踩坑：skimage中对图像做的归一化处理
skimage在读使用io.imread读取灰度图像时（as_grey=True / as_gray=True）会做归一化处理数据类型转化为float64；
图像缩放transform.resize同样会将uint8的图像转化为float64类型，这里注意的是！！！！！！！如果已经归一化，但是类型依然是uint8的图像，在缩放之后图像的范围将不再是（0-1）。
总结：skimage需要慎用，cv2，PIL都挺好用的！
import skimage
from skimage import io,transform
import numpy as np
image= io.imread('test.jpg',as_grey=False)
# 第一个参数是文件名可以是网络地址，第二个参数默认为False，True时为灰度图
print type(image) # out: numpy.ndarray
print image.dtype # out: dtype('uint8')
print image.shape # out: (300, 400, 3) (h,w,c)前面介绍了ndarray的特点
# mode也是RGB
print image
'''
注意此时image里都是整数uint8,范围[0-255]
array([
[ [143, 198, 201 (dim=3)],[143, 198, 201],... (w=200)],
[ [143, 198, 201],[143, 198, 201],... ],
...(h=100)
], dtype=uint8)
'''
image= io.imread('test.jpg',as_grey=True)
print image.shape # out: (300, 400)
print image
'''
此时image范围变为[0-1]
array([[ 0.73148549, 0.73148549, 0.73148549, ..., 0.73148549,
0.73148549, 0.73148549],
[ 0.73148549, 0.73148549, 0.73148549, ..., 0.73148549,
.....]])
'''
print image.dtype # out: dtype('float64')
image = io.imread('test.jpg',as_grey=False)
# h*w
image = transform.resize(image,(100, 200),order=1) # order默认是1，双线性
#resize后image范围又变成[0-1]
print image.dtype # out: dtype('float64')
print image.shape # out: (100, 200, 3)
print image
'''
array([[[ 0.56078431, 0.77647059, 0.78823529],
[ 0.56078431, 0.77647059, 0.78823529],
[ 0.56078431, 0.77647059, 0.78823529],
..., ...]])
'''
'''
resize函数接口
resize(image, output_shape, order=1, mode='constant', cval=0, clip=True, preserve_range=False)
order : int, optional
The order of interpolation. The order has to be in the range 0-5:
- 0: Nearest-neighbor
- 1: Bi-linear (default)
- 2: Bi-quadratic
- 3: Bi-cubic
- 4: Bi-quartic
- 5: Bi-quintic
'''
print skimage.img_as_float(image).dtype # out: float64
# img_as_float可以把image转为double，即float64
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作者：爆米花好美啊
来源：CSDN
原文：https://blog.csdn.net/u013010889/article/details/54347089
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