Python PIL.Image.html() Examples

def brightness(x, gamma=1, gain=1, is_random=False):
    """Change the brightness of a single image, randomly or non-randomly.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    gamma : float, small than 1 means brighter.
        Non negative real number. Default value is 1.

        - If is_random is True, gamma in a range of (1-gamma, 1+gamma).
    gain : float
        The constant multiplier. Default value is 1.
    is_random : boolean, default False
        - If True, randomly change brightness.

    References
    -----------
    - `skimage.exposure.adjust_gamma <http://scikit-image.org/docs/dev/api/skimage.exposure.html>`_
    - `chinese blog <http://www.cnblogs.com/denny402/p/5124402.html>`_
    """
    if is_random:
        gamma = np.random.uniform(1-gamma, 1+gamma)
    x = exposure.adjust_gamma(x, gamma, gain)
    return x 

def dilation(x, radius=3):
    """ Return greyscale morphological dilation of an image,
    see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`_.

    Parameters
    -----------
    x : 2D array image.
    radius : int for the radius of mask.
    """
    from skimage.morphology import disk, dilation
    mask = disk(radius)
    x = dilation(x, selem=mask)
    return x




## Sequence 

def brightness(x, gamma=1, gain=1, is_random=False):
    """Change the brightness of a single image, randomly or non-randomly.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    gamma : float, small than 1 means brighter.
        Non negative real number. Default value is 1.

        - If is_random is True, gamma in a range of (1-gamma, 1+gamma).
    gain : float
        The constant multiplier. Default value is 1.
    is_random : boolean, default False
        - If True, randomly change brightness.

    References
    -----------
    - `skimage.exposure.adjust_gamma <http://scikit-image.org/docs/dev/api/skimage.exposure.html>`_
    - `chinese blog <http://www.cnblogs.com/denny402/p/5124402.html>`_
    """
    if is_random:
        gamma = np.random.uniform(1-gamma, 1+gamma)
    x = exposure.adjust_gamma(x, gamma, gain)
    return x 

def brightness(x, gamma=1, gain=1, is_random=False):
    """Change the brightness of a single image, randomly or non-randomly.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    gamma : float, small than 1 means brighter.
        Non negative real number. Default value is 1.

        - If is_random is True, gamma in a range of (1-gamma, 1+gamma).
    gain : float
        The constant multiplier. Default value is 1.
    is_random : boolean, default False
        - If True, randomly change brightness.

    References
    -----------
    - `skimage.exposure.adjust_gamma <http://scikit-image.org/docs/dev/api/skimage.exposure.html>`_
    - `chinese blog <http://www.cnblogs.com/denny402/p/5124402.html>`_
    """
    if is_random:
        gamma = np.random.uniform(1-gamma, 1+gamma)
    x = exposure.adjust_gamma(x, gamma, gain)
    return x 

def dilation(x, radius=3):
    """ Return greyscale morphological dilation of an image,
    see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`_.

    Parameters
    -----------
    x : 2D array image.
    radius : int for the radius of mask.
    """
    from skimage.morphology import disk, dilation
    mask = disk(radius)
    x = dilation(x, selem=mask)
    return x




## Sequence 

def imresize(x, size=[100, 100], interp='bilinear', mode=None):
    """Resize an image by given output size and method. Warning, this function
    will rescale the value to [0, 255].

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    size : int, float or tuple (h, w)
        - int, Percentage of current size.
        - float, Fraction of current size.
        - tuple, Size of the output image.
    interp : str, optional
        Interpolation to use for re-sizing (‘nearest’, ‘lanczos’, ‘bilinear’, ‘bicubic’ or ‘cubic’).
    mode : str, optional
        The PIL image mode (‘P’, ‘L’, etc.) to convert arr before resizing.

    Returns
    --------
    imresize : ndarray
    The resized array of image.

    References
    ------------
    - `scipy.misc.imresize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.imresize.html>`_
    """
    if x.shape[-1] == 1:
        # greyscale
        x = scipy.misc.imresize(x[:,:,0], size, interp=interp, mode=mode)
        return x[:, :, np.newaxis]
    elif x.shape[-1] == 3:
        # rgb, bgr ..
        return scipy.misc.imresize(x, size, interp=interp, mode=mode)
    else:
        raise Exception("Unsupported channel %d" % x.shape[-1])

# normailization 

def imresize(x, size=[100, 100], interp='bilinear', mode=None):
    """Resize an image by given output size and method. Warning, this function
    will rescale the value to [0, 255].

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    size : int, float or tuple (h, w)
        - int, Percentage of current size.
        - float, Fraction of current size.
        - tuple, Size of the output image.
    interp : str, optional
        Interpolation to use for re-sizing (‘nearest’, ‘lanczos’, ‘bilinear’, ‘bicubic’ or ‘cubic’).
    mode : str, optional
        The PIL image mode (‘P’, ‘L’, etc.) to convert arr before resizing.

    Returns
    --------
    imresize : ndarray
    The resized array of image.

    References
    ------------
    - `scipy.misc.imresize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.imresize.html>`_
    """
    if x.shape[-1] == 1:
        # greyscale
        x = scipy.misc.imresize(x[:,:,0], size, interp=interp, mode=mode)
        return x[:, :, np.newaxis]
    elif x.shape[-1] == 3:
        # rgb, bgr ..
        return scipy.misc.imresize(x, size, interp=interp, mode=mode)
    else:
        raise Exception("Unsupported channel %d" % x.shape[-1])

# normailization 

def channel_shift(x, intensity, is_random=False, channel_index=2):
    """Shift the channels of an image, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    intensity : float
        Intensity of shifting.
    is_random : boolean, default False
        If True, randomly shift.
    channel_index : int
        Index of channel, default 2.
    """
    if is_random:
        factor = np.random.uniform(-intensity, intensity)
    else:
        factor = intensity
    x = np.rollaxis(x, channel_index, 0)
    min_x, max_x = np.min(x), np.max(x)
    channel_images = [np.clip(x_channel + factor, min_x, max_x)
                      for x_channel in x]
    x = np.stack(channel_images, axis=0)
    x = np.rollaxis(x, 0, channel_index+1)
    return x
    # x = np.rollaxis(x, channel_index, 0)
    # min_x, max_x = np.min(x), np.max(x)
    # channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x)
    #                   for x_channel in x]
    # x = np.stack(channel_images, axis=0)
    # x = np.rollaxis(x, 0, channel_index+1)
    # return x 

def channel_shift_multi(x, intensity, channel_index=2):
    """Shift the channels of images with the same arguments, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_ .
    Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

    Parameters
    -----------
    x : list of numpy array
        List of images with dimension of [n_images, row, col, channel] (default).
    others : see ``channel_shift``.
    """
    if is_random:
        factor = np.random.uniform(-intensity, intensity)
    else:
        factor = intensity

    results = []
    for data in x:
        data = np.rollaxis(data, channel_index, 0)
        min_x, max_x = np.min(data), np.max(data)
        channel_images = [np.clip(x_channel + factor, min_x, max_x)
                          for x_channel in x]
        data = np.stack(channel_images, axis=0)
        data = np.rollaxis(x, 0, channel_index+1)
        results.append( data )
    return np.asarray(results)

# noise 

def apply_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0.):
    """Return transformed images by given transform_matrix from ``transform_matrix_offset_center``.

    Parameters
    ----------
    x : numpy array
        Batch of images with dimension of 3, [batch_size, row, col, channel].
    transform_matrix : numpy array
        Transform matrix (offset center), can be generated by ``transform_matrix_offset_center``
    channel_index : int
        Index of channel, default 2.
    fill_mode : string
        Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    cval : scalar, optional
        Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_

    Examples
    --------
    - See ``rotation``, ``shift``, ``shear``, ``zoom``.
    """
    x = np.rollaxis(x, channel_index, 0)
    final_affine_matrix = transform_matrix[:2, :2]
    final_offset = transform_matrix[:2, 2]
    channel_images = [ndi.interpolation.affine_transform(x_channel, final_affine_matrix,
                      final_offset, order=0, mode=fill_mode, cval=cval) for x_channel in x]
    x = np.stack(channel_images, axis=0)
    x = np.rollaxis(x, 0, channel_index+1)
    return x 

def array_to_img(x, dim_ordering=(0,1,2), scale=True):
    """Converts a numpy array to PIL image object (uint8 format).

    Parameters
    ----------
    x : numpy array
        A image with dimension of 3 and channels of 1 or 3.
    dim_ordering : list or tuple of 3 int
        Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
    scale : boolean, default is True
        If True, converts image to [0, 255] from any range of value like [-1, 2].

    References
    -----------
    - `PIL Image.fromarray <http://pillow.readthedocs.io/en/3.1.x/reference/Image.html?highlight=fromarray>`_
    """
    from PIL import Image
    # if dim_ordering == 'default':
    #     dim_ordering = K.image_dim_ordering()
    # if dim_ordering == 'th':  # theano
    #     x = x.transpose(1, 2, 0)
    x = x.transpose(dim_ordering)
    if scale:
        x += max(-np.min(x), 0)
        x_max = np.max(x)
        if x_max != 0:
            # print(x_max)
            # x /= x_max
            x = x / x_max
        x *= 255
    if x.shape[2] == 3:
        # RGB
        return Image.fromarray(x.astype('uint8'), 'RGB')
    elif x.shape[2] == 1:
        # grayscale
        return Image.fromarray(x[:, :, 0].astype('uint8'), 'L')
    else:
        raise Exception('Unsupported channel number: ', x.shape[2])


## Sequence 

def _(event):
    if event.fwd_from:
        return
    await event.edit("Processing ...")
    if not os.path.isdir(Config.TMP_DOWNLOAD_DIRECTORY):
        os.makedirs(Config.TMP_DOWNLOAD_DIRECTORY)
    if event.reply_to_msg_id:
        downloaded_file_name = await borg.download_media(
            await event.get_reply_message(),
            Config.TMP_DOWNLOAD_DIRECTORY
        )
        if downloaded_file_name.endswith(".mp4"):
            downloaded_file_name = get_video_thumb(
                downloaded_file_name
            )
        metadata = extractMetadata(createParser(downloaded_file_name))
        height = 0
        if metadata.has("height"):
            height = metadata.get("height")
        # resize image
        # ref: https://t.me/PyrogramChat/44663
        # https://stackoverflow.com/a/21669827/4723940
        Image.open(downloaded_file_name).convert("RGB").save(downloaded_file_name)
        img = Image.open(downloaded_file_name)
        # https://stackoverflow.com/a/37631799/4723940
        # img.thumbnail((320, 320))
        img.resize((320, height))
        img.save(thumb_image_path, "JPEG")
        # https://pillow.readthedocs.io/en/3.1.x/reference/Image.html#create-thumbnails
        os.remove(downloaded_file_name)
        await event.edit(
            "Custom video / file thumbnail saved. " + \
            "This image will be used in the upload, till `.clearthumbnail`."
        )
    else:
        await event.edit("Reply to a photo to save custom thumbnail") 

def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2,
                    fill_mode='nearest', cval=0.):
    """Rotate an image randomly or non-randomly.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    rg : int or float
        Degree to rotate, usually 0 ~ 180.
    is_random : boolean, default False
        If True, randomly rotate.
    row_index, col_index, channel_index : int
        Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
    fill_mode : string
        Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    cval : scalar, optional
        Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_

    Examples
    ---------
    >>> x --> [row, col, 1] greyscale
    >>> x = rotation(x, rg=40, is_random=False)
    >>> tl.visualize.frame(x[:,:,0], second=0.01, saveable=True, name='temp',cmap='gray')
    """
    if is_random:
        theta = np.pi / 180 * np.random.uniform(-rg, rg)
    else:
        theta = np.pi /180 * rg
    rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
                                [np.sin(theta), np.cos(theta), 0],
                                [0, 0, 1]])

    h, w = x.shape[row_index], x.shape[col_index]
    transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
    return x 

def shift(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
                 fill_mode='nearest', cval=0.):
    """Shift an image randomly or non-randomly.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    wrg : float
        Percentage of shift in axis x, usually -0.25 ~ 0.25.
    hrg : float
        Percentage of shift in axis y, usually -0.25 ~ 0.25.
    is_random : boolean, default False
        If True, randomly shift.
    row_index, col_index, channel_index : int
        Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
    fill_mode : string
        Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’.

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    cval : scalar, optional
        Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0.

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    """
    h, w = x.shape[row_index], x.shape[col_index]
    if is_random:
        tx = np.random.uniform(-hrg, hrg) * h
        ty = np.random.uniform(-wrg, wrg) * w
    else:
        tx, ty = hrg * h, wrg * w
    translation_matrix = np.array([[1, 0, tx],
                                   [0, 1, ty],
                                   [0, 0, 1]])

    transform_matrix = translation_matrix  # no need to do offset
    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
    return x 

def shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2,
                 fill_mode='nearest', cval=0.):
    """Shear an image randomly or non-randomly.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    intensity : float
        Percentage of shear, usually -0.5 ~ 0.5 (is_random==True), 0 ~ 0.5 (is_random==False),
        you can have a quick try by shear(X, 1).
    is_random : boolean, default False
        If True, randomly shear.
    row_index, col_index, channel_index : int
        Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
    fill_mode : string
        Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’.

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    cval : scalar, optional
        Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0.

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    """
    if is_random:
        shear = np.random.uniform(-intensity, intensity)
    else:
        shear = intensity
    shear_matrix = np.array([[1, -np.sin(shear), 0],
                             [0, np.cos(shear), 0],
                             [0, 0, 1]])

    h, w = x.shape[row_index], x.shape[col_index]
    transform_matrix = transform_matrix_offset_center(shear_matrix, h, w)
    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
    return x 

def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2,
                    fill_mode='nearest', cval=0.):
    """Rotate an image randomly or non-randomly.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    rg : int or float
        Degree to rotate, usually 0 ~ 180.
    is_random : boolean, default False
        If True, randomly rotate.
    row_index, col_index, channel_index : int
        Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
    fill_mode : string
        Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    cval : scalar, optional
        Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_

    Examples
    ---------
    >>> x --> [row, col, 1] greyscale
    >>> x = rotation(x, rg=40, is_random=False)
    >>> tl.visualize.frame(x[:,:,0], second=0.01, saveable=True, name='temp',cmap='gray')
    """
    if is_random:
        theta = np.pi / 180 * np.random.uniform(-rg, rg)
    else:
        theta = np.pi /180 * rg
    rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
                                [np.sin(theta), np.cos(theta), 0],
                                [0, 0, 1]])

    h, w = x.shape[row_index], x.shape[col_index]
    transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
    x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval)
    return x 

def channel_shift(x, intensity, is_random=False, channel_index=2):
    """Shift the channels of an image, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    intensity : float
        Intensity of shifting.
    is_random : boolean, default False
        If True, randomly shift.
    channel_index : int
        Index of channel, default 2.
    """
    if is_random:
        factor = np.random.uniform(-intensity, intensity)
    else:
        factor = intensity
    x = np.rollaxis(x, channel_index, 0)
    min_x, max_x = np.min(x), np.max(x)
    channel_images = [np.clip(x_channel + factor, min_x, max_x)
                      for x_channel in x]
    x = np.stack(channel_images, axis=0)
    x = np.rollaxis(x, 0, channel_index+1)
    return x
    # x = np.rollaxis(x, channel_index, 0)
    # min_x, max_x = np.min(x), np.max(x)
    # channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x)
    #                   for x_channel in x]
    # x = np.stack(channel_images, axis=0)
    # x = np.rollaxis(x, 0, channel_index+1)
    # return x 

def channel_shift_multi(x, intensity, channel_index=2):
    """Shift the channels of images with the same arguments, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_ .
    Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

    Parameters
    -----------
    x : list of numpy array
        List of images with dimension of [n_images, row, col, channel] (default).
    others : see ``channel_shift``.
    """
    if is_random:
        factor = np.random.uniform(-intensity, intensity)
    else:
        factor = intensity

    results = []
    for data in x:
        data = np.rollaxis(data, channel_index, 0)
        min_x, max_x = np.min(data), np.max(data)
        channel_images = [np.clip(x_channel + factor, min_x, max_x)
                          for x_channel in x]
        data = np.stack(channel_images, axis=0)
        data = np.rollaxis(x, 0, channel_index+1)
        results.append( data )
    return np.asarray(results)

# noise 

def apply_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0.):
    """Return transformed images by given transform_matrix from ``transform_matrix_offset_center``.

    Parameters
    ----------
    x : numpy array
        Batch of images with dimension of 3, [batch_size, row, col, channel].
    transform_matrix : numpy array
        Transform matrix (offset center), can be generated by ``transform_matrix_offset_center``
    channel_index : int
        Index of channel, default 2.
    fill_mode : string
        Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_
    cval : scalar, optional
        Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0

        - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_

    Examples
    --------
    - See ``rotation``, ``shift``, ``shear``, ``zoom``.
    """
    x = np.rollaxis(x, channel_index, 0)
    final_affine_matrix = transform_matrix[:2, :2]
    final_offset = transform_matrix[:2, 2]
    channel_images = [ndi.interpolation.affine_transform(x_channel, final_affine_matrix,
                      final_offset, order=0, mode=fill_mode, cval=cval) for x_channel in x]
    x = np.stack(channel_images, axis=0)
    x = np.rollaxis(x, 0, channel_index+1)
    return x 

def array_to_img(x, dim_ordering=(0,1,2), scale=True):
    """Converts a numpy array to PIL image object (uint8 format).

    Parameters
    ----------
    x : numpy array
        A image with dimension of 3 and channels of 1 or 3.
    dim_ordering : list or tuple of 3 int
        Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
    scale : boolean, default is True
        If True, converts image to [0, 255] from any range of value like [-1, 2].

    References
    -----------
    - `PIL Image.fromarray <http://pillow.readthedocs.io/en/3.1.x/reference/Image.html?highlight=fromarray>`_
    """
    from PIL import Image
    # if dim_ordering == 'default':
    #     dim_ordering = K.image_dim_ordering()
    # if dim_ordering == 'th':  # theano
    #     x = x.transpose(1, 2, 0)
    x = x.transpose(dim_ordering)
    if scale:
        x += max(-np.min(x), 0)
        x_max = np.max(x)
        if x_max != 0:
            # print(x_max)
            # x /= x_max
            x = x / x_max
        x *= 255
    if x.shape[2] == 3:
        # RGB
        return Image.fromarray(x.astype('uint8'), 'RGB')
    elif x.shape[2] == 1:
        # grayscale
        return Image.fromarray(x[:, :, 0].astype('uint8'), 'L')
    else:
        raise Exception('Unsupported channel number: ', x.shape[2]) 

def find_contours(x, level=0.8, fully_connected='low', positive_orientation='low'):
    """ Find iso-valued contours in a 2D array for a given level value, returns list of (n, 2)-ndarrays
    see `skimage.measure.find_contours <http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.find_contours>`_ .

    Parameters
    ------------
    x : 2D ndarray of double. Input data in which to find contours.
    level : float. Value along which to find contours in the array.
    fully_connected : str, {‘low’, ‘high’}.  Indicates whether array elements below the given level value are to be considered fully-connected (and hence elements above the value will only be face connected), or vice-versa. (See notes below for details.)
    positive_orientation : either ‘low’ or ‘high’. Indicates whether the output contours will produce positively-oriented polygons around islands of low- or high-valued elements. If ‘low’ then contours will wind counter-clockwise around elements below the iso-value. Alternately, this means that low-valued elements are always on the left of the contour.
    """
    return skimage.measure.find_contours(x, level, fully_connected='low', positive_orientation='low') 

def binary_dilation(x, radius=3):
    """ Return fast binary morphological dilation of an image.
    see `skimage.morphology.binary_dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_dilation>`_.

    Parameters
    -----------
    x : 2D array image.
    radius : int for the radius of mask.
    """
    from skimage.morphology import disk, binary_dilation
    mask = disk(radius)
    x = binary_dilation(image, selem=mask)
    return x 

def ringmaker(r=10., dr=1):
    """
    Creates ring of radius r and thickness dr.

    Parameters
    ----------
    r : float
        Ring radius
    dr : int
        Ring thickness (cv2.circle requires int)
    """
    # See <http://docs.opencv.org/2.4/modules/core/doc/
    # drawing_functions.html#circle>, supplemented by
    # <http://docs.opencv.org/3.1.0/dc/da5/tutorial_py_drawing_functions.html>
    # and <https://github.com/opencv/opencv/blob/
    # 05b15943d6a42c99e5f921b7dbaa8323f3c042c6/modules/imgproc/
    # src/drawing.cpp>.

    # mask grid extent (dr/2 +1 to ensure we capture ring width
    # and radius); same philosophy as above
    rhext = int(np.ceil(r + dr / 2.)) + 1

    # cv2.circle requires integer radius
    mask = np.zeros([2 * rhext + 1, 2 * rhext + 1], np.uint8)

    # Generate ring
    ring = cv2.circle(mask, (rhext, rhext), int(np.round(r)), 1, thickness=dr)

    return ring.astype(float) 

def pil_to_array(pilImage):
    """Load a `PIL image`_ and return it as a numpy array.

    .. _PIL image: https://pillow.readthedocs.io/en/latest/reference/Image.html

    Returns
    -------
    numpy.array

        The array shape depends on the image type:

        - (M, N) for grayscale images.
        - (M, N, 3) for RGB images.
        - (M, N, 4) for RGBA images.

    """
    if pilImage.mode in ['RGBA', 'RGBX', 'RGB', 'L']:
        # return MxNx4 RGBA, MxNx3 RBA, or MxN luminance array
        return np.asarray(pilImage)
    elif pilImage.mode.startswith('I;16'):
        # return MxN luminance array of uint16
        raw = pilImage.tobytes('raw', pilImage.mode)
        if pilImage.mode.endswith('B'):
            x = np.fromstring(raw, '>u2')
        else:
            x = np.fromstring(raw, '<u2')
        return x.reshape(pilImage.size[::-1]).astype('=u2')
    else:  # try to convert to an rgba image
        try:
            pilImage = pilImage.convert('RGBA')
        except ValueError:
            raise RuntimeError('Unknown image mode')
        return np.asarray(pilImage)  # return MxNx4 RGBA array 

def pil_to_array(pilImage):
    """Load a `PIL image`_ and return it as a numpy array.

    .. _PIL image: https://pillow.readthedocs.io/en/latest/reference/Image.html

    Returns
    -------
    numpy.array

        The array shape depends on the image type:

        - (M, N) for grayscale images.
        - (M, N, 3) for RGB images.
        - (M, N, 4) for RGBA images.

    """
    if pilImage.mode in ['RGBA', 'RGBX', 'RGB', 'L']:
        # return MxNx4 RGBA, MxNx3 RBA, or MxN luminance array
        return np.asarray(pilImage)
    elif pilImage.mode.startswith('I;16'):
        # return MxN luminance array of uint16
        raw = pilImage.tobytes('raw', pilImage.mode)
        if pilImage.mode.endswith('B'):
            x = np.frombuffer(raw, '>u2')
        else:
            x = np.frombuffer(raw, '<u2')
        return x.reshape(pilImage.size[::-1]).astype('=u2')
    else:  # try to convert to an rgba image
        try:
            pilImage = pilImage.convert('RGBA')
        except ValueError:
            raise RuntimeError('Unknown image mode')
        return np.asarray(pilImage)  # return MxNx4 RGBA array 

def mode(self) -> str:
        """
        Return the image mode string such as 'RGBA', 'RGB', 'L', etc, or None according to PIL.
        See http://pillow.readthedocs.org/en/3.0.x/handbook/concepts.html#modes
        :return: A string indicating the image mode
        """
        pass 

def mode(self) -> str:
        """
        Return the image mode string such as 'RGBA', 'RGB', 'L', etc, or None according to PIL.
        See http://pillow.readthedocs.org/en/3.0.x/handbook/concepts.html#modes
        :return: A string indicating the image mode
        """ 

def imresize(x, size=[100, 100], interp='bilinear', mode=None):
    """Resize an image by given output size and method. Warning, this function
    will rescale the value to [0, 255].

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    size : int, float or tuple (h, w)
        - int, Percentage of current size.
        - float, Fraction of current size.
        - tuple, Size of the output image.
    interp : str, optional
        Interpolation to use for re-sizing (‘nearest’, ‘lanczos’, ‘bilinear’, ‘bicubic’ or ‘cubic’).
    mode : str, optional
        The PIL image mode (‘P’, ‘L’, etc.) to convert arr before resizing.

    Returns
    --------
    imresize : ndarray
    The resized array of image.

    References
    ------------
    - `scipy.misc.imresize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.imresize.html>`_
    """
    if x.shape[-1] == 1:
        # greyscale
        x = scipy.misc.imresize(x[:,:,0], size, interp=interp, mode=mode)
        return x[:, :, np.newaxis]
    elif x.shape[-1] == 3:
        # rgb, bgr ..
        return scipy.misc.imresize(x, size, interp=interp, mode=mode)
    else:
        raise Exception("Unsupported channel %d" % x.shape[-1])

# normailization 

def channel_shift(x, intensity, is_random=False, channel_index=2):
    """Shift the channels of an image, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_.

    Parameters
    -----------
    x : numpy array
        An image with dimension of [row, col, channel] (default).
    intensity : float
        Intensity of shifting.
    is_random : boolean, default False
        If True, randomly shift.
    channel_index : int
        Index of channel, default 2.
    """
    if is_random:
        factor = np.random.uniform(-intensity, intensity)
    else:
        factor = intensity
    x = np.rollaxis(x, channel_index, 0)
    min_x, max_x = np.min(x), np.max(x)
    channel_images = [np.clip(x_channel + factor, min_x, max_x)
                      for x_channel in x]
    x = np.stack(channel_images, axis=0)
    x = np.rollaxis(x, 0, channel_index+1)
    return x
    # x = np.rollaxis(x, channel_index, 0)
    # min_x, max_x = np.min(x), np.max(x)
    # channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x)
    #                   for x_channel in x]
    # x = np.stack(channel_images, axis=0)
    # x = np.rollaxis(x, 0, channel_index+1)
    # return x 

def channel_shift_multi(x, intensity, channel_index=2):
    """Shift the channels of images with the same arguments, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_ .
    Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

    Parameters
    -----------
    x : list of numpy array
        List of images with dimension of [n_images, row, col, channel] (default).
    others : see ``channel_shift``.
    """
    if is_random:
        factor = np.random.uniform(-intensity, intensity)
    else:
        factor = intensity

    results = []
    for data in x:
        data = np.rollaxis(data, channel_index, 0)
        min_x, max_x = np.min(data), np.max(data)
        channel_images = [np.clip(x_channel + factor, min_x, max_x)
                          for x_channel in x]
        data = np.stack(channel_images, axis=0)
        data = np.rollaxis(x, 0, channel_index+1)
        results.append( data )
    return np.asarray(results)

# noise