transform-image-operator-template/README.md

# **Template: Image Preprocessing Operator**

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**