# 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 os
from paddleseg.utils import logger

from tools.model import get_model
import cv2
import numpy as np
from ppmatting.core import predict
from ppmatting.utils import get_image_list

current_path = os.path.abspath(os.path.dirname(__file__))


def get_rel_path(path: str):
    return "{}/../{}".format(current_path, path)


def seg(img_path: str, save_dir: str):
    image_list, image_dir = get_image_list(img_path)
    logger.info('Number of predict images = {}'.format(len(image_list)))

    model = get_model()

    return predict(
        model=model.model,
        model_path=model.path,
        transforms=model.transforms,
        image_list=image_list,
        image_dir=image_dir,
        trimap_list=None,
        save_dir=save_dir,
        fg_estimate=True)


def replace(img_path: str, save_dir: str, background: str = None, width: int = None, height: int = None):
    logger.info("replace: {}, {},{},{},{}".format(img_path, save_dir, background, width, height))
    image_list, image_dir = get_image_list(img_path)
    model = get_model()
    alpha, fg, _, p = predict(
        model=model.model,
        model_path=model.path,
        transforms=model.transforms,
        image_list=image_list,
        trimap_list=None,
        save_dir=save_dir,
        fg_estimate=False)

    if background is None:
        return p

    img_ori = cv2.imread(img_path)
    bg = get_bg(background, img_ori.shape)
    if bg is None:
        return p

    alpha = alpha / 255.0
    alpha = alpha[:, :, np.newaxis]
    com = alpha * fg + (1 - alpha) * bg
    com = com.astype('uint8')
    com_save_path = os.path.join(save_dir, os.path.basename(img_path))
    cv2.imwrite(com_save_path, com)
    return com_save_path


def get_bg(background, img_shape):
    # 1、纯色
    # 2、通道颜色
    # 3、图片
    bg = np.zeros(img_shape)
    if background is None:
        return None
    if os.path.exists(background):
        bg = cv2.imread(background)
        bg = cv2.resize(bg, (img_shape[1], img_shape[0]))
    elif background == 'r':
        bg[:, :, 2] = 255
    elif background == 'g':
        bg[:, :, 1] = 255
    elif background == 'b':
        bg[:, :, 0] = 255
    elif background == 'w':
        bg[:, :, :] = 255
    elif is_color_hex(background):
        r, g, b, _ = hex_to_rgb(background)
        bg[:, :, 2] = r
        bg[:, :, 1] = g
        bg[:, :, 0] = b
    else:
        return None

    return bg


def is_color_hex(color: str):
    size = len(color)
    if color.startswith("#"):
        return size == 7 or size == 9
    return False


def hex_to_rgb(color: str):
    if color.startswith("#"):
        color = color[1:len(color)]
    r = int(color[0:2], 16)
    g = int(color[2:4], 16)
    b = int(color[4:6], 16)
    a = 100
    if len(color) == 8:
        a = int(color[6:8], 16)
    return r, g, b, a