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User
import gradio as gr
from image_dataset import ImageDataset
from image_wgan import ImageWgan
import os
from os.path import exists
from PIL import Image, ImageEnhance
import numpy
print(gr.__version__)
def init():
generated_samples_folder = "."
discriminator_saved_model = "discriminator64.model"
generator_saved_model = "generator64.model"
latent_space = 100
image_wgan = ImageWgan(
image_shape = (4,64,64),
latent_space_dimension=latent_space,
generator_saved_model=generator_saved_model if exists(generator_saved_model) else None,
discriminator_saved_model=discriminator_saved_model if exists(discriminator_saved_model) else None
)
image_wgan.generate(
sample_folder=generated_samples_folder
)
crop()
def crop():
import generator
res = 64
if res != 0:
results = "generated.png"
img = Image.open(results)
width,height = img.size
top = 2
bottom = 2
for i in range(4):
left = (res+2)*i +2
right = width-(res+2)*i
imgcrop = img.crop((left,top,left+res,res+2))
imgcrop.save(str(i)+".png")
init()
import numpy
def gen(seed):
numpy.random.seed(int(seed))
init()
crop()
imgArr = []
for i in range(4):
img = Image.open(str(i)+".png")
imgArr.append(img)
return imgArr
iface = gr.Interface(
fn=gen,
inputs=gr.Slider(0,1000,500,step = 0.01),
outputs=gr.Gallery(label="Generated Skins"),
title = "Minecraft Skin Generator",
css = "html{image-rendering:pixelated}",
debug = True,
)
iface.launch(width=64,height=64,enable_queue=True) |