demo.py

# python demo.py --config config/vox-256-spade.yml --checkpoint checkpoints/00000189-checkpoint.pth.tar --source_image  /home/cxu-serve/p61/rzhu14/lsong11_workspace/Thin-Plate-Spline-Motion-Model/assets/test.png --driving_video  /home/cxu-serve/p61/rzhu14/lsong11_workspace/Thin-Plate-Spline-Motion-Model/assets/driving.mp4 --relative --adapt_scale --find_best_frame --gen spade
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
import torch.nn.functional as F
from sync_batchnorm import DataParallelWithCallback

from modules.generator import OcclusionAwareGenerator, OcclusionAwareSPADEGenerator
from modules.keypoint_detector import KPDetector, HEEstimator
from animate import normalize_kp
from scipy.spatial import ConvexHull
import warnings
warnings.filterwarnings("ignore")


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, gen, cpu=False):

    with open(config_path) as f:
        config = yaml.load(f)

    if gen == 'original':
        generator = OcclusionAwareGenerator(**config['model_params']['generator_params'],
                                            **config['model_params']['common_params'])
    elif gen == 'spade':
        generator = OcclusionAwareSPADEGenerator(**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()

    he_estimator = HEEstimator(**config['model_params']['he_estimator_params'],
                               **config['model_params']['common_params'])
    if not cpu:
        he_estimator.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'])
    he_estimator.load_state_dict(checkpoint['he_estimator'])
    
    if not cpu:
        generator = DataParallelWithCallback(generator)
        kp_detector = DataParallelWithCallback(kp_detector)
        he_estimator = DataParallelWithCallback(he_estimator)

    generator.eval()
    kp_detector.eval()
    he_estimator.eval()
    
    return generator, kp_detector, he_estimator


def headpose_pred_to_degree(pred):
    device = pred.device
    idx_tensor = [idx for idx in range(66)]
    idx_tensor = torch.FloatTensor(idx_tensor).to(device)
    pred = F.softmax(pred)
    degree = torch.sum(pred*idx_tensor, axis=1) * 3 - 99

    return degree

'''
# beta version
def get_rotation_matrix(yaw, pitch, roll):
    yaw = yaw / 180 * 3.14
    pitch = pitch / 180 * 3.14
    roll = roll / 180 * 3.14

    roll = roll.unsqueeze(1)
    pitch = pitch.unsqueeze(1)
    yaw = yaw.unsqueeze(1)

    roll_mat = torch.cat([torch.ones_like(roll), torch.zeros_like(roll), torch.zeros_like(roll), 
                          torch.zeros_like(roll), torch.cos(roll), -torch.sin(roll),
                          torch.zeros_like(roll), torch.sin(roll), torch.cos(roll)], dim=1)
    roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)

    pitch_mat = torch.cat([torch.cos(pitch), torch.zeros_like(pitch), torch.sin(pitch), 
                           torch.zeros_like(pitch), torch.ones_like(pitch), torch.zeros_like(pitch),
                           -torch.sin(pitch), torch.zeros_like(pitch), torch.cos(pitch)], dim=1)
    pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)

    yaw_mat = torch.cat([torch.cos(yaw), -torch.sin(yaw), torch.zeros_like(yaw),  
                         torch.sin(yaw), torch.cos(yaw), torch.zeros_like(yaw),
                         torch.zeros_like(yaw), torch.zeros_like(yaw), torch.ones_like(yaw)], dim=1)
    yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)

    rot_mat = torch.einsum('bij,bjk,bkm->bim', roll_mat, pitch_mat, yaw_mat)

    return rot_mat

'''
def get_rotation_matrix(yaw, pitch, roll):
    yaw = yaw / 180 * 3.14
    pitch = pitch / 180 * 3.14
    roll = roll / 180 * 3.14

    roll = roll.unsqueeze(1)
    pitch = pitch.unsqueeze(1)
    yaw = yaw.unsqueeze(1)

    pitch_mat = torch.cat([torch.ones_like(pitch), torch.zeros_like(pitch), torch.zeros_like(pitch), 
                          torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch),
                          torch.zeros_like(pitch), torch.sin(pitch), torch.cos(pitch)], dim=1)
    pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)

    yaw_mat = torch.cat([torch.cos(yaw), torch.zeros_like(yaw), torch.sin(yaw), 
                           torch.zeros_like(yaw), torch.ones_like(yaw), torch.zeros_like(yaw),
                           -torch.sin(yaw), torch.zeros_like(yaw), torch.cos(yaw)], dim=1)
    yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)

    roll_mat = torch.cat([torch.cos(roll), -torch.sin(roll), torch.zeros_like(roll),  
                         torch.sin(roll), torch.cos(roll), torch.zeros_like(roll),
                         torch.zeros_like(roll), torch.zeros_like(roll), torch.ones_like(roll)], dim=1)
    roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)

    rot_mat = torch.einsum('bij,bjk,bkm->bim', pitch_mat, yaw_mat, roll_mat)

    return rot_mat

def keypoint_transformation(kp_canonical, he, estimate_jacobian=True, free_view=False, yaw=0, pitch=0, roll=0):
    kp = kp_canonical['value']
    if not free_view:
        yaw, pitch, roll = he['yaw'], he['pitch'], he['roll']
        yaw = headpose_pred_to_degree(yaw)
        pitch = headpose_pred_to_degree(pitch)
        roll = headpose_pred_to_degree(roll)
    else:
        if yaw is not None:
            yaw = torch.tensor([yaw]).cuda()
        else:
            yaw = he['yaw']
            yaw = headpose_pred_to_degree(yaw)
        if pitch is not None:
            pitch = torch.tensor([pitch]).cuda()
        else:
            pitch = he['pitch']
            pitch = headpose_pred_to_degree(pitch)
        if roll is not None:
            roll = torch.tensor([roll]).cuda()
        else:
            roll = he['roll']
            roll = headpose_pred_to_degree(roll)

    t, exp = he['t'], he['exp']

    rot_mat = get_rotation_matrix(yaw, pitch, roll)
    
    # keypoint rotation
    kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)

    # keypoint translation
    t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1)
    kp_t = kp_rotated + t

    # add expression deviation 
    exp = exp.view(exp.shape[0], -1, 3)
    kp_transformed = kp_t + exp

    if estimate_jacobian:
        jacobian = kp_canonical['jacobian']
        jacobian_transformed = torch.einsum('bmp,bkps->bkms', rot_mat, jacobian)
    else:
        jacobian_transformed = None

    return {'value': kp_transformed, 'jacobian': jacobian_transformed}

def make_animation(source_image, driving_video, generator, kp_detector, he_estimator, relative=True, adapt_movement_scale=True, estimate_jacobian=True, cpu=False, free_view=False, yaw=0, pitch=0, roll=0):
    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_canonical = kp_detector(source)
        he_source = he_estimator(source)
        he_driving_initial = he_estimator(driving[:, :, 0])

        kp_source = keypoint_transformation(kp_canonical, he_source, estimate_jacobian)
        kp_driving_initial = keypoint_transformation(kp_canonical, he_driving_initial, estimate_jacobian)
        # kp_driving_initial = keypoint_transformation(kp_canonical, he_driving_initial, free_view=free_view, yaw=yaw, pitch=pitch, roll=roll)

        for frame_idx in tqdm(range(driving.shape[2])):
            driving_frame = driving[:, :, frame_idx]
            if not cpu:
                driving_frame = driving_frame.cuda()
            he_driving = he_estimator(driving_frame)
            kp_driving = keypoint_transformation(kp_canonical, he_driving, estimate_jacobian, free_view=free_view, yaw=yaw, pitch=pitch, roll=roll)

            # np.save('all_kps/%05d.npy'%frame_idx, kp_driving['value'].cpu().detach().numpy())
            # import pdb; pdb.set_trace()
            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=estimate_jacobian, adapt_movement_scale=adapt_movement_scale)
            out = generator(source, frame_idx, 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')

    fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, 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", default='config/vox-256.yaml', help="path to config")
    parser.add_argument("--checkpoint", default='', help="path to checkpoint to restore")

    parser.add_argument("--source_image", default='', help="path to source image")
    parser.add_argument("--driving_video", default='', help="path to driving video")
    parser.add_argument("--result_video", default='./results_hq.mp4', help="path to output")

    parser.add_argument("--gen", default="spade", choices=["original", "spade"])
 
    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.add_argument("--free_view", dest="free_view", action="store_true", help="control head pose")
    parser.add_argument("--yaw", dest="yaw", type=int, default=None, help="yaw")
    parser.add_argument("--pitch", dest="pitch", type=int, default=None, help="pitch")
    parser.add_argument("--roll", dest="roll", type=int, default=None, help="roll")
 

    parser.set_defaults(relative=False)
    parser.set_defaults(adapt_scale=False)
    parser.set_defaults(free_view=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, (512, 512))[..., :3]
    driving_video = [resize(frame, (512, 512))[..., :3] for frame in driving_video]
    generator, kp_detector, he_estimator = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, gen=opt.gen, cpu=opt.cpu)

    with open(opt.config) as f:
        config = yaml.load(f)
    estimate_jacobian = config['model_params']['common_params']['estimate_jacobian']
    print(f'estimate jacobian: {estimate_jacobian}')

    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, he_estimator, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, estimate_jacobian=estimate_jacobian, cpu=opt.cpu, free_view=opt.free_view, yaw=opt.yaw, pitch=opt.pitch, roll=opt.roll)
        predictions_backward = make_animation(source_image, driving_backward, generator, kp_detector, he_estimator, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, estimate_jacobian=estimate_jacobian, cpu=opt.cpu, free_view=opt.free_view, yaw=opt.yaw, pitch=opt.pitch, roll=opt.roll)
        predictions = predictions_backward[::-1] + predictions_forward[1:]
    else:
        predictions = make_animation(source_image, driving_video, generator, kp_detector, he_estimator, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, estimate_jacobian=estimate_jacobian, cpu=opt.cpu, free_view=opt.free_view, yaw=opt.yaw, pitch=opt.pitch, roll=opt.roll)
    imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps)