用numpy在网格中显示图像的更加自我的方式

Question

是否有更惯用的方式来显示图像网格,如下例所示？

import numpy as np

def gallery(array, ncols=3):
    nrows = np.math.ceil(len(array)/float(ncols))
    cell_w = array.shape[2]
    cell_h = array.shape[1]
    channels = array.shape[3]
    result = np.zeros((cell_h*nrows, cell_w*ncols, channels), dtype=array.dtype)
    for i in range(0, nrows):
        for j in range(0, ncols):
            result[i*cell_h:(i+1)*cell_h, j*cell_w:(j+1)*cell_w, :] = array[i*ncols+j]
    return result

我试着用hstack和reshape等,但不能得到正确的行为.

我有兴趣使用numpy这样做,因为使用matplotlib调用subplot和可以绘制多少图像是有限制的imshow.

如果您需要测试样本数据,可以像下面这样使用网络摄像头:

import cv2
import matplotlib.pyplot as plt
_, img = cv2.VideoCapture(0).read()

plt.imshow(gallery(np.array([img]*6)))

Answer 1

import numpy as np
import matplotlib.pyplot as plt

def gallery(array, ncols=3):
    nindex, height, width, intensity = array.shape
    nrows = nindex//ncols
    assert nindex == nrows*ncols
    # want result.shape = (height*nrows, width*ncols, intensity)
    result = (array.reshape(nrows, ncols, height, width, intensity)
              .swapaxes(1,2)
              .reshape(height*nrows, width*ncols, intensity))
    return result

def make_array():
    from PIL import Image
    return np.array([np.asarray(Image.open('face.png').convert('RGB'))]*12)

array = make_array()
result = gallery(array)
plt.imshow(result)
plt.show()

产量

我们有一系列的形状(nrows*ncols, height, weight, intensity).我们想要一个形状的阵列(height*nrows, width*ncols, intensity).

所以这里的想法是首先使用reshape将第一轴分成两个轴,一个是长度nrows,另一个是长度ncols:

array.reshape(nrows, ncols, height, width, intensity)

这允许我们用来swapaxes(1,2)重新排序轴,使形状变为 (nrows, height, ncols, weight, intensity).请注意,这位于nrows旁边height和ncols旁边width.

由于reshape不改变数据的raveled顺序,reshape(height*nrows, width*ncols, intensity)现在产生所需的数组.

这(在精神上)与unblockshaped功能中使用的想法相同.

Answer 2

另一种方法是使用view_as_blocks。然后你避免手动交换轴：

from skimage.util import view_as_blocks
import numpy as np

def refactor(im_in,ncols=3):
    n,h,w,c = im_in.shape
    dn = (-n)%ncols # trailing images
    im_out = (np.empty((n+dn)*h*w*c,im_in.dtype)
           .reshape(-1,w*ncols,c))
    view=view_as_blocks(im_out,(h,w,c))
    for k,im in enumerate( list(im_in) + dn*[0] ):
        view[k//ncols,k%ncols,0] = im 
    return im_out