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import argparse
import os
import cv2
import numpy as np
import torch
from model import Generator
from psp_encoder.psp_encoders import PSPEncoder
from utils import ten2cv, cv2ten
import glob
from tqdm import tqdm
import random
seed = 0
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def sigmoid(x, w=1):
return 1. / (1 + np.exp(-w * x))
def get_alphas(start=-5, end=5, step=0.5, len_tail=10):
return [0] + [sigmoid(alpha) for alpha in np.arange(start, end, step)] + [1] * len_tail
def slide(entries, margin=32):
"""Returns a sliding reference window.
Args:
entries: a list containing two reference images, x_prev and x_next,
both of which has a shape (1, 3, H, W)
Returns:
canvas: output slide of shape (num_frames, 3, H*2, W+margin)
"""
_, C, H, W = entries[0].shape
alphas = get_alphas()
T = len(alphas) # number of frames
canvas = - torch.ones((T, C, H*2, W + margin))
merged = torch.cat(entries, dim=2) # (1, 3, H*2, W)
for t, alpha in enumerate(alphas):
top = int(H * (1 - alpha)) # top, bottom for canvas
bottom = H * 2
m_top = 0 # top, bottom for merged
m_bottom = 2 * H - top
canvas[t, :, top:bottom, :W] = merged[:, :, m_top:m_bottom, :]
return canvas
def slide_one_window(entries, margin=32):
"""Returns a sliding reference window.
Args:
entries: a list containing two reference images, x_prev and x_next,
both of which has a shape (1, 3, H, W)
Returns:
canvas: output slide of shape (num_frames, 3, H, W+margin)
"""
_, C, H, W = entries[0].shape
device = entries[0].device
alphas = get_alphas()
T = len(alphas) # number of frames
canvas = - torch.ones((T, C, H, W + margin)).to(device)
merged = torch.cat(entries, dim=2) # (1, 3, H*2, W)
for t, alpha in enumerate(alphas):
m_top = int(H * alpha) # top, bottom for merged
m_bottom = m_top + H
canvas[t, :, :, :W] = merged[:, :, m_top:m_bottom, :]
return canvas
def tensor2ndarray255(images):
images = torch.clamp(images * 0.5 + 0.5, 0, 1)
return (images.cpu().numpy().transpose(0, 2, 3, 1) * 255).astype(np.uint8)
@torch.no_grad()
def interpolate(args, g, sample_in, sample_style_prev, sample_style_next):
''' returns T x C x H x W '''
frames_ten = []
alphas = get_alphas()
for alpha in alphas:
sample_style = torch.lerp(sample_style_prev, sample_style_next, alpha)
frame_ten, _ = g([sample_in], z_embed=sample_style, add_weight_index=args.add_weight_index,
input_is_latent=True, return_latents=False, randomize_noise=False)
frames_ten.append(frame_ten)
frames_ten = torch.cat(frames_ten)
return frames_ten
@torch.no_grad()
def video_ref(args, g, psp_encoder, img_in_ten, img_style_tens):
video = []
sample_in = psp_encoder(img_in_ten)
img_style_ten_prev, sample_style_prev = None, None
for idx in tqdm(range(len(img_style_tens))):
img_style_ten_next = img_style_tens[idx]
sample_style_next = g_ema.get_z_embed(img_style_ten_next)
if img_style_ten_prev is None:
img_style_ten_prev, sample_style_prev = img_style_ten_next, sample_style_next
continue
interpolated = interpolate(args, g, sample_in, sample_style_prev, sample_style_next)
entries = [img_style_ten_prev, img_style_ten_next]
slided = slide_one_window(entries, margin=0) # [T, C, H, W)
frames = torch.cat([img_in_ten.expand_as(interpolated), slided, interpolated], dim=3).cpu() # [T, C, H, W*3)
video.append(frames)
img_style_ten_prev, sample_style_prev = img_style_ten_next, sample_style_next
# append last frame 10 time
for _ in range(10):
video.append(frames[-1:])
video = tensor2ndarray255(torch.cat(video)) # [T, H, W*3, C)
return video
def save_video(fname, images, output_fps=30):
print('save video to: %s' % fname)
assert isinstance(images, np.ndarray), "images should be np.array: NHWC"
num_frames, height, width, channels = images.shape
fourcc = cv2.VideoWriter_fourcc(*'XVID')
videoWriter = cv2.VideoWriter(fname, fourcc, output_fps, (width, height))
for idx in tqdm(range(num_frames)):
frame = images[idx][:, :, ::-1] # [H, W*3, C)
videoWriter.write(frame)
videoWriter.release()
if __name__ == '__main__':
device = 'cuda'
parser = argparse.ArgumentParser()
parser.add_argument('--size', type=int, default=1024)
parser.add_argument('--ckpt', type=str, default='', help='path to BlendGAN checkpoint')
parser.add_argument('--psp_encoder_ckpt', type=str, default='', help='path to psp_encoder checkpoint')
parser.add_argument('--style_img_path', type=str, default=None, help='path to style image')
parser.add_argument('--input_img_path', type=str, default=None, help='path to input image')
parser.add_argument('--add_weight_index', type=int, default=7)
parser.add_argument('--channel_multiplier', type=int, default=2)
parser.add_argument('--outdir', type=str, default="")
args = parser.parse_args()
outdir = args.outdir
if not os.path.exists(outdir):
os.makedirs(outdir, exist_ok=True)
args.latent = 512
args.n_mlp = 8
checkpoint = torch.load(args.ckpt)
model_dict = checkpoint['g_ema']
print('ckpt: ', args.ckpt)
g_ema = Generator(
args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier
).to(device)
g_ema.load_state_dict(model_dict)
g_ema.eval()
psp_encoder = PSPEncoder(args.psp_encoder_ckpt, output_size=args.size).to(device)
psp_encoder.eval()
input_img_paths = sorted(glob.glob(os.path.join(args.input_img_path, '*.*')))
style_img_paths = sorted(glob.glob(os.path.join(args.style_img_path, '*.*')))[:]
for input_img_path in input_img_paths:
print('process: %s' % input_img_path)
name_in = os.path.splitext(os.path.basename(input_img_path))[0]
img_in = cv2.imread(input_img_path, 1)
img_in = cv2.resize(img_in, (args.size, args.size))
img_in_ten = cv2ten(img_in, device)
img_style_tens = []
style_img_path_rand = random.choices(style_img_paths, k=8)
for style_img_path in style_img_path_rand:
name_style = os.path.splitext(os.path.basename(style_img_path))[0]
img_style = cv2.imread(style_img_path, 1)
img_style = cv2.resize(img_style, (args.size, args.size))
img_style_ten = cv2ten(img_style, device)
img_style_tens.append(img_style_ten)
fname = f'{args.outdir}/{name_in}.mp4'
video = video_ref(args, g_ema, psp_encoder, img_in_ten, img_style_tens)
save_video(fname, video, output_fps=30)
print('Done!')
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