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Merge remote-tracking branch 'origin/develop' into develop

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xupei
2025-01-14 09:41:46 +08:00
parent edcd9b68cc 94000ceb21
commit 8b406cd10d
13 changed files with 208 additions and 29 deletions
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4
app/service/design/items/pipelines/segmentation.py
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@@ -53,7 +53,7 @@ class Segmentation(object):
file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
try:
np.save(file_path, seg_result)
logger.info(f"保存成功 ：{os.path.abspath(file_path)}")
logger.debug(f"保存成功 ：{os.path.abspath(file_path)}")
except Exception as e:
logger.error(f"保存失败: {e}")
@@ -64,7 +64,7 @@ class Segmentation(object):
seg_result = np.load(file_path)
return True, seg_result
except FileNotFoundError:
logger.warning("文件不存在")
# logger.warning("文件不存在")
return False, None
except Exception as e:
logger.error(f"加载失败: {e}")

6
app/service/design_batch/pipeline/segmentation.py
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@@ -51,19 +51,19 @@ class Segmentation:
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)}")
logger.debug(f"保存成功 ：{os.path.abspath(file_path)}")
except Exception as e:
logger.error(f"保存失败: {e}")
@staticmethod
def load_seg_result(image_id):
file_path = f"seg_cache/{image_id}.npy"
logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
# logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
try:
seg_result = np.load(file_path)
return True, seg_result
except FileNotFoundError:
logger.warning("文件不存在")
# logger.warning("文件不存在")
return False, None
except Exception as e:
logger.error(f"加载失败: {e}")

2
app/service/design_fast/design_generate.py
View File

@@ -207,7 +207,7 @@ def design_generate_v2(request_data):
'Connection': "keep-alive",
'Content-Type': "application/json"
}
logger.info(items_response)
# logger.info(items_response)
response = post_request(url, json_data=items_response, headers=headers)
if response:
# 打印结果

16
app/service/design_fast/pipeline/segmentation.py
View File

@@ -36,11 +36,11 @@ class Segmentation:
# preview 过模型 不缓存
if "preview_submit" in result.keys() and result['preview_submit'] == "preview":
# 推理获得seg 结果
seg_result = get_seg_result(result["image_id"], result['image'])[0]
seg_result = get_seg_result(result["image_id"], result['image'])
# submit 过模型 缓存
elif "preview_submit" in result.keys() and result['preview_submit'] == "submit":
# 推理获得seg 结果
seg_result = get_seg_result(result["image_id"], result['image'])[0]
seg_result = get_seg_result(result["image_id"], result['image'])
self.save_seg_result(seg_result, result['image_id'])
# null 正常流程 加载本地缓存 无缓存则过模型
else:
@@ -49,14 +49,14 @@ class Segmentation:
# 判断缓存和实际图片size是否相同
if not _ or result["image"].shape[:2] != seg_result.shape:
# 推理获得seg 结果
seg_result = get_seg_result(result["image_id"], result['image'])[0]
seg_result = get_seg_result(result["image_id"], result['image'])
self.save_seg_result(seg_result, result['image_id'])
result['seg_result'] = seg_result
# 处理前片后片
temp_front = seg_result == 1.0
temp_front = seg_result == 1
result['front_mask'] = (255 * (temp_front + 0).astype(np.uint8))
temp_back = seg_result == 2.0
temp_back = seg_result == 2
result['back_mask'] = (255 * (temp_back + 0).astype(np.uint8))
result['mask'] = result['front_mask'] + result['back_mask']
return result
@@ -66,19 +66,19 @@ class Segmentation:
file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
try:
np.save(file_path, seg_result)
logger.info(f"保存成功 ：{os.path.abspath(file_path)}")
logger.debug(f"保存成功 ：{os.path.abspath(file_path)}")
except Exception as e:
logger.error(f"保存失败: {e}")
@staticmethod
def load_seg_result(image_id):
file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
# logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
try:
seg_result = np.load(file_path)
return True, seg_result
except FileNotFoundError:
logger.warning("文件不存在")
# logger.warning("文件不存在")
return False, None
except Exception as e:
logger.error(f"加载失败: {e}")

12
app/service/design_fast/utils/design_ensemble.py
View File

@@ -13,7 +13,6 @@ import cv2
import mmcv
import numpy as np
import torch
import torch.nn.functional as F
import tritonclient.http as httpclient
from app.core.config import *
@@ -85,7 +84,10 @@ def seg_preprocess(img_path):
if ori_shape != (img_scale_w, img_scale_h):
# mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
img = cv2.resize(img, (img_scale_h, img_scale_w))
# img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
# 扩充25的白边
img = cv2.copyMakeBorder(img, 25, 25, 25, 25, cv2.BORDER_CONSTANT, value=[255, 255, 255])
img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
return preprocessed_img, ori_shape
@@ -114,9 +116,9 @@ def get_seg_result(image_id, image):
# no cache
def seg_postprocess(image_id, output, ori_shape):
seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
seg_pred = seg_logit.cpu().numpy()
return seg_pred[0]
seg_logit = cv2.resize(output[0][0].astype(np.uint8), (ori_shape[1] + 50, ori_shape[0] + 50))
seg_logit = seg_logit[25: - 25, 25: - 25]
return seg_logit
def key_point_show(image_path, key_point_result=None):

4
app/service/design_pre_processing/service.py
View File

@@ -266,7 +266,7 @@ class DesignPreprocessing:
seg_result = np.load(file_path)
return True, seg_result
except FileNotFoundError:
logging.info("文件不存在")
# logging.info("文件不存在")
return False, None
except Exception as e:
logging.warning(f"加载失败: {e}")
@@ -277,7 +277,7 @@ class DesignPreprocessing:
file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
try:
np.save(file_path, seg_result)
logging.info(f"保存成功,{os.path.abspath(file_path)}")
logging.debug(f"保存成功,{os.path.abspath(file_path)}")
except Exception as e:
logging.warning(f"保存失败: {e}")

126
app/service/generate_image/service_generate_multi_view.py Normal file
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@@ -0,0 +1,126 @@
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：trinity_client
@File ：service_att_recognition.py
@Author ：周成融
@Date ：2023/7/26 12:01:05
@detail ：
"""
import json
import logging
import time
import numpy as np
import redis
import tritonclient.grpc as grpcclient
from app.core.config import *
from app.schemas.generate_image import GenerateMultiViewModel
from app.service.generate_image.utils.upload_sd_image import upload_png_sd
from app.service.utils.oss_client import oss_get_image
logger = logging.getLogger()
class GenerateMultiView:
def __init__(self, request_data):
if DEBUG is False:
self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
self.channel = self.connection.channel()
# self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
# self.channel = self.connection.channel()
# self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
self.grpc_client = grpcclient.InferenceServerClient(url=GMV_MODEL_URL)
self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
self.image = self.get_image(request_data.image_url)
self.tasks_id = request_data.tasks_id
self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
self.generate_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''}
self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
self.redis_client.expire(self.tasks_id, 600)
def get_image(self, image_url):
try:
image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL")
return image
except Exception as e:
logger.error(e)
def callback(self, result, error):
if error:
self.generate_data['status'] = "FAILURE"
self.generate_data['message'] = str(error)
# self.generate_data['data'] = str(error)
self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
else:
# pil图像转成numpy数组
images = result.as_numpy("generated_image")
# for id, img in enumerate(images):
# cv2.imwrite(f"{id}.png", img)
# image_url = ""
image_url = upload_png_sd(images[6], user_id=self.user_id, category="multi_view", file_name=f"{self.tasks_id}.png")
# logger.info(f"upload image SUCCESS ： {image_url}")
self.generate_data['status'] = "SUCCESS"
self.generate_data['message'] = "success"
self.generate_data['image_url'] = str(image_url)
self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
def read_tasks_status(self):
status_data = self.redis_client.get(self.tasks_id)
return json.loads(status_data), status_data
def get_result(self):
try:
images = [np.array(self.image).astype(np.uint8)] * 1
image_obj = np.array(images, dtype=np.uint8)
input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
input_image.set_data_from_numpy(image_obj)
inputs = [input_image]
ctx = self.grpc_client.async_infer(model_name=GMV_MODEL_NAME, inputs=inputs, callback=self.callback)
time_out = 600
generate_data = None
while time_out > 0:
generate_data, _ = self.read_tasks_status()
if generate_data['status'] in ["REVOKED", "FAILURE"]:
ctx.cancel()
break
elif generate_data['status'] == "SUCCESS":
break
time_out -= 1
time.sleep(0.1)
return generate_data
except Exception as e:
self.generate_data['status'] = "FAILURE"
self.generate_data['message'] = str(e)
self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
raise Exception(str(e))
finally:
dict_generate_data, str_generate_data = self.read_tasks_status()
if DEBUG is False:
self.channel.basic_publish(exchange='', routing_key=GMV_RABBITMQ_QUEUES, body=str_generate_data)
# self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=str_generate_data)
logger.info(f" [x] Sent {json.dumps(dict_generate_data, indent=4)}")
def infer_cancel(tasks_id):
redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'}
generate_data = json.dumps(data)
redis_client.set(tasks_id, generate_data)
return data
if __name__ == '__main__':
rd = GenerateMultiViewModel(
tasks_id="123-89",
image_url="aida-sys-image/images/female/outwear/0628000123.jpg",
)
server = GenerateMultiView(rd)
print(server.get_result())

9
app/service/utils/decorator.py
View File

@@ -7,9 +7,9 @@ def RunTime(func):
t1 = time.time()
res = func(*args, **kwargs)
t2 = time.time()
# if t2 - t1 > 0.05:
# logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
if t2 - t1 > 0.05:
logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
# logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
return res
return wrapper
@@ -22,7 +22,8 @@ def ClassCallRunTime(func):
end_time = time.time()
execution_time = end_time - start_time
class_name = args[0].__class__.__name__ # 获取类名
print(f"class name: {class_name} , run time is : {execution_time} s")
if execution_time > 0.05:
logging.info(f"class name: {class_name} , run time is : {execution_time} s")
return result
return wrapper

2
app/service/utils/new_oss_client.py
View File

@@ -82,7 +82,7 @@ if __name__ == '__main__':
# url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg"
# 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/test/123-89.png"
url = "aida-users/89/123-89.png"
# url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png"
read_type = "2"