Numpy uint8_t arrays to vtkImageData

A slightly more complete answer (generalizing to 1-3 channels, different datatypes).

import vtkimport numpy as npfrom vtk.util import numpy_supportdef numpy_array_as_vtk_image_data(source_numpy_array):    """    :param source_numpy_array: source array with 2-3 dimensions. If used, the third dimension represents the channel count.    Note: Channels are flipped, i.e. source is assumed to be BGR instead of RGB (which works if you're using cv2.imread function to read three-channel images)    Note: Assumes array value at [0,0] represents the upper-left pixel.    :type source_numpy_array: np.ndarray    :return: vtk-compatible image, if conversion is successful. Raises exception otherwise    :rtype vtk.vtkImageData    """    if len(source_numpy_array.shape) > 2:        channel_count = source_numpy_array.shape[2]    else:        channel_count = 1    output_vtk_image = vtk.vtkImageData()    output_vtk_image.SetDimensions(source_numpy_array.shape[1], source_numpy_array.shape[0], channel_count)    vtk_type_by_numpy_type = {        np.uint8: vtk.VTK_UNSIGNED_CHAR,        np.uint16: vtk.VTK_UNSIGNED_SHORT,        np.uint32: vtk.VTK_UNSIGNED_INT,        np.uint64: vtk.VTK_UNSIGNED_LONG if vtk.VTK_SIZEOF_LONG == 64 else vtk.VTK_UNSIGNED_LONG_LONG,        np.int8: vtk.VTK_CHAR,        np.int16: vtk.VTK_SHORT,        np.int32: vtk.VTK_INT,        np.int64: vtk.VTK_LONG if vtk.VTK_SIZEOF_LONG == 64 else vtk.VTK_LONG_LONG,        np.float32: vtk.VTK_FLOAT,        np.float64: vtk.VTK_DOUBLE    }    vtk_datatype = vtk_type_by_numpy_type[source_numpy_array.dtype.type]    source_numpy_array = np.flipud(source_numpy_array)    # Note: don't flip (take out next two lines) if input is RGB.    # Likewise, BGRA->RGBA would require a different reordering here.    if channel_count > 1:        source_numpy_array = np.flip(source_numpy_array, 2)    depth_array = numpy_support.numpy_to_vtk(source_numpy_array.ravel(), deep=True, array_type = vtk_datatype)    depth_array.SetNumberOfComponents(channel_count)    output_vtk_image.SetSpacing([1, 1, 1])    output_vtk_image.SetOrigin([-1, -1, -1])    output_vtk_image.GetPointData().SetScalars(depth_array)    output_vtk_image.Modified()    return output_vtk_image

Using the numpy_support library one can convert numpy arrays into a vtk data arrays

from vtk.util import numpy_supportdef updateFrames(self, depthFrame, rgbFrame=None):   #Build vtkImageData here from the given numpy uint8_t arrays.   self.__depthImageData = vtk.vtkImageData()   depthArray = numpy_support.numpy_to_vtk(depthFrame.ravel(), deep=True, array_type=vtk.VTK_UNSIGNED_CHAR)    # .transpose(2, 0, 1) may be required depending on numpy array order see - https://github.com/quentan/Test_ImageData/blob/master/TestImageData.py   __depthImageData.SetDimensions(depthFrame.shape)  #assume 0,0 origin and 1,1 spacing.   __depthImageData.SetSpacing([1,1])   __depthImageData.SetOrigin([0,0])   __depthImageData.GetPointData().SetScalars(depthArray)

Should provide a working example of how to generate the depthFrame as a starting point