阅读 332

Python实现GIF动图以及视频卡通化详解

本文主要介绍了如何使用Python中的animegan2-pytorch实现动图以及视频的卡通化效果，文中的代码具有一定的学习价值，需要的朋友可以参考一下

目录

  • 前言

  • 环境依赖

  • 核心代码

    • gif动图卡通化

    • 视频卡通化

  • 总结

    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：

    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    31
    32
    33
    34
    35
    36
    37
    38
    39
    40
    41
    42
    43
    44
    45
    46
    47
    48
    49
    50
    51
    52
    53
    54
    55
    56
    57
    58
    59
    60
    61
    62
    63
    64
    65
    66
    67
    68
    69
    70
    71
    72
    73
    74
    75
    76
    77
    78
    79
    80
    81
    82
    83
    84
    85
    86
    87
    88
    89
    90
    91
    92
    93
    94
    95
    96
    97
    98
    99
    100
    101
    102
    103
    104
    105
    106
    107
    108
    109
    110
    111
    112
    113
    114
    115
    116
    117
    118
    119
    120
    121
    122
    123
    124
    125
    126
    127
    128
    129
    130
    131
    132
    133
    134
    135
    136
    137
    138
    139
    140
    141
    142
    143
    144
    145
    146
    147
    148
    149
    150
    151
    152
    153
    154
    155
    156
    157
    158
    159
    160
    161
    162
    163
    164
    165
    166
    167
    168
    169
    170
    171
    172
    #!/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, ImageSequence
    import torch
    from torchvision.transforms.functional import to_tensor, to_pil_image
    from torch import nn
    import os
    import torch.nn.functional as F
    import uuid
    import 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)