<gh_stars>10-100
import numpy as np
try:
    from skimage import filters
except ImportError:
    from skimage import filter as filters

from nilabels.tools.aux_methods.utils_nib import set_new_data


def normalise_below_labels(im_input, im_segm, labels_list=None, stats=np.median, exclude_first_label=True):
    """
    Normalise the intensities of im_input for the stats obtained of the values found under input labels.
    :param im_input: nibabel image
    :param im_segm: nibabel segmentation
    :param labels_list: list of labels you want to normalise below
    :param stats: required statistic e.g. np.median
    :param exclude_first_label: remove the first label from the labels list.
    :return: divide for the statistics required for only the non-zero values below.
    """
    if labels_list is not None:
        if exclude_first_label:
            labels_list = labels_list[1:]
        mask_data = np.zeros_like(im_segm.get_data(), dtype=np.bool)
        for label_k in labels_list:
            mask_data += im_segm.get_data() == label_k
    else:
        mask_data = np.zeros_like(im_segm.get_data(), dtype=np.bool)
        mask_data[im_segm.get_data() > 0] = 1

    masked_im_data = np.nan_to_num((mask_data.astype(np.float64) * im_input.get_data().astype(np.float64)).flatten())
    non_zero_masked_im_data = masked_im_data[np.where(masked_im_data > 1e-6)]
    s = stats(non_zero_masked_im_data)
    assert isinstance(s, float)
    output_im = set_new_data(im_input, (1 / float(s)) * im_input.get_data())

    return output_im


def intensities_normalisation_linear(im_input, im_segm, im_mask_foreground=None,
                                     toll=1e-12, percentile_range=(1, 99), output_range=(0.1, 10)):
    """
    Normalise the values below the binarised segmentation so that the normalised image
    will be between 0 and 1, based on a linear transformation whose parameters are learned
    from the values below the segmentation.
    E.G.

    min_intensities = 1% of the intensities below the mask
    max_intensities = 99% of the intensities above the mask

    compute the parameters a and b so that:

    a * min_intensities  + b = 0.1
    a * max_intensities  + b = 10

    To all the values in the image is then applied the linear function:
    f(x) = a * x  + b in the foreground,
    f(x) = 0 in the background.
    :return:
    """
    mask_data = np.zeros_like(im_segm.get_data(), dtype=np.bool)
    mask_data[im_segm.get_data() > 0] = 1

    non_zero_below_mask = im_input.get_data()[np.where(mask_data > toll)].flatten()

    min_intensities = np.percentile(non_zero_below_mask, percentile_range[0])
    max_intensities = np.percentile(non_zero_below_mask, percentile_range[1])

    a = (output_range[1] - output_range[0]) / (max_intensities - min_intensities)
    b = output_range[0] - a * min_intensities

    if im_mask_foreground is None:
        im_mask_foreground_data = np.ones_like(im_input.get_data())
    else:
        im_mask_foreground_data = im_mask_foreground.get_data()

    return set_new_data(im_input, im_mask_foreground_data * (a * im_input.get_data() + b))


def mahalanobis_distance_map(im, im_mask=None, trim=False):
    """
    From an image to its Mahalanobis distance map
    :param im: input image acquired with some modality.
    :param im_mask: considering only the data below the given mask.
    :param trim: if mask is provided the output image is masked with zeros values outside the mask.
    :return: nibabel image same shape as im, with the corresponding Mahalanobis map
    """
    if im_mask is None:
        mu = np.mean(im.get_data().flatten())
        sigma2 = np.std(im.get_data().flatten())
        return set_new_data(im, np.sqrt((im.get_data() - mu) * sigma2 * (im.get_data() - mu)))
    else:
        np.testing.assert_array_equal(im.affine, im_mask.affine)
        np.testing.assert_array_equal(im.shape, im_mask.shape)
        mu = np.mean(im.get_data().flatten() * im_mask.get_data().flatten())
        sigma2 = np.std(im.get_data().flatten() * im_mask.get_data().flatten())
        new_data = np.sqrt((im.get_data() - mu) * sigma2**(-1) * (im.get_data() - mu))
        if trim:
            new_data = new_data * im_mask.get_data().astype(np.bool)
        return set_new_data(im, new_data)

import numpy as np
try:
    from skimage import filters
except ImportError:
    from skimage import filter as filters

from nilabels.tools.aux_methods.utils_nib import set_new_data


def normalise_below_labels(im_input, im_segm, labels_list=None, stats=np.median, exclude_first_label=True):
    """
    Normalise the intensities of im_input for the stats obtained of the values found under input labels.
    :param im_input: nibabel image
    :param im_segm: nibabel segmentation
    :param labels_list: list of labels you want to normalise below
    :param stats: required statistic e.g. np.median
    :param exclude_first_label: remove the first label from the labels list.
    :return: divide for the statistics required for only the non-zero values below.
    """
    if labels_list is not None:
        if exclude_first_label:
            labels_list = labels_list[1:]
        mask_data = np.zeros_like(im_segm.get_data(), dtype=np.bool)
        for label_k in labels_list:
            mask_data += im_segm.get_data() == label_k
    else:
        mask_data = np.zeros_like(im_segm.get_data(), dtype=np.bool)
        mask_data[im_segm.get_data() > 0] = 1

    masked_im_data = np.nan_to_num((mask_data.astype(np.float64) * im_input.get_data().astype(np.float64)).flatten())
    non_zero_masked_im_data = masked_im_data[np.where(masked_im_data > 1e-6)]
    s = stats(non_zero_masked_im_data)
    assert isinstance(s, float)
    output_im = set_new_data(im_input, (1 / float(s)) * im_input.get_data())

    return output_im


def intensities_normalisation_linear(im_input, im_segm, im_mask_foreground=None,
                                     toll=1e-12, percentile_range=(1, 99), output_range=(0.1, 10)):
    """
    Normalise the values below the binarised segmentation so that the normalised image
    will be between 0 and 1, based on a linear transformation whose parameters are learned
    from the values below the segmentation.
    E.G.

    min_intensities = 1% of the intensities below the mask
    max_intensities = 99% of the intensities above the mask

    compute the parameters a and b so that:

    a * min_intensities  + b = 0.1
    a * max_intensities  + b = 10

    To all the values in the image is then applied the linear function:
    f(x) = a * x  + b in the foreground,
    f(x) = 0 in the background.
    :return:
    """
    mask_data = np.zeros_like(im_segm.get_data(), dtype=np.bool)
    mask_data[im_segm.get_data() > 0] = 1

    non_zero_below_mask = im_input.get_data()[np.where(mask_data > toll)].flatten()

    min_intensities = np.percentile(non_zero_below_mask, percentile_range[0])
    max_intensities = np.percentile(non_zero_below_mask, percentile_range[1])

    a = (output_range[1] - output_range[0]) / (max_intensities - min_intensities)
    b = output_range[0] - a * min_intensities

    if im_mask_foreground is None:
        im_mask_foreground_data = np.ones_like(im_input.get_data())
    else:
        im_mask_foreground_data = im_mask_foreground.get_data()

    return set_new_data(im_input, im_mask_foreground_data * (a * im_input.get_data() + b))


def mahalanobis_distance_map(im, im_mask=None, trim=False):
    """
    From an image to its Mahalanobis distance map
    :param im: input image acquired with some modality.
    :param im_mask: considering only the data below the given mask.
    :param trim: if mask is provided the output image is masked with zeros values outside the mask.
    :return: nibabel image same shape as im, with the corresponding Mahalanobis map
    """
    if im_mask is None:
        mu = np.mean(im.get_data().flatten())
        sigma2 = np.std(im.get_data().flatten())
        return set_new_data(im, np.sqrt((im.get_data() - mu) * sigma2 * (im.get_data() - mu)))
    else:
        np.testing.assert_array_equal(im.affine, im_mask.affine)
        np.testing.assert_array_equal(im.shape, im_mask.shape)
        mu = np.mean(im.get_data().flatten() * im_mask.get_data().flatten())
        sigma2 = np.std(im.get_data().flatten() * im_mask.get_data().flatten())
        new_data = np.sqrt((im.get_data() - mu) * sigma2**(-1) * (im.get_data() - mu))
        if trim:
            new_data = new_data * im_mask.get_data().astype(np.bool)
        return set_new_data(im, new_data)
Normalise the intensities of im_input for the stats obtained of the values found under input labels.     :param im_input: nibabel image     :param im_segm: nibabel segmentation     :param labels_list: list of labels you want to normalise below     :param stats: required statistic e.g. np.median     :param exclude_first_label: remove the first label from the labels list.     :return: divide for the statistics required for only the non-zero values below. Normalise the values below the binarised segmentation so that the normalised image     will be between 0 and 1, based on a linear transformation whose parameters are learned     from the values below the segmentation.     E.G.      min_intensities = 1% of the intensities below the mask     max_intensities = 99% of the intensities above the mask      compute the parameters a and b so that:      a * min_intensities  + b = 0.1     a * max_intensities  + b = 10      To all the values in the image is then applied the linear function:     f(x) = a * x  + b in the foreground,     f(x) = 0 in the background.     :return: From an image to its Mahalanobis distance map     :param im: input image acquired with some modality.     :param im_mask: considering only the data below the given mask.     :param trim: if mask is provided the output image is masked with zeros values outside the mask.     :return: nibabel image same shape as im, with the corresponding Mahalanobis map