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1. 显示时，灰度值转化到0~255
2. 对0~1的灰度值，转化到0~255，在显示。
3. 对小于0的灰度值，将其视为0，然后 转化为0~255，在显示。
import numpy as np
import torchvision
import torch
from torch.utils.data import DataLoader
from torchvision import datasets,transforms
import os
import time
import matplotlib.pyplot as plt
data_dir="E:\PycharmProjects\PytorchLearn\DogsVSCats\\test1\\1.jpg"
image=plt.imread(data_dir)
image1=image/255.0
mean=image.mean()
std=image.std()
image2=(image-mean)/std
image3=image2.copy()
image3[image3<0]=0
plt.figure(1)
plt.imshow(image)
plt.figure(2)
plt.imshow(image1)
plt.figure(3)
plt.imshow(image2)
plt.figure(4)
plt.imshow(image3)
print("-"*10)
print(image)
print("-"*10)
print(image1)
print("-"*10)
print(image2)
print("-"*10)
print(image3)
plt.show()C:\ProgramData\Anaconda3\python.exe E:/PycharmProjects/PytorchLearn/TransformLearning/test.py
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
----------
[[[ 37 64 94]
[ 35 62 92]
[ 34 60 93]
...
[ 71 108 199]
[ 49 86 175]
[ 48 85 173]]
[[ 41 68 98]
[ 39 66 96]
[ 37 63 96]
...
[ 71 106 198]
[ 48 85 174]
[ 47 84 172]]
[[ 45 72 102]
[ 43 70 100]
[ 40 66 99]
...
[ 70 105 199]
[ 48 83 173]
[ 47 83 171]]
...
[[165 169 170]
[167 171 174]
[171 174 179]
...
[ 97 89 70]
[ 85 77 54]
[ 87 77 52]]
[[168 172 173]
[169 173 176]
[172 175 180]
...
[116 108 89]
[105 97 74]
[108 98 73]]
[[173 177 178]
[172 176 179]
[172 175 180]
...
[125 117 98]
[115 107 84]
[118 108 83]]]
----------
[[[0.14509804 0.25098039 0.36862745]
[0.1372549 0.24313725 0.36078431]
[0.13333333 0.23529412 0.36470588]
...
[0.27843137 0.42352941 0.78039216]
[0.19215686 0.3372549 0.68627451]
[0.18823529 0.33333333 0.67843137]]
[[0.16078431 0.26666667 0.38431373]
[0.15294118 0.25882353 0.37647059]
[0.14509804 0.24705882 0.37647059]
...
[0.27843137 0.41568627 0.77647059]
[0.18823529 0.33333333 0.68235294]
[0.18431373 0.32941176 0.6745098 ]]
[[0.17647059 0.28235294 0.4 ]
[0.16862745 0.2745098 0.39215686]
[0.15686275 0.25882353 0.38823529]
...
[0.2745098 0.41176471 0.78039216]
[0.18823529 0.3254902 0.67843137]
[0.18431373 0.3254902 0.67058824]]
...
[[0.64705882 0.6627451 0.66666667]
[0.65490196 0.67058824 0.68235294]
[0.67058824 0.68235294 0.70196078]
...
[0.38039216 0.34901961 0.2745098 ]
[0.33333333 0.30196078 0.21176471]
[0.34117647 0.30196078 0.20392157]]
[[0.65882353 0.6745098 0.67843137]
[0.6627451 0.67843137 0.69019608]
[0.6745098 0.68627451 0.70588235]
...
[0.45490196 0.42352941 0.34901961]
[0.41176471 0.38039216 0.29019608]
[0.42352941 0.38431373 0.28627451]]
[[0.67843137 0.69411765 0.69803922]
[0.6745098 0.69019608 0.70196078]
[0.6745098 0.68627451 0.70588235]
...
[0.49019608 0.45882353 0.38431373]
[0.45098039 0.41960784 0.32941176]
[0.4627451 0.42352941 0.3254902 ]]]
----------
[[[-1.28147369 -0.85929993 -0.39021797]
[-1.31274582 -0.89057206 -0.4214901 ]
[-1.32838189 -0.92184419 -0.40585403]
...
[-0.74984747 -0.17131305 1.25156889]
[-1.09384091 -0.51530649 0.87630332]
[-1.10947697 -0.53094256 0.84503119]]
[[-1.21892943 -0.79675567 -0.32767371]
[-1.25020156 -0.8280278 -0.35894584]
[-1.28147369 -0.87493599 -0.35894584]
...
[-0.74984747 -0.20258519 1.23593282]
[-1.10947697 -0.53094256 0.86066725]
[-1.12511304 -0.54657862 0.82939512]]
[[-1.15638517 -0.7342114 -0.26512945]
[-1.1876573 -0.76548354 -0.29640158]
[-1.23456549 -0.8280278 -0.31203764]
...
[-0.76548354 -0.21822125 1.25156889]
[-1.10947697 -0.56221469 0.84503119]
[-1.12511304 -0.56221469 0.81375906]]
...
[[ 0.71994267 0.78248693 0.79812299]
[ 0.7512148 0.81375906 0.86066725]
[ 0.81375906 0.86066725 0.93884758]
...
[-0.34330977 -0.4683983 -0.76548354]
[-0.53094256 -0.65603108 -1.01566058]
[-0.49967043 -0.65603108 -1.04693271]]
[[ 0.76685086 0.82939512 0.84503119]
[ 0.78248693 0.84503119 0.89193938]
[ 0.82939512 0.87630332 0.95448365]
...
[-0.04622453 -0.17131305 -0.4683983 ]
[-0.21822125 -0.34330977 -0.70293927]
[-0.17131305 -0.32767371 -0.71857534]]
[[ 0.84503119 0.90757545 0.92321152]
[ 0.82939512 0.89193938 0.93884758]
[ 0.82939512 0.87630332 0.95448365]
...
[ 0.09450006 -0.03058847 -0.32767371]
[-0.0618606 -0.18694912 -0.54657862]
[-0.0149524 -0.17131305 -0.56221469]]]
----------
[[[0. 0. 0. ]
[0. 0. 0. ]
[0. 0. 0. ]
...
[0. 0. 1.25156889]
[0. 0. 0.87630332]
[0. 0. 0.84503119]]
[[0. 0. 0. ]
[0. 0. 0. ]
[0. 0. 0. ]
...
[0. 0. 1.23593282]
[0. 0. 0.86066725]
[0. 0. 0.82939512]]
[[0. 0. 0. ]
[0. 0. 0. ]
[0. 0. 0. ]
...
[0. 0. 1.25156889]
[0. 0. 0.84503119]
[0. 0. 0.81375906]]
...
[[0.71994267 0.78248693 0.79812299]
[0.7512148 0.81375906 0.86066725]
[0.81375906 0.86066725 0.93884758]
...
[0. 0. 0. ]
[0. 0. 0. ]
[0. 0. 0. ]]
[[0.76685086 0.82939512 0.84503119]
[0.78248693 0.84503119 0.89193938]
[0.82939512 0.87630332 0.95448365]
...
[0. 0. 0. ]
[0. 0. 0. ]
[0. 0. 0. ]]
[[0.84503119 0.90757545 0.92321152]
[0.82939512 0.89193938 0.93884758]
[0.82939512 0.87630332 0.95448365]
...
[0.09450006 0. 0. ]
[0. 0. 0. ]
[0. 0. 0. ]]]
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Process finished with exit code 0
