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demo.py
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demo.py
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import matplotlib
matplotlib.use('Agg')
import os, sys
import yaml
from argparse import ArgumentParser
from tqdm import tqdm
import imageio
import numpy as np
from skimage.transform import resize
from skimage import img_as_ubyte
import torch
from sync_batchnorm import DataParallelWithCallback
from modules.generator import OcclusionAwareGenerator_SPADE
from modules.keypoint_detector import KPDetector
from scipy.spatial import ConvexHull
if sys.version_info[0] < 3:
raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7")
def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,
use_relative_movement=False, use_relative_jacobian=False):
kp_new = {k: v for k, v in kp_driving.items()}
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)
# kp_new['value'] = kp_driving['value'] * adapt_movement_scale # for reenactment demo
else:
adapt_movement_scale = 1
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 load_checkpoints(config_path, checkpoint_path, cpu=False):
with open(config_path) as f:
# config = yaml.load(f)
config = yaml.load(f, Loader=yaml.FullLoader)
generator = OcclusionAwareGenerator_SPADE(**config['model_params']['generator_params'],
**config['model_params']['common_params'])
if not cpu:
generator.cuda()
kp_detector = KPDetector(**config['model_params']['kp_detector_params'],
**config['model_params']['common_params'])
if not cpu:
kp_detector.cuda()
if cpu:
checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
else:
checkpoint = torch.load(checkpoint_path)
generator.load_state_dict(checkpoint['generator'])
kp_detector.load_state_dict(checkpoint['kp_detector'])
if not cpu:
generator = DataParallelWithCallback(generator)
kp_detector = DataParallelWithCallback(kp_detector)
generator.eval()
kp_detector.eval()
return generator, kp_detector
def make_animation(source_image, driving_video, generator, kp_detector, relative=True, adapt_movement_scale=True,
cpu=False):
with torch.no_grad():
predictions = []
source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
if not cpu:
source = source.cuda()
driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3)
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]
if not cpu:
driving_frame = driving_frame.cuda()
kp_driving = kp_detector(driving_frame)
kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving,
kp_driving_initial=kp_driving_initial, use_relative_movement=relative,
use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale)
out = generator(source, kp_source=kp_source, kp_driving=kp_norm)
predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
return predictions
def find_best_frame(source, driving, cpu=False):
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)):
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
return frame_num
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--config", required=True, help="path to config")
parser.add_argument("--checkpoint", default='cpk/00000099-checkpoint.pth.tar', help="path to checkpoint to restore")
parser.add_argument("--source_image", default='sup-mat/source.png', help="path to source image")
parser.add_argument("--driving_video", default='sup-mat/driving.mp4', help="path to driving video")
parser.add_argument("--result_video", default='result.mp4', help="path to output")
parser.add_argument("--mode", default='reenactment',choices=['reenactment','reconstruction'], help="path to output")
parser.add_argument("--relative", dest="relative", action="store_true",
help="use relative or absolute keypoint coordinates")
parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true",
help="adapt movement scale based on convex hull of keypoints")
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("--best_frame", dest="best_frame", type=int, default=None,
help="Set frame to start from.")
parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
parser.set_defaults(relative=False)
parser.set_defaults(adapt_scale=False)
parser.set_defaults(find_best_frame=True)
opt = parser.parse_args()
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.mode == 'reenactment':
source_image = imageio.imread(opt.source_image)
elif opt.mode == 'reconstruction':
source_image = driving_video[0]
source_image = resize(source_image, (256, 256))[..., :3]
driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video]
generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu)
if opt.find_best_frame or opt.best_frame is not None:
i = opt.best_frame if opt.best_frame is not None else find_best_frame(source_image, driving_video, cpu=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, generator, kp_detector,
relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu)
predictions_backward = make_animation(source_image, driving_backward, generator, kp_detector,
relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu)
predictions = predictions_backward[::-1] + predictions_forward[1:]
else:
predictions = make_animation(source_image, driving_video, generator, kp_detector, relative=opt.relative,
adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu)
imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps)