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  1. 35
      .gitattributes
  2. 209
      .gitignore
  3. 54
      README.md
  4. 13
      __init__.py
  5. 1
      config.py
  6. BIN
      readme_res/operator.png
  7. 0
      requirements.txt
  8. BIN
      test_data/test.jpg
  9. 29
      test_transform_image_operator_template.py
  10. 35
      transform_image_operator_template.py
  11. 13
      transform_image_operator_template.yaml

35
.gitattributes

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# .gitattributes
# Source files
# ============
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*.msgpack filter=lfs diff=lfs merge=lfs -text

209
.gitignore

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### Linux ###
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54
README.md

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# transform-image-operator-template
# **Template: Image Preprocessing Operator**
This is another test repo
Authors:
## **Overview**
> <font style="color: red">**Note:** this is just a **template**, not a runnable pipeline.</font>
This **transform image operator class** defines image embedding functions, as well as the standard inputs and outputs. You can complete the operator by filling in functions (`__init__` & `__call__`) in [transform_image_operator_template.py](http://./transform_image_operator_template.py) and update this README file. FYI, [transform-image](https://hub.towhee.io/towhee/transform-image) is based on this template.
This operator is used to transform the image, such as cropping, PIL.Image and Tensor conversion, normalization and other operations on the image.
## **Interface**
```python
__init__(self, size: int)
```
**Args:**
- size:
- The size of the output image
- supported types: `int`
```python
int__call__(self, img: Union[Image.Image, torch.Tensor, str])
```
**Args:**
- img:
- the input image to be processed
- supported type: `PIL.image`, `torch.Tensor` or `str` (path of the image)
**Returns:**
The Operator returns a tuple `Tuple[('img_transformed', torch.Tensor)]` containing following fields:
- img_transformed:
- the tensor of the transformed image
- data type: `torch.Tensor`
## **Requirements**
You can get the required python package by [requirements.txt](https://zilliverse.feishu.cn/docs/requirements.txt).
## **How it works**
The `towhee/transform-image-operator-template` Operator is used for image transformation and is an important part of data preprocessing. It can be added to the pipeline and is usually used as the first custom operator of the pipeline. For example, it's the first Operator named processing within [image-embedding-pipeline-template](https://hub.towhee.io/towhee/image-embedding-pipeline-template) pipeline, and it is the red box in the picture below.
![img](./readme_res/operator.png)
## **Reference**

13
__init__.py

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# Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

1
config.py

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TEST_IMG = './test_data/test.jpg'

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test_transform_image_operator_template.py

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import unittest
from PIL import Image
from torchvision import transforms
from transform_image_operator_template import TransformImageOperatorTemplate
from config import TEST_IMG, SIZE
class TestTransformImageOperatorTemplate(unittest.TestCase):
test_img = Image.open(TEST_IMG)
tfms = transforms.Compose(
[
transforms.Resize(SIZE),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
img1 = tfms(test_img).unsqueeze(0)
def test_transform_image(self):
op = TransformImageOperatorTemplate(SIZE)
outputs = op(self.test_img)
print("The output tyep of operator:", type(outputs.img_transformed))
c = (self.img1.numpy() == outputs.img_transformed.numpy())
self.assertEqual(c.all(), True)
if __name__ == '__main__':
unittest.main()

35
transform_image_operator_template.py

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# Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import NamedTuple, Union
from towhee.operator import Operator
class TransformImageOperatorTemplate(Operator):
"""
Transform an image (resize, crop, normalize, etc...)
Args:
size (`int`):
Image size to use. A resize to `size x size` followed by center crop and
image normalization will be done.
"""
def __init__(self, size: int) -> None:
super().__init__()
#user defined initialization
def __call__(self, img: Union[Image.Image, torch.Tensor, str]) -> NamedTuple('Outputs', [('img_transformed', torch.Tensor)]):
results = #user defined function: get the transformed image
Outputs = NamedTuple('Outputs', [('img_transformed', torch.Tensor)])
return Outputs(results)

13
transform_image_operator_template.yaml

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name: 'transform-image'
labels:
recommended_framework: #the recommend framework, such as 'pytorch1.2.0'
class: image-transform
others:
operator: 'towhee/transform-image-operator-class'
init:
size: int
call:
input:
img: Union[np.ndarray, Image.Image, torch.Tensor, str]
output:
img_transformed: torch.Tensor
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