# Copyright (c) OpenMMLab. All rights reserved.

import argparse

import os

import os.path as osp

import shutil

import cv2

import mmcv

import numpy as np

import torch

from mmcv import DictAction

from mmaction.apis import inference_recognizer, init_recognizer

from mmaction.utils import import_module_error_func

try:

    from mmdet.apis import inference_detector, init_detector

    from mmpose.apis import (init_pose_model, inference_top_down_pose_model,

                             vis_pose_result)

except (ImportError, ModuleNotFoundError):

    @import_module_error_func('mmdet')

    def inference_detector(*args, **kwargs):

        pass

    @import_module_error_func('mmdet')

    def init_detector(*args, **kwargs):

        pass

    @import_module_error_func('mmpose')

    def init_pose_model(*args, **kwargs):

        pass

    @import_module_error_func('mmpose')

    def inference_top_down_pose_model(*args, **kwargs):

        pass

    @import_module_error_func('mmpose')

    def vis_pose_result(*args, **kwargs):

        pass

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_u48_240e_ntu120_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_ntu120_xsub_keypoint/'

                 'slowonly_r50_u48_240e_ntu120_xsub_keypoint-6736b03f.pth'),

        help='skeleton model checkpoint file/url')

    parser.add_argument(

        '--det-config',

        default='demo/faster_rcnn_r50_fpn_2x_coco.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(

        '--pose-config',

        default='demo/hrnet_w32_coco_256x192.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(

        '--det-score-thr',

        type=float,

        default=0.9,

        help='the threshold of human detection score')

    parser.add_argument(

        '--label-map',

        default='tools/data/skeleton/label_map_ntu120.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 frame_extraction(video_path, short_side):

    """Extract frames given video_path.

    Args:

        video_path (str): The video_path.

    """

    # Load the video, extract frames into ./tmp/video_name

    target_dir = osp.join('./tmp', osp.basename(osp.splitext(video_path)[0]))

    os.makedirs(target_dir, exist_ok=True)

    # Should be able to handle videos up to several hours

    frame_tmpl = osp.join(target_dir, 'img_{:06d}.jpg')

    vid = cv2.VideoCapture(video_path)

    frames = []

    frame_paths = []

    flag, frame = vid.read()

    cnt = 0

    new_h, new_w = None, None

    while flag:

        if new_h is None:

            h, w, _ = frame.shape

            new_w, new_h = mmcv.rescale_size((w, h), (short_side, np.Inf))

        frame = mmcv.imresize(frame, (new_w, new_h))

        frames.append(frame)

        frame_path = frame_tmpl.format(cnt + 1)

        frame_paths.append(frame_path)

        cv2.imwrite(frame_path, frame)

        cnt += 1

        flag, frame = vid.read()

    return frame_paths, frames

def detection_inference(args, frame_paths):

    """Detect human boxes given frame paths.

    Args:

        args (argparse.Namespace): The arguments.

        frame_paths (list[str]): The paths of frames to do detection inference.

    Returns:

        list[np.ndarray]: The human detection results.

    """

    model = init_detector(args.det_config, args.det_checkpoint, args.device)

    assert model.CLASSES[0] == 'person', ('We require you to use a detector '

                                          'trained on COCO')

    results = []

    print('Performing Human Detection for each frame')

    prog_bar = mmcv.ProgressBar(len(frame_paths))

    for frame_path in frame_paths:

        result = inference_detector(model, frame_path)

        # We only keep human detections with score larger than det_score_thr

        result = result[0][result[0][:, 4] >= args.det_score_thr]

        results.append(result)

        prog_bar.update()

    return results

def pose_inference(args, frame_paths, det_results):

    model = init_pose_model(args.pose_config, args.pose_checkpoint,

                            args.device)

    ret = []

    print('Performing Human Pose Estimation for each frame')

    prog_bar = mmcv.ProgressBar(len(frame_paths))

    for f, d in zip(frame_paths, det_results):

        # Align input format

        d = [dict(bbox=x) for x in list(d)]

        pose = inference_top_down_pose_model(model, f, d, format='xyxy')[0]

        ret.append(pose)

        prog_bar.update()

    return ret

def main():

    args = parse_args()

    frame_paths, original_frames = frame_extraction(args.video,

                                                    args.short_side)

    num_frame = len(frame_paths)

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

    # Get clip_len, frame_interval and calculate center index of each clip

    config = mmcv.Config.fromfile(args.config)

    config.merge_from_dict(args.cfg_options)

    for component in config.data.test.pipeline:

        if component['type'] == 'PoseNormalize':

            component['mean'] = (w // 2, h // 2, .5)

            component['max_value'] = (w, h, 1.)

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

    # Load label_map

    label_map = [x.strip() for x in open(args.label_map).readlines()]

    # Get Human detection results

    det_results = detection_inference(args, frame_paths)

    torch.cuda.empty_cache()

    pose_results = pose_inference(args, frame_paths, det_results)

    torch.cuda.empty_cache()

    fake_anno = dict(

        frame_dir='',

        label=-1,

        img_shape=(h, w),

        original_shape=(h, w),

        start_index=0,

        modality='Pose',

        total_frames=num_frame)

    num_person = max([len(x) for x in pose_results])

    num_keypoint = 17

    keypoint = np.zeros((num_person, num_frame, num_keypoint, 2),

                        dtype=np.float16)

    keypoint_score = np.zeros((num_person, num_frame, num_keypoint),

                              dtype=np.float16)

    for i, poses in enumerate(pose_results):

        for j, pose in enumerate(poses):

            pose = pose['keypoints']

            keypoint[j, i] = pose[:, :2]

            keypoint_score[j, i] = pose[:, 2]

    fake_anno['keypoint'] = keypoint

    fake_anno['keypoint_score'] = keypoint_score

    results = inference_recognizer(model, fake_anno)

    action_label = label_map[results[0][0]]

    pose_model = init_pose_model(args.pose_config, args.pose_checkpoint,

                                 args.device)

    vis_frames = [

        vis_pose_result(pose_model, frame_paths[i], pose_results[i])

        for i in range(num_frame)

    ]

    for frame in vis_frames:

        cv2.putText(frame, action_label, (10, 30), FONTFACE, FONTSCALE,

                    FONTCOLOR, THICKNESS, LINETYPE)

    vid = mpy.ImageSequenceClip([x[:, :, ::-1] for x in vis_frames], fps=24)

    vid.write_videofile(args.out_filename, remove_temp=True)

    tmp_frame_dir = osp.dirname(frame_paths[0])

    shutil.rmtree(tmp_frame_dir)

if __name__ == '__main__':

    main()