图像相关层之卷积和池化锐化图片示例

前情提要：黑白图只有1个通道数，彩色图却含有三个通道数。

在这里插入图片描述

一、彩色图像输入，灰度输出（代码和结果如下）

1、卷积未池化

1.1 代码一

from PIL import Image
import torch as t
from torch import nn
from torch.autograd import Variable as V
from torchvision.transforms import ToTensor,ToPILImage
to_tensor = ToTensor() # 创建Totensor对象，可以将图片转换为tensor
to_pil = ToPILImage() # 创建ToPILImage对象，可以将tensor转换成图像
image = Image.open('E:/pythonProjecttest/mg.png') # 图片提取
# if image.mode != 'L':
#     image = image.convert('L')
input = to_tensor(image).unsqueeze(0) # 将图片转换为tensor

# 锐化卷积核
kernel = t.ones(3,3,3)/9 # 卷积核大小设置
kernel[1][1][1] = 1
conv = nn.Conv2d(3,1,(3,3,3),1,bias=False) # 设置输入通道数、输出通道数、卷积核大小、步长、...偏置
conv.weight.data = kernel.view(1,3,3,3) # 卷积核必须数量一致
out = conv(V(input))
to_pil(out.data.squeeze(0)).show() # 将tensor转换成图像并显示图片

1.2 代码二

from PIL import Image
import torch as t
from torch import nn
from torch.autograd import Variable as V
from torchvision.transforms import ToTensor,ToPILImage
to_tensor = ToTensor() # 创建Totensor对象，可以将图片转换为tensor
to_pil = ToPILImage() # 创建ToPILImage对象，可以将tensor转换成图像
image = Image.open('E:/pythonProjecttest/mg.png') # 图片提取
if image.mode != 'L': # 彩色图转换为黑白图
    image = image.convert('L')
input = to_tensor(image).unsqueeze(0)

# 锐化卷积核
kernel = t.ones(3,3)/9
kernel[1][1] = 1
conv = nn.Conv2d(1,1,(3,3),1,bias=False) # 设置输入通道数、输出通道数、卷积核大小、步长、...偏置
conv.weight.data = kernel.view(1,1,3,3) # 卷积核必须数量一致
out = conv(V(input))
to_pil(out.data.squeeze(0)).show() # 将tensor转换成图像并显示图片

在这里插入图片描述

2、池化未卷积

from PIL import Image
import torch as t
from torch import nn
from torch.autograd import Variable as V
from torchvision.transforms import ToTensor,ToPILImage
to_tensor = ToTensor() # 创建Totensor对象，可以将图片转换为tensor
to_pil = ToPILImage() # 创建ToPILImage对象，可以将tensor转换成图像
image = Image.open('E:/pythonProjecttest/mg.png') # 图片提取
if image.mode != 'L': # 彩色图转换为黑白图
    image = image.convert('L')
input = to_tensor(image).unsqueeze(0)

# 锐化卷积核
# kernel = t.ones(3,3)/9
# kernel[1][1] = 1
# conv = nn.Conv2d(1,1,(3,3),1,bias=False) # 设置输入通道数、输出通道数、卷积核大小、步长、...偏置
# conv.weight.data = kernel.view(1,1,3,3) # 卷积核必须数量一致
# out = conv(V(input))
# 池化层
pool = nn.AvgPool2d(2,2)
out = pool(V(input))

to_pil(out.data.squeeze(0)).show() # 显示图片

在这里插入图片描述

3、卷积及池化

from PIL import Image
import torch as t
from torch import nn
from torch.autograd import Variable as V
from torchvision.transforms import ToTensor,ToPILImage
to_tensor = ToTensor() # 创建Totensor对象，可以将图片转换为tensor
to_pil = ToPILImage() # 创建ToPILImage对象，可以将tensor转换成图像
image = Image.open('E:/pythonProjecttest/mg.png') # 图片提取
if image.mode != 'L': # 彩色图转换为黑白图
    image = image.convert('L')
input = to_tensor(image).unsqueeze(0)

# 锐化卷积核
kernel = t.ones(3,3)/9
kernel[1][1] = 1
conv = nn.Conv2d(1,1,(3,3),1,bias=False) # 设置输入通道数、输出通道数、卷积核大小、步长、...偏置
conv.weight.data = kernel.view(1,1,3,3) # 卷积核必须数量一致
out = conv(V(input))
# 池化层
pool = nn.AvgPool2d(2,2)
out = pool(out)

to_pil(out.data.squeeze(0)).show() # 显示图片

在这里插入图片描述

二、彩色图像输入，彩色输出（代码和结果如下）

1、卷积未池化

from PIL import Image
import torch as t
from torch import nn
from torch.autograd import Variable as V
from torchvision.transforms import ToTensor,ToPILImage
to_tensor = ToTensor() # 创建Totensor对象，可以将图片转换为tensor
to_pil = ToPILImage() # 创建ToPILImage对象，可以将tensor转换成图像
image = Image.open('E:/pythonProjecttest/mg.png') # 图片提取
# if image.mode != 'L':
#     image = image.convert('L')
input = to_tensor(image).unsqueeze(0)

# 锐化卷积核
kernel = t.ones(3,3,3,3)/9
kernel[1][1][1][1] = 1
conv = nn.Conv2d(3,3,(3,3,3,3),1,bias=False) # 设置输入通道数、输出通道数、卷积核大小、步长、...偏置
conv.weight.data = kernel.view(3,3,3,3) # 卷积核必须数量一致
out = conv(V(input))
to_pil(out.data.squeeze(0)).show() # 显示图片

在这里插入图片描述

2、池化未卷积

from PIL import Image
import torch as t
from torch import nn
from torch.autograd import Variable as V
from torchvision.transforms import ToTensor,ToPILImage
to_tensor = ToTensor() # 创建Totensor对象，可以将图片转换为tensor
to_pil = ToPILImage() # 创建ToPILImage对象，可以将tensor转换成图像
image = Image.open('E:/pythonProjecttest/mg.png') # 图片提取
# if image.mode != 'L': # 彩色图转换为黑白图
#     image = image.convert('L')
input = to_tensor(image).unsqueeze(0)

# 锐化卷积核
# kernel = t.ones(3,3)/9
# kernel[1][1] = 1
# conv = nn.Conv2d(1,1,(3,3),1,bias=False) # 设置输入通道数、输出通道数、卷积核大小、步长、...偏置
# conv.weight.data = kernel.view(1,1,3,3) # 卷积核必须数量一致
# out = conv(V(input))
# 池化层
pool = nn.AvgPool2d(2,2)
out = pool(V(input))

to_pil(out.data.squeeze(0)).show() # 显示图片

在这里插入图片描述

3、卷积及池化

from PIL import Image
import torch as t
from torch import nn
from torch.autograd import Variable as V
from torchvision.transforms import ToTensor,ToPILImage
to_tensor = ToTensor() # 创建Totensor对象，可以将图片转换为tensor
to_pil = ToPILImage() # 创建ToPILImage对象，可以将tensor转换成图像
image = Image.open('E:/pythonProjecttest/mg.png') # 图片提取
# if image.mode != 'L': # 彩色图转换为黑白图
#     image = image.convert('L')
input = to_tensor(image).unsqueeze(0)

# 锐化卷积核
kernel = t.ones(3,3,3,3)/9
kernel[1][1][1][1] = 1
conv = nn.Conv2d(3,3,(3,3,3,3),1,bias=False) # 设置输入通道数、输出通道数、卷积核大小、步长、...偏置
conv.weight.data = kernel.view(3,3,3,3) # 卷积核必须数量一致
out = conv(V(input))
# 池化层
pool = nn.AvgPool2d(2,2)
out = pool(out)

to_pil(out.data.squeeze(0)).show() # 显示图片