# Copyright (c) OpenMMLab. All rights reserved.
import math
import os
import warnings

import cv2
import mmcv
import numpy as np
from matplotlib import pyplot as plt
from mmcv.utils.misc import deprecated_api_warning
from mmcv.visualization.color import color_val

try:
    import trimesh
    has_trimesh = True
except (ImportError, ModuleNotFoundError):
    has_trimesh = False

try:
    os.environ['PYOPENGL_PLATFORM'] = 'osmesa'
    import pyrender
    has_pyrender = True
except (ImportError, ModuleNotFoundError):
    has_pyrender = False


def imshow_bboxes(img,
                  bboxes,
                  labels=None,
                  colors='green',
                  text_color='white',
                  thickness=1,
                  font_scale=0.5,
                  show=True,
                  win_name='',
                  wait_time=0,
                  out_file=None):
    """Draw bboxes with labels (optional) on an image. This is a wrapper of
    mmcv.imshow_bboxes.

    Args:
        img (str or ndarray): The image to be displayed.
        bboxes (ndarray): ndarray of shape (k, 4), each row is a bbox in
            format [x1, y1, x2, y2].
        labels (str or list[str], optional): labels of each bbox.
        colors (list[str or tuple or :obj:`Color`]): A list of colors.
        text_color (str or tuple or :obj:`Color`): Color of texts.
        thickness (int): Thickness of lines.
        font_scale (float): Font scales of texts.
        show (bool): Whether to show the image.
        win_name (str): The window name.
        wait_time (int): Value of waitKey param.
        out_file (str, optional): The filename to write the image.

    Returns:
        ndarray: The image with bboxes drawn on it.
    """

    # adapt to mmcv.imshow_bboxes input format
    bboxes = np.split(
        bboxes, bboxes.shape[0], axis=0) if bboxes.shape[0] > 0 else []
    if not isinstance(colors, list):
        colors = [colors for _ in range(len(bboxes))]
    colors = [mmcv.color_val(c) for c in colors]
    assert len(bboxes) == len(colors)

    img = mmcv.imshow_bboxes(
        img,
        bboxes,
        colors,
        top_k=-1,
        thickness=thickness,
        show=False,
        out_file=None)

    if labels is not None:
        if not isinstance(labels, list):
            labels = [labels for _ in range(len(bboxes))]
        assert len(labels) == len(bboxes)

        for bbox, label, color in zip(bboxes, labels, colors):
            if label is None:
                continue
            bbox_int = bbox[0, :4].astype(np.int32)
            # roughly estimate the proper font size
            text_size, text_baseline = cv2.getTextSize(label,
                                                       cv2.FONT_HERSHEY_DUPLEX,
                                                       font_scale, thickness)
            text_x1 = bbox_int[0]
            text_y1 = max(0, bbox_int[1] - text_size[1] - text_baseline)
            text_x2 = bbox_int[0] + text_size[0]
            text_y2 = text_y1 + text_size[1] + text_baseline
            cv2.rectangle(img, (text_x1, text_y1), (text_x2, text_y2), color,
                          cv2.FILLED)
            cv2.putText(img, label, (text_x1, text_y2 - text_baseline),
                        cv2.FONT_HERSHEY_DUPLEX, font_scale,
                        mmcv.color_val(text_color), thickness)

    if show:
        mmcv.imshow(img, win_name, wait_time)
    if out_file is not None:
        mmcv.imwrite(img, out_file)
    return img


@deprecated_api_warning({'pose_limb_color': 'pose_link_color'})
def imshow_keypoints(img,
                     pose_result,
                     skeleton=None,
                     kpt_score_thr=0.3,
                     pose_kpt_color=None,
                     pose_link_color=None,
                     radius=4,
                     thickness=1,
                     show_keypoint_weight=False):
    """Draw keypoints and links on an image.

    Args:
            img (str or Tensor): The image to draw poses on. If an image array
                is given, id will be modified in-place.
            pose_result (list[kpts]): The poses to draw. Each element kpts is
                a set of K keypoints as an Kx3 numpy.ndarray, where each
                keypoint is represented as x, y, score.
            kpt_score_thr (float, optional): Minimum score of keypoints
                to be shown. Default: 0.3.
            pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None,
                the keypoint will not be drawn.
            pose_link_color (np.array[Mx3]): Color of M links. If None, the
                links will not be drawn.
            thickness (int): Thickness of lines.
    """

    img = mmcv.imread(img)
    img_h, img_w, _ = img.shape

    for kpts in pose_result:

        kpts = np.array(kpts, copy=False)

        # draw each point on image
        if pose_kpt_color is not None:
            assert len(pose_kpt_color) == len(kpts)
            for kid, kpt in enumerate(kpts):
                x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2]
                if kpt_score > kpt_score_thr:
                    color = tuple(int(c) for c in pose_kpt_color[kid])
                    if show_keypoint_weight:
                        img_copy = img.copy()
                        cv2.circle(img_copy, (int(x_coord), int(y_coord)),
                                   radius, color, -1)
                        transparency = max(0, min(1, kpt_score))
                        cv2.addWeighted(
                            img_copy,
                            transparency,
                            img,
                            1 - transparency,
                            0,
                            dst=img)
                    else:
                        cv2.circle(img, (int(x_coord), int(y_coord)), radius,
                                   color, -1)

        # draw links
        if skeleton is not None and pose_link_color is not None:
            assert len(pose_link_color) == len(skeleton)
            for sk_id, sk in enumerate(skeleton):
                pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1]))
                pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1]))
                if (pos1[0] > 0 and pos1[0] < img_w and pos1[1] > 0
                        and pos1[1] < img_h and pos2[0] > 0 and pos2[0] < img_w
                        and pos2[1] > 0 and pos2[1] < img_h
                        and kpts[sk[0], 2] > kpt_score_thr
                        and kpts[sk[1], 2] > kpt_score_thr):
                    color = tuple(int(c) for c in pose_link_color[sk_id])
                    if show_keypoint_weight:
                        img_copy = img.copy()
                        X = (pos1[0], pos2[0])
                        Y = (pos1[1], pos2[1])
                        mX = np.mean(X)
                        mY = np.mean(Y)
                        length = ((Y[0] - Y[1])**2 + (X[0] - X[1])**2)**0.5
                        angle = math.degrees(
                            math.atan2(Y[0] - Y[1], X[0] - X[1]))
                        stickwidth = 2
                        polygon = cv2.ellipse2Poly(
                            (int(mX), int(mY)),
                            (int(length / 2), int(stickwidth)), int(angle), 0,
                            360, 1)
                        cv2.fillConvexPoly(img_copy, polygon, color)
                        transparency = max(
                            0, min(1, 0.5 * (kpts[sk[0], 2] + kpts[sk[1], 2])))
                        cv2.addWeighted(
                            img_copy,
                            transparency,
                            img,
                            1 - transparency,
                            0,
                            dst=img)
                    else:
                        cv2.line(img, pos1, pos2, color, thickness=thickness)

    return img


def imshow_keypoints_3d(
    pose_result,
    img=None,
    skeleton=None,
    pose_kpt_color=None,
    pose_link_color=None,
    vis_height=400,
    kpt_score_thr=0.3,
    num_instances=-1,
    *,
    axis_azimuth=70,
    axis_limit=1.7,
    axis_dist=10.0,
    axis_elev=15.0,
):
    """Draw 3D keypoints and links in 3D coordinates.

    Args:
        pose_result (list[dict]): 3D pose results containing:
            - "keypoints_3d" ([K,4]): 3D keypoints
            - "title" (str): Optional. A string to specify the title of the
                visualization of this pose result
        img (str|np.ndarray): Opptional. The image or image path to show input
            image and/or 2D pose. Note that the image should be given in BGR
            channel order.
        skeleton (list of [idx_i,idx_j]): Skeleton described by a list of
            links, each is a pair of joint indices.
        pose_kpt_color (np.ndarray[Nx3]`): Color of N keypoints. If None, do
            not nddraw keypoints.
        pose_link_color (np.array[Mx3]): Color of M links. If None, do not
            draw links.
        vis_height (int): The image height of the visualization. The width
                will be N*vis_height depending on the number of visualized
                items.
        kpt_score_thr (float): Minimum score of keypoints to be shown.
            Default: 0.3.
        num_instances (int): Number of instances to be shown in 3D. If smaller
            than 0, all the instances in the pose_result will be shown.
            Otherwise, pad or truncate the pose_result to a length of
            num_instances.
        axis_azimuth (float): axis azimuth angle for 3D visualizations.
        axis_dist (float): axis distance for 3D visualizations.
        axis_elev (float): axis elevation view angle for 3D visualizations.
        axis_limit (float): The axis limit to visualize 3d pose. The xyz
            range will be set as:
            - x: [x_c - axis_limit/2, x_c + axis_limit/2]
            - y: [y_c - axis_limit/2, y_c + axis_limit/2]
            - z: [0, axis_limit]
            Where x_c, y_c is the mean value of x and y coordinates
        figsize: (float): figure size in inch.
    """

    show_img = img is not None
    if num_instances < 0:
        num_instances = len(pose_result)
    else:
        if len(pose_result) > num_instances:
            pose_result = pose_result[:num_instances]
        elif len(pose_result) < num_instances:
            pose_result += [dict()] * (num_instances - len(pose_result))
    num_axis = num_instances + 1 if show_img else num_instances

    plt.ioff()
    fig = plt.figure(figsize=(vis_height * num_axis * 0.01, vis_height * 0.01))

    if show_img:
        img = mmcv.imread(img, channel_order='bgr')
        img = mmcv.bgr2rgb(img)
        img = mmcv.imrescale(img, scale=vis_height / img.shape[0])

        ax_img = fig.add_subplot(1, num_axis, 1)
        ax_img.get_xaxis().set_visible(False)
        ax_img.get_yaxis().set_visible(False)
        ax_img.set_axis_off()
        ax_img.set_title('Input')
        ax_img.imshow(img, aspect='equal')

    for idx, res in enumerate(pose_result):
        dummy = len(res) == 0
        kpts = np.zeros((1, 3)) if dummy else res['keypoints_3d']
        if kpts.shape[1] == 3:
            kpts = np.concatenate([kpts, np.ones((kpts.shape[0], 1))], axis=1)
        valid = kpts[:, 3] >= kpt_score_thr

        ax_idx = idx + 2 if show_img else idx + 1
        ax = fig.add_subplot(1, num_axis, ax_idx, projection='3d')
        ax.view_init(
            elev=axis_elev,
            azim=axis_azimuth,
        )
        x_c = np.mean(kpts[valid, 0]) if sum(valid) > 0 else 0
        y_c = np.mean(kpts[valid, 1]) if sum(valid) > 0 else 0
        ax.set_xlim3d([x_c - axis_limit / 2, x_c + axis_limit / 2])
        ax.set_ylim3d([y_c - axis_limit / 2, y_c + axis_limit / 2])
        ax.set_zlim3d([0, axis_limit])
        ax.set_aspect('auto')
        ax.set_xticks([])
        ax.set_yticks([])
        ax.set_zticks([])
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_zticklabels([])
        ax.dist = axis_dist

        if not dummy and pose_kpt_color is not None:
            pose_kpt_color = np.array(pose_kpt_color)
            assert len(pose_kpt_color) == len(kpts)
            x_3d, y_3d, z_3d = np.split(kpts[:, :3], [1, 2], axis=1)
            # matplotlib uses RGB color in [0, 1] value range
            _color = pose_kpt_color[..., ::-1] / 255.
            ax.scatter(
                x_3d[valid],
                y_3d[valid],
                z_3d[valid],
                marker='o',
                color=_color[valid],
            )

        if not dummy and skeleton is not None and pose_link_color is not None:
            pose_link_color = np.array(pose_link_color)
            assert len(pose_link_color) == len(skeleton)
            for link, link_color in zip(skeleton, pose_link_color):
                link_indices = [_i for _i in link]
                xs_3d = kpts[link_indices, 0]
                ys_3d = kpts[link_indices, 1]
                zs_3d = kpts[link_indices, 2]
                kpt_score = kpts[link_indices, 3]
                if kpt_score.min() > kpt_score_thr:
                    # matplotlib uses RGB color in [0, 1] value range
                    _color = link_color[::-1] / 255.
                    ax.plot(xs_3d, ys_3d, zs_3d, color=_color, zdir='z')

        if 'title' in res:
            ax.set_title(res['title'])

    # convert figure to numpy array
    fig.tight_layout()
    fig.canvas.draw()
    img_w, img_h = fig.canvas.get_width_height()
    img_vis = np.frombuffer(
        fig.canvas.tostring_rgb(), dtype=np.uint8).reshape(img_h, img_w, -1)
    img_vis = mmcv.rgb2bgr(img_vis)

    plt.close(fig)

    return img_vis


def imshow_mesh_3d(img,
                   vertices,
                   faces,
                   camera_center,
                   focal_length,
                   colors=(76, 76, 204)):
    """Render 3D meshes on background image.

    Args:
        img(np.ndarray): Background image.
        vertices (list of np.ndarray): Vetrex coordinates in camera space.
        faces (list of np.ndarray): Faces of meshes.
        camera_center ([2]): Center pixel.
        focal_length ([2]): Focal length of camera.
        colors (list[str or tuple or Color]): A list of mesh colors.
    """

    H, W, C = img.shape

    if not has_pyrender:
        warnings.warn('pyrender package is not installed.')
        return img

    if not has_trimesh:
        warnings.warn('trimesh package is not installed.')
        return img

    try:
        renderer = pyrender.OffscreenRenderer(
            viewport_width=W, viewport_height=H)
    except (ImportError, RuntimeError):
        warnings.warn('pyrender package is not installed correctly.')
        return img

    if not isinstance(colors, list):
        colors = [colors for _ in range(len(vertices))]
    colors = [color_val(c) for c in colors]

    depth_map = np.ones([H, W]) * np.inf
    output_img = img
    for idx in range(len(vertices)):
        color = colors[idx]
        color = [c / 255.0 for c in color]
        color.append(1.0)
        vert = vertices[idx]
        face = faces[idx]

        material = pyrender.MetallicRoughnessMaterial(
            metallicFactor=0.2, alphaMode='OPAQUE', baseColorFactor=color)

        mesh = trimesh.Trimesh(vert, face)
        rot = trimesh.transformations.rotation_matrix(
            np.radians(180), [1, 0, 0])
        mesh.apply_transform(rot)
        mesh = pyrender.Mesh.from_trimesh(mesh, material=material)

        scene = pyrender.Scene(ambient_light=(0.5, 0.5, 0.5))
        scene.add(mesh, 'mesh')

        camera_pose = np.eye(4)
        camera = pyrender.IntrinsicsCamera(
            fx=focal_length[0],
            fy=focal_length[1],
            cx=camera_center[0],
            cy=camera_center[1],
            zfar=1e5)
        scene.add(camera, pose=camera_pose)

        light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=1)
        light_pose = np.eye(4)

        light_pose[:3, 3] = np.array([0, -1, 1])
        scene.add(light, pose=light_pose)

        light_pose[:3, 3] = np.array([0, 1, 1])
        scene.add(light, pose=light_pose)

        light_pose[:3, 3] = np.array([1, 1, 2])
        scene.add(light, pose=light_pose)

        color, rend_depth = renderer.render(
            scene, flags=pyrender.RenderFlags.RGBA)

        valid_mask = (rend_depth < depth_map) * (rend_depth > 0)
        depth_map[valid_mask] = rend_depth[valid_mask]
        valid_mask = valid_mask[:, :, None]
        output_img = (
            valid_mask * color[:, :, :3] + (1 - valid_mask) * output_img)

    return output_img
