干净地平铺以平面1D格式存储的图像的numpy数组

Question

I'm loading in a bunch of 16x16 images from a .csv file in with Numpy. Each row is a list of 256 grayscale values stored in CMO (so the shape is (n,256) where n is the number of images). This means that I can display any individual image with pyplot as:

plot.imshow(np.reshape(images[index], (16,16), order='F'), cmap=cm.Greys_r)

I want to tile these images with a certain number of images per row. I do have a working solution:

def TileImage(imgs, picturesPerRow=16):
    # Convert to a true list of 16x16 images
    tmp = np.reshape(imgs, (-1, 16, 16), order='F')
    img = ""
    for i in range(0, tmp.shape[0], picturesPerRow):
        # On the last iteration, we may not have exactly picturesPerRow
        # images left so we need to pad        
        if tmp.shape[0] - i >= picturesPerRow:
            mid = np.concatenate(tmp[i:i+picturesPerRow], axis=1)
        else:
            padding = np.zeros((picturesPerRow - (tmp.shape[0] -i), 16, 16))
            mid = np.concatenate(np.concatenate((tmp[i:tmp.shape[0]], padding), axis=0), axis=1)

        if img == "":
            img = mid
        else:
            img = np.concatenate((img, mid), axis=0)

    return img

This works perfectly fine, but it feels like there should be a much cleaner way to do this sort of thing. I'm a bit of a novice at Numpy and I was wondering if there was a cleaner way to tile the flattened data in a way without all the manual padding and conditional concatenation.

Usually these sorts of simple array reshaping operations can be done in a couple of lines with Numpy, so I feel like I'm missing something. (Also, using a "" as a flag as if it were a null pointer seems a bit messy)

Answer 1

Here is a simplified version of your implementation.

Could not think about any simpler way of doing it.

def TileImage(imgs, picturesPerRow=16):
    """ Convert to a true list of 16x16 images
    """

    # Calculate how many columns
    picturesPerColumn = imgs.shape[0]/picturesPerRow + 1*((imgs.shape[0]%picturesPerRow)!=0)

    # Padding
    rowPadding = picturesPerRow - imgs.shape[0]%picturesPerRow
    imgs = vstack([imgs,zeros([rowPadding,imgs.shape[1]])])

    # Reshaping all images
    imgs = imgs.reshape(imgs.shape[0],16,16)

    # Tiling Loop (The conditionals are not necessary anymore)
    tiled = []
    for i in range(0,picturesPerColumn*picturesPerRow,picturesPerRow):
        tiled.append(hstack(imgs[i:i+picturesPerRow,:,:]))


    return vstack(tiled)

Hope it helps.