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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
import depth
from sync_batchnorm import DataParallelWithCallback
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, checkpoint, log_dir, dataset,opt):
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)
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)
depth_encoder = depth.ResnetEncoder(18, False).cuda()
depth_decoder = depth.DepthDecoder(num_ch_enc=depth_encoder.num_ch_enc, scales=range(4)).cuda()
loaded_dict_enc = torch.load('depth/models/weights_19/encoder.pth')
loaded_dict_dec = torch.load('depth/models/weights_19/depth.pth')
filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in depth_encoder.state_dict()}
depth_encoder.load_state_dict(filtered_dict_enc)
depth_decoder.load_state_dict(loaded_dict_dec)
depth_decoder.eval()
depth_encoder.eval()
generator.eval()
kp_detector.eval()
for it, x in tqdm(enumerate(dataloader)):
with torch.no_grad():
predictions = []
visualizations = []
driving_video = x['driving_video'].cuda()
source_frame = x['source_video'][:, :, 0, :, :].cuda()
outputs = depth_decoder(depth_encoder(source_frame))
depth_source = outputs[("disp", 0)]
outputs = depth_decoder(depth_encoder(driving_video[:, :, 0]))
depth_driving = outputs[("disp", 0)]
source = torch.cat((source_frame,depth_source),1)
driving = torch.cat((driving_video[:, :, 0],depth_driving),1)
kp_source = kp_detector(source)
kp_driving_initial = kp_detector(driving)
for frame_idx in range(driving_video.shape[2]):
driving_frame = driving_video[:, :, frame_idx].cuda()
outputs = depth_decoder(depth_encoder(driving_frame))
depth_map = outputs[("disp", 0)]
driving = torch.cat((driving_frame,depth_map),1)
kp_driving = kp_detector(driving)
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)
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)
visualization = visualization
visualizations.append(visualization)
predictions = np.concatenate(predictions, axis=1)
result_name = "-".join([x['driving_name'][0], x['source_name'][0]])
imageio.imsave(os.path.join(png_dir, result_name + '.png'), (255 * predictions).astype(np.uint8))
image_name = result_name + animate_params['format']
imageio.mimsave(os.path.join(log_dir, image_name), visualizations)