client = minio_client
def __call__(self, result):
single_print = result['print']['single']
overall_print = result['print']['overall']
@@ -356,8 +359,7 @@ class PrintPainting:
print_image = cv2.add(img_bg, img_fg)
return print_image
@staticmethod
def get_print(print_dict):
def get_print(self, print_dict):
if 'print_scale_list' not in print_dict.keys() or print_dict['print_scale_list'][0] < 0.3:
print_dict['scale'] = 0.3
else:
@@ -365,7 +367,7 @@ class PrintPainting:
bucket_name = print_dict['print_path_list'][0].split("/", 1)[0]
object_name = print_dict['print_path_list'][0].split("/", 1)[1]
image = oss_get_image(bucket=bucket_name, object_name=object_name, data_type="PIL")
image = oss_get_image(oss_client=self.minio_client, bucket=bucket_name, object_name=object_name, data_type="PIL")
# 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))
@@ -480,9 +482,8 @@ class PrintPainting:
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")
def read_image(self, image_url):
image = oss_get_image(oss_client=self.minio_client, bucket=image_url.split("/", 1)[0], object_name=image_url.split("/", 1)[1], data_type="cv2")
if image.shape[2] == 4:
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
image = Image.fromarray(image_rgb)

0
app/service/design/design_batch/items/pipeline/scale.py → app/service/design_batch/pipeline/scale.py
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13
app/service/design/design_batch/items/pipeline/segmentation.py → app/service/design_batch/pipeline/segmentation.py
View File

@@ -5,16 +5,19 @@ import cv2
import numpy as np
from app.core.config import SEG_CACHE_PATH
from app.service.design.utils.design_ensemble import get_seg_result
from app.service.utils.oss_client import oss_get_image
from app.service.design_batch.utils.design_ensemble import get_seg_result
from app.service.utils.new_oss_client import oss_get_image
logger = logging.getLogger()
class Segmentation:
def __init__(self, minio_client):
self.minio_client = minio_client
def __call__(self, result):
if "seg_mask_url" in result.keys() and result['seg_mask_url'] != "":
seg_mask = oss_get_image(bucket=result['seg_mask_url'].split('/')[0], object_name=result['seg_mask_url'][result['seg_mask_url'].find('/') + 1:], data_type="cv2")
seg_mask = oss_get_image(oss_client=self.minio_client, bucket=result['seg_mask_url'].split('/')[0], object_name=result['seg_mask_url'][result['seg_mask_url'].find('/') + 1:], data_type="cv2")
seg_mask = cv2.resize(seg_mask, (result['img_shape'][1], result['img_shape'][0]), interpolation=cv2.INTER_NEAREST)
# 转换颜色空间为 RGB（OpenCV 默认是 BGR）
image_rgb = cv2.cvtColor(seg_mask, cv2.COLOR_BGR2RGB)
@@ -45,7 +48,7 @@ class Segmentation:
@staticmethod
def save_seg_result(seg_result, image_id):
file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
file_path = f"seg_cache/{image_id}.npy"
try:
np.save(file_path, seg_result)
logger.info(f"保存成功 ：{os.path.abspath(file_path)}")
@@ -54,7 +57,7 @@ class Segmentation:
@staticmethod
def load_seg_result(image_id):
file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
file_path = f"seg_cache/{image_id}.npy"
logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
try:
seg_result = np.load(file_path)

19
app/service/design/design_batch/items/pipeline/split.py → app/service/design_batch/pipeline/split.py
View File

@@ -7,13 +7,16 @@ from PIL import Image
from cv2 import cvtColor, COLOR_BGR2RGBA
from app.core.config import AIDA_CLOTHING
from app.service.design.utils.conversion_image import rgb_to_rgba
from app.service.design.utils.upload_image import upload_png_mask
from app.service.design_batch.utils.conversion_image import rgb_to_rgba
from app.service.design_batch.utils.upload_image import upload_png_mask
from app.service.utils.generate_uuid import generate_uuid
from app.service.utils.oss_client import oss_upload_image
from app.service.utils.new_oss_client import oss_upload_image
class Split(object):
def __init__(self, minio_client):
self.minio_client = minio_client
def __call__(self, result):
try:
@@ -27,7 +30,7 @@ class Split(object):
front_mask = cv2.resize(front_mask, new_size)
result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
result['front_image'], result["front_image_url"], _ = upload_png_mask(result_front_image_pil, f'{generate_uuid()}', mask=None)
result['front_image'], result["front_image_url"], _ = upload_png_mask(self.minio_client, result_front_image_pil, f'{generate_uuid()}', mask=None)
height, width = front_mask.shape
mask_image = np.zeros((height, width, 3))
@@ -38,7 +41,7 @@ class Split(object):
back_mask = cv2.resize(back_mask, new_size)
result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
result['back_image'], result["back_image_url"], _ = upload_png_mask(result_back_image_pil, f'{generate_uuid()}', mask=None)
result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None)
mask_image[back_mask != 0] = [0, 255, 0]
rbga_mask = rgb_to_rgba(mask_image, front_mask + back_mask)
@@ -47,7 +50,7 @@ class Split(object):
mask_pil.save(image_data, format='PNG')
image_data.seek(0)
image_bytes = image_data.read()
req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
result['mask_url'] = req.bucket_name + "/" + req.object_name
else:
rbga_mask = rgb_to_rgba(mask_image, front_mask)
@@ -56,7 +59,7 @@ class Split(object):
mask_pil.save(image_data, format='PNG')
image_data.seek(0)
image_bytes = image_data.read()
req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
result['mask_url'] = req.bucket_name + "/" + req.object_name
result['back_image'] = None
result["back_image_url"] = None
@@ -65,7 +68,7 @@ class Split(object):
# 创建中间图层
result_pattern_image_rgba = rgb_to_rgba(result['pattern_image'], result['mask'])
result_pattern_image_pil = Image.fromarray(cvtColor(result_pattern_image_rgba, COLOR_BGR2RGBA))
result['pattern_image'], result['pattern_image_url'], _ = upload_png_mask(result_pattern_image_pil, f'{generate_uuid()}')
result['pattern_image'], result['pattern_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_image_pil, f'{generate_uuid()}')
return result
except Exception as e:
logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}")

12
app/service/design_batch/service.py Normal file
View File

@@ -0,0 +1,12 @@
import json
import pika
from app.service.design_batch.design_batch_celery import batch_design
from app.service.design_batch.utils.MQ import publish_status
async def start_design_batch_generate(data, file):
generate_clothes_task = batch_design.delay(json.loads(file.decode())['objects'], data.total, data.tasks_id)
print(generate_clothes_task)
publish_status(data.tasks_id, "0/100", "")
return {"task_id": data.tasks_id}

162
app/service/design_batch/test.py Normal file
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@@ -0,0 +1,162 @@
from app.service.design_batch.design_batch_celery import batch_design
if __name__ == '__main__':
data = {
"objects": [
{
"basic": {
"body_point_test": {
"waistband_right": [
200,
241
],
"hand_point_right": [
223,
297
],
"waistband_left": [
112,
241
],
"hand_point_left": [
92,
305
],
"shoulder_left": [
99,
116
],
"shoulder_right": [
215,
116
]
},
"layer_order": True,
"scale_bag": 0.7,
"scale_earrings": 0.16,
"self_template": True,
"single_overall": "overall",
"switch_category": ""
},
"items": [
{
"businessId": 270372,
"color": "30 28 28",
"image_id": 69780,
"offset": [
0,
0
],
"path": "aida-sys-image/images/female/trousers/0825000630.jpg",
"print": {
"element": {
"element_angle_list": [],
"element_path_list": [],
"element_scale_list": [],
"location": []
},
"overall": {
"location": [],
"print_angle_list": [],
"print_path_list": [],
"print_scale_list": []
},
"single": {
"location": [],
"print_angle_list": [],
"print_path_list": [],
"print_scale_list": []
}
},
"priority": 10,
"resize_scale": [
1.0,
1.0
],
"type": "Trousers"
},
{
"businessId": 270373,
"color": "30 28 28",
"image_id": 98243,
"offset": [
0,
0
],
"path": "aida-sys-image/images/female/blouse/0902003811.jpg",
"print": {
"element": {
"element_angle_list": [],
"element_path_list": [],
"element_scale_list": [],
"location": []
},
"overall": {
"location": [],
"print_angle_list": [],
"print_path_list": [],
"print_scale_list": []
},
"single": {
"location": [],
"print_angle_list": [],
"print_path_list": [],
"print_scale_list": []
}
},
"priority": 11,
"resize_scale": [
1.0,
1.0
],
"type": "Blouse"
},
{
"businessId": 270374,
"color": "172 68 68",
"image_id": 98244,
"offset": [
0,
0
],
"path": "aida-sys-image/images/female/outwear/0825000410.jpg",
"print": {
"element": {
"element_angle_list": [],
"element_path_list": [],
"element_scale_list": [],
"location": []
},
"overall": {
"location": [],
"print_angle_list": [],
"print_path_list": [],
"print_scale_list": []
},
"single": {
"location": [],
"print_angle_list": [],
"print_path_list": [],
"print_scale_list": []
}
},
"priority": 12,
"resize_scale": [
1.0,
1.0
],
"type": "Outwear"
},
{
"body_path": "aida-sys-image/models/female/5bdfe7ca-64eb-44e4-b03d-8e517520c795.png",
"image_id": 96090,
"type": "Body"
}
]
}
],
"process_id": "83"
}
task_id = 1
json_name = "test.json"
batch_design.delay(data['objects'], task_id, json_name)

17
app/service/design_batch/utils/MQ.py Normal file
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@@ -0,0 +1,17 @@
import json
import pika
def publish_status(task_id, progress, result):
connection = pika.BlockingConnection(pika.ConnectionParameters('10.1.2.213'))
channel = connection.channel()
channel.queue_declare(queue='DesignBatch', durable=True)
message = {'task_id': task_id, 'progress': progress, "result": result}
channel.basic_publish(exchange='',
routing_key='DesignBatch',
body=json.dumps(message),
properties=pika.BasicProperties(
delivery_mode=2,
))
connection.close()

0
app/service/design/core/__init__.py → app/service/design_batch/utils/__init__.py
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0
app/service/design/design_batch/items/utils/conversion_image.py → app/service/design_batch/utils/conversion_image.py
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0
app/service/design/design_batch/items/utils/design_ensemble.py → app/service/design_batch/utils/design_ensemble.py
View File

77
app/service/design_batch/utils/organize.py Normal file
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@@ -0,0 +1,77 @@
import cv2
from app.core.config import PRIORITY_DICT
def organize_body(layer):
body_layer = dict(priority=0,
name=layer["name"].lower(),
image=layer['body_image'],
image_url=layer['body_path'],
mask_image=None,
mask_url=None,
sacle=1,
# mask=layer['body_mask'],
position=(0, 0))
return body_layer
def organize_clothing(layer):
# 起始坐标
start_point = calculate_start_point(layer['keypoint'], layer['scale'], layer['clothes_keypoint'], layer['body_point_test'], layer["offset"], layer["resize_scale"])
# 前片数据
front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_front', None),
name=f'{layer["name"].lower()}_front',
image=layer["front_image"],
# mask_image=layer['front_mask_image'],
image_url=layer['front_image_url'],
mask_url=layer['mask_url'],
sacle=layer['scale'],
clothes_keypoint=layer['clothes_keypoint'],
position=start_point,
resize_scale=layer["resize_scale"],
mask=cv2.resize(layer['mask'], layer["front_image"].size),
gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
pattern_image_url=layer['pattern_image_url'],
pattern_image=layer['pattern_image']
)
# 后片数据
back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None),
name=f'{layer["name"].lower()}_back',
image=layer["back_image"],
# mask_image=layer['back_mask_image'],
image_url=layer['back_image_url'],
mask_url=layer['mask_url'],
sacle=layer['scale'],
clothes_keypoint=layer['clothes_keypoint'],
position=start_point,
resize_scale=layer["resize_scale"],
mask=cv2.resize(layer['mask'], layer["front_image"].size),
gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
pattern_image_url=layer['pattern_image_url'],
)
return front_layer, back_layer
def calculate_start_point(keypoint_type, scale, clothes_point, body_point, offset, resize_scale):
"""
Align left
Args:
keypoint_type: string, "waistband" | "shoulder" | "ear_point"
scale: float
clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]}
body_point: dict, containing keypoint data of body figure
Returns:
start_point: tuple (x', y')
x' = y_body - y1 * scale + offset
y' = x_body - x1 * scale + offset
"""
side_indicator = f'{keypoint_type}_left'
start_point = (
int(body_point[side_indicator][1] + offset[1] - int(clothes_point[side_indicator][0]) * scale), # y
int(body_point[side_indicator][0] + offset[0] - int(clothes_point[side_indicator][1]) * scale) # x
)
return start_point

30
app/service/design_batch/utils/progress.py Normal file
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@@ -0,0 +1,30 @@
import logging
from app.service.design_fast.utils.redis_utils import Redis
logger = logging.getLogger(__name__)
def update_progress(process_id, total):
# logger.info(f"{process_id} , {total}")
r = Redis()
progress = r.read(key=process_id)
if progress and total != 1:
if int(progress) <= 100:
r.write(key=process_id, value=int(progress) + int(100 / total))
else:
r.write(key=process_id, value=99)
return progress
elif total == 1:
r.write(key=process_id, value=100)
return progress
else:
r.write(key=process_id, value=int(100 / total))
return progress
def final_progress(process_id):
r = Redis()
progress = r.read(key=process_id)
r.write(key=process_id, value=100)
return progress

0
app/service/design/design_batch/items/utils/redis_utils.py → app/service/design_batch/utils/redis_utils.py
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13
app/service/design_batch/utils/save_json.py Normal file
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@@ -0,0 +1,13 @@
import json
import logging
logger = logging.getLogger()
def oss_upload_json(oss_client, json_data, object_name):
try:
with open(f"app/service/design_batch/response_json/{object_name}", 'w') as file:
json.dump(json_data, file, indent=4)
oss_client.fput_object("test", object_name, f"app/service/design_batch/response_json/{object_name}")
except Exception as e:
logger.warning(str(e))

16
app/service/design/design_batch/items/utils/synthesis_item.py → app/service/design_batch/utils/synthesis_item.py
View File

@@ -179,3 +179,19 @@ def synthesis_single(front_image, back_image):
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}"
def update_base_size_priority(layers, size):
# 计算透明背景图片的宽度
min_x = min(info['position'][1] for info in layers)
x_list = []
for info in layers:
if info['image'] is not None:
x_list.append(info['position'][1] + info['image'].width)
max_x = max(x_list)
new_width = max_x - min_x
new_height = 700
# 更新坐标
for info in layers:
info['adaptive_position'] = (info['position'][0], info['position'][1] - min_x)
return layers, (new_width, new_height)

8
app/service/design/utils/upload_image.py → app/service/design_batch/utils/upload_image.py
View File

@@ -13,11 +13,11 @@ import logging
import cv2
from app.core.config import *
from app.service.utils.oss_client import oss_upload_image
from app.service.utils.new_oss_client import oss_upload_image
# @RunTime
def upload_png_mask(front_image, object_name, mask=None):
def upload_png_mask(minio_client, front_image, object_name, mask=None):
try:
mask_url = None
if mask is not None:
@@ -25,14 +25,14 @@ 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]
req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"mask/mask_{object_name}.png", image_bytes=cv2.imencode('.png', rgba_image)[1])
req = oss_upload_image(oss_client=minio_client, 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()
req = oss_upload_image(bucket=AIDA_CLOTHING, object_name=f"image/image_{object_name}.png", image_bytes=image_bytes)
req = oss_upload_image(oss_client=minio_client, 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:

2
app/service/design_fast/pipeline/keypoint.py
View File

@@ -4,7 +4,7 @@ import numpy as np
from pymilvus import MilvusClient
from app.core.config import *
from app.service.design.utils.design_ensemble import get_keypoint_result
from app.service.design_fast.utils.design_ensemble import get_keypoint_result
logger = logging.getLogger(__name__)

2
app/service/design_fast/pipeline/segmentation.py
View File

@@ -5,7 +5,7 @@ import cv2
import numpy as np
from app.core.config import SEG_CACHE_PATH
from app.service.design.utils.design_ensemble import get_seg_result
from app.service.design_fast.utils.design_ensemble import get_seg_result
from app.service.utils.new_oss_client import oss_get_image
logger = logging.getLogger()

2
app/service/design_fast/pipeline/split.py
View File

@@ -7,7 +7,7 @@ from PIL import Image
from cv2 import cvtColor, COLOR_BGR2RGBA
from app.core.config import AIDA_CLOTHING
from app.service.design.utils.conversion_image import rgb_to_rgba
from app.service.design_fast.utils.conversion_image import rgb_to_rgba
from app.service.design_fast.utils.upload_image import upload_png_mask
from app.service.utils.generate_uuid import generate_uuid
from app.service.utils.new_oss_client import oss_upload_image

2
app/service/design_pre_processing/service.py
View File

@@ -10,7 +10,7 @@ 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, get_seg_result
from app.service.design_fast.utils.design_ensemble import get_seg_result, get_keypoint_result
from app.service.utils.oss_client import oss_get_image, oss_upload_image
logger = logging.getLogger()

18
app/service/utils/oss_client.py
View File

@@ -1,8 +1,6 @@
import io
import logging
from io import BytesIO
import boto3
import cv2
import numpy as np
import urllib3
@@ -42,12 +40,8 @@ def oss_get_image(bucket, object_name, data_type):
# cv2 默认全通道读取
image_object = None
try:
if OSS == "minio":
oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE, http_client=http_client)
image_data = oss_client.get_object(bucket_name=bucket, object_name=object_name)
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)
image_data = oss_client.get_object(Bucket=bucket, Key=object_name)['Body']
oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE, http_client=http_client)
image_data = oss_client.get_object(bucket_name=bucket, object_name=object_name)
if data_type == "cv2":
image_bytes = image_data.read()
image_array = np.frombuffer(image_bytes, np.uint8) # 转成8位无符号整型
@@ -65,12 +59,8 @@ def oss_get_image(bucket, object_name, data_type):
def oss_upload_image(bucket, object_name, image_bytes):
req = None
try:
if OSS == "minio":
oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
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=bucket, Key=object_name, Body=io.BytesIO(image_bytes), ContentType='image/png')
oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
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')
except Exception as e:
logger.warning(f"{OSS} | 上传图片出现异常 ######: {e}")
return req