113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
"""
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file_name: util
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project_name: DBM
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created: 2020-20-07
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"""
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import os
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import glob
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import numpy as np
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import subprocess
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def filter_path(video_url, out_dir):
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"""
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Filtering video uri path to prepare input and ouptut location
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Args:
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video_url: S3 bucket path for video
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out_dir: Output directory path
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"""
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fl_name,_ = os.path.splitext(os.path.basename(video_url))
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input_loc = os.path.dirname(video_url)
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out_loc = os.path.join(out_dir, fl_name)
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return input_loc, out_loc, fl_name
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def save_output(df, out_loc, fl_name, f_dir, f_ext):
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"""
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creating output directory for Audio features
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Args:
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df: (dataframe) feature dataframe[ex: Formant freq, pitch]
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out_loc: (dir) Output location where we want to save raw output
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fl_name: file name
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f_dir: directory name for a feature
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f_ext: extension for a feature [ex: '_pose.csv']
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"""
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full_f_name = fl_name + f_ext
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dir_path = os.path.join(out_loc, f_dir)
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if not os.path.exists(dir_path):
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os.makedirs(dir_path)
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sav_path = os.path.join(dir_path,full_f_name)
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df.to_csv(sav_path, index=False)
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def audio_process(base_dir,video_url):
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"""
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Parsing cleaned audio files(Audio files without IMA voice)
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Args:
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base_dir: Base path for raw data
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video_url: Raw video file path
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"""
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new_video_url = base_dir+'/'.join(video_url[2:])
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split_val = new_video_url.split('/')
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wav_path = '/'.join(split_val[0:len(split_val)-1])
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audio_split_check = glob.glob(wav_path + '/*_split.wav')
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return audio_split_check
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def compute_open_face_features(input_filepath,
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output_directory,
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open_face_executable,
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au_static=False,
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tracked_visualization=False,
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clobber=False,
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verbose=True):
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"""
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Runs OpenFace on an input video.
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See https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments
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Args:
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input_filepath:
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output_directory:
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au_static:
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tracked_visualization:
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open_face_executable:
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clobber: (bool) if True existing files will be overwritten
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verbose:
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Returns:
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(str) path to output csv file
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Raises:
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IOError if OpenFace executable is missing
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"""
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if not os.path.isfile(open_face_executable):
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raise IOError("OpenFace executable {} could not be found.".format(open_face_executable))
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bn, _ = os.path.splitext(os.path.basename(input_filepath))
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if not output_directory:
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output_directory = os.path.join(os.path.dirname(input_filepath), bn + '_openface')
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output_csv = os.path.join(output_directory, bn + '.csv')
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if not os.path.isfile(output_csv) or clobber:
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call = [open_face_executable, ]
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if au_static:
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call += ['-au_static', ]
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if tracked_visualization:
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call += ['-tracked', ]
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call += ['-q', '-2Dfp', '-3Dfp', '-pdmparams', '-pose', '-aus', '-gaze']
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call += ['-f', input_filepath, '-out_dir', output_directory]
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if verbose:
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print('Computing OpenFace features {} from video file'.format(input_filepath))
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subprocess.check_output(call)
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if verbose:
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print('OpenFace features saved to {}'.format(output_directory))
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else:
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if verbose:
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print('Output file {} already exists'.format(output_csv))
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return os.path.join(output_directory, bn + '.csv')
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