1. opencv-python包
opencv的像素值在
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:
即通道数在最后面
其中
分离方法为:
b, g, r = cv2.split(img)
2. scikit-image包
scikit-image的像素值在
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
通道顺序是
3. matplotlib包
matplotlib的像素值在[-1,1]之间,存储的时候转换到
import matplotlib.pyplot as plt
img = plt.imread("img_name")
plt.imshow(img)
matplotlib读取图片也是
4. tifffile包
import 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