Generating code for processing images#

In case chatGPT’s knowledge base contains solutions for tasks we ask for, such as image analysis tasks, it can create quite sophisticated image analysis scripts.

We define a helper function for making prompts first.

import openai
import matplotlib.pyplot as plt

def prompt(message:str, model="gpt-3.5-turbo"):
    """A prompt helper function that sends a message to openAI
    and returns only the text response.
    client = openai.OpenAI()
    response =
        messages=[{"role": "user", "content": message}]
    return response.choices[0].message.content

A simple task could be described like in the following. We explictly specify that this should execute from Jupyter to prevent windows popping up.

simple_question = """
Write Python code only and no additional explanatory text.

Write a python program, that 
* loads the file `../../data/blobs.tif`,
* labels objects in this image, and
* visualize results.

Assume this program would be executed in a Jupyter notebook.
It is not necessary to save the results. Show the results in Jupyter.

The generated code looks like this.

code = prompt(simple_question)
import matplotlib.pyplot as plt
from import imread
from skimage.feature import peak_local_max
from skimage.filters import threshold_otsu
from skimage.measure import label
from skimage.segmentation import mark_boundaries

# Load the image
image = imread('../../data/blobs.tif')

# Threshold the image
thresh = threshold_otsu(image)
binary = image > thresh

# Label the objects
labeled_image, num_labels = label(binary, connectivity=2, return_num=True)

# Find object centroids
centroids = peak_local_max(image, labels=labeled_image, min_distance=10)

# Visualize results
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(mark_boundaries(image, labeled_image, color=(1, 0, 0)))
ax.plot(centroids[:, 1], centroids[:, 0], 'b+', markersize=10)

This code needs a little cleaning, before we can execute it.

cleaner_code = code.replace("```python", "").replace("```", "")

We now excute the code. Note in more advanced scenarios there is a risk when executing generated code. We could delete files unintentionally for example.



  • Rerun the code above. Is the output the same? If not, why?

  • Modify the code above so that the output of the script is the number of blobs in the image.