# Image Captioning with MAGIC

*author: David Wang*


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## Description

This operator generates the caption with [MAGIC](https://arxiv.org/abs/2205.02655) which describes the content of the given image. MAGIC is a simple yet efficient plug-and-play framework, which directly combines an off-the-shelf LM (i.e., GPT-2) and an image-text matching model (i.e., CLIP) for image-grounded text generation. During decoding, MAGIC influences the generation of the LM by introducing a CLIP-induced score, called magic score, which regularizes the generated result to be semantically related to a given image while being coherent to the previously generated context. This is an adaptation from [yxuansu / MAGIC](https://github.com/yxuansu/MAGIC).


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## Code Example

Load an image from path './image.jpg' to generate the caption. 

 *Write the pipeline in simplified style*:

```python
import towhee

towhee.glob('./image.jpg') \
      .image_decode() \
      .image_captioning.magic(model_name='expansionnet_rf') \
      .show()
```
<img src="./cap.png" alt="result1" style="height:20px;"/>

*Write a same pipeline with explicit inputs/outputs name specifications:*

```python
import towhee

towhee.glob['path']('./image.jpg') \
      .image_decode['path', 'img']() \
      .image_captioning.magic['img', 'text'](model_name='expansionnet_rf') \
      .select['img', 'text']() \
      .show()
```
<img src="./tabular.png" alt="result2" style="height:60px;"/>


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## Factory Constructor

Create the operator via the following factory method

***expansionnet_v2(model_name)***

**Parameters:**

​   ***model_name:*** *str*

​   The model name of MAGIC. Supported model names: 
- magic_mscoco    

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## Interface

An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) as input and generate the correspoing caption.


**Parameters:**

​	***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)* 

​  The image to generate embedding.	



**Returns:** *str*

​   The caption generated by model.