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

import cv2
import mmcv
import numpy as np
import torch
from mmcv.transforms import Compose
from torchvision.transforms import functional as F

from mmdet.apis import init_detector

try:
    import ffmpegcv
except ImportError:
    raise ImportError(
        'Please install ffmpegcv with:\n\n    pip install ffmpegcv')


def parse_args():
    parser = argparse.ArgumentParser(
        description='MMDetection video demo with GPU acceleration')
    parser.add_argument('video', help='Video file')
    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(
        '--score-thr', type=float, default=0.3, help='Bbox score threshold')
    parser.add_argument('--out', type=str, help='Output video file')
    parser.add_argument('--show', action='store_true', help='Show video')
    parser.add_argument(
        '--nvdecode', action='store_true', help='Use NVIDIA decoder')
    parser.add_argument(
        '--wait-time',
        type=float,
        default=1,
        help='The interval of show (s), 0 is block')
    args = parser.parse_args()
    return args


def prefetch_img_metas(cfg, ori_wh):
    w, h = ori_wh
    cfg.data.test.pipeline[0].type = 'LoadImageFromNDArray'
    test_pipeline = Compose(cfg.data.test.pipeline)
    data = {'img': np.zeros((h, w, 3), dtype=np.uint8)}
    data = test_pipeline(data)
    img_metas = data['img_metas'][0].data
    return img_metas


def process_img(frame_resize, img_metas, device):
    assert frame_resize.shape == img_metas['pad_shape']
    frame_cuda = torch.from_numpy(frame_resize).to(device).float()
    frame_cuda = frame_cuda.permute(2, 0, 1)  # HWC to CHW
    mean = torch.from_numpy(img_metas['img_norm_cfg']['mean']).to(device)
    std = torch.from_numpy(img_metas['img_norm_cfg']['std']).to(device)
    frame_cuda = F.normalize(frame_cuda, mean=mean, std=std, inplace=True)
    frame_cuda = frame_cuda[None, :, :, :]  # NCHW
    data = {'img': [frame_cuda], 'img_metas': [[img_metas]]}
    return data


def main():
    args = parse_args()
    assert args.out or args.show, \
        ('Please specify at least one operation (save/show the '
         'video) with the argument "--out" or "--show"')

    model = init_detector(args.config, args.checkpoint, device=args.device)

    if args.nvdecode:
        VideoCapture = ffmpegcv.VideoCaptureNV
    else:
        VideoCapture = ffmpegcv.VideoCapture
    video_origin = VideoCapture(args.video)
    img_metas = prefetch_img_metas(model.cfg,
                                   (video_origin.width, video_origin.height))
    resize_wh = img_metas['pad_shape'][1::-1]
    video_resize = VideoCapture(
        args.video,
        resize=resize_wh,
        resize_keepratio=True,
        resize_keepratioalign='topleft',
        pix_fmt='rgb24')
    video_writer = None
    if args.out:
        video_writer = ffmpegcv.VideoWriter(args.out, fps=video_origin.fps)

    with torch.no_grad():
        for frame_resize, frame_origin in zip(
                mmcv.track_iter_progress(video_resize), video_origin):
            data = process_img(frame_resize, img_metas, args.device)
            result = model(return_loss=False, rescale=True, **data)[0]
            frame_mask = model.show_result(
                frame_origin, result, score_thr=args.score_thr)
            if args.show:
                cv2.namedWindow('video', 0)
                mmcv.imshow(frame_mask, 'video', args.wait_time)
            if args.out:
                video_writer.write(frame_mask)

    if video_writer:
        video_writer.release()
    video_origin.release()
    video_resize.release()

    cv2.destroyAllWindows()


if __name__ == '__main__':
    main()

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

import cv2
import mmcv
import numpy as np
import torch
from torchvision.transforms import functional as F

from mmdet.apis import init_detector
from mmdet.datasets.pipelines import Compose

try:
    import ffmpegcv
except ImportError:
    raise ImportError(
        'Please install ffmpegcv with:\n\n    pip install ffmpegcv')


def parse_args():
    parser = argparse.ArgumentParser(
        description='MMDetection video demo with GPU acceleration')
    parser.add_argument('video', help='Video file')
    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(
        '--score-thr', type=float, default=0.3, help='Bbox score threshold')
    parser.add_argument('--out', type=str, help='Output video file')
    parser.add_argument('--show', action='store_true', help='Show video')
    parser.add_argument(
        '--nvdecode', action='store_true', help='Use NVIDIA decoder')
    parser.add_argument(
        '--wait-time',
        type=float,
        default=1,
        help='The interval of show (s), 0 is block')
    args = parser.parse_args()
    return args


def prefetch_img_metas(cfg, ori_wh):
    w, h = ori_wh
    cfg.data.test.pipeline[0].type = 'LoadImageFromNDArray'
    test_pipeline = Compose(cfg.data.test.pipeline)
    data = {'img': np.zeros((h, w, 3), dtype=np.uint8)}
    data = test_pipeline(data)
    img_metas = data['img_metas'][0].data
    return img_metas


def process_img(frame_resize, img_metas, device):
    assert frame_resize.shape == img_metas['pad_shape']
    frame_cuda = torch.from_numpy(frame_resize).to(device).float()
    frame_cuda = frame_cuda.permute(2, 0, 1)  # HWC to CHW
    mean = torch.from_numpy(img_metas['img_norm_cfg']['mean']).to(device)
    std = torch.from_numpy(img_metas['img_norm_cfg']['std']).to(device)
    frame_cuda = F.normalize(frame_cuda, mean=mean, std=std, inplace=True)
    frame_cuda = frame_cuda[None, :, :, :]  # NCHW
    data = {'img': [frame_cuda], 'img_metas': [[img_metas]]}
    return data


def main():
    args = parse_args()
    assert args.out or args.show, \
        ('Please specify at least one operation (save/show the '
         'video) with the argument "--out" or "--show"')

    model = init_detector(args.config, args.checkpoint, device=args.device)

    if args.nvdecode:
        VideoCapture = ffmpegcv.VideoCaptureNV
    else:
        VideoCapture = ffmpegcv.VideoCapture
    video_origin = VideoCapture(args.video)
    img_metas = prefetch_img_metas(model.cfg,
                                   (video_origin.width, video_origin.height))
    resize_wh = img_metas['pad_shape'][1::-1]
    video_resize = VideoCapture(
        args.video,
        resize=resize_wh,
        resize_keepratio=True,
        resize_keepratioalign='topleft',
        pix_fmt='rgb24')
    video_writer = None
    if args.out:
        video_writer = ffmpegcv.VideoWriter(args.out, fps=video_origin.fps)

    with torch.no_grad():
        for frame_resize, frame_origin in zip(
                mmcv.track_iter_progress(video_resize), video_origin):
            data = process_img(frame_resize, img_metas, args.device)
            result = model(return_loss=False, rescale=True, **data)[0]
            frame_mask = model.show_result(
                frame_origin, result, score_thr=args.score_thr)
            if args.show:
                cv2.namedWindow('video', 0)
                mmcv.imshow(frame_mask, 'video', args.wait_time)
            if args.out:
                video_writer.write(frame_mask)

    if video_writer:
        video_writer.release()
    video_origin.release()
    video_resize.release()

    cv2.destroyAllWindows()


if __name__ == '__main__':
    main()
