What is dimension order of numpy shape for image data?

Question

I am using nibabel lib to load data from nii file. I read the document of the lib at http://nipy.org/nibabel/gettingstarted.html, and found that

This information is available without the need to load anything of the main image data into the memory. Of course there is also access to the image data as a NumPy array

This is my code to load the data and it shapes

import nibabel as nib img = nib.load('example.nii') data = img.get_data() data = np.squeeze(data) data = np.copy(data, order="C") print data.shape 

I got the result

128, 128, 64 

What is order of data shape? Is it WidthxHeightxDepth? And my input must arranged as depth, height, width. So I will use input=data.transpose(2,0,1). Is it right? Thanks all

Update: I found that the Numpy will read the image by order Height x Width x Depth as the reference http://www.python-course.eu/images/axis.jpeg

Answer 1

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

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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.

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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) 

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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]