这篇教程Python实现GIF动图以及视频卡通化详解写得很实用，希望能帮到您。
前言参考文章：Python实现照片卡通化 我继续魔改一下，让该模型可以支持将gif动图或者视频，也做成卡通化效果。毕竟一张图可以那就带边视频也可以，没毛病。所以继给次元壁来了一拳，我在加两脚。 项目github地址：github地址
环境依赖除了参考文章中的依赖，还需要加一些其他依赖，requirements.txt如下： 
其他环境不太清楚的，可以看我前言链接地址的文章，有具体说明。
核心代码不废话了，先上gif代码。
gif动图卡通化实现代码如下： #!/usr/bin/env python# -*- coding: utf-8 -*-# @Time : 2021/12/5 18:10# @Author : 剑客阿良_ALiang# @Site : # @File : gif_cartoon_tool.py# !/usr/bin/env python# -*- coding: utf-8 -*-# @Time : 2021/12/5 0:26# @Author : 剑客阿良_ALiang# @Site :# @File : video_cartoon_tool.py # !/usr/bin/env python# -*- coding: utf-8 -*-# @Time : 2021/12/4 22:34# @Author : 剑客阿良_ALiang# @Site :# @File : image_cartoon_tool.py from PIL import Image, ImageEnhance, ImageSequenceimport torchfrom torchvision.transforms.functional import to_tensor, to_pil_imagefrom torch import nnimport osimport torch.nn.functional as Fimport uuidimport imageio # -------------------------- hy add 01 --------------------------class ConvNormLReLU(nn.Sequential): def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=1, pad_mode="reflect", groups=1, bias=False): pad_layer = { "zero": nn.ZeroPad2d, "same": nn.ReplicationPad2d, "reflect": nn.ReflectionPad2d, } if pad_mode not in pad_layer: raise NotImplementedError super(ConvNormLReLU, self).__init__( pad_layer[pad_mode](padding), nn.Conv2d(in_ch, out_ch, kernel_size=kernel_size, stride=stride, padding=0, groups=groups, bias=bias), nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True), nn.LeakyReLU(0.2, inplace=True) ) class InvertedResBlock(nn.Module): def __init__(self, in_ch, out_ch, expansion_ratio=2): super(InvertedResBlock, self).__init__() self.use_res_connect = in_ch == out_ch bottleneck = int(round(in_ch * expansion_ratio)) layers = [] if expansion_ratio != 1: layers.append(ConvNormLReLU(in_ch, bottleneck, kernel_size=1, padding=0)) # dw layers.append(ConvNormLReLU(bottleneck, bottleneck, groups=bottleneck, bias=True)) # pw layers.append(nn.Conv2d(bottleneck, out_ch, kernel_size=1, padding=0, bias=False)) layers.append(nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True)) self.layers = nn.Sequential(*layers) def forward(self, input): out = self.layers(input) if self.use_res_connect: out = input + out return out class Generator(nn.Module): def __init__(self, ): super().__init__() self.block_a = nn.Sequential( ConvNormLReLU(3, 32, kernel_size=7, padding=3), ConvNormLReLU(32, 64, stride=2, padding=(0, 1, 0, 1)), ConvNormLReLU(64, 64) ) self.block_b = nn.Sequential( ConvNormLReLU(64, 128, stride=2, padding=(0, 1, 0, 1)), ConvNormLReLU(128, 128) ) self.block_c = nn.Sequential( ConvNormLReLU(128, 128), InvertedResBlock(128, 256, 2), InvertedResBlock(256, 256, 2), InvertedResBlock(256, 256, 2), InvertedResBlock(256, 256, 2), ConvNormLReLU(256, 128), ) self.block_d = nn.Sequential( ConvNormLReLU(128, 128), ConvNormLReLU(128, 128) ) self.block_e = nn.Sequential( ConvNormLReLU(128, 64), ConvNormLReLU(64, 64), ConvNormLReLU(64, 32, kernel_size=7, padding=3) ) self.out_layer = nn.Sequential( nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0, bias=False), nn.Tanh() ) def forward(self, input, align_corners=True): out = self.block_a(input) half_size = out.size()[-2:] out = self.block_b(out) out = self.block_c(out) if align_corners: out = F.interpolate(out, half_size, mode="bilinear", align_corners=True) else: out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False) out = self.block_d(out) if align_corners: out = F.interpolate(out, input.size()[-2:], mode="bilinear", align_corners=True) else: out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False) out = self.block_e(out) out = self.out_layer(out) return out # -------------------------- hy add 02 -------------------------- def handle(gif_path: str, output_dir: str, type: int, device='cpu'): _ext = os.path.basename(gif_path).strip().split('.')[-1] if type == 1: _checkpoint = './weights/paprika.pt' elif type == 2: _checkpoint = './weights/face_paint_512_v1.pt' elif type == 3: _checkpoint = './weights/face_paint_512_v2.pt' elif type == 4: _checkpoint = './weights/celeba_distill.pt' else: raise Exception('type not support') os.makedirs(output_dir, exist_ok=True) net = Generator() net.load_state_dict(torch.load(_checkpoint, map_location="cpu")) net.to(device).eval() result = os.path.join(output_dir, '{}.{}'.format(uuid.uuid1().hex, _ext)) img = Image.open(gif_path) out_images = [] for frame in ImageSequence.Iterator(img): frame = frame.convert("RGB") with torch.no_grad(): image = to_tensor(frame).unsqueeze(0) * 2 - 1 out = net(image.to(device), False).cpu() out = out.squeeze(0).clip(-1, 1) * 0.5 + 0.5 out = to_pil_image(out) out_images.append(out) # out_images[0].save(result, save_all=True, loop=True, append_images=out_images[1:], duration=100) imageio.mimsave(result, out_images, fps=15) return result if __name__ == '__main__': print(handle('samples/gif/128.gif', 'samples/gif_result/', 3, 'cuda'))代码说明： 1、主要的handle方法入参分别为：gif地址、输出目录、类型、设备使用（默认cpu，可选cuda使用显卡）。 2、类型主要是选择模型，最好用3，人像处理更生动一些。 执行验证一下 下面是我准备的gif素材 
执行结果如下 
看一下效果 
哈哈，有点意思哦。
视频卡通化实现代码如下： #!/usr/bin/env python# -*- coding: utf-8 -*-# @Time : 2021/12/5 0:26# @Author : 剑客阿良_ALiang# @Site : # @File : video_cartoon_tool.py # !/usr/bin/env python# -*- coding: utf-8 -*-# @Time : 2021/12/4 22:34# @Author : 剑客阿良_ALiang# @Site :# @File : image_cartoon_tool.py from PIL import Image, ImageEnhanceimport torchfrom torchvision.transforms.functional import to_tensor, to_pil_imagefrom torch import nnimport osimport torch.nn.functional as Fimport uuidimport cv2import numpy as npimport timefrom ffmpy import FFmpeg # -------------------------- hy add 01 --------------------------class ConvNormLReLU(nn.Sequential): def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=1, pad_mode="reflect", groups=1, bias=False): pad_layer = { "zero": nn.ZeroPad2d, "same": nn.ReplicationPad2d, "reflect": nn.ReflectionPad2d, } if pad_mode not in pad_layer: raise NotImplementedError super(ConvNormLReLU, self).__init__( pad_layer[pad_mode](padding), nn.Conv2d(in_ch, out_ch, kernel_size=kernel_size, stride=stride, padding=0, groups=groups, bias=bias), nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True), nn.LeakyReLU(0.2, inplace=True) ) class InvertedResBlock(nn.Module): def __init__(self, in_ch, out_ch, expansion_ratio=2): super(InvertedResBlock, self).__init__() self.use_res_connect = in_ch == out_ch bottleneck = int(round(in_ch * expansion_ratio)) layers = [] if expansion_ratio != 1: layers.append(ConvNormLReLU(in_ch, bottleneck, kernel_size=1, padding=0)) # dw layers.append(ConvNormLReLU(bottleneck, bottleneck, groups=bottleneck, bias=True)) # pw layers.append(nn.Conv2d(bottleneck, out_ch, kernel_size=1, padding=0, bias=False)) layers.append(nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True)) self.layers = nn.Sequential(*layers) def forward(self, input): out = self.layers(input) if self.use_res_connect: out = input + out return out class Generator(nn.Module): def __init__(self, ): super().__init__() self.block_a = nn.Sequential( ConvNormLReLU(3, 32, kernel_size=7, padding=3), ConvNormLReLU(32, 64, stride=2, padding=(0, 1, 0, 1)), ConvNormLReLU(64, 64) ) self.block_b = nn.Sequential( ConvNormLReLU(64, 128, stride=2, padding=(0, 1, 0, 1)), ConvNormLReLU(128, 128) ) self.block_c = nn.Sequential( ConvNormLReLU(128, 128), InvertedResBlock(128, 256, 2), InvertedResBlock(256, 256, 2), InvertedResBlock(256, 256, 2), InvertedResBlock(256, 256, 2), ConvNormLReLU(256, 128), ) self.block_d = nn.Sequential( ConvNormLReLU(128, 128), ConvNormLReLU(128, 128) ) self.block_e = nn.Sequential( ConvNormLReLU(128, 64), ConvNormLReLU(64, 64), ConvNormLReLU(64, 32, kernel_size=7, padding=3) ) self.out_layer = nn.Sequential( nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0, bias=False), nn.Tanh() ) def forward(self, input, align_corners=True): out = self.block_a(input) half_size = out.size()[-2:] out = self.block_b(out) out = self.block_c(out) if align_corners: out = F.interpolate(out, half_size, mode="bilinear", align_corners=True) else: out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False) out = self.block_d(out) if align_corners: out = F.interpolate(out, input.size()[-2:], mode="bilinear", align_corners=True) else: out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False) out = self.block_e(out) out = self.out_layer(out) return out # -------------------------- hy add 02 -------------------------- def handle(video_path: str, output_dir: str, type: int, fps: int, device='cpu'): _ext = os.path.basename(video_path).strip().split('.')[-1] if type == 1: _checkpoint = './weights/paprika.pt' elif type == 2: _checkpoint = './weights/face_paint_512_v1.pt' elif type == 3: _checkpoint = './weights/face_paint_512_v2.pt' elif type == 4: _checkpoint = './weights/celeba_distill.pt' else: raise Exception('type not support') os.makedirs(output_dir, exist_ok=True) # 获取视频音频 _audio = extract(video_path, output_dir, 'wav') net = Generator() net.load_state_dict(torch.load(_checkpoint, map_location="cpu")) net.to(device).eval() result = os.path.join(output_dir, '{}.{}'.format(uuid.uuid1().hex, _ext)) capture = cv2.VideoCapture(video_path) size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))) print(size) videoWriter = cv2.VideoWriter(result, cv2.VideoWriter_fourcc(*'mp4v'), fps, size) cul = 0 with torch.no_grad(): while True: ret, frame = capture.read() if ret: print(ret) image = to_tensor(frame).unsqueeze(0) * 2 - 1 out = net(image.to(device), False).cpu() out = out.squeeze(0).clip(-1, 1) * 0.5 + 0.5 out = to_pil_image(out) contrast_enhancer = ImageEnhance.Contrast(out) img_enhanced_image = contrast_enhancer.enhance(2) enhanced_image = np.asarray(img_enhanced_image) videoWriter.write(enhanced_image) cul += 1 print('第{}张图'.format(cul)) else: break videoWriter.release() # 视频添加原音频 _final_video = video_add_audio(result, _audio, output_dir) return _final_video # -------------------------- hy add 03 --------------------------def extract(video_path: str, tmp_dir: str, ext: str): file_name = '.'.join(os.path.basename(video_path).split('.')[0:-1]) print('文件名:{}，提取音频'.format(file_name)) if ext == 'mp3': return _run_ffmpeg(video_path, os.path.join(tmp_dir, '{}.{}'.format(uuid.uuid1().hex, ext)), 'mp3') if ext == 'wav': return _run_ffmpeg(video_path, os.path.join(tmp_dir, '{}.{}'.format(uuid.uuid1().hex, ext)), 'wav') def _run_ffmpeg(video_path: str, audio_path: str, format: str): ff = FFmpeg(inputs={video_path: None}, outputs={audio_path: '-f {} -vn'.format(format)}) print(ff.cmd) ff.run() return audio_path # 视频添加音频def video_add_audio(video_path: str, audio_path: str, output_dir: str): _ext_video = os.path.basename(video_path).strip().split('.')[-1] _ext_audio = os.path.basename(audio_path).strip().split('.')[-1] if _ext_audio not in ['mp3', 'wav']: raise Exception('audio format not support') _codec = 'copy' if _ext_audio == 'wav': _codec = 'aac' result = os.path.join( output_dir, '{}.{}'.format( uuid.uuid4(), _ext_video)) ff = FFmpeg( inputs={video_path: None, audio_path: None}, outputs={result: '-map 0:v -map 1:a -c:v copy -c:a {} -shortest'.format(_codec)}) print(ff.cmd) ff.run() return result if __name__ == '__main__': print(handle('samples/video/981.mp4', 'samples/video_result/', 3, 25, 'cuda'))代码说明 1、主要的实现方法入参分别为：视频地址、输出目录、类型、fps（帧率）、设备类型（默认cpu，可选择cuda显卡模式）。 2、类型主要是选择模型，最好用3，人像处理更生动一些。 3、代码设计思路：先将视频音频提取出来、将视频逐帧处理后写入新视频、新视频和原视频音频融合。 关于如何视频提取音频可以参考我的另一篇文章：python 提取视频中的音频 关于如何视频融合音频可以参考我的另一篇文章：Python 视频添加音频 4、视频中间会产生临时文件，没有清理，如需要可以修改代码自行清理。 验证一下 下面是我准备的视频素材截图，我会上传到github上。 
执行结果 
看看效果截图 
还是很不错的哦。
总结这次可不是没什么好总结的，总结的东西蛮多的。首先我说一下这个开源项目目前模型的一些问题。 1、我测试了不少图片，总的来说对亚洲人的脸型不能很好的卡通化，但是欧美的脸型都比较好。所以还是训练的数据不是很够，但是能理解，毕竟要专门做卡通化的标注数据想想就是蛮头疼的事。所以我建议大家在使用的时候，多关注一下项目是否更新了最新的模型。 2、视频一但有字幕，会对字幕也做处理。所以可以考虑找一些视频和字幕分开的素材，效果会更好一些。 以上就是Python实现GIF动图以及视频卡通化详解的详细内容，更多关于Python 动图 视频卡通化的资料请关注51zixue.net其它相关文章！
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