我想在 Python 中調(diào)用 Matlab .m 文件和函數(shù)，但是由于 Matlab 和 Python 之間的數(shù)據(jù)類型不同，出現(xiàn)了關(guān)于 .m 文件的錯誤TypeError: unsupported Python data type: numpy.ndarray。作為以下代碼中的示例VoxelSizeUnification，我想在 Python 中調(diào)用它的 Matlab 函數(shù)，其輸入來自 Python 數(shù)據(jù)類型：import matlab.engineeng = matlab.engine.start_matlab()xyzSpacing = [dcm_image.SliceThickness, dcm_image.PixelSpacing[1], dcm_image.PixelSpacing[0]]xyzNewSpacing = [1.25, 1.25, 1.25]eng.VoxelSizeUnification(volume_image, xyzNewSpacing, xyzSpacing)  # TypeError: unsupported Python data type: numpy.ndarray那：volume_image is {ndarray} and includes images as: volume_image[number of slices in 3rd dimenson = 133, rows=512, columns=512].xyzNewSpacing and xyzSpacing are <class 'list'> with size of (1 x 3)此外，我使用link1 進(jìn)行搜索，但我不想保存文件然后加載它們。同樣在link2 中，mlab 應(yīng)該使用 python>=2.7 并且我的 Python 是 3.6.6 和 Matlab 2017b。另外，我已經(jīng)matlab.double用一個例子嘗試并測試了上面的代碼，沒有任何錯誤：xyzNewSpacing = matlab.double([1.25, 1.25, 1.25])xyzSpacing = matlab.double([1.5, 1.5, 1.5])vol = matlab.double([[[1, 2, 1], [3, 1, 5], [2, 1, 2]],                     [[2, 3, 1], [1, 2, 3], [2, 1, 3]],                     [[4, 2, 1], [2, 3, 1], [3, 2, 1]]])ret = eng.VoxelSizeUnification(vol, xyzNewSpacing, xyzSpacing)但是，對于volume_image這是圖像的3D陣列，收到錯誤約：ValueError: initializer must be a rectangular nested sequence。Python：xyzNewSpacing = matlab.double([1.25, 1.25, 1.25])xyzSpacing = matlab.double([1.5, 1.5, 1.5])d = matlab.double(volume_image) # ValueError: initializer must be a rectangular nested sequenceret = eng.VoxelSizeUnification(d, xyzSpacing, xyzNewSpacing)MATLAB：function outputSize = VoxelSizeUnification(d, xyzSpacing, xyzNewSpacing)         outputSize = [ceil((d(1)*xyzSpacing(1))/xyzNewSpacing(1))...                      ceil((d(2)*xyzSpacing(2))/xyzNewSpacing(2))...                       ceil((d(3)*xyzSpacing(3))/xyzNewSpacing(3))];end的原因是ValueError: initializer must be a rectangular nested sequence什么？謝謝。