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 from modules.keypoint_detector import KPDetector from animate import normalize_kp 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 load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**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='vox-cpk.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/source.png', help="path to driving video") parser.add_argument("--result_video", default='result.mp4', 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) opt = parser.parse_args() 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() 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)