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# -*- coding: utf-8 -*-
"""
Created on Tue Apr 26 21:02:31 2022
@author: pc
"""
import pickle
import numpy as np
import torch
import gradio as gr
import sys
import subprocess
import os
from typing import Tuple
import PIL.Image
os.system("git clone https://github.com/NVlabs/stylegan3")
sys.path.append("stylegan3")
DESCRIPTION = f'''This model generates healthy MR Brain Images.
![Example]("https://huggingface.co/spaces/SerdarHelli/Brain-MR-Image-Generation-GAN/blob/main/ex.png")
'''
def make_transform(translate: Tuple[float,float], angle: float):
m = np.eye(3)
s = np.sin(angle/360.0*np.pi*2)
c = np.cos(angle/360.0*np.pi*2)
m[0][0] = c
m[0][1] = s
m[0][2] = translate[0]
m[1][0] = -s
m[1][1] = c
m[1][2] = translate[1]
return m
network_pkl='braingan-400.pkl'
with open(network_pkl, 'rb') as f:
G = pickle.load(f)['G_ema']
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
G.eval()
G.to(device)
def predict(Seed,noise_mode,truncation_psi,trans_x,trans_y,angle):
# Generate images.
z = torch.from_numpy(np.random.RandomState(Seed).randn(1, G.z_dim)).to(device)
label = torch.zeros([1, G.c_dim], device=device)
# Construct an inverse rotation/translation matrix and pass to the generator. The
# generator expects this matrix as an inverse to avoid potentially failing numerical
# operations in the network.
if hasattr(G.synthesis, 'input'):
m = make_transform((trans_x,trans_y), angle)
m = np.linalg.inv(m)
G.synthesis.input.transform.copy_(torch.from_numpy(m))
img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
return (PIL.Image.fromarray(img[0].cpu().numpy()[:,:,0])).resize((512,512))
noises=['const', 'random', 'none']
interface=gr.Interface(fn=predict, title="Brain MR Image Generation with StyleGAN-2",
description = DESCRIPTION,
article = "Author: S.Serdar Helli",
inputs=[gr.inputs.Slider( minimum=0, maximum=2**12,label='Seed'),gr.inputs.Radio( choices=noises, default='const',label='Noise Mods'),
gr.inputs.Slider(0, 2, step=0.05, default=1, label='Truncation psi'),
gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate X'),
gr.inputs.Slider(-1, 1, step=0.05, default=0, label='Translate Y'),
gr.inputs.Slider(-180, 180, step=5, default=0, label='Angle'),],
outputs=gr.outputs.Image( type="numpy", label="Output"))
interface.launch(debug=True) |