bg_replace.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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 argparse
import os

import cv2
import numpy as np
import paddle
from paddleseg.cvlibs import manager, Config
from paddleseg.utils import get_sys_env, logger

from core import predict
import model
from dataset import MattingDataset
from utils import get_image_list


def parse_args():
    parser = argparse.ArgumentParser(
        description='PP-HumanSeg inference for video')
    parser.add_argument(
        "--config",
        dest="cfg",
        help="The config file.",
        default=None,
        type=str,
        required=True)
    parser.add_argument(
        '--model_path',
        dest='model_path',
        help='The path of model for prediction',
        type=str,
        default=None)
    parser.add_argument(
        '--image_path',
        dest='image_path',
        help='Image including human',
        type=str,
        default=None)
    parser.add_argument(
        '--trimap_path',
        dest='trimap_path',
        help='The path of trimap',
        type=str,
        default=None)
    parser.add_argument(
        '--bg_path',
        dest='bg_path',
        help=
        'Background image path for replacing. If not specified, a white background is used',
        type=str,
        default=None)
    parser.add_argument(
        '--save_dir',
        dest='save_dir',
        help='The directory for saving the inference results',
        type=str,
        default='./output')

    return parser.parse_args()


def main(args):
    env_info = get_sys_env()
    place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
        'GPUs used'] else 'cpu'
    paddle.set_device(place)
    if not args.cfg:
        raise RuntimeError('No configuration file specified.')

    cfg = Config(args.cfg)
    val_dataset = cfg.val_dataset
    if val_dataset is None:
        raise RuntimeError(
            'The verification dataset is not specified in the configuration file.'
        )
    elif len(val_dataset) == 0:
        raise ValueError(
            'The length of val_dataset is 0. Please check if your dataset is valid'
        )

    msg = '\n---------------Config Information---------------\n'
    msg += str(cfg)
    msg += '------------------------------------------------'
    logger.info(msg)

    model = cfg.model
    transforms = val_dataset.transforms

    alpha = predict(
        model,
        model_path=args.model_path,
        transforms=transforms,
        image_list=[args.image_path],
        trimap_list=[args.trimap_path],
        save_dir=args.save_dir)

    img_ori = cv2.imread(args.image_path)
    bg = get_bg(args.bg_path, img_ori.shape)
    alpha = alpha / 255
    alpha = alpha[:, :, np.newaxis]
    com = alpha * img_ori + (1 - alpha) * bg
    com = com.astype('uint8')
    com_save_path = os.path.join(args.save_dir,
                                 os.path.basename(args.image_path))
    cv2.imwrite(com_save_path, com)


def get_bg(bg_path, img_shape):
    if bg_path is None:
        bg = np.zeros(img_shape)
        bg[:, :, 1] = 255

    elif not os.path.exists(bg_path):
        raise Exception('The --bg_path is not existed: {}'.format(bg_path))
    else:
        bg = cv2.imread(bg_path)
        bg = cv2.resize(bg, (img_shape[1], img_shape[0]))
    return bg


if __name__ == "__main__":
    args = parse_args()
    main(args)