def scad_propseg_validation(folder_input, contrast):
    from sct_get_centerline import ind2sub
    import time
    import math
    import numpy
    import sct_convert as cnv

    t0 = time.time()

    current_folder = os.getcwd()
    os.chdir(folder_input)

    try:
        patients = next(os.walk('.'))[1]
        for i in patients:
            directory = i + "/" + str(contrast)
            try:
                os.chdir(directory)
            except Exception, e:
                print str(i)+" : "+contrast+" directory not found"
            try:
                if os.path.isfile(i+"_"+contrast+".nii.gz"):
                    raw_image = Image(i+"_"+contrast+".nii.gz")
                elif os.path.isfile(contrast+".nii.gz"):
                    raw_image = Image(contrast+".nii.gz")
                else:
                    raise Exception("Patient scan not found")

                if os.path.isfile(i+"_"+contrast+"_manual_segmentation.nii.gz"):
                    manual_segmentation  = i+"_"+contrast+"_manual_segmentation.nii.gz"
                    # Using propseg default
                    sct.run("sct_propseg -i "+raw_image.absolutepath+" -t "+contrast)
                    cnv.convert(raw_image.file_name+"_seg.nii.gz", "propseg_default.nii.gz")
                    # Using scad
                    scad = SCAD(raw_image, contrast=contrast, rm_tmp_file=1, verbose=1)
                    scad.execute()
                    # Using propseg with scad
                    sct.run("sct_propseg -i "+raw_image.absolutepath+" -t "+contrast+" -init-centerline "+scad.output_filename)
                    cnv.convert(raw_image.file_name+"_seg.nii.gz", "propseg_scad.nii.gz")
                    # Calculate dice of propseg_default
                    sct.run("sct_dice_coefficient propseg_default.nii.gz "+manual_segmentation+" -o propseg_default_result.txt")
                    # Calculate dice of propseg_scad
                    sct.run("sct_dice_coefficient propseg_scad.nii.gz "+manual_segmentation+" -o propseg_scad_result.txt")
                else:
                    printv("Cannot find the manual segmentation", type="warning")

            except Exception, e:
                print e.message

            os.chdir(folder_input)
def validate_scad(folder_input, contrast):
    """
    Expecting folder to have the following structure :
    errsm_01:
    - t2
    -- errsm_01.nii.gz or t2.nii.gz
    --
    :param folder_input:
    :return:
    """
    from sct_get_centerline import ind2sub
    import time
    import math
    import numpy

    t0 = time.time()

    current_folder = os.getcwd()
    os.chdir(folder_input)

    try:
        patients = next(os.walk('.'))[1]
        overall_distance = {}
        max_distance = {}
        standard_deviation = 0
        overall_std = {}
        rmse = {}
        for i in patients:
            directory = i + "/" + str(contrast)
            try:
                os.chdir(directory)
            except Exception, e:
                print str(i)+" : "+contrast+" directory not found"

            try:
                if os.path.isfile(i+"_"+contrast+".nii.gz"):
                    raw_image = Image(i+"_"+contrast+".nii.gz")
                elif os.path.isfile(contrast+".nii.gz"):
                    raw_image = Image(contrast+".nii.gz")
                else:
                    raise Exception("Patient scan not found")

                if os.path.isfile(i+"_"+contrast+"_manual_segmentation.nii.gz"):
                    raw_orientation = raw_image.change_orientation()
                    scad = SCAD(raw_image, contrast=contrast, rm_tmp_file=1, verbose=1)
                    scad.execute()

                    manual_seg = Image(i+"_"+contrast+"_manual_segmentation.nii.gz")
                    manual_orientation = manual_seg.change_orientation()

                    from scipy.ndimage.measurements import center_of_mass
                    # find COM
                    iterator = range(manual_seg.data.shape[2])
                    com_x = [0 for ix in iterator]
                    com_y = [0 for iy in iterator]

                    for iz in iterator:
                        com_x[iz], com_y[iz] = center_of_mass(manual_seg.data[:, :, iz])

                    centerline_scad = Image(i+"_"+contrast+"_centerline.nii.gz")
                    # os.remove(i+"_"+contrast+"_centerline.nii.gz")

                    centerline_scad.change_orientation()
                    distance = {}
                    for iz in range(1, centerline_scad.data.shape[2]-1):
                        ind1 = np.argmax(centerline_scad.data[:, :, iz])
                        X,Y = ind2sub(centerline_scad.data[:, :, iz].shape,ind1)
                        com_phys = np.array(manual_seg.transfo_pix2phys([[com_x[iz], com_y[iz], iz]]))
                        scad_phys = np.array(centerline_scad.transfo_pix2phys([[X, Y, iz]]))
                        distance_magnitude = np.linalg.norm([com_phys[0][0]-scad_phys[0][0], com_phys[0][1]-scad_phys[0][1], 0])
                        if math.isnan(distance_magnitude):
                            print "Value is nan"
                        else:
                            distance[iz] = distance_magnitude

                    f = open(i+"_"+contrast+"_results.txt", 'w+')
                    f.write("Patient,Slice,Distance")
                    for key, value in distance.items():
                        f.write(i+","+str(key)+","+str(value))

                    standard_deviation = np.std(np.array(distance.values()))
                    average = sum(distance.values())/len(distance)
                    root_mean_square = np.sqrt(np.mean(np.square(distance.values())))

                    f.write("\nAverage : "+str(average))
                    f.write("\nStandard Deviation : "+str(standard_deviation))

                    f.close()

                    overall_distance[i] = average
                    max_distance[i] = max(distance.values())
                    overall_std[i] = standard_deviation
                    rmse[i] = root_mean_square

                else:
                    printv("Cannot find the manual segmentation", type="warning")

            except Exception, e:
                print e.message

            os.chdir(folder_input)