image-embedding-resnet50/README.md

# Image Embedding Pipeline with Resnet50

Authors: derekdqc, shiyu22

## Overview

This pipeline is used to **extract the feature vector of the image**. First step is to normalize the image, and then use resnet50 model to generate the vector.

## Interface

**Args:**

- img_tensor(`PIL.Image`), 

  The image to be encoded.

**Returns:**

- (`Tuple[('cnn', numpy.ndarray)]`)

  The embedding of the image.

## How to use

1. Install [Towhee](https://github.com/towhee-io/towhee)

```bash
$ pip3 install towhee
```

> You can refer to [Getting Started with Towhee](https://towhee.io/) for more details. If you have any questions, you can [submit an issue to the towhee repository](https://github.com/towhee-io/towhee/issues).

2. Run it with Towhee

```python
>>> from towhee import pipeline
>>> from PIL import Image

>>> img = Image.open('path/to/your/image') # for example, './test_data/test.jpg'
>>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50')
>>> embedding = embedding_pipeline(img)
```

## How it works

You can learn the pipeline and operator in [Towhee architecture](https://github.com/towhee-io/towhee#towhee-architecture). This pipeline includes two main functions: [towhee/transform-image](https://hub.towhee.io/towhee/transform-image) and [towhee/resnet50-image-embedding](https://hub.towhee.io/towhee/resnet50-image-embedding). It is necessary to ensure that the input and output of the four Operators correspond to each other, and the input and output data types can be defined by DataFrame.

![img](./readme_res/pipeline.png)