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[to #43259593] refacor image preprocess

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Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9501913

    * [to #43259593] refacor image preprocess
This commit is contained in:
yingda.chen
2022-07-25 17:00:21 +08:00
parent badc91aafa
commit 590bc52f97
13 changed files with 56 additions and 116 deletions
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2
modelscope/models/multi_modal/clip/clip_model.py
View File

@@ -1,6 +1,6 @@
import os.path as osp
from typing import Any, Dict
import cv2
import json
import numpy as np
import torch

1
modelscope/pipelines/audio/ans_pipeline.py
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@@ -11,7 +11,6 @@ from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.utils.constant import Tasks
from modelscope.utils.torch_utils import create_device
def audio_norm(x):

1
modelscope/pipelines/base.py
View File

@@ -8,7 +8,6 @@ from typing import Any, Dict, Generator, List, Mapping, Union
import numpy as np
from modelscope.hub.snapshot_download import snapshot_download
from modelscope.models.base import Model
from modelscope.msdatasets import MsDataset
from modelscope.outputs import TASK_OUTPUTS

16
modelscope/pipelines/cv/animal_recog_pipeline.py
View File

@@ -13,7 +13,7 @@ from modelscope.models.cv.animal_recognition import resnet
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import load_image
from modelscope.preprocessors import LoadImage, load_image
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
@@ -79,19 +79,7 @@ class AnimalRecogPipeline(Pipeline):
logger.info('load model done')
def preprocess(self, input: Input) -> Dict[str, Any]:
if isinstance(input, str):
img = load_image(input)
elif isinstance(input, PIL.Image.Image):
img = input.convert('RGB')
elif isinstance(input, np.ndarray):
if len(input.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = input[:, :, ::-1]
img = Image.fromarray(img.astype('uint8')).convert('RGB')
else:
raise TypeError(f'input should be either str, PIL.Image,'
f' np.array, but got {type(input)}')
img = LoadImage.convert_to_img(input)
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
test_transforms = transforms.Compose([

15
modelscope/pipelines/cv/image_cartoon_pipeline.py
View File

@@ -3,7 +3,6 @@ from typing import Any, Dict
import cv2
import numpy as np
import PIL
import tensorflow as tf
from modelscope.metainfo import Pipelines
@@ -14,7 +13,7 @@ from modelscope.models.cv.cartoon.utils import get_f5p, padTo16x, resize_size
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import load_image
from modelscope.preprocessors import LoadImage
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
@@ -65,17 +64,7 @@ class ImageCartoonPipeline(Pipeline):
return sess
def preprocess(self, input: Input) -> Dict[str, Any]:
if isinstance(input, str):
img = np.array(load_image(input))
elif isinstance(input, PIL.Image.Image):
img = np.array(input.convert('RGB'))
elif isinstance(input, np.ndarray):
if len(input.shape) == 2:
input = cv2.cvtColor(input, cv2.COLOR_GRAY2BGR)
img = input[:, :, ::-1]
else:
raise TypeError(f'input should be either str, PIL.Image,'
f' np.array, but got {type(input)}')
img = LoadImage.convert_to_ndarray(input)
img = img.astype(np.float)
result = {'img': img}
return result

15
modelscope/pipelines/cv/image_color_enhance_pipeline.py
View File

@@ -13,7 +13,7 @@ from modelscope.models.cv.image_color_enhance.image_color_enhance import \
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input
from modelscope.preprocessors import (ImageColorEnhanceFinetunePreprocessor,
load_image)
LoadImage, load_image)
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.logger import get_logger
from ..base import Pipeline
@@ -47,18 +47,7 @@ class ImageColorEnhancePipeline(Pipeline):
self._device = torch.device('cpu')
def preprocess(self, input: Input) -> Dict[str, Any]:
if isinstance(input, str):
img = load_image(input)
elif isinstance(input, PIL.Image.Image):
img = input.convert('RGB')
elif isinstance(input, np.ndarray):
if len(input.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
img = Image.fromarray(img.astype('uint8')).convert('RGB')
else:
raise TypeError(f'input should be either str, PIL.Image,'
f' np.array, but got {type(input)}')
img = LoadImage.convert_to_img(input)
test_transforms = transforms.Compose([transforms.ToTensor()])
img = test_transforms(img)
result = {'src': img.unsqueeze(0).to(self._device)}

2
modelscope/pipelines/cv/image_colorization_pipeline.py
View File

@@ -88,7 +88,7 @@ class ImageColorizationPipeline(Pipeline):
img = input.convert('LA').convert('RGB')
elif isinstance(input, np.ndarray):
if len(input.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = cv2.cvtColor(input, cv2.COLOR_GRAY2BGR)
img = input[:, :, ::-1] # in rgb order
img = PIL.Image.fromarray(img).convert('LA').convert('RGB')
else:

15
modelscope/pipelines/cv/image_matting_pipeline.py
View File

@@ -3,13 +3,12 @@ from typing import Any, Dict
import cv2
import numpy as np
import PIL
from modelscope.metainfo import Pipelines
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import load_image
from modelscope.preprocessors import LoadImage
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.logger import get_logger
@@ -47,17 +46,7 @@ class ImageMattingPipeline(Pipeline):
logger.info('load model done')
def preprocess(self, input: Input) -> Dict[str, Any]:
if isinstance(input, str):
img = np.array(load_image(input))
elif isinstance(input, PIL.Image.Image):
img = np.array(input.convert('RGB'))
elif isinstance(input, np.ndarray):
if len(input.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = input[:, :, ::-1] # in rgb order
else:
raise TypeError(f'input should be either str, PIL.Image,'
f' np.array, but got {type(input)}')
img = LoadImage.convert_to_img(input)
img = img.astype(np.float)
result = {'img': img}
return result

15
modelscope/pipelines/cv/image_super_resolution_pipeline.py
View File

@@ -10,7 +10,7 @@ from modelscope.models.cv.super_resolution import rrdbnet_arch
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import load_image
from modelscope.preprocessors import LoadImage, load_image
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.logger import get_logger
@@ -46,18 +46,7 @@ class ImageSuperResolutionPipeline(Pipeline):
logger.info('load model done')
def preprocess(self, input: Input) -> Dict[str, Any]:
if isinstance(input, str):
img = np.array(load_image(input))
elif isinstance(input, PIL.Image.Image):
img = np.array(input.convert('RGB'))
elif isinstance(input, np.ndarray):
if len(input.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = input[:, :, ::-1] # in rgb order
else:
raise TypeError(f'input should be either str, PIL.Image,'
f' np.array, but got {type(input)}')
img = LoadImage.convert_to_ndarray(input)
img = torch.from_numpy(img).to(self.device).permute(
2, 0, 1).unsqueeze(0) / 255.
result = {'img': img}

16
modelscope/pipelines/cv/ocr_detection_pipeline.py
View File

@@ -3,14 +3,13 @@ from typing import Any, Dict
import cv2
import numpy as np
import PIL
import tensorflow as tf
from modelscope.metainfo import Pipelines
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import load_image
from modelscope.preprocessors import LoadImage
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.logger import get_logger
from .ocr_utils import model_resnet_mutex_v4_linewithchar, ops, utils
@@ -112,17 +111,8 @@ class OCRDetectionPipeline(Pipeline):
model_loader.restore(sess, model_path)
def preprocess(self, input: Input) -> Dict[str, Any]:
if isinstance(input, str):
img = np.array(load_image(input))
elif isinstance(input, PIL.Image.Image):
img = np.array(input.convert('RGB'))
elif isinstance(input, np.ndarray):
if len(input.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = input[:, :, ::-1] # in rgb order
else:
raise TypeError(f'input should be either str, PIL.Image,'
f' np.array, but got {type(input)}')
img = LoadImage.convert_to_ndarray(input)
h, w, c = img.shape
img_pad = np.zeros((max(h, w), max(h, w), 3), dtype=np.float32)
img_pad[:h, :w, :] = img

30
modelscope/pipelines/cv/style_transfer_pipeline.py
View File

@@ -3,13 +3,12 @@ from typing import Any, Dict
import cv2
import numpy as np
import PIL
from modelscope.metainfo import Pipelines
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import load_image
from modelscope.preprocessors import LoadImage
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.logger import get_logger
@@ -61,35 +60,12 @@ class StyleTransferPipeline(Pipeline):
return pipeline_parameters, {}, {}
def preprocess(self, content: Input, style: Input) -> Dict[str, Any]:
if isinstance(content, str):
content = np.array(load_image(content))
elif isinstance(content, PIL.Image.Image):
content = np.array(content.convert('RGB'))
elif isinstance(content, np.ndarray):
if len(content.shape) == 2:
content = cv2.cvtColor(content, cv2.COLOR_GRAY2BGR)
content = content[:, :, ::-1] # in rgb order
else:
raise TypeError(
f'modelscope error: content should be either str, PIL.Image,'
f' np.array, but got {type(content)}')
content = LoadImage.convert_to_ndarray(content)
if len(content.shape) == 2:
content = cv2.cvtColor(content, cv2.COLOR_GRAY2BGR)
content_img = content.astype(np.float)
if isinstance(style, str):
style_img = np.array(load_image(style))
elif isinstance(style, PIL.Image.Image):
style_img = np.array(style.convert('RGB'))
elif isinstance(style, np.ndarray):
if len(style.shape) == 2:
style_img = cv2.cvtColor(style, cv2.COLOR_GRAY2BGR)
style_img = style_img[:, :, ::-1] # in rgb order
else:
raise TypeError(
f'modelscope error: style should be either str, PIL.Image,'
f' np.array, but got {type(style)}')
style_img = LoadImage.convert_to_ndarray(style)
if len(style_img.shape) == 2:
style_img = cv2.cvtColor(style_img, cv2.COLOR_GRAY2BGR)
style_img = style_img.astype(np.float)

8
modelscope/pipelines/cv/virtual_tryon_pipeline.py
View File

@@ -1,21 +1,18 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
import os.path as osp
from abc import ABC, abstractmethod
from typing import Any, Dict, Generator, List, Union
from typing import Any, Dict
import cv2
import numpy as np
import PIL
import torch
from PIL import Image
from torchvision import transforms
from modelscope.hub.snapshot_download import snapshot_download
from modelscope.metainfo import Pipelines
from modelscope.models.cv.virual_tryon.sdafnet import SDAFNet_Tryon
from modelscope.outputs import TASK_OUTPUTS, OutputKeys
from modelscope.pipelines.util import is_model, is_official_hub_path
from modelscope.outputs import OutputKeys
from modelscope.preprocessors import load_image
from modelscope.utils.constant import ModelFile, Tasks
from ..base import Pipeline
@@ -67,6 +64,7 @@ class VirtualTryonPipeline(Pipeline):
load_pretrained(self.model, src_params)
self.model = self.model.eval()
self.size = 192
from torchvision import transforms
self.test_transforms = transforms.Compose([
transforms.Resize(self.size, interpolation=2),
transforms.ToTensor(),

36
modelscope/preprocessors/image.py
View File

@@ -2,7 +2,10 @@
import io
from typing import Any, Dict, Union
import torch
import cv2
import numpy as np
import PIL
from numpy import ndarray
from PIL import Image, ImageOps
from modelscope.fileio import File
@@ -60,6 +63,37 @@ class LoadImage:
repr_str = f'{self.__class__.__name__}(' f'mode={self.mode})'
return repr_str
@staticmethod
def convert_to_ndarray(input) -> ndarray:
if isinstance(input, str):
img = np.array(load_image(input))
elif isinstance(input, PIL.Image.Image):
img = np.array(input.convert('RGB'))
elif isinstance(input, np.ndarray):
if len(input.shape) == 2:
input = cv2.cvtColor(input, cv2.COLOR_GRAY2BGR)
img = input[:, :, ::-1]
else:
raise TypeError(f'input should be either str, PIL.Image,'
f' np.array, but got {type(input)}')
return img
@staticmethod
def convert_to_img(input) -> ndarray:
if isinstance(input, str):
img = load_image(input)
elif isinstance(input, PIL.Image.Image):
img = input.convert('RGB')
elif isinstance(input, np.ndarray):
if len(input.shape) == 2:
img = cv2.cvtColor(input, cv2.COLOR_GRAY2BGR)
img = input[:, :, ::-1]
img = Image.fromarray(img.astype('uint8')).convert('RGB')
else:
raise TypeError(f'input should be either str, PIL.Image,'
f' np.array, but got {type(input)}')
return img
def load_image(image_path_or_url: str) -> Image.Image:
""" simple interface to load an image from file or url