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168 lines
7.3 KiB
168 lines
7.3 KiB
import sys
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import yaml
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from argparse import ArgumentParser
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from tqdm.auto import tqdm
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import imageio
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import numpy as np
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from skimage.transform import resize
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from skimage import img_as_ubyte
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import torch
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from sync_batchnorm import DataParallelWithCallback
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from modules.generator import OcclusionAwareGenerator
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from modules.keypoint_detector import KPDetector
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from animate import normalize_kp
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import ffmpeg
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from os.path import splitext
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from shutil import copyfileobj
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from tempfile import NamedTemporaryFile
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if sys.version_info[0] < 3:
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raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7")
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def load_checkpoints(config_path, checkpoint_path, cpu=True):
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with open(config_path) as f:
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config = yaml.full_load(f)
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generator = OcclusionAwareGenerator(**config['model_params']['generator_params'],
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**config['model_params']['common_params'])
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if not cpu:
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generator.cuda()
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kp_detector = KPDetector(**config['model_params']['kp_detector_params'],
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**config['model_params']['common_params'])
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if not cpu:
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kp_detector.cuda()
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if cpu:
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checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
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else:
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checkpoint = torch.load(checkpoint_path)
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generator.load_state_dict(checkpoint['generator'])
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kp_detector.load_state_dict(checkpoint['kp_detector'])
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if not cpu:
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generator = DataParallelWithCallback(generator)
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kp_detector = DataParallelWithCallback(kp_detector)
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generator.eval()
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kp_detector.eval()
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return generator, kp_detector
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def make_animation(source_image, driving_video, generator, kp_detector, relative=True, adapt_movement_scale=True, cpu=True):
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with torch.no_grad():
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predictions = []
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source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
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if not cpu:
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source = source.cuda()
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driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3)
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kp_source = kp_detector(source)
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kp_driving_initial = kp_detector(driving[:, :, 0])
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for frame_idx in tqdm(range(driving.shape[2])):
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driving_frame = driving[:, :, frame_idx]
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if not cpu:
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driving_frame = driving_frame.cuda()
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kp_driving = kp_detector(driving_frame)
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kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving,
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kp_driving_initial=kp_driving_initial, use_relative_movement=relative,
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use_relative_jacobian=relative, adapt_movement_scale=adapt_movement_scale)
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out = generator(source, kp_source=kp_source, kp_driving=kp_norm)
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predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
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return predictions
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def find_best_frame(source, driving, cpu=False):
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import face_alignment # type: ignore (local file)
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from scipy.spatial import ConvexHull
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def normalize_kp(kp):
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kp = kp - kp.mean(axis=0, keepdims=True)
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area = ConvexHull(kp[:, :2]).volume
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area = np.sqrt(area)
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kp[:, :2] = kp[:, :2] / area
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return kp
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fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True,
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device='cpu' if cpu else 'cuda')
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kp_source = fa.get_landmarks(255 * source)[0]
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kp_source = normalize_kp(kp_source)
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norm = float('inf')
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frame_num = 0
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for i, image in tqdm(enumerate(driving)):
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kp_driving = fa.get_landmarks(255 * image)[0]
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kp_driving = normalize_kp(kp_driving)
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new_norm = (np.abs(kp_source - kp_driving) ** 2).sum()
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if new_norm < norm:
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norm = new_norm
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frame_num = i
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return frame_num
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--config", required=True, help="path to config")
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parser.add_argument("--checkpoint", default='vox-cpk.pth.tar', help="path to checkpoint to restore")
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parser.add_argument("--source_image", default='sup-mat/source.png', help="path to source image")
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parser.add_argument("--driving_video", default='driving.mp4', help="path to driving video")
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parser.add_argument("--result_video", default='result.mp4', help="path to output")
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parser.add_argument("--relative", dest="relative", action="store_true", help="use relative or absolute keypoint coordinates")
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parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", help="adapt movement scale based on convex hull of keypoints")
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parser.add_argument("--find_best_frame", dest="find_best_frame", action="store_true",
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help="Generate from the frame that is the most alligned with source. (Only for faces, requires face_aligment lib)")
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parser.add_argument("--best_frame", dest="best_frame", type=int, default=None, help="Set frame to start from.")
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parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
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parser.add_argument("--audio", dest="audio", action="store_true", help="copy audio to output from the driving video" )
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parser.set_defaults(relative=False)
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parser.set_defaults(adapt_scale=False)
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parser.set_defaults(audio_on=False)
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opt = parser.parse_args()
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source_image = imageio.imread(opt.source_image)
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reader = imageio.get_reader(opt.driving_video)
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fps = reader.get_meta_data()['fps']
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driving_video = []
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try:
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for im in reader:
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driving_video.append(im)
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except RuntimeError:
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pass
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reader.close()
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source_image = resize(source_image, (256, 256))[..., :3]
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driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video]
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generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu)
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if opt.find_best_frame or opt.best_frame is not None:
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i = opt.best_frame if opt.best_frame is not None else find_best_frame(source_image, driving_video, cpu=opt.cpu)
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print("Best frame: " + str(i))
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driving_forward = driving_video[i:]
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driving_backward = driving_video[:(i+1)][::-1]
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predictions_forward = make_animation(source_image, driving_forward, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu)
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predictions_backward = make_animation(source_image, driving_backward, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu)
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predictions = predictions_backward[::-1] + predictions_forward[1:]
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else:
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predictions = make_animation(source_image, driving_video, generator, kp_detector, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, cpu=opt.cpu)
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imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps)
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if opt.audio:
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try:
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with NamedTemporaryFile(suffix=splitext(opt.result_video)[1]) as output:
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ffmpeg.output(ffmpeg.input(opt.result_video).video, ffmpeg.input(opt.driving_video).audio, output.name, c='copy').run()
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with open(opt.result_video, 'wb') as result:
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copyfileobj(output, result)
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except ffmpeg.Error:
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print("Failed to copy audio: the driving video may have no audio track or the audio format is invalid.") |