What is dimension order of numpy shape for image data?

OK, here's my take:

Using scipy.ndimage.imread('img.jpg', mode='RGB'), the resulting array will always have this order: (H, W, D) i.e. (height, width, depth) because of the terminology that numpy uses for ndarrays (axis=0, axis=1, axis=2) or analogously (Y, X, Z) if one would like to visualize in 3 dimensions.

# read imageIn [21]: img = scipy.ndimage.imread('suza.jpg', mode='RGB')# image shape as (H, W, D)In [22]: img.shapeOut[22]: (634, 1366, 3)# transpose to shape as (D, H, W)In [23]: tr_img = img.transpose((-1, 0, 1))    In [23]: tr_img.shapeOut[23]: (3, 634, 1366)

If you consider the img_shape as a tuple,

#  index    (0,   1,    2)img_shape = (634, 1366, 3)# or index  (-3,  -2,  -1)

Choose which one is a convenient way for you to remember.

NOTE: The scipy.ndimage.imread() API has been removed since Scipy 1.2.0. So, it is now recommended to use imageio.imread(), which reads the image and returns Array, a subclass of numpy array, following the same conventions discussed above.

# read image$ img = imageio.imread('suza.jpg', format='jpg')# convert the image to a numpy array$ img_np = np.asarray(img)

PS: It should also be noted that libraries like tensorflow also (almost) follows the same convention as numpy.

tf.image_decode_jpeg() returns:

A Tensor of type uint8. 3-D with shape [height, width, channels]