image-embedding-resnet50/README.md

# Image Embedding Pipeline with Resnet50

Authors: derekdqc, shiyu22

## Overview

This pipeline is used to **extract the feature vector of a given image**.

The pipeline performs two major steps. First, normalize the image, and then use resnet50 model to generate the vector.

## Interface

**Pipeline Input Arguments:**

- img(`PIL.Image`):

  The image to be encoded. `PIL.Image` is the currently supported image type. 

**Pipeline Returns:**

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

  The pipeline returns a tuple.

  - The `feature_vector` field contains 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

This pipeline includes two main operators: transform image (implemented as [towhee/transform-image](https://hub.towhee.io/towhee/transform-image)) and image embedding (implemented as [towhee/resnet50-image-embedding](https://hub.towhee.io/towhee/resnet50-image-embedding)). The transform image operator will first convert the original image into a normalized format, such as with 512x512 resolutions. Then, the normalized image will be encoded via image embedding operator, and finally we get a feature vector of the given image. (The basic concepts of pipeline and operator are introduced in [Towhee architecture](https://github.com/towhee-io/towhee#towhee-architecture).)

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