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import gradio as gr
import torch
import imageio
import imageio_ffmpeg
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from skimage.transform import resize
import warnings
import os
from model import load_checkpoints
from model import make_animation
from skimage import img_as_ubyte
from PIL import Image
import time
warnings.filterwarnings("ignore")
device = torch.device('cuda:0')
#device = torch.device('cpu')
dataset_name = 'vox' # ['vox', 'taichi', 'ted', 'mgif']
source_image_path = './assets/source.png'
driving_video_path = './assets/driving.mp4'
output_video_path = './generated.mp4'
config_path = './config/vox-256.yaml'
checkpoint_path = './checkpoints/vox.pth.tar'
predict_mode = 'relative' # ['standard', 'relative', 'avd']
find_best_frame = False # when use the relative mode to animate a face, use 'find_best_frame=True' can get better quality result
pixel = 256 # for vox, taichi and mgif, the resolution is 256*256
if(dataset_name == 'ted'): # for ted, the resolution is 384*384
pixel = 384
if find_best_frame:
#!pip install face_alignment
pass
def create_video(tt):
source_image = imageio.imread(f"assets/img_{tt}.jpg")
reader = imageio.get_reader(f"assets/ref_{tt}.mp4")
source_image = resize(source_image, (pixel, pixel))[..., :3]
fps = reader.get_meta_data()['fps']
driving_video = []
try:
for im in reader:
driving_video.append(im)
except RuntimeError:
pass
reader.close()
driving_video = [resize(frame, (pixel, pixel))[..., :3] for frame in driving_video]
def display(source, driving, generated=None):
fig = plt.figure(figsize=(8 + 4 * (generated is not None), 6))
ims = []
for i in range(len(driving)):
cols = [source]
cols.append(driving[i])
if generated is not None:
cols.append(generated[i])
im = plt.imshow(np.concatenate(cols, axis=1), animated=True)
plt.axis('off')
ims.append([im])
ani = animation.ArtistAnimation(fig, ims, interval=50, repeat_delay=1000)
plt.close()
return ani
#HTML(display(source_image, driving_video).to_html5_video())
inpainting, kp_detector, dense_motion_network, avd_network = load_checkpoints(config_path = config_path, checkpoint_path = checkpoint_path, device = device)
if predict_mode=='relative' and find_best_frame:
from model import find_best_frame as _find
i = _find(source_image, driving_video, device.type=='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, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = predict_mode)
predictions_backward = make_animation(source_image, driving_backward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = predict_mode)
predictions = predictions_backward[::-1] + predictions_forward[1:]
else:
predictions = make_animation(source_image, driving_video, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = predict_mode)
#save resulting video
imageio.mimsave(f"./assets/output_{tt}.mp4", [img_as_ubyte(frame) for frame in predictions], fps=fps)
def greet(img,video):
tt=str(time.time())
os.replace(video, f"assets/ref_{tt}.mp4")
img.save(f"assets/img_{tt}.jpg")
create_video(tt)
return f"./assets/output_{tt}.mp4"
iface = gr.Interface(fn=greet, inputs=
[gr.inputs.Image(type="pil",label="Foto yükleyin"),gr.inputs.Video(label="Video yükleyin")], outputs=gr.inputs.Video(label="Yeni video"))
iface.launch() |