# Image Captioning with BLIP

*author: David Wang*


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## Description

This operator generates the caption with [BLIP](https://arxiv.org/abs/2201.12086) which describes the content of the given image. This is an adaptation from [salesforce/BLIP](https://github.com/salesforce/BLIP).


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## Code Example

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

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

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

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

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


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## Factory Constructor

Create the operator via the following factory method

***blip(model_name)***

**Parameters:**

​   ***model_name:*** *str*

​   The model name of BLIP. Supported model names: 
- blip_base     

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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:**

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

​  The image to generate caption.	



**Returns:** *str*

​   The caption generated by model.


# More Resources

- [CLIP Object Detection: Merging AI Vision with Language Understanding - Zilliz blog](https://zilliz.com/learn/CLIP-object-detection-merge-AI-vision-with-language-understanding): CLIP Object Detection combines CLIP's text-image understanding with object detection tasks, allowing CLIP to locate and identify objects in images using texts.
- [Supercharged Semantic Similarity Search in Production - Zilliz blog](https://zilliz.com/learn/supercharged-semantic-similarity-search-in-production): Building a Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus, the most advanced open-source vector database.
- [The guide to clip-vit-base-patch32 | OpenAI](https://zilliz.com/ai-models/clip-vit-base-patch32): clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding.
- [An LLM Powered Text to Image Prompt Generation with Milvus - Zilliz blog](https://zilliz.com/blog/llm-powered-text-to-image-prompt-generation-with-milvus): An interesting LLM project powered by the Milvus vector database for generating more efficient text-to-image prompts.
- [From Text to Image: Fundamentals of CLIP - Zilliz blog](https://zilliz.com/blog/fundamentals-of-clip): Search algorithms rely on semantic similarity to retrieve the most relevant results. With the CLIP model, the semantics of texts and images can be connected in a high-dimensional vector space. Read this simple introduction to see how CLIP can help you build a powerful text-to-image service.