基于深度学习的图像生成方法，具体来说是使用了生成对抗网络（GAN）的变体架构进行图像生成任务通过构建特定的网络结构和进行适当的 4.38MB

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资源文件列表:

photo turn animation.zip 大约有4个文件

photo turn animation/
photo turn animation/AIpic.py 3.53KB
photo turn animation/input.jpg 21.6KB
photo turn animation/weight.pth 4.7MB

资源介绍:

基于深度学习的图像生成方法，具体来说是使用了生成对抗网络（GAN）的变体架构进行图像生成任务通过构建特定的网络结构和进行适当的

import os import cv2 import torch import numpy as np import torch.nn as nn class ResBlock(nn.Module): def __init__(self, num_channel): super(ResBlock, self).__init__() self.conv_layer = nn.Sequential( nn.Conv2d(num_channel, num_channel, 3, 1, 1), nn.BatchNorm2d(num_channel), nn.ReLU(inplace=True), nn.Conv2d(num_channel, num_channel, 3, 1, 1), nn.BatchNorm2d(num_channel)) self.activation = nn.ReLU(inplace=True) def forward(self, inputs): output = self.conv_layer(inputs) output = self.activation(output + inputs) return output class DownBlock(nn.Module): def __init__(self, in_channel, out_channel): super(DownBlock, self).__init__() self.conv_layer = nn.Sequential( nn.Conv2d(in_channel, out_channel, 3, 2, 1), nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True), nn.Conv2d(out_channel, out_channel, 3, 1, 1), nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True)) def forward(self, inputs): output = self.conv_layer(inputs) return output class UpBlock(nn.Module): def __init__(self, in_channel, out_channel, is_last=False): super(UpBlock, self).__init__() self.is_last = is_last self.conv_layer = nn.Sequential( nn.Conv2d(in_channel, in_channel, 3, 1, 1), nn.BatchNorm2d(in_channel), nn.ReLU(inplace=True), nn.Upsample(scale_factor=2), nn.Conv2d(in_channel, out_channel, 3, 1, 1)) self.act = nn.Sequential( nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True)) self.last_act = nn.Tanh() def forward(self, inputs): output = self.conv_layer(inputs) if self.is_last: output = self.last_act(output) else: output = self.act(output) return output class SimpleGenerator(nn.Module): def __init__(self, num_channel=32, num_blocks=4): super(SimpleGenerator, self).__init__() self.down1 = DownBlock(3, num_channel) self.down2 = DownBlock(num_channel, num_channel*2) self.down3 = DownBlock(num_channel*2, num_channel*3) self.down4 = DownBlock(num_channel*3, num_channel*4) res_blocks = [ResBlock(num_channel*4)]*num_blocks self.res_blocks = nn.Sequential(*res_blocks) self.up1 = UpBlock(num_channel*4, num_channel*3) self.up2 = UpBlock(num_channel*3, num_channel*2) self.up3 = UpBlock(num_channel*2, num_channel) self.up4 = UpBlock(num_channel, 3, is_last=True) def forward(self, inputs): down1 = self.down1(inputs) down2 = self.down2(down1) down3 = self.down3(down2) down4 = self.down4(down3) down4 = self.res_blocks(down4) up1 = self.up1(down4) up2 = self.up2(up1+down3) up3 = self.up3(up2+down2) up4 = self.up4(up3+down1) return up4 weight = torch.load('weight.pth', map_location='cpu') model = SimpleGenerator() model.load_state_dict(weight) model.eval() img = cv2.imread(r'input.jpg') image = img/127.5 - 1 image = image.transpose(2, 0, 1) image = torch.tensor(image).unsqueeze(0) output = model(image.float()) output = output.squeeze(0).detach().numpy() output = output.transpose(1, 2, 0) output = (output + 1) * 127.5 output = np.clip(output, 0, 255).astype(np.uint8) cv2.imwrite('output.jpg', output)

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