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

import cv2
import mmcv
import mmengine
import torch
from mmengine import DictAction
from mmengine.utils import track_iter_progress

from mmaction.apis import (detection_inference, inference_skeleton,
                           init_recognizer, pose_inference)
from mmaction.registry import VISUALIZERS
from mmaction.utils import frame_extract

try:
    import moviepy.editor as mpy
except ImportError:
    raise ImportError('Please install moviepy to enable output file')

FONTFACE = cv2.FONT_HERSHEY_DUPLEX
FONTSCALE = 0.75
FONTCOLOR = (255, 255, 255)  # BGR, white
THICKNESS = 1
LINETYPE = 1


def parse_args():
    parser = argparse.ArgumentParser(description='MMAction2 demo')
    parser.add_argument('video', help='video file/url')
    parser.add_argument('out_filename', help='output filename')
    parser.add_argument(
        '--config',
        default=('configs/skeleton/posec3d/'
                 'slowonly_r50_8xb16-u48-240e_ntu60-xsub-keypoint.py'),
        help='skeleton model config file path')
    parser.add_argument(
        '--checkpoint',
        default=('https://download.openmmlab.com/mmaction/skeleton/posec3d/'
                 'slowonly_r50_u48_240e_ntu60_xsub_keypoint/'
                 'slowonly_r50_u48_240e_ntu60_xsub_keypoint-f3adabf1.pth'),
        help='skeleton model checkpoint file/url')
    parser.add_argument(
        '--det-config',
        default='demo/demo_configs/faster-rcnn_r50_fpn_2x_coco_infer.py',
        help='human detection config file path (from mmdet)')
    parser.add_argument(
        '--det-checkpoint',
        default=('http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/'
                 'faster_rcnn_r50_fpn_2x_coco/'
                 'faster_rcnn_r50_fpn_2x_coco_'
                 'bbox_mAP-0.384_20200504_210434-a5d8aa15.pth'),
        help='human detection checkpoint file/url')
    parser.add_argument(
        '--det-score-thr',
        type=float,
        default=0.9,
        help='the threshold of human detection score')
    parser.add_argument(
        '--det-cat-id',
        type=int,
        default=0,
        help='the category id for human detection')
    parser.add_argument(
        '--pose-config',
        default='demo/demo_configs/'
        'td-hm_hrnet-w32_8xb64-210e_coco-256x192_infer.py',
        help='human pose estimation config file path (from mmpose)')
    parser.add_argument(
        '--pose-checkpoint',
        default=('https://download.openmmlab.com/mmpose/top_down/hrnet/'
                 'hrnet_w32_coco_256x192-c78dce93_20200708.pth'),
        help='human pose estimation checkpoint file/url')
    parser.add_argument(
        '--label-map',
        default='tools/data/skeleton/label_map_ntu60.txt',
        help='label map file')
    parser.add_argument(
        '--device', type=str, default='cuda:0', help='CPU/CUDA device option')
    parser.add_argument(
        '--short-side',
        type=int,
        default=480,
        help='specify the short-side length of the image')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        default={},
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. For example, '
        "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
    args = parser.parse_args()
    return args


def visualize(args, frames, data_samples, action_label):
    pose_config = mmengine.Config.fromfile(args.pose_config)
    visualizer = VISUALIZERS.build(pose_config.visualizer)
    visualizer.set_dataset_meta(data_samples[0].dataset_meta)

    vis_frames = []
    print('Drawing skeleton for each frame')
    for d, f in track_iter_progress(list(zip(data_samples, frames))):
        f = mmcv.imconvert(f, 'bgr', 'rgb')
        visualizer.add_datasample(
            'result',
            f,
            data_sample=d,
            draw_gt=False,
            draw_heatmap=False,
            draw_bbox=True,
            show=False,
            wait_time=0,
            out_file=None,
            kpt_thr=0.3)
        vis_frame = visualizer.get_image()
        cv2.putText(vis_frame, action_label, (10, 30), FONTFACE, FONTSCALE,
                    FONTCOLOR, THICKNESS, LINETYPE)
        vis_frames.append(vis_frame)

    vid = mpy.ImageSequenceClip(vis_frames, fps=24)
    vid.write_videofile(args.out_filename, remove_temp=True)


def main():
    args = parse_args()

    tmp_dir = tempfile.TemporaryDirectory()
    frame_paths, frames = frame_extract(args.video, args.short_side,
                                        tmp_dir.name)

    h, w, _ = frames[0].shape

    # Get Human detection results.
    det_results, _ = detection_inference(args.det_config, args.det_checkpoint,
                                         frame_paths, args.det_score_thr,
                                         args.det_cat_id, args.device)
    torch.cuda.empty_cache()

    # Get Pose estimation results.
    pose_results, pose_data_samples = pose_inference(args.pose_config,
                                                     args.pose_checkpoint,
                                                     frame_paths, det_results,
                                                     args.device)
    torch.cuda.empty_cache()

    config = mmengine.Config.fromfile(args.config)
    config.merge_from_dict(args.cfg_options)

    model = init_recognizer(config, args.checkpoint, args.device)
    result = inference_skeleton(model, pose_results, (h, w))

    max_pred_index = result.pred_scores.item.argmax().item()
    label_map = [x.strip() for x in open(args.label_map).readlines()]
    action_label = label_map[max_pred_index]

    visualize(args, frames, pose_data_samples, action_label)

    tmp_dir.cleanup()


if __name__ == '__main__':
    main()