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import gradio as gr | |
from image_dataset import ImageDataset | |
from image_wgan import ImageWgan | |
import os | |
from os.path import exists | |
def init(): | |
generated_samples_folder = "results64" | |
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 | |
) | |
init() | |
def crop(): | |
from PIL import Image | |
import generator | |
res = 64 | |
if res != 0: | |
results = "results64/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("results64/"+str(i)+".png") | |
crop() | |
def greet(name): | |
return "Hello " + name + "!!" | |
iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
iface.launch() |