目前在计算机视觉中应用的数组维度最多有四维&＃xff0c;可以表示为 (Batch_size, Row, Column, Channel)
以下将要从二维数组到四维数组进行代码的简单说明&＃xff1a;
Tips&＃xff1a;
1) 在numpy中所有的index都是从0开始。
2) axis &＃61; 0 对Cloumn(Width)操作&＃xff1b; axis &＃61; 1 对Row(Height)操作&＃xff1b; axis &＃61; 2 or -1 对Channel(Depth)操作
1. 二维数组 (Row, Column)
import numpy as np
# Set a matrix with (2*3)
array &＃61; np.array([
[1,2,3],
[4,5,6]
]) print(array)
[[1 2 3][4 5 6]]print(array.shape) # (Row, Column)
(2, 3) print(array[0,1])
2
2. 三维数组 (Row, Column, Channel)
import numpy as np# Set a matrix with (2*3*4)
array &＃61; np.array([[[1,2,3,4],[5,6,7,8],[9,10,11,12]],[[13,14,15,16],[17,18,19,20],[21,22,23,24]]])print(array)
[[[ 1 2 3 4][ 5 6 7 8][ 9 10 11 12]][[13 14 15 16][17 18 19 20][21 22 23 24]]]print(array.shape)
(2, 3, 4) #(Row, Column, Channel)print(array[0,1,2])
7
3. 四维数组(Batch_size, Row, Column, Channel)
import numpy as np
# Set a matrix with (2*2*3*4)
array &＃61; np.array([[[[1,2,3,4],[5,6,7,8],[9,10,11,12]],[[13,14,15,16],[17,18,19,20],[21,22,23,24]]],[[[21,22,23,24],[17,18,19,20],[13,14,15,16]],[[9,10,11,12],[5,6,7,8],[1,2,3,4]]]])print(array)
[[[[ 1 2 3 4][ 5 6 7 8][ 9 10 11 12]][[13 14 15 16][17 18 19 20][21 22 23 24]]][[[21 22 23 24][17 18 19 20][13 14 15 16]][[ 9 10 11 12][ 5 6 7 8][ 1 2 3 4]]]]print(array.shape) #(Batch_size, Row, Column, Channel)
(2, 2, 3, 4)print(array[1,0,1,2])
19print(array[1]) # Choice Batch_size 1
[[[21 22 23 24][17 18 19 20][13 14 15 16]][[ 9 10 11 12][ 5 6 7 8][ 1 2 3 4]]]
以上。
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