<filename>explain/utils/display.py
""" Module for display related operations. """
import warnings
import math

import numpy as np
import cv2

import tensorflow as tf


def grid_display(array, num_rows=None, num_columns=None):
    """
    Display a list of images as a grid.

    Args:
        array (numpy.ndarray): 4D Tensor (batch_size, height, width, channels)

    Returns:
        numpy.ndarray: 3D Tensor as concatenation of input images on a grid
    """
    if num_rows is not None and num_columns is not None:
        total_grid_size = num_rows * num_columns
        if total_grid_size < len(array):
            warnings.warn(
                Warning(
                    "Given values for num_rows and num_columns doesn't allow to display "
                    "all images. Values have been overrided to respect at least num_columns"
                )
            )
            num_rows = math.ceil(len(array) / num_columns)
    elif num_rows is not None:
        num_columns = math.ceil(len(array) / num_rows)
    elif num_columns is not None:
        num_rows = math.ceil(len(array) / num_columns)
    else:
        num_rows = math.ceil(math.sqrt(len(array)))
        num_columns = math.ceil(math.sqrt(len(array)))

    number_of_missing_elements = num_columns * num_rows - len(array)
    # We fill the array with np.zeros elements to obtain a perfect square
    array = np.append(
        array,
        np.zeros((number_of_missing_elements, *array[0].shape)).astype(array.dtype),
        axis=0,
    )

    grid = np.concatenate(
        [
            np.concatenate(
                array[index * num_columns : (index + 1) * num_columns], axis=1
            )
            for index in range(num_rows)
        ],
        axis=0,
    )

    return grid


def filter_display(array, num_rows=None, num_columns=None):
    """
    Display a list of filter outputs as a greyscale images grid.

    Args:
        array (numpy.ndarray): 4D Tensor (batch_size, height, width, channels)

    Returns:
        numpy.ndarray: 3D Tensor as concatenation of input images on a grid
    """
    return grid_display(
        np.concatenate(np.rollaxis(array, 3, 1), axis=0), num_rows, num_columns
    )

def display_all_imgs(array):
    """
    Display a list of filter outputs as a greyscale images.

    Args:
        array (numpy.ndarray): 4D Tensor (batch_size, height, width, channels)

    Returns:
        numpy.ndarray: 4D Tensor (batch_size, height, width, 1)
    """
    # The input array comes with a lot of channels (depending on the network architecture).
    # So, this function changes the batch_size and channels values.

    return np.swapaxes(array, 0, 3)

    """
    return np.reshape(
        array,
        (array.shape[0] * array.shape[3], array.shape[1], array.shape[2], 1)
    )
    """


def image_to_uint_255(image):
    """
    Convert float images to int 0-255 images.

    Args:
        image (numpy.ndarray): Input image. Can be either [0, 255], [0, 1], [-1, 1]

    Returns:
        numpy.ndarray:
    """
    if image.dtype == np.uint8:
        return image

    if image.min() < 0:
        image = (image + 1.0) / 2.0

    return (image * 255).astype("uint8")


def heatmap_display(
    heatmap, original_image, colormap=cv2.COLORMAP_VIRIDIS, image_weight=0.7
):
    """
    Apply a heatmap (as an np.ndarray) on top of an original image.

    Args:
        heatmap (numpy.ndarray): Array corresponding to the heatmap
        original_image (numpy.ndarray): Image on which we apply the heatmap
        colormap (int): OpenCV Colormap to use for heatmap visualization
        image_weight (float): An optional `float` value in range [0,1] indicating the weight of
            the input image to be overlaying the calculated attribution maps. Defaults to `0.7`

    Returns:
        np.ndarray: Original image with heatmap applied
    """
    heatmap = cv2.resize(heatmap, (original_image.shape[1], original_image.shape[0]))

    image = image_to_uint_255(original_image)

    heatmap = (heatmap - np.min(heatmap)) / (heatmap.max() - heatmap.min())

    heatmap = cv2.applyColorMap(
        cv2.cvtColor((heatmap * 255).astype("uint8"), cv2.COLOR_GRAY2BGR), colormap
    )

    output = cv2.addWeighted(
        cv2.cvtColor(image, cv2.COLOR_RGB2BGR), image_weight, heatmap, 1, 0
    )

    return cv2.cvtColor(output, cv2.COLOR_BGR2RGB)

<filename>explain/utils/display.py
""" Module for display related operations. """
import warnings
import math

import numpy as np
import cv2

import tensorflow as tf


def grid_display(array, num_rows=None, num_columns=None):
    """
    Display a list of images as a grid.

    Args:
        array (numpy.ndarray): 4D Tensor (batch_size, height, width, channels)

    Returns:
        numpy.ndarray: 3D Tensor as concatenation of input images on a grid
    """
    if num_rows is not None and num_columns is not None:
        total_grid_size = num_rows * num_columns
        if total_grid_size < len(array):
            warnings.warn(
                Warning(
                    "Given values for num_rows and num_columns doesn't allow to display "
                    "all images. Values have been overrided to respect at least num_columns"
                )
            )
            num_rows = math.ceil(len(array) / num_columns)
    elif num_rows is not None:
        num_columns = math.ceil(len(array) / num_rows)
    elif num_columns is not None:
        num_rows = math.ceil(len(array) / num_columns)
    else:
        num_rows = math.ceil(math.sqrt(len(array)))
        num_columns = math.ceil(math.sqrt(len(array)))

    number_of_missing_elements = num_columns * num_rows - len(array)
    # We fill the array with np.zeros elements to obtain a perfect square
    array = np.append(
        array,
        np.zeros((number_of_missing_elements, *array[0].shape)).astype(array.dtype),
        axis=0,
    )

    grid = np.concatenate(
        [
            np.concatenate(
                array[index * num_columns : (index + 1) * num_columns], axis=1
            )
            for index in range(num_rows)
        ],
        axis=0,
    )

    return grid


def filter_display(array, num_rows=None, num_columns=None):
    """
    Display a list of filter outputs as a greyscale images grid.

    Args:
        array (numpy.ndarray): 4D Tensor (batch_size, height, width, channels)

    Returns:
        numpy.ndarray: 3D Tensor as concatenation of input images on a grid
    """
    return grid_display(
        np.concatenate(np.rollaxis(array, 3, 1), axis=0), num_rows, num_columns
    )

def display_all_imgs(array):
    """
    Display a list of filter outputs as a greyscale images.

    Args:
        array (numpy.ndarray): 4D Tensor (batch_size, height, width, channels)

    Returns:
        numpy.ndarray: 4D Tensor (batch_size, height, width, 1)
    """
    # The input array comes with a lot of channels (depending on the network architecture).
    # So, this function changes the batch_size and channels values.

    return np.swapaxes(array, 0, 3)

    """
    return np.reshape(
        array,
        (array.shape[0] * array.shape[3], array.shape[1], array.shape[2], 1)
    )
    """


def image_to_uint_255(image):
    """
    Convert float images to int 0-255 images.

    Args:
        image (numpy.ndarray): Input image. Can be either [0, 255], [0, 1], [-1, 1]

    Returns:
        numpy.ndarray:
    """
    if image.dtype == np.uint8:
        return image

    if image.min() < 0:
        image = (image + 1.0) / 2.0

    return (image * 255).astype("uint8")


def heatmap_display(
    heatmap, original_image, colormap=cv2.COLORMAP_VIRIDIS, image_weight=0.7
):
    """
    Apply a heatmap (as an np.ndarray) on top of an original image.

    Args:
        heatmap (numpy.ndarray): Array corresponding to the heatmap
        original_image (numpy.ndarray): Image on which we apply the heatmap
        colormap (int): OpenCV Colormap to use for heatmap visualization
        image_weight (float): An optional `float` value in range [0,1] indicating the weight of
            the input image to be overlaying the calculated attribution maps. Defaults to `0.7`

    Returns:
        np.ndarray: Original image with heatmap applied
    """
    heatmap = cv2.resize(heatmap, (original_image.shape[1], original_image.shape[0]))

    image = image_to_uint_255(original_image)

    heatmap = (heatmap - np.min(heatmap)) / (heatmap.max() - heatmap.min())

    heatmap = cv2.applyColorMap(
        cv2.cvtColor((heatmap * 255).astype("uint8"), cv2.COLOR_GRAY2BGR), colormap
    )

    output = cv2.addWeighted(
        cv2.cvtColor(image, cv2.COLOR_RGB2BGR), image_weight, heatmap, 1, 0
    )

    return cv2.cvtColor(output, cv2.COLOR_BGR2RGB)

Module for display related operations. Display a list of images as a grid.      Args:         array (numpy.ndarray): 4D Tensor (batch_size, height, width, channels)      Returns:         numpy.ndarray: 3D Tensor as concatenation of input images on a grid # We fill the array with np.zeros elements to obtain a perfect square Display a list of filter outputs as a greyscale images grid.      Args:         array (numpy.ndarray): 4D Tensor (batch_size, height, width, channels)      Returns:         numpy.ndarray: 3D Tensor as concatenation of input images on a grid Display a list of filter outputs as a greyscale images.      Args:         array (numpy.ndarray): 4D Tensor (batch_size, height, width, channels)      Returns:         numpy.ndarray: 4D Tensor (batch_size, height, width, 1) # The input array comes with a lot of channels (depending on the network architecture). # So, this function changes the batch_size and channels values. return np.reshape(         array,         (array.shape[0] * array.shape[3], array.shape[1], array.shape[2], 1)     ) Convert float images to int 0-255 images.      Args:         image (numpy.ndarray): Input image. Can be either [0, 255], [0, 1], [-1, 1]      Returns:         numpy.ndarray: Apply a heatmap (as an np.ndarray) on top of an original image.      Args:         heatmap (numpy.ndarray): Array corresponding to the heatmap         original_image (numpy.ndarray): Image on which we apply the heatmap         colormap (int): OpenCV Colormap to use for heatmap visualization         image_weight (float): An optional `float` value in range [0,1] indicating the weight of             the input image to be overlaying the calculated attribution maps. Defaults to `0.7`      Returns:         np.ndarray: Original image with heatmap applied