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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 | |
| 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) | |