Generate image data using Dall-E

! pip install openai

# Imports
import openai
import os
import requests

# Notebook Imports
from IPython.display import Image
from IPython.display import display
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# You can set your API key and org as environment variables in your system like this:
# os.environ['OPENAI_API_KEY'] = 'api string'

# Set up OpenAI API key and organization
openai.api_key = os.getenv("OPENAI_API_KEY")

image_prompt = "A webcam image of a human 1m from the camera sitting at a desk showing that they are wearing gloves with their hands up to the camera."
# image_prompt = "A webcam image of a person 1m from the camera sitting at a desk with their bare hands up to the camera."
# image_prompt = "A webcam image of a human 1m from the camera sitting at a desk showing that they are wearing wool gloves with their hands up to the camera."

response = openai.Image.create(
    prompt=image_prompt,
    n=1,
    size="256x256",
)
Image(url=response["data"][0]["url"])

response2 = openai.Image.create_variation(
  image=requests.get(response['data'][0]['url']).content,
  n=3,
  size="256x256"
)
imgs = []
for img in response2['data']:
  imgs.append(Image(url=img['url']))

display(*imgs)

labels = [{"prompt": "A webcam image of a human 1m from the camera sitting at a desk showing that they are wearing wool gloves with their hands up to the camera.",
          "label": "gloves"},
          {"prompt": "A webcam image of a person 1m from the camera sitting at a desk with their bare hands up to the camera.",
          "label": "no-gloves"}
        ]
output_folder = "output"
base_images_number = 10
variation_per_image = 3
# Check if output directory for noisey files exists and create it if it doesn't
if not os.path.exists(output_folder):
    os.makedirs(output_folder)

for option in labels:
    for i in range(base_images_number):
        response = openai.Image.create(
            prompt=option["prompt"],
            n=1,
            size="256x256",
        )
        try:
            img = response["data"][0]["url"]
            with open(f'{output_folder}/{option["label"]}.{img.split("/")[-1]}.png', 'wb+') as f:
                f.write(requests.get(img).content)
            response2 = openai.Image.create_variation(
                image=requests.get(img).content,
                n=variation_per_image,
                size="256x256"
            )
        except Exception as e:
            print(e)
        for img in response2['data']:
            try:
                with open(f'{output_folder}/{option["label"]}.{img["url"].split("/")[-1]}.png', 'wb') as f:
                    f.write(requests.get(img["url"]).content)
            except Exception as e:
                print(e)

import os


# Define the folder containing the images
folder_path = './output'

# Get a list of all the image files in the folder
image_files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f)) and f.endswith('.png')]

# Set up the plot
fig, axs = plt.subplots(nrows=20, ncols=20, figsize=(10, 10))

# Loop through each image and plot it in a grid cell
for i in range(20):
    for j in range(20):
        img = mpimg.imread(os.path.join(folder_path, image_files[i*10+j]))
        axs[i,j].imshow(img)
        axs[i,j].axis('off')

# Make the plot look clean
fig.subplots_adjust(hspace=0, wspace=0)
plt.tight_layout()
plt.show()

! cd output
! edge-impulse-uploader .