In [ ]:
!git clone https://github.com/AliaksandrSiarohin/first-order-model.git

fatal: destination path 'first-order-model' already exists and is not an empty directory.
In [ ]:
!pip install PyYAML==5.3.1

Requirement already satisfied: PyYAML==5.3.1 in /usr/local/lib/python3.7/dist-packages (5.3.1)
In [ ]:
pwd
Out[ ]:
'/content'
In [ ]:
ls

drive/  first-order-model/  sample_data/
In [ ]:
cd first-order-model/

/content/first-order-model
In [ ]:
ls

animate.py       demo.ipynb         logger.py          requirements.txt
augmentation.py  demo.py            modules/           run.py
config/          Dockerfile         old_demo.ipynb     sup-mat/
crop-video.py    frames_dataset.py  README.md          sync_batchnorm/
data/            LICENSE.md         reconstruction.py  train.py
In [ ]:
import imageio
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from skimage.transform import resize
from IPython.display import HTML
import warnings
warnings.filterwarnings("ignore")


source_image = imageio.imread('/content/drive/MyDrive/Datasets/First_Order_Model_Data/Emily.png')
driver_video = imageio.get_reader('/content/drive/MyDrive/Datasets/First_Order_Model_Data/driver.mp4')


source_image = resize(source_image, (256, 256))[..., :3]
driver_video = [resize(frame, (256, 256))[..., :3] for frame in driver_video]

def display(source, driving, generated=None):
    fig = plt.figure(figsize=(8 + 4 * (generated is not None), 6))

    arr = []
    for i in range(len(driving)):
        cols = [source]
        cols.append(driving[i])
        if generated is not None:
            cols.append(generated[i])
        visual = plt.imshow(np.concatenate(cols, axis=1), animated=True)
        plt.axis('off')
        arr.append([visual])

    anime = animation.ArtistAnimation(fig, arr, interval=50, repeat_delay=1000)
    plt.close()
    return anime
In [ ]:
HTML(display(source_image, driver_video).to_html5_video())
Out[ ]:
In [ ]:
from demo import load_checkpoints

generator, kp_detector = load_checkpoints(config_path='/content/first-order-model/config/vox-256.yaml',
                                          checkpoint_path='/content/drive/MyDrive/Datasets/First_Order_Model_Data/vox-adv-cpk.pth.tar')
In [ ]:
from demo import make_animation
from skimage import img_as_ubyte

prediction = make_animation(source_image, driver_video, generator, kp_detector)

100%|██████████| 852/852 [01:51<00:00,  7.66it/s]
In [ ]:
HTML(display(source_image, driver_video, prediction).to_html5_video())
Out[ ]:
In [ ]:
# Saving the video
imageio.mimsave('../new)_video.mp4', [img_as_ubyte(i) for i in prediction])
In [ ]:
prediction
Out[ ]:
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