Esempio n. 1
0
def _multiframe_to_block(multiframe_dicom):
    """
    Generate a full datablock containing all stacks
    """
    # Calculate the amount of stacks and slices in the stack
    number_of_stack_slices = int(common.get_ss_value(multiframe_dicom[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)]))
    number_of_stacks = int(int(multiframe_dicom.NumberOfFrames) / number_of_stack_slices)

    # We create a numpy array
    size_x = multiframe_dicom.pixel_array.shape[2]
    size_y = multiframe_dicom.pixel_array.shape[1]
    size_z = number_of_stack_slices
    size_t = number_of_stacks
    # get the format
    format_string = common.get_numpy_type(multiframe_dicom)

    # get header info needed for ordering
    frame_info = multiframe_dicom[0x5200, 0x9230]

    data_4d = numpy.zeros((size_z, size_y, size_x, size_t), dtype=format_string)

    # loop over each slice and insert in datablock
    t_location_index = _get_t_position_index(multiframe_dicom)
    for slice_index in range(0, size_t * size_z):

        z_location = frame_info[slice_index].FrameContentSequence[0].InStackPositionNumber - 1
        if t_location_index is None:
            t_location = frame_info[slice_index].FrameContentSequence[0].TemporalPositionIndex - 1
        else:
            t_location = frame_info[slice_index].FrameContentSequence[0].DimensionIndexValues[t_location_index] - 1

        block_data = multiframe_dicom.pixel_array[slice_index, :, :]
        # apply scaling
        rescale_intercept = frame_info[slice_index].PixelValueTransformationSequence[0].RescaleIntercept
        rescale_slope = frame_info[slice_index].PixelValueTransformationSequence[0].RescaleSlope
        block_data = common.do_scaling(block_data,
                                       rescale_slope, rescale_intercept)
        # switch to float if needed
        if block_data.dtype != data_4d.dtype:
            data_4d = data_4d.astype(block_data.dtype)
        data_4d[z_location, :, :, t_location] = block_data

    full_block = numpy.zeros((size_x, size_y, size_z, size_t), dtype=data_4d.dtype)

    # loop over each stack and reorganize the data
    for t_index in range(0, size_t):
        # transpose the block so the directions are correct
        data_3d = numpy.transpose(data_4d[:, :, :, t_index], (2, 1, 0))
        # add the block the the full data
        full_block[:, :, :, t_index] = data_3d

    return full_block
Esempio n. 2
0
def _multiframe_to_block(multiframe_dicom):
    """
    Generate a full datablock containing all stacks
    """
    # Calculate the amount of stacks and slices in the stack
    number_of_stack_slices = int(common.get_ss_value(multiframe_dicom[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)]))
    number_of_stacks = int(int(multiframe_dicom.NumberOfFrames) / number_of_stack_slices)

    # We create a numpy array
    size_x = multiframe_dicom.pixel_array.shape[2]
    size_y = multiframe_dicom.pixel_array.shape[1]
    size_z = number_of_stack_slices
    size_t = number_of_stacks
    # get the format
    format_string = common.get_numpy_type(multiframe_dicom)

    # get header info needed for ordering
    frame_info = multiframe_dicom[0x5200, 0x9230]

    data_4d = numpy.zeros((size_z, size_y, size_x, size_t), dtype=format_string)

    # loop over each slice and insert in datablock
    t_location_index = _get_t_position_index(multiframe_dicom)
    for slice_index in range(0, size_t * size_z):

        z_location = frame_info[slice_index].FrameContentSequence[0].InStackPositionNumber - 1
        if t_location_index is None:
            t_location = frame_info[slice_index].FrameContentSequence[0].TemporalPositionIndex - 1
        else:
            t_location = frame_info[slice_index].FrameContentSequence[0].DimensionIndexValues[t_location_index] - 1

        block_data = multiframe_dicom.pixel_array[slice_index, :, :]
        # apply scaling
        rescale_intercept = frame_info[slice_index].PixelValueTransformationSequence[0].RescaleIntercept
        rescale_slope = frame_info[slice_index].PixelValueTransformationSequence[0].RescaleSlope
        block_data = common.do_scaling(block_data,
                                       rescale_slope, rescale_intercept)
        # switch to float if needed
        if block_data.dtype != data_4d.dtype:
            data_4d = data_4d.astype(block_data.dtype)
        data_4d[z_location, :, :, t_location] = block_data

    full_block = numpy.zeros((size_x, size_y, size_z, size_t), dtype=data_4d.dtype)

    # loop over each stack and reorganize the data
    for t_index in range(0, size_t):
        # transpose the block so the directions are correct
        data_3d = numpy.transpose(data_4d[:, :, :, t_index], (2, 1, 0))
        # add the block the the full data
        full_block[:, :, :, t_index] = data_3d

    return full_block