python之如何从 Python 中的补丁中恢复 3D 图像

我有一个形状为 DxHxW 的 3D 图像。我成功地将图像提取到补丁 pdxphxpw(重叠补丁)中。对于每个补丁，我都做了一些处理。现在，我想从处理过的补丁中生成图像，这样新图像的形状必须与原始图像相同。你能帮我做吗？

这是我提取补丁的代码

def patch_extract_3D(input,patch_shape,xstep=1,ystep=1,zstep=1): 
    patches_3D = np.lib.stride_tricks.as_strided(input, ((input.shape[0] - patch_shape[0] + 1) / xstep, (input.shape[1] - patch_shape[1] + 1) / ystep, 
                                                  (input.shape[2] - patch_shape[2] + 1) / zstep, patch_shape[0], patch_shape[1], patch_shape[2]), 
                                                  (input.strides[0] * xstep, input.strides[1] * ystep,input.strides[2] * zstep, input.strides[0], input.strides[1],input.strides[2])) 
    patches_3D= patches_3D.reshape(patches_3D.shape[0]*patches_3D.shape[1]*patches_3D.shape[2], patch_shape[0],patch_shape[1],patch_shape[2]) 
    return patches_3D 

这是处理补丁(只是简单的乘以 2

for i in range(patches_3D.shape[0]): 
    patches_3D[i]=patches_3D[i]; 
    patches_3D[i]=patches_3D[i]*2; 

现在，我需要的是 patches_3D，我想将它重新整形为原始图像。谢谢

这是示例代码

patch_shape=[2, 2, 2] 
input=np.arange(4*4*6).reshape(4,4,6) 
patches_3D=patch_extract_3D(input,patch_shape) 
print  patches_3D.shape 
for i in range(patches_3D.shape[0]): 
    patches_3D[i]=patches_3D[i]*2 
print  patches_3D.shape 

请您参考如下方法:

然而，这会反过来，因为你的补丁重叠，只有当它们的值在它们重叠的地方一致时，这才会被明确定义

def stuff_patches_3D(out_shape,patches,xstep=12,ystep=12,zstep=12): 
    out = np.zeros(out_shape, patches.dtype) 
    patch_shape = patches.shape[-3:] 
    patches_6D = np.lib.stride_tricks.as_strided(out, ((out.shape[0] - patch_shape[0] + 1) // xstep, (out.shape[1] - patch_shape[1] + 1) // ystep, 
                                                  (out.shape[2] - patch_shape[2] + 1) // zstep, patch_shape[0], patch_shape[1], patch_shape[2]), 
                                                  (out.strides[0] * xstep, out.strides[1] * ystep,out.strides[2] * zstep, out.strides[0], out.strides[1],out.strides[2])) 
    patches_6D[...] = patches.reshape(patches_6D.shape) 
    return out 

更新:这是一个更安全的版本，它平均重叠像素:

def stuff_patches_3D(out_shape,patches,xstep=12,ystep=12,zstep=12): 
    out = np.zeros(out_shape, patches.dtype) 
    denom = np.zeros(out_shape, patches.dtype) 
    patch_shape = patches.shape[-3:] 
    patches_6D = np.lib.stride_tricks.as_strided(out, ((out.shape[0] - patch_shape[0] + 1) // xstep, (out.shape[1] - patch_shape[1] + 1) // ystep, 
                                                  (out.shape[2] - patch_shape[2] + 1) // zstep, patch_shape[0], patch_shape[1], patch_shape[2]), 
                                                  (out.strides[0] * xstep, out.strides[1] * ystep,out.strides[2] * zstep, out.strides[0], out.strides[1],out.strides[2])) 
    denom_6D = np.lib.stride_tricks.as_strided(denom, ((denom.shape[0] - patch_shape[0] + 1) // xstep, (denom.shape[1] - patch_shape[1] + 1) // ystep, 
                                                  (denom.shape[2] - patch_shape[2] + 1) // zstep, patch_shape[0], patch_shape[1], patch_shape[2]), 
                                                  (denom.strides[0] * xstep, denom.strides[1] * ystep,denom.strides[2] * zstep, denom.strides[0], denom.strides[1],denom.strides[2])) 
    np.add.at(patches_6D, tuple(x.ravel() for x in np.indices(patches_6D.shape)), patches.ravel()) 
    np.add.at(denom_6D, tuple(x.ravel() for x in np.indices(patches_6D.shape)), 1) 
    return out/denom