python - 2D 可变迭代器/生成器

Question

我有一个 NxN 矩阵，我想将其拆分为非重叠的 KxK block 。对于每个 block ，我想为元素分配新值。

由于这看起来是生成器的好地方，所以我实现了:

def extracted_patches(im, top_left, patch_size, grid_size):
    '''Extract patches in row-major order following a specific configuration

    Parameters
    ----------
    im : the input image (2D numpy array)
    top_left : (y,x) coordinate of the top left point (e.g. (3,5))
    grid_size : (cy, cx) how many patches in the y-direction and in the x-direction
    patch_size : (h, w) how many pixels for the size of each patch

    Returns
    -------
    a generator that goes through each patch (a numpy array view) in row-major order
    '''
    for i in xrange(grid_size[0]):
        for j in xrange(grid_size[1]):
            yield im[top_left[0] + patch_size[0]*i : top_left[0] + patch_size[0]*(i+1)
                    ,top_left[1] + patch_size[1]*j : top_left[1] + patch_size[1]*(j+1)]

然后，当我尝试更改每个补丁的值时，赋值会更改变量值而不是生成器给出的值

output_im = np.zeros((patch_size[0]*grid_size[0], patch_size[1]*grid_size[1]))        
output_im_it = extracted_patches(output_im, (0,0), patch_size, grid_size)

for i in xrange(grid_size[0]*grid_size[1]):
    output_im_it = np.random.random(patch_size)

我的生成器可以是可变的吗？

Answer 1

与保存 numpy 数组的任何变量一样，要更改“指向”的值，您需要避免分配给该变量，而是分配给它的一个切片。试试这个:

for submat in output_im_it:
     submat[:] = np.random.random(patch_size)

作为对您编辑的回应:您似乎将生成器对象与其生成的值混淆了。您不能分配给生成器对象本身的切片。您可以分配给 numpy 数组的切片，您可以使用例如获得output_im_it.next() 或使用 for 循环，如上所述。