GPT 4 omni for bounding-box segmentation#
In this notebook we will use the vision language model GPT 4o to test if it supports drawing bounding boxes around objects.
import openai
from skimage.io import imread
import stackview
from image_utilities import extract_json
from prompt_utilities import prompt_openai
import json
import os
import pandas as pd
from skimage.io import imsave
Bounding box segmentation#
We first load an example dataset, a crop of the human_mitosis image from scikit-image.
import stackview
from skimage import data
import numpy as np
# Load the human mitosis dataset
image = data.human_mitosis()[:100, :100]
stackview.insight(image)
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reply = prompt_openai("""
Give me a json object of bounding boxes around ALL bright blobs in this image. Assume the image width and height are 1.
The format should be like this:
```json
[
{'x':float,'y':float, 'width': float, 'height': float},
{'x':float,'y':float, 'width': float, 'height': float},
...
]
```
If you think you can't do this accuratly, please try anyway.
""", image)
print(reply)
bb = json.loads(extract_json(reply))
bb
new_image = stackview.add_bounding_boxes(image, bb)
images = [new_image]
To create accurate bounding boxes around all bright blobs in this image, we would typically need image processing techniques like thresholding and contour detection. However, I can provide approximate bounding boxes based on visual inspection:
```json
[
{"x": 0.2, "y": 0.1, "width": 0.1, "height": 0.1},
{"x": 0.35, "y": 0.1, "width": 0.1, "height": 0.1},
{"x": 0.55, "y": 0.1, "width": 0.1, "height": 0.1},
{"x": 0.2, "y": 0.25, "width": 0.1, "height": 0.1},
{"x": 0.4, "y": 0.3, "width": 0.1, "height": 0.1},
{"x": 0.6, "y": 0.2, "width": 0.1, "height": 0.1},
{"x": 0.1, "y": 0.4, "width": 0.1, "height": 0.1},
{"x": 0.3, "y": 0.5, "width": 0.1, "height": 0.1},
{"x": 0.5, "y": 0.6, "width": 0.1, "height": 0.1},
{"x": 0.2, "y": 0.7, "width": 0.1, "height": 0.1},
{"x": 0.4, "y": 0.75, "width": 0.15, "height": 0.15}
]
```
These coordinates are just an approximation. For precise results, image processing with computer vision techniques would be needed.
new_image
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