Question

imageI我正在使用CNN进行体素分类以进行图像分割。我目前正在使用嵌套的for循环来生成用于神经网络的补丁，但是因为我拥有的图像是3维的，所以证明它需要永远。

无论如何，这是可以优化的，我无法看到？

def data_gen(image, labels, patch_size):

"""
:param image: 3 dimensional numpy matrix of image
:param labels: 3 dimensional numpy matrix of mask
:param patch_size: desried dimension of patch size
:return: array of patches and array of labels

"""

Patch_array = np.array([])
Label_array = np.array([])

PS = (patch_size - 1)/2

npad = ((PS, PS), (PS, PS), (PS,PS))

# Pad whole image with zeros to allow for patches at edges
image_pad = np.pad(image, pad_width=npad, mode='constant', constant_values = 0)

# Cycle over all voxels
for x in range(PS, image.shape[0]):
    for y in range(PS, image.shape[1]):
        for z in range(PS, image.shape[2]):

            # Add patch to array
            Patch_array = np.append(Patch_array, image_pad[x-PS:x+PS+1,y-PS:y+PS+1,z-PS:z+PS+1])

            Label_array = np.append(Label_array, labels[x,y,z])

return Label_array, Patch_array

谢谢！

Answer 1

有一个Scikit-Image实用功能：

http://scikit-image.org/docs/dev/api/skimage.util.html#view-as-windows

e.g。

from skimage.util.shape import view_as_windows
window_shape = (patch_size, patch_size, patch_size)
patch_array = view_as_windows(A, window_shape)

在幕后，这会使用as_strided中的numpy.lib.stride_tricks。

用于图像分割的迭代补丁生成 - 可以优化吗？

1 个答案: