深度学习图像去雾代码实现pytorch - CSDN文库

深度学习图像去雾代码的实现可以使用AOD-Net去雾网络的Python源代码。这个代码使用了卷积神经网络来对雾霾图像进行去雾，并且采用了pytorch作为实现框架。你可以在官方的pytorch网站上找到更多关于pytorch的详细信息和文档。根据你的需求，你可以在anaconda-prompt中复制生成的代码，并使用conda命令来安装pytorch。具体的安装命令是"conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch"。

相关问题

深度学习图像去雾python代码

以下是使用深度学习进行图像去雾的Python代码示例，使用的是AOD-Net去雾网络的PyTorch实现： ```python import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from PIL import Image # 定义AOD-Net去雾网络 class AODNet(nn.Module): def __init__(self): super(AODNet, self).__init__() self.conv1 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(3) self.relu1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(3) self.relu2 = nn.ReLU(inplace=True) self.conv3 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn3 = nn.BatchNorm2d(3) self.relu3 = nn.ReLU(inplace=True) self.conv4 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn4 = nn.BatchNorm2d(3) self.relu4 = nn.ReLU(inplace=True) self.conv5 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn5 = nn.BatchNorm2d(3) self.relu5 = nn.ReLU(inplace=True) self.conv6 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn6 = nn.BatchNorm2d(3) self.relu6 = nn.ReLU(inplace=True) self.conv7 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn7 = nn.BatchNorm2d(3) self.relu7 = nn.ReLU(inplace=True) self.conv8 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn8 = nn.BatchNorm2d(3) self.relu8 = nn.ReLU(inplace=True) self.conv9 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn9 = nn.BatchNorm2d(3) self.relu9 = nn.ReLU(inplace=True) self.conv10 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn10 = nn.BatchNorm2d(3) self.relu10 = nn.ReLU(inplace=True) self.conv11 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn11 = nn.BatchNorm2d(3) self.relu11 = nn.ReLU(inplace=True) self.conv12 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn12 = nn.BatchNorm2d(3) self.relu12 = nn.ReLU(inplace=True) self.conv13 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn13 = nn.BatchNorm2d(3) self.relu13 = nn.ReLU(inplace=True) self.conv14 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn14 = nn.BatchNorm2d(3) self.relu14 = nn.ReLU(inplace=True) self.conv15 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn15 = nn.BatchNorm2d(3) self.relu15 = nn.ReLU(inplace=True) self.conv16 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn16 = nn.BatchNorm2d(3) self.relu16 = nn.ReLU(inplace=True) self.conv17 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn17 = nn.BatchNorm2d(3) self.relu17 = nn.ReLU(inplace=True) self.conv18 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn18 = nn.BatchNorm2d(3) self.relu18 = nn.ReLU(inplace=True) self.conv19 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn19 = nn.BatchNorm2d(3) self.relu19 = nn.ReLU(inplace=True) self.conv20 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn20 = nn.BatchNorm2d(3) self.relu20 = nn.ReLU(inplace=True) self.conv21 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn21 = nn.BatchNorm2d(3) self.relu21 = nn.ReLU(inplace=True) self.conv22 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn22 = nn.BatchNorm2d(3) self.relu22 = nn.ReLU(inplace=True) self.conv23 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn23 = nn.BatchNorm2d(3) self.relu23 = nn.ReLU(inplace=True) self.conv24 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn24 = nn.BatchNorm2d(3) self.relu24 = nn.ReLU(inplace=True) self.conv25 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn25 = nn.BatchNorm2d(3) self.relu25 = nn.ReLU(inplace=True) self.conv26 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn26 = nn.BatchNorm2d(3) self.relu26 = nn.ReLU(inplace=True) self.conv27 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn27 = nn.BatchNorm2d(3) self.relu27 = nn.ReLU(inplace=True) self.conv28 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn28 = nn.BatchNorm2d(3) self.relu28 = nn.ReLU(inplace=True) self.conv29 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn29 = nn.BatchNorm2d(3) self.relu29 = nn.ReLU(inplace=True) self.conv30 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn30 = nn.BatchNorm2d(3) self.relu30 = nn.ReLU(inplace=True) self.conv31 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn31 = nn.BatchNorm2d(3) self.relu31 = nn.ReLU(inplace=True) self.conv32 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn32 = nn.BatchNorm2d(3) self.relu32 = nn.ReLU(inplace=True) self.conv33 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn33 = nn.BatchNorm2d(3) self.relu33 = nn.ReLU(inplace=True) self.conv34 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn34 = nn.BatchNorm2d(3) self.relu34 = nn.ReLU(inplace=True) self.conv35 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn35 = nn.BatchNorm2d(3) self.relu35 = nn.ReLU(inplace=True) self.conv36 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn36 = nn.BatchNorm2d(3) self.relu36 = nn.ReLU(inplace=True) self.conv37 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn37 = nn.BatchNorm2d(3) self.relu37 = nn.ReLU(inplace=True) self.conv38 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn38 = nn.BatchNorm2d(3) self.relu38 = nn.ReLU(inplace=True) self.conv39 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn39 = nn.BatchNorm2d(3) self.relu39 = nn.ReLU(inplace=True) self.conv40 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn40 = nn.BatchNorm2d(3) self.relu40 = nn.ReLU(inplace=True) self.conv41 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn41 = nn.BatchNorm2d(3) self.relu41 = nn.ReLU(inplace=True) self.conv42 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn42 = nn.BatchNorm2d(3) self.relu42 = nn.ReLU(inplace=True) self.conv43 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn43 = nn.BatchNorm2d(3) self.relu43 = nn.ReLU(inplace=True) self.conv44 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn44 = nn.BatchNorm2d(3) self.relu44 = nn.ReLU(inplace=True) self.conv45 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn45 = nn.BatchNorm2d(3) self.relu45 = nn.ReLU(inplace=True) self.conv46 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn46 = nn.BatchNorm2d(3) self.relu46 = nn.ReLU(inplace=True) self.conv47 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn47 = nn.BatchNorm2d(3) self.relu47 = nn.ReLU(inplace=True) self.conv48 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn48 = nn.BatchNorm2d(3) self.relu48 = nn.ReLU(inplace=True) self.conv49 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn49 = nn.BatchNorm2d(3) self.relu49 = nn.ReLU(inplace=True) self.conv50 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn50 = nn.BatchNorm2d(3) self.relu50 = nn.ReLU(inplace=True) self.conv51 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn51 = nn.BatchNorm2d(3) self.relu51 = nn.ReLU(inplace=True) self.conv52 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn52 = nn.BatchNorm2d(3) self.relu52 = nn.ReLU(inplace=True) self.conv53 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn53 = nn.BatchNorm2d(3) self.relu53 = nn.ReLU(inplace=True) self.conv54 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn54 = nn.BatchNorm2d(3) self.relu54 = nn.ReLU(inplace=True) self.conv55 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn55 = nn.BatchNorm2d(3) self.relu55 = nn.ReLU(inplace=True) self.conv56 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False) self.bn56 = nn.BatchNorm2d(3) self.relu56 = nn.ReLU(inplace=True) self.conv57 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)

gcanet图像去雾python实现

GCANet是一种图像去雾的深度学习模型，它可以通过神经网络来恢复雾化图像的清晰度。以下是使用Python实现GCANet图像去雾的步骤： 1. 下载GCANet模型的代码和预训练权重，可以从GitHub上找到。 2. 安装PyTorch和其他必要的Python库，例如numpy、opencv和scikit-image。 3. 加载预训练的GCANet模型和权重，可以使用PyTorch的torch.load函数来加载模型。 4. 加载要去雾的图像，并使用opencv将其转换为灰度图像。 5. 对图像进行预处理，例如将其缩放为模型所需的大小，并将其转换为PyTorch张量。 6. 将图像传递给GCANet模型并运行前向传递以生成去雾图像。 7. 将生成的去雾图像转换为numpy数组，并使用opencv将其保存为图像文件。以下是一个简单的示例代码： ```python import torch import cv2 import numpy as np from skimage import transform # Load GCANet model and weights model = torch.load('gcanet_model.pth') # Load input image and convert to grayscale img = cv2.imread('input_image.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Preprocess input image gray = transform.resize(gray, (256, 256)) gray = np.expand_dims(gray, axis=0) gray = np.expand_dims(gray, axis=0) gray = torch.Tensor(gray) # Generate output image with torch.no_grad(): output = model(gray) output = output.squeeze().cpu().numpy() output = np.clip(output, 0, 1) # Save output image output = output * 255 cv2.imwrite('output_image.jpg', output) ``` 请注意，这只是一个简单的示例代码，您需要根据自己的需求进行修改和调整。

相关推荐

CSDN会员

开通CSDN年卡参与万元壕礼抽奖

海量 VIP免费资源千本正版电子书商城会员专享价千门课程&专栏

全年可省5,000元立即开通