Suck the cat with code! This paper is participating in[Cat Essay Campaign].
Batch generation unique NFT cat map, this project wang Duoyu will vote?
Zero, preface,
Our project is based on the NFT-image-Generator written by Benyamin Ahmed’s children. The teen made 2.5 million yuan in a summer holiday selling NFT whale memes, weird- Whales Images. In addition, he also open source the NFT picture generator, today we are based on his open source tool to generate cat emoticons.
First, set the weight of the composite parts
cat = ["01"."."."3"."04"."5"."6"."7"]
cat_weights = [10.40.15.5.10.10.10]
title = ["01"."."."3"."04"]
title_weights = [20.40.15.15]
Copy the codeWe have 7 and 4 cat pictures here. We combine these seven cats with four cats to create a unique image.
P.S. the weights add up to 100.
2. Name of the file corresponding to each picture
cat_files = {
"01": "cat1".".": "cat2"."3": "cat3"."04": "cat4"."5": "cat5"."6": "cat6"."7": "cat7"
}
title_files = {
"01": "title01".".": "title02"."3": "title03"."04": "title04"
}
Copy the codeWe are putting the file name in the folder into the dictionary.
Thirdly, the image combination based on weight is generated
TOTAL_IMAGES = 10 # Number of random unique images we want to generate
all_images = []
# A recursive function to generate unique image combinations
def create_new_image() :
new_image = {} #
# For each trait category, select a random trait based on the weightings
new_image ["Cat"] = random.choices(cat, cat_weights)[0]
new_image ["Title"] = random.choices(title, title_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)
Copy the codeThe TOTAL_IMAGES variable is the maximum number of images we want to generate.
Create_new_image is a recursion that randomly selects an image for each image category based on its weight.
Judge whether all the pictures are unique
def all_images_unique(all_images) :
seen = list(a)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))
Copy the codeHere we go through all the images to see if there are duplicate images.
5. Add Token Id to each image
# Add token Id to each image
i = 0
for item in all_images:
item["tokenId"] = i
i = i + 1
print(all_images)
Copy the codeAfter that, all_images will read:
[{'Cat': '05'.'Title': '01'.'tokenId': 0}, {'Cat': '02'.'Title': '04'.'tokenId': 1}, {'Cat': '01'.'Title': '03'.'tokenId': 2}, {'Cat': '01'.'Title': '02'.'tokenId': 3}, {'Cat': '02'.'Title': '03'.'tokenId': 4}, {'Cat': '07'.'Title': '02'.'tokenId': 5}, {'Cat': '02'.'Title': '02'.'tokenId': 6}, {'Cat': '07'.'Title': '01'.'tokenId': 7}, {'Cat': '03'.'Title': '02'.'tokenId': 8}, {'Cat': '05'.'Title': '02'.'tokenId': 9}]
Copy the codeContains pictures of tokenId and two types of cats.
Six, get the number of pictures used
# Get Trait Counts
title_count = {}
for item in title:
title_count[item] = 0
cat_count = {}
for item in cat:
cat_count[item] = 0
for image in all_images:
cat_count[image["Cat"]] + =1
title_count[image["Title"]] + =1
print(cat_count)
print(title_count)
Copy the codeUse a dictionary to store the number of times each image has been used.
7. Make up pictures
#### Generate Images
for item in all_images:
im1 = Image.open(f'./trait-layers/cat/{cat_files[item["Cat"]]}.png').convert('RGBA')
im2 = Image.open(f'./trait-layers/title/{title_files[item["Title"]]}.png').convert('RGBA')
im1 = im1.resize((400.400))
im2 = im2.resize((400.400))
#Create each composite
com = Image.alpha_composite(im1, im2)
#Convert to RGB
rgb_im = com.convert('RGB')
file_name = str(item["tokenId"]) + ".png"
rgb_im.save("./images/" + file_name)
Copy the codeThe idea here is simple, convert the Image to the same size, overlay it with image.alpha_composite, and then save the Image against tokenId.
