NFT images generator using Python Jupyter Notebook

Generate NFT images using python and Jupyter Notebook
Let develop a nft image generator which generates a series of unique images using a collection of layers.
  • Install Python
  • Install PIP Download PIP get-pip.py
  • curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
    
    python get-pip.py
  • Install Python Pillow
  • pip install pillow
  • Install Python display
  • pip install display
  • Install Jupyter Notebook
  • pip install jupyter
  • Set up developing folders similar to the following structure Alt Text Alt Text Alt TextAlt TextAlt TextAlt TextAlt Text
  • Shift + right click => choose PowerShell

  • Run Jupyter in your generator folder

  • jupyter notebook
  • Choose New => Python 3 to create a new notebook

  • Import necessary packages.

  • from PIL import Image 
    from IPython.display import display 
    import random
    import json
  • Inject all the shapes and set their weights
  • # Each image is made up a series of traits
    # The weightings for each trait drive the rarity and add up to 100%
    
    background = ["Blue", "Orange"] 
    background_weights = [30, 70]
    
    circle = ["Blue", "Orange"] 
    circle_weights = [30, 70]
    
    square = ["Blue","Orange"] 
    square_weights = [30, 70]
    
    # Dictionary variable for each trait. 
    # Eech trait corresponds to its file name
    # Add more shapes and colours as you wish
    
    background_files = {
        "Blue": "blue",
        "Orange": "orange",
    }
    
    square_files = {
        "Blue": "blue-square",
        "Orange": "orange-square",     
    }
    
    circle_files = {
        "Blue": "blue-circle",
        "Orange": "orange-circle", 
    }
  • Create a function to generate unique image combinations
  • TOTAL_IMAGES = 8 # Number of random unique images we want to generate ( 2 x 2 x 2 = 8)
    
    all_images = [] 
    
    def create_new_image():
    
        new_image = {} #
    
        # For each trait category, select a random trait based on the weightings 
        new_image ["Background"] = random.choices(background, background_weights)[0]
        new_image ["Circle"] = random.choices(circle, circle_weights)[0]
        new_image ["Square"] = random.choices(square, square_weights)[0]
    
        if new_image in all_images:
            return create_new_image()
        else:
            return new_image
    
    
    # Generate the unique combinations based on trait weightings
    for i in range(TOTAL_IMAGES): 
    
        new_trait_image = create_new_image()
    
        all_images.append(new_trait_image)
  • Return true if all images are unique
  • def all_images_unique(all_images):
        seen = list()
        return not any(i in seen or seen.append(i) for i in all_images)
    
    print("Are all images unique?", all_images_unique(all_images))
  • Add token Id to each image
  • i = 0
    for item in all_images:
        item["tokenId"] = i
        i = i + 1
  • Print all images
  • print(all_images)
  • Get traits count
  • background_count = {}
    for item in background:
        background_count[item] = 0
    
    circle_count = {}
    for item in circle:
        circle_count[item] = 0
    
    square_count = {}
    for item in square:
        square_count[item] = 0
    
    for image in all_images:
        background_count[image["Background"]] += 1
        circle_count[image["Circle"]] += 1
        square_count[image["Square"]] += 1
    
    print(background_count)
    print(circle_count)
    print(square_count)
  • Generate Metadata for all Traits
  • METADATA_FILE_NAME = './metadata/all-traits.json'; 
    with open(METADATA_FILE_NAME, 'w') as outfile:
        json.dump(all_images, outfile, indent=4)
  • Generate Images
  • for item in all_images:
    
        im1 = Image.open(f'./layers/backgrounds/{background_files[item["Background"]]}.jpg').convert('RGBA')
        im2 = Image.open(f'./layers/circles/{circle_files[item["Circle"]]}.png').convert('RGBA')
        im3 = Image.open(f'./layers/squares/{square_files[item["Square"]]}.png').convert('RGBA')
    
        #Create each composite
        com1 = Image.alpha_composite(im1, im2)
        com2 = Image.alpha_composite(com1, im3)
    
        #Convert to RGB
        rgb_im = com2.convert('RGB')
        file_name = str(item["tokenId"]) + ".png"
        rgb_im.save("./images/" + file_name)
  • Generate Metadata for each Image
  • f = open('./metadata/all-traits.json',) 
    data = json.load(f)
    
    IMAGES_BASE_URI = "ADD_IMAGES_BASE_URI_HERE"
    PROJECT_NAME = "ADD_PROJECT_NAME_HERE"
    
    def getAttribute(key, value):
        return {
            "trait_type": key,
            "value": value
        }
    for i in data:
        token_id = i['tokenId']
        token = {
            "image": IMAGES_BASE_URI + str(token_id) + '.png',
            "tokenId": token_id,
            "name": PROJECT_NAME + ' ' + str(token_id),
            "attributes": []
        }
        token["attributes"].append(getAttribute("Background", i["Background"]))
        token["attributes"].append(getAttribute("Circle", i["Circle"]))
        token["attributes"].append(getAttribute("Square", i["Square"]))
    
        with open('./metadata/' + str(token_id), 'w') as outfile:
            json.dump(token, outfile, indent=4)
    f.close()
  • It will output all the generated images to the /images folder, and the metadata to the /metadata folder. The filenames will refer to tokenIds.
  • This blog will show you how to upload your first nft to Opensea.
    My latest blog which shows you how to make an nft generator using JAVASCRIPT

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    NFT images generator using Python Jupyter Notebook