# Copyright (c) OpenMMLab. All rights reserved.

import cv2
import matplotlib.pyplot as plt
import mmcv
import numpy as np
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
from mmdet.core.visualization import palette_val
from mmdet.core.visualization.image import draw_labels, draw_masks

from mmrotate.core.visualization.palette import get_palette

EPS = 1e-2


def _get_adaptive_scales(areas, min_area=800, max_area=30000):
    """Get adaptive scales according to areas.

    The scale range is [0.5, 1.0]. When the area is less than
    ``'min_area'``, the scale is 0.5 while the area is larger than
    ``'max_area'``, the scale is 1.0.

    Args:
        areas (ndarray): The areas of bboxes or masks with the
            shape of (n, ).
        min_area (int): Lower bound areas for adaptive scales.
            Default: 800.
        max_area (int): Upper bound areas for adaptive scales.
            Default: 30000.

    Returns:
        ndarray: The adaotive scales with the shape of (n, ).
    """
    scales = 0.5 + (areas - min_area) / (max_area - min_area)
    scales = np.clip(scales, 0.5, 1.0)
    return scales


def draw_rbboxes(ax, bboxes, color='g', alpha=0.8, thickness=2):
    """Draw oriented bounding boxes on the axes.

    Args:
        ax (matplotlib.Axes): The input axes.
        bboxes (ndarray): The input bounding boxes with the shape
            of (n, 5).
        color (list[tuple] | matplotlib.color): the colors for each
            bounding boxes.
        alpha (float): Transparency of bounding boxes. Default: 0.8.
        thickness (int): Thickness of lines. Default: 2.

    Returns:
        matplotlib.Axes: The result axes.
    """
    polygons = []
    for i, bbox in enumerate(bboxes):
        xc, yc, w, h, ag = bbox[:5]
        wx, wy = w / 2 * np.cos(ag), w / 2 * np.sin(ag)
        hx, hy = -h / 2 * np.sin(ag), h / 2 * np.cos(ag)
        p1 = (xc - wx - hx, yc - wy - hy)
        p2 = (xc + wx - hx, yc + wy - hy)
        p3 = (xc + wx + hx, yc + wy + hy)
        p4 = (xc - wx + hx, yc - wy + hy)
        poly = np.int0(np.array([p1, p2, p3, p4]))
        polygons.append(Polygon(poly))
    p = PatchCollection(
        polygons,
        facecolor='none',
        edgecolors=color,
        linewidths=thickness,
        alpha=alpha)
    ax.add_collection(p)

    return ax


def imshow_det_rbboxes(img,
                       bboxes=None,
                       labels=None,
                       segms=None,
                       class_names=None,
                       score_thr=0,
                       bbox_color='green',
                       text_color='green',
                       mask_color=None,
                       thickness=2,
                       font_size=13,
                       win_name='',
                       show=True,
                       wait_time=0,
                       out_file=None):
    """Draw bboxes and class labels (with scores) on an image.

    Args:
        img (str | ndarray): The image to be displayed.
        bboxes (ndarray): Bounding boxes (with scores), shaped (n, 5) or
            (n, 6).
        labels (ndarray): Labels of bboxes.
        segms (ndarray | None): Masks, shaped (n,h,w) or None.
        class_names (list[str]): Names of each classes.
        score_thr (float): Minimum score of bboxes to be shown. Default: 0.
        bbox_color (list[tuple] | tuple | str | None): Colors of bbox lines.
           If a single color is given, it will be applied to all classes.
           The tuple of color should be in RGB order. Default: 'green'.
        text_color (list[tuple] | tuple | str | None): Colors of texts.
           If a single color is given, it will be applied to all classes.
           The tuple of color should be in RGB order. Default: 'green'.
        mask_color (list[tuple] | tuple | str | None, optional): Colors of
           masks. If a single color is given, it will be applied to all
           classes. The tuple of color should be in RGB order.
           Default: None.
        thickness (int): Thickness of lines. Default: 2.
        font_size (int): Font size of texts. Default: 13.
        show (bool): Whether to show the image. Default: True.
        win_name (str): The window name. Default: ''.
        wait_time (float): Value of waitKey param. Default: 0.
        out_file (str, optional): The filename to write the image.
            Default: None.

    Returns:
        ndarray: The image with bboxes drawn on it.
    """
    assert bboxes is None or bboxes.ndim == 2, \
        f' bboxes ndim should be 2, but its ndim is {bboxes.ndim}.'
    assert labels.ndim == 1, \
        f' labels ndim should be 1, but its ndim is {labels.ndim}.'
    assert bboxes is None or bboxes.shape[1] == 5 or bboxes.shape[1] == 6, \
        f' bboxes.shape[1] should be 5 or 6, but its {bboxes.shape[1]}.'
    assert bboxes is None or bboxes.shape[0] <= labels.shape[0], \
        'labels.shape[0] should not be less than bboxes.shape[0].'
    assert segms is None or segms.shape[0] == labels.shape[0], \
        'segms.shape[0] and labels.shape[0] should have the same length.'
    assert segms is not None or bboxes is not None, \
        'segms and bboxes should not be None at the same time.'

    img = mmcv.imread(img).astype(np.uint8)

    if score_thr > 0:
        assert bboxes is not None and bboxes.shape[1] == 6
        scores = bboxes[:, -1]
        inds = scores > score_thr
        bboxes = bboxes[inds, :]
        labels = labels[inds]
        if segms is not None:
            segms = segms[inds, ...]

    img = mmcv.bgr2rgb(img)
    width, height = img.shape[1], img.shape[0]
    img = np.ascontiguousarray(img)

    fig = plt.figure(win_name, frameon=False)
    plt.title(win_name)
    canvas = fig.canvas
    dpi = fig.get_dpi()
    # add a small EPS to avoid precision lost due to matplotlib's truncation
    # (https://github.com/matplotlib/matplotlib/issues/15363)
    fig.set_size_inches((width + EPS) / dpi, (height + EPS) / dpi)

    # remove white edges by set subplot margin
    plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
    ax = plt.gca()
    ax.axis('off')

    max_label = int(max(labels) if len(labels) > 0 else 0)
    text_palette = palette_val(get_palette(text_color, max_label + 1))
    text_colors = [text_palette[label] for label in labels]

    num_bboxes = 0
    if bboxes is not None:
        num_bboxes = bboxes.shape[0]
        bbox_palette = palette_val(get_palette(bbox_color, max_label + 1))
        colors = [bbox_palette[label] for label in labels[:num_bboxes]]
        draw_rbboxes(ax, bboxes, colors, alpha=0.8, thickness=thickness)

        horizontal_alignment = 'left'
        positions = bboxes[:, :2].astype(np.int32) + thickness
        areas = bboxes[:, 2] * bboxes[:, 3]
        scales = _get_adaptive_scales(areas)
        scores = bboxes[:, 5] if bboxes.shape[1] == 6 else None
        # draw_labels(
        #     ax,
        #     labels[:num_bboxes],
        #     positions,
        #     scores=scores,
        #     class_names=class_names,
        #     color=text_colors,
        #     font_size=font_size,
        #     scales=scales,
        #     horizontal_alignment=horizontal_alignment)

    if segms is not None:
        mask_palette = get_palette(mask_color, max_label + 1)
        colors = [mask_palette[label] for label in labels]
        colors = np.array(colors, dtype=np.uint8)
        draw_masks(ax, img, segms, colors, with_edge=True)

        if num_bboxes < segms.shape[0]:
            segms = segms[num_bboxes:]
            horizontal_alignment = 'center'
            areas = []
            positions = []
            for mask in segms:
                _, _, stats, centroids = cv2.connectedComponentsWithStats(
                    mask.astype(np.uint8), connectivity=8)
                largest_id = np.argmax(stats[1:, -1]) + 1
                positions.append(centroids[largest_id])
                areas.append(stats[largest_id, -1])
            areas = np.stack(areas, axis=0)
            scales = _get_adaptive_scales(areas)
            draw_labels(
                ax,
                labels[num_bboxes:],
                positions,
                class_names=class_names,
                color=text_colors,
                font_size=font_size,
                scales=scales,
                horizontal_alignment=horizontal_alignment)

    plt.imshow(img)

    stream, _ = canvas.print_to_buffer()
    buffer = np.frombuffer(stream, dtype='uint8')
    img_rgba = buffer.reshape(height, width, 4)
    rgb, alpha = np.split(img_rgba, [3], axis=2)
    img = rgb.astype('uint8')
    img = mmcv.rgb2bgr(img)

    if show:
        # We do not use cv2 for display because in some cases, opencv will
        # conflict with Qt, it will output a warning: Current thread
        # is not the object's thread. You can refer to
        # https://github.com/opencv/opencv-python/issues/46 for details
        if wait_time == 0:
            plt.show()
        else:
            plt.show(block=False)
            plt.pause(wait_time)
    if out_file is not None:
        mmcv.imwrite(img, out_file)

    plt.close()

    return img
