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demo.py
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demo.py
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import matplotlib
matplotlib.use('Agg')
import sys
import yaml
from argparse import ArgumentParser
from tqdm import tqdm
from scipy.spatial import ConvexHull
import numpy as np
import imageio
from skimage.transform import resize
from skimage import img_as_ubyte
import torch
import os
from modules.inpainting_network import InpaintingNetwork
from modules.keypoint_detector import KPDetector
from modules.dense_motion import DenseMotionNetwork
from modules.avd_network import AVDNetwork
if sys.version_info[0] < 3:
raise Exception("You must use Python 3 or higher. Recommended version is Python 3.9")
def relative_kp(kp_source, kp_driving, kp_driving_initial):
source_area = ConvexHull(kp_source['fg_kp'][0].data.cpu().numpy()).volume
driving_area = ConvexHull(kp_driving_initial['fg_kp'][0].data.cpu().numpy()).volume
adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
kp_new = {k: v for k, v in kp_driving.items()}
kp_value_diff = (kp_driving['fg_kp'] - kp_driving_initial['fg_kp'])
kp_value_diff *= adapt_movement_scale
kp_new['fg_kp'] = kp_value_diff + kp_source['fg_kp']
return kp_new
def load_checkpoints(config_path, checkpoint_path, device):
with open(config_path) as f:
config = yaml.full_load(f)
inpainting = InpaintingNetwork(**config['model_params']['generator_params'],
**config['model_params']['common_params'])
kp_detector = KPDetector(**config['model_params']['common_params'])
dense_motion_network = DenseMotionNetwork(**config['model_params']['common_params'],
**config['model_params']['dense_motion_params'])
avd_network = AVDNetwork(num_tps=config['model_params']['common_params']['num_tps'],
**config['model_params']['avd_network_params'])
kp_detector.to(device)
dense_motion_network.to(device)
inpainting.to(device)
avd_network.to(device)
checkpoint = torch.load(checkpoint_path, map_location=device)
inpainting.load_state_dict(checkpoint['inpainting_network'])
kp_detector.load_state_dict(checkpoint['kp_detector'])
dense_motion_network.load_state_dict(checkpoint['dense_motion_network'])
if 'avd_network' in checkpoint:
avd_network.load_state_dict(checkpoint['avd_network'])
inpainting.eval()
kp_detector.eval()
dense_motion_network.eval()
avd_network.eval()
return inpainting, kp_detector, dense_motion_network, avd_network
def make_animation(source_image, driving_video, inpainting_network, kp_detector, dense_motion_network, avd_network, device, mode = 'relative'):
assert mode in ['standard', 'relative', 'avd']
# with torch.no_grad():
predictions = []
source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
source = source.to(device)
driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3).to(device)
kp_source = kp_detector(source)
kp_driving_initial = kp_detector(driving[:, :, 0])
for frame_idx in tqdm(range(driving.shape[2])):
driving_frame = driving[:, :, frame_idx]
driving_frame = driving_frame.to(device)
kp_driving = kp_detector(driving_frame)
if mode == 'standard':
kp_norm = kp_driving
elif mode=='relative':
kp_norm = relative_kp(kp_source=kp_source, kp_driving=kp_driving,
kp_driving_initial=kp_driving_initial)
elif mode == 'avd':
kp_norm = avd_network(kp_source, kp_driving)
dense_motion = dense_motion_network(source_image=source, kp_driving=kp_norm,
kp_source=kp_source, bg_param = None,
dropout_flag = False)
out = inpainting_network(source, dense_motion)
predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
return predictions
def find_best_frame(source, driving, cpu):
import face_alignment
def normalize_kp(kp):
kp = kp - kp.mean(axis=0, keepdims=True)
area = ConvexHull(kp[:, :2]).volume
area = np.sqrt(area)
kp[:, :2] = kp[:, :2] / area
return kp
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True,
device= 'cpu' if cpu else 'cuda')
kp_source = fa.get_landmarks(255 * source)[0]
kp_source = normalize_kp(kp_source)
norm = float('inf')
frame_num = 0
for i, image in tqdm(enumerate(driving)):
try:
kp_driving = fa.get_landmarks(255 * image)[0]
kp_driving = normalize_kp(kp_driving)
new_norm = (np.abs(kp_source - kp_driving) ** 2).sum()
if new_norm < norm:
norm = new_norm
frame_num = i
except:
pass
return frame_num
if __name__ == "__main__":
# os.environ['CUDA_VISIBLE_DEVICES'] = '1'
parser = ArgumentParser()
parser.add_argument("--config", required=True, help="path to config")
parser.add_argument("--checkpoint", default='checkpoints/vox.pth.tar', help="path to checkpoint to restore")
parser.add_argument("--source_image", default='./assets/source.png', help="path to source image")
parser.add_argument("--driving_video", default='./assets/driving.mp4', help="path to driving video")
parser.add_argument("--result_video", default='./result.mp4', help="path to output")
parser.add_argument("--img_shape", default="256,256", type=lambda x: list(map(int, x.split(','))),
help='Shape of image, that the model was trained on.')
parser.add_argument("--mode", default='relative', choices=['standard', 'relative', 'avd'], help="Animate mode: ['standard', 'relative', 'avd'], when use the relative mode to animate a face, use '--find_best_frame' can get better quality result")
parser.add_argument("--find_best_frame", dest="find_best_frame", action="store_true",
help="Generate from the frame that is the most alligned with source. (Only for faces, requires face_aligment lib)")
parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
parser.add_argument("--gpu", default='0', help="gpu id to be used")
opt = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
source_image = imageio.imread(opt.source_image)
reader = imageio.get_reader(opt.driving_video)
fps = reader.get_meta_data()['fps']
driving_video = []
try:
for im in reader:
driving_video.append(im)
except RuntimeError:
pass
reader.close()
if opt.cpu:
device = torch.device('cpu')
else:
device = torch.device('cuda')
source_image = resize(source_image, opt.img_shape)[..., :3]
driving_video = [resize(frame, opt.img_shape)[..., :3] for frame in driving_video]
inpainting, kp_detector, dense_motion_network, avd_network = load_checkpoints(config_path = opt.config, checkpoint_path = opt.checkpoint, device = device)
if opt.find_best_frame:
i = find_best_frame(source_image, driving_video, opt.cpu)
print ("Best frame: " + str(i))
driving_forward = driving_video[i:]
driving_backward = driving_video[:(i+1)][::-1]
predictions_forward = make_animation(source_image, driving_forward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
predictions_backward = make_animation(source_image, driving_backward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
predictions = predictions_backward[::-1] + predictions_forward[1:]
else:
predictions = make_animation(source_image, driving_video, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps)