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  • [图像处理]基于 PyTorch 的高斯核卷积

    import torch
    import numpy as np
    import torch.nn as nn
    import torch.nn.functional as F
    import cv2
    import matplotlib.pyplot as plt
    from PIL import Image
    
    
    class GaussianBlurConv(nn.Module):
        def __init__(self, channels=3):
            super(GaussianBlurConv, self).__init__()
            self.channels = channels
            # print("channels: ", channels.shape)
            kernel = [[0.00078633, 0.00655965, 0.01330373, 0.00655965, 0.00078633],
                      [0.00655965, 0.05472157, 0.11098164, 0.05472157, 0.00655965],
                      [0.01330373, 0.11098164, 0.22508352, 0.11098164, 0.01330373],
                      [0.00655965, 0.05472157, 0.11098164, 0.05472157, 0.00655965],
                      [0.00078633, 0.00655965, 0.01330373, 0.00655965, 0.00078633]]
    
            kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)    # (H, W) -> (1, 1, H, W)
            kernel = kernel.expand((int(channels), 1, 5, 5))
            self.weight = nn.Parameter(data=kernel, requires_grad=False)
    
        def __call__(self, x):
            x = F.conv2d(x, self.weight, padding=2, groups=self.channels)
            return x
    
    
    path = r"/home/curry/Pictures/lenna/lenna.jpg"
    img = Image.open(path)
    img = np.array(img)
    
    img = torch.from_numpy(img).unsqueeze(dim=0).permute(0, 3, 1, 2).contiguous().float() # (1, H, W, C) -> (1, C, H, W)
    
    gaussian = GaussianBlurConv(channels=img.shape[1])	
    
    result = gaussian(img).squeeze().permute(1, 2, 0).contiguous().numpy().astype(np.int32)	# 做高斯处理
    
    plt.imshow(result)
    plt.show()
    
    
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  • 原文地址:https://www.cnblogs.com/xxxxxxxxx/p/12207719.html
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