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animate.py
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animate.py
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import os
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
from frames_dataset import PairedDataset
from logger import Logger, Visualizer
import imageio
from scipy.spatial import ConvexHull
import numpy as np
def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,
use_relative_movement=False, use_relative_jacobian=False):
if adapt_movement_scale:
source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume
driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume
adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
else:
adapt_movement_scale = 1
kp_new = {k: v for k, v in kp_driving.items()}
if use_relative_movement:
kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])
kp_value_diff *= adapt_movement_scale
kp_new['value'] = kp_value_diff + kp_source['value']
if use_relative_jacobian:
jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))
kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])
return kp_new
def animate(config, generator, kp_detector, tdmm, checkpoint, log_dir, dataset, with_eye):
def batch_orth_proj(X, camera):
camera = camera.clone().view(-1, 1, 3)
X_trans = X[:, :, :2] + camera[:, :, 1:]
X_trans = torch.cat([X_trans, X[:,:,2:]], 2)
shape = X_trans.shape
Xn = (camera[:, :, 0:1] * X_trans)
return Xn
log_dir = os.path.join(log_dir, 'animation')
png_dir = os.path.join(log_dir, 'png')
animate_params = config['animate_params']
dataset = PairedDataset(initial_dataset=dataset, number_of_pairs=animate_params['num_pairs'])
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
if checkpoint is not None:
Logger.load_cpk(checkpoint, generator=generator, kp_detector=kp_detector, tdmm=tdmm)
else:
raise AttributeError("Checkpoint should be specified for mode='animate'.")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(png_dir):
os.makedirs(png_dir)
if torch.cuda.is_available():
generator = generator.cuda()
kp_detector = kp_detector.cuda()
tdmm = tdmm.cuda()
generator.eval()
kp_detector.eval()
tdmm.eval()
for it, x in tqdm(enumerate(dataloader)):
with torch.no_grad():
predictions = []
visualizations = []
driving_video = x['driving_video']
source_frame = x['source_video'][:, :, 0, :, :]
driving_name = x['driving_name'][0]
source_name = x['source_name'][0]
if torch.cuda.is_available():
driving_video = driving_video.cuda()
source_frame = source_frame.cuda()
kp_source = kp_detector(source_frame)
kp_driving_initial = kp_detector(driving_video[:, :, 0])
source_codedict = tdmm.encode(source_frame)
source_verts, source_transformed_verts, _ = tdmm.decode_flame(source_codedict)
source_albedo = tdmm.extract_texture(source_frame, source_transformed_verts, with_eye=with_eye)
driving_init_codedict = tdmm.encode(driving_video[:, :, 0])
driving_init_verts, driving_init_transformed_verts, _ = tdmm.decode_flame(driving_init_codedict)
for frame_idx in range(driving_video.shape[2]):
driving_frame = driving_video[:, :, frame_idx]
kp_driving = kp_detector(driving_frame)
driving_codedict = tdmm.encode(driving_frame)
# calculate relative 3D motion in the code space
if relative:
delta_shape = source_codedict['shape'] + driving_codedict['shape'] - driving_init_codedict['shape']
delta_exp = source_codedict['exp'] + driving_codedict['exp'] - driving_init_codedict['exp']
delta_pose = source_codedict['pose'] + driving_codedict['pose'] - driving_init_codedict['pose']
else:
delta_shape = source_codedict['shape']
delta_exp = driving_codedict['exp']
delta_pose = driving_codedict['pose']
delta_source_verts, _, _ = tdmm.flame(shape_params=delta_shape,
expression_params=delta_exp,
pose_params=delta_pose)
if relative:
delta_scale = source_codedict['cam'][:, 0:1] * driving_codedict['cam'][:, 0:1] / driving_init_codedict['cam'][:, 0:1]
delta_trans = source_codedict['cam'][:, 1:] + driving_codedict['cam'][:, 1:] - driving_init_codedict['cam'][:, 1:]
else:
delta_scale = driving_codedict['cam'][:, 0:1]
delta_trans = driving_codedict['cam'][:, 1:]
delta_cam = torch.cat([delta_scale, delta_trans], dim=1)
delta_source_transformed_verts = batch_orth_proj(delta_source_verts, delta_cam)
delta_source_transformed_verts[:, :, 1:] = - delta_source_transformed_verts[:, :, 1:]
render_ops = tdmm.render(source_transformed_verts, delta_source_transformed_verts, source_albedo)
kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving,
kp_driving_initial=kp_driving_initial, **animate_params['normalization_params'])
out = generator(source_frame, kp_source=kp_source, kp_driving=kp_norm, render_ops=render_ops,
driving_features=driving_codedict)
out['kp_driving'] = kp_driving
out['kp_source'] = kp_source
out['kp_norm'] = kp_norm
del out['sparse_deformed']
predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
visualization = Visualizer(**config['visualizer_params']).visualize(source=source_frame,
driving=driving_frame, out=out)
visualizations.append(visualization)
result_name = "-".join([x['driving_name'][0].split('.')[0], x['source_name'][0].split('.')[0]])
if not os.path.exists(os.path.join(png_dir, result_name)):
os.mkdir(os.path.join(png_dir, result_name))
# save png
for i in range(len(predictions)):
imageio.imsave(os.path.join(png_dir, result_name + '/%07d.png' % i), (255 * predictions[i]).astype(np.uint8))
# save gif/mp4
image_name = result_name + animate_params['format']
imageio.mimsave(os.path.join(log_dir, image_name), visualizations)