import numpy as np

def vis_image(img, ax=None):

    """Visualize a color image.

    Args:

        img (~numpy.ndarray): An array of shape :math:`(3, height, width)`.

            This is in RGB format and the range of its value is

            :math:`[0, 255]`.

        ax (matplotlib.axes.Axis): The visualization is displayed on this

            axis. If this is :obj:`None` (default), a new axis is created.

    Returns:

        ~matploblib.axes.Axes:

        Returns the Axes object with the plot for further tweaking.

    """

    from matplotlib import pyplot as plot

    if ax is None:

        fig = plot.figure()

        ax = fig.add_subplot(1, 1, 1)

    # CHW -> HWC

    img = img.transpose((1, 2, 0))[:, :, 0]

    ax.imshow(img.astype(np.uint8), cmap='gray')

    return ax

def vis_bbox(img, bbox, label=None, score=None, label_names=None, ax=None):

    """Visualize bounding boxes inside image.

    Example:

        >>> from chainercv.datasets import VOCDetectionDataset

        >>> from chainercv.datasets import voc_bbox_label_names

        >>> from chainercv.visualizations import vis_bbox

        >>> import matplotlib.pyplot as plot

        >>> dataset = VOCDetectionDataset()

        >>> img, bbox, label = dataset[60]

        >>> vis_bbox(img, bbox, label,

        ...         label_names=voc_bbox_label_names)

        >>> plot.show()

    Args:

        img (~numpy.ndarray): An array of shape :math:`(3, height, width)`.

            This is in RGB format and the range of its value is

            :math:`[0, 255]`.

        bbox (~numpy.ndarray): An array of shape :math:`(R, 4)`, where

            :math:`R` is the number of bounding boxes in the image.

            Each element is organized

            by :math:`(y_{min}, x_{min}, y_{max}, x_{max})` in the second axis.

        label (~numpy.ndarray): An integer array of shape :math:`(R,)`.

            The values correspond to id for label names stored in

            :obj:`label_names`. This is optional.

        score (~numpy.ndarray): A float array of shape :math:`(R,)`.

             Each value indicates how confident the prediction is.

             This is optional.

        label_names (iterable of strings): Name of labels ordered according

            to label ids. If this is :obj:`None`, labels will be skipped.

        ax (matplotlib.axes.Axis): The visualization is displayed on this

            axis. If this is :obj:`None` (default), a new axis is created.

    Returns:

        ~matploblib.axes.Axes:

        Returns the Axes object with the plot for further tweaking.

    """

    from matplotlib import pyplot as plot

    #if label is not None and not len(bbox) == len(label):

    #    raise ValueError('The length of label must be same as that of bbox')

    #if score is not None and not len(bbox) == len(score):

    #    raise ValueError('The length of score must be same as that of bbox')

    # Returns newly instantiated matplotlib.axes.Axes object if ax is None

    ax = vis_image(img, ax=ax)

    # If there is no bounding box to display, visualize the image and exit.

    if len(bbox) == 0:

        return ax

    if len(bbox) != 0:

        #print(bbox)

        bb = bbox[0]

        i = 0

        xy = (bb[1], bb[0])

        height = bb[2] - bb[0]

        width = bb[3] - bb[1]

        ax.add_patch(plot.Rectangle(

            xy, width, height, fill=False, edgecolor='red', linewidth=3))

        caption = list()

        if label is not None and label_names is not None:

            lb = label[i]

            if not (0 <= lb < len(label_names)):

                raise ValueError('No corresponding name is given')

            caption.append(label_names[lb])

        if score is not None:

            sc = score[i]

            caption.append('{:.2f}'.format(sc))

        if len(caption) > 0:

            ax.text(bb[1], bb[0],

                    ': '.join(caption),

                    style='italic')

#                     bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 5})

    return ax