# 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 a pipeline with explicit inputs/outputs name specifications:*

```python
from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('url')
    .map('url', 'img', ops.image_decode.cv2_rgb())
    .map('img', 'text', ops.image_captioning.magic(model_name='magic_mscoco')) 
    .output('img', 'text')
)

DataCollection(p('./image.jpg')).show()
``` 
<img src="./tab.png" alt="result2" style="height:60px;"/>


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## Factory Constructor

Create the operator via the following factory method

***magic(model_name)***

**Parameters:**

​   ***model_name:*** *str*

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

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## Interface

An image captioning 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 caption.	

**Returns:** *str*

​   The caption generated by model.