# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cv2
import numpy as np

end_list = np.array([17, 22, 27, 42, 48, 31, 36, 68], dtype=np.int32) - 1


def plot_kpt(image, kpt):
    ''' Draw 68 key points
    Args:
        image: the input image
        kpt: (68, 3).
    '''
    image = image.copy()
    kpt = np.round(kpt).astype(np.int32)
    for i in range(kpt.shape[0]):
        st = kpt[i, :2]
        image = cv2.circle(image, (st[0], st[1]), 1, (0, 0, 255), 2)
        if i in end_list:
            continue
        ed = kpt[i + 1, :2]
        image = cv2.line(image, (st[0], st[1]), (ed[0], ed[1]), (255, 255, 255), 1)
    return image


def plot_vertices(image, vertices):
    image = image.copy()
    vertices = np.round(vertices).astype(np.int32)
    for i in range(0, vertices.shape[0], 2):
        st = vertices[i, :2]
        image = cv2.circle(image, (st[0], st[1]), 1, (255, 0, 0), -1)
    return image


def plot_pose_box(image, P, kpt, color=(0, 255, 0), line_width=2):
    ''' Draw a 3D box as annotation of pose. Ref:https://github.com/yinguobing/head-pose-estimation/blob/master/pose_estimator.py
    Args:
        image: the input image
        P: (3, 4). Affine Camera Matrix.
        kpt: (68, 3).
    '''
    image = image.copy()

    point_3d = []
    rear_size = 90
    rear_depth = 0
    point_3d.append((-rear_size, -rear_size, rear_depth))
    point_3d.append((-rear_size, rear_size, rear_depth))
    point_3d.append((rear_size, rear_size, rear_depth))
    point_3d.append((rear_size, -rear_size, rear_depth))
    point_3d.append((-rear_size, -rear_size, rear_depth))

    front_size = 105
    front_depth = 110
    point_3d.append((-front_size, -front_size, front_depth))
    point_3d.append((-front_size, front_size, front_depth))
    point_3d.append((front_size, front_size, front_depth))
    point_3d.append((front_size, -front_size, front_depth))
    point_3d.append((-front_size, -front_size, front_depth))
    point_3d = np.array(point_3d, dtype=np.float).reshape(-1, 3)

    # Map to 2d image points
    point_3d_homo = np.hstack((point_3d, np.ones([point_3d.shape[0], 1])))  #n x 4
    point_2d = point_3d_homo.dot(P.T)[:, :2]
    point_2d[:, :2] = point_2d[:, :2] - np.mean(point_2d[:4, :2], 0) + np.mean(kpt[:27, :2], 0)
    point_2d = np.int32(point_2d.reshape(-1, 2))

    # Draw all the lines
    cv2.polylines(image, [point_2d], True, color, line_width, cv2.LINE_AA)
    cv2.line(image, tuple(point_2d[1]), tuple(point_2d[6]), color, line_width, cv2.LINE_AA)
    cv2.line(image, tuple(point_2d[2]), tuple(point_2d[7]), color, line_width, cv2.LINE_AA)
    cv2.line(image, tuple(point_2d[3]), tuple(point_2d[8]), color, line_width, cv2.LINE_AA)

    return image