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李云龙二次元风格化！一键fork你也能行

本文介绍用AnimeGANv2实现李云龙视频二次元风格化的方法。先说明AnimeGANv2的改进，接着讲实现思路与目录结构，然后阐述素材准备、环境配置和代码实操过程，包括安装依赖、导入环境、用预训练模型进行视频风格迁移，最后说明合并音视频的步骤，还提供了相关展示和技术详解链接。

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李云龙二次元风格化！一键star、fork，你也可以生成这样的团长！

0.效果展示

打滚卖萌求star求fork!

视频效果如下，前往B站观看效果更佳：李云龙二次元风格化：

github开源repo：李云龙二次元风格化
CSDN技术详解：李云龙二次元风格化！

1.模型简介

1.1AnimeGANv2

本文使用了animeGANv2进行了视频的风格迁移。
animeGANv2，顾名思义，是其前作AnimeGAN的改进版，改进方向主要在以下4点：

解决了生成的图像中的高频伪影问题。
它易于训练，并能直接达到论文所述的效果。
进一步减少生成器网络的参数数量。(现在生成器大小 8.17Mb)
尽可能多地使用来自BD电影的新的高质量的风格数据。
效果图参考：

本文则是使用了paddlepaddle预训练好的animeGANv2模型对李云龙名场面视频进行了风格化迁移，详情请看下文分解。

2.实现思路

2.1目录结构

├── codes 代码、数据都在这里面
│   ├── PaddleGAN-develop 代码，基于paddleGAN进行了一些改动，鸣谢
│   ├── videos 所需要转换的视频及音频信息
│   ├── images notebook展示用的图片，没啥用

3.素材准备

首先要找到自己要操作的视频素材，将视频的音频单独提取出来备用
我自己找的资源放在了codes/videos/liyunlong文件夹下，是李云龙名场面：
你咋不敢跟旅长干一架呢！→旅长我给你跪下了

4.代码实操：

话不多说，首先是环境的基本配置

In [ ]

# 安装基本环境!pip install -r codes/PaddleGAN-develop/requirements.txt

Looking in indexes: https://mirror.baidu.com/pypi/simple/
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In [ ]

# 导入基本环境import paddle 
import os 
import sys 
sys.path.insert(0,'codes/PaddleGAN-develop')from ppgan.apps import AnimeGANPredictor

codes/PaddleGAN-develop/ppgan/modules/dense_motion.py:116: DeprecationWarning: invalid escape sequence \h
  """

5.GAN它！

友情提示：此处最好使用GPU环境，cpu推理属实是有点点慢进行模型的推理：

In [ ]

# 使用paddlepaddle预训练好的animeGANv2模型对视频进行风格迁移：from ppgan.apps import AnimeGANPredictorimport cv2
    
predictor = AnimeGANPredictor('',None,)
video_src = 'codes/videos/liyunlong/格式工厂混流 亮剑-03+亮剑-03+亮剑-04 00_00_23-.mp4'video_ = cv2.VideoCapture(video_src)
video_name_ = os.path.basename(video_src)
total_frames = video_.get(cv2.CAP_PROP_FRAME_COUNT)
fps_ = video_.get(cv2.CAP_PROP_FPS)print("video {}, fps:{}, total frames:{}...".format(video_name_, fps_, total_frames))
frame_count_ = 0save_per_frames = 1dst_dir = 'codes/videos/liyunlong/'
    out_video = cv2.VideoWriter('{}/hayao_{}'.format(dst_dir, video_name_),
                                cv2.VideoWriter_fourcc(*'DIVX'), int(fps_),
                                (int(video_.get(3)), int(video_.get(4))))print('now begin...')while True:
    ret_, frame_ = video_.read()    if not ret_:  # or len(fps_list_) == 0:
        print('end of video...')        break
        
    result_frame = predictor.anime_image_only(frame_)    if frame_count_ % save_per_frames == 0:
        out_video.write(result_frame)
    frame_count_ = frame_count_ + 1
    if frame_count_ % 100 == 0:        print("{}/{} processed...".format(frame_count_, int(total_frames)), flush=False)

[03/17 14:08:11] ppgan INFO: Found /home/aistudio/.cache/ppgan/animeganv2_hayao.pdparams
video 格式工厂混流 亮剑-03+亮剑-03+亮剑-04 00_00_23-.mp4, fps:23.0, total frames:5739.0...
now begin...
100/5739 processed...
200/5739 processed...
300/5739 processed...

代码解释

6.最终视频

合成最终所需要的视频：

In [ ]

# 合并生成的视频和之前分离的音频：!ffmpeg -i codes/videos/liyunlong/hayao_格式工厂混流 亮剑-03+亮剑-03+亮剑-04 00_00_23-.mp4 -i codes/videos/liyunlong/音频1.aac -c:v copy -c:a aac -strict experimental codes/videos/liyunlong/李云龙二次元化.mp4

这样就大功告成啦~~~

你可以在此基础上：