前言
下载和安装资源
安装补充
验证工具代码
总结
前言
看一个很有意思的项目，其实之前在百度飞浆等平台上也看到了类似的实现效果。
根据视频的表情，可以移动照片。看看项目给出的效果。
还是一样的，不管作者给出什么效果，自己试试。
下载和安装资源
让我们先看看README关于项目的基本信息，我们可以看到，除了表情驱动照片外，还可以移动姿势。
模型文件提供在线下载地址。
文件又大又难下，我把它放在我的云盘上，可以从下面的云盘下载。
链接：https://pan.baidu.com/s/1ANQjl4SBEjBZuX87KPXmnA 提取码：tuan
新建的模型文件放在根目录下checkpoint文件夹下。
将requirements.txt依赖安装。
在测试README在命令中，如有报错。
Traceback (most recent call last): File "demo.py", line 17, in <module> from animate import normalize_kp File "D:\spyder\first-order-model\animate.py", line 7, in <module> from frames_dataset import PairedDataset File "D:\spyder\first-order-model\frames_dataset.py", line 10, in <module> from augmentation import AllAugmentationTransform File "D:\spyder\first-order-model\augmentation.py", line 13, in <module> import torchvision File "C:\Users\huyi\.conda\envs\fom\lib\site-packages\torchvision\__init__.py", line 2, in <module> from torchvision import datasets File "C:\Users\huyi\.conda\envs\fom\lib\site-packages\torchvision\datasets\__init__.py", line 9, in <module> from .fakedata import FakeData File "C:\Users\huyi\.conda\envs\fom\lib\site-packages\torchvision\datasets\fakedata.py", line 3, in <module> from .. import transforms File "C:\Users\huyi\.conda\envs\fom\lib\site-packages\torchvision\transforms\__init__.py", line 1, in <module> from .transforms import * File "C:\Users\huyi\.conda\envs\fom\lib\site-packages\torchvision\transforms\transforms.py", line 16, in <module> from . import functional as F File "C:\Users\huyi\.conda\envs\fom\lib\site-packages\torchvision\transforms\functional.py", line 5, in <module> from PIL import Image, ImageOps, ImageEnhance, PILLOW_VERSION ImportError: cannot import name 'PILLOW_VERSION' from 'PIL' (C:\Users\huyi\.conda\envs\fom\lib\site-packages\PIL\__init__.py)
这个问题主要是我用的。pillow如果你不想找到相应的低版本，你可以用我的方式解决。
1、修改functional.py代码，将PILLOW_VERSION调整为__version__。
2、将imageio升级。
pip install --upgrade imageio -i https://pypi.douban.com/simple3、安装imageio_ffmpeg模块。
pip install imageio-ffmpeg -i https://pypi.douban.com/simple验证工具代码
我不会重复官方给出的使用方法，你可以按照下面的命令进行测试。
在这里，我推荐一个可视化的库gradio，下面我将demo.py代码改造了。
新的工具文件代码如下：
#!/user/bin/env python # coding=utf-8 """ @project : first-order-model @author : 剑客阿良_ALiang @file : hy_gradio.py @ide : PyCharm @time : 2022-06-23 14:35:28 """ import uuid from typing import Optional import gradio as gr 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 ython 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
def h_interface(input_image: str):
parser = ArgumentParser()
opt = parser.parse_args()
opt.config = "./config/vox-256.yaml"
opt.checkpoint = "./checkpoint/vox-cpk.pth.tar"
opt.source_image = input_image
opt.driving_video = "./data/input/ts.mp4"
opt.result_video = "./data/result/{}.mp4".format(uuid.uuid1().hex)
opt.relative = True
opt.adapt_scale = True
opt.cpu = True
opt.find_best_frame = False
opt.best_frame = False
# source_image = imageio.imread(opt.source_image)
source_image = 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)
return opt.result_video
if __name__ == "__main__":
demo = gr.Interface(h_interface, inputs=[gr.Image(shape=(500, 500))], outputs=[gr.Video()])
demo.launch()
# h_interface("C:\\Users\\huyi\\Desktop\\xx3.jpg")
1、将原demo.py中的main函数内容，重新编辑为h_interface方法，输入是想要驱动的图片。
2、其中driving_video参数使用了我自己录制的一段表情视频ts.mp4，我建议在使用的时候可以自己用手机录制一段替换。
3、使用gradio来生成方法的页面，下面会展示给大家看。
4、使用uuid为结果视频命名。
Running on local URL: http://127.0.0.1:7860/
To create a public link, set `share=True` in `launch()`.
打开本地的地址：http://localhost:7860/
可以看到我们实现的交互界面如下：
我们上传一下我准备的样例图片，提交制作。
看一下执行的日志，如下图。
看一下制作结果。
由于上传不了视频，我将视频转成了gif。
还是蛮有意思的，具体的参数调优我就不弄了，大家可能根据需要调整我提供的方法里面的参数。
总结
还是非常推荐gradio，大家有兴趣还是可以玩玩。
分享：
人们觉得你只能在以下二者中居其一：要么你是条鲨鱼，要么你只得躺在那里，任鲨鱼活生生地把你吃掉——这个世界就是这样。而我，我是那种会走出去，与鲨鱼搏斗的人。
——《十一种孤独》