1. opencv-python包
   opencv的像素值在0,1之间，存储的时候转换到0,255，show的时候转换到[0,255]

import cv2
img = cv2.imread("imgfile")
cv2.imshow("img_win_name", img)
cv2.waitKey(0) # 无限期等待输入

cv2.imwrite("write_file_name", img)

注意，opencv打开图片的格式为：
height×width×channels
height×width×channels

即通道数在最后面
其中33个通道的顺序分别是BB、GG、RR。
分离方法为:
b, g, r = cv2.split(img)

1. scikit-image包
   scikit-image的像素值在−1,1之间，存储的时候转换到0,255，show的时候转换到[0,255]

import skimage.io as io
import matplotlib.pyplot as plt

img = io.imread("a.jpg")
io.imshow(img)
plt.show()

注意skimage读取图片也是height×width×channelsheight×width×channels
通道顺序是R,G,BR,G,B

1. matplotlib包
   matplotlib的像素值在[-1,1]之间，存储的时候转换到0,255，show的时候转换到[0,255]

import matplotlib.pyplot as plt

img = plt.imread("img_name")
plt.imshow(img)

matplotlib读取图片也是height×widht×channelsheight×widht×channels，也是R,G,BR,G,B

1. tifffile包
2. tifffile as tiff

将图片的像素值放缩到[0,1]之间

def scale_percentile(matrix):

w, h, d = matrix.shape
matrix = np.reshape(matrix, [w * h, d]).astype(np.float64)
# Get 2nd and 98th percentile
mins = np.percentile(matrix, 1, axis=0)
maxs = np.percentile(matrix, 99, axis=0) - mins
matrix = (matrix - mins[None, :]) / maxs[None, :]
matrix = np.reshape(matrix, [w, h, d])
matrix = matrix.clip(0, 1)
return matrix

img = tiff.imread("file_name")
tiff.imshow(scale_percentile(img))

注意tifffile的图片的读取顺序height×width×channelsheight×width×channels。R,G,BR,G,B按照波段来获取。