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fix:  替换项目中所有mmcv的依赖
This commit is contained in:
zcr
2026-02-10 11:17:31 +08:00
parent 23a6a30cc4
commit bafcb68028
16 changed files with 74 additions and 121 deletions
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6
.dockerignore
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@@ -1,2 +1,6 @@
seg_cache
test
test
.venv
__pycache__/
*.pyc
.git/

1
README.md
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@@ -20,7 +20,6 @@
$ conda activate trinity_client_aida
$ pip install -r requirements.txt
$ conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -y
$ pip install mmcv==1.4.2 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13/index.html
1. 启动服务器

5
app/service/attribute/service_att_recognition.py
View File

@@ -3,7 +3,6 @@
from pprint import pprint
import cv2
import mmcv
import numpy as np
import pandas as pd
import torch
@@ -12,6 +11,7 @@ from minio import Minio
from app.core.config import settings, DESIGN_MODEL_URL
from app.schemas.attribute_retrieve import AttributeRecognitionModel
from app.service.utils.image_normalize import my_imnormalize
from app.service.utils.new_oss_client import oss_get_image
minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
@@ -109,10 +109,9 @@ class AttributeRecognition:
@staticmethod
def preprocess(img):
img = mmcv.imread(img)
img_scale = (224, 224)
img = cv2.resize(img, img_scale)
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)
img = my_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

5
app/service/attribute/service_category_recognition.py
View File

@@ -10,7 +10,6 @@
from minio import Minio
from skimage import transform
import cv2
import mmcv
import numpy as np
import pandas as pd
import tritonclient.http as httpclient
@@ -18,6 +17,7 @@ import torch
from app.core.config import settings, DESIGN_MODEL_URL
from app.schemas.attribute_retrieve import CategoryRecognitionModel
from app.service.utils.image_normalize import my_imnormalize
from app.service.utils.new_oss_client import oss_get_image
minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
@@ -39,11 +39,10 @@ class CategoryRecognition:
@staticmethod
def preprocess(img):
img = mmcv.imread(img)
# ori_shape = img.shape[:2]
img_scale = (224, 224)
img = cv2.resize(img, img_scale)
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)
img = my_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

11
app/service/brand_dna/service.py
View File

@@ -1,7 +1,6 @@
import logging
import cv2
import mmcv
import numpy as np
import pandas as pd
import torch
@@ -14,6 +13,7 @@ from app.core.config import settings
from app.schemas.brand_dna import BrandDnaModel
from app.service.attribute.config import const
from app.service.utils.generate_uuid import generate_uuid
from app.service.utils.image_normalize import my_imnormalize
from app.service.utils.new_oss_client import oss_upload_image, oss_get_image
minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
@@ -202,7 +202,7 @@ class BrandDna:
# 服装分割预处理
@staticmethod
def seg_product_preprocess(image):
img = mmcv.imread(image)
img = image
ori_shape = img.shape[:2]
img_scale_w, img_scale_h = ori_shape
if ori_shape[0] > 1024:
@@ -211,9 +211,9 @@ class BrandDna:
img_scale_h = 1024
# 如果图片size任意一边 大于 1024， 则会resize 成1024
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
# my_imnormalize(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)
img = my_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
@@ -227,11 +227,10 @@ class BrandDna:
# 类别检测模型预处理
@staticmethod
def category_preprocess(img):
img = mmcv.imread(img)
# ori_shape = img.shape[:2]
img_scale = (224, 224)
img = cv2.resize(img, img_scale)
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)
img = my_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

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

@@ -10,13 +10,13 @@
import logging
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 DESIGN_MODEL_URL, DESIGN_MODEL_NAME
from app.service.utils.image_normalize import my_imnormalize
"""
keypoint
@@ -25,13 +25,13 @@ from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME
def keypoint_preprocess(img_path):
img = mmcv.imread(img_path)
img = img_path
img_scale = (256, 256)
h, w = img.shape[:2]
img = cv2.resize(img, img_scale)
w_scale = img_scale[0] / w
h_scale = img_scale[1] / h
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)
img = my_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, (w_scale, h_scale)
@@ -74,7 +74,7 @@ def keypoint_postprocess(output, scale_factor):
# KNet
def seg_preprocess(img_path):
img = mmcv.imread(img_path)
img = img_path
ori_shape = img.shape[:2]
img_scale_w, img_scale_h = ori_shape
if ori_shape[0] > 1024:
@@ -83,9 +83,9 @@ def seg_preprocess(img_path):
img_scale_h = 1024
# 如果图片size任意一边 大于 1024， 则会resize 成1024
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
# my_imnormalize(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)
img = my_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

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

@@ -10,12 +10,12 @@
import logging
import cv2
import mmcv
import numpy as np
import torch
import tritonclient.http as httpclient
from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME
from app.service.utils.image_normalize import my_imnormalize
"""
keypoint
@@ -24,14 +24,14 @@ from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME
def keypoint_preprocess(img_path):
img = mmcv.imread(img_path)
img = img_path
img = cv2.copyMakeBorder(img, 25, 25, 25, 25, cv2.BORDER_CONSTANT, value=[255, 255, 255])
img_scale = (256, 256)
h, w = img.shape[:2]
img = cv2.resize(img, img_scale)
w_scale = img_scale[0] / w
h_scale = img_scale[1] / h
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)
img = my_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, (w_scale, h_scale)
@@ -78,7 +78,7 @@ def keypoint_postprocess(output, scale_factor):
# KNet
def seg_preprocess(img_path):
img = mmcv.imread(img_path)
img = img_path
ori_shape = img.shape[:2]
img_scale_w, img_scale_h = ori_shape
if ori_shape[0] > 1024:
@@ -87,12 +87,12 @@ def seg_preprocess(img_path):
img_scale_h = 1024
# 如果图片size任意一边 大于 1024， 则会resize 成1024
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
# my_imnormalize(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))
# 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)
img = my_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

5
app/service/generate_image/service_agent_tool_generate_image.py
View File

@@ -11,7 +11,6 @@ import logging
import uuid
import cv2
import mmcv
import numpy as np
import pandas as pd
import torch
@@ -21,6 +20,7 @@ from minio import Minio
from tritonclient.utils import np_to_triton_dtype
from app.core.config import settings, FAST_GI_MODEL_URL, GI_MODEL_URL, DESIGN_MODEL_URL, FAST_GI_MODEL_NAME, GI_MODEL_NAME
from app.service.utils.image_normalize import my_imnormalize
from app.service.utils.new_oss_client import oss_upload_image
logger = logging.getLogger()
@@ -86,10 +86,9 @@ class AgentToolGenerateImage:
@staticmethod
def preprocess(img):
img = mmcv.imread(img)
img_scale = (224, 224)
img = cv2.resize(img, img_scale)
img = mmcv.imnormalize(
img = my_imnormalize(
img,
mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]),
to_rgb=True)

4
app/service/generate_image/service_generate_image.py
View File

@@ -189,10 +189,10 @@ if __name__ == '__main__':
tasks_id="123-89",
prompt="a single item of sketch of dress, 4k, white background",
image_url="aida-collection-element/89/Sketchboard/95f20cdc-e059-435c-b8b1-d04cc9e80c3d.png",
mode='img2img',
mode='txt2img',
category="sketch",
gender="Female",
version="fast"
version="hight"
)
server = GenerateImage(rd)
print(server.get_result())

9
app/service/generate_image/utils/image_processing.py
View File

@@ -2,23 +2,23 @@ import logging
import time
import cv2
import mmcv
import numpy as np
import torch
import tritonclient.http as httpclient
from app.core.config import settings, DESIGN_MODEL_URL, DESIGN_MODEL_NAME
from app.service.generate_image.utils.upload_sd_image import upload_stain_png_sd, upload_face_png_sd
from app.service.utils.image_normalize import my_imnormalize
logger = logging.getLogger()
def seg_preprocess(img_path):
img = mmcv.imread(img_path)
img = img_path
ori_shape = img.shape[:2]
img_scale = ori_shape
img = cv2.resize(img, img_scale)
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)
img = my_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
@@ -242,10 +242,9 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100):
def generate_category_recognition(image, gender):
def preprocess(img):
img = mmcv.imread(img)
img_scale = (224, 224)
img = cv2.resize(img, img_scale)
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)
img = my_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

8
app/service/lineart/service.py
View File

@@ -1,7 +1,6 @@
import logging
import cv2
import mmcv
import numpy as np
import torch
import torch.nn.functional as F
@@ -10,6 +9,7 @@ from minio import Minio
from app.core.config import settings
from app.core.config import DESIGN_MODEL_URL
from app.schemas.image2sketch import Image2SketchModel
from app.service.utils.image_normalize import my_imnormalize
from app.service.utils.new_oss_client import oss_get_image, oss_upload_image
logger = logging.getLogger()
@@ -67,7 +67,7 @@ class LineArtService:
@staticmethod
def line_art_preprocess(image):
img = mmcv.imread(image)
img = image
ori_shape = img.shape[:2]
img_scale_w, img_scale_h = ori_shape
if ori_shape[0] > 1024:
@@ -76,9 +76,9 @@ class LineArtService:
img_scale_h = 1024
# 如果图片size任意一边 大于 1024， 则会resize 成1024
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
# my_imnormalize(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)
img = my_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

27
app/service/utils/image_normalize.py Normal file
View File

@@ -0,0 +1,27 @@
import cv2
import np
def my_imnormalize(img, mean, std, to_rgb=True):
"""Inplace normalize an image with mean and std.
Args:
img (ndarray): Image to be normalized.
mean (ndarray): The mean to be used for normalize.
std (ndarray): The std to be used for normalize.
to_rgb (bool): Whether to convert to rgb.
Returns:
ndarray: The normalized image.
"""
# cv2 inplace normalization does not accept uint8
img = img.copy().astype(np.float32)
assert img.dtype != np.uint8
mean = np.float64(mean.reshape(1, -1))
stdinv = 1 / np.float64(std.reshape(1, -1))
if to_rgb:
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace
cv2.subtract(img, mean, img) # inplace
cv2.multiply(img, stdinv, img) # inplace
return img

2
pyproject.toml
View File

@@ -23,8 +23,8 @@ dependencies = [
"load-dotenv>=0.1.0",
"loguru>=0.7.3",
"minio>=7.2.20",
"mmcv>=2.2.0",
"moviepy==1.0.3",
"np>=1.0.2",
"numpy<2",
"ollama>=0.6.1",
"opencv-python>=4.11.0.86",

BIN
requirements.txt
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BIN
requirements_2.txt
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88
uv.lock generated
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@@ -8,15 +8,6 @@ resolution-markers = [
"(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')",
]
[[package]]
name = "addict"
version = "2.4.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/85/ef/fd7649da8af11d93979831e8f1f8097e85e82d5bfeabc8c68b39175d8e75/addict-2.4.0.tar.gz", hash = "sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494", size = 9186, upload-time = "2020-11-21T16:21:31.416Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl", hash = "sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc", size = 3832, upload-time = "2020-11-21T16:21:29.588Z" },
]
[[package]]
name = "agentaction"
version = "0.1.7"
@@ -1671,43 +1662,6 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/3e/9a/b697530a882588a84db616580f2ba5d1d515c815e11c30d219145afeec87/minio-7.2.20-py3-none-any.whl", hash = "sha256:eb33dd2fb80e04c3726a76b13241c6be3c4c46f8d81e1d58e757786f6501897e", size = 93751, upload-time = "2025-11-27T00:37:13.993Z" },
]
[[package]]
name = "mmcv"
version = "2.2.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "addict" },
{ name = "mmengine" },
{ name = "numpy" },
{ name = "opencv-python" },
{ name = "packaging" },
{ name = "pillow" },
{ name = "pyyaml" },
{ name = "regex", marker = "sys_platform == 'win32'" },
{ name = "yapf" },
]
sdist = { url = "https://files.pythonhosted.org/packages/e9/a2/57a733e7e84985a8a0e3101dfb8170fc9db92435c16afad253069ae3f9df/mmcv-2.2.0.tar.gz", hash = "sha256:ac479247e808d8802f89eadf04d4118de86bdfe81361ec5aed0cc1bf731c67c9", size = 479121, upload-time = "2024-04-24T14:24:28.064Z" }
[[package]]
name = "mmengine"
version = "0.10.7"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "addict" },
{ name = "matplotlib" },
{ name = "numpy" },
{ name = "opencv-python" },
{ name = "pyyaml" },
{ name = "regex", marker = "sys_platform == 'win32'" },
{ name = "rich" },
{ name = "termcolor" },
{ name = "yapf" },
]
sdist = { url = "https://files.pythonhosted.org/packages/17/14/959360bbd8374e23fc1b720906999add16a3ac071a501636db12c5861ff5/mmengine-0.10.7.tar.gz", hash = "sha256:d20ffcc31127567e53dceff132612a87f0081de06cbb7ab2bdb7439125a69225", size = 378090, upload-time = "2025-03-04T12:23:09.568Z" }
wheels = [
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]
[[package]]
name = "mmh3"
version = "5.2.0"
@@ -1801,6 +1755,12 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" },
]
[[package]]
name = "np"
version = "1.0.2"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/40/7d/749666e5a9976dcbc4d16d487bbe571efc6bbf4cdf3f4620c0ccc52b57ef/np-1.0.2.tar.gz", hash = "sha256:781265283f3823663ad8fb48741aae62abcf4c78bc19f908f8aa7c1d3eb132f8", size = 7419, upload-time = "2017-10-05T11:26:00.956Z" }
[[package]]
name = "numpy"
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