Generating Magnetic Resonance images using DALL-E#

In this notebook we will demonstrate how to ask openAI’s DALL-E model to generate medial images (for fun).

Read more:

import openai
from import imread, imshow
from numpy import random
from matplotlib import pyplot as plt

def prompt_image(message:str, width:int=512, height:int=512):
    response = openai.Image.create(
    image_url = response['data'][0]['url']
    image = imread(image_url)
    return image

As a real example of an orange slice imaged with MR, we use the example dataset “Credit: Mandarin orange, axial view, MRI.” is licensed (CC-BY 4.0) by Alexandr Khrapichev, University of Oxford

images = [imread('../../data/mri_fruit_sxm89b3x.jpg')[3063:4087:4,1024:2048:4,0]]
mri_prompt = """
A single, high resolution, black-white image of 
a realistically looking orange fruit slice 
imaged with T2-weighted magnetic resonance imaging (MRI).
for _ in range(3):
    images.append(prompt_image(mri_prompt, width=256, height=256)[::2,::2])
fix, ax = plt.subplots(1,len(images), figsize=(15,15))
for i, image in enumerate(images):
    ax[i].imshow(image, cmap='Greys_r')


There is another example data set available. Crop out the star fruit from that image and repeat the experiment: Write a prompt that generates images looking similar.

The example dataset “Collage of mixed fruits and vegetables, MRI.” is licensed (CC-BY 4.0) by Alexandr Khrapichev, University of Oxford

image2 = imread('../../data/mri_fruit_bvtnk4mm.jpg')
<matplotlib.image.AxesImage at 0x1e55334b070>