You have a truncated array representation. Let's look at a full example:
>>> a = np.zeros((2, 3, 4))
>>> a
array([[[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]],
[[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]]])
Arrays in NumPy are printed as the word array followed by structure, similar to embedded Python lists. Let's create a similar list:
>>> l = [[[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]],
[[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]]]
>>> l
[[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]
The first level of this compound list l has exactly 2 elements, just as the first dimension of the array a (# of rows). Each of these elements is itself a list with 3 elements, which is equal to the second dimension of a (# of columns). Finally, the most nested lists have 4 elements each, same as the third dimension of a (depth/# of colors).
So you've got exactly the same structure (in terms of dimensions) as in Matlab, just printed in another way.
Some caveats:
Matlab stores data column by column ("Fortran order"), while NumPy by default stores them row by row ("C order"). This doesn't affect indexing, but may affect performance. For example, in Matlab efficient loop will be over columns (e.g.
for n = 1:10 a(:, n) end), while in NumPy it's preferable to iterate over rows (e.g.for n in range(10): a[n, :]-- notenin the first position, not the last).If you work with colored images in OpenCV, remember that:
2.1. It stores images in BGR format and not RGB, like most Python libraries do.
2.2. Most functions work on image coordinates (
x, y), which are opposite to matrix coordinates (i, j).
You have a truncated array representation. Let's look at a full example:
>>> a = np.zeros((2, 3, 4))
>>> a
array([[[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]],
[[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]]])
Arrays in NumPy are printed as the word array followed by structure, similar to embedded Python lists. Let's create a similar list:
>>> l = [[[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]],
[[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]]]
>>> l
[[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]
The first level of this compound list l has exactly 2 elements, just as the first dimension of the array a (# of rows). Each of these elements is itself a list with 3 elements, which is equal to the second dimension of a (# of columns). Finally, the most nested lists have 4 elements each, same as the third dimension of a (depth/# of colors).
So you've got exactly the same structure (in terms of dimensions) as in Matlab, just printed in another way.
Some caveats:
Matlab stores data column by column ("Fortran order"), while NumPy by default stores them row by row ("C order"). This doesn't affect indexing, but may affect performance. For example, in Matlab efficient loop will be over columns (e.g.
for n = 1:10 a(:, n) end), while in NumPy it's preferable to iterate over rows (e.g.for n in range(10): a[n, :]-- notenin the first position, not the last).If you work with colored images in OpenCV, remember that:
2.1. It stores images in BGR format and not RGB, like most Python libraries do.
2.2. Most functions work on image coordinates (
x, y), which are opposite to matrix coordinates (i, j).
No need to go in such deep technicalities, and get yourself blasted. Let me explain it in the most easiest way. We all have studied "Sets" during our school-age in Mathematics. Just consider 3D numpy array as the formation of "sets".
x = np.zeros((2,3,4))
Simply Means:
2 Sets, 3 Rows per Set, 4 Columns
Example:
Input
x = np.zeros((2,3,4))
Output
Set # 1 ---- [[[ 0., 0., 0., 0.], ---- Row 1
[ 0., 0., 0., 0.], ---- Row 2
[ 0., 0., 0., 0.]], ---- Row 3
Set # 2 ---- [[ 0., 0., 0., 0.], ---- Row 1
[ 0., 0., 0., 0.], ---- Row 2
[ 0., 0., 0., 0.]]] ---- Row 3
Explanation: See? we have 2 Sets, 3 Rows per Set, and 4 Columns.
Note: Whenever you see a "Set of numbers" closed in double brackets from both ends. Consider it as a "set". And 3D and 3D+ arrays are always built on these "sets".
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Hey everyone,
Currently I am learning arrays in python for learning machine learning, and I learned 1D array and 2D array now I want to learn 3D array, but I don't get any resource which explaining 3D arrays in python, I searched on google, Gemini, ChatGPT, Bing ai, YouTube. But anyone is not explaining 3D array properly,
Can anyone please Explain me 3D arrays and How 3D arrays look like?