How can I specify a subset of a numpy matrix for placement of smaller matrix?
<p>I have an image pyramid with a down sample rate of 2. That is, the bottom of my pyramid is a an image of shape <code>(256, 256)</code>, where the next level is <code>(128, 128)</code>, etc.</p>

<p>My goal is to display this pyramid into a single image. The first image is placed on the left. The second is placed in the top right corner. Each subsequent image must be placed beneath the previous and wedged into the corner.</p>

<p>Here is my current function:</p>

<pre><code>def pyramid2img(pmd):
    '''
    Given a pre-constructed pyramid, this is a helper
    function to display the pyramid in a single image.
    '''

    # orignal shape (pyramid goes from biggest to smallest)
    org_img_shp = pmd[0].shape

    # the output will have to have 1.5 times the width
    out_shp = tuple(int(x*y) \
        for (x,y) in zip(org_img_shp, (1, 1.5)))
    new_img = np.zeros(out_shp, dtype=np.int8)

    # i keep track of the top left corner of where I want to 
    # place the current image matrix
    origin = [0, 0]
    for lvl, img_mtx in enumerate(pmd):

        # trying to specify the subset to place the next img_mtx in
        sub = new_img[origin[0]:origin[0]+pmd[lvl].shape[0],
            origin[1]:origin[1]+pmd[lvl].shape[1]]# = img_mtx

        # some prints to see exactly whats being called above ^
        print 'level {}, sub {}, mtx {}'.format(
            lvl, sub.shape, img_mtx.shape)
        print 'sub = new_img[{}:{}, {}:{}]'.format(
            origin[0], origin[0]+pmd[lvl].shape[0],
            origin[1], origin[1]+pmd[lvl].shape[1])

        # first shift moves the origin to the right
        if lvl == 0:
            origin[0] += pmd[lvl].shape[0]
        # the rest move the origin downward
        else:
            origin[1] += pmd[lvl].shape[1]

    return new_img
</code></pre>

<p>OUTPUT FROM THE PRINT STATEMENTS:</p>

<pre><code>level 0, sub (256, 256), mtx (256, 256)
sub = new_img[0:256, 0:256]


level 1, sub (0, 128), mtx (128, 128)
sub = new_img[256:384, 0:128]


level 2, sub (0, 64), mtx (64, 64)
sub = new_img[256:320, 128:192]


level 3, sub (0, 32), mtx (32, 32)
sub = new_img[256:288, 192:224]


level 4, sub (0, 16), mtx (16, 16)
sub = new_img[256:272, 224:240]


level 5, sub (0, 8), mtx (8, 8)
sub = new_img[256:264, 240:248]


level 6, sub (0, 4), mtx (4, 4)
sub = new_img[256:260, 248:252]
</code></pre>

<p>If you look at the output, you can see that I am trying to reference a 2d-slice of the output image so that I can place the next level of the pyramid inside it.</p>

<p>The problem is that the slicing I am performing is not giving a 2d-array with the shape I expect it too. It thinks I am trying to put a (n,n) matrix into a (0, n) matrix.</p>

<p>How come when I specify a slice like <code>new_img[256:320, 128:192]</code>, it returns an object with shape <code>(0, 64)</code>, NOT <code>(64, 64)</code>?</p>

<p>Is there an easier way to do what I am trying to do?</p>

<p>Here is an example.</p>

<p>Create a pyramid first:</p>

<pre><code>import numpy as np
import pylab as pl
import cv2

img = cv2.imread("earth.jpg")[:, :, ::-1]

size = 512
imgs = []
while size &gt;= 2:
    imgs.append(cv2.resize(img, (size, size)))
    size //= 2
</code></pre>

<p>And here is the code to merge the images:</p>

<pre><code>def align(size, width, loc):
    if loc in ("left", "top"):
        return 0
    elif loc in ("right", "bottom"):
        return size - width
    elif loc == "center":
        return (size - width) // 2

def resize_canvas(img, shape, loc, fill=255):
    new_img = np.full(shape + img.shape[2:], fill, dtype=img.dtype)
    y = align(shape[0], img.shape[0], loc[0])
    x = align(shape[1], img.shape[1], loc[1])
    new_img[y:y+img.shape[0], x:x+img.shape[1], ...] = img
    return new_img

def vbox(imgs, align="right", fill=255):
    width = max(img.shape[1] for img in imgs)
    return np.concatenate([
            resize_canvas(img, (img.shape[0], width), ("top", align), fill=fill) 
            for img in imgs
        ])

def hbox(imgs, align="top", fill=255):
    height = max(img.shape[0] for img in imgs)
    return np.concatenate([
            resize_canvas(img, (height, img.shape[1]), (align, "left"), fill=fill) 
            for img in imgs
        ], axis=1)
</code></pre>

<p>the output of:</p>

<pre><code>pl.imshow(hbox([imgs[0], vbox(imgs[1:])]))
</code></pre>

<p><a href="http://i.stack.imgur.com/pCSVp.png" rel="nofollow"><img src="http://i.stack.imgur.com/pCSVp.png" alt="enter image description here"></a></p>

python numpy image-processing