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render_video_from_dataset.py
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import argparse
import math
import glob
import numpy as np
import sys
import os
import torch
from torchvision.utils import save_image
from tqdm import tqdm
import curriculums as curriculums
import pandas as pd
from torch_ema import ExponentialMovingAverage
import datasets as datasets
import torchvision.transforms as transforms
import skvideo.io
import copy
from PIL import Image
from moviepy.editor import *
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def show(tensor_img):
if len(tensor_img.shape) > 3:
tensor_img = tensor_img.squeeze(0)
tensor_img = tensor_img.permute(1, 2, 0).squeeze().cpu().numpy()
plt.imshow(tensor_img)
plt.show()
def generate_img(gen, z, **kwargs):
with torch.no_grad():
img = generator.staged_forward(z, None, None, max_batch_size=opt.max_batch_size, **kwargs)[0].to(device)
tensor_img = img.detach()
img_min = img.min()
img_max = img.max()
img = (img - img_min)/(img_max-img_min)
img = img.permute(0, 2, 3, 1).squeeze().cpu().numpy()
return img, tensor_img
def generate_img_recon(gen, z, pos_z, **kwargs):
with torch.no_grad():
img = generator.staged_forward(z, pos_z[:, 0], pos_z[:, 1], mode='recon', **kwargs)[0].to(device)
tensor_img = img.detach()
img_min = img.min()
img_max = img.max()
img = (img - img_min)/(img_max-img_min)
img = img.permute(0, 2, 3, 1).squeeze().cpu().numpy()
return img, tensor_img
def tensor_to_PIL(img):
img = img.squeeze() * 0.5 + 0.5
return Image.fromarray(img.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy())
inv_normalize = transforms.Normalize(
mean=[-0.5/0.5],
std=[1/0.5]
)
# inv_tensor = inv_normalize(tensor)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, default='')
parser.add_argument('--num_obj', type=int, default=5000)
parser.add_argument('--output_dir', type=str, default='')
parser.add_argument('--max_batch_size', type=int, default=60000)
parser.add_argument('--lock_view_dependence', action='store_true')
parser.add_argument('--image_size', type=int, default=128)
parser.add_argument('--ray_steps', type=int, default=96)
parser.add_argument('--curriculum', type=str, default='celeba')
parser.add_argument('--dataset_path', type=str, default='')
parser.add_argument('--trajectory', type=str, default='front')
parser.add_argument('--num_frames', type=int, default=64)
opt = parser.parse_args()
curriculum = getattr(curriculums, opt.curriculum)
curriculum['num_steps'] = opt.ray_steps
curriculum['img_size'] = opt.image_size
curriculum['v_stddev'] = 0
curriculum['h_stddev'] = 0
curriculum['lock_view_dependence'] = opt.lock_view_dependence
curriculum['last_back'] = True
curriculum['nerf_noise'] = 0
curriculum = {key: value for key, value in curriculum.items() if type(key) is str}
os.makedirs(opt.output_dir, exist_ok=True)
checkpoint = torch.load(opt.path, map_location=torch.device(device))
generator = checkpoint['generator.pth'].to(device)
encoder = checkpoint['encoder.pth'].to(device)
ema = ExponentialMovingAverage(generator.parameters(), decay=0.999)
ema_encoder = ExponentialMovingAverage(encoder.parameters(), decay=0.999)
ema.load_state_dict(checkpoint['ema.pth'])
ema_encoder.load_state_dict(checkpoint['ema_encoder.pth'])
ema.copy_to(generator.parameters())
ema_encoder.copy_to(encoder.parameters())
generator.set_device(device)
generator.eval()
encoder.eval()
if opt.trajectory == 'front':
trajectory = []
for t in np.linspace(0, 1, opt.num_frames):
pitch = 0.2 * np.cos(t * 2 * math.pi) + math.pi/2
yaw = 0.4 * np.sin(t * 2 * math.pi) + math.pi/2
fov = 12
# fov = 12 + 5 + np.sin(t * 2 * math.pi) * 5
trajectory.append((pitch, yaw, fov))
elif opt.trajectory == 'orbit':
trajectory = []
for t in np.linspace(0, 1, opt.num_frames):
pitch = math.pi/4
yaw = t * 2 * math.pi
fov = curriculum['fov']
trajectory.append((pitch, yaw, fov))
dataloader, _ = datasets.get_dataset(opt.curriculum,
dataset_dir=opt.dataset_path,
batch_size=1,
img_size=64,
split='test'
)
dataloader_iterator = iter(dataloader)
for seed in tqdm(range(opt.num_obj)):
real_imgs, _ = next(dataloader_iterator)
real_imgs = real_imgs.to(device, non_blocking=True)
frames = []
z, pos = encoder(real_imgs.to(device), alpha=1.0)
output_name = f'{seed}.mp4'
this_output_dir = os.path.join(opt.output_dir, str(seed))
os.makedirs(this_output_dir, exist_ok=True)
writer = skvideo.io.FFmpegWriter(os.path.join(this_output_dir, output_name), outputdict={'-pix_fmt': 'yuv420p', '-crf': '21'})
for pitch, yaw, fov in tqdm(trajectory):
curriculum_copy = copy.deepcopy(curriculum)
curriculum_copy['h_mean'] = yaw
curriculum_copy['v_mean'] = pitch
curriculum_copy['fov'] = fov
curriculum_copy['h_stddev'] = 0
curriculum_copy['v_stddev'] = 0
frame = generator.staged_forward(z, None, None, max_batch_size=opt.max_batch_size, **curriculum_copy)[0].to(device)
frames.append(tensor_to_PIL(frame))
for frame in frames:
writer.writeFrame(np.array(frame))
writer.close()
real_imgs = inv_normalize(real_imgs)
save_image(real_imgs, os.path.join(this_output_dir, f'input.png'), normalize=False)
clip = (VideoFileClip(os.path.join(this_output_dir, output_name)))
gif_name = f'{seed}.gif'
clip.write_gif(os.path.join(this_output_dir, gif_name))