Vision Large Language Models for Counting objects

Vision Large Language Models for Counting objects#

In this notebook we use OpenAI’s LLMs with Vision capabilities to see how well they can count blobs in blobs.tif.

Note: It is not recommended to use this approach for counting objects in microscopy images. The author of this notebook is not aware of any publication showing that this approach works well.

import openai
import PIL
import stackview
from skimage.io import imread
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

We will need some helper functions for assembling a prompt and submitting it to the openai server.

def prompt_with_image(message:str, image, model="gpt-4o-2024-05-13"):
    """A prompt helper function that sends a text message and an image
    to openAI and returns the text response.
    """
    import os
    
    # convert message in the right format if necessary
    if isinstance(message, str):
        message = [{"role": "user", "content": message}]
    
    image_message = image_to_message(image)
        
    # setup connection to the LLM
    client = openai.OpenAI()
    
    # submit prompt
    response = client.chat.completions.create(
        model=model,
        messages=message + image_message
    )
    
    # extract answer
    return response.choices[0].message.content


def image_to_message(image):
    import base64

    from stackview._image_widget import _img_to_rgb

    rgb_image = _img_to_rgb(image)
    byte_stream = numpy_to_bytestream(rgb_image)
    base64_image = base64.b64encode(byte_stream).decode('utf-8')

    return [{"role": "user", "content": [{
        "type": "image_url",
        "image_url": {
            "url": f"data:image/jpeg;base64,{base64_image}"
        }

    }]}]


def numpy_to_bytestream(data):
    """Turn a NumPy array into a bytestream"""
    import numpy as np
    from PIL import Image
    import io

    # Convert the NumPy array to a PIL Image
    image = Image.fromarray(data.astype(np.uint8)).convert("RGBA")

    # Create a BytesIO object
    bytes_io = io.BytesIO()

    # Save the PIL image to the BytesIO object as a PNG
    image.save(bytes_io, format='PNG')

    # return the beginning of the file as a bytestream
    bytes_io.seek(0)
    return bytes_io.read()

This is the example image we will be using.

image = imread("../../data/blobs.tif")
stackview.insight(image)
shape(254, 256)
dtypeuint8
size63.5 kB
min8
max248

This is the prompt we submit to the server.

my_prompt = """
Analyse the following image by counting the bright blobs. Respond with the number only.
"""

prompt_with_image(my_prompt, image)

'64'

Benchmarking vision-LLMs#

We can run this prompt in a loop for a couple of vision models.

num_samples = 25

models = {
    "gpt-4-vision-preview":[],
    "gpt-4-turbo-2024-04-09":[],    
    "gpt-4o-2024-05-13":[],
}
for model in models.keys():
    samples = []

    while len(samples) < num_samples:
        result = prompt_with_image(my_prompt, image)

        try:
            samples.append(int(result))
        except:
            print("Error processing result:", result)
    
    models[model] = samples

sampled_models = pd.DataFrame(models)

Let’s get an overview about samples:

# Extract the two columns for comparison
columns_to_plot = sampled_models[models.keys()]

# Melt the dataframe to prepare for plotting
df_melted = columns_to_plot.melt(var_name='Model', value_name='Blob count')

# Draw the violin plot
plt.figure(figsize=(8, 4))
sns.violinplot(x='Model', y='Blob count', data=df_melted)
plt.title('Vision models counting blobs')
plt.show()
../_images/b067770f05204286cbed4728569a7f49df266db02a9785042919e0746ab7ff1c.png

These are the results in detail:

sampled_models
gpt-4-vision-preview gpt-4-turbo-2024-04-09 gpt-4o-2024-05-13
0 56 56 58
1 52 52 54
2 53 54 69
3 48 59 50
4 62 51 63
5 58 54 55
6 56 55 56
7 69 58 57
8 53 60 50
9 50 78 51
10 63 52 54
11 120 56 65
12 56 64 55
13 61 57 57
14 52 56 46
15 64 52 54
16 74 53 63
17 51 57 52
18 52 49 63
19 52 72 51
20 48 47 51
21 52 54 50
22 67 50 58
23 52 56 48
24 65 54 54 
sampled_models.describe()
gpt-4-vision-preview gpt-4-turbo-2024-04-09 gpt-4o-2024-05-13
count 25.000000 25.000000 25.000000
mean 59.440000 56.240000 55.360000
std 14.399306 6.765599 5.692685
min 48.000000 47.000000 46.000000
25% 52.000000 52.000000 51.000000
50% 56.000000 55.000000 54.000000
75% 63.000000 57.000000 58.000000
max 120.000000 78.000000 69.000000
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