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#1、打开图像,转化为灰度图
import numpy as np
import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from PIL import Image
import matplotlib.pyplot as plt
im=Image.open('D:/1234.jpeg').convert('L')
im=np.array(im,dtype='float32')#将其转换为一个矩阵
plt.imshow(im.astype('uint8'),cmap='gray')
#2、将图像数据转换为tensor张量,并适配卷积输入的要求
#3、创建Pytorch二维卷积神经网络对象,(卷积在pytorch中有两种方式,一种是torch.nn.Conv2d(),一种是torch.nn.functional.conv2d())
im=torch.from_numpy(im.reshape((1,1,im.shape[0],im.shape[1])))
conv1=nn.Conv2d(1,1,3,bias=False)#定义卷积
#定义轮廓检测卷积核
sobel_kernel=np.array([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]],dtype='float32')
sobel_kernel=sobel_kernel.reshape((1,1,3,3))#适配卷积的输入输出
#给卷积的kernel赋值
conv1.weight.data=torch.from_numpy(sobel_kernel)
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