"""make_highlight_video.py -- use the pipeline to make a highlight from a video

Usage:
    make_highlight_clip.py <input_file> [--max-duration=<seconds>] [--output-dir=<dir>] [--output-filename=<filename>] [--save-json]

Options:
    -h --help                   Show this screen
    --max-duration=<seconds>    Maximum duration of the highlight clip [default: 60]
    --output-dir=<dir>          Directory to save the highlight clip [default: ./]
    --output-filename=<filename>    Filename of the highlight clip [default: highlights.mp4]
    --save-json                 Save the feature data as a JSON file [default: False]

Notes:
    <input_file>                The input file can be a video or JSON file
"""

import os
import sys
import time
from pipeline.feature_extractors import RandomFeatureExtractor,\
    LaughterFeatureExtractor, LoudAudioFeatureExtractor,\
    VideoActivityFeatureExtractor, JSONFeatureExtractor
from pipeline.utils import SourceMedia, Source
from pipeline.producers import FfmpegVideoProducer, JSONProducer
from pipeline.consolidators import OverlapConsolidator
from pipeline.adjusters import TargetTimeAdjuster

from docopt import docopt

def main():
    start_time = time.time()
    PRINT_SOMETHING = False
    if PRINT_SOMETHING:
        print(f"+{'-'*78}+")
        print(f"| Starting pipeline to make highlight video at {time.strftime('%Y-%m-%d %H:%M:%S')}{' '*13}|")
        print(f"+{'-'*78}+")
        time.sleep(1)

    args = docopt(__doc__)
    input_file = args['<input_file>']
    max_duration = float(args['--max-duration'])
    output_dir = args['--output-dir']
    # expand "./" to current directory
    if output_dir == "./":
        output_dir = os.getcwd()
    output_filename = args['--output-filename']
    save_json = args['--save-json']

    source_media = SourceMedia(sources=[Source(input_file, input_file, "manual")])

    combined_features = []

    # feature extractors: laughter, loudness, video activity, words
    if input_file.endswith(".json"):
        feature_extractors = [JSONFeatureExtractor]
    else:
        feature_extractors = [LaughterFeatureExtractor, LoudAudioFeatureExtractor,
                              VideoActivityFeatureExtractor
                              ]
    # NOTE: words FE crashes OOM due to noted issues on their GH
    # so omitted for now

    for fe in feature_extractors:
        fe_instance = fe(input_files=source_media)
        fe_instance.setup()
        fe_instance.run()
        fe_instance.teardown()
        if fe_instance.features:
            combined_features.extend(fe_instance.features)

    if combined_features:
        if PRINT_SOMETHING:
            for f in combined_features:
                print(f"Feature: {f}")

        # consolidate features
        oc = OverlapConsolidator(features=combined_features)
        oc.consolidate()

        # adjust features to target time
        tta = TargetTimeAdjuster(features=oc.features, target_time=max_duration)
        tta.adjust()

        # output before video
        if save_json:
            jsonprod = JSONProducer(features=combined_features)
            jsonprod.produce()

        # make video from tta features:
        ffprod = FfmpegVideoProducer(features=tta.features,
                                     output_dir=output_dir,
                                     output_filename=output_filename)
        ffprod.produce()



    end_time = time.time()
    if PRINT_SOMETHING:
        print(f"+{'-'*78}+")
        print(f"| Pipeline finished creating highlight video at {time.strftime('%Y-%m-%d %H:%M:%S')}{' '*12}|")
        print(f"| Total time: {end_time - start_time:.2f} seconds {' '*51}|")
        print(f"+{'-'*78}+")


if __name__ == '__main__':
    main()