Python Image.ext方法代码示例

# 需要导入模块: from msct_image import Image [as 别名]
# 或者: from msct_image.Image import ext [as 别名]
    def validation(self):
        name_ref_gm_seg = sct.extract_fname(self.ref_gm_seg)
        im_ref_gm_seg = Image("../" + self.ref_gm_seg)

        res_gm_seg_bin = Image("../" + self.res_names["gm_seg"])
        res_wm_seg_bin = Image("../" + self.res_names["wm_seg"])

        sct.run("cp ../" + self.ref_gm_seg + " ./ref_gm_seg.nii.gz")
        im_ref_wm_seg = inverse_gmseg_to_wmseg(im_ref_gm_seg, Image("../" + self.sc_seg_fname), "ref_gm_seg")
        im_ref_wm_seg.file_name = "ref_wm_seg"
        im_ref_wm_seg.ext = ".nii.gz"
        im_ref_wm_seg.save()

        if self.param.res_type == "prob":
            res_gm_seg_bin.data = np.asarray((res_gm_seg_bin.data >= 0.5).astype(int))
            res_wm_seg_bin.data = np.asarray((res_wm_seg_bin.data >= 0.50001).astype(int))

        res_gm_seg_bin.path = "./"
        res_gm_seg_bin.file_name = "res_gm_seg_bin"
        res_gm_seg_bin.ext = ".nii.gz"
        res_gm_seg_bin.save()
        res_wm_seg_bin.path = "./"
        res_wm_seg_bin.file_name = "res_wm_seg_bin"
        res_wm_seg_bin.ext = ".nii.gz"
        res_wm_seg_bin.save()
        try:
            status_gm, output_gm = sct.run(
                "sct_dice_coefficient ref_gm_seg.nii.gz res_gm_seg_bin.nii.gz  -2d-slices 2",
                error_exit="warning",
                raise_exception=True,
            )
        except Exception:
            sct.run("c3d res_gm_seg_bin.nii.gz  ref_gm_seg.nii.gz -reslice-identity -o ref_in_res_space_gm.nii.gz ")
            status_gm, output_gm = sct.run(
                "sct_dice_coefficient ref_in_res_space_gm.nii.gz res_gm_seg_bin.nii.gz  -2d-slices 2",
                error_exit="warning",
            )
        try:
            status_wm, output_wm = sct.run(
                "sct_dice_coefficient ref_wm_seg.nii.gz res_wm_seg_bin.nii.gz  -2d-slices 2",
                error_exit="warning",
                raise_exception=True,
            )
        except Exception:
            sct.run("c3d res_wm_seg_bin.nii.gz  ref_wm_seg.nii.gz -reslice-identity -o ref_in_res_space_wm.nii.gz ")
            status_wm, output_wm = sct.run(
                "sct_dice_coefficient ref_in_res_space_wm.nii.gz res_wm_seg_bin.nii.gz  -2d-slices 2",
                error_exit="warning",
            )
        dice_name = "dice_" + self.param.res_type + ".txt"
        dice_fic = open("../" + dice_name, "w")
        if self.param.res_type == "prob":
            dice_fic.write(
                "WARNING : the probabilistic segmentations were binarized with a threshold at 0.5 to compute the dice coefficient \n"
            )
        dice_fic.write(
            "\n--------------------------------------------------------------\nDice coefficient on the Gray Matter segmentation:\n"
        )
        dice_fic.write(output_gm)
        dice_fic.write(
            "\n\n--------------------------------------------------------------\nDice coefficient on the White Matter segmentation:\n"
        )
        dice_fic.write(output_wm)
        dice_fic.close()
        # sct.run(' mv ./' + dice_name + ' ../')

        return dice_name

# 需要导入模块: from msct_image import Image [as 别名]
# 或者: from msct_image.Image import ext [as 别名]
def resample():
    # extract resampling factor
    sct.printv('\nParse resampling factor...', param.verbose)
    new_size_split = param.new_size.split('x')
    new_size = [float(new_size_split[i]) for i in range(len(new_size_split))]
    # check if it has three values
    if not len(new_size) == 3:
        sct.printv('\nERROR: new size should have three dimensions. E.g., 2x2x1.\n', 1, 'error')
    else:
        ns_x, ns_y, ns_z = new_size

    # Extract path/file/extension
    path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
    path_out, file_out, ext_out = '', file_data, ext_data
    if param.fname_out != '':
        path_out, file_out, ext_out = sct.extract_fname(param.fname_out)
    else:
        file_out += param.file_suffix
    param.fname_out = path_out+file_out+ext_out

    input_im = Image(param.fname_data)

    # Get dimensions of data
    sct.printv('\nGet dimensions of data...', param.verbose)
    nx, ny, nz, nt, px, py, pz, pt = input_im.dim
    sct.printv('  ' + str(px) + ' x ' + str(py) + ' x ' + str(pz)+ ' x ' + str(pt)+'mm', param.verbose)
    dim = 4  # by default, will be adjusted later
    if nt == 1:
        dim = 3
    if nz == 1:
        dim = 2
        sct.run('ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program', param.verbose, 'error')

    # Calculate new dimensions
    sct.printv('\nCalculate new dimensions...', param.verbose)
    if param.new_size_type == 'factor':
        px_new = px/ns_x
        py_new = py/ns_y
        pz_new = pz/ns_z
    elif param.new_size_type == 'vox':
        px_new = px*nx/ns_x
        py_new = py*ny/ns_y
        pz_new = pz*nz/ns_z
    else:
        px_new = ns_x
        py_new = ns_y
        pz_new = ns_z

    sct.printv('  ' + str(px_new) + ' x ' + str(py_new) + ' x ' + str(pz_new)+ ' x ' + str(pt)+'mm', param.verbose)

    zooms = (px, py, pz)  # input_im.hdr.get_zooms()[:3]
    affine = input_im.hdr.get_qform()  # get_base_affine()
    new_zooms = (px_new, py_new, pz_new)

    if type(param.interpolation) == int:
        order = param.interpolation
    elif type(param.interpolation) == str and param.interpolation in param.x_to_order.keys():
        order = param.x_to_order[param.interpolation]
    else:
        order = 1
        sct.printv('WARNING: wrong input for the interpolation. Using default value = linear', param.verbose, 'warning')

    new_data, new_affine = dp_iso.reslice(input_im.data, affine, zooms, new_zooms, mode=param.mode, order=order)

    new_im = Image(param=new_data)
    new_im.absolutepath = param.fname_out
    new_im.path = path_out
    new_im.file_name = file_out
    new_im.ext = ext_out

    zooms_to_set = list(new_zooms)
    if dim == 4:
        zooms_to_set.append(nt)

    new_im.hdr = input_im.hdr
    new_im.hdr.set_zooms(zooms_to_set)

    # Set the new sform and qform:
    new_im.hdr.set_sform(new_affine)
    new_im.hdr.set_qform(new_affine)

    new_im.save()

    # to view results
    sct.printv('\nDone! To view results, type:', param.verbose)
    sct.printv('fslview '+param.fname_out+' &', param.verbose, 'info')

# 需要导入模块: from msct_image import Image [as 别名]
# 或者: from msct_image.Image import ext [as 别名]
def resample():
    # extract resampling factor
    sct.printv('\nParse resampling factor...', param.verbose)
    factor_split = param.factor.split('x')
    factor = [float(factor_split[i]) for i in range(len(factor_split))]
    # check if it has three values
    if not len(factor) == 3:
        sct.printv('\nERROR: factor should have three dimensions. E.g., 2x2x1.\n', 1, 'error')
    else:
        fx, fy, fz = [float(factor_split[i]) for i in range(len(factor_split))]

    # Extract path/file/extension
    path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
    path_out, file_out, ext_out = path_data, file_data, ext_data
    if param.fname_out != '':
        file_out = sct.extract_fname(param.fname_out)[1]
    else:
        file_out.append(param.file_suffix)

    input_im = Image(param.fname_data)

    # Get dimensions of data
    sct.printv('\nGet dimensions of data...', param.verbose)
    nx, ny, nz, nt, px, py, pz, pt = input_im.dim
    sct.printv('  ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), param.verbose)
    dim = 4  # by default, will be adjusted later
    if nt == 1:
        dim = 3
    if nz == 1:
        dim = 2
        #TODO : adapt for 2D too or change description
        sct.run('ERROR (sct_resample): Dimension of input data is different from 3 or 4. Exit program', param.verbose, 'error')

    # Calculate new dimensions
    sct.printv('\nCalculate new dimensions...', param.verbose)
    nx_new = int(round(nx*fx))
    ny_new = int(round(ny*fy))
    nz_new = int(round(nz*fz))
    px_new = px/fx
    py_new = py/fy
    pz_new = pz/fz
    sct.printv('  ' + str(nx_new) + ' x ' + str(ny_new) + ' x ' + str(nz_new)+ ' x ' + str(nt), param.verbose)


    zooms = input_im.hdr.get_zooms()[:3]
    affine = input_im.hdr.get_base_affine()
    new_zooms = (px_new, py_new, pz_new)

    if type(param.interpolation) == int:
        order = param.interpolation
    elif type(param.interpolation) == str and param.interpolation in param.x_to_order.keys():
        order = param.x_to_order[param.interpolation]
    else:
        order = 1
        sct.printv('WARNING: wrong input for the interpolation. Using default value = trilinear', param.verbose, 'warning')

    new_data, new_affine = dp_iso.reslice(input_im.data, affine, zooms, new_zooms, mode=param.mode, order=order)

    new_im = Image(param=new_data)
    new_im.absolutepath = path_out+file_out+ext_out
    new_im.path = path_out
    new_im.file_name = file_out
    new_im.ext = ext_out

    zooms_to_set = list(new_zooms)
    if dim == 4:
        zooms_to_set.append(nt)

    new_im.hdr = input_im.hdr
    new_im.hdr.set_zooms(zooms_to_set)
    new_im.save()

    # to view results
    sct.printv('\nDone! To view results, type:', param.verbose)
    sct.printv('fslview '+param.fname_out+' &', param.verbose, 'info')
    print