![]() For C++, creating a non contiguously defined 4D array results in an array ]]. This makes more sense when you think of creating multidimensional arrays in C like langauges. doing np.zeros((2,2,3,4)).shape instead would mean (metaframs, frames, rows, columns). Which is actually (frames, rows, columns). In numpy, shape is largest stride first, ie, in a 3d vector, it would be the least contiguous dimension, Z, or pages, 3rd dim etc. Numpy's shape further has its own order in which it displays the shape. You can also do FORTRAN ordering ("F"), this instead orders elements based on columns, indexing contiguous elements. There, precisely how your data is layed out and how shape is represented in numpy is very important.īy default, numpy uses C ordering, which means contiguous elements in memory are the elements stored in rows. If you take a look to matrix c you will see it is a 100x200x3 matrix which is exactly what it is shown in the image (in red as we have set the R coordinate to 255 and the other two remain at 0).Īs much as people like to say "order doesn't matter its just convention" this breaks down when entering cross domain interfaces, IE transfer from C ordering to Fortran ordering or some other ordering scheme. Take a look to Numpy for Matlab users, it will guide you converting Matlab code to Numpy.įor example if you are using OpenCV, you can build an image using numpy taking into account that OpenCV uses BGR representation: import cv2 However you are calculating the same matrix. Numpy prints matrixes different to Matlab: You are right, you are creating a matrix with 2 rows, 3 columns and 4 depth. Most functions work on image coordinates ( x, y), which are opposite to matrix coordinates ( i, j). It stores images in BGR format and not RGB, like most Python libraries do.Ģ.2. If you work with colored images in OpenCV, remember that:Ģ.1. for n in range(10): a - note n in the first position, not the last). for n = 1:10 a(:, n) end), while in NumPy it's preferable to iterate over rows (e.g. For example, in Matlab efficient loop will be over columns (e.g. This doesn't affect indexing, but may affect performance. ![]() ![]() Matlab stores data column by column ("Fortran order"), while NumPy by default stores them row by row ("C order"). ![]() So you've got exactly the same structure (in terms of dimensions) as in Matlab, just printed in another way. Finally, the most nested lists have 4 elements each, same as the third dimension of a (depth/# of colors). Each of these elements is itself a list with 3 elements, which is equal to the second dimension of a (# of columns). The first level of this compound list l has exactly 2 elements, just as the first dimension of the array a (# of rows). Let's look at a full example: > a = np.zeros((2, 3, 4))Īrrays in NumPy are printed as the word array followed by structure, similar to embedded Python lists. You have a truncated array representation. Have I misunderstood something? If not, why the heck is numpy using such a unintuitive way of working with 3D-dimensional arrays? ![]() This complicates things greatly if all I want to do is try something on a known smaller 3-dimensional array. To further add to this problem, importing an image with OpenCV the color dimension is the last dimension, that is, I see the color information as the depth dimension. That is, the first dimension is the "depth". That is, 3 rows, 4 column and 2 depth dimensions. Instead it is presented as [0 0 0 0 [0 0 0 0 In my world this should result in 2 rows, 3 columns and 4 depth dimensions and it should be presented as: [0 0 0 [0 0 0 [0 0 0 [0 0 0 In fact the order doesn't make sense at all. My problem is that the order of the dimensions are off compared to Matlab. New at Python and Numpy, trying to create 3-dimensional arrays. ![]()
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