# Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser

import cv2
from mmengine.model.utils import revert_sync_batchnorm

from mmseg.apis import inference_model, init_model
from mmseg.apis.inference import show_result_pyplot


def main():
    parser = ArgumentParser()
    parser.add_argument('video', help='Video file or webcam id')
    parser.add_argument('config', help='Config file')
    parser.add_argument('checkpoint', help='Checkpoint file')
    parser.add_argument(
        '--device', default='cuda:0', help='Device used for inference')
    parser.add_argument(
        '--palette',
        default='cityscapes',
        help='Color palette used for segmentation map')
    parser.add_argument(
        '--show', action='store_true', help='Whether to show draw result')
    parser.add_argument(
        '--show-wait-time', default=1, type=int, help='Wait time after imshow')
    parser.add_argument(
        '--output-file', default=None, type=str, help='Output video file path')
    parser.add_argument(
        '--output-fourcc',
        default='MJPG',
        type=str,
        help='Fourcc of the output video')
    parser.add_argument(
        '--output-fps', default=-1, type=int, help='FPS of the output video')
    parser.add_argument(
        '--output-height',
        default=-1,
        type=int,
        help='Frame height of the output video')
    parser.add_argument(
        '--output-width',
        default=-1,
        type=int,
        help='Frame width of the output video')
    parser.add_argument(
        '--opacity',
        type=float,
        default=0.5,
        help='Opacity of painted segmentation map. In (0, 1] range.')
    args = parser.parse_args()

    assert args.show or args.output_file, \
        'At least one output should be enabled.'

    # build the model from a config file and a checkpoint file
    model = init_model(args.config, args.checkpoint, device=args.device)
    if args.device == 'cpu':
        model = revert_sync_batchnorm(model)

    # build input video
    if args.video.isdigit():
        args.video = int(args.video)
    cap = cv2.VideoCapture(args.video)
    assert (cap.isOpened())
    input_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
    input_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
    input_fps = cap.get(cv2.CAP_PROP_FPS)

    # init output video
    writer = None
    output_height = None
    output_width = None
    if args.output_file is not None:
        fourcc = cv2.VideoWriter_fourcc(*args.output_fourcc)
        output_fps = args.output_fps if args.output_fps > 0 else input_fps
        output_height = args.output_height if args.output_height > 0 else int(
            input_height)
        output_width = args.output_width if args.output_width > 0 else int(
            input_width)
        writer = cv2.VideoWriter(args.output_file, fourcc, output_fps,
                                 (output_width, output_height), True)

    # start looping
    try:
        while True:
            flag, frame = cap.read()
            if not flag:
                break

            # test a single image
            result = inference_model(model, frame)

            # blend raw image and prediction
            draw_img = show_result_pyplot(model, frame, result)

            if args.show:
                cv2.imshow('video_demo', draw_img)
                cv2.waitKey(args.show_wait_time)
            if writer:
                if draw_img.shape[0] != output_height or draw_img.shape[
                        1] != output_width:
                    draw_img = cv2.resize(draw_img,
                                          (output_width, output_height))
                writer.write(draw_img)
    finally:
        if writer:
            writer.release()
        cap.release()


if __name__ == '__main__':
    main()