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