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| import os | |
| import pickle | |
| import sys | |
| import subprocess | |
| import imageio | |
| import numpy as np | |
| import scipy.interpolate | |
| import torch | |
| from tqdm import tqdm | |
| import gradio as gr | |
| os.system("git clone https://github.com/NVlabs/stylegan3") | |
| sys.path.append("stylegan3") | |
| def layout_grid(img, grid_w=None, grid_h=1, float_to_uint8=True, chw_to_hwc=True, to_numpy=True): | |
| batch_size, channels, img_h, img_w = img.shape | |
| if grid_w is None: | |
| grid_w = batch_size // grid_h | |
| assert batch_size == grid_w * grid_h | |
| if float_to_uint8: | |
| img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8) | |
| img = img.reshape(grid_h, grid_w, channels, img_h, img_w) | |
| img = img.permute(2, 0, 3, 1, 4) | |
| img = img.reshape(channels, grid_h * img_h, grid_w * img_w) | |
| if chw_to_hwc: | |
| img = img.permute(1, 2, 0) | |
| if to_numpy: | |
| img = img.cpu().numpy() | |
| return img | |
| network_pkl='braingan-400.pkl' | |
| with open(network_pkl, 'rb') as f: | |
| G = pickle.load(f)['G_ema'] | |
| device = torch.device('cuda') | |
| G.eval() | |
| G.to(device) | |
| def predict(Seed,choices): | |
| shuffle_seed=None | |
| w_frames=60*4 | |
| kind='cubic' | |
| num_keyframes=None | |
| wraps=2 | |
| psi=1 | |
| if choices=='4x2': | |
| grid_w = 4 | |
| grid_h = 2 | |
| s1=Seed | |
| seeds=(np.arange(s1-8,s1)).tolist() | |
| if choices=='2x1': | |
| grid_w = 2 | |
| grid_h = 1 | |
| s1=Seed | |
| seeds=(np.arange(s1-2,s1)).tolist() | |
| mp4='ex.mp4' | |
| truncation_psi=1 | |
| num_keyframes=None | |
| if num_keyframes is None: | |
| if len(seeds) % (grid_w*grid_h) != 0: | |
| raise ValueError('Number of input seeds must be divisible by grid W*H') | |
| num_keyframes = len(seeds) // (grid_w*grid_h) | |
| all_seeds = np.zeros(num_keyframes*grid_h*grid_w, dtype=np.int64) | |
| for idx in range(num_keyframes*grid_h*grid_w): | |
| all_seeds[idx] = seeds[idx % len(seeds)] | |
| if shuffle_seed is not None: | |
| rng = np.random.RandomState(seed=shuffle_seed) | |
| rng.shuffle(all_seeds) | |
| zs = torch.from_numpy(np.stack([np.random.RandomState(seed).randn(G.z_dim) for seed in all_seeds])).to(device) | |
| ws = G.mapping(z=zs, c=None, truncation_psi=psi) | |
| _ = G.synthesis(ws[:1]) # warm up | |
| ws = ws.reshape(grid_h, grid_w, num_keyframes, *ws.shape[1:]) | |
| # Interpolation. | |
| grid = [] | |
| for yi in range(grid_h): | |
| row = [] | |
| for xi in range(grid_w): | |
| x = np.arange(-num_keyframes * wraps, num_keyframes * (wraps + 1)) | |
| y = np.tile(ws[yi][xi].cpu().numpy(), [wraps * 2 + 1, 1, 1]) | |
| interp = scipy.interpolate.interp1d(x, y, kind=kind, axis=0) | |
| row.append(interp) | |
| grid.append(row) | |
| # Render video. | |
| video_out = imageio.get_writer(mp4, mode='I', fps=60, codec='libx264') | |
| for frame_idx in tqdm(range(num_keyframes * w_frames)): | |
| imgs = [] | |
| for yi in range(grid_h): | |
| for xi in range(grid_w): | |
| interp = grid[yi][xi] | |
| w = torch.from_numpy(interp(frame_idx / w_frames)).to(device) | |
| img = G.synthesis(ws=w.unsqueeze(0), noise_mode='const')[0] | |
| imgs.append(img) | |
| video_out.append_data(layout_grid(torch.stack(imgs), grid_w=grid_w, grid_h=grid_h)) | |
| video_out.close() | |
| return 'ex.mp4' | |
| choices=['4x2','2x1'] | |
| interface=gr.Interface(fn=predict, title="Brain MR Image Generation with StyleGAN-2", | |
| description = "", | |
| article = "Author: S.Serdar Helli", | |
| inputs=[gr.inputs.Slider( minimum=16, maximum=2**10,label='Seed'),gr.inputs.Radio( choices=choices, default='4x2',label='Image Grid')], | |
| outputs=gr.outputs.Video(label='Video')) | |
| interface.launch(debug=True) |