如何從Python中的補丁恢復3D圖像？

Question

我有一個形狀爲DxHxW的3D圖像。我成功地將圖像提取到補丁pdxphxpw（重疊補丁）。對於每個補丁，我都會做一些處理。現在，我想從處理過的補丁中生成圖像，以使新圖像必須與原始圖像具有相同的形狀。你能幫我做到嗎？

這是我的代碼提取補丁

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 

這是處理補丁（只是簡單的多用2

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

現在，我需要的是從patches_3D ，我想重塑它到原始圖像。謝謝

這是示例代碼

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 

Answer 1

這將反過來做，但是，因爲你的補丁重疊，這樣只會被明確定義，如果他們的價值認同它們重疊

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：這裏是一個平均重疊像素的安全版本：

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