9d673a4694047ef68d9e5c9ba4c43e75f06bf2df,invesalius/data/imagedata_utils.py,,dcm2memmap,#Any#Any#Any#Any#,520

Before Change

        if resolution_percentage == 1.0:
            shape = slice_size[1], len(files), slice_size[0]
        else:
            shape = math.ceil(slice_size[1]*resolution_percentage), len(files),\
                                        math.ceil(slice_size[0]*resolution_percentage)
    else:
        if resolution_percentage == 1.0:
            shape = len(files), slice_size[1], slice_size[0]
        else:
            shape = len(files), math.ceil(slice_size[1]*resolution_percentage),\
                                        math.ceil(slice_size[0]*resolution_percentage)

    matrix = numpy.memmap(temp_file, mode="w+", dtype="int16", shape=shape)
    dcm_reader = vtkgdcm.vtkGDCMImageReader()
    cont = 0
    max_scalar = None
    min_scalar = None

    for n, f in enumerate(files):
        dcm_reader.SetFileName(f)
        dcm_reader.Update()
        image = dcm_reader.GetOutput()

        if resolution_percentage != 1.0:
            image_resized = ResampleImage2D(image, px=None, py=None,\
                                resolution_percentage = resolution_percentage, update_progress = None)

            image = image_resized

        min_aux, max_aux = image.GetScalarRange()
        if min_scalar is None or min_aux < min_scalar:
            min_scalar = min_aux

        if max_scalar is None or max_aux > max_scalar:
            max_scalar = max_aux

        array = numpy_support.vtk_to_numpy(image.GetPointData().GetScalars())
        if orientation == "CORONAL":
            array.shape = matrix.shape[0], matrix.shape[2]
            matrix[:, shape[1] - n - 1, :] = array
        elif orientation == "SAGITTAL":
            array.shape = matrix.shape[0], matrix.shape[1]
            // TODO: Verify if it"s necessary to add the slices swapped only in
            // sagittal rmi or only in // Rasiane"s case or is necessary in all
            // sagittal cases.

After Change

        if resolution_percentage == 1.0:
            shape = slice_size[1], len(files), slice_size[0]
        else:
            shape = int(math.ceil(slice_size[1]*resolution_percentage)), len(files),\
                                        int(math.ceil(slice_size[0]*resolution_percentage))
    else:
        if resolution_percentage == 1.0:
            shape = len(files), slice_size[1], slice_size[0]
        else:
            shape = len(files), int(math.ceil(slice_size[1]*resolution_percentage)),\
                                int(math.ceil(slice_size[0]*resolution_percentage))

    matrix = numpy.memmap(temp_file, mode="w+", dtype="int16", shape=shape)
    dcm_reader = vtkgdcm.vtkGDCMImageReader()
    cont = 0
    max_scalar = None
    min_scalar = None

    for n, f in enumerate(files):
        dcm_reader.SetFileName(f)
        dcm_reader.Update()
        image = dcm_reader.GetOutput()

        if resolution_percentage != 1.0:
            image_resized = ResampleImage2D(image, px=None, py=None,\
                                resolution_percentage = resolution_percentage, update_progress = None)

            image = image_resized

        min_aux, max_aux = image.GetScalarRange()
        if min_scalar is None or min_aux < min_scalar:
            min_scalar = min_aux

        if max_scalar is None or max_aux > max_scalar:
            max_scalar = max_aux

        array = numpy_support.vtk_to_numpy(image.GetPointData().GetScalars())
        if orientation == "CORONAL":
            array.shape = matrix.shape[0], matrix.shape[2]
            matrix[:, shape[1] - n - 1, :] = array
        elif orientation == "SAGITTAL":
            array.shape = matrix.shape[0], matrix.shape[1]
            // TODO: Verify if it"s necessary to add the slices swapped only in
            // sagittal rmi or only in // Rasiane"s case or is necessary in all
            // sagittal cases.

Instances