clip-bbox 0.3.1

Python library for detecting image objects with natural language text labels

CLIP_BBox

CLIP_BBox is a Python library for detecting image objects with natural language text labels.

Overview / About

CLIP is a neural network, pretrained on image-text pairs, that can predict the most relevant text snippet for a given image.

Given an image and a natural language text label, CLIP_BBox will obtain the image's spatial embedding and text label's embedding from CLIP, compute the similarity heatmap between the embeddings, then draw bounding boxes around the image regions with the highest image-text correspondences.

Note

The files for building the CLIP model (clip.py, model.py, newpad.py, simple_tokenizer.py) are third-party code from the CLIP repo. They are not included in test coverage.

Features

The library provides functions for the following operations:

• Getting and appropriately reshaping an image's spatial embedding from the CLIP model before it performs attention-pooling
• Getting a text snippet's embedding from CLIP
• Computing the similarity heatmap between an image's spatial and text embeddings from CLIP
• Drawing bounding boxes on an image, given a similarity heatmap

Install

Use pip to install clip_bbox as a Python package:

$ pip install clip-bbox

Usage Examples

Command Line Script

usage: python -m clip_bbox [-h] imgpath caption outpath

positional arguments:
  imgpath     path to input image
  caption     caption of input image
  outpath     path to output image displaying bounding boxes

optional arguments:
  -h, --help  show this help message and exit

To draw bounding boxes on an image based on its caption, run

$ python -m clip_bbox "path/to/img.png" "caption of your image" "path/to/output_path.png"

Python Module

To draw bounding boxes on an image based on its caption, do the following:

from clip_bbox import run_clip_bbox

run_clip_bbox("path/to/img.png", "caption of your image", "path/to/output_path.png")

Example Output

Here is an example output image for the caption "a camera on a tripod":