Image Captioning with BLIP

author: David Wang

Description

This operator generates the caption with BLIP which describes the content of the given image. This is an adaptation from salesforce/BLIP.

Code Example

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

Write a pipeline with explicit inputs/outputs name specifications:

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()

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

Interface

An image captioning operator takes a towhee image 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: 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: 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: 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: 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: 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.