Merge pull request #40 from jordihasianta/master
Refactoring OpenDBM Core Module
This commit is contained in:
Andre Daniel Paredes
76
.gitignore
vendored
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76
.gitignore
vendored
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@@ -0,0 +1,76 @@
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.DS_Store
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.ipynb_checkpoints/
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docs/node_modules
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*/.pyc
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.vscode
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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#Pydoc generated_file for OpenDBM Python API Documentation
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docs/website/api/*api.md
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#docker dependencies for mac
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.github/brew-colima
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.github/brew-docker
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# Distribution / packaging
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speech/
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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#pycharm
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.idea/
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#documentation
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docs/.docusaurus
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.docusaurus
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docs/.nvmrc
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.nvmrc
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node_modules
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docs/website/build/
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docs/sync-api-docs/generatedComponentApiDocs.js
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docs/sync-api-docs/extracted.json
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14
Dockerfile
14
Dockerfile
@@ -1,8 +1,12 @@
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FROM python:3.6
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FROM python:3.7
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FROM ubuntu:18.04
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MAINTAINER fnndsc "vijay.yadav@aicure.com"
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ENV LANG C.UTF-8
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ENV LC_ALL C.UTF-8
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RUN apt-get update && apt-get install -y python3-pip \
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&& apt-get install -y wget \
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&& apt-get install -y automake --upgrade \
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@@ -20,21 +24,21 @@ RUN ln -sfn /usr/bin/pip3 /usr/bin/pip
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COPY . /app
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#cloning openface
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WORKDIR /app/pkg
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WORKDIR /app/opendbm/pkg
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RUN git clone https://github.com/AiCure/open_dbm.git -b openface
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RUN echo "Installing OpenFace..."
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WORKDIR /app/pkg/open_dbm/OpenFace
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WORKDIR /app/opendbm/pkg/open_dbm/OpenFace
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RUN bash ./download_models.sh
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RUN dpkg --configure -a
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RUN su -c ./install.sh
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RUN echo "Done OpenFace!"
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RUN echo "Cloning DeepSpeech..."
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WORKDIR /app/pkg
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WORKDIR /app/opendbm/pkg
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RUN git clone https://github.com/mozilla/DeepSpeech.git
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WORKDIR /app/pkg/DeepSpeech
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WORKDIR /app/opendbm/pkg/DeepSpeech
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RUN wget https://github.com/mozilla/DeepSpeech/releases/download/v0.9.1/deepspeech-0.9.1-models.pbmm
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RUN wget https://github.com/mozilla/DeepSpeech/releases/download/v0.9.1/deepspeech-0.9.1-models.scorer
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@@ -1,16 +0,0 @@
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"""
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file_name: init
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project_name: DBM
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created: 2020-20-07
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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DBMLIB_PATH = os.path.dirname(__file__)
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DBMLIB_SERVICE_CONFIG = os.path.abspath(os.path.join(DBMLIB_PATH, '../resources/services/services.yml'))
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DBMLIB_FEATURE_CONFIG = os.path.abspath(os.path.join(DBMLIB_PATH, '../resources/features/raw_feature.yml'))
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DBMLIB_DERIVE_FEATURE_CONFIG = os.path.abspath(os.path.join(DBMLIB_PATH, '../resources/features/derived_feature.yml'))
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@@ -1,277 +0,0 @@
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"""
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file_name: config_raw_feature
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project_name: DBM
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created: 2020-20-07
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"""
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import yaml
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from dbm_lib import DBMLIB_FEATURE_CONFIG
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class ConfigRawReader(object):
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"""Summary
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Read sevice end ponit
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"""
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def __init__(self,
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feature_config_yml=None):
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"""Summary
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Args:
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feature_config_yml (None, optional): yml file defined service configuration
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"""
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if feature_config_yml is None:
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feature_config = DBMLIB_FEATURE_CONFIG
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else:
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feature_config = feature_config_yml
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with open(feature_config, 'r') as ymlfile:
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config = yaml.load(ymlfile)
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#Verbal features
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self.base_raw = config
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self.err_reason = config['raw_feature']['error_reason']
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#Output range
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self.mov_headvel_start = config['raw_feature']['mov_headvel_start']
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self.mov_headvel_end = config['raw_feature']['mov_headvel_end']
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#Acoustic variable
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self.aco_int = config['raw_feature']['aco_int']
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self.aco_ff = config['raw_feature']['aco_ff']
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self.aco_voiceLabel = config['raw_feature']['aco_voiceLabel']
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self.aco_hnr = config['raw_feature']['aco_hnr']
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self.aco_gne = config['raw_feature']['aco_gne']
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self.aco_fm1 = config['raw_feature']['aco_fm1']
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self.aco_fm2 = config['raw_feature']['aco_fm2']
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self.aco_fm3 = config['raw_feature']['aco_fm3']
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self.aco_fm4 = config['raw_feature']['aco_fm4']
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self.aco_jitter = config['raw_feature']['aco_jitter']
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self.aco_shimmer = config['raw_feature']['aco_shimmer']
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self.aco_mfcc1 = config['raw_feature']['aco_mfcc1']
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self.aco_mfcc2 = config['raw_feature']['aco_mfcc2']
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self.aco_mfcc3 = config['raw_feature']['aco_mfcc3']
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self.aco_mfcc4 = config['raw_feature']['aco_mfcc4']
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self.aco_mfcc5 = config['raw_feature']['aco_mfcc5']
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self.aco_mfcc6 = config['raw_feature']['aco_mfcc6']
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self.aco_mfcc7 = config['raw_feature']['aco_mfcc7']
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self.aco_mfcc8 = config['raw_feature']['aco_mfcc8']
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self.aco_mfcc9 = config['raw_feature']['aco_mfcc9']
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self.aco_mfcc10 = config['raw_feature']['aco_mfcc10']
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self.aco_mfcc11 = config['raw_feature']['aco_mfcc11']
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self.aco_mfcc12 = config['raw_feature']['aco_mfcc12']
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self.aco_voiceFrame = config['raw_feature']['aco_voiceFrame']
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self.aco_totVoiceFrame = config['raw_feature']['aco_totVoiceFrame']
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self.aco_voicePct = config['raw_feature']['aco_voicePct']
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self.aco_pausetime = config['raw_feature']['aco_pausetime']
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self.aco_totaltime = config['raw_feature']['aco_totaltime']
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self.aco_speakingtime = config['raw_feature']['aco_speakingtime']
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self.aco_numpauses = config['raw_feature']['aco_numpauses']
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self.aco_pausefrac = config['raw_feature']['aco_pausefrac']
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#Facial Action Unit (for consistency)
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self.fac_AU01int = config['raw_feature']['fac_AU01int']
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self.fac_AU02int = config['raw_feature']['fac_AU02int']
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self.fac_AU04int = config['raw_feature']['fac_AU04int']
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self.fac_AU05int = config['raw_feature']['fac_AU05int']
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self.fac_AU06int = config['raw_feature']['fac_AU06int']
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self.fac_AU07int = config['raw_feature']['fac_AU07int']
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self.fac_AU09int = config['raw_feature']['fac_AU09int']
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self.fac_AU10int = config['raw_feature']['fac_AU10int']
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self.fac_AU12int = config['raw_feature']['fac_AU12int']
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self.fac_AU14int = config['raw_feature']['fac_AU14int']
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self.fac_AU15int = config['raw_feature']['fac_AU15int']
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self.fac_AU17int = config['raw_feature']['fac_AU17int']
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self.fac_AU20int = config['raw_feature']['fac_AU20int']
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self.fac_AU23int = config['raw_feature']['fac_AU23int']
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self.fac_AU25int = config['raw_feature']['fac_AU25int']
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self.fac_AU26int = config['raw_feature']['fac_AU26int']
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self.fac_AU45int = config['raw_feature']['fac_AU45int']
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self.fac_AU01pres = config['raw_feature']['fac_AU01pres']
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self.fac_AU02pres = config['raw_feature']['fac_AU02pres']
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self.fac_AU04pres = config['raw_feature']['fac_AU04pres']
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self.fac_AU05pres = config['raw_feature']['fac_AU05pres']
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self.fac_AU06pres = config['raw_feature']['fac_AU06pres']
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self.fac_AU07pres = config['raw_feature']['fac_AU07pres']
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self.fac_AU09pres = config['raw_feature']['fac_AU09pres']
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self.fac_AU10pres = config['raw_feature']['fac_AU10pres']
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self.fac_AU12pres = config['raw_feature']['fac_AU12pres']
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self.fac_AU14pres = config['raw_feature']['fac_AU14pres']
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self.fac_AU15pres = config['raw_feature']['fac_AU15pres']
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self.fac_AU17pres = config['raw_feature']['fac_AU17pres']
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self.fac_AU20pres = config['raw_feature']['fac_AU20pres']
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self.fac_AU23pres = config['raw_feature']['fac_AU23pres']
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self.fac_AU25pres = config['raw_feature']['fac_AU25pres']
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self.fac_AU26pres = config['raw_feature']['fac_AU26pres']
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self.fac_AU28pres = config['raw_feature']['fac_AU28pres']
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self.fac_AU45pres = config['raw_feature']['fac_AU45pres']
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#Facial Landmarks (for consistency)
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self.fac_LMK00disp = config['raw_feature']['fac_LMK00disp']
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self.fac_LMK01disp = config['raw_feature']['fac_LMK01disp']
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self.fac_LMK02disp = config['raw_feature']['fac_LMK02disp']
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self.fac_LMK03disp = config['raw_feature']['fac_LMK03disp']
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self.fac_LMK04disp = config['raw_feature']['fac_LMK04disp']
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self.fac_LMK05disp = config['raw_feature']['fac_LMK05disp']
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self.fac_LMK06disp = config['raw_feature']['fac_LMK06disp']
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self.fac_LMK07disp = config['raw_feature']['fac_LMK07disp']
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self.fac_LMK08disp = config['raw_feature']['fac_LMK08disp']
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self.fac_LMK09disp = config['raw_feature']['fac_LMK09disp']
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self.fac_LMK10disp = config['raw_feature']['fac_LMK10disp']
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self.fac_LMK11disp = config['raw_feature']['fac_LMK11disp']
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self.fac_LMK12disp = config['raw_feature']['fac_LMK12disp']
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self.fac_LMK13disp = config['raw_feature']['fac_LMK13disp']
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self.fac_LMK14disp = config['raw_feature']['fac_LMK14disp']
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self.fac_LMK15disp = config['raw_feature']['fac_LMK15disp']
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self.fac_LMK16disp = config['raw_feature']['fac_LMK16disp']
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self.fac_LMK17disp = config['raw_feature']['fac_LMK17disp']
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self.fac_LMK18disp = config['raw_feature']['fac_LMK18disp']
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self.fac_LMK19disp = config['raw_feature']['fac_LMK19disp']
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self.fac_LMK20disp = config['raw_feature']['fac_LMK20disp']
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self.fac_LMK21disp = config['raw_feature']['fac_LMK21disp']
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self.fac_LMK22disp = config['raw_feature']['fac_LMK22disp']
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self.fac_LMK23disp = config['raw_feature']['fac_LMK23disp']
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self.fac_LMK24disp = config['raw_feature']['fac_LMK24disp']
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self.fac_LMK25disp = config['raw_feature']['fac_LMK25disp']
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self.fac_LMK26disp = config['raw_feature']['fac_LMK26disp']
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self.fac_LMK27disp = config['raw_feature']['fac_LMK27disp']
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self.fac_LMK28disp = config['raw_feature']['fac_LMK28disp']
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self.fac_LMK29disp = config['raw_feature']['fac_LMK29disp']
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self.fac_LMK30disp = config['raw_feature']['fac_LMK30disp']
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self.fac_LMK31disp = config['raw_feature']['fac_LMK31disp']
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self.fac_LMK32disp = config['raw_feature']['fac_LMK32disp']
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self.fac_LMK33disp = config['raw_feature']['fac_LMK33disp']
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self.fac_LMK34disp = config['raw_feature']['fac_LMK34disp']
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self.fac_LMK35disp = config['raw_feature']['fac_LMK35disp']
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self.fac_LMK36disp = config['raw_feature']['fac_LMK36disp']
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self.fac_LMK37disp = config['raw_feature']['fac_LMK37disp']
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self.fac_LMK38disp = config['raw_feature']['fac_LMK38disp']
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self.fac_LMK39disp = config['raw_feature']['fac_LMK39disp']
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self.fac_LMK40disp = config['raw_feature']['fac_LMK40disp']
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self.fac_LMK41disp = config['raw_feature']['fac_LMK41disp']
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self.fac_LMK42disp = config['raw_feature']['fac_LMK42disp']
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self.fac_LMK43disp = config['raw_feature']['fac_LMK43disp']
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self.fac_LMK44disp = config['raw_feature']['fac_LMK44disp']
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self.fac_LMK45disp = config['raw_feature']['fac_LMK45disp']
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self.fac_LMK46disp = config['raw_feature']['fac_LMK46disp']
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self.fac_LMK47disp = config['raw_feature']['fac_LMK47disp']
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self.fac_LMK48disp = config['raw_feature']['fac_LMK48disp']
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self.fac_LMK49disp = config['raw_feature']['fac_LMK49disp']
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self.fac_LMK50disp = config['raw_feature']['fac_LMK50disp']
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self.fac_LMK51disp = config['raw_feature']['fac_LMK51disp']
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self.fac_LMK52disp = config['raw_feature']['fac_LMK52disp']
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self.fac_LMK53disp = config['raw_feature']['fac_LMK53disp']
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self.fac_LMK54disp = config['raw_feature']['fac_LMK54disp']
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self.fac_LMK55disp = config['raw_feature']['fac_LMK55disp']
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||||
self.fac_LMK56disp = config['raw_feature']['fac_LMK56disp']
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||||
self.fac_LMK57disp = config['raw_feature']['fac_LMK57disp']
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||||
self.fac_LMK58disp = config['raw_feature']['fac_LMK58disp']
|
||||
self.fac_LMK59disp = config['raw_feature']['fac_LMK59disp']
|
||||
self.fac_LMK60disp = config['raw_feature']['fac_LMK60disp']
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||||
self.fac_LMK61disp = config['raw_feature']['fac_LMK61disp']
|
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self.fac_LMK62disp = config['raw_feature']['fac_LMK62disp']
|
||||
self.fac_LMK63disp = config['raw_feature']['fac_LMK63disp']
|
||||
self.fac_LMK64disp = config['raw_feature']['fac_LMK64disp']
|
||||
self.fac_LMK65disp = config['raw_feature']['fac_LMK65disp']
|
||||
self.fac_LMK66disp = config['raw_feature']['fac_LMK66disp']
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self.fac_LMK67disp = config['raw_feature']['fac_LMK67disp']
|
||||
|
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#Facial features
|
||||
self.hap_exp = config['raw_feature']['hap_exp']
|
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self.sad_exp = config['raw_feature']['sad_exp']
|
||||
self.sur_exp = config['raw_feature']['sur_exp']
|
||||
self.fea_exp = config['raw_feature']['fea_exp']
|
||||
self.ang_exp = config['raw_feature']['ang_exp']
|
||||
self.dis_exp = config['raw_feature']['dis_exp']
|
||||
self.con_exp = config['raw_feature']['con_exp']
|
||||
self.happ_occ = config['raw_feature']['happ_occ']
|
||||
self.sad_occ = config['raw_feature']['sad_occ']
|
||||
self.sur_occ = config['raw_feature']['sur_occ']
|
||||
self.fea_occ = config['raw_feature']['fea_occ']
|
||||
self.ang_occ = config['raw_feature']['ang_occ']
|
||||
self.dis_occ = config['raw_feature']['dis_occ']
|
||||
self.con_occ = config['raw_feature']['con_occ']
|
||||
self.pos_exp = config['raw_feature']['pos_exp']
|
||||
self.neg_exp = config['raw_feature']['neg_exp']
|
||||
self.neu_exp = config['raw_feature']['neu_exp']
|
||||
self.cai_exp = config['raw_feature']['cai_exp']
|
||||
self.com_exp = config['raw_feature']['com_exp']
|
||||
self.com_lower_exp = config['raw_feature']['com_lower_exp']
|
||||
self.com_upper_exp = config['raw_feature']['com_upper_exp']
|
||||
self.pai_exp = config['raw_feature']['pai_exp']
|
||||
self.hap_exp_full = config['raw_feature']['hap_exp_full']
|
||||
self.sad_exp_full = config['raw_feature']['sad_exp_full']
|
||||
self.sur_exp_full = config['raw_feature']['sur_exp_full']
|
||||
self.fea_exp_full = config['raw_feature']['fea_exp_full']
|
||||
self.ang_exp_full = config['raw_feature']['ang_exp_full']
|
||||
self.dis_exp_full = config['raw_feature']['dis_exp_full']
|
||||
self.con_exp_full = config['raw_feature']['con_exp_full']
|
||||
self.pos_exp_full = config['raw_feature']['pos_exp_full']
|
||||
self.neg_exp_full = config['raw_feature']['neg_exp_full']
|
||||
self.neu_exp_full = config['raw_feature']['neu_exp_full']
|
||||
self.cai_exp_full = config['raw_feature']['cai_exp_full']
|
||||
self.com_exp_full = config['raw_feature']['com_exp_full']
|
||||
self.com_lower_exp_full = config['raw_feature']['com_lower_exp_full']
|
||||
self.com_upper_exp_full = config['raw_feature']['com_upper_exp_full']
|
||||
self.pai_exp_full = config['raw_feature']['pai_exp_full']
|
||||
self.fac_AsymMaskMouth = config['raw_feature']['fac_AsymMaskMouth']
|
||||
self.fac_AsymMaskEye = config['raw_feature']['fac_AsymMaskEye']
|
||||
self.fac_AsymMaskEyebrow = config['raw_feature']['fac_AsymMaskEyebrow']
|
||||
self.fac_AsymMaskCom = config['raw_feature']['fac_AsymMaskCom']
|
||||
|
||||
#Movement features
|
||||
self.head_vel = config['raw_feature']['head_vel']
|
||||
self.mov_blink_ear = config['raw_feature']['mov_blink_ear']
|
||||
self.vid_dur = config['raw_feature']['vid_dur']
|
||||
self.fps = config['raw_feature']['fps']
|
||||
self.mov_blinkframes = config['raw_feature']['mov_blinkframes']
|
||||
self.mov_blinkdur = config['raw_feature']['mov_blinkdur']
|
||||
self.mov_Hpose_Pitch = config['raw_feature']['mov_Hpose_Pitch']
|
||||
self.mov_Hpose_Yaw = config['raw_feature']['mov_Hpose_Yaw']
|
||||
self.mov_Hpose_Roll = config['raw_feature']['mov_Hpose_Roll']
|
||||
self.mov_Hpose_Dist = config['raw_feature']['mov_Hpose_Dist']
|
||||
|
||||
self.mov_freq_trem_freq = config['raw_feature']['mov_freq_trem_freq']
|
||||
self.mov_freq_trem_index = config['raw_feature']['mov_freq_trem_index']
|
||||
self.mov_freq_trem_pindex = config['raw_feature']['mov_freq_trem_pindex']
|
||||
self.mov_amp_trem_freq = config['raw_feature']['mov_amp_trem_freq']
|
||||
self.mov_amp_trem_index = config['raw_feature']['mov_amp_trem_index']
|
||||
self.mov_amp_trem_pindex = config['raw_feature']['mov_amp_trem_pindex']
|
||||
|
||||
self.fac_tremor_median_5 = config['raw_feature']['fac_tremor_median_5']
|
||||
self.fac_tremor_median_12 = config['raw_feature']['fac_tremor_median_12']
|
||||
self.fac_tremor_median_8 = config['raw_feature']['fac_tremor_median_8']
|
||||
self.fac_tremor_median_48 = config['raw_feature']['fac_tremor_median_48']
|
||||
self.fac_tremor_median_54 = config['raw_feature']['fac_tremor_median_54']
|
||||
self.fac_tremor_median_28 = config['raw_feature']['fac_tremor_median_28']
|
||||
self.fac_tremor_median_51 = config['raw_feature']['fac_tremor_median_51']
|
||||
self.fac_tremor_median_66 = config['raw_feature']['fac_tremor_median_66']
|
||||
self.fac_tremor_median_57 = config['raw_feature']['fac_tremor_median_57']
|
||||
|
||||
self.mov_leye_x = config['raw_feature']['mov_leye_x']
|
||||
self.mov_leye_y = config['raw_feature']['mov_leye_y']
|
||||
self.mov_leye_z = config['raw_feature']['mov_leye_z']
|
||||
self.mov_reye_x = config['raw_feature']['mov_reye_x']
|
||||
self.mov_reye_y = config['raw_feature']['mov_reye_y']
|
||||
self.mov_reye_z = config['raw_feature']['mov_reye_z']
|
||||
self.mov_eleft_disp = config['raw_feature']['mov_eleft_disp']
|
||||
self.mov_eright_disp = config['raw_feature']['mov_eright_disp']
|
||||
|
||||
#NLP features
|
||||
self.nlp_transcribe = config['raw_feature']['nlp_transcribe']
|
||||
self.nlp_numSentences = config['raw_feature']['nlp_numSentences']
|
||||
self.nlp_singPronPerAns = config['raw_feature']['nlp_singPronPerAns']
|
||||
self.nlp_singPronPerSen = config['raw_feature']['nlp_singPronPerSen']
|
||||
self.nlp_pastTensePerAns = config['raw_feature']['nlp_pastTensePerAns']
|
||||
self.nlp_pastTensePerSen = config['raw_feature']['nlp_pastTensePerSen']
|
||||
self.nlp_pronounsPerAns = config['raw_feature']['nlp_pronounsPerAns']
|
||||
self.nlp_pronounsPerSen = config['raw_feature']['nlp_pronounsPerSen']
|
||||
self.nlp_verbsPerAns = config['raw_feature']['nlp_verbsPerAns']
|
||||
self.nlp_verbsPerSen = config['raw_feature']['nlp_verbsPerSen']
|
||||
self.nlp_adjectivesPerAns = config['raw_feature']['nlp_adjectivesPerAns']
|
||||
self.nlp_adjectivesPerSen = config['raw_feature']['nlp_adjectivesPerSen']
|
||||
self.nlp_nounsPerAns = config['raw_feature']['nlp_nounsPerAns']
|
||||
self.nlp_nounsPerSen = config['raw_feature']['nlp_nounsPerSen']
|
||||
self.nlp_sentiment_mean = config['raw_feature']['nlp_sentiment_mean']
|
||||
self.nlp_mattr = config['raw_feature']['nlp_mattr']
|
||||
self.nlp_wordsPerMin = config['raw_feature']['nlp_wordsPerMin']
|
||||
self.nlp_totalTime = config['raw_feature']['nlp_totalTime']
|
||||
|
||||
@@ -1,164 +0,0 @@
|
||||
"""
|
||||
file_name: process_features
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.audio import intensity, pitch_freq, hnr, gne, voice_frame_score, formant_freq
|
||||
from dbm_lib.dbm_features.raw_features.audio import pause_segment, jitter, shimmer, mfcc
|
||||
from dbm_lib.dbm_features.raw_features.video import face_asymmetry, face_au, face_emotion_expressivity, face_landmark
|
||||
from dbm_lib.dbm_features.raw_features.movement import head_motion, eye_blink, eye_gaze, voice_tremor, facial_tremor
|
||||
from dbm_lib.dbm_features.raw_features.nlp import transcribe, speech_features
|
||||
|
||||
|
||||
import subprocess
|
||||
import logging
|
||||
from os.path import isfile, splitext, basename, dirname, join
|
||||
import glob
|
||||
import os
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
def audio_to_wav(input_filepath):
|
||||
""" Extracts a video's audio file and saves it to wav
|
||||
Args:
|
||||
input_filepath: (str)
|
||||
Returns:
|
||||
"""
|
||||
try:
|
||||
|
||||
fname, _ = splitext(input_filepath)
|
||||
output_filepath = fname + '.wav'
|
||||
|
||||
if not isfile(output_filepath):
|
||||
call = ['ffmpeg', '-i', input_filepath, '-vn', '-acodec', 'pcm_s16le', '-ar', '44100', output_filepath]
|
||||
|
||||
logger.info('Converting audio from {} to wav'.format(input_filepath))
|
||||
subprocess.check_output(call)
|
||||
logger.info('wav output saved in {}'.format(output_filepath))
|
||||
else:
|
||||
logger.info('Output file {} already exists'.format(output_filepath))
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to extract audio from Video')
|
||||
|
||||
def process_acoustic(video_uri, out_dir, dbm_group, r_config):
|
||||
"""
|
||||
processing acoustic features
|
||||
Args:
|
||||
video_uri: video path; out_dir: raw variable output dir
|
||||
dbm_group: list of features group to process; r_config: raw feature config object
|
||||
"""
|
||||
if dbm_group != None and len(dbm_group)>0 and 'acoustic' not in dbm_group:
|
||||
return
|
||||
|
||||
logger.info('Processing acoustic variables from data in {}'.format(video_uri))
|
||||
logger.info('processing audio intensity....')
|
||||
intensity.run_intensity(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing audio pitch freq....')
|
||||
pitch_freq.run_pitch(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing HNR....')
|
||||
hnr.run_hnr(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing GNE....')
|
||||
gne.run_gne(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing voice frame score....')
|
||||
voice_frame_score.run_vfs(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing formant frequency....')
|
||||
formant_freq.run_formant(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing pause segment....')
|
||||
pause_segment.run_pause_segment(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing jitter....')
|
||||
jitter.run_jitter(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing shimmer....')
|
||||
shimmer.run_shimmer(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing mfcc....')
|
||||
mfcc.run_mfcc(video_uri, out_dir, r_config)
|
||||
|
||||
def process_facial(video_uri, out_dir, dbm_group, r_config):
|
||||
"""
|
||||
processing facial features
|
||||
Args:
|
||||
video_uri: video path; out_dir: raw variable output dir
|
||||
dbm_group: list of features to process; r_config: raw feature config object
|
||||
"""
|
||||
if dbm_group != None and len(dbm_group)>0 and 'facial' not in dbm_group:
|
||||
return
|
||||
|
||||
logger.info('Processing facial variables from data in {}'.format(video_uri))
|
||||
logger.info('processing facial asymmetry....')
|
||||
face_asymmetry.run_face_asymmetry(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing facial Action Unit....')
|
||||
face_au.run_face_au(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing facial expressivity....')
|
||||
face_emotion_expressivity.run_face_expressivity(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing facial landmark....')
|
||||
face_landmark.run_face_landmark(video_uri, out_dir, r_config)
|
||||
|
||||
def process_movement(video_uri, out_dir, dbm_group, r_config, dlib_model):
|
||||
"""
|
||||
processing facial features
|
||||
Args:
|
||||
video_uri: video path; out_dir: raw variable output dir
|
||||
dbm_group: list of features to process; r_config: raw feature config object
|
||||
dlib_model: shape predictor model path
|
||||
"""
|
||||
if dbm_group != None and len(dbm_group)>0 and 'movement' not in dbm_group:
|
||||
return
|
||||
|
||||
logger.info('Processing movement variables from data in {}'.format(video_uri))
|
||||
|
||||
logger.info('processing head movement....')
|
||||
head_motion.run_head_movement(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing eye blink....')
|
||||
eye_blink.run_eye_blink(video_uri, out_dir, r_config, dlib_model)
|
||||
|
||||
logger.info('processing eye gaze....')
|
||||
eye_gaze.run_eye_gaze(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing voice tremor....')
|
||||
voice_tremor.run_vtremor(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info('processing facial tremor....')
|
||||
facial_tremor.fac_tremor_process(video_uri, out_dir, r_config, model_output=True)
|
||||
|
||||
|
||||
def process_nlp(video_uri, out_dir, dbm_group, tran_tog, r_config, deep_path):
|
||||
"""
|
||||
processing nlp features
|
||||
Args:
|
||||
video_uri: video path; out_dir: raw variable output dir
|
||||
dbm_group: list of features to process; r_config: raw feature config object
|
||||
deep_path: deep speech build path
|
||||
"""
|
||||
if dbm_group != None and len(dbm_group)>0 and 'speech' not in dbm_group:
|
||||
return
|
||||
|
||||
logger.info('Processing nlp variables from data in {}'.format(video_uri))
|
||||
transcribe.run_transcribe(video_uri, out_dir, r_config, deep_path)
|
||||
speech_features.run_speech_feature(video_uri, out_dir, r_config, tran_tog)
|
||||
|
||||
|
||||
def remove_file(file_path, file_ext = '.wav'):
|
||||
"""
|
||||
removing wav file
|
||||
"""
|
||||
file_dir = dirname(file_path)
|
||||
file_name, _ = splitext(basename(file_path))
|
||||
wav_file = glob.glob(join(file_dir, file_name + file_ext))
|
||||
|
||||
if len(wav_file)> 0:
|
||||
os.remove(wav_file[0])
|
||||
@@ -1,10 +0,0 @@
|
||||
"""
|
||||
file_name: init
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
@@ -1,133 +0,0 @@
|
||||
"""
|
||||
file_name: formant_freq
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
import numpy as np
|
||||
import parselmouth
|
||||
import librosa
|
||||
import glob
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
formant_dir = 'acoustic/formant_freq'
|
||||
csv_ext = '_formant.csv'
|
||||
error_txt = 'error: length less than 0.064'
|
||||
|
||||
def formant_list(formant,snd):
|
||||
"""
|
||||
Getting formant frequency per second
|
||||
Args:
|
||||
formant: Formant object for sound wave
|
||||
snd: Parselmouth sound object
|
||||
Returns:
|
||||
List of first through fourth formant for each frame
|
||||
"""
|
||||
f1_list = []
|
||||
f2_list = []
|
||||
f3_list = []
|
||||
f4_list = []
|
||||
|
||||
dur = snd.duration-0.02
|
||||
dur_round = round(dur, 2)
|
||||
|
||||
time_list = np.arange(0.001, dur_round, 0.001)
|
||||
for time in time_list:
|
||||
|
||||
f1 = formant.get_value_at_time(1,time)
|
||||
f2 = formant.get_value_at_time(2,time)
|
||||
f3 = formant.get_value_at_time(3,time)
|
||||
f4 = formant.get_value_at_time(4,time)
|
||||
|
||||
f1_list.append(f1)
|
||||
f2_list.append(f2)
|
||||
f3_list.append(f3)
|
||||
f4_list.append(f4)
|
||||
return f1_list,f2_list,f3_list,f4_list
|
||||
|
||||
def formant_score(path):
|
||||
"""
|
||||
Using parselmouth library fetching Formant Frequency
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of Formant freq for each voice frame
|
||||
"""
|
||||
sound_pat = parselmouth.Sound(path)
|
||||
formant = sound_pat.to_formant_burg(time_step=.001)
|
||||
f_score = formant_list(formant,sound_pat)
|
||||
return f_score
|
||||
|
||||
def calc_formant(video_uri, audio_file, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing Formant freq matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file; fl_name: input file name
|
||||
out_loc: (str) Output directory; r_config: raw variable config
|
||||
"""
|
||||
|
||||
f1_list,f2_list,f3_list,f4_list = formant_score(audio_file)
|
||||
df_formant = pd.DataFrame(f1_list, columns=[r_config.aco_fm1])
|
||||
|
||||
df_formant[r_config.aco_fm2] = f2_list
|
||||
df_formant[r_config.aco_fm3] = f3_list
|
||||
df_formant[r_config.aco_fm4] = f4_list
|
||||
|
||||
df_formant.replace('', np.nan, regex=True,inplace=True)
|
||||
df_formant[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
|
||||
df_formant['Frames'] = df_formant.index
|
||||
df_formant['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_formant, out_loc, fl_name, formant_dir, csv_ext)
|
||||
|
||||
def empty_fm(video_uri, out_loc, fl_name, r_config):
|
||||
|
||||
"""
|
||||
Preparing empty formant frequency matrix if something fails
|
||||
"""
|
||||
cols = ['Frames', r_config.aco_fm1, r_config.aco_fm2, r_config.aco_fm3, r_config.aco_fm4, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, np.nan, np.nan, np.nan, error_txt]]
|
||||
df_fm = pd.DataFrame(out_val, columns = cols)
|
||||
df_fm['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_fm, out_loc, fl_name, formant_dir, csv_ext)
|
||||
|
||||
def run_formant(video_uri, out_dir, r_config):
|
||||
|
||||
"""
|
||||
Processing all patient's for fetching Formant freq
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
|
||||
empty_fm(video_uri, out_loc, fl_name, r_config)
|
||||
return
|
||||
|
||||
calc_formant(video_uri, audio_file, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
@@ -1,161 +0,0 @@
|
||||
"""
|
||||
file_name: gne
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import glob
|
||||
import parselmouth
|
||||
import librosa
|
||||
import more_itertools as mit
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
gne_dir = 'acoustic/glottal_noise'
|
||||
ff_dir = 'acoustic/pitch'
|
||||
csv_ext = '_gne.csv'
|
||||
|
||||
def gne_ratio(sound):
|
||||
"""
|
||||
Using parselmouth library fetching glottal noise excitation ratio
|
||||
Args:
|
||||
sound: parselmouth object
|
||||
Returns:
|
||||
(list) list of gne ratio for each voice frame
|
||||
"""
|
||||
harmonicity_gne = sound.to_harmonicity_gne()
|
||||
gne_all_bands = harmonicity_gne.values
|
||||
gne_all_bands = np.where(gne_all_bands==-200, np.NaN, gne_all_bands)
|
||||
|
||||
gne = np.nanmax(gne_all_bands) # following http://www.fon.hum.uva.nl/rob/NKI_TEVA/TEVA/HTML/NKI_TEVA.pdf
|
||||
return gne
|
||||
|
||||
def empty_gne(video_uri, out_loc, fl_name, r_config, error_txt):
|
||||
"""
|
||||
Preparing empty GNE matrix if something fails
|
||||
"""
|
||||
cols = ['Frames', r_config.aco_gne, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
|
||||
df_gne = pd.DataFrame(out_val, columns = cols)
|
||||
df_gne['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_gne, out_loc, fl_name, gne_dir, csv_ext)
|
||||
|
||||
def segment_pitch(dir_path, r_config):
|
||||
"""
|
||||
segmenting pitch freq for each voice segment
|
||||
"""
|
||||
com_speech_sort, voiced_yes, voiced_no = ([], ) * 3
|
||||
for file in os.listdir(dir_path):
|
||||
try:
|
||||
|
||||
if file.endswith('_pitch.csv'):
|
||||
|
||||
ff_df = pd.read_csv((dir_path+'/'+file))
|
||||
voice_label = ff_df[r_config.aco_voiceLabel]
|
||||
|
||||
indices_yes = [i for i, x in enumerate(voice_label) if x == "yes"]
|
||||
voiced_yes = [list(group) for group in mit.consecutive_groups(indices_yes)]
|
||||
|
||||
indices_no = [i for i, x in enumerate(voice_label) if x == "no"]
|
||||
voiced_no = [list(group) for group in mit.consecutive_groups(indices_no)]
|
||||
|
||||
com_speech = voiced_yes + voiced_no
|
||||
com_speech_sort = sorted(com_speech, key=lambda x: x[0])
|
||||
except:
|
||||
pass
|
||||
|
||||
return com_speech_sort, voiced_yes, voiced_no
|
||||
|
||||
def segment_gne(com_speech_sort, voiced_yes, voiced_no, gne_all_frames, audio_file):
|
||||
"""
|
||||
calculating gne for each voice segment
|
||||
"""
|
||||
snd = parselmouth.Sound(audio_file)
|
||||
pitch = snd.to_pitch(time_step=.001)
|
||||
|
||||
for idx, vs in enumerate(com_speech_sort):
|
||||
try:
|
||||
|
||||
max_gne = np.NaN
|
||||
if vs in voiced_yes and len(vs)>1:
|
||||
|
||||
start_time = pitch.get_time_from_frame_number(vs[0])
|
||||
end_time = pitch.get_time_from_frame_number(vs[-1])
|
||||
|
||||
snd_start = int(snd.get_frame_number_from_time(start_time))
|
||||
snd_end = int(snd.get_frame_number_from_time(end_time))
|
||||
|
||||
samples = parselmouth.Sound(snd.as_array()[0][snd_start:snd_end])
|
||||
max_gne = gne_ratio(samples)
|
||||
except:
|
||||
pass
|
||||
|
||||
gne_all_frames[idx] = max_gne
|
||||
return gne_all_frames
|
||||
|
||||
def calc_gne(video_uri, audio_file, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing gne matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv's
|
||||
"""
|
||||
dir_path = os.path.join(out_loc, ff_dir)
|
||||
if os.path.isdir(dir_path):
|
||||
voice_seg = segment_pitch(dir_path, r_config)
|
||||
|
||||
gne_all_frames = [np.NaN] * len(voice_seg[0])
|
||||
gne_segment_frames = segment_gne(voice_seg[0], voice_seg[1], voice_seg[2], gne_all_frames, audio_file)
|
||||
|
||||
df_gne = pd.DataFrame(gne_segment_frames, columns=[r_config.aco_gne])
|
||||
df_gne[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
|
||||
df_gne['Frames'] = df_gne.index
|
||||
df_gne['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Processing Output file {} '.format(out_loc))
|
||||
ut.save_output(df_gne, out_loc, fl_name, gne_dir, csv_ext)
|
||||
|
||||
else:
|
||||
error_txt = 'error: pitch freq not available'
|
||||
empty_gne(video_uri, out_loc, fl_name, r_config, error_txt)
|
||||
|
||||
def run_gne(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patient's for fetching glottal noise ratio
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
|
||||
error_txt = 'error: length less than 0.064'
|
||||
empty_gne(video_uri, out_loc, fl_name, r_config, error_txt)
|
||||
return
|
||||
|
||||
calc_gne(video_uri, audio_file, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
@@ -1,96 +0,0 @@
|
||||
"""
|
||||
file_name: hnr
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import glob
|
||||
import parselmouth
|
||||
import librosa
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
hnr_dir = 'acoustic/harmonic_noise'
|
||||
csv_ext = '_hnr.csv'
|
||||
error_txt = 'error: length less than 0.064'
|
||||
|
||||
def hnr_ratio(filepath):
|
||||
"""
|
||||
Using parselmouth library fetching harmonic noise ratio ratio
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of hnr ratio for each voice frame, min,max and mean hnr
|
||||
"""
|
||||
sound = parselmouth.Sound(filepath)
|
||||
harmonicity = sound.to_harmonicity_ac(time_step=.001)
|
||||
|
||||
hnr_all_frames = harmonicity.values#[harmonicity.values != -200] nan it (****)
|
||||
hnr_all_frames = np.where(hnr_all_frames==-200, np.NaN, hnr_all_frames)
|
||||
return hnr_all_frames.transpose()
|
||||
|
||||
def calc_hnr(video_uri, audio_file, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing harmonic noise matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv's
|
||||
"""
|
||||
|
||||
hnr_all_frames = hnr_ratio(audio_file)
|
||||
df_hnr = pd.DataFrame(hnr_all_frames, columns=[r_config.aco_hnr])
|
||||
|
||||
df_hnr['Frames'] = df_hnr.index
|
||||
df_hnr['dbm_master_url'] = video_uri
|
||||
df_hnr[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)
|
||||
|
||||
def empty_hnr(video_uri, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing empty HNR matrix if something fails
|
||||
"""
|
||||
cols = ['Frames', r_config.aco_hnr, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_hnr = pd.DataFrame(out_val, columns = cols)
|
||||
df_hnr['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)
|
||||
|
||||
def run_hnr(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patient's for fetching harmonic noise ratio
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
|
||||
empty_hnr(video_uri, out_loc, fl_name, r_config)
|
||||
return
|
||||
|
||||
calc_hnr(video_uri, audio_file, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
@@ -1,92 +0,0 @@
|
||||
"""
|
||||
file_name: intensity
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import glob
|
||||
import parselmouth
|
||||
import librosa
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
intensity_dir = 'acoustic/intensity'
|
||||
csv_ext = '_intensity.csv'
|
||||
error_txt = 'error: length less than 0.064'
|
||||
|
||||
def intensity_score(path):
|
||||
"""
|
||||
Using parselmouth library fetching Intensity
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of Intensity for each voice frame
|
||||
"""
|
||||
sound_pat = parselmouth.Sound(path)
|
||||
intensity = sound_pat.to_intensity(time_step=.001)
|
||||
return intensity.values[0]
|
||||
|
||||
def calc_intensity(video_uri, audio_file, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing Intensity matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv's
|
||||
"""
|
||||
|
||||
intensity_frames = intensity_score(audio_file)
|
||||
df_intensity = pd.DataFrame(intensity_frames, columns=[r_config.aco_int])
|
||||
|
||||
df_intensity['Frames'] = df_intensity.index
|
||||
df_intensity['dbm_master_url'] = video_uri
|
||||
df_intensity[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_intensity, out_loc, fl_name, intensity_dir, csv_ext)
|
||||
|
||||
def empty_intensity(video_uri, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing empty Intensity matrix if something fails
|
||||
"""
|
||||
cols = ['Frames', r_config.aco_int, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_int = pd.DataFrame(out_val, columns = cols)
|
||||
df_int['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_int, out_loc, fl_name, intensity_dir, csv_ext)
|
||||
|
||||
def run_intensity(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patient's for fetching Intensity
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
|
||||
empty_intensity(video_uri, out_loc, fl_name, r_config)
|
||||
return
|
||||
|
||||
calc_intensity(video_uri, audio_file, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
@@ -1,159 +0,0 @@
|
||||
"""
|
||||
file_name: jitter_processing
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import glob
|
||||
import parselmouth
|
||||
import librosa
|
||||
import numpy as np
|
||||
import more_itertools as mit
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
jitter_dir = 'acoustic/jitter'
|
||||
ff_dir = 'acoustic/pitch'
|
||||
csv_ext = '_jitter.csv'
|
||||
|
||||
def audio_jitter(sound):
|
||||
"""
|
||||
Using parselmouth library fetching jitter
|
||||
Args:
|
||||
sound: parselmouth object
|
||||
Returns:
|
||||
(list) list of jitters for each voice frame
|
||||
"""
|
||||
pointProcess = parselmouth.praat.call(sound, "To PointProcess (periodic, cc)...", 80, 500)
|
||||
jitter = parselmouth.praat.call(pointProcess, "Get jitter (local)", 0, 0, 0.0001, 0.02, 1.3)
|
||||
return jitter
|
||||
|
||||
def empty_jitter(video_uri, out_loc, fl_name, r_config, error_txt):
|
||||
"""
|
||||
Preparing empty jitter matrix if something fails
|
||||
"""
|
||||
cols = ['Frames', r_config.aco_jitter, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_jitter = pd.DataFrame(out_val, columns = cols)
|
||||
df_jitter['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_jitter, out_loc, fl_name, jitter_dir, csv_ext)
|
||||
|
||||
def segment_pitch(dir_path, r_config):
|
||||
"""
|
||||
segmenting pitch freq for each voice segment
|
||||
"""
|
||||
com_speech_sort, voiced_yes, voiced_no = ([], ) * 3
|
||||
for file in os.listdir(dir_path):
|
||||
try:
|
||||
|
||||
if file.endswith('_pitch.csv'):
|
||||
|
||||
ff_df = pd.read_csv((dir_path+'/'+file))
|
||||
voice_label = ff_df[r_config.aco_voiceLabel]
|
||||
|
||||
indices_yes = [i for i, x in enumerate(voice_label) if x == "yes"]
|
||||
voiced_yes = [list(group) for group in mit.consecutive_groups(indices_yes)]
|
||||
|
||||
indices_no = [i for i, x in enumerate(voice_label) if x == "no"]
|
||||
voiced_no = [list(group) for group in mit.consecutive_groups(indices_no)]
|
||||
|
||||
com_speech = voiced_yes + voiced_no
|
||||
com_speech_sort = sorted(com_speech, key=lambda x: x[0])
|
||||
except:
|
||||
pass
|
||||
|
||||
return com_speech_sort, voiced_yes, voiced_no
|
||||
|
||||
def segment_jitter(com_speech_sort, voiced_yes, voiced_no, jitter_frames, audio_file):
|
||||
"""
|
||||
calculating jitter for each voice segment
|
||||
"""
|
||||
snd = parselmouth.Sound(audio_file)
|
||||
pitch = snd.to_pitch(time_step=.001)
|
||||
|
||||
for idx, vs in enumerate(com_speech_sort):
|
||||
try:
|
||||
|
||||
jitter = np.NaN
|
||||
if vs in voiced_yes and len(vs)>1:
|
||||
|
||||
start_time = pitch.get_time_from_frame_number(vs[0])
|
||||
end_time = pitch.get_time_from_frame_number(vs[-1])
|
||||
|
||||
snd_start = int(snd.get_frame_number_from_time(start_time))
|
||||
snd_end = int(snd.get_frame_number_from_time(end_time))
|
||||
|
||||
samples = parselmouth.Sound(snd.as_array()[0][snd_start:snd_end])
|
||||
jitter = audio_jitter(samples)
|
||||
except:
|
||||
pass
|
||||
|
||||
jitter_frames[idx] = jitter
|
||||
return jitter_frames
|
||||
|
||||
def calc_jitter(video_uri, audio_file, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing jitter matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv
|
||||
r_config: config.config_raw_feature.pyConfigFeatureNmReader object
|
||||
"""
|
||||
dir_path = os.path.join(out_loc, ff_dir)
|
||||
if os.path.isdir(dir_path):
|
||||
voice_seg = segment_pitch(dir_path, r_config)
|
||||
|
||||
jitter_frames = [np.NaN] * len(voice_seg[0])
|
||||
jitter_segment_frames = segment_jitter(voice_seg[0], voice_seg[1], voice_seg[2], jitter_frames, audio_file)
|
||||
|
||||
df_jitter = pd.DataFrame(jitter_segment_frames, columns=[r_config.aco_jitter])
|
||||
df_jitter[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
|
||||
df_jitter['Frames'] = df_jitter.index
|
||||
df_jitter['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Processing Output file {} '.format(out_loc))
|
||||
ut.save_output(df_jitter, out_loc, fl_name, jitter_dir, csv_ext)
|
||||
|
||||
else:
|
||||
error_txt = 'error: fundamental freq not available'
|
||||
empty_jitter(video_uri, out_loc, fl_name, r_config, error_txt)
|
||||
|
||||
def run_jitter(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patient's videos for fetching jitter
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
|
||||
error_txt = 'error: length less than 0.064'
|
||||
empty_jitter(video_uri, out_loc, fl_name, r_config, error_txt)
|
||||
return
|
||||
|
||||
calc_jitter(video_uri, audio_file, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
@@ -1,105 +0,0 @@
|
||||
"""
|
||||
file_name: mfcc
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import os
|
||||
import glob
|
||||
import parselmouth
|
||||
import librosa
|
||||
import numpy as np
|
||||
import librosa
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
mfcc_dir = 'acoustic/mfcc'
|
||||
csv_ext = '_mfcc.csv'
|
||||
error_txt = 'error: length less than 0.064'
|
||||
|
||||
def empty_mfcc(video_uri, out_loc, fl_name, r_config):
|
||||
|
||||
"""
|
||||
Preparing empty empty_mfcc matrix if something fails
|
||||
"""
|
||||
cols = ['Frames', r_config.aco_mfcc1, r_config.aco_mfcc2, r_config.aco_mfcc3, r_config.aco_mfcc4, r_config.aco_mfcc5,
|
||||
r_config.aco_mfcc6, r_config.aco_mfcc7, r_config.aco_mfcc8, r_config.aco_mfcc9, r_config.aco_mfcc10,
|
||||
r_config.aco_mfcc11, r_config.aco_mfcc12, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,
|
||||
error_txt]]
|
||||
df_mfcc = pd.DataFrame(out_val, columns = cols)
|
||||
df_mfcc['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_mfcc, out_loc, fl_name, mfcc_dir, csv_ext)
|
||||
|
||||
def audio_mfcc(path):
|
||||
"""
|
||||
Using parselmouth library fetching mfccs
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of mfccs for each voice frame
|
||||
"""
|
||||
sound = parselmouth.Sound(path)
|
||||
mfcc_object = sound.to_mfcc(time_step=.001,number_of_coefficients=12)
|
||||
mfccs = mfcc_object.to_array()
|
||||
mfccs = np.delete(mfccs, (0), axis=0)
|
||||
return mfccs
|
||||
|
||||
def calc_mfcc(video_uri, audio_file, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing mfcc matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: output location to save csv
|
||||
fl_name: (str) name of audio file
|
||||
r_config: config.config_raw_feature.pyConfigFeatureNmReader object
|
||||
"""
|
||||
dict_ = {}
|
||||
mfccs = audio_mfcc(audio_file)
|
||||
|
||||
for i in range(1,13):
|
||||
conf_str = r_config.base_raw['raw_feature']
|
||||
dict_[conf_str['aco_mfcc' + str(i)]] = mfccs[i-1, :]
|
||||
|
||||
df = pd.DataFrame(dict_)
|
||||
df['Frames'] = df.index
|
||||
|
||||
df[r_config.err_reason] = 'Pass'# may replace based on threshold in future release
|
||||
df['dbm_master_url'] = video_uri
|
||||
|
||||
ut.save_output(df, out_loc, fl_name, mfcc_dir, csv_ext)
|
||||
|
||||
def run_mfcc(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patients to fetch mfccs
|
||||
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
|
||||
empty_mfcc(video_uri, out_loc, fl_name, r_config)
|
||||
return
|
||||
|
||||
calc_mfcc(video_uri, audio_file, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
@@ -1,113 +0,0 @@
|
||||
"""
|
||||
file_name: pitch_freq
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import os
|
||||
import glob
|
||||
import parselmouth
|
||||
import librosa
|
||||
import numpy as np
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
ff_dir = 'acoustic/pitch'
|
||||
csv_ext = '_pitch.csv'
|
||||
error_txt = 'error: length less than 0.064'
|
||||
|
||||
def audio_pitch(path):
|
||||
"""
|
||||
Using parselmouth library fetching pitch/fundamental frequency
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of pitch/fundamental frequency for each voice frame
|
||||
"""
|
||||
sound_pat = parselmouth.Sound(path)
|
||||
pitch = sound_pat.to_pitch(time_step=.001)
|
||||
pitch_values = pitch.selected_array['frequency']
|
||||
|
||||
return list(pitch_values)
|
||||
|
||||
def label_speech(row,fd_freq):
|
||||
"""
|
||||
identify whether frame is voiced or not
|
||||
Args:
|
||||
row: (item) pitch frequency value
|
||||
Returns:
|
||||
(str) yes or no indicator for voice
|
||||
"""
|
||||
if row[fd_freq] > 0 :
|
||||
return 'yes'
|
||||
else:
|
||||
return 'no'
|
||||
|
||||
def calc_pitch(video_uri, audio_file, out_loc, fl_name, r_config):
|
||||
|
||||
"""
|
||||
Preparing pitch frequency matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
row: (dataframe) subject details from master csv
|
||||
new_out_base_dir: (str) Output directory for csv
|
||||
"""
|
||||
|
||||
ff_frames = audio_pitch(audio_file)
|
||||
df_ffreq = pd.DataFrame(ff_frames, columns=[r_config.aco_ff])
|
||||
|
||||
df_ffreq['Frames'] = df_ffreq.index
|
||||
df_ffreq[r_config.aco_voiceLabel] = df_ffreq.apply(lambda row: label_speech(row, r_config.aco_ff),axis=1)
|
||||
|
||||
df_ffreq[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
df_ffreq['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Processing Output file {} '.format(out_loc))
|
||||
ut.save_output(df_ffreq, out_loc, fl_name, ff_dir, csv_ext)
|
||||
|
||||
def empty_pitch(video_uri, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing empty pitch frequency matrix if something fails
|
||||
"""
|
||||
|
||||
df_ffreq = pd.DataFrame([[np.nan, np.nan, 'no', error_txt]],
|
||||
columns=['Frames', r_config.aco_ff, r_config.aco_voiceLabel, r_config.err_reason])
|
||||
df_ffreq['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_ffreq, out_loc, fl_name, ff_dir, csv_ext)
|
||||
|
||||
def run_pitch(video_uri, out_dir, r_config):
|
||||
|
||||
"""
|
||||
Processing audio for fetching pitch
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
|
||||
empty_pitch(video_uri, out_loc, fl_name, r_config)
|
||||
return
|
||||
|
||||
calc_pitch(video_uri, audio_file, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
@@ -1,160 +0,0 @@
|
||||
"""
|
||||
file_name: shimmer_processing
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import glob
|
||||
import parselmouth
|
||||
import librosa
|
||||
import numpy as np
|
||||
import more_itertools as mit
|
||||
from os.path import join
|
||||
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
shimmer_dir = 'acoustic/shimmer'
|
||||
ff_dir = 'acoustic/pitch'
|
||||
csv_ext = '_shimmer.csv'
|
||||
|
||||
def audio_shimmer(sound):
|
||||
"""
|
||||
Using parselmouth library fetching shimmer
|
||||
Args:
|
||||
sound: parselmouth object
|
||||
Returns:
|
||||
(list) list of shimmers for each voice frame
|
||||
"""
|
||||
pointProcess = parselmouth.praat.call(sound, "To PointProcess (periodic, cc)...", 80, 500)
|
||||
shimmer = parselmouth.praat.call([sound, pointProcess], "Get shimmer (local)", 0, 0, 0.0001, 0.02, 1.3, 1.6)
|
||||
return shimmer
|
||||
|
||||
def empty_shimmer(video_uri, out_loc, fl_name, r_config, error_txt):
|
||||
"""
|
||||
Preparing empty shimmer matrix if something fails
|
||||
"""
|
||||
cols = ['Frames', r_config.aco_shimmer, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_shimmer = pd.DataFrame(out_val, columns = cols)
|
||||
df_shimmer['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_shimmer, out_loc, fl_name, shimmer_dir, csv_ext)
|
||||
|
||||
def segment_pitch(dir_path, r_config):
|
||||
"""
|
||||
segmenting pitch freq for each voice segment
|
||||
"""
|
||||
com_speech_sort, voiced_yes, voiced_no = ([], ) * 3
|
||||
for file in os.listdir(dir_path):
|
||||
try:
|
||||
|
||||
if file.endswith('_pitch.csv'):
|
||||
|
||||
ff_df = pd.read_csv((dir_path+'/'+file))
|
||||
voice_label = ff_df[r_config.aco_voiceLabel]
|
||||
|
||||
indices_yes = [i for i, x in enumerate(voice_label) if x == "yes"]
|
||||
voiced_yes = [list(group) for group in mit.consecutive_groups(indices_yes)]
|
||||
|
||||
indices_no = [i for i, x in enumerate(voice_label) if x == "no"]
|
||||
voiced_no = [list(group) for group in mit.consecutive_groups(indices_no)]
|
||||
|
||||
com_speech = voiced_yes + voiced_no
|
||||
com_speech_sort = sorted(com_speech, key=lambda x: x[0])
|
||||
except:
|
||||
pass
|
||||
|
||||
return com_speech_sort, voiced_yes, voiced_no
|
||||
|
||||
def segment_shimmer(com_speech_sort, voiced_yes, voiced_no, shimmer_frames, audio_file):
|
||||
"""
|
||||
calculating shimmer for each voice segment
|
||||
"""
|
||||
snd = parselmouth.Sound(audio_file)
|
||||
pitch = snd.to_pitch(time_step=.001)
|
||||
|
||||
for idx, vs in enumerate(com_speech_sort):
|
||||
try:
|
||||
|
||||
shimmer = np.NaN
|
||||
if vs in voiced_yes and len(vs)>1:
|
||||
|
||||
start_time = pitch.get_time_from_frame_number(vs[0])
|
||||
end_time = pitch.get_time_from_frame_number(vs[-1])
|
||||
|
||||
snd_start = int(snd.get_frame_number_from_time(start_time))
|
||||
snd_end = int(snd.get_frame_number_from_time(end_time))
|
||||
|
||||
samples = parselmouth.Sound(snd.as_array()[0][snd_start:snd_end])
|
||||
shimmer = audio_shimmer(samples)
|
||||
except:
|
||||
pass
|
||||
|
||||
shimmer_frames[idx] = shimmer
|
||||
return shimmer_frames
|
||||
|
||||
def calc_shimmer(video_uri, audio_file, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing shimmer matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv
|
||||
r_config: config.config_raw_feature.pyConfigFeatureNmReader object
|
||||
"""
|
||||
dir_path = os.path.join(out_loc, ff_dir)
|
||||
if os.path.isdir(dir_path):
|
||||
voice_seg = segment_pitch(dir_path, r_config)
|
||||
|
||||
shimmer_frames = [np.NaN] * len(voice_seg[0])
|
||||
shimmer_segment_frames = segment_shimmer(voice_seg[0], voice_seg[1], voice_seg[2], shimmer_frames, audio_file)
|
||||
|
||||
df_shimmer = pd.DataFrame(shimmer_segment_frames, columns=[r_config.aco_shimmer])
|
||||
df_shimmer[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
|
||||
df_shimmer['Frames'] = df_shimmer.index
|
||||
df_shimmer['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Processing Output file {} '.format(out_loc))
|
||||
ut.save_output(df_shimmer, out_loc, fl_name, shimmer_dir, csv_ext)
|
||||
|
||||
else:
|
||||
error_txt = 'error: fundamental freq not available'
|
||||
empty_shimmer(video_uri, out_loc, fl_name, r_config, error_txt)
|
||||
|
||||
def run_shimmer(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patients to fetch shimmer
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
|
||||
error_txt = 'error: length less than 0.064'
|
||||
empty_shimmer(video_uri, out_loc, fl_name, r_config, error_txt)
|
||||
return
|
||||
|
||||
calc_shimmer(video_uri, audio_file, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
@@ -1,111 +0,0 @@
|
||||
"""
|
||||
file_name: voice_frame_score
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import parselmouth
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import glob
|
||||
import librosa
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
vfs_dir = 'acoustic/voice_frame_score'
|
||||
csv_ext = '_voiceprev.csv'
|
||||
error_txt = 'error: length less than 0.064'
|
||||
|
||||
def audio_pitch_frame(pitch):
|
||||
"""
|
||||
Computing total number of speech and participant voiced frames
|
||||
Args:
|
||||
pitch: speech pitch
|
||||
Returns:
|
||||
(float) total voice frames and participant voiced frames
|
||||
"""
|
||||
total_frames = pitch.get_number_of_frames()
|
||||
voiced_frames = pitch.count_voiced_frames()
|
||||
return total_frames, voiced_frames
|
||||
|
||||
def voice_segment(path):
|
||||
"""
|
||||
Using parselmouth library for fundamental frequency
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(float) total voice frames, participant voiced frames and voiced frames percentage
|
||||
"""
|
||||
sound_pat = parselmouth.Sound(path)
|
||||
pitch = sound_pat.to_pitch()
|
||||
total_frames,voiced_frames = audio_pitch_frame(pitch)
|
||||
|
||||
voiced_percentage = (voiced_frames/total_frames)*100
|
||||
return voiced_percentage, voiced_frames, total_frames
|
||||
|
||||
def calc_vfs(video_uri, audio_file, out_loc, fl_name, r_config):
|
||||
"""
|
||||
creating dataframe matrix for voice frame score
|
||||
Args:
|
||||
audio_file: Audio file path
|
||||
new_out_base_dir: AWS instance output base directory path
|
||||
f_nm_config: Config file object
|
||||
"""
|
||||
|
||||
voice_percentage,voiced_frames, total_frames = voice_segment(audio_file)
|
||||
df_vfs = pd.DataFrame([voiced_frames], columns=[r_config.aco_voiceFrame])
|
||||
|
||||
df_vfs[r_config.aco_totVoiceFrame] = [total_frames]
|
||||
df_vfs[r_config.aco_voicePct] = [voice_percentage]
|
||||
df_vfs[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
|
||||
df_vfs['Frames'] = df_vfs.index
|
||||
df_vfs['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_vfs, out_loc, fl_name, vfs_dir, csv_ext)
|
||||
|
||||
def empty_vfs(video_uri, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Preparing empty VFS matrix if something fails
|
||||
"""
|
||||
cols = ['Frames', r_config.aco_voiceFrame, r_config.aco_totVoiceFrame, r_config.aco_voicePct, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, np.nan, np.nan, error_txt]]
|
||||
df_vfs = pd.DataFrame(out_val, columns = cols)
|
||||
df_vfs['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_vfs, out_loc, fl_name, vfs_dir, csv_ext)
|
||||
|
||||
def run_vfs(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all participants for fetching voice frame score
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
|
||||
empty_vfs(video_uri, out_loc, fl_name, r_config)
|
||||
return
|
||||
|
||||
calc_vfs(video_uri, audio_file, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
@@ -1,17 +0,0 @@
|
||||
"""
|
||||
file_name: init
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
|
||||
DBMLIB_PATH = os.path.dirname(__file__)
|
||||
DBMLIB_VTREMOR_LIB = os.path.abspath(os.path.join(DBMLIB_PATH,
|
||||
'../../../../resources/libraries/voice_tremor.praat'))
|
||||
DBMLIB_FTREMOR_CONFIG = os.path.abspath(os.path.join(DBMLIB_PATH, '../../../../resources/features/facial/config.json'))
|
||||
|
||||
@@ -1,160 +0,0 @@
|
||||
"""
|
||||
file_name: eye_blink
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
import glob
|
||||
from scipy.spatial import distance as dist
|
||||
from scipy.signal import find_peaks
|
||||
from imutils.video import FileVideoStream
|
||||
from imutils.video import VideoStream
|
||||
from imutils import face_utils
|
||||
from moviepy.editor import VideoFileClip
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import imutils
|
||||
import time
|
||||
import dlib
|
||||
import cv2
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
movement_expr_dir = 'movement/eye_blink'
|
||||
csv_ext = '_eyeblinks.csv'
|
||||
|
||||
def eye_aspect_ratio(eye):
|
||||
"""
|
||||
Computing eye aspect ratio for an individual frame
|
||||
Args:
|
||||
eye: Eye landmarks
|
||||
Return:
|
||||
Eye aspect ratio for a frame
|
||||
"""
|
||||
# euclidean distance for vertical eye landmarks
|
||||
dist_cor1 = dist.euclidean(eye[1], eye[5])
|
||||
dist_cor2 = dist.euclidean(eye[2], eye[4])
|
||||
|
||||
# euclidean distance for horizontal eye landmark
|
||||
dist_cor3 = dist.euclidean(eye[0], eye[3])
|
||||
|
||||
ear = (dist_cor1 + dist_cor2) / (2.0 * dist_cor3)
|
||||
return ear
|
||||
|
||||
def blink_detection(video_path,facial_landmarks,raw_config):
|
||||
"""
|
||||
Blink detection for each frame
|
||||
Args:
|
||||
video_path: MP4 file location
|
||||
facial_landmarks: Facial landmark pre-trained model path
|
||||
raw_config: Raw configuration file object
|
||||
Return:
|
||||
Dataframe with blink informatiom like blink frame, duration etc.
|
||||
"""
|
||||
TOT_FRAME = 1
|
||||
blink_frame = []
|
||||
ear_frame = []
|
||||
|
||||
clip = VideoFileClip(video_path, has_mask=True)
|
||||
vid_length = clip.duration
|
||||
|
||||
identifier = dlib.get_frontal_face_detector() #dlib's face detector (HOG-based)
|
||||
forecaster = dlib.shape_predictor(facial_landmarks) # the facial landmark predictor
|
||||
|
||||
#left and right eye landmarks
|
||||
(left_beg, left_end) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
|
||||
(right_beg, right_end) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
|
||||
|
||||
f_stream = True
|
||||
vid_stream = FileVideoStream(video_path).start()
|
||||
|
||||
while True:
|
||||
try:
|
||||
#check if stream/frame available in video
|
||||
if f_stream and not vid_stream.more():
|
||||
break
|
||||
|
||||
#reading & converting frame into grayscale
|
||||
vid_frame = vid_stream.read()
|
||||
vid_frame = imutils.resize(vid_frame, width=450)
|
||||
gray = cv2.cvtColor(vid_frame, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
#detecting face
|
||||
rects = identifier(gray, 0)
|
||||
for rect in rects:
|
||||
|
||||
lmk = forecaster(gray, rect)
|
||||
lmk = face_utils.shape_to_np(lmk)
|
||||
|
||||
l_eye = lmk[left_beg:left_end] #Extracting left eye ratio
|
||||
r_eye = lmk[right_beg:right_end] #Extracting right eye ratio
|
||||
l_ear = eye_aspect_ratio(l_eye) # eye aspect ratio for left eye
|
||||
r_ear = eye_aspect_ratio(r_eye) # eye aspect ratio for right eye
|
||||
|
||||
ear = (l_ear + r_ear) / 2.0 # average the eye aspect ratio
|
||||
blink_frame.append(TOT_FRAME)
|
||||
ear_frame.append(ear)
|
||||
|
||||
TOT_FRAME += 1
|
||||
except Exception as e:
|
||||
#logger.error("blink detection processing failed for: {}".format(video_path))
|
||||
continue
|
||||
|
||||
blink_df = pd.DataFrame(ear_frame, columns =[raw_config.mov_blink_ear])
|
||||
blink_df[raw_config.vid_dur] = vid_length
|
||||
blink_df[raw_config.fps] = int(TOT_FRAME/vid_length)
|
||||
blink_df[raw_config.mov_blinkframes] = blink_frame
|
||||
|
||||
peaks, _ = find_peaks(blink_df[raw_config.mov_blink_ear]*-1, prominence=0.1)#prominence = 0.1 based on tuning
|
||||
final_blink_df = blink_df.iloc[peaks,:].reset_index(drop=True)
|
||||
|
||||
u_blink_df = blink_dur(final_blink_df,raw_config)
|
||||
u_blink_df['dbm_master_url'] = video_path
|
||||
return u_blink_df
|
||||
|
||||
def blink_dur(blink_df,raw_config):
|
||||
"""
|
||||
Computing blink duration between each blink
|
||||
Args:
|
||||
blink_df : Dataframe with blink informatiom like blink frame
|
||||
raw_config: Raw configuration file object
|
||||
Returns:
|
||||
Updated dataframe with blink duration
|
||||
"""
|
||||
dur_list = []
|
||||
if len(blink_df)>0:
|
||||
blink_df[raw_config.mov_blinkdur] = blink_df[raw_config.mov_blinkframes].diff().fillna(
|
||||
blink_df[raw_config.mov_blinkframes])
|
||||
else:
|
||||
blink_df[raw_config.mov_blinkdur] = np.nan
|
||||
blink_df[raw_config.mov_blinkdur] = blink_df[raw_config.mov_blinkdur]/blink_df[raw_config.fps]
|
||||
return blink_df
|
||||
|
||||
def run_eye_blink(video_uri, out_dir, r_config, facial_landmarks):
|
||||
"""
|
||||
Processing all patient's for getting eye blink artifacts
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; input_dir : input directory for video's
|
||||
out_dir: (str) Output directory for processed output; r_config: raw variable config object;
|
||||
facial_landmarks: landmark model path
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
vid_file_path = os.path.exists(video_uri)
|
||||
if vid_file_path==True:
|
||||
|
||||
logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
|
||||
df_blink = blink_detection(video_uri, facial_landmarks, r_config)
|
||||
ut.save_output(df_blink, out_loc, fl_name, movement_expr_dir, csv_ext)
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
|
||||
@@ -1,148 +0,0 @@
|
||||
"""
|
||||
file_name: eye_gaze
|
||||
project_name: DBM
|
||||
created: 2020-30-11
|
||||
"""
|
||||
|
||||
import os
|
||||
import glob
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from scipy.spatial import distance
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
eye_pose_dir = 'movement/gaze'
|
||||
eye_pose_ext = '_eyegaze.csv'
|
||||
|
||||
def eye_motion_df(l_disp, r_disp, error_list, r_config):
|
||||
"""
|
||||
Generating eye movement dataframe
|
||||
|
||||
Args:
|
||||
l_disp: displacement list(left eye); l_disp: displacement list(right eye)
|
||||
r_config: raw variable config file object
|
||||
|
||||
Reutrns:
|
||||
Final eye displacement dataframe
|
||||
"""
|
||||
df_eye_left = pd.DataFrame(l_disp, columns=[r_config.mov_eleft_disp])
|
||||
df_eye_right = pd.DataFrame(r_disp, columns=[r_config.mov_eright_disp])
|
||||
|
||||
df_eye_motion = pd.concat([df_eye_left, df_eye_right], axis=1, sort=False)
|
||||
df_eye_motion[r_config.err_reason] = error_list
|
||||
return df_eye_motion
|
||||
|
||||
def filter_motion(df_of, df_disp, col_l, col_r, r_config):
|
||||
"""
|
||||
Filtering final eye movement dataframe
|
||||
|
||||
Args:
|
||||
df_of: Openface raw out dataframe; col_r: right eye column
|
||||
col_l: left eye column; r_config: raw variable config file object
|
||||
"""
|
||||
|
||||
df_of = df_of[col_l + col_r + [' confidence']]
|
||||
df_of.loc[(df_of[' confidence'].astype(float) < 0.8), col_l + col_r] = np.nan
|
||||
|
||||
df_filter = df_of[col_l + col_r]
|
||||
df_filter.columns = [r_config.mov_leye_x, r_config.mov_leye_y, r_config.mov_leye_z,
|
||||
r_config.mov_reye_x, r_config.mov_reye_y, r_config.mov_reye_z]
|
||||
|
||||
df_motion = pd.concat([df_filter, df_disp], axis=1, sort=False)
|
||||
return df_motion
|
||||
|
||||
def eye_disp(of_results, col, r_config):
|
||||
"""
|
||||
Computing head velocity frame by frame
|
||||
|
||||
Args:
|
||||
of_results: Openface raw out dataframe
|
||||
r_config: Face config file object
|
||||
|
||||
Reutrns:
|
||||
Final head velocity frame by frame output
|
||||
"""
|
||||
distance_list = []
|
||||
error_list = []
|
||||
|
||||
of_results = of_results[col+ [' confidence']]
|
||||
for index, row in of_results.iterrows():
|
||||
dst = np.nan
|
||||
|
||||
if index == 0 or float(row[' confidence']) < 0.8: #Threshold < 0.8
|
||||
distance_list.append(dst)
|
||||
|
||||
if float(row[' confidence']) < 0.8:
|
||||
error_list.append('confidence less than 80%')
|
||||
|
||||
else:
|
||||
error_list.append('Pass')
|
||||
continue
|
||||
|
||||
if index > 0:
|
||||
|
||||
point_x = (of_results[col[0]][index-1], of_results[col[1]][index-1], of_results[col[2]][index-1])
|
||||
point_y = (row[col[0]],row[col[1]],row[col[2]])
|
||||
try:
|
||||
dst = distance.euclidean(point_x, point_y)
|
||||
except:
|
||||
pass
|
||||
|
||||
distance_list.append(abs(dst))
|
||||
error_list.append('Pass')
|
||||
|
||||
return distance_list, error_list
|
||||
|
||||
def calc_eye_mov(video_uri, df_of, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Computing eye motion variables
|
||||
Args:
|
||||
df_of: Openface dataframe
|
||||
out_loc: Output path for saving output csv's
|
||||
fl_name: file name for output csv
|
||||
r_config: raw variable config file object
|
||||
|
||||
"""
|
||||
|
||||
col_l = [ ' gaze_0_x', ' gaze_0_y', ' gaze_0_z']
|
||||
col_r = [ ' gaze_1_x', ' gaze_1_y', ' gaze_1_z']
|
||||
|
||||
gazel_disp, err_l = eye_disp(df_of, col_l, r_config)
|
||||
gazer_disp, err_r = eye_disp(df_of, col_r, r_config)
|
||||
|
||||
df_disp = eye_motion_df(gazel_disp, gazer_disp, err_l, r_config)
|
||||
df_disp['dbm_master_url'] = video_uri
|
||||
|
||||
df_motion = filter_motion(df_of, df_disp, col_l, col_r, r_config)
|
||||
ut.save_output(df_motion, out_loc, fl_name, eye_pose_dir, eye_pose_ext)
|
||||
|
||||
def run_eye_gaze(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patient's for getting eye movement artifacts
|
||||
--------------------------------
|
||||
--------------------------------
|
||||
Args:
|
||||
video_uri: video path; input_dir : input directory for video's
|
||||
out_dir: (str) Output directory for processed output; r_config: raw variable config object
|
||||
"""
|
||||
try:
|
||||
|
||||
#filtering path to generate input & output path
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + '_openface/*.csv'))
|
||||
|
||||
if len(of_csv_path)>0:
|
||||
|
||||
of_csv = of_csv_path[0]
|
||||
df_of = pd.read_csv(of_csv, error_bad_lines=False)
|
||||
|
||||
logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
|
||||
calc_eye_mov(video_uri, df_of, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
@@ -1,164 +0,0 @@
|
||||
import sys, os, glob, cv2, re
|
||||
import pickle, json
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import numpy.ma as ma
|
||||
import logging
|
||||
from os.path import join
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
from dbm_lib.dbm_features.raw_features.util.math_util import *
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.movement import DBMLIB_FTREMOR_CONFIG
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
ft_dir = 'movement/facial_tremor'
|
||||
csv_ext = '_fac_tremor.csv'
|
||||
model_ext = '_fac_model.csv'
|
||||
fac_features_ext = '_fac_features.csv'
|
||||
|
||||
def compute_features(out_dir, df_of, r_config):
|
||||
""" Computes features
|
||||
|
||||
Returns: features in vector format
|
||||
"""
|
||||
config = json.loads(open(DBMLIB_FTREMOR_CONFIG,'r').read())
|
||||
|
||||
pattern_x = re.compile("l\d+_x")
|
||||
pattern_y = re.compile("l\d+_y")
|
||||
|
||||
# assumption: distance of face to camera remains at roughly static
|
||||
|
||||
# logic break
|
||||
landmark_columns = []
|
||||
for col in df_of.columns:
|
||||
if pattern_x.match(col) or pattern_y.match(col):
|
||||
landmark_columns.append(col)
|
||||
|
||||
df_of= df_of[(df_of[landmark_columns]!= 0).any(axis=1)]
|
||||
df_of.reset_index(inplace=True)
|
||||
|
||||
num_frames = len(df_of)
|
||||
logger.info("Number of frames to be processed: {}".format(str(num_frames)))
|
||||
landmarks = config['landmarks']
|
||||
|
||||
try:
|
||||
if num_frames == 0:
|
||||
error_reason = "No frames with visible face."
|
||||
logger.error(error_reason)
|
||||
return empty_frame(landmarks, r_config, error_reason)
|
||||
|
||||
# if num_frames < 60:
|
||||
# error_reason = 'Number of frames with visible face < 60. Video too short'
|
||||
# logger.error(error_reason)
|
||||
# return empty_frame(landmarks, f_cfg, error_reason)
|
||||
|
||||
first_row = df_of.iloc[0]
|
||||
|
||||
facew = abs(first_row[config['face_width_left']] - first_row[config['face_width_right']])
|
||||
faceh = abs(first_row[config['face_height_left']] - first_row[config['face_height_right']])
|
||||
|
||||
if facew == 0 or faceh == 0:
|
||||
error_reason = 'face width or height = 0. Check landmark values'
|
||||
logger.error(error_reason)
|
||||
return empty_frame(landmarks, r_config)
|
||||
|
||||
fac_disp = calc_displacement_vec(df_of, landmarks, num_frames)
|
||||
|
||||
# if verbose:
|
||||
# logger.info("Displacement output: {}".format(str(fac_disp)))
|
||||
|
||||
fac_disp_median = np.median(fac_disp, axis = 1)
|
||||
fac_disp_mean = np.mean(fac_disp, axis = 1)
|
||||
|
||||
if len(fac_disp.shape)!=2:
|
||||
error_reason = 'fac_disp is not 2D. smth went wrong with disp calc'
|
||||
logger.error(error_reason)
|
||||
return empty_frame(landmarks, r_config, error_reason)
|
||||
|
||||
if len(fac_disp[0])<=1:
|
||||
error_reason = 'Video too short. smth went wrong with disp calc'
|
||||
logger.error(error_reason)
|
||||
return empty_frame(landmarks, r_config, error_reason)
|
||||
|
||||
fac_corr_mat = np.corrcoef(fac_disp, rowvar = True)
|
||||
# extract relevant row from cov matrix
|
||||
ref_lmk_index = [i for i, lmk in enumerate(landmarks) if config['ref_lmk']==lmk]
|
||||
fac_corr = fac_corr_mat[ref_lmk_index][0]
|
||||
|
||||
fac_area = config['ref_area'] / (facew * faceh)
|
||||
|
||||
# if verbose:
|
||||
# logger.info("Face area: {}".format(fac_area))
|
||||
# logger.info("Face Displacement Median: {}".format(str(fac_disp_median)))
|
||||
# logger.info("Face Displacement Mean: {}".format(str(fac_disp_mean)))
|
||||
|
||||
fac_features1 = np.multiply(fac_area * fac_disp_median, (1. - fac_corr))
|
||||
fac_features2 = np.multiply(fac_area * fac_disp_mean, (1. - fac_corr))
|
||||
|
||||
# base_fac_features = np.dot(fac_area * fac_disp_median, (1. - fac_corr))
|
||||
|
||||
fac_features_dict = {}
|
||||
for i, landmark in enumerate(landmarks):
|
||||
fac_features_dict['fac_features_mean_{}'.format(landmark)] = [fac_features2[i]]
|
||||
raw_variable_map = 'fac_tremor_median_{}'.format(landmark)
|
||||
fac_features_dict[r_config.base_raw['raw_feature'][raw_variable_map]] = [fac_features1[i]]
|
||||
|
||||
fac_features_dict['fac_disp_median_{}'.format(landmark)] = [fac_disp_median[i]]
|
||||
fac_features_dict['fac_corr_{}'.format(landmark)] = [fac_corr[i]]
|
||||
|
||||
fac_features_dict[r_config.err_reason] = ['']
|
||||
data = pd.DataFrame.from_dict(fac_features_dict)
|
||||
logger.info('Concluded computing tremor features')
|
||||
|
||||
return data
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Error computing tremor features: {}'.format(str(e)))
|
||||
return empty_frame(landmarks, r_config, str(e))
|
||||
|
||||
def empty_frame(landmarks, r_config, error_reason):
|
||||
fac_features_dict = {}
|
||||
for i, landmark in enumerate(landmarks):
|
||||
raw_variable_map = 'fac_tremor_median_{}'.format(landmark)
|
||||
fac_features_dict[r_config.base_raw['raw_feature'][raw_variable_map]] = [np.nan]
|
||||
|
||||
fac_features_dict['fac_features_mean_{}'.format(landmark)] = [np.nan]
|
||||
fac_features_dict['fac_disp_median_{}'.format(landmark)] = [np.nan]
|
||||
fac_features_dict['fac_corr_{}'.format(landmark)] = [np.nan]
|
||||
|
||||
fac_features_dict[r_config.err_reason] = [error_reason]
|
||||
empty_frame = pd.DataFrame.from_dict(fac_features_dict)
|
||||
return empty_frame
|
||||
|
||||
def fac_tremor_process(video_uri, out_dir, r_config, model_output=False):
|
||||
"""
|
||||
processing input videos
|
||||
|
||||
|
||||
"""
|
||||
# try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + '_openface_lmk/*.csv'))
|
||||
|
||||
if len(of_csv_path)>0:
|
||||
of_csv = of_csv_path[0]
|
||||
df_of = pd.read_csv(of_csv, error_bad_lines=False)
|
||||
|
||||
logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
|
||||
|
||||
feats = compute_features(of_csv_path , df_of, r_config)
|
||||
|
||||
# if model_output:
|
||||
# result = score(feats, r_config)
|
||||
# feats = pd.concat([feats, result], axis=1)
|
||||
|
||||
ut.save_output(feats, out_loc, fl_name, ft_dir, csv_ext)
|
||||
|
||||
|
||||
|
||||
# except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
@@ -1,196 +0,0 @@
|
||||
"""
|
||||
file_name: head_mov
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
import glob
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from scipy.spatial import distance
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
h_mov_dir = 'movement/head_movement'
|
||||
h_pose_dir = 'movement/head_pose'
|
||||
h_mov_ext = '_headmov.csv'
|
||||
h_pose_ext = '_headpose.csv'
|
||||
|
||||
def head_pose_dist(of_results):
|
||||
"""
|
||||
Computing head pose distance frame by frame
|
||||
|
||||
Args:
|
||||
of_results: Openface raw out dataframe
|
||||
f_nm_config: Face config file object
|
||||
|
||||
Reutrns:
|
||||
Final head pose distance frame by frame output
|
||||
"""
|
||||
distance_list = []
|
||||
error_list = []
|
||||
for index, row in of_results.iterrows():
|
||||
dst = np.nan
|
||||
|
||||
if index == 0 or float(row[' confidence']) < 0.2: #Threshold < 0.2
|
||||
distance_list.append(dst)
|
||||
|
||||
if float(row[' confidence']) < 0.2:
|
||||
error_list.append('confidence less than 20%')
|
||||
|
||||
else:
|
||||
error_list.append('Pass')
|
||||
continue
|
||||
|
||||
if index > 0:
|
||||
|
||||
point_x = (of_results[' pose_Rx'][index-1], of_results[' pose_Ry'][index-1], of_results[' pose_Rz'][index-1])
|
||||
point_y = (row[' pose_Rx'],row[' pose_Ry'],row[' pose_Rz'])
|
||||
try:
|
||||
dst = distance.euclidean(point_x, point_y)
|
||||
except:
|
||||
pass
|
||||
distance_list.append(abs(dst))
|
||||
error_list.append('Pass')
|
||||
return distance_list, error_list
|
||||
|
||||
def head_pose(of_results,r_config):
|
||||
"""
|
||||
Generating head pose estimation dataframe
|
||||
|
||||
Args:
|
||||
distance_val: distance list
|
||||
f_nm_config: raw variable config file object
|
||||
|
||||
Reutrns:
|
||||
Final head pose estimation dataframe
|
||||
"""
|
||||
pose_dist_list, error_list = head_pose_dist(of_results)
|
||||
of_results.loc[(of_results[' confidence'].astype(float) < 0.2), [' pose_Rx',' pose_Ry',' pose_Rz']] = np.nan
|
||||
pose_of = of_results[[' pose_Rx',' pose_Ry',' pose_Rz']]
|
||||
pose_of.columns = [r_config.mov_Hpose_Pitch, r_config.mov_Hpose_Yaw, r_config.mov_Hpose_Roll]
|
||||
pose_of[r_config.mov_Hpose_Dist] = pose_dist_list
|
||||
pose_of[r_config.err_reason] = error_list
|
||||
|
||||
return pose_of
|
||||
|
||||
def head_motion_df(distance_val, error_list, r_config):
|
||||
"""
|
||||
Generating head movement dataframe
|
||||
|
||||
Args:
|
||||
distance_val: distance list
|
||||
r_config: raw variable config file object
|
||||
|
||||
Reutrns:
|
||||
Final head velocity dataframe
|
||||
"""
|
||||
head_motion = r_config.head_vel
|
||||
df_head_motion = pd.DataFrame(distance_val, columns=[head_motion])
|
||||
df_head_motion['Frames'] = df_head_motion.index
|
||||
|
||||
new_df_intensity = df_head_motion[['Frames', head_motion]]
|
||||
new_df_intensity[r_config.err_reason] = error_list
|
||||
|
||||
return new_df_intensity
|
||||
|
||||
def head_vel(of_results, r_config):
|
||||
"""
|
||||
Computing head velocity frame by frame
|
||||
|
||||
Args:
|
||||
of_results: Openface raw out dataframe
|
||||
r_config: Face config file object
|
||||
|
||||
Reutrns:
|
||||
Final head velocity frame by frame output
|
||||
"""
|
||||
distance_list = []
|
||||
error_list = []
|
||||
for index, row in of_results.iterrows():
|
||||
dst = np.nan
|
||||
|
||||
if index == 0 or float(row[' confidence']) < 0.2: #Threshold < 0.2
|
||||
distance_list.append(dst)
|
||||
|
||||
if float(row[' confidence']) < 0.2:
|
||||
error_list.append('confidence less than 20%')
|
||||
|
||||
else:
|
||||
error_list.append('Pass')
|
||||
continue
|
||||
|
||||
if index > 0:
|
||||
|
||||
point_x = (of_results[' pose_Tx'][index-1], of_results[' pose_Ty'][index-1], of_results[' pose_Tz'][index-1])
|
||||
point_y = (row[' pose_Tx'],row[' pose_Ty'],row[' pose_Tz'])
|
||||
try:
|
||||
dst = distance.euclidean(point_x, point_y)
|
||||
except:
|
||||
pass
|
||||
|
||||
if abs(dst)>200:
|
||||
dst = np.nan
|
||||
error_list.append('Out of range')
|
||||
|
||||
else:
|
||||
error_list.append('Pass')
|
||||
distance_list.append(dst)
|
||||
df_velocity = head_motion_df(distance_list, error_list, r_config)
|
||||
|
||||
return df_velocity
|
||||
|
||||
def calc_head_mov(video_uri, df_of, out_loc, fl_name, r_config):
|
||||
"""
|
||||
Computing head motion and head pose variables
|
||||
Args:
|
||||
df_of: Openface dataframe
|
||||
out_loc: Output path for saving output csv's
|
||||
fl_name: file name for output csv
|
||||
r_config: raw variable config file object
|
||||
|
||||
"""
|
||||
|
||||
col = [' confidence',' pose_Rx',' pose_Ry',' pose_Rz',' pose_Tx', ' pose_Ty', ' pose_Tz']
|
||||
df_of = df_of[col]
|
||||
|
||||
df_hmotion = head_vel(df_of, r_config)
|
||||
df_hmotion['dbm_master_url'] = video_uri
|
||||
|
||||
df_pose = head_pose(df_of, r_config)
|
||||
df_pose['dbm_master_url'] = video_uri
|
||||
|
||||
ut.save_output(df_hmotion, out_loc, fl_name, h_mov_dir, h_mov_ext)
|
||||
ut.save_output(df_pose, out_loc, fl_name, h_pose_dir, h_pose_ext)
|
||||
|
||||
def run_head_movement(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patient's for getting movement artifacts for cdx_analysis workflow
|
||||
--------------------------------
|
||||
--------------------------------
|
||||
Args:
|
||||
video_uri: video path; input_dir : input directory for video's
|
||||
out_dir: (str) Output directory for processed output; r_config: raw variable config object
|
||||
"""
|
||||
try:
|
||||
|
||||
#filtering path to generate input & output path
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + '_openface/*.csv'))
|
||||
|
||||
|
||||
if len(of_csv_path)>0:
|
||||
|
||||
of_csv = of_csv_path[0]
|
||||
df_of = pd.read_csv(of_csv, error_bad_lines=False)
|
||||
|
||||
logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
|
||||
calc_head_mov(video_uri, df_of, out_loc, fl_name, r_config)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
@@ -1,94 +0,0 @@
|
||||
import pandas as pd
|
||||
import os
|
||||
import glob
|
||||
from os.path import join
|
||||
import parselmouth
|
||||
from parselmouth.praat import call, run_file
|
||||
import numpy as np
|
||||
import librosa
|
||||
import json
|
||||
import re
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
from dbm_lib.dbm_features.raw_features.movement import DBMLIB_VTREMOR_LIB
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
vt_dir = 'movement/voice_tremor'
|
||||
csv_ext = '_vtremor.csv'
|
||||
|
||||
#Executing praat script using parselmouth function
|
||||
def tremor_praat(snd_file,r_cfg):
|
||||
"""
|
||||
Generating Voice tremor endpoint dataframe
|
||||
Args:
|
||||
snd_file: (.wav) parsed audio file
|
||||
r_cfg: Raw variable configuration file
|
||||
Returns tremor endpoint dataframe
|
||||
"""
|
||||
snd = parselmouth.Sound(snd_file)
|
||||
tremor_var = run_file(snd,DBMLIB_VTREMOR_LIB,capture_output=True)
|
||||
new_tremor_var = re.sub('--undefined--', '0', tremor_var[1])
|
||||
res = json.loads(new_tremor_var)
|
||||
tremor_df = pd.DataFrame(res,index=['0',])
|
||||
tremor_df.columns = [r_cfg.mov_freq_trem_freq,r_cfg.mov_amp_trem_freq,r_cfg.mov_freq_trem_index,
|
||||
r_cfg.mov_amp_trem_index,r_cfg.mov_freq_trem_pindex,r_cfg.mov_amp_trem_pindex]
|
||||
return tremor_df
|
||||
|
||||
def prepare_vtrem_output(audio_file, out_loc, r_config, fl_name):
|
||||
"""
|
||||
Preparing voice tremor matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file ; r_config: raw config object
|
||||
out_loc: (str) Output directory for csv ; fl_name: file name
|
||||
"""
|
||||
df_tremor = tremor_praat(audio_file, r_config)
|
||||
df_tremor[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
|
||||
logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
|
||||
ut.save_output(df_tremor, out_loc, fl_name, vt_dir, csv_ext)
|
||||
|
||||
def prepare_empty_vt(out_loc, fl_name, r_config, error_txt):
|
||||
|
||||
"""
|
||||
Preparing empty voice tremor matrix
|
||||
"""
|
||||
cols = [r_config.mov_freq_trem_freq, r_config.mov_amp_trem_freq, r_config.mov_freq_trem_index,
|
||||
r_config.mov_amp_trem_index, r_config.mov_freq_trem_pindex, r_config.mov_amp_trem_pindex, r_config.err_reason]
|
||||
|
||||
out_val = [[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, error_txt]]
|
||||
df_tremor = pd.DataFrame(out_val, columns = cols)
|
||||
|
||||
logger.info('Saving Output file {} '.format(os.path.join(out_loc, fl_name)))
|
||||
ut.save_output(df_tremor, out_loc, fl_name, vt_dir, csv_ext)
|
||||
|
||||
def run_vtremor(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patient's for fetching Formant freq
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.5:
|
||||
logger.info('Output file {} size is less than 0.5sec'.format(audio_file))
|
||||
|
||||
error_txt = 'error: length less than 0.5 sec'
|
||||
prepare_empty_vt(video_uri, out_loc, fl_name, error_txt)
|
||||
return
|
||||
prepare_vtrem_output(audio_file, out_loc, r_config, fl_name)
|
||||
except Exception as e:
|
||||
logger.error('Failed to compute Voice Tremor {} for {}'.format(e,video_uri))
|
||||
prepare_empty_vt(out_loc, fl_name, r_config, e)
|
||||
@@ -1,51 +0,0 @@
|
||||
"""
|
||||
file_name: speech_features
|
||||
project_name: DBM
|
||||
created: 2020-13-11
|
||||
"""
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import glob
|
||||
from os.path import join
|
||||
import logging
|
||||
import shutil
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
from dbm_lib.dbm_features.raw_features.util import nlp_util as n_util
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
speech_dir = 'speech/speech_feature'
|
||||
speech_ext = '_nlp.csv'
|
||||
transcribe_ext = 'speech/deepspeech/*_transcribe.csv'
|
||||
|
||||
def run_speech_feature(video_uri, out_dir, r_config, tran_tog):
|
||||
"""
|
||||
Processing all patient's for fetching nlp features
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
|
||||
transcribe_path = glob.glob(join(out_loc, transcribe_ext))
|
||||
if len(transcribe_path)>0:
|
||||
|
||||
transcribe_df = pd.read_csv(transcribe_path[0])
|
||||
df_speech= n_util.process_speech(transcribe_df, r_config)
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_speech, out_loc, fl_name, speech_dir, speech_ext)
|
||||
|
||||
if (tran_tog == None) or (tran_tog != 'on'):
|
||||
shutil.rmtree(os.path.dirname(transcribe_path[0]))
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
@@ -1,84 +0,0 @@
|
||||
"""
|
||||
file_name: transcribe
|
||||
project_name: DBM
|
||||
created: 2020-10-11
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import librosa
|
||||
import glob
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
from dbm_lib.dbm_features.raw_features.util import nlp_util as n_util
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
formant_dir = 'speech/deepspeech'
|
||||
csv_ext = '_transcribe.csv'
|
||||
error_txt = 'error: length less than 0.1'
|
||||
|
||||
def calc_transcribe(video_uri, audio_file, out_loc, fl_name, r_config, deep_path, aud_dur):
|
||||
"""
|
||||
Preparing Formant freq matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file; fl_name: input file name
|
||||
out_loc: (str) Output directory; r_config: raw variable config
|
||||
"""
|
||||
|
||||
text = n_util.process_deepspeech(audio_file, deep_path)
|
||||
df_formant = pd.DataFrame([text], columns=[r_config.nlp_transcribe])
|
||||
|
||||
df_formant.replace('', np.nan, regex=True,inplace=True)
|
||||
df_formant[r_config.nlp_totalTime] = aud_dur
|
||||
df_formant[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
df_formant['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_formant, out_loc, fl_name, formant_dir, csv_ext)
|
||||
|
||||
def empty_transcribe(video_uri, out_loc, fl_name, r_config):
|
||||
|
||||
"""
|
||||
Preparing empty formant frequency matrix if something fails
|
||||
"""
|
||||
cols = [r_config.nlp_transcribe, r_config.nlp_totalTime, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_fm = pd.DataFrame(out_val, columns = cols)
|
||||
df_fm['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
ut.save_output(df_fm, out_loc, fl_name, formant_dir, csv_ext)
|
||||
|
||||
def run_transcribe(video_uri, out_dir, r_config, deep_path):
|
||||
|
||||
"""
|
||||
Processing all patient's for fetching Formant freq
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output; deep_path: deepspeech build path
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
|
||||
if float(aud_dur) < 0.1:
|
||||
logger.info('Output file {} size is less than 0.1 sec'.format(audio_file))
|
||||
|
||||
empty_transcribe(video_uri, out_loc, fl_name, r_config)
|
||||
return
|
||||
|
||||
calc_transcribe(video_uri, audio_file, out_loc, fl_name, r_config, deep_path, aud_dur)
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
|
||||
@@ -1,212 +0,0 @@
|
||||
"""
|
||||
file_name: nlp_util
|
||||
project_name: DBM
|
||||
created: 2020-10-11
|
||||
"""
|
||||
|
||||
import subprocess
|
||||
import json
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import os
|
||||
import logging
|
||||
|
||||
import nltk
|
||||
import re
|
||||
from lexicalrichness import LexicalRichness
|
||||
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
#Speech to text using Deepspeech 0.9.1
|
||||
def deepspeech(AUDIO_FILE,deep_path):
|
||||
"""
|
||||
Extracting text from audio using Deep Speech neural network trained model
|
||||
Returns:
|
||||
Text: text which is extracted from audio
|
||||
"""
|
||||
api = 'deepspeech'
|
||||
arg_speech0 = '--model'
|
||||
arg_speech_path0 = os.path.join(deep_path, 'deepspeech-0.9.1-models.pbmm')
|
||||
arg_speech1 = '--scorer'
|
||||
arg_speech_path1 = os.path.join(deep_path, 'deepspeech-0.9.1-models.scorer')
|
||||
arg_audio = "--audio"
|
||||
|
||||
out = subprocess.Popen([api, arg_speech0, arg_speech_path0, arg_speech1, arg_speech_path1, arg_audio, AUDIO_FILE],
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT)
|
||||
logger.info('Deepspeech output...... {}'.format(out))
|
||||
try:
|
||||
stdout,stderr = out.communicate()
|
||||
except:
|
||||
return "error", "error"
|
||||
#print(stderr)
|
||||
return stdout,stderr
|
||||
|
||||
def deep_speech_output_clean(result):
|
||||
"""
|
||||
Parsing deep speech output(text)
|
||||
Return:
|
||||
Text from speech
|
||||
"""
|
||||
text = ""
|
||||
if len(result)>0:
|
||||
res_split = str(result[0]).split('\\n')
|
||||
|
||||
if len(res_split)>0:
|
||||
for i in range(len(res_split)):
|
||||
if 'Inference took' in res_split[i]:
|
||||
text = res_split[i + 1]
|
||||
return text
|
||||
return text
|
||||
|
||||
def process_deepspeech(audio_file,deep_path):
|
||||
"""
|
||||
Transcribing audio to extract text from speech
|
||||
"""
|
||||
deep_output = deepspeech(audio_file,deep_path)
|
||||
deep_text= deep_speech_output_clean(deep_output)
|
||||
|
||||
return deep_text
|
||||
|
||||
def nltk_download():
|
||||
|
||||
try:
|
||||
nltk.data.find('tokenizers/punkt')
|
||||
|
||||
except LookupError:
|
||||
logger.info('punkt is not available')
|
||||
nltk.download('punkt')
|
||||
|
||||
try:
|
||||
nltk.data.find('averaged_perceptron_tagger')
|
||||
|
||||
except LookupError:
|
||||
logger.info('averaged_perceptron_tagger is not available')
|
||||
nltk.download('averaged_perceptron_tagger')
|
||||
|
||||
def empty_speech(r_config, master_url, error_txt):
|
||||
"""
|
||||
Preparing empty speech matrix with error
|
||||
Args:
|
||||
r_config: raw config file object
|
||||
error_txt: Error message during transcription
|
||||
|
||||
Returns:
|
||||
Empty dataframe for speech features with error
|
||||
"""
|
||||
|
||||
col = [r_config.nlp_numSentences, r_config.nlp_singPronPerAns, r_config.nlp_singPronPerSen, r_config.nlp_pastTensePerAns,
|
||||
r_config.nlp_pastTensePerSen, r_config.nlp_pronounsPerAns, r_config.nlp_pronounsPerSen, r_config.nlp_verbsPerAns,
|
||||
r_config.nlp_verbsPerSen, r_config.nlp_adjectivesPerAns, r_config.nlp_adjectivesPerSen, r_config.nlp_nounsPerAns,
|
||||
r_config.nlp_nounsPerSen, r_config.nlp_sentiment_mean, r_config.nlp_mattr, r_config.nlp_wordsPerMin,
|
||||
r_config.nlp_totalTime, r_config.err_reason]
|
||||
|
||||
df_speech = pd.DataFrame([[np.nan] * len(col) + [error_txt]], columns = col)
|
||||
df_speech['dbm_master_url'] = master_url
|
||||
|
||||
return df_speech
|
||||
|
||||
def divide_var(speech_var1, spech_var2):
|
||||
"""
|
||||
divide variables
|
||||
"""
|
||||
speech_var = np.nan
|
||||
if spech_var2!=0:
|
||||
speech_var = speech_var1/spech_var2
|
||||
return speech_var
|
||||
|
||||
def process_speech(transcribe_df,r_config):
|
||||
"""
|
||||
Preparing speech features
|
||||
Args:
|
||||
transcribe_df: Transcribed dataframe
|
||||
r_config: raw config file object
|
||||
Returns:
|
||||
Dataframe for speech features
|
||||
"""
|
||||
transcribe_df = transcribe_df.replace(np.nan, '', regex=True)
|
||||
err_transcribe = transcribe_df[r_config.err_reason].iloc[0]
|
||||
transcribe = transcribe_df[r_config.nlp_transcribe].iloc[0]
|
||||
total_time = transcribe_df[r_config.nlp_totalTime].iloc[0]
|
||||
master_url = transcribe_df['dbm_master_url'].iloc[0]
|
||||
|
||||
#clean transcribe
|
||||
transcribe = transcribe.replace(",", "")
|
||||
transcribe = " ".join(re.findall(r"[\w']+|[.!?]", transcribe))
|
||||
|
||||
if err_transcribe != 'Pass':
|
||||
df_speech = empty_speech(r_config, master_url, error_txt)
|
||||
|
||||
return df_speech
|
||||
|
||||
speech_dict = {}
|
||||
nltk_download()
|
||||
|
||||
sentences = nltk.tokenize.sent_tokenize(transcribe)
|
||||
words_all = nltk.tokenize.word_tokenize(transcribe)
|
||||
num_sentences = len(sentences)
|
||||
|
||||
speech_dict[r_config.nlp_numSentences] = num_sentences
|
||||
|
||||
#nlp_singPron
|
||||
i_s = transcribe.count('I')
|
||||
me_s = transcribe.count('me')
|
||||
my_s = transcribe.count('my')
|
||||
sing_count = i_s + me_s + my_s
|
||||
|
||||
speech_dict[r_config.nlp_singPronPerAns] = sing_count if len(words_all)>0 else np.nan
|
||||
speech_dict[r_config.nlp_singPronPerSen] = divide_var(speech_dict[r_config.nlp_singPronPerAns], num_sentences)
|
||||
|
||||
tagged = nltk.pos_tag(transcribe.split())
|
||||
tagged_df = pd.DataFrame(tagged, columns=['word', 'pos_tag'])
|
||||
|
||||
#Past tense per answer
|
||||
all_POSs = tagged_df['pos_tag'].tolist()
|
||||
speech_dict[r_config.nlp_pastTensePerAns] = all_POSs.count('VBD') if len(words_all)>0 else np.nan
|
||||
speech_dict[r_config.nlp_pastTensePerSen] = divide_var(speech_dict[r_config.nlp_pastTensePerAns], num_sentences)
|
||||
|
||||
#Pronoun per answer
|
||||
pronounsPerAns = all_POSs.count('PRP') + all_POSs.count('PRP$')
|
||||
speech_dict[r_config.nlp_pronounsPerAns] = pronounsPerAns if len(words_all)>0 else np.nan
|
||||
speech_dict[r_config.nlp_pronounsPerSen] = divide_var(speech_dict[r_config.nlp_pronounsPerAns], num_sentences)
|
||||
|
||||
#Verb per answer
|
||||
verbPerAns = all_POSs.count('VB') + all_POSs.count('VBD') + all_POSs.count('VBG') \
|
||||
+ all_POSs.count('VBN') + all_POSs.count('VBP') + all_POSs.count('VBZ')
|
||||
speech_dict[r_config.nlp_verbsPerAns] = verbPerAns if len(words_all) > 0 else np.nan
|
||||
speech_dict[r_config.nlp_verbsPerSen] = divide_var(speech_dict[r_config.nlp_verbsPerAns], num_sentences)
|
||||
|
||||
#Adjective per answer
|
||||
adjectivesAns = all_POSs.count('JJ') + all_POSs.count('JJR') + all_POSs.count('JJS')
|
||||
speech_dict[r_config.nlp_adjectivesPerAns] = adjectivesAns if len(words_all) > 0 else np.nan
|
||||
speech_dict[r_config.nlp_adjectivesPerSen] = divide_var(speech_dict[r_config.nlp_adjectivesPerAns], num_sentences)
|
||||
|
||||
#Noun per answer
|
||||
nounsAns = all_POSs.count('NN') + all_POSs.count('NNP') + all_POSs.count('NNS')
|
||||
speech_dict[r_config.nlp_nounsPerAns] = nounsAns if len(words_all) > 0 else np.nan
|
||||
speech_dict[r_config.nlp_nounsPerSen] = divide_var(speech_dict[r_config.nlp_nounsPerAns], num_sentences)
|
||||
|
||||
#Sentiment analysis
|
||||
vader = SentimentIntensityAnalyzer()
|
||||
sentence_valences = []
|
||||
|
||||
for s in sentences:
|
||||
sentiment_dict = vader.polarity_scores(s)
|
||||
sentence_valences.append(sentiment_dict['compound'])
|
||||
|
||||
speech_dict[r_config.nlp_sentiment_mean] = np.mean(sentence_valences) if len(sentence_valences) > 0 else np.nan
|
||||
non_punc = list(value for value in words_all if value not in ['.','!','?'])
|
||||
|
||||
non_punc_as_str = " ".join(str(non_punc))
|
||||
lex = LexicalRichness(non_punc_as_str)
|
||||
speech_dict[r_config.nlp_mattr] = lex.mattr(window_size=lex.words) if lex.words > 0 else np.nan
|
||||
|
||||
#Number of words per minute
|
||||
speech_dict[r_config.nlp_wordsPerMin] = divide_var(len(non_punc), total_time)*60
|
||||
speech_dict[r_config.nlp_totalTime] = total_time
|
||||
speech_dict['dbm_master_url'] = master_url
|
||||
|
||||
df_speech = pd.DataFrame([speech_dict])
|
||||
return df_speech
|
||||
@@ -1,112 +0,0 @@
|
||||
"""
|
||||
file_name: util
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
import glob
|
||||
import numpy as np
|
||||
import subprocess
|
||||
|
||||
def filter_path(video_url, out_dir):
|
||||
|
||||
"""
|
||||
Filtering video uri path to prepare input and ouptut location
|
||||
|
||||
Args:
|
||||
video_url: S3 bucket path for video
|
||||
out_dir: Output directory path
|
||||
|
||||
"""
|
||||
|
||||
fl_name,_ = os.path.splitext(os.path.basename(video_url))
|
||||
input_loc = os.path.dirname(video_url)
|
||||
out_loc = os.path.join(out_dir, fl_name)
|
||||
return input_loc, out_loc, fl_name
|
||||
|
||||
def save_output(df, out_loc, fl_name, f_dir, f_ext):
|
||||
"""
|
||||
creating output directory for Audio features
|
||||
Args:
|
||||
df: (dataframe) feature dataframe[ex: Formant freq, pitch]
|
||||
out_loc: (dir) Output location where we want to save raw output
|
||||
fl_name: file name
|
||||
f_dir: directory name for a feature
|
||||
f_ext: extension for a feature [ex: '_pose.csv']
|
||||
"""
|
||||
full_f_name = fl_name + f_ext
|
||||
dir_path = os.path.join(out_loc, f_dir)
|
||||
|
||||
if not os.path.exists(dir_path):
|
||||
os.makedirs(dir_path)
|
||||
|
||||
sav_path = os.path.join(dir_path,full_f_name)
|
||||
df.to_csv(sav_path, index=False)
|
||||
|
||||
def audio_process(base_dir,video_url):
|
||||
"""
|
||||
Parsing cleaned audio files(Audio files without IMA voice)
|
||||
Args:
|
||||
base_dir: Base path for raw data
|
||||
video_url: Raw video file path
|
||||
"""
|
||||
new_video_url = base_dir+'/'.join(video_url[2:])
|
||||
split_val = new_video_url.split('/')
|
||||
wav_path = '/'.join(split_val[0:len(split_val)-1])
|
||||
audio_split_check = glob.glob(wav_path + '/*_split.wav')
|
||||
return audio_split_check
|
||||
|
||||
def compute_open_face_features(input_filepath,
|
||||
output_directory,
|
||||
open_face_executable,
|
||||
au_static=False,
|
||||
tracked_visualization=False,
|
||||
clobber=False,
|
||||
verbose=True):
|
||||
"""
|
||||
Runs OpenFace on an input video.
|
||||
See https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments
|
||||
Args:
|
||||
input_filepath:
|
||||
output_directory:
|
||||
au_static:
|
||||
tracked_visualization:
|
||||
open_face_executable:
|
||||
clobber: (bool) if True existing files will be overwritten
|
||||
verbose:
|
||||
Returns:
|
||||
(str) path to output csv file
|
||||
Raises:
|
||||
IOError if OpenFace executable is missing
|
||||
"""
|
||||
|
||||
if not os.path.isfile(open_face_executable):
|
||||
raise IOError("OpenFace executable {} could not be found.".format(open_face_executable))
|
||||
|
||||
bn, _ = os.path.splitext(os.path.basename(input_filepath))
|
||||
if not output_directory:
|
||||
output_directory = os.path.join(os.path.dirname(input_filepath), bn + '_openface')
|
||||
|
||||
output_csv = os.path.join(output_directory, bn + '.csv')
|
||||
if not os.path.isfile(output_csv) or clobber:
|
||||
call = [open_face_executable, ]
|
||||
if au_static:
|
||||
call += ['-au_static', ]
|
||||
|
||||
if tracked_visualization:
|
||||
call += ['-tracked', ]
|
||||
|
||||
call += ['-q', '-2Dfp', '-3Dfp', '-pdmparams', '-pose', '-aus', '-gaze']
|
||||
call += ['-f', input_filepath, '-out_dir', output_directory]
|
||||
|
||||
if verbose:
|
||||
print('Computing OpenFace features {} from video file'.format(input_filepath))
|
||||
subprocess.check_output(call)
|
||||
if verbose:
|
||||
print('OpenFace features saved to {}'.format(output_directory))
|
||||
else:
|
||||
if verbose:
|
||||
print('Output file {} already exists'.format(output_csv))
|
||||
|
||||
return os.path.join(output_directory, bn + '.csv')
|
||||
@@ -1,190 +0,0 @@
|
||||
"""
|
||||
file_name: video_util
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import glob
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
def smooth(x,window_len=11,window='hanning'):
|
||||
"""smooth the data using a window with requested size.
|
||||
|
||||
This method is based on the convolution of a scaled window with the signal.
|
||||
The signal is prepared by introducing reflected copies of the signal
|
||||
(with the window size) in both ends so that transient parts are minimized
|
||||
in the begining and end part of the output signal.
|
||||
|
||||
input:
|
||||
x: the input signal
|
||||
window_len: the dimension of the smoothing window; should be an odd integer
|
||||
window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
|
||||
flat window will produce a moving average smoothing.
|
||||
|
||||
output:
|
||||
the smoothed signal
|
||||
|
||||
example:
|
||||
|
||||
t=linspace(-2,2,0.1)
|
||||
x=sin(t)+randn(len(t))*0.1
|
||||
y=smooth(x)
|
||||
|
||||
see also:
|
||||
|
||||
numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
|
||||
scipy.signal.lfilter
|
||||
|
||||
TODO: the window parameter could be the window itself if an array instead of a string
|
||||
NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.
|
||||
"""
|
||||
if x.ndim != 1:
|
||||
raise (ValueError, "smooth only accepts 1 dimension arrays.")
|
||||
if x.size < window_len:
|
||||
raise (ValueError, "Input vector needs to be bigger than window size.")
|
||||
if window_len<3:
|
||||
return x
|
||||
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
|
||||
raise (ValueError, "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
|
||||
s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]
|
||||
#print(len(s))
|
||||
if window == 'flat': #moving average
|
||||
w=np.ones(window_len,'d')
|
||||
else:
|
||||
w=eval('np.'+window+'(window_len)')
|
||||
y=np.convolve(w/w.sum(),s,mode='valid')
|
||||
return y[int(window_len/2):-int(window_len/2)]
|
||||
|
||||
def filter_by_confidence_and_thresh(x, fea, thresh):
|
||||
if x['s_confidence'] > 0.2 and np.fabs(x[fea]) < thresh:
|
||||
return x[fea]
|
||||
else:
|
||||
return np.NaN
|
||||
|
||||
def add_au_emotion(x, emotion,emotion_type,exp_type):
|
||||
"""
|
||||
computing individula emotion expressivity matrix
|
||||
Args:
|
||||
emotion: Action Unit
|
||||
"""
|
||||
error_reason = 'Pass'
|
||||
if x['s_confidence'] > 0.8: #if using smooth, no need for 'success'
|
||||
sum_r = 0
|
||||
cnt = 0
|
||||
for au in emotion:
|
||||
au_c_label = " AU{:02d}_c".format(au)
|
||||
au_r_label = " AU{:02d}_r".format(au)
|
||||
if x[au_c_label]==1 and (not np.isnan(x[au_r_label])): #there are data with face in, but au_c=0
|
||||
sum_r += x[au_r_label]
|
||||
cnt += 6
|
||||
if exp_type=='full' and x[au_c_label]==0: #Logic to compute emotion expressivity when all AU's are present
|
||||
cnt = 0
|
||||
break
|
||||
if cnt > 0:
|
||||
sum_r /= cnt
|
||||
else:
|
||||
sum_r = 0
|
||||
v_emo = x[emotion_type] + sum_r
|
||||
else:
|
||||
v_emo = np.NaN
|
||||
error_reason = 'confidence less than 80%'
|
||||
|
||||
return v_emo, error_reason
|
||||
|
||||
def add_au_occ(x, emotion,emotion_type):
|
||||
"""
|
||||
computing individula emotion presence
|
||||
Args:
|
||||
emotion: Action Unit
|
||||
"""
|
||||
au_pres = []
|
||||
em_pres = 0
|
||||
error_reason = 'Pass'
|
||||
if x['s_confidence'] > 0.8: #if using smooth, no need for 'success'
|
||||
for au in emotion:
|
||||
au_c_label = " AU{:02d}_c".format(au)
|
||||
if x[au_c_label]==1: #there are data with face in, but au_c=0
|
||||
au_pres.append(1)
|
||||
|
||||
if len(au_pres) == len(emotion):
|
||||
em_pres = 1
|
||||
else:
|
||||
em_pres = np.NaN
|
||||
error_reason = 'confidence less than 80%'
|
||||
return em_pres, error_reason
|
||||
|
||||
def emotion_exp(em_au,of,em_col,err_col):
|
||||
"""
|
||||
Computing individual emotion expressivity and adding it to dataframe
|
||||
"""
|
||||
for emotion in em_au:
|
||||
of[[em_col[0],err_col]]=of.apply(add_au_emotion, args=(emotion,em_col[0],'partial',), axis=1, result_type='expand')
|
||||
of[[em_col[1],err_col]]=of.apply(add_au_emotion, args=(emotion,em_col[1],'full',), axis=1, result_type='expand')
|
||||
|
||||
def emotion_pres(em_au,of,em_col,err_col):
|
||||
"""
|
||||
Computing individual emotion expressivity and adding it to dataframe
|
||||
"""
|
||||
for emotion in em_au:
|
||||
of[[em_col,err_col]]=of.apply(add_au_occ, args=(emotion,em_col,), axis=1, result_type='expand')
|
||||
|
||||
def calc_of_for_video(of,face_cfg,fe_cfg):
|
||||
"""
|
||||
Creating dataframe for emotion expressivity
|
||||
"""
|
||||
new_cols = [fe_cfg.hap_exp,fe_cfg.sad_exp,fe_cfg.sur_exp,fe_cfg.fea_exp,fe_cfg.ang_exp,fe_cfg.dis_exp,fe_cfg.con_exp,
|
||||
fe_cfg.pai_exp,fe_cfg.neg_exp,fe_cfg.pos_exp,fe_cfg.neu_exp,fe_cfg.com_lower_exp,fe_cfg.com_upper_exp,
|
||||
fe_cfg.cai_exp,fe_cfg.com_exp,fe_cfg.happ_occ,fe_cfg.sad_occ,fe_cfg.sur_occ,fe_cfg.fea_occ,fe_cfg.ang_occ,
|
||||
fe_cfg.dis_occ,fe_cfg.con_occ,fe_cfg.hap_exp_full,fe_cfg.sad_exp_full,fe_cfg.sur_exp_full,fe_cfg.fea_exp_full,
|
||||
fe_cfg.ang_exp_full,fe_cfg.dis_exp_full,fe_cfg.con_exp_full,fe_cfg.pai_exp_full,fe_cfg.neg_exp_full,
|
||||
fe_cfg.pos_exp_full,fe_cfg.neu_exp_full,fe_cfg.cai_exp_full,fe_cfg.com_lower_exp_full,fe_cfg.com_upper_exp_full,
|
||||
fe_cfg.com_exp_full]
|
||||
of[new_cols] = pd.DataFrame([[0] * len(new_cols)], index=of.index)
|
||||
of[fe_cfg.err_reason] = 'Pass'
|
||||
|
||||
#Composite happiness expressivity
|
||||
emotion_exp(face_cfg.happiness,of,[fe_cfg.hap_exp,fe_cfg.hap_exp_full],fe_cfg.err_reason)
|
||||
#Composite sadness expressivity
|
||||
emotion_exp(face_cfg.sadness,of,[fe_cfg.sad_exp,fe_cfg.sad_exp_full],fe_cfg.err_reason)
|
||||
#Composite surprise expressivity
|
||||
emotion_exp(face_cfg.surprise,of,[fe_cfg.sur_exp,fe_cfg.sur_exp_full],fe_cfg.err_reason)
|
||||
#Composite fear expressivity
|
||||
emotion_exp(face_cfg.fear,of,[fe_cfg.fea_exp,fe_cfg.fea_exp_full],fe_cfg.err_reason)
|
||||
#Composite anger expressivity
|
||||
emotion_exp(face_cfg.anger,of,[fe_cfg.ang_exp,fe_cfg.ang_exp_full],fe_cfg.err_reason)
|
||||
#Composite disgust expressivity
|
||||
emotion_exp(face_cfg.disgust,of,[fe_cfg.dis_exp,fe_cfg.dis_exp_full],fe_cfg.err_reason)
|
||||
#Composite contempt expressivity
|
||||
emotion_exp(face_cfg.contempt,of,[fe_cfg.con_exp,fe_cfg.con_exp_full],fe_cfg.err_reason)
|
||||
#Composite Negative Expressivity
|
||||
emotion_exp(face_cfg.NEG_ACTION_UNITS,of,[fe_cfg.neg_exp,fe_cfg.neg_exp_full],fe_cfg.err_reason)
|
||||
#Composite Positive Expressivity
|
||||
emotion_exp(face_cfg.POS_ACTION_UNITS,of,[fe_cfg.pos_exp,fe_cfg.pos_exp_full],fe_cfg.err_reason)
|
||||
#Composite Neutral Expressivity
|
||||
emotion_exp(face_cfg.NET_ACTION_UNITS,of,[fe_cfg.neu_exp,fe_cfg.neu_exp_full],fe_cfg.err_reason)
|
||||
#Composite Activation Expressivity
|
||||
emotion_exp(face_cfg.cai,of,[fe_cfg.cai_exp,fe_cfg.cai_exp_full],fe_cfg.err_reason)
|
||||
#Composite Expressivity
|
||||
emotion_exp(face_cfg.ACTION_UNITS,of,[fe_cfg.com_exp,fe_cfg.com_exp_full],fe_cfg.err_reason)
|
||||
#Composite lower face expressivity
|
||||
emotion_exp(face_cfg.LOWER_ACTION_UNITS,of,[fe_cfg.com_lower_exp,fe_cfg.com_lower_exp_full],fe_cfg.err_reason)
|
||||
#Composite upper face Expressivity
|
||||
emotion_exp(face_cfg.UPPER_ACTION_UNITS,of,[fe_cfg.com_upper_exp,fe_cfg.com_upper_exp_full],fe_cfg.err_reason)
|
||||
#Composite pain expressivity
|
||||
emotion_exp(face_cfg.pain,of,[fe_cfg.pai_exp,fe_cfg.pai_exp_full],fe_cfg.err_reason)
|
||||
#AU happiness presence
|
||||
emotion_pres(face_cfg.happiness,of,fe_cfg.happ_occ,fe_cfg.err_reason)
|
||||
#AU Sad presence
|
||||
emotion_pres(face_cfg.sadness,of,fe_cfg.sad_occ,fe_cfg.err_reason)
|
||||
#AU Surprise presence
|
||||
emotion_pres(face_cfg.surprise,of,fe_cfg.sur_occ,fe_cfg.err_reason)
|
||||
#AU fear presence
|
||||
emotion_pres(face_cfg.fear,of,fe_cfg.fea_occ,fe_cfg.err_reason)
|
||||
#AU anger presence
|
||||
emotion_pres(face_cfg.anger,of,fe_cfg.ang_occ,fe_cfg.err_reason)
|
||||
#AU disgust presence
|
||||
emotion_pres(face_cfg.disgust,of,fe_cfg.dis_occ,fe_cfg.err_reason)
|
||||
#AU contempt presence
|
||||
emotion_pres(face_cfg.contempt,of,fe_cfg.con_occ,fe_cfg.err_reason)
|
||||
@@ -1,14 +0,0 @@
|
||||
"""
|
||||
file_name: __init__
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
|
||||
DBMLIB_PATH = os.path.dirname(__file__)
|
||||
DBMLIB_FACE_CONFIG = os.path.abspath(os.path.join(DBMLIB_PATH, '../../../../resources/services/face_util.yml'))
|
||||
@@ -1,355 +0,0 @@
|
||||
"""
|
||||
file_name: face_asymmetry.py
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
from mpl_toolkits import mplot3d
|
||||
from matplotlib import pyplot as plt
|
||||
import time
|
||||
import numpy as np
|
||||
import os
|
||||
import datetime
|
||||
import glob
|
||||
import cv2
|
||||
from scipy.spatial.transform import Rotation as R
|
||||
import subprocess
|
||||
import pandas as pd
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.video.face_config.face_config_reader import ConfigFaceReader
|
||||
from dbm_lib.dbm_features.raw_features.util import video_util as vu
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
face_asym_dir = 'facial/face_asymmetry'
|
||||
csv_ext = '_facasym.csv'
|
||||
|
||||
cv2_color_purple = (254,19,188)
|
||||
color_blue = (0,0,1.0)
|
||||
color_green = (0,1.0,0)
|
||||
color_red = (1.0,0,0)
|
||||
color_y = (1.0,1.0,0)
|
||||
|
||||
error_code_message = {
|
||||
0: 'pass',
|
||||
1: 'confidence less than 80%',
|
||||
}
|
||||
error_message_code = {y:x for x,y in error_code_message.items()}
|
||||
|
||||
def visualize_vid(fn, attr=None, write_out=False):
|
||||
|
||||
vid = cv2.VideoCapture(fn)
|
||||
tot = int(vid.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
fps = vid.get(cv2.CAP_PROP_FPS)
|
||||
frame_width = int(vid.get(3))
|
||||
frame_height = int(vid.get(4))
|
||||
|
||||
if write_out:
|
||||
fig_w = 680 #680 667 676 #frame_width in order of Ali, Vennessa, synthesis
|
||||
fig_h = 659 #659 659 659 #frame_height
|
||||
out_vid = cv2.VideoWriter('out.mp4',cv2.VideoWriter_fourcc(*'MP4V'), fps, (fig_w,fig_h))
|
||||
|
||||
plt.figure(figsize=(8, 8))
|
||||
try:
|
||||
frameid = 0
|
||||
while(True):
|
||||
ret, frame = vid.read()
|
||||
if not ret:
|
||||
# Release the Video Device if ret is false
|
||||
vid.release()
|
||||
print('Released Video Resource')
|
||||
break
|
||||
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
frameid += 1
|
||||
logger.info(frameid, frame.shape)
|
||||
|
||||
if 'lmks_frms' in attr:
|
||||
lmks_frms = attr['lmks_frms']
|
||||
for i in range(lmks_frms[frameid].shape[0]):
|
||||
cv2.circle(frame,(int(lmks_frms[frameid][i,0]),int(lmks_frms[frameid][i,1])), 2, cv2_color_purple, -1)
|
||||
|
||||
if write_out:
|
||||
cv2.putText(frame,'Frame: '+str(frameid), (10,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 3)
|
||||
|
||||
plt.subplot(211)
|
||||
plt.imshow(frame)
|
||||
plt.axis('off'); plt.pause(0.2);
|
||||
|
||||
if 'score_asym' in attr:
|
||||
ax = plt.subplot(212)
|
||||
ax.cla()
|
||||
ax.set_xlim(0,140) #ax.set_xlim(0,300)
|
||||
ax.set_ylim(0,10)
|
||||
|
||||
sa = attr['score_asym']
|
||||
s = sa[np.where(sa[:,0] <= frameid),:][0,:,:]
|
||||
|
||||
for i in range(1,s.shape[1]):
|
||||
plt.plot(s[:,0], s[:,i])
|
||||
|
||||
plt.legend(['mouth', 'eyebrow', 'eye', 'mouth+eye+eyebrow'])
|
||||
plt.minorticks_on()
|
||||
plt.grid(b=True, which='major', color='r', linestyle='-')
|
||||
plt.grid(b=True, which='minor', color='r', linestyle='--')
|
||||
|
||||
plt.savefig('tmp.png', bbox_inches='tight')
|
||||
print(cv2.imread('tmp.png').shape)
|
||||
|
||||
plt.clf()
|
||||
if write_out:
|
||||
out_vid.write(cv2.imread('tmp.png'))
|
||||
|
||||
except KeyboardInterrupt:
|
||||
# Release the Video Device
|
||||
vid.release()
|
||||
if write_out:
|
||||
out_vid.release()
|
||||
logger.info('Exception, and Video Resource Released')
|
||||
|
||||
if write_out:
|
||||
out_vid.release()
|
||||
|
||||
|
||||
def retrieve_attr(of_df):
|
||||
'''
|
||||
Retrieve landmarks and pose_translation for each frame from openface output
|
||||
Args:
|
||||
of_df: dataframe output from openface, including detected landmark coordinates
|
||||
Returns:
|
||||
lmks_frms: dictionary, with frame id as key and 68 landmark set as value
|
||||
pose_p: dictionary, with frame id as key and pose param as value
|
||||
'''
|
||||
tot_lmks = 68 # openface specific
|
||||
if len([i for i in of_df.columns.to_list() if ' x_' in i]) != tot_lmks:
|
||||
return {}
|
||||
|
||||
lmks_frms = {}
|
||||
pose_p = {}
|
||||
|
||||
for fi in sorted(of_df['frame'].to_list()):
|
||||
lmks = np.zeros((tot_lmks,6))
|
||||
r = of_df[of_df['frame']==fi]
|
||||
|
||||
for i in range(tot_lmks):
|
||||
lmk_y = r[' y_'+str(i)].iloc[0]
|
||||
lmk_x = r[' x_'+str(i)].iloc[0]
|
||||
lmk_X = r[' X_'+str(i)].iloc[0]
|
||||
lmk_Y = r[' Y_'+str(i)].iloc[0]
|
||||
lmk_Z = r[' Z_'+str(i)].iloc[0]
|
||||
|
||||
confi = r[' confidence']
|
||||
lmks[i,:] = [lmk_x, lmk_y, lmk_X, lmk_Y, lmk_Z, confi]
|
||||
|
||||
lmks_frms[fi] = lmks
|
||||
pose_p[fi] = [r[' pose_Tx'].iloc[0], r[' pose_Ty'].iloc[0], r[' pose_Tz'].iloc[0],
|
||||
r[' pose_Rx'].iloc[0], r[' pose_Ry'].iloc[0], r[' pose_Rz'].iloc[0]]
|
||||
|
||||
return lmks_frms, pose_p
|
||||
|
||||
|
||||
def mirror_point(a, b, c, d, x1, y1, z1):
|
||||
# mirror a point w.r.t a 3D plane
|
||||
k =(-a * x1-b * y1-c * z1-d)/float((a * a + b * b + c * c))
|
||||
|
||||
x2 = a * k + x1
|
||||
y2 = b * k + y1
|
||||
z2 = c * k + z1
|
||||
|
||||
x3 = 2 * x2-x1
|
||||
y3 = 2 * y2-y1
|
||||
z3 = 2 * z2-z1
|
||||
return [x3, y3, z3]
|
||||
|
||||
|
||||
def dist_vec2plane(vec, nrm):
|
||||
# Calculate the projected length of a vector (vec) to a plane defined by its normal (nrm)
|
||||
return np.sqrt(np.dot(vec, vec) - np.dot(vec, nrm)**2)
|
||||
|
||||
|
||||
def vis_lmks3d(lmks_frms, vis_idx):
|
||||
"""
|
||||
Visualizing facial landmarks
|
||||
"""
|
||||
fig = plt.figure()
|
||||
color_type = ['b','g','r','y','c']
|
||||
assert len(color_type) > len(vis_idx)
|
||||
|
||||
for fi in sorted(list(lmks_frms.keys())):
|
||||
ax = plt.axes(projection="3d")
|
||||
for i,vi in enumerate(vis_idx):
|
||||
ax.scatter(lmks_frms[fi][vi,2], lmks_frms[fi][vi,3], lmks_frms[fi][vi,4], c=color_type[i])
|
||||
|
||||
ax.axes.set_xlim3d(left=-75, right=100)
|
||||
ax.axes.set_ylim3d(bottom=-200, top=25)
|
||||
ax.axes.set_zlim3d(bottom=440, top=560)
|
||||
ax.view_init(-89, -90) #elev, ariz
|
||||
plt.title(str(fi)); ax.set_xlabel('X'); ax.set_ylabel('Y'); ax.set_zlabel('Z')
|
||||
plt.pause(0.2)
|
||||
plt.cla()
|
||||
plt.draw()
|
||||
|
||||
def calc_fac_asymmetry(attr, is_vis=False):
|
||||
'''
|
||||
Quantify facial asymmetry
|
||||
Args:
|
||||
attr: attribute dictionary containing necessary features for calculation, e.g.,
|
||||
lmks_frms: dictionary, with frame id as key and 68 landmark set (OpenFace) as value
|
||||
pose_param: dictionary, with frame id as key and pose param as value
|
||||
Returns:
|
||||
score_asym: 2D array of size (num_frms, num_asymm_fea), with frame id as the 0th column, and each remaining column as one asymmetry feature
|
||||
'''
|
||||
# openface landmark indices
|
||||
lmks_ref_idx = list(range(0,17)) + list(range(27,36))
|
||||
lmks_mid_idx = [27,28,29,30,33,51,62,66,57,8]
|
||||
lmks_rgt_idx = [0,1,2,3,4,5,6,7,
|
||||
17,18,19,20,21,
|
||||
36,37,38,39,40,41,
|
||||
48,49,50,
|
||||
59,58,
|
||||
60,61,
|
||||
67]
|
||||
lmks_lft_idx = [16,15,14,13,12,11,10,9,
|
||||
26,25,24,23,22,
|
||||
45,44,43,42,47,46,
|
||||
54,53,52,
|
||||
55,56,
|
||||
64,63,
|
||||
65]
|
||||
|
||||
lmks_mth_idx = list(range(48,68))
|
||||
lmks_ebr_idx = list(range(17,27))
|
||||
lmks_eye_idx = list(range(36,48))
|
||||
assert len(lmks_lft_idx)==len(lmks_rgt_idx)
|
||||
|
||||
fea_list = ['mouth', 'eyebrow', 'eye', 'composite']
|
||||
score_asym = np.empty(shape=(0, 0))
|
||||
|
||||
if ('lmks_frms' in attr) and ('pose_param' in attr):
|
||||
lmks_frms = attr['lmks_frms']
|
||||
pose_p = attr['pose_param']
|
||||
|
||||
if is_vis:
|
||||
vis_lmks3d(lmks_frms, [lmks_lft_idx, lmks_rgt_idx, lmks_mid_idx, lmks_ref_idx])
|
||||
|
||||
score_asym = np.zeros((len(lmks_frms),len(fea_list)+1+1)) # +1: extra column for error code
|
||||
if is_vis:
|
||||
fig = plt.figure()
|
||||
ax = plt.axes(projection="3d")
|
||||
|
||||
for s,fi in enumerate(sorted(list(lmks_frms.keys()))):
|
||||
lmks_3d = lmks_frms[fi][:,2:5]
|
||||
pose = pose_p[fi]
|
||||
err_code = error_message_code['pass']
|
||||
|
||||
if lmks_frms[fi][0,5] < 0.8:
|
||||
err_code = error_message_code['confidence less than 80%']
|
||||
score_asym[s,:] = [fi,np.NaN,np.NaN,np.NaN,np.NaN,err_code]
|
||||
continue
|
||||
|
||||
rx = R.from_euler('x', pose[3])
|
||||
ry = R.from_euler('y', pose[4])
|
||||
rz = R.from_euler('z', pose[5])
|
||||
|
||||
vec_pose = rz.apply(ry.apply(rx.apply([0,0,1])))
|
||||
anc_idx = [30, 27, 8] # for central plane estimation
|
||||
nrm = np.cross(lmks_3d[anc_idx[2],:] - lmks_3d[anc_idx[0],:],
|
||||
lmks_3d[anc_idx[1],:] - lmks_3d[anc_idx[0],:])
|
||||
|
||||
nrm = nrm / np.linalg.norm(nrm)
|
||||
a,b,c = nrm
|
||||
d = np.dot(nrm, lmks_3d[anc_idx[0],:])
|
||||
|
||||
dist_L2R_mth = []
|
||||
dist_L2R_ebr = []
|
||||
dist_L2R_eye = []
|
||||
dist_com = []
|
||||
|
||||
lmks_rfl = np.empty((0,3))
|
||||
src_idx = lmks_lft_idx
|
||||
|
||||
for k,idx in enumerate(src_idx):
|
||||
p_rfl = np.array(mirror_point(a, b, c, -d, lmks_3d[idx,0], lmks_3d[idx,1], lmks_3d[idx,2]))
|
||||
lmks_rfl = np.vstack((lmks_rfl, p_rfl))
|
||||
dist = dist_vec2plane((p_rfl-lmks_3d[lmks_rgt_idx[k],:]), vec_pose)
|
||||
|
||||
if idx in lmks_mth_idx:
|
||||
dist_L2R_mth.append(dist)
|
||||
if idx in lmks_ebr_idx:
|
||||
dist_L2R_ebr.append(dist)
|
||||
if idx in lmks_eye_idx:
|
||||
dist_L2R_eye.append(dist)
|
||||
if (idx in lmks_mth_idx) or (idx in lmks_ebr_idx) or (idx in lmks_eye_idx):
|
||||
dist_com.append(dist)
|
||||
score_asym[s,:] = [fi,np.mean(dist_L2R_mth),np.mean(dist_L2R_ebr),np.mean(dist_L2R_eye),np.mean(dist_com),err_code]
|
||||
|
||||
if is_vis:
|
||||
ax.scatter(lmks_3d[:,0], lmks_3d[:,1], lmks_3d[:,2])
|
||||
ax.scatter(lmks_rfl[:,0], lmks_rfl[:,1], lmks_rfl[:,2], c='y')
|
||||
ax.scatter(pose_p[fi][0], pose_p[fi][1], pose_p[fi][2], c='c')
|
||||
plt.title('mirrored landmarks, frame: '+str(fi)); ax.set_xlabel('X'); ax.set_ylabel('Y'); ax.set_zlabel('Z')
|
||||
plt.pause(0.2)
|
||||
plt.cla()
|
||||
plt.draw()
|
||||
|
||||
return score_asym
|
||||
|
||||
|
||||
def calc_asym_feature(open_face_csv, f_cfg):
|
||||
"""
|
||||
Calculating facial asymmetry features and preparing final df
|
||||
"""
|
||||
df_list = []
|
||||
|
||||
of_df = pd.read_csv(open_face_csv, error_bad_lines=False)
|
||||
lmks_frms, pose_p = retrieve_attr(of_df)
|
||||
|
||||
attr = {'lmks_frms': lmks_frms, 'pose_param': pose_p}
|
||||
score_asym = calc_fac_asymmetry(attr)
|
||||
|
||||
df_score_asym = pd.DataFrame(score_asym, columns=['frame', f_cfg.fac_AsymMaskMouth, f_cfg.fac_AsymMaskEyebrow,
|
||||
f_cfg.fac_AsymMaskEye, f_cfg.fac_AsymMaskCom, f_cfg.err_reason])
|
||||
df_score_asym[f_cfg.err_reason] = df_score_asym[f_cfg.err_reason].apply(lambda x: error_code_message[x])
|
||||
|
||||
df_score_asym['frame'] = of_df['frame']
|
||||
df_score_asym['face_id'] = of_df[' face_id']
|
||||
df_score_asym['timestamp'] = of_df[' timestamp']
|
||||
df_score_asym['confidence'] = of_df[' confidence']
|
||||
df_score_asym['success'] = of_df[' success']
|
||||
|
||||
df_list.append(df_score_asym)
|
||||
return df_list
|
||||
|
||||
|
||||
def run_face_asymmetry(video_uri, out_dir, f_cfg):
|
||||
"""
|
||||
Processing all patient's for calculating facial asymmetry
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; f_cfg: face config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
#Baseline logic
|
||||
cfr = ConfigFaceReader()
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + '_openface/*.csv'))
|
||||
if len(of_csv_path)>0:
|
||||
|
||||
of_csv = of_csv_path[0]
|
||||
asym_df_list = calc_asym_feature(of_csv, f_cfg)
|
||||
|
||||
asym_final_df = pd.concat(asym_df_list, ignore_index=True)
|
||||
asym_final_df['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
|
||||
ut.save_output(asym_final_df, out_loc, fl_name, face_asym_dir, csv_ext)
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
@@ -1,98 +0,0 @@
|
||||
"""
|
||||
file_name: face_au.py
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import datetime
|
||||
import glob
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.video.face_config.face_config_reader import ConfigFaceReader
|
||||
from dbm_lib.dbm_features.raw_features.util import video_util as vu
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
face_au_dir = 'facial/face_au'
|
||||
csv_ext = '_facau.csv'
|
||||
|
||||
|
||||
def extract_col_nm_au(cols):
|
||||
"""
|
||||
Extract action unit (au) column names from openface output (csv)
|
||||
Args:
|
||||
cols: column names from open face output (csv)
|
||||
Returns:
|
||||
(list) list of au column names
|
||||
"""
|
||||
cols_lmk = []
|
||||
au_tags = ' AU'
|
||||
cols_au = [c for c in cols if au_tags in c]
|
||||
return cols_au
|
||||
|
||||
|
||||
def au_col_nm_map(df):
|
||||
"""
|
||||
Rename dataframe action unit column names to match functional specifications v1.0
|
||||
Args:
|
||||
df: dataframe
|
||||
Returns:
|
||||
dataframe with mapped variables
|
||||
"""
|
||||
dict_au_cols = {}
|
||||
for col in list(df):
|
||||
if ' AU' in col:
|
||||
idx = col.rfind('_')
|
||||
if idx > -1:
|
||||
au_id = col[idx-2:idx]
|
||||
if '_r' in col:
|
||||
dict_au_cols[col] = 'fac_AU' + au_id + 'int'
|
||||
if '_c' in col:
|
||||
dict_au_cols[col] = 'fac_AU' + au_id + 'pres'
|
||||
df.rename(columns=dict_au_cols, inplace=True)
|
||||
return df
|
||||
|
||||
|
||||
def run_face_au(video_uri, out_dir, f_cfg):
|
||||
"""
|
||||
Processing all patient's for fetching action units
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; f_cfg: face config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
#Baseline logic
|
||||
cfr = ConfigFaceReader()
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + '_openface/*.csv'))
|
||||
if len(of_csv_path)>0:
|
||||
|
||||
df_of = pd.read_csv(of_csv_path[0], error_bad_lines=False)
|
||||
df_au = df_of[extract_col_nm_au(df_of)]
|
||||
df_au = df_au.copy()
|
||||
|
||||
df_au['frame'] = df_of['frame']
|
||||
df_au['face_id'] = df_of[' face_id']
|
||||
df_au['timestamp'] = df_of[' timestamp']
|
||||
df_au['confidence'] = df_of[' confidence']
|
||||
df_au['success'] = df_of[' success']
|
||||
|
||||
df_au = au_col_nm_map(df_au)
|
||||
df_au['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
|
||||
ut.save_output(df_au, out_loc, fl_name, face_au_dir, csv_ext)
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
|
||||
@@ -1,86 +0,0 @@
|
||||
"""
|
||||
file_name: process_emotion_expressivity
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import datetime
|
||||
import glob
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.video.face_config.face_config_reader import ConfigFaceReader
|
||||
from dbm_lib.dbm_features.raw_features.util import video_util as vu
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
face_expr_dir = 'facial/face_expressivity'
|
||||
csv_ext = '_facemo.csv'
|
||||
|
||||
#Openface feature extraction
|
||||
def of_feature(df_of, cfr, f_cfg):
|
||||
"""
|
||||
Creating dataframe for face expressivity
|
||||
Args:
|
||||
of: open face attributes
|
||||
Returns:
|
||||
(list) list of expressivity score for emotions
|
||||
"""
|
||||
df_list = []
|
||||
df_of['s_confidence'] = vu.smooth(df_of[' confidence'].values, window='flat').tolist()
|
||||
|
||||
if 'AU' in cfr.SELECTED_FEATURES :
|
||||
vu.calc_of_for_video(df_of, cfr, f_cfg)
|
||||
#Normalizing facial expressivity for Composite and Negative expr(Range 0 to 1)
|
||||
|
||||
if len(df_of[f_cfg.neg_exp])>0:
|
||||
df_of[f_cfg.neg_exp] = df_of[f_cfg.neg_exp]/5
|
||||
|
||||
if len(df_of[f_cfg.neg_exp_full])>0:
|
||||
df_of[f_cfg.neg_exp_full] = df_of[f_cfg.neg_exp_full]/5
|
||||
|
||||
if len(df_of[f_cfg.com_exp])>0:
|
||||
df_of[f_cfg.com_exp] = df_of[f_cfg.com_exp]/7
|
||||
|
||||
if len(df_of[f_cfg.com_exp_full])>0:
|
||||
df_of[f_cfg.com_exp_full] = df_of[f_cfg.com_exp_full]/7
|
||||
|
||||
df_list.append(df_of)
|
||||
return df_list
|
||||
|
||||
|
||||
def run_face_expressivity(video_uri, out_dir, f_cfg):
|
||||
"""
|
||||
Processing all patient's for fetching facial landmarks
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; f_cfg: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
#Baseline logic
|
||||
cfr = ConfigFaceReader()
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + '_openface/*.csv'))
|
||||
if len(of_csv_path)>0:
|
||||
|
||||
df_of = pd.read_csv(of_csv_path[0], error_bad_lines=False)
|
||||
df_of = df_of[cfr.AU_fl]
|
||||
expr_df_list = of_feature(df_of, cfr, f_cfg)
|
||||
|
||||
exp_final_df = pd.concat(expr_df_list, ignore_index=True)
|
||||
exp_final_df['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Processing Output file {} '.format(os.path.join(out_loc, fl_name)))
|
||||
ut.save_output(exp_final_df, out_loc, fl_name, face_expr_dir, csv_ext)
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
@@ -1,122 +0,0 @@
|
||||
"""
|
||||
file_name: face_landmark
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import datetime
|
||||
import glob
|
||||
from os.path import join
|
||||
import logging
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.video.face_config.face_config_reader import ConfigFaceReader
|
||||
from dbm_lib.dbm_features.raw_features.util import video_util as vu
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
|
||||
face_lmk_dir = 'facial/face_landmark'
|
||||
csv_ext = '_faclmk.csv'
|
||||
|
||||
def extract_col_nm_lmk(cols):
|
||||
"""
|
||||
Extract landmark column names from openface output (csv)
|
||||
Args:
|
||||
cols: column names from open face output (csv)
|
||||
Returns:
|
||||
(list) list of landmark column names
|
||||
"""
|
||||
cols_lmk = []
|
||||
lmk_tags = [' y_', ' x_', ' X_', ' Y_', ' Z_']
|
||||
for c in cols:
|
||||
if any(t in c for t in lmk_tags):
|
||||
cols_lmk.append(c)
|
||||
return cols_lmk
|
||||
|
||||
|
||||
def lmk_col_nm_map(df):
|
||||
"""
|
||||
Rename dataframe landmark column names to match functional specifications v1.0
|
||||
Args:
|
||||
df: dataframe
|
||||
"""
|
||||
dict_lmk_cols = {}
|
||||
for col in list(df):
|
||||
idx = col.rfind('_')+1
|
||||
if idx > 0:
|
||||
lmk_id = col[idx:] if len(col[idx:])>1 else '0'+col[idx:]
|
||||
if ' y_' in col:
|
||||
dict_lmk_cols[col] = 'fac_LMK' + lmk_id + 'r'
|
||||
if ' x_' in col:
|
||||
dict_lmk_cols[col] = 'fac_LMK' + lmk_id + 'c'
|
||||
if ' X_' in col:
|
||||
dict_lmk_cols[col] = 'fac_LMK' + lmk_id + 'X'
|
||||
if ' Y_' in col:
|
||||
dict_lmk_cols[col] = 'fac_LMK' + lmk_id + 'Y'
|
||||
if ' Z_' in col:
|
||||
dict_lmk_cols[col] = 'fac_LMK' + lmk_id + 'Z'
|
||||
df.rename(columns=dict_lmk_cols, inplace=True)
|
||||
return df
|
||||
|
||||
|
||||
def add_disp_3D(df):
|
||||
"""
|
||||
Add 3D displacement for each landmark
|
||||
Args:
|
||||
df: landmark dataframe
|
||||
"""
|
||||
df = df.sort_values(by=['frame'], ascending=False)
|
||||
cols_lmk = [col for col in list(df) if 'fac_LMK' in col]
|
||||
df_t = df[cols_lmk]
|
||||
df_diff = df_t.diff()
|
||||
df_diff = df_diff.pow(2)
|
||||
|
||||
tot_lmk = 68 # 68 landmark model
|
||||
for i in range(tot_lmk):
|
||||
lmk_id = '{:02d}'.format(i)
|
||||
df['fac_LMK'+lmk_id+'disp'] = df_diff[['fac_LMK'+lmk_id+'X', 'fac_LMK'+lmk_id+'Y', 'fac_LMK'+lmk_id+'Z']].sum(axis=1).apply(np.sqrt)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def run_face_landmark(video_uri, out_dir, f_cfg):
|
||||
"""
|
||||
Processing all patient's for fetching facial landmarks
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; f_cfg: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
#Baseline logic
|
||||
cfr = ConfigFaceReader()
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + '_openface/*.csv'))
|
||||
if len(of_csv_path)>0:
|
||||
|
||||
df_of = pd.read_csv(of_csv_path[0], error_bad_lines=False)
|
||||
df_lmk = df_of[extract_col_nm_lmk(df_of)]
|
||||
df_lmk = df_lmk.copy()
|
||||
|
||||
df_lmk['frame'] = df_of['frame']
|
||||
df_lmk['face_id'] = df_of[' face_id']
|
||||
df_lmk['timestamp'] = df_of[' timestamp']
|
||||
df_lmk['confidence'] = df_of[' confidence']
|
||||
df_lmk['success'] = df_of[' success']
|
||||
|
||||
df_lmk = lmk_col_nm_map(df_lmk)
|
||||
df_lmk = add_disp_3D(df_lmk)
|
||||
df_lmk['dbm_master_url'] = video_uri
|
||||
|
||||
logger.info('Processing Output file {} '.format(join(out_loc, fl_name)))
|
||||
ut.save_output(df_lmk, out_loc, fl_name, face_lmk_dir, csv_ext)
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process video file')
|
||||
4
opendbm/__init__.py
Normal file
4
opendbm/__init__.py
Normal file
@@ -0,0 +1,4 @@
|
||||
from opendbm.api_lib.facial_activity import FacialActivity
|
||||
from opendbm.api_lib.movement import Movement
|
||||
from opendbm.api_lib.speech import Speech
|
||||
from opendbm.api_lib.verbal_acoustics import VerbalAcoustics
|
||||
1
opendbm/api_lib/__init__.py
Normal file
1
opendbm/api_lib/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
from .model import DEEEPSPEECH_URL, DEEPSPEECH_MODELS, OPENDBM_DATA, AudioModel
|
||||
1
opendbm/api_lib/facial_activity/__init__.py
Normal file
1
opendbm/api_lib/facial_activity/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
from opendbm.api_lib.facial_activity.api import FacialActivity
|
||||
49
opendbm/api_lib/facial_activity/_action_unit.py
Normal file
49
opendbm/api_lib/facial_activity/_action_unit.py
Normal file
@@ -0,0 +1,49 @@
|
||||
import tempfile
|
||||
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.video.face_au import run_face_au
|
||||
|
||||
|
||||
class ActionUnit(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = [
|
||||
"fac_AU01int",
|
||||
"fac_AU02int",
|
||||
"fac_AU04int",
|
||||
"fac_AU05int",
|
||||
"fac_AU06int",
|
||||
"fac_AU07int",
|
||||
"fac_AU09int",
|
||||
"fac_AU10int",
|
||||
"fac_AU12int",
|
||||
"fac_AU14int",
|
||||
"fac_AU15int",
|
||||
"fac_AU17int",
|
||||
"fac_AU20int",
|
||||
"fac_AU23int",
|
||||
"fac_AU25int",
|
||||
"fac_AU26int",
|
||||
"fac_AU45int",
|
||||
"fac_AU01pres",
|
||||
"fac_AU02pres",
|
||||
"fac_AU04pres",
|
||||
"fac_AU05pres",
|
||||
"fac_AU06pres",
|
||||
"fac_AU07pres",
|
||||
"fac_AU09pres",
|
||||
"fac_AU10pres",
|
||||
"fac_AU12pres",
|
||||
"fac_AU14pres",
|
||||
"fac_AU15pres",
|
||||
"fac_AU17pres",
|
||||
"fac_AU20pres",
|
||||
"fac_AU23pres",
|
||||
"fac_AU25pres",
|
||||
"fac_AU26pres",
|
||||
"fac_AU28pres",
|
||||
"fac_AU45pres",
|
||||
]
|
||||
|
||||
def _fit_transform(self, path):
|
||||
return run_face_au(path, f"{tempfile.gettempdir()}/", self.r_config, save=False)
|
||||
20
opendbm/api_lib/facial_activity/_asymmetry.py
Normal file
20
opendbm/api_lib/facial_activity/_asymmetry.py
Normal file
@@ -0,0 +1,20 @@
|
||||
import tempfile
|
||||
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.dbm_lib import run_face_asymmetry
|
||||
|
||||
|
||||
class Asymmetry(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = [
|
||||
"fac_asymmaskmouth",
|
||||
"fac_asymmaskeye",
|
||||
"fac_asymmaskeyebrow",
|
||||
"fac_asymmaskcom",
|
||||
]
|
||||
|
||||
def _fit_transform(self, path):
|
||||
return run_face_asymmetry(
|
||||
path, f"{tempfile.gettempdir()}/", self.r_config, save=False
|
||||
)
|
||||
89
opendbm/api_lib/facial_activity/_expressivity.py
Normal file
89
opendbm/api_lib/facial_activity/_expressivity.py
Normal file
@@ -0,0 +1,89 @@
|
||||
import tempfile
|
||||
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.video import face_emotion_expressivity
|
||||
|
||||
|
||||
class Expressivity(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = [
|
||||
" AU01_r",
|
||||
" AU02_r",
|
||||
" AU04_r",
|
||||
" AU05_r",
|
||||
" AU06_r",
|
||||
" AU07_r",
|
||||
" AU09_r",
|
||||
" AU10_r",
|
||||
" AU12_r",
|
||||
" AU14_r",
|
||||
" AU15_r",
|
||||
" AU17_r",
|
||||
" AU20_r",
|
||||
" AU25_r",
|
||||
" AU26_r",
|
||||
" AU45_r",
|
||||
" AU01_c",
|
||||
" AU02_c",
|
||||
" AU04_c",
|
||||
" AU05_c",
|
||||
" AU06_c",
|
||||
" AU07_c",
|
||||
" AU10_c",
|
||||
" AU12_c",
|
||||
" AU14_c",
|
||||
" AU15_c",
|
||||
" AU17_c",
|
||||
" AU20_c",
|
||||
" AU23_c",
|
||||
" AU25_c",
|
||||
" AU26_c",
|
||||
" AU28_c",
|
||||
" AU45_c",
|
||||
" AU09_c",
|
||||
" AU23_r",
|
||||
"s_confidence",
|
||||
"fac_hapintsoft",
|
||||
"fac_sadintsoft",
|
||||
"fac_surintsoft",
|
||||
"fac_feaintsoft",
|
||||
"fac_angintsoft",
|
||||
"fac_disintsoft",
|
||||
"fac_conintsoft",
|
||||
"fac_paiintsoft",
|
||||
"fac_negintsoft",
|
||||
"fac_posintsoft",
|
||||
"neu_exp",
|
||||
"fac_comlowintsoft",
|
||||
"fac_comuppintsoft",
|
||||
"cai_exp",
|
||||
"fac_comintsoft",
|
||||
"fac_happres",
|
||||
"fac_sadpres",
|
||||
"fac_surpres",
|
||||
"fac_feapres",
|
||||
"fac_angpres",
|
||||
"fac_dispres",
|
||||
"fac_conpres",
|
||||
"fac_hapinthard",
|
||||
"fac_sadinthard",
|
||||
"fac_surinthard",
|
||||
"fac_feainthard",
|
||||
"fac_anginthard",
|
||||
"fac_disinthard",
|
||||
"fac_coninthard",
|
||||
"fac_paiinthard",
|
||||
"fac_neginthard",
|
||||
"fac_posinthard",
|
||||
"neu_exp_full",
|
||||
"cai_exp_full",
|
||||
"fac_comlowinthard",
|
||||
"fac_comuppinthard",
|
||||
"fac_cominthard",
|
||||
]
|
||||
|
||||
def _fit_transform(self, path):
|
||||
return face_emotion_expressivity.run_face_expressivity(
|
||||
path, f"{tempfile.gettempdir()}/", self.r_config, save=False
|
||||
)
|
||||
24
opendbm/api_lib/facial_activity/_landmark.py
Normal file
24
opendbm/api_lib/facial_activity/_landmark.py
Normal file
@@ -0,0 +1,24 @@
|
||||
import tempfile
|
||||
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.dbm_lib import run_face_landmark
|
||||
|
||||
|
||||
def r_num_fmt(fmt, rnum):
|
||||
return list(map(lambda x: fmt.format(i="%02d" % x), rnum))
|
||||
|
||||
|
||||
lcols = []
|
||||
for vr in ["r", "c", "X", "Y", "Z"]:
|
||||
lcols += r_num_fmt(f"fac_LMK{{i}}{vr}", range(68))
|
||||
|
||||
|
||||
class Landmark(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = lcols
|
||||
|
||||
def _fit_transform(self, path):
|
||||
return run_face_landmark(
|
||||
path, f"{tempfile.gettempdir()}/", self.r_config, save=False
|
||||
)
|
||||
84
opendbm/api_lib/facial_activity/api.py
Normal file
84
opendbm/api_lib/facial_activity/api.py
Normal file
@@ -0,0 +1,84 @@
|
||||
import shutil
|
||||
import tempfile
|
||||
from collections import OrderedDict
|
||||
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.api_lib.util import check_isfile
|
||||
|
||||
from ._action_unit import ActionUnit
|
||||
from ._asymmetry import Asymmetry
|
||||
from ._expressivity import Expressivity
|
||||
from ._landmark import Landmark
|
||||
|
||||
|
||||
class FacialActivity(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
self._landmark = Landmark()
|
||||
self._action_unit = ActionUnit()
|
||||
self._asymmetry = Asymmetry()
|
||||
self._expressivity = Expressivity()
|
||||
|
||||
self._models = OrderedDict(
|
||||
{
|
||||
"landmark": self._landmark,
|
||||
"action_unit": self._action_unit,
|
||||
"asymmetry": self._asymmetry,
|
||||
"expressivity": self._expressivity,
|
||||
}
|
||||
)
|
||||
|
||||
def fit(self, path):
|
||||
"""Fit a file in filepath to OpenFace Model. Make sure to set the Docker to be active first.
|
||||
For installation, see https://aicure.github.io/open_dbm/docs/openface-docker-installation
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : string,
|
||||
File Path of MP4/MOV file.
|
||||
"""
|
||||
|
||||
check_isfile(path)
|
||||
result_path, bn = super()._fit(path, "facial")
|
||||
|
||||
for k, v in self._models.items():
|
||||
v._df = v._fit_transform(result_path)
|
||||
|
||||
shutil.rmtree(f"{tempfile.gettempdir()}/{bn}/")
|
||||
|
||||
def get_landmark(self):
|
||||
"""
|
||||
Get the model object of Landmark
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._landmark
|
||||
|
||||
def get_action_unit(self):
|
||||
"""
|
||||
Get the model object of Action Unit
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._action_unit
|
||||
|
||||
def get_asymmetry(self):
|
||||
"""
|
||||
Get the model object of Facial Asymmetry
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._asymmetry
|
||||
|
||||
def get_expressivity(self):
|
||||
"""
|
||||
Get the model object of Facial Expressivity
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._expressivity
|
||||
205
opendbm/api_lib/model.py
Normal file
205
opendbm/api_lib/model.py
Normal file
@@ -0,0 +1,205 @@
|
||||
import os
|
||||
import platform
|
||||
import subprocess
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
from opendbm.api_lib.util import docker_command_dec, wsllize
|
||||
from opendbm.dbm_lib import config_derive_feature, config_raw_feature, config_reader
|
||||
|
||||
OPENFACE_PATH_VIDEO = "pkg/open_dbm/OpenFace/build/bin/FaceLandmarkVid"
|
||||
OPENFACE_PATH = "pkg/open_dbm/OpenFace/build/bin/FeatureExtraction"
|
||||
DEEEPSPEECH_URL = "https://github.com/mozilla/DeepSpeech/releases/download/v0.9.1"
|
||||
DEEPSPEECH_MODELS = ["deepspeech-0.9.1-models.pbmm", "deepspeech-0.9.1-models.scorer"]
|
||||
MODEL_PATH = os.path.dirname(__file__)
|
||||
OPENDBM_DATA = Path.home() / ".opendbm"
|
||||
DLIB_SHAPE_MODEL = os.path.abspath(
|
||||
os.path.join(
|
||||
MODEL_PATH, "../pkg/shape_detector/shape_predictor_68_face_landmarks.dat"
|
||||
)
|
||||
)
|
||||
FACIAL_ACTIVITY_ARGS = [
|
||||
"-q",
|
||||
"-2Dfp",
|
||||
"-3Dfp",
|
||||
"-pdmparams",
|
||||
"-pose",
|
||||
"-aus",
|
||||
"-gaze",
|
||||
"-f",
|
||||
]
|
||||
|
||||
|
||||
class Model(object):
|
||||
def __init__(self):
|
||||
self.s_config = config_reader.ConfigReader()
|
||||
self.r_config = config_raw_feature.ConfigRawReader()
|
||||
self.d_config = config_derive_feature.ConfigDeriveReader()
|
||||
self._df = None
|
||||
self._params = []
|
||||
|
||||
def to_dataframe(self):
|
||||
"""
|
||||
Convert the result of the processed data into dataframe.
|
||||
Returns:
|
||||
pandas dataframe
|
||||
"""
|
||||
if self._df is None:
|
||||
raise Exception("Model has not been fit yet")
|
||||
else:
|
||||
return self._df
|
||||
|
||||
def mean(self):
|
||||
"""
|
||||
get mean/average of data
|
||||
Returns:
|
||||
pandas.Series
|
||||
"""
|
||||
return self._df[self._params].mean()
|
||||
|
||||
def std(self):
|
||||
"""
|
||||
get std of data
|
||||
Returns:
|
||||
pandas.Series
|
||||
"""
|
||||
return self._df[self._params].std()
|
||||
|
||||
|
||||
class VideoModel(Model):
|
||||
"""
|
||||
A class to process the data of facial and Movement.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
@docker_command_dec
|
||||
def _fit(self, path, dbm_group):
|
||||
"""
|
||||
A function where the model is processing the data.
|
||||
The model lived in the docker image,
|
||||
where the full path of the model is stated in a variable named openface_call
|
||||
Args:
|
||||
path: input path of the file
|
||||
dbm_group: self-explanatory. This function only accept dbm_group of facial and movement.
|
||||
|
||||
Returns:
|
||||
output path of the processed file by the model.
|
||||
"""
|
||||
docker_temp_dir = "/app/tmp/"
|
||||
wsl_cmd, temp_dir = wsllize((tempfile.gettempdir()))
|
||||
filename = os.path.basename(path)
|
||||
bn, _ = os.path.splitext(filename)
|
||||
|
||||
facial_args = " ".join(FACIAL_ACTIVITY_ARGS)
|
||||
docker_call = wsl_cmd + ["docker", "exec", "dbm_container", "/bin/bash", "-c"]
|
||||
|
||||
openface_call = [
|
||||
docker_call
|
||||
+ [f"{OPENFACE_PATH} {facial_args} {path} -out_dir {docker_temp_dir}"],
|
||||
docker_call
|
||||
+ [
|
||||
f"{OPENFACE_PATH_VIDEO} {facial_args} {path} -out_dir {docker_temp_dir}"
|
||||
],
|
||||
]
|
||||
|
||||
out_dir_openface = [
|
||||
f"{temp_dir}/{bn}/{bn}_openface/",
|
||||
f"{temp_dir}/{bn}_landmark_output/{bn}_landmark_output_openface_lmk/",
|
||||
]
|
||||
result_path = [
|
||||
docker_temp_dir + bn + ".csv",
|
||||
docker_temp_dir + bn + "_landmark_output.csv",
|
||||
]
|
||||
|
||||
if dbm_group == "facial":
|
||||
openface_csv = self._processing_video(
|
||||
dbm_group,
|
||||
openface_call[0],
|
||||
out_dir_openface[0],
|
||||
result_path[0],
|
||||
wsl_cmd,
|
||||
temp_dir,
|
||||
bn,
|
||||
)
|
||||
|
||||
return openface_csv, bn
|
||||
else:
|
||||
|
||||
openface_csv = self._processing_video(
|
||||
"facial",
|
||||
openface_call[0],
|
||||
out_dir_openface[0],
|
||||
result_path[0],
|
||||
wsl_cmd,
|
||||
temp_dir,
|
||||
bn,
|
||||
)
|
||||
openface_lmk_csv = self._processing_video(
|
||||
"movement",
|
||||
openface_call[1],
|
||||
out_dir_openface[1],
|
||||
result_path[1],
|
||||
wsl_cmd,
|
||||
temp_dir,
|
||||
bn,
|
||||
)
|
||||
|
||||
return openface_csv, openface_lmk_csv, bn
|
||||
|
||||
def _processing_video(
|
||||
self, dbm_group, call, out_dir, result_path, wsl_cmd, temp_dir, bn
|
||||
):
|
||||
"""
|
||||
Helper function for _fit method
|
||||
"""
|
||||
|
||||
subprocess.Popen(
|
||||
call,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.PIPE,
|
||||
).wait()
|
||||
mkdir_cmd = wsl_cmd + ["mkdir", "-p", out_dir]
|
||||
|
||||
copy_cmd = wsl_cmd + ["docker", "cp", f"dbm_container:/{result_path}", out_dir]
|
||||
subprocess.Popen(
|
||||
mkdir_cmd,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.PIPE,
|
||||
).wait()
|
||||
subprocess.Popen(
|
||||
copy_cmd,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.PIPE,
|
||||
).wait()
|
||||
|
||||
if platform.system() == "Windows":
|
||||
path_in_temp = out_dir[len(temp_dir) :]
|
||||
out_dir = (tempfile.gettempdir()) + path_in_temp
|
||||
|
||||
if dbm_group == "facial":
|
||||
return out_dir + bn + ".csv"
|
||||
else:
|
||||
return out_dir + bn + "_landmark_output.csv"
|
||||
|
||||
|
||||
class AudioModel(Model):
|
||||
"""
|
||||
A class to process the data of speech and acoustic
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def prep_func(func):
|
||||
def wrapper(self, *args, **kwargs):
|
||||
path = args[0]
|
||||
|
||||
df = func(self, path, **kwargs)
|
||||
return df
|
||||
|
||||
return wrapper
|
||||
1
opendbm/api_lib/movement/__init__.py
Normal file
1
opendbm/api_lib/movement/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
from opendbm.api_lib.movement.api import Movement
|
||||
11
opendbm/api_lib/movement/_eye_blink.py
Normal file
11
opendbm/api_lib/movement/_eye_blink.py
Normal file
@@ -0,0 +1,11 @@
|
||||
from opendbm.api_lib.model import DLIB_SHAPE_MODEL, VideoModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.movement.eye_blink import run_eye_blink
|
||||
|
||||
|
||||
class EyeBlink(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["mov_blink_ear", "mov_blinkframes", "mov_blinkdur", "fps"]
|
||||
|
||||
def _fit_transform(self, path):
|
||||
return run_eye_blink(path, ".", self.r_config, DLIB_SHAPE_MODEL, save=False)
|
||||
24
opendbm/api_lib/movement/_eye_gaze.py
Normal file
24
opendbm/api_lib/movement/_eye_gaze.py
Normal file
@@ -0,0 +1,24 @@
|
||||
import tempfile
|
||||
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.movement.eye_gaze import run_eye_gaze
|
||||
|
||||
|
||||
class EyeGaze(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = [
|
||||
"mov_lefteyex",
|
||||
"mov_lefteyey",
|
||||
"mov_lefteyez",
|
||||
"mov_righteyex",
|
||||
"mov_righteyey",
|
||||
"mov_righteyez",
|
||||
"mov_leyedisp",
|
||||
"mov_reyedisp",
|
||||
]
|
||||
|
||||
def _fit_transform(self, path):
|
||||
return run_eye_gaze(
|
||||
path, f"{tempfile.gettempdir()}/", self.r_config, save=False
|
||||
)
|
||||
52
opendbm/api_lib/movement/_facial_tremor.py
Normal file
52
opendbm/api_lib/movement/_facial_tremor.py
Normal file
@@ -0,0 +1,52 @@
|
||||
import tempfile
|
||||
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.dbm_lib import fac_tremor_process
|
||||
|
||||
|
||||
class FacialTremor(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = [
|
||||
"fac_features_mean_5",
|
||||
"fac_tremor_median_5",
|
||||
"fac_disp_median_5",
|
||||
"fac_corr_5",
|
||||
"fac_features_mean_12",
|
||||
"fac_tremor_median_12",
|
||||
"fac_disp_median_12",
|
||||
"fac_corr_12",
|
||||
"fac_features_mean_8",
|
||||
"fac_tremor_median_8",
|
||||
"fac_disp_median_8",
|
||||
"fac_corr_8",
|
||||
"fac_features_mean_48",
|
||||
"fac_tremor_median_48",
|
||||
"fac_disp_median_48",
|
||||
"fac_corr_48",
|
||||
"fac_features_mean_54",
|
||||
"fac_tremor_median_54",
|
||||
"fac_disp_median_54",
|
||||
"fac_corr_54",
|
||||
"fac_features_mean_28",
|
||||
"fac_tremor_median_28",
|
||||
"fac_disp_median_28",
|
||||
"fac_corr_28",
|
||||
"fac_features_mean_51",
|
||||
"fac_tremor_median_51",
|
||||
"fac_disp_median_51",
|
||||
"fac_corr_51",
|
||||
"fac_features_mean_66",
|
||||
"fac_tremor_median_66",
|
||||
"fac_disp_median_66",
|
||||
"fac_corr_66",
|
||||
"fac_features_mean_57",
|
||||
"fac_tremor_median_57",
|
||||
"fac_disp_median_57",
|
||||
"fac_corr_57",
|
||||
]
|
||||
|
||||
def _fit_transform(self, path):
|
||||
return fac_tremor_process(
|
||||
path, f"{tempfile.gettempdir()}/", self.r_config, save=False
|
||||
)
|
||||
19
opendbm/api_lib/movement/_head_movement.py
Normal file
19
opendbm/api_lib/movement/_head_movement.py
Normal file
@@ -0,0 +1,19 @@
|
||||
import tempfile
|
||||
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.dbm_lib import run_head_movement
|
||||
|
||||
|
||||
class HeadMovement(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = [
|
||||
"mov_headvel",
|
||||
"mov_hposepitch",
|
||||
"mov_hposeyaw",
|
||||
"mov_hposeroll",
|
||||
"mov_hposedist",
|
||||
]
|
||||
|
||||
def _fit_transform(self, path):
|
||||
return run_head_movement(path, f"{tempfile.gettempdir()}/", self.r_config)
|
||||
18
opendbm/api_lib/movement/_vocal_tremor.py
Normal file
18
opendbm/api_lib/movement/_vocal_tremor.py
Normal file
@@ -0,0 +1,18 @@
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.movement.voice_tremor import run_vtremor
|
||||
|
||||
|
||||
class VocalTremor(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = [
|
||||
"mov_freqtremfreq",
|
||||
"mov_freqtremindex",
|
||||
"mov_freqtrempindex",
|
||||
"mov_amptremfreq",
|
||||
"mov_amptremindex",
|
||||
"mov_amptrempindex",
|
||||
]
|
||||
|
||||
def _fit_transform(self, path):
|
||||
return run_vtremor(path, ".", self.r_config, save=False)
|
||||
105
opendbm/api_lib/movement/api.py
Normal file
105
opendbm/api_lib/movement/api.py
Normal file
@@ -0,0 +1,105 @@
|
||||
import os
|
||||
import shutil
|
||||
import tempfile
|
||||
from collections import OrderedDict
|
||||
|
||||
from opendbm.api_lib.model import VideoModel
|
||||
from opendbm.api_lib.util import check_isfile
|
||||
from opendbm.dbm_lib.controller import process_feature as pf
|
||||
|
||||
from ._eye_blink import EyeBlink
|
||||
from ._eye_gaze import EyeGaze
|
||||
from ._facial_tremor import FacialTremor
|
||||
from ._head_movement import HeadMovement
|
||||
from ._vocal_tremor import VocalTremor
|
||||
|
||||
|
||||
class Movement(VideoModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._eye_blink = EyeBlink()
|
||||
self._eye_gaze = EyeGaze()
|
||||
self._facial_tremor = FacialTremor()
|
||||
self._head_movement = HeadMovement()
|
||||
self._vocal_tremor = VocalTremor()
|
||||
|
||||
self._models = OrderedDict(
|
||||
{
|
||||
"eye_blink": self._eye_blink,
|
||||
"eye_gaze": self._eye_gaze,
|
||||
"facial_tremor": self._facial_tremor,
|
||||
"head_movement": self._head_movement,
|
||||
"vocal_tremor": self._vocal_tremor,
|
||||
}
|
||||
)
|
||||
|
||||
def fit(self, path):
|
||||
"""Fit a file in filepath to OpenFace Model. Make sure to set the Docker to be active first.
|
||||
For installation, see https://aicure.github.io/open_dbm/docs/openface-docker-installation
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : string,
|
||||
File Path of MP4/MOV file.
|
||||
|
||||
"""
|
||||
check_isfile(path)
|
||||
result_path, result_path_lmk, bn = super()._fit(path, "movement")
|
||||
wav_path = pf.audio_to_wav(path, tmp=True)
|
||||
|
||||
for k, v in self._models.items():
|
||||
if k in ["eye_gaze", "head_movement"]:
|
||||
v._df = v._fit_transform(result_path)
|
||||
elif k == "facial_tremor":
|
||||
v._df = v._fit_transform(result_path_lmk)
|
||||
elif k == "vocal_tremor":
|
||||
v._df = v._fit_transform(wav_path)
|
||||
else:
|
||||
v._df = v._fit_transform(path)
|
||||
shutil.rmtree(f"{tempfile.gettempdir()}/{bn}/")
|
||||
shutil.rmtree(f"{tempfile.gettempdir()}/{bn}_landmark_output/")
|
||||
os.remove(wav_path)
|
||||
|
||||
def get_eye_blink(self):
|
||||
"""
|
||||
Get the model object of Eye Blink
|
||||
Returns:
|
||||
self: object
|
||||
"""
|
||||
return self._eye_blink
|
||||
|
||||
def get_eye_gaze(self):
|
||||
"""
|
||||
Get the model object of Eye Gaze
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._eye_gaze
|
||||
|
||||
def get_facial_tremor(self):
|
||||
"""
|
||||
Get the model object of Facial Tremor
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._facial_tremor
|
||||
|
||||
def get_head_movement(self):
|
||||
"""
|
||||
Get the model object of Head Movement
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._head_movement
|
||||
|
||||
def get_vocal_tremor(self):
|
||||
"""
|
||||
Get the model object of Vocal Tremor
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._vocal_tremor
|
||||
1
opendbm/api_lib/speech/__init__.py
Normal file
1
opendbm/api_lib/speech/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
from opendbm.api_lib.speech.api import Speech
|
||||
34
opendbm/api_lib/speech/_speech_features.py
Normal file
34
opendbm/api_lib/speech/_speech_features.py
Normal file
@@ -0,0 +1,34 @@
|
||||
import tempfile
|
||||
|
||||
from opendbm.api_lib.model import OPENDBM_DATA, AudioModel
|
||||
from opendbm.dbm_lib import run_speech_feature
|
||||
|
||||
|
||||
class SpeechFeature(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = [
|
||||
"nlp_numSentences",
|
||||
"nlp_singPronPerAns",
|
||||
"nlp_singPronPerSen",
|
||||
"nlp_pastTensePerAns",
|
||||
"nlp_pastTensePerSen",
|
||||
"nlp_pronounsPerAns",
|
||||
"nlp_pronounsPerSen",
|
||||
"nlp_verbsPerAns",
|
||||
"nlp_verbsPerSen",
|
||||
"nlp_adjectivesPerAns",
|
||||
"nlp_adjectivesPerSen",
|
||||
"nlp_nounsPerAns",
|
||||
"nlp_nounsPerSen",
|
||||
"nlp_sentiment_mean",
|
||||
"nlp_mattr",
|
||||
"nlp_wordsPerMin",
|
||||
"nlp_totalTime",
|
||||
]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path):
|
||||
return run_speech_feature(
|
||||
path, f"{tempfile.gettempdir()}/", self.r_config, OPENDBM_DATA, save=False
|
||||
)
|
||||
16
opendbm/api_lib/speech/_transcribe.py
Normal file
16
opendbm/api_lib/speech/_transcribe.py
Normal file
@@ -0,0 +1,16 @@
|
||||
import tempfile
|
||||
|
||||
from opendbm.api_lib.model import OPENDBM_DATA, AudioModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.nlp.transcribe import run_transcribe
|
||||
|
||||
|
||||
class Transcribe(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["nlp_transcribe", "nlp_totalTime"]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path):
|
||||
return run_transcribe(
|
||||
path, f"{tempfile.gettempdir()}/", self.r_config, OPENDBM_DATA, save=False
|
||||
)
|
||||
73
opendbm/api_lib/speech/api.py
Normal file
73
opendbm/api_lib/speech/api.py
Normal file
@@ -0,0 +1,73 @@
|
||||
import logging
|
||||
import os
|
||||
|
||||
from opendbm.api_lib import DEEEPSPEECH_URL, DEEPSPEECH_MODELS, OPENDBM_DATA, AudioModel
|
||||
from opendbm.api_lib.util import check_file, check_isfile, download_url
|
||||
|
||||
from ._speech_features import SpeechFeature
|
||||
from ._transcribe import Transcribe
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
|
||||
class Speech(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._transcribe = Transcribe()
|
||||
self._speech_features = SpeechFeature()
|
||||
self._models = {
|
||||
"transcribe": self._transcribe,
|
||||
"speech_features": self._speech_features,
|
||||
}
|
||||
|
||||
def fit(self, path):
|
||||
"""Fit a file in filepath to Deepspeech Model.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : string,
|
||||
File Path of Video/Sound file format.
|
||||
"""
|
||||
check_isfile(path)
|
||||
self._check_model_exists()
|
||||
path, is_wav = check_file(path)
|
||||
for v in self._models.values():
|
||||
v._df = v._fit_transform(path)
|
||||
if not is_wav:
|
||||
os.remove(path)
|
||||
|
||||
@staticmethod
|
||||
def _check_model_exists():
|
||||
"""
|
||||
Check if deepspeech model is exists. if not, download to OPENDBM Directory.
|
||||
"""
|
||||
if not OPENDBM_DATA.exists():
|
||||
os.mkdir(OPENDBM_DATA)
|
||||
for dm in DEEPSPEECH_MODELS:
|
||||
pt = OPENDBM_DATA / dm
|
||||
if not pt.exists():
|
||||
logger.info(
|
||||
f"{dm} not exists. Automatically downloading to {OPENDBM_DATA}/"
|
||||
)
|
||||
download_url(f"{DEEEPSPEECH_URL}/{dm}", pt)
|
||||
else:
|
||||
continue
|
||||
|
||||
def get_transcribe(self):
|
||||
"""
|
||||
Get the model object of Transcribe
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._transcribe
|
||||
|
||||
def get_speech_features(self):
|
||||
"""
|
||||
Get the model object of Speech Features
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._speech_features
|
||||
196
opendbm/api_lib/util.py
Normal file
196
opendbm/api_lib/util.py
Normal file
@@ -0,0 +1,196 @@
|
||||
# import urllib, os
|
||||
import logging
|
||||
import os
|
||||
import platform
|
||||
import subprocess
|
||||
import tempfile
|
||||
import urllib.request as ur
|
||||
|
||||
from tqdm import tqdm
|
||||
|
||||
from opendbm.dbm_lib.controller import process_feature as pf
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
|
||||
# urllib = getattr(urllib, 'request', urllib)
|
||||
|
||||
|
||||
class TqdmUpTo(tqdm):
|
||||
"""Provides `update_to(n)` which uses `tqdm.update(delta_n)`."""
|
||||
|
||||
def update_to(self, b=1, bsize=1, tsize=None):
|
||||
"""
|
||||
b : int, optional
|
||||
Number of blocks transferred so far [default: 1].
|
||||
bsize : int, optional
|
||||
Size of each block (in tqdm units) [default: 1].
|
||||
tsize : int, optional
|
||||
Total size (in tqdm units). If [default: None] remains unchanged.
|
||||
"""
|
||||
if tsize is not None:
|
||||
self.total = tsize
|
||||
return self.update(b * bsize - self.n) # also sets self.n = b * bsize
|
||||
|
||||
|
||||
def download_url(url, local_path):
|
||||
"""
|
||||
Function to download url and drop it to the local path
|
||||
"""
|
||||
with TqdmUpTo(
|
||||
unit="B",
|
||||
unit_scale=True,
|
||||
unit_divisor=1024,
|
||||
miniters=1,
|
||||
desc=url.split("/")[-1],
|
||||
) as t: # all optional kwargs
|
||||
ur.urlretrieve(url, filename=local_path, reporthook=t.update_to, data=None)
|
||||
t.total = t.n
|
||||
|
||||
|
||||
def wsllize(path):
|
||||
"""
|
||||
Add WSL prefix if the platform using windows.
|
||||
This function will also convert input path to wsl structure based on given path.
|
||||
Args:
|
||||
path: path of the input data
|
||||
|
||||
Returns:
|
||||
wsl prefix
|
||||
"""
|
||||
if platform.system() == "Windows":
|
||||
wsl_cmd = ["wsl"]
|
||||
path = subprocess.check_output(["wsl", "wslpath", repr(path)]).decode("utf-8")
|
||||
if path.endswith("\n"):
|
||||
path = path[:-1]
|
||||
return wsl_cmd, path
|
||||
else:
|
||||
return [], path
|
||||
|
||||
|
||||
def check_isfile(path):
|
||||
if not os.path.isfile(path):
|
||||
raise FileNotFoundError("File not found. Make sure specify the correct path")
|
||||
|
||||
|
||||
def check_file(path):
|
||||
"""
|
||||
Check if file is in wav format. if not, convert to wav.
|
||||
Args:
|
||||
path: Input path
|
||||
|
||||
Returns:
|
||||
path: output path of the new wav file
|
||||
bool: returns True if file is wav format
|
||||
"""
|
||||
return (
|
||||
(pf.audio_to_wav(path, tmp=True), False)
|
||||
if not path.endswith(".wav")
|
||||
else (path, True)
|
||||
)
|
||||
|
||||
|
||||
def check_docker_model_exist(wsl_cmd, model_name):
|
||||
"""
|
||||
check if docker model is present or not.
|
||||
|
||||
Args:
|
||||
wsl_cmd: wsl prefix is platform is Windows
|
||||
model_name: self-explanatory
|
||||
|
||||
"""
|
||||
|
||||
try:
|
||||
check_docker_model_exist = subprocess.check_output(
|
||||
wsl_cmd + ["docker", "image", "ls"]
|
||||
).decode("utf-8")
|
||||
if model_name not in check_docker_model_exist:
|
||||
raise FileNotFoundError(
|
||||
f"""
|
||||
{model_name} model not found. Make sure to
|
||||
download the model first. For further instruction about download,
|
||||
please see our web documentation.
|
||||
"""
|
||||
)
|
||||
except subprocess.CalledProcessError:
|
||||
raise EnvironmentError("Make sure to set the Docker to be active")
|
||||
|
||||
|
||||
def docker_command_dec(fn):
|
||||
"""
|
||||
Decorator to execute model in Docker environment.
|
||||
Starting the container and exit state is handled here
|
||||
Args:
|
||||
fn: any fn that need to access docker
|
||||
|
||||
Returns:
|
||||
decorated fn
|
||||
"""
|
||||
import os
|
||||
|
||||
def inner(*args, **kwargs):
|
||||
wsl_cmd, path = wsllize((args[1]))
|
||||
|
||||
check_docker_model_exist(wsl_cmd, "dbm-openface")
|
||||
|
||||
create_docker = wsl_cmd + [
|
||||
"docker",
|
||||
"create",
|
||||
"-ti",
|
||||
"--name",
|
||||
"dbm_container",
|
||||
"dbm-openface",
|
||||
"bash",
|
||||
]
|
||||
|
||||
copy_file_to_docker = wsl_cmd + ["docker", "cp", path, "dbm_container:/app/"]
|
||||
start_container = wsl_cmd + ["docker", "start", "dbm_container"]
|
||||
terminate_container = wsl_cmd + ["docker", "stop", "dbm_container"]
|
||||
remove_container = wsl_cmd + ["docker", "rm", "dbm_container"]
|
||||
|
||||
subprocess.Popen(
|
||||
create_docker,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.PIPE,
|
||||
).wait()
|
||||
subprocess.Popen(
|
||||
copy_file_to_docker,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.PIPE,
|
||||
).wait()
|
||||
subprocess.Popen(
|
||||
start_container,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.PIPE,
|
||||
).wait()
|
||||
|
||||
try:
|
||||
|
||||
args = args[0], "/app/" + os.path.basename(args[1]), args[2]
|
||||
|
||||
result = fn(*args, **kwargs)
|
||||
|
||||
return result
|
||||
except Exception as e:
|
||||
|
||||
logger.info(f"Failed: {e}")
|
||||
|
||||
finally:
|
||||
subprocess.Popen(
|
||||
terminate_container,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.PIPE,
|
||||
).wait()
|
||||
subprocess.Popen(
|
||||
remove_container,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.PIPE,
|
||||
).wait()
|
||||
|
||||
return inner
|
||||
1
opendbm/api_lib/verbal_acoustics/__init__.py
Normal file
1
opendbm/api_lib/verbal_acoustics/__init__.py
Normal file
@@ -0,0 +1 @@
|
||||
from opendbm.api_lib.verbal_acoustics.api import VerbalAcoustics
|
||||
12
opendbm/api_lib/verbal_acoustics/_audio_intensity.py
Normal file
12
opendbm/api_lib/verbal_acoustics/_audio_intensity.py
Normal file
@@ -0,0 +1,12 @@
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib import run_intensity
|
||||
|
||||
|
||||
class AudioIntensity(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["aco_int"]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_intensity(path, ".", self.r_config, save=False)
|
||||
14
opendbm/api_lib/verbal_acoustics/_formant_frequency.py
Normal file
14
opendbm/api_lib/verbal_acoustics/_formant_frequency.py
Normal file
@@ -0,0 +1,14 @@
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib import run_formant
|
||||
|
||||
|
||||
class FormantFrequency(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["aco_fm1", "aco_fm2", "aco_fm3", "aco_fm4"]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_formant(path, ".", self.r_config, save=False)
|
||||
14
opendbm/api_lib/verbal_acoustics/_glottal_noise.py
Normal file
14
opendbm/api_lib/verbal_acoustics/_glottal_noise.py
Normal file
@@ -0,0 +1,14 @@
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.audio.gne import run_gne
|
||||
|
||||
|
||||
class GlottalNoiseRatio(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["aco_gne"]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_gne(path, ".", self.r_config, save=False, ff_df=kwargs["ff_df"])
|
||||
14
opendbm/api_lib/verbal_acoustics/_harmonic_noise.py
Normal file
14
opendbm/api_lib/verbal_acoustics/_harmonic_noise.py
Normal file
@@ -0,0 +1,14 @@
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.audio.hnr import run_hnr
|
||||
|
||||
|
||||
class HarmonicsNoiseRatio(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["aco_hnr"]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_hnr(path, ".", self.r_config, save=False)
|
||||
14
opendbm/api_lib/verbal_acoustics/_jitter.py
Normal file
14
opendbm/api_lib/verbal_acoustics/_jitter.py
Normal file
@@ -0,0 +1,14 @@
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.audio.jitter import run_jitter
|
||||
|
||||
|
||||
class Jitter(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["aco_jitter"]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_jitter(path, ".", self.r_config, save=False, ff_df=kwargs["ff_df"])
|
||||
12
opendbm/api_lib/verbal_acoustics/_mfcc.py
Normal file
12
opendbm/api_lib/verbal_acoustics/_mfcc.py
Normal file
@@ -0,0 +1,12 @@
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib import run_mfcc
|
||||
|
||||
|
||||
class MFCC(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["aco_mfcc" + str(i) for i in range(1, 13)]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_mfcc(path, ".", self.r_config, save=False)
|
||||
20
opendbm/api_lib/verbal_acoustics/_pause_characteristics.py
Normal file
20
opendbm/api_lib/verbal_acoustics/_pause_characteristics.py
Normal file
@@ -0,0 +1,20 @@
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib import run_pause_segment
|
||||
|
||||
|
||||
class PauseCharacteristics(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = [
|
||||
"aco_totaltime",
|
||||
"aco_speakingtime",
|
||||
"aco_numpauses",
|
||||
"aco_pausetime",
|
||||
"aco_pausefrac",
|
||||
]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_pause_segment(path, ".", self.r_config, save=False)
|
||||
14
opendbm/api_lib/verbal_acoustics/_pitch_frequency.py
Normal file
14
opendbm/api_lib/verbal_acoustics/_pitch_frequency.py
Normal file
@@ -0,0 +1,14 @@
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.audio.pitch_freq import run_pitch
|
||||
|
||||
|
||||
class PitchFrequency(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["aco_ff"]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_pitch(path, ".", self.r_config, save=False)
|
||||
14
opendbm/api_lib/verbal_acoustics/_shimmer.py
Normal file
14
opendbm/api_lib/verbal_acoustics/_shimmer.py
Normal file
@@ -0,0 +1,14 @@
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.audio.shimmer import run_shimmer
|
||||
|
||||
|
||||
class Shimmer(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["aco_shimmer"]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_shimmer(path, ".", self.r_config, save=False, ff_df=kwargs["ff_df"])
|
||||
14
opendbm/api_lib/verbal_acoustics/_voice_prevalence.py
Normal file
14
opendbm/api_lib/verbal_acoustics/_voice_prevalence.py
Normal file
@@ -0,0 +1,14 @@
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.audio.voice_frame_score import run_vfs
|
||||
|
||||
|
||||
class VoicePrevalence(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._params = ["aco_voiceframe", "aco_totvoiceframe", "aco_voicepct"]
|
||||
|
||||
@AudioModel.prep_func
|
||||
def _fit_transform(self, path, **kwargs):
|
||||
return run_vfs(path, ".", self.r_config, save=False)
|
||||
154
opendbm/api_lib/verbal_acoustics/api.py
Normal file
154
opendbm/api_lib/verbal_acoustics/api.py
Normal file
@@ -0,0 +1,154 @@
|
||||
import os
|
||||
from collections import OrderedDict
|
||||
|
||||
from opendbm.api_lib.model import AudioModel
|
||||
from opendbm.api_lib.util import check_file, check_isfile
|
||||
from opendbm.dbm_lib.controller import process_feature as pf
|
||||
|
||||
from ._audio_intensity import AudioIntensity
|
||||
from ._formant_frequency import FormantFrequency
|
||||
from ._glottal_noise import GlottalNoiseRatio
|
||||
from ._harmonic_noise import HarmonicsNoiseRatio
|
||||
from ._jitter import Jitter
|
||||
from ._mfcc import MFCC
|
||||
from ._pause_characteristics import PauseCharacteristics
|
||||
from ._pitch_frequency import PitchFrequency
|
||||
from ._shimmer import Shimmer
|
||||
from ._voice_prevalence import VoicePrevalence
|
||||
|
||||
|
||||
class VerbalAcoustics(AudioModel):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self._auint = AudioIntensity()
|
||||
self._pitchfreq = PitchFrequency()
|
||||
self._forfreq = FormantFrequency()
|
||||
self._hnr = HarmonicsNoiseRatio()
|
||||
self._gne = GlottalNoiseRatio()
|
||||
self._jitter = Jitter()
|
||||
self._shimmer = Shimmer()
|
||||
self._pchar = PauseCharacteristics()
|
||||
self._vopre = VoicePrevalence()
|
||||
self._mfcc = MFCC()
|
||||
self._models = OrderedDict(
|
||||
{
|
||||
"audio_intensity": self._auint,
|
||||
"pitch_frequency": self._pitchfreq,
|
||||
"formant_frequency": self._forfreq,
|
||||
"harmonic_noise": self._hnr,
|
||||
"glottal_noise": self._gne,
|
||||
"jitter": self._jitter,
|
||||
"shimmer": self._shimmer,
|
||||
"pause_characteristics": self._pchar,
|
||||
"voice_prevalence": self._vopre,
|
||||
"mfcc": self._mfcc,
|
||||
}
|
||||
)
|
||||
|
||||
def fit(self, path):
|
||||
"""Fit a file in filepath to parselmouth Model.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : string,
|
||||
File Path of Video/Sound file format.
|
||||
"""
|
||||
check_isfile(path)
|
||||
path, is_wav = check_file(path)
|
||||
for k, v in self._models.items():
|
||||
if k in ["glottal_noise", "jitter", "shimmer"]:
|
||||
v._df = v._fit_transform(path, ff_df=self._pitchfreq._df)
|
||||
else:
|
||||
v._df = v._fit_transform(path)
|
||||
if not is_wav:
|
||||
os.remove(path)
|
||||
|
||||
def get_audio_intensity(self):
|
||||
"""
|
||||
Get the model object of Audio Intensity
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._auint
|
||||
|
||||
def get_pitch_frequency(self):
|
||||
"""
|
||||
Get the model object of Pitch Frequency
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._pitchfreq
|
||||
|
||||
def get_formant_frequency(self):
|
||||
"""
|
||||
Get the model object of Formant Frequency
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._forfreq
|
||||
|
||||
def get_harmonic_noise(self):
|
||||
"""
|
||||
Get the model object of Harmonic Noise
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._hnr
|
||||
|
||||
def get_glottal_noise(self):
|
||||
"""
|
||||
Get the model object of Glottal Noise
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._gne
|
||||
|
||||
def get_jitter(self):
|
||||
"""
|
||||
Get the model object of Jitter
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._jitter
|
||||
|
||||
def get_shimmer(self):
|
||||
"""
|
||||
Get the model object of Shimmer
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._shimmer
|
||||
|
||||
def get_pause_characteristics(self):
|
||||
"""
|
||||
Get the model object of Pause Characteristics
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._pchar
|
||||
|
||||
def get_voice_prevalence(self):
|
||||
"""
|
||||
Get the model object of Vocal Prevalence
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._vopre
|
||||
|
||||
def get_mfcc(self):
|
||||
"""
|
||||
Get the model object of MFCC
|
||||
Returns:
|
||||
self: object
|
||||
Model Object
|
||||
"""
|
||||
return self._mfcc
|
||||
19
opendbm/dbm_lib/__init__.py
Normal file
19
opendbm/dbm_lib/__init__.py
Normal file
@@ -0,0 +1,19 @@
|
||||
"""
|
||||
file_name: init
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
from .config import config_derive_feature, config_raw_feature, config_reader
|
||||
from .dbm_features.raw_features.audio.formant_freq import run_formant
|
||||
from .dbm_features.raw_features.audio.intensity import run_intensity
|
||||
from .dbm_features.raw_features.audio.mfcc import run_mfcc
|
||||
from .dbm_features.raw_features.audio.pause_segment import run_pause_segment
|
||||
from .dbm_features.raw_features.movement.facial_tremor import fac_tremor_process
|
||||
from .dbm_features.raw_features.movement.head_motion import run_head_movement
|
||||
from .dbm_features.raw_features.nlp.speech_features import run_speech_feature
|
||||
from .dbm_features.raw_features.video import ConfigFaceReader
|
||||
from .dbm_features.raw_features.video.face_asymmetry import run_face_asymmetry
|
||||
from .dbm_features.raw_features.video.face_landmark import run_face_landmark
|
||||
0
opendbm/dbm_lib/config/__init__.py
Normal file
0
opendbm/dbm_lib/config/__init__.py
Normal file
@@ -4,15 +4,22 @@ project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
import yaml
|
||||
from dbm_lib import DBMLIB_DERIVE_FEATURE_CONFIG
|
||||
|
||||
DBMLIB_PATH = os.path.dirname(__file__)
|
||||
DBMLIB_DERIVE_FEATURE_CONFIG = os.path.abspath(
|
||||
os.path.join(DBMLIB_PATH, "../../resources/features/derived_feature.yml")
|
||||
)
|
||||
|
||||
|
||||
class ConfigDeriveReader(object):
|
||||
"""Summary
|
||||
Read sevice end ponit
|
||||
"""
|
||||
def __init__(self,
|
||||
feature_config_yml=None):
|
||||
|
||||
def __init__(self, feature_config_yml=None):
|
||||
"""Summary
|
||||
Args:
|
||||
feature_config_yml (None, optional): yml file defined service configuration
|
||||
@@ -23,7 +30,6 @@ class ConfigDeriveReader(object):
|
||||
else:
|
||||
feature_config = feature_config_yml
|
||||
|
||||
with open(feature_config, 'r') as ymlfile:
|
||||
config = yaml.load(ymlfile)
|
||||
with open(feature_config, "r") as ymlfile:
|
||||
config = yaml.load(ymlfile, Loader=yaml.CLoader)
|
||||
self.base_derive = config
|
||||
|
||||
283
opendbm/dbm_lib/config/config_raw_feature.py
Normal file
283
opendbm/dbm_lib/config/config_raw_feature.py
Normal file
@@ -0,0 +1,283 @@
|
||||
"""
|
||||
file_name: config_raw_feature
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
import yaml
|
||||
|
||||
DBMLIB_PATH = os.path.dirname(__file__)
|
||||
DBMLIB_FEATURE_CONFIG = os.path.abspath(
|
||||
os.path.join(DBMLIB_PATH, "../../resources/features/raw_feature.yml")
|
||||
)
|
||||
|
||||
|
||||
class ConfigRawReader(object):
|
||||
"""Summary
|
||||
Read sevice end ponit
|
||||
"""
|
||||
|
||||
def __init__(self, feature_config_yml=None):
|
||||
"""Summary
|
||||
Args:
|
||||
feature_config_yml (None, optional): yml file defined service configuration
|
||||
"""
|
||||
|
||||
if feature_config_yml is None:
|
||||
feature_config = DBMLIB_FEATURE_CONFIG
|
||||
else:
|
||||
feature_config = feature_config_yml
|
||||
|
||||
with open(feature_config, "r") as ymlfile:
|
||||
config = yaml.load(ymlfile, Loader=yaml.CLoader)
|
||||
|
||||
# Verbal features
|
||||
self.base_raw = config
|
||||
self.err_reason = config["raw_feature"]["error_reason"]
|
||||
|
||||
# Output range
|
||||
self.mov_headvel_start = config["raw_feature"]["mov_headvel_start"]
|
||||
self.mov_headvel_end = config["raw_feature"]["mov_headvel_end"]
|
||||
|
||||
# Acoustic variable
|
||||
self.aco_int = config["raw_feature"]["aco_int"]
|
||||
self.aco_ff = config["raw_feature"]["aco_ff"]
|
||||
self.aco_voiceLabel = config["raw_feature"]["aco_voiceLabel"]
|
||||
self.aco_hnr = config["raw_feature"]["aco_hnr"]
|
||||
self.aco_gne = config["raw_feature"]["aco_gne"]
|
||||
self.aco_fm1 = config["raw_feature"]["aco_fm1"]
|
||||
self.aco_fm2 = config["raw_feature"]["aco_fm2"]
|
||||
self.aco_fm3 = config["raw_feature"]["aco_fm3"]
|
||||
self.aco_fm4 = config["raw_feature"]["aco_fm4"]
|
||||
self.aco_jitter = config["raw_feature"]["aco_jitter"]
|
||||
self.aco_shimmer = config["raw_feature"]["aco_shimmer"]
|
||||
self.aco_mfcc1 = config["raw_feature"]["aco_mfcc1"]
|
||||
self.aco_mfcc2 = config["raw_feature"]["aco_mfcc2"]
|
||||
self.aco_mfcc3 = config["raw_feature"]["aco_mfcc3"]
|
||||
self.aco_mfcc4 = config["raw_feature"]["aco_mfcc4"]
|
||||
self.aco_mfcc5 = config["raw_feature"]["aco_mfcc5"]
|
||||
self.aco_mfcc6 = config["raw_feature"]["aco_mfcc6"]
|
||||
self.aco_mfcc7 = config["raw_feature"]["aco_mfcc7"]
|
||||
self.aco_mfcc8 = config["raw_feature"]["aco_mfcc8"]
|
||||
self.aco_mfcc9 = config["raw_feature"]["aco_mfcc9"]
|
||||
self.aco_mfcc10 = config["raw_feature"]["aco_mfcc10"]
|
||||
self.aco_mfcc11 = config["raw_feature"]["aco_mfcc11"]
|
||||
self.aco_mfcc12 = config["raw_feature"]["aco_mfcc12"]
|
||||
self.aco_voiceFrame = config["raw_feature"]["aco_voiceFrame"]
|
||||
self.aco_totVoiceFrame = config["raw_feature"]["aco_totVoiceFrame"]
|
||||
self.aco_voicePct = config["raw_feature"]["aco_voicePct"]
|
||||
self.aco_pausetime = config["raw_feature"]["aco_pausetime"]
|
||||
self.aco_totaltime = config["raw_feature"]["aco_totaltime"]
|
||||
self.aco_speakingtime = config["raw_feature"]["aco_speakingtime"]
|
||||
self.aco_numpauses = config["raw_feature"]["aco_numpauses"]
|
||||
self.aco_pausefrac = config["raw_feature"]["aco_pausefrac"]
|
||||
|
||||
# Facial Action Unit (for consistency)
|
||||
self.fac_AU01int = config["raw_feature"]["fac_AU01int"]
|
||||
self.fac_AU02int = config["raw_feature"]["fac_AU02int"]
|
||||
self.fac_AU04int = config["raw_feature"]["fac_AU04int"]
|
||||
self.fac_AU05int = config["raw_feature"]["fac_AU05int"]
|
||||
self.fac_AU06int = config["raw_feature"]["fac_AU06int"]
|
||||
self.fac_AU07int = config["raw_feature"]["fac_AU07int"]
|
||||
self.fac_AU09int = config["raw_feature"]["fac_AU09int"]
|
||||
self.fac_AU10int = config["raw_feature"]["fac_AU10int"]
|
||||
self.fac_AU12int = config["raw_feature"]["fac_AU12int"]
|
||||
self.fac_AU14int = config["raw_feature"]["fac_AU14int"]
|
||||
self.fac_AU15int = config["raw_feature"]["fac_AU15int"]
|
||||
self.fac_AU17int = config["raw_feature"]["fac_AU17int"]
|
||||
self.fac_AU20int = config["raw_feature"]["fac_AU20int"]
|
||||
self.fac_AU23int = config["raw_feature"]["fac_AU23int"]
|
||||
self.fac_AU25int = config["raw_feature"]["fac_AU25int"]
|
||||
self.fac_AU26int = config["raw_feature"]["fac_AU26int"]
|
||||
self.fac_AU45int = config["raw_feature"]["fac_AU45int"]
|
||||
self.fac_AU01pres = config["raw_feature"]["fac_AU01pres"]
|
||||
self.fac_AU02pres = config["raw_feature"]["fac_AU02pres"]
|
||||
self.fac_AU04pres = config["raw_feature"]["fac_AU04pres"]
|
||||
self.fac_AU05pres = config["raw_feature"]["fac_AU05pres"]
|
||||
self.fac_AU06pres = config["raw_feature"]["fac_AU06pres"]
|
||||
self.fac_AU07pres = config["raw_feature"]["fac_AU07pres"]
|
||||
self.fac_AU09pres = config["raw_feature"]["fac_AU09pres"]
|
||||
self.fac_AU10pres = config["raw_feature"]["fac_AU10pres"]
|
||||
self.fac_AU12pres = config["raw_feature"]["fac_AU12pres"]
|
||||
self.fac_AU14pres = config["raw_feature"]["fac_AU14pres"]
|
||||
self.fac_AU15pres = config["raw_feature"]["fac_AU15pres"]
|
||||
self.fac_AU17pres = config["raw_feature"]["fac_AU17pres"]
|
||||
self.fac_AU20pres = config["raw_feature"]["fac_AU20pres"]
|
||||
self.fac_AU23pres = config["raw_feature"]["fac_AU23pres"]
|
||||
self.fac_AU25pres = config["raw_feature"]["fac_AU25pres"]
|
||||
self.fac_AU26pres = config["raw_feature"]["fac_AU26pres"]
|
||||
self.fac_AU28pres = config["raw_feature"]["fac_AU28pres"]
|
||||
self.fac_AU45pres = config["raw_feature"]["fac_AU45pres"]
|
||||
|
||||
# Facial Landmarks (for consistency)
|
||||
self.fac_LMK00disp = config["raw_feature"]["fac_LMK00disp"]
|
||||
self.fac_LMK01disp = config["raw_feature"]["fac_LMK01disp"]
|
||||
self.fac_LMK02disp = config["raw_feature"]["fac_LMK02disp"]
|
||||
self.fac_LMK03disp = config["raw_feature"]["fac_LMK03disp"]
|
||||
self.fac_LMK04disp = config["raw_feature"]["fac_LMK04disp"]
|
||||
self.fac_LMK05disp = config["raw_feature"]["fac_LMK05disp"]
|
||||
self.fac_LMK06disp = config["raw_feature"]["fac_LMK06disp"]
|
||||
self.fac_LMK07disp = config["raw_feature"]["fac_LMK07disp"]
|
||||
self.fac_LMK08disp = config["raw_feature"]["fac_LMK08disp"]
|
||||
self.fac_LMK09disp = config["raw_feature"]["fac_LMK09disp"]
|
||||
self.fac_LMK10disp = config["raw_feature"]["fac_LMK10disp"]
|
||||
self.fac_LMK11disp = config["raw_feature"]["fac_LMK11disp"]
|
||||
self.fac_LMK12disp = config["raw_feature"]["fac_LMK12disp"]
|
||||
self.fac_LMK13disp = config["raw_feature"]["fac_LMK13disp"]
|
||||
self.fac_LMK14disp = config["raw_feature"]["fac_LMK14disp"]
|
||||
self.fac_LMK15disp = config["raw_feature"]["fac_LMK15disp"]
|
||||
self.fac_LMK16disp = config["raw_feature"]["fac_LMK16disp"]
|
||||
self.fac_LMK17disp = config["raw_feature"]["fac_LMK17disp"]
|
||||
self.fac_LMK18disp = config["raw_feature"]["fac_LMK18disp"]
|
||||
self.fac_LMK19disp = config["raw_feature"]["fac_LMK19disp"]
|
||||
self.fac_LMK20disp = config["raw_feature"]["fac_LMK20disp"]
|
||||
self.fac_LMK21disp = config["raw_feature"]["fac_LMK21disp"]
|
||||
self.fac_LMK22disp = config["raw_feature"]["fac_LMK22disp"]
|
||||
self.fac_LMK23disp = config["raw_feature"]["fac_LMK23disp"]
|
||||
self.fac_LMK24disp = config["raw_feature"]["fac_LMK24disp"]
|
||||
self.fac_LMK25disp = config["raw_feature"]["fac_LMK25disp"]
|
||||
self.fac_LMK26disp = config["raw_feature"]["fac_LMK26disp"]
|
||||
self.fac_LMK27disp = config["raw_feature"]["fac_LMK27disp"]
|
||||
self.fac_LMK28disp = config["raw_feature"]["fac_LMK28disp"]
|
||||
self.fac_LMK29disp = config["raw_feature"]["fac_LMK29disp"]
|
||||
self.fac_LMK30disp = config["raw_feature"]["fac_LMK30disp"]
|
||||
self.fac_LMK31disp = config["raw_feature"]["fac_LMK31disp"]
|
||||
self.fac_LMK32disp = config["raw_feature"]["fac_LMK32disp"]
|
||||
self.fac_LMK33disp = config["raw_feature"]["fac_LMK33disp"]
|
||||
self.fac_LMK34disp = config["raw_feature"]["fac_LMK34disp"]
|
||||
self.fac_LMK35disp = config["raw_feature"]["fac_LMK35disp"]
|
||||
self.fac_LMK36disp = config["raw_feature"]["fac_LMK36disp"]
|
||||
self.fac_LMK37disp = config["raw_feature"]["fac_LMK37disp"]
|
||||
self.fac_LMK38disp = config["raw_feature"]["fac_LMK38disp"]
|
||||
self.fac_LMK39disp = config["raw_feature"]["fac_LMK39disp"]
|
||||
self.fac_LMK40disp = config["raw_feature"]["fac_LMK40disp"]
|
||||
self.fac_LMK41disp = config["raw_feature"]["fac_LMK41disp"]
|
||||
self.fac_LMK42disp = config["raw_feature"]["fac_LMK42disp"]
|
||||
self.fac_LMK43disp = config["raw_feature"]["fac_LMK43disp"]
|
||||
self.fac_LMK44disp = config["raw_feature"]["fac_LMK44disp"]
|
||||
self.fac_LMK45disp = config["raw_feature"]["fac_LMK45disp"]
|
||||
self.fac_LMK46disp = config["raw_feature"]["fac_LMK46disp"]
|
||||
self.fac_LMK47disp = config["raw_feature"]["fac_LMK47disp"]
|
||||
self.fac_LMK48disp = config["raw_feature"]["fac_LMK48disp"]
|
||||
self.fac_LMK49disp = config["raw_feature"]["fac_LMK49disp"]
|
||||
self.fac_LMK50disp = config["raw_feature"]["fac_LMK50disp"]
|
||||
self.fac_LMK51disp = config["raw_feature"]["fac_LMK51disp"]
|
||||
self.fac_LMK52disp = config["raw_feature"]["fac_LMK52disp"]
|
||||
self.fac_LMK53disp = config["raw_feature"]["fac_LMK53disp"]
|
||||
self.fac_LMK54disp = config["raw_feature"]["fac_LMK54disp"]
|
||||
self.fac_LMK55disp = config["raw_feature"]["fac_LMK55disp"]
|
||||
self.fac_LMK56disp = config["raw_feature"]["fac_LMK56disp"]
|
||||
self.fac_LMK57disp = config["raw_feature"]["fac_LMK57disp"]
|
||||
self.fac_LMK58disp = config["raw_feature"]["fac_LMK58disp"]
|
||||
self.fac_LMK59disp = config["raw_feature"]["fac_LMK59disp"]
|
||||
self.fac_LMK60disp = config["raw_feature"]["fac_LMK60disp"]
|
||||
self.fac_LMK61disp = config["raw_feature"]["fac_LMK61disp"]
|
||||
self.fac_LMK62disp = config["raw_feature"]["fac_LMK62disp"]
|
||||
self.fac_LMK63disp = config["raw_feature"]["fac_LMK63disp"]
|
||||
self.fac_LMK64disp = config["raw_feature"]["fac_LMK64disp"]
|
||||
self.fac_LMK65disp = config["raw_feature"]["fac_LMK65disp"]
|
||||
self.fac_LMK66disp = config["raw_feature"]["fac_LMK66disp"]
|
||||
self.fac_LMK67disp = config["raw_feature"]["fac_LMK67disp"]
|
||||
|
||||
# Facial features
|
||||
self.hap_exp = config["raw_feature"]["hap_exp"]
|
||||
self.sad_exp = config["raw_feature"]["sad_exp"]
|
||||
self.sur_exp = config["raw_feature"]["sur_exp"]
|
||||
self.fea_exp = config["raw_feature"]["fea_exp"]
|
||||
self.ang_exp = config["raw_feature"]["ang_exp"]
|
||||
self.dis_exp = config["raw_feature"]["dis_exp"]
|
||||
self.con_exp = config["raw_feature"]["con_exp"]
|
||||
self.happ_occ = config["raw_feature"]["happ_occ"]
|
||||
self.sad_occ = config["raw_feature"]["sad_occ"]
|
||||
self.sur_occ = config["raw_feature"]["sur_occ"]
|
||||
self.fea_occ = config["raw_feature"]["fea_occ"]
|
||||
self.ang_occ = config["raw_feature"]["ang_occ"]
|
||||
self.dis_occ = config["raw_feature"]["dis_occ"]
|
||||
self.con_occ = config["raw_feature"]["con_occ"]
|
||||
self.pos_exp = config["raw_feature"]["pos_exp"]
|
||||
self.neg_exp = config["raw_feature"]["neg_exp"]
|
||||
self.neu_exp = config["raw_feature"]["neu_exp"]
|
||||
self.cai_exp = config["raw_feature"]["cai_exp"]
|
||||
self.com_exp = config["raw_feature"]["com_exp"]
|
||||
self.com_lower_exp = config["raw_feature"]["com_lower_exp"]
|
||||
self.com_upper_exp = config["raw_feature"]["com_upper_exp"]
|
||||
self.pai_exp = config["raw_feature"]["pai_exp"]
|
||||
self.hap_exp_full = config["raw_feature"]["hap_exp_full"]
|
||||
self.sad_exp_full = config["raw_feature"]["sad_exp_full"]
|
||||
self.sur_exp_full = config["raw_feature"]["sur_exp_full"]
|
||||
self.fea_exp_full = config["raw_feature"]["fea_exp_full"]
|
||||
self.ang_exp_full = config["raw_feature"]["ang_exp_full"]
|
||||
self.dis_exp_full = config["raw_feature"]["dis_exp_full"]
|
||||
self.con_exp_full = config["raw_feature"]["con_exp_full"]
|
||||
self.pos_exp_full = config["raw_feature"]["pos_exp_full"]
|
||||
self.neg_exp_full = config["raw_feature"]["neg_exp_full"]
|
||||
self.neu_exp_full = config["raw_feature"]["neu_exp_full"]
|
||||
self.cai_exp_full = config["raw_feature"]["cai_exp_full"]
|
||||
self.com_exp_full = config["raw_feature"]["com_exp_full"]
|
||||
self.com_lower_exp_full = config["raw_feature"]["com_lower_exp_full"]
|
||||
self.com_upper_exp_full = config["raw_feature"]["com_upper_exp_full"]
|
||||
self.pai_exp_full = config["raw_feature"]["pai_exp_full"]
|
||||
self.fac_AsymMaskMouth = config["raw_feature"]["fac_AsymMaskMouth"]
|
||||
self.fac_AsymMaskEye = config["raw_feature"]["fac_AsymMaskEye"]
|
||||
self.fac_AsymMaskEyebrow = config["raw_feature"]["fac_AsymMaskEyebrow"]
|
||||
self.fac_AsymMaskCom = config["raw_feature"]["fac_AsymMaskCom"]
|
||||
|
||||
# Movement features
|
||||
self.head_vel = config["raw_feature"]["head_vel"]
|
||||
self.mov_blink_ear = config["raw_feature"]["mov_blink_ear"]
|
||||
self.vid_dur = config["raw_feature"]["vid_dur"]
|
||||
self.fps = config["raw_feature"]["fps"]
|
||||
self.mov_blinkframes = config["raw_feature"]["mov_blinkframes"]
|
||||
self.mov_blinkdur = config["raw_feature"]["mov_blinkdur"]
|
||||
self.mov_Hpose_Pitch = config["raw_feature"]["mov_Hpose_Pitch"]
|
||||
self.mov_Hpose_Yaw = config["raw_feature"]["mov_Hpose_Yaw"]
|
||||
self.mov_Hpose_Roll = config["raw_feature"]["mov_Hpose_Roll"]
|
||||
self.mov_Hpose_Dist = config["raw_feature"]["mov_Hpose_Dist"]
|
||||
|
||||
self.mov_freq_trem_freq = config["raw_feature"]["mov_freq_trem_freq"]
|
||||
self.mov_freq_trem_index = config["raw_feature"]["mov_freq_trem_index"]
|
||||
self.mov_freq_trem_pindex = config["raw_feature"]["mov_freq_trem_pindex"]
|
||||
self.mov_amp_trem_freq = config["raw_feature"]["mov_amp_trem_freq"]
|
||||
self.mov_amp_trem_index = config["raw_feature"]["mov_amp_trem_index"]
|
||||
self.mov_amp_trem_pindex = config["raw_feature"]["mov_amp_trem_pindex"]
|
||||
|
||||
self.fac_tremor_median_5 = config["raw_feature"]["fac_tremor_median_5"]
|
||||
self.fac_tremor_median_12 = config["raw_feature"]["fac_tremor_median_12"]
|
||||
self.fac_tremor_median_8 = config["raw_feature"]["fac_tremor_median_8"]
|
||||
self.fac_tremor_median_48 = config["raw_feature"]["fac_tremor_median_48"]
|
||||
self.fac_tremor_median_54 = config["raw_feature"]["fac_tremor_median_54"]
|
||||
self.fac_tremor_median_28 = config["raw_feature"]["fac_tremor_median_28"]
|
||||
self.fac_tremor_median_51 = config["raw_feature"]["fac_tremor_median_51"]
|
||||
self.fac_tremor_median_66 = config["raw_feature"]["fac_tremor_median_66"]
|
||||
self.fac_tremor_median_57 = config["raw_feature"]["fac_tremor_median_57"]
|
||||
|
||||
self.mov_leye_x = config["raw_feature"]["mov_leye_x"]
|
||||
self.mov_leye_y = config["raw_feature"]["mov_leye_y"]
|
||||
self.mov_leye_z = config["raw_feature"]["mov_leye_z"]
|
||||
self.mov_reye_x = config["raw_feature"]["mov_reye_x"]
|
||||
self.mov_reye_y = config["raw_feature"]["mov_reye_y"]
|
||||
self.mov_reye_z = config["raw_feature"]["mov_reye_z"]
|
||||
self.mov_eleft_disp = config["raw_feature"]["mov_eleft_disp"]
|
||||
self.mov_eright_disp = config["raw_feature"]["mov_eright_disp"]
|
||||
|
||||
# NLP features
|
||||
self.nlp_transcribe = config["raw_feature"]["nlp_transcribe"]
|
||||
self.nlp_numSentences = config["raw_feature"]["nlp_numSentences"]
|
||||
self.nlp_singPronPerAns = config["raw_feature"]["nlp_singPronPerAns"]
|
||||
self.nlp_singPronPerSen = config["raw_feature"]["nlp_singPronPerSen"]
|
||||
self.nlp_pastTensePerAns = config["raw_feature"]["nlp_pastTensePerAns"]
|
||||
self.nlp_pastTensePerSen = config["raw_feature"]["nlp_pastTensePerSen"]
|
||||
self.nlp_pronounsPerAns = config["raw_feature"]["nlp_pronounsPerAns"]
|
||||
self.nlp_pronounsPerSen = config["raw_feature"]["nlp_pronounsPerSen"]
|
||||
self.nlp_verbsPerAns = config["raw_feature"]["nlp_verbsPerAns"]
|
||||
self.nlp_verbsPerSen = config["raw_feature"]["nlp_verbsPerSen"]
|
||||
self.nlp_adjectivesPerAns = config["raw_feature"]["nlp_adjectivesPerAns"]
|
||||
self.nlp_adjectivesPerSen = config["raw_feature"]["nlp_adjectivesPerSen"]
|
||||
self.nlp_nounsPerAns = config["raw_feature"]["nlp_nounsPerAns"]
|
||||
self.nlp_nounsPerSen = config["raw_feature"]["nlp_nounsPerSen"]
|
||||
self.nlp_sentiment_mean = config["raw_feature"]["nlp_sentiment_mean"]
|
||||
self.nlp_mattr = config["raw_feature"]["nlp_mattr"]
|
||||
self.nlp_wordsPerMin = config["raw_feature"]["nlp_wordsPerMin"]
|
||||
self.nlp_totalTime = config["raw_feature"]["nlp_totalTime"]
|
||||
@@ -4,15 +4,22 @@ project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
import yaml
|
||||
from dbm_lib import DBMLIB_SERVICE_CONFIG
|
||||
|
||||
DBMLIB_PATH = os.path.dirname(__file__)
|
||||
DBMLIB_SERVICE_CONFIG = os.path.abspath(
|
||||
os.path.join(DBMLIB_PATH, "../../resources/services/services.yml")
|
||||
)
|
||||
|
||||
|
||||
class ConfigReader(object):
|
||||
"""Summary
|
||||
Read sevice end ponit
|
||||
"""
|
||||
def __init__(self,
|
||||
service_config_yml=None):
|
||||
|
||||
def __init__(self, service_config_yml=None):
|
||||
"""Summary
|
||||
Args:
|
||||
service_config_yml (None, optional): yml file defined service configuration
|
||||
@@ -22,14 +29,14 @@ class ConfigReader(object):
|
||||
else:
|
||||
service_config = service_config_yml
|
||||
|
||||
with open(service_config, 'r') as ymlfile:
|
||||
config = yaml.load(ymlfile)
|
||||
self.input_dir = config['cdx_configuration']['input_dir']
|
||||
self.output_dir = config['cdx_configuration']['output_dir']
|
||||
self.out_derived_dir = config['cdx_configuration']['out_derived_dir']
|
||||
self.of_path = config['cdx_configuration']['open_face_path']
|
||||
self.facial_landmarks = config['cdx_configuration']['facial_landmarks']
|
||||
self.feature_group = config['cdx_configuration']['feature_group']
|
||||
with open(service_config, "r") as ymlfile:
|
||||
config = yaml.load(ymlfile, Loader=yaml.CLoader)
|
||||
self.input_dir = config["cdx_configuration"]["input_dir"]
|
||||
self.output_dir = config["cdx_configuration"]["output_dir"]
|
||||
self.out_derived_dir = config["cdx_configuration"]["out_derived_dir"]
|
||||
self.of_path = config["cdx_configuration"]["open_face_path"]
|
||||
self.facial_landmarks = config["cdx_configuration"]["facial_landmarks"]
|
||||
self.feature_group = config["cdx_configuration"]["feature_group"]
|
||||
|
||||
def get_open_face_path(self):
|
||||
"""Summary
|
||||
0
opendbm/dbm_lib/controller/__init__.py
Normal file
0
opendbm/dbm_lib/controller/__init__.py
Normal file
187
opendbm/dbm_lib/controller/process_feature.py
Normal file
187
opendbm/dbm_lib/controller/process_feature.py
Normal file
@@ -0,0 +1,187 @@
|
||||
"""
|
||||
file_name: process_features
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
import subprocess
|
||||
import tempfile
|
||||
from os.path import basename, dirname, isfile, join, splitext
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features import audio, movement, nlp, video
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
|
||||
def audio_to_wav(input_filepath, tmp=False):
|
||||
"""Extracts a video's audio file and saves it to wav
|
||||
Args:
|
||||
input_filepath: (str)
|
||||
Returns:
|
||||
"""
|
||||
try:
|
||||
|
||||
fname, _ = splitext(input_filepath)
|
||||
if tmp:
|
||||
fname = os.path.basename(input_filepath)
|
||||
output_filepath = f"{tempfile.gettempdir()}/{fname}.wav"
|
||||
else:
|
||||
output_filepath = fname + ".wav"
|
||||
|
||||
if not isfile(output_filepath):
|
||||
call = [
|
||||
"ffmpeg",
|
||||
"-i",
|
||||
input_filepath,
|
||||
"-vn",
|
||||
"-acodec",
|
||||
"pcm_s16le",
|
||||
"-ar",
|
||||
"44100",
|
||||
output_filepath,
|
||||
]
|
||||
|
||||
logger.info("Converting audio from {} to wav".format(input_filepath))
|
||||
subprocess.Popen(
|
||||
call,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.PIPE,
|
||||
).wait()
|
||||
# subprocess.check_output(call)
|
||||
logger.info("wav output saved in {}".format(output_filepath))
|
||||
else:
|
||||
logger.info("Output file {} already exists".format(output_filepath))
|
||||
return output_filepath
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Failed to extract audio from Video", e)
|
||||
|
||||
|
||||
def process_acoustic(video_uri, out_dir, dbm_group, r_config):
|
||||
"""
|
||||
processing acoustic features
|
||||
Args:
|
||||
video_uri: video path; out_dir: raw variable output dir
|
||||
dbm_group: list of features group to process; r_config: raw feature config object
|
||||
"""
|
||||
if dbm_group is not None and len(dbm_group) > 0 and "acoustic" not in dbm_group:
|
||||
return
|
||||
|
||||
logger.info("Processing acoustic variables from data in {}".format(video_uri))
|
||||
logger.info("processing audio intensity....")
|
||||
audio.intensity.run_intensity(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing audio pitch freq....")
|
||||
audio.pitch_freq.run_pitch(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing HNR....")
|
||||
audio.hnr.run_hnr(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing GNE....")
|
||||
audio.gne.run_gne(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing voice frame score....")
|
||||
audio.voice_frame_score.run_vfs(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing formant frequency....")
|
||||
audio.formant_freq.run_formant(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing pause segment....")
|
||||
audio.pause_segment.run_pause_segment(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing jitter....")
|
||||
audio.jitter.run_jitter(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing shimmer....")
|
||||
audio.shimmer.run_shimmer(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing mfcc....")
|
||||
audio.mfcc.run_mfcc(video_uri, out_dir, r_config)
|
||||
|
||||
|
||||
def process_facial(video_uri, out_dir, dbm_group, r_config):
|
||||
"""
|
||||
processing facial features
|
||||
Args:
|
||||
video_uri: video path; out_dir: raw variable output dir
|
||||
dbm_group: list of features to process; r_config: raw feature config object
|
||||
"""
|
||||
if dbm_group is not None and len(dbm_group) > 0 and "facial" not in dbm_group:
|
||||
return
|
||||
|
||||
logger.info("Processing facial variables from data in {}".format(video_uri))
|
||||
logger.info("processing facial asymmetry....")
|
||||
video.face_asymmetry.run_face_asymmetry(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing facial Action Unit....")
|
||||
video.face_au.run_face_au(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing facial expressivity....")
|
||||
video.face_emotion_expressivity.run_face_expressivity(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing facial landmark....")
|
||||
video.face_landmark.run_face_landmark(video_uri, out_dir, r_config)
|
||||
|
||||
|
||||
def process_movement(video_uri, out_dir, dbm_group, r_config, dlib_model):
|
||||
"""
|
||||
processing facial features
|
||||
Args:
|
||||
video_uri: video path; out_dir: raw variable output dir
|
||||
dbm_group: list of features to process; r_config: raw feature config object
|
||||
dlib_model: shape predictor model path
|
||||
"""
|
||||
if dbm_group is not None and len(dbm_group) > 0 and "movement" not in dbm_group:
|
||||
return
|
||||
|
||||
logger.info("Processing movement variables from data in {}".format(video_uri))
|
||||
|
||||
logger.info("processing head movement....")
|
||||
movement.head_motion.run_head_movement(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing eye blink....")
|
||||
movement.eye_blink.run_eye_blink(video_uri, out_dir, r_config, dlib_model)
|
||||
|
||||
logger.info("processing eye gaze....")
|
||||
movement.eye_gaze.run_eye_gaze(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing voice tremor....")
|
||||
movement.voice_tremor.run_vtremor(video_uri, out_dir, r_config)
|
||||
|
||||
logger.info("processing facial tremor....")
|
||||
movement.facial_tremor.fac_tremor_process(
|
||||
video_uri, out_dir, r_config, model_output=True
|
||||
)
|
||||
|
||||
|
||||
def process_nlp(video_uri, out_dir, dbm_group, tran_tog, r_config, deep_path):
|
||||
"""
|
||||
processing nlp features
|
||||
Args:
|
||||
video_uri: video path; out_dir: raw variable output dir
|
||||
dbm_group: list of features to process; r_config: raw feature config object
|
||||
deep_path: deep speech build path
|
||||
"""
|
||||
if dbm_group is not None and len(dbm_group) > 0 and "speech" not in dbm_group:
|
||||
return
|
||||
|
||||
logger.info("Processing nlp variables from data in {}".format(video_uri))
|
||||
nlp.transcribe.run_transcribe(video_uri, out_dir, r_config, deep_path)
|
||||
nlp.speech_features.run_speech_feature(video_uri, out_dir, r_config, tran_tog)
|
||||
|
||||
|
||||
def remove_file(file_path, file_ext=".wav"):
|
||||
"""
|
||||
removing wav file
|
||||
"""
|
||||
file_dir = dirname(file_path)
|
||||
file_name, _ = splitext(basename(file_path))
|
||||
wav_file = glob.glob(join(file_dir, file_name + file_ext))
|
||||
|
||||
if len(wav_file) > 0:
|
||||
os.remove(wav_file[0])
|
||||
0
opendbm/dbm_lib/dbm_features/__init__.py
Normal file
0
opendbm/dbm_lib/dbm_features/__init__.py
Normal file
@@ -0,0 +1,7 @@
|
||||
"""
|
||||
file_name: init
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import, division, print_function
|
||||
@@ -4,15 +4,17 @@ project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import glob
|
||||
import os
|
||||
import logging
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
logger = logging.getLogger()
|
||||
|
||||
|
||||
def dict_to_df(feature_dict, file):
|
||||
"""
|
||||
@@ -21,26 +23,29 @@ def dict_to_df(feature_dict, file):
|
||||
final_dict = {k: v for d in feature_dict for k, v in d.items()}
|
||||
|
||||
feature_df = pd.DataFrame([final_dict])
|
||||
feature_df['Filename'] = file
|
||||
feature_df["Filename"] = file
|
||||
|
||||
return feature_df
|
||||
|
||||
|
||||
def save_derive_output(df_list, out_loc):
|
||||
"""
|
||||
Saving derive variable output
|
||||
"""
|
||||
try:
|
||||
if len(df_list)>0:
|
||||
if len(df_list) > 0:
|
||||
df = df_list[0]
|
||||
|
||||
file_name = os.path.join(out_loc, 'derived_output.csv')
|
||||
file_name = os.path.join(out_loc, "derived_output.csv")
|
||||
if not os.path.exists(out_loc):
|
||||
|
||||
os.makedirs(out_loc)
|
||||
df.to_csv(file_name, index=False)
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to save derived variable csv')
|
||||
e
|
||||
logger.error("Failed to save derived variable csv")
|
||||
|
||||
|
||||
def feature_output(df_fea, exp_var, cal_type):
|
||||
"""
|
||||
@@ -52,40 +57,41 @@ def feature_output(df_fea, exp_var, cal_type):
|
||||
df_ = df_fea[exp_var].astype(float).copy()
|
||||
df_ = df_.dropna().reset_index(drop=True)
|
||||
|
||||
if len(df_)>0:
|
||||
if len(df_) > 0:
|
||||
|
||||
if cal_type == 'mean':
|
||||
exp_val = df_.mean(axis = 0, skipna = True)
|
||||
if cal_type == "mean":
|
||||
exp_val = df_.mean(axis=0, skipna=True)
|
||||
|
||||
elif cal_type == 'std':
|
||||
exp_val = df_.std(axis = 0, skipna = True)
|
||||
elif cal_type == "std":
|
||||
exp_val = df_.std(axis=0, skipna=True)
|
||||
|
||||
elif cal_type == 'count':#use case for eye blink
|
||||
exp_var = 'mov_blink'
|
||||
exp_val = (len(df_)/df_[0])*60
|
||||
elif cal_type == "count": # use case for eye blink
|
||||
exp_var = "mov_blink"
|
||||
exp_val = (len(df_) / df_[0]) * 60
|
||||
|
||||
elif cal_type == 'pct':
|
||||
if len(df_)>0:
|
||||
exp_val = len(df_[df_ > 0])/len(df_)
|
||||
elif cal_type == "pct":
|
||||
if len(df_) > 0:
|
||||
exp_val = len(df_[df_ > 0]) / len(df_)
|
||||
|
||||
elif cal_type == 'range':
|
||||
elif cal_type == "range":
|
||||
exp_val = max(df_) - min(df_)
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to compute calculation: {}'.format(e))
|
||||
logger.error("Failed to compute calculation: {}".format(e))
|
||||
pass
|
||||
|
||||
var_name = exp_var + '_' + cal_type
|
||||
var_name = exp_var + "_" + cal_type
|
||||
exp_val = float("{0:.4f}".format(exp_val))
|
||||
var_val = (var_name, exp_val)
|
||||
|
||||
return var_val
|
||||
|
||||
|
||||
def cal_type_dict(var_df, raw_df, d_cfg_Obj, r_cfg_Obj):
|
||||
|
||||
var_name = str(var_df['var_id'])
|
||||
var_name = str(var_df["var_id"])
|
||||
|
||||
#fetching key based on variable name from raw config
|
||||
# fetching key based on variable name from raw config
|
||||
var_key = list(r_cfg_Obj.keys())[list(r_cfg_Obj.values()).index(var_name)]
|
||||
cal_type = d_cfg_Obj[var_key] # calculation type from config
|
||||
|
||||
@@ -94,19 +100,29 @@ def cal_type_dict(var_df, raw_df, d_cfg_Obj, r_cfg_Obj):
|
||||
|
||||
return var_val_dict
|
||||
|
||||
|
||||
def compute_feature(raw_df, var_cols, d_cfg_Obj, r_cfg_Obj):
|
||||
"""
|
||||
Computing features
|
||||
"""
|
||||
#Variable data frame for each feature group
|
||||
var_df = pd.DataFrame(var_cols,columns=['var_id'])
|
||||
# Variable data frame for each feature group
|
||||
var_df = pd.DataFrame(var_cols, columns=["var_id"])
|
||||
feature_dict = {}
|
||||
|
||||
if len(raw_df)>0:
|
||||
feature_dict = var_df.apply(cal_type_dict, args=(raw_df, d_cfg_Obj, r_cfg_Obj, ), axis=1)
|
||||
if len(raw_df) > 0:
|
||||
feature_dict = var_df.apply(
|
||||
cal_type_dict,
|
||||
args=(
|
||||
raw_df,
|
||||
d_cfg_Obj,
|
||||
r_cfg_Obj,
|
||||
),
|
||||
axis=1,
|
||||
)
|
||||
|
||||
return feature_dict
|
||||
|
||||
|
||||
def calc_derive(input_file, input_dir, r_cfg_Obj, d_cfg_Obj, feature):
|
||||
"""
|
||||
Calculating derived variable
|
||||
@@ -120,45 +136,47 @@ def calc_derive(input_file, input_dir, r_cfg_Obj, d_cfg_Obj, feature):
|
||||
|
||||
var_cols = [r_cfg_Obj[x] for x in d_cfg_Obj[feature]]
|
||||
|
||||
fea_loc = d_cfg_Obj[feature + '_LOC']
|
||||
fea_res = glob.glob(os.path.join(input_loc, '*/*/*' + fea_loc + '.csv'))
|
||||
fea_loc = d_cfg_Obj[feature + "_LOC"]
|
||||
fea_res = glob.glob(os.path.join(input_loc, "*/*/*" + fea_loc + ".csv"))
|
||||
|
||||
if len(fea_res)>0:
|
||||
if len(fea_res) > 0:
|
||||
raw_df = pd.read_csv(fea_res[0])
|
||||
feature_dict = compute_feature(raw_df, var_cols, d_cfg_Obj, r_cfg_Obj)
|
||||
|
||||
if len(feature_dict)>0:
|
||||
if len(feature_dict) > 0:
|
||||
feature_df = dict_to_df(feature_dict, file)
|
||||
df_list.append(feature_df)
|
||||
|
||||
if len(df_list)>0:
|
||||
if len(df_list) > 0:
|
||||
df = pd.concat(df_list, ignore_index=True)
|
||||
return df
|
||||
|
||||
|
||||
def run_derive(input_file, input_dir, output_dir, r_config, d_config):
|
||||
"""
|
||||
Processing derived variable
|
||||
"""
|
||||
d_cfg_Obj = d_config.base_derive['derive_feature']
|
||||
r_cfg_Obj = r_config.base_raw['raw_feature']
|
||||
feature_group = d_cfg_Obj['FEATURE_GROUP']
|
||||
d_cfg_Obj = d_config.base_derive["derive_feature"]
|
||||
r_cfg_Obj = r_config.base_raw["raw_feature"]
|
||||
feature_group = d_cfg_Obj["FEATURE_GROUP"]
|
||||
|
||||
#Iterating over feature group
|
||||
# Iterating over feature group
|
||||
df_list = []
|
||||
for feature in feature_group:
|
||||
try:
|
||||
|
||||
df_fea = calc_derive(input_file, input_dir, r_cfg_Obj, d_cfg_Obj, feature)
|
||||
if len(df_fea)>0:
|
||||
if len(df_fea) > 0:
|
||||
|
||||
if len(df_list) == 0:
|
||||
df_list.append(df_fea)
|
||||
else:
|
||||
result = pd.merge(df_list[0], df_fea, how='outer', on=['Filename'])
|
||||
result = pd.merge(df_list[0], df_fea, how="outer", on=["Filename"])
|
||||
df_list = [result]
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process derived variables {}'.format(feature))
|
||||
e
|
||||
logger.error("Failed to process derived variables {}".format(feature))
|
||||
|
||||
logger.info("Saving derived variable output...")
|
||||
save_derive_output(df_list, output_dir)
|
||||
10
opendbm/dbm_lib/dbm_features/raw_features/audio/__init__.py
Normal file
10
opendbm/dbm_lib/dbm_features/raw_features/audio/__init__.py
Normal file
@@ -0,0 +1,10 @@
|
||||
from .formant_freq import run_formant
|
||||
from .gne import run_gne
|
||||
from .hnr import run_hnr
|
||||
from .intensity import run_intensity
|
||||
from .jitter import run_jitter
|
||||
from .mfcc import run_mfcc
|
||||
from .pause_segment import run_pause_segment
|
||||
from .pitch_freq import run_pitch
|
||||
from .shimmer import run_shimmer
|
||||
from .voice_frame_score import run_vfs
|
||||
155
opendbm/dbm_lib/dbm_features/raw_features/audio/formant_freq.py
Normal file
155
opendbm/dbm_lib/dbm_features/raw_features/audio/formant_freq.py
Normal file
@@ -0,0 +1,155 @@
|
||||
"""
|
||||
file_name: formant_freq
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
formant_dir = "acoustic/formant_freq"
|
||||
csv_ext = "_formant.csv"
|
||||
error_txt = "error: length less than 0.064"
|
||||
|
||||
|
||||
def formant_list(formant, snd):
|
||||
"""
|
||||
Getting formant frequency per second
|
||||
Args:
|
||||
formant: Formant object for sound wave
|
||||
snd: Parselmouth sound object
|
||||
Returns:
|
||||
List of first through fourth formant for each frame
|
||||
"""
|
||||
f1_list = []
|
||||
f2_list = []
|
||||
f3_list = []
|
||||
f4_list = []
|
||||
|
||||
dur = snd.duration - 0.02
|
||||
dur_round = round(dur, 2)
|
||||
|
||||
time_list = np.arange(0.001, dur_round, 0.001)
|
||||
for time in time_list:
|
||||
|
||||
f1 = formant.get_value_at_time(1, time)
|
||||
f2 = formant.get_value_at_time(2, time)
|
||||
f3 = formant.get_value_at_time(3, time)
|
||||
f4 = formant.get_value_at_time(4, time)
|
||||
|
||||
f1_list.append(f1)
|
||||
f2_list.append(f2)
|
||||
f3_list.append(f3)
|
||||
f4_list.append(f4)
|
||||
return f1_list, f2_list, f3_list, f4_list
|
||||
|
||||
|
||||
def formant_score(path):
|
||||
"""
|
||||
Using parselmouth library fetching Formant Frequency
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of Formant freq for each voice frame
|
||||
"""
|
||||
sound_pat = parselmouth.Sound(path)
|
||||
formant = sound_pat.to_formant_burg(time_step=0.001)
|
||||
f_score = formant_list(formant, sound_pat)
|
||||
return f_score
|
||||
|
||||
|
||||
def calc_formant(video_uri, audio_file, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Preparing Formant freq matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file; fl_name: input file name
|
||||
out_loc: (str) Output directory; r_config: raw variable config
|
||||
"""
|
||||
|
||||
f1_list, f2_list, f3_list, f4_list = formant_score(audio_file)
|
||||
df_formant = pd.DataFrame(f1_list, columns=[r_config.aco_fm1])
|
||||
|
||||
df_formant[r_config.aco_fm2] = f2_list
|
||||
df_formant[r_config.aco_fm3] = f3_list
|
||||
df_formant[r_config.aco_fm4] = f4_list
|
||||
|
||||
df_formant.replace("", np.nan, regex=True, inplace=True)
|
||||
df_formant[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
|
||||
df_formant["Frames"] = df_formant.index
|
||||
df_formant["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_formant, out_loc, fl_name, formant_dir, csv_ext)
|
||||
return df_formant
|
||||
|
||||
|
||||
def empty_fm(video_uri, out_loc, fl_name, r_config, save=True):
|
||||
|
||||
"""
|
||||
Preparing empty formant frequency matrix if something fails
|
||||
"""
|
||||
cols = [
|
||||
"Frames",
|
||||
r_config.aco_fm1,
|
||||
r_config.aco_fm2,
|
||||
r_config.aco_fm3,
|
||||
r_config.aco_fm4,
|
||||
r_config.err_reason,
|
||||
]
|
||||
out_val = [[np.nan, np.nan, np.nan, np.nan, np.nan, error_txt]]
|
||||
df_fm = pd.DataFrame(out_val, columns=cols)
|
||||
df_fm["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_fm, out_loc, fl_name, formant_dir, csv_ext)
|
||||
return df_fm
|
||||
|
||||
|
||||
def run_formant(video_uri, out_dir, r_config, save=True):
|
||||
|
||||
"""
|
||||
Processing all patient's for fetching Formant freq
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.064sec".format(audio_file)
|
||||
)
|
||||
|
||||
df = empty_fm(video_uri, out_loc, fl_name, r_config, save=save)
|
||||
else:
|
||||
df = calc_formant(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, save=save
|
||||
)
|
||||
return df
|
||||
except Exception as e:
|
||||
e
|
||||
logger.error("Failed to process audio file")
|
||||
166
opendbm/dbm_lib/dbm_features/raw_features/audio/gne.py
Normal file
166
opendbm/dbm_lib/dbm_features/raw_features/audio/gne.py
Normal file
@@ -0,0 +1,166 @@
|
||||
"""
|
||||
file_name: gne
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
from os.path import join
|
||||
|
||||
import more_itertools as mit
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
gne_dir = "acoustic/glottal_noise"
|
||||
ff_dir = "acoustic/pitch"
|
||||
csv_ext = "_gne.csv"
|
||||
|
||||
|
||||
def gne_ratio(sound):
|
||||
"""
|
||||
Using parselmouth library fetching glottal noise excitation ratio
|
||||
Args:
|
||||
sound: parselmouth object
|
||||
Returns:
|
||||
(list) list of gne ratio for each voice frame
|
||||
"""
|
||||
harmonicity_gne = sound.to_harmonicity_gne()
|
||||
gne_all_bands = harmonicity_gne.values
|
||||
gne_all_bands = np.where(gne_all_bands == -200, np.NaN, gne_all_bands)
|
||||
|
||||
gne = np.nanmax(
|
||||
gne_all_bands
|
||||
) # following http://www.fon.hum.uva.nl/rob/NKI_TEVA/TEVA/HTML/NKI_TEVA.pdf
|
||||
return gne
|
||||
|
||||
|
||||
def empty_gne(video_uri, out_loc, fl_name, r_config, error_txt, save=True):
|
||||
"""
|
||||
Preparing empty GNE matrix if something fails
|
||||
"""
|
||||
cols = ["Frames", r_config.aco_gne, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
|
||||
df_gne = pd.DataFrame(out_val, columns=cols)
|
||||
df_gne["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_gne, out_loc, fl_name, gne_dir, csv_ext)
|
||||
return df_gne
|
||||
|
||||
|
||||
def segment_gne(com_speech_sort, voiced_yes, voiced_no, gne_all_frames, audio_file):
|
||||
"""
|
||||
calculating gne for each voice segment
|
||||
"""
|
||||
snd = parselmouth.Sound(audio_file)
|
||||
pitch = snd.to_pitch(time_step=0.001)
|
||||
|
||||
for idx, vs in enumerate(com_speech_sort):
|
||||
try:
|
||||
|
||||
max_gne = np.NaN
|
||||
if vs in voiced_yes and len(vs) > 1:
|
||||
|
||||
start_time = pitch.get_time_from_frame_number(vs[0])
|
||||
end_time = pitch.get_time_from_frame_number(vs[-1])
|
||||
|
||||
snd_start = int(snd.get_frame_number_from_time(start_time))
|
||||
snd_end = int(snd.get_frame_number_from_time(end_time))
|
||||
|
||||
samples = parselmouth.Sound(snd.as_array()[0][snd_start:snd_end])
|
||||
max_gne = gne_ratio(samples)
|
||||
except:
|
||||
pass
|
||||
|
||||
gne_all_frames[idx] = max_gne
|
||||
return gne_all_frames
|
||||
|
||||
|
||||
def calc_gne(video_uri, audio_file, out_loc, fl_name, r_config, save=True, ff_df=None):
|
||||
"""
|
||||
Preparing gne matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv's
|
||||
"""
|
||||
dir_path = os.path.join(out_loc, ff_dir)
|
||||
if os.path.isdir(dir_path) or ff_df is not None:
|
||||
if ff_df is not None:
|
||||
voice_seg = ut.process_segment_pitch(ff_df, r_config)
|
||||
else:
|
||||
voice_seg = ut.segment_pitch(dir_path, r_config, ff_df=ff_df)
|
||||
|
||||
gne_all_frames = [np.NaN] * len(voice_seg[0])
|
||||
gne_segment_frames = segment_gne(
|
||||
voice_seg[0], voice_seg[1], voice_seg[2], gne_all_frames, audio_file
|
||||
)
|
||||
|
||||
df_gne = pd.DataFrame(gne_segment_frames, columns=[r_config.aco_gne])
|
||||
df_gne[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
|
||||
df_gne["Frames"] = df_gne.index
|
||||
df_gne["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Processing Output file {} ".format(out_loc))
|
||||
ut.save_output(df_gne, out_loc, fl_name, gne_dir, csv_ext)
|
||||
return df_gne
|
||||
|
||||
else:
|
||||
error_txt = "error: pitch freq not available"
|
||||
return empty_gne(video_uri, out_loc, fl_name, r_config, error_txt, save=save)
|
||||
|
||||
|
||||
def run_gne(video_uri, out_dir, r_config, save=True, ff_df=None):
|
||||
"""
|
||||
Processing all patient's for fetching glottal noise ratio
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.064sec".format(audio_file)
|
||||
)
|
||||
|
||||
error_txt = "error: length less than 0.064"
|
||||
df = empty_gne(
|
||||
video_uri, out_loc, fl_name, r_config, error_txt, save=save
|
||||
)
|
||||
else:
|
||||
df = calc_gne(
|
||||
video_uri,
|
||||
audio_file,
|
||||
out_loc,
|
||||
fl_name,
|
||||
r_config,
|
||||
save=save,
|
||||
ff_df=ff_df,
|
||||
)
|
||||
return df
|
||||
except Exception as e:
|
||||
e
|
||||
logger.error("Failed to process audio file")
|
||||
112
opendbm/dbm_lib/dbm_features/raw_features/audio/hnr.py
Normal file
112
opendbm/dbm_lib/dbm_features/raw_features/audio/hnr.py
Normal file
@@ -0,0 +1,112 @@
|
||||
"""
|
||||
file_name: hnr
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
hnr_dir = "acoustic/harmonic_noise"
|
||||
csv_ext = "_hnr.csv"
|
||||
error_txt = "error: length less than 0.064"
|
||||
|
||||
|
||||
def hnr_ratio(filepath):
|
||||
"""
|
||||
Using parselmouth library fetching harmonic noise ratio ratio
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of hnr ratio for each voice frame, min,max and mean hnr
|
||||
"""
|
||||
sound = parselmouth.Sound(filepath)
|
||||
harmonicity = sound.to_harmonicity_ac(time_step=0.001)
|
||||
|
||||
hnr_all_frames = harmonicity.values # [harmonicity.values != -200] nan it (****)
|
||||
hnr_all_frames = np.where(hnr_all_frames == -200, np.NaN, hnr_all_frames)
|
||||
return hnr_all_frames.transpose()
|
||||
|
||||
|
||||
def calc_hnr(video_uri, audio_file, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Preparing harmonic noise matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv's
|
||||
"""
|
||||
|
||||
hnr_all_frames = hnr_ratio(audio_file)
|
||||
df_hnr = pd.DataFrame(hnr_all_frames, columns=[r_config.aco_hnr])
|
||||
|
||||
df_hnr["Frames"] = df_hnr.index
|
||||
df_hnr["dbm_master_url"] = video_uri
|
||||
df_hnr[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)
|
||||
return df_hnr
|
||||
|
||||
|
||||
def empty_hnr(video_uri, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Preparing empty HNR matrix if something fails
|
||||
"""
|
||||
cols = ["Frames", r_config.aco_hnr, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_hnr = pd.DataFrame(out_val, columns=cols)
|
||||
df_hnr["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)
|
||||
return df_hnr
|
||||
|
||||
|
||||
def run_hnr(video_uri, out_dir, r_config, save=True):
|
||||
"""
|
||||
Processing all patient's for fetching harmonic noise ratio
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.064sec".format(audio_file)
|
||||
)
|
||||
|
||||
df = empty_hnr(video_uri, out_loc, fl_name, r_config, save=save)
|
||||
|
||||
else:
|
||||
df = calc_hnr(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, save=save
|
||||
)
|
||||
return df
|
||||
except Exception as e:
|
||||
e
|
||||
logger.error("Failed to process audio file")
|
||||
107
opendbm/dbm_lib/dbm_features/raw_features/audio/intensity.py
Normal file
107
opendbm/dbm_lib/dbm_features/raw_features/audio/intensity.py
Normal file
@@ -0,0 +1,107 @@
|
||||
"""
|
||||
file_name: intensity
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
intensity_dir = "acoustic/intensity"
|
||||
csv_ext = "_intensity.csv"
|
||||
error_txt = "error: length less than 0.064"
|
||||
|
||||
|
||||
def intensity_score(path):
|
||||
"""
|
||||
Using parselmouth library fetching Intensity
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of Intensity for each voice frame
|
||||
"""
|
||||
sound_pat = parselmouth.Sound(path)
|
||||
intensity = sound_pat.to_intensity(time_step=0.001)
|
||||
return intensity.values[0]
|
||||
|
||||
|
||||
def calc_intensity(video_uri, audio_file, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Preparing Intensity matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv's
|
||||
"""
|
||||
|
||||
intensity_frames = intensity_score(audio_file)
|
||||
df_intensity = pd.DataFrame(intensity_frames, columns=[r_config.aco_int])
|
||||
|
||||
df_intensity["Frames"] = df_intensity.index
|
||||
df_intensity["dbm_master_url"] = video_uri
|
||||
df_intensity[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_intensity, out_loc, fl_name, intensity_dir, csv_ext)
|
||||
return df_intensity
|
||||
|
||||
|
||||
def empty_intensity(video_uri, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Preparing empty Intensity matrix if something fails
|
||||
"""
|
||||
cols = ["Frames", r_config.aco_int, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_int = pd.DataFrame(out_val, columns=cols)
|
||||
df_int["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_int, out_loc, fl_name, intensity_dir, csv_ext)
|
||||
return df_int
|
||||
|
||||
|
||||
def run_intensity(video_uri, out_dir, r_config, save=True):
|
||||
"""
|
||||
Processing all patient's for fetching Intensity
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.064sec".format(audio_file)
|
||||
)
|
||||
|
||||
df = empty_intensity(video_uri, out_loc, fl_name, r_config, save=save)
|
||||
else:
|
||||
df = calc_intensity(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, save=save
|
||||
)
|
||||
return df
|
||||
except Exception as e:
|
||||
e
|
||||
logger.error("Failed to process audio file")
|
||||
166
opendbm/dbm_lib/dbm_features/raw_features/audio/jitter.py
Normal file
166
opendbm/dbm_lib/dbm_features/raw_features/audio/jitter.py
Normal file
@@ -0,0 +1,166 @@
|
||||
"""
|
||||
file_name: jitter_processing
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
jitter_dir = "acoustic/jitter"
|
||||
ff_dir = "acoustic/pitch"
|
||||
csv_ext = "_jitter.csv"
|
||||
|
||||
|
||||
def audio_jitter(sound):
|
||||
"""
|
||||
Using parselmouth library fetching jitter
|
||||
Args:
|
||||
sound: parselmouth object
|
||||
Returns:
|
||||
(list) list of jitters for each voice frame
|
||||
"""
|
||||
pointProcess = parselmouth.praat.call(
|
||||
sound, "To PointProcess (periodic, cc)...", 80, 500
|
||||
)
|
||||
jitter = parselmouth.praat.call(
|
||||
pointProcess, "Get jitter (local)", 0, 0, 0.0001, 0.02, 1.3
|
||||
)
|
||||
return jitter
|
||||
|
||||
|
||||
def empty_jitter(video_uri, out_loc, fl_name, r_config, error_txt, save=True):
|
||||
"""
|
||||
Preparing empty jitter matrix if something fails
|
||||
"""
|
||||
cols = ["Frames", r_config.aco_jitter, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_jitter = pd.DataFrame(out_val, columns=cols)
|
||||
df_jitter["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_jitter, out_loc, fl_name, jitter_dir, csv_ext)
|
||||
return df_jitter
|
||||
|
||||
|
||||
def segment_jitter(com_speech_sort, voiced_yes, voiced_no, jitter_frames, audio_file):
|
||||
"""
|
||||
calculating jitter for each voice segment
|
||||
"""
|
||||
snd = parselmouth.Sound(audio_file)
|
||||
pitch = snd.to_pitch(time_step=0.001)
|
||||
|
||||
for idx, vs in enumerate(com_speech_sort):
|
||||
try:
|
||||
|
||||
jitter = np.NaN
|
||||
if vs in voiced_yes and len(vs) > 1:
|
||||
|
||||
start_time = pitch.get_time_from_frame_number(vs[0])
|
||||
end_time = pitch.get_time_from_frame_number(vs[-1])
|
||||
|
||||
snd_start = int(snd.get_frame_number_from_time(start_time))
|
||||
snd_end = int(snd.get_frame_number_from_time(end_time))
|
||||
|
||||
samples = parselmouth.Sound(snd.as_array()[0][snd_start:snd_end])
|
||||
jitter = audio_jitter(samples)
|
||||
except:
|
||||
pass
|
||||
|
||||
jitter_frames[idx] = jitter
|
||||
return jitter_frames
|
||||
|
||||
|
||||
def calc_jitter(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, save=True, ff_df=None
|
||||
):
|
||||
"""
|
||||
Preparing jitter matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv
|
||||
r_config: config.config_raw_feature.pyConfigFeatureNmReader object
|
||||
"""
|
||||
dir_path = os.path.join(out_loc, ff_dir)
|
||||
if os.path.isdir(dir_path) or ff_df is not None:
|
||||
|
||||
if ff_df is not None:
|
||||
voice_seg = ut.process_segment_pitch(ff_df, r_config)
|
||||
else:
|
||||
voice_seg = ut.segment_pitch(dir_path, r_config, ff_df=ff_df)
|
||||
|
||||
jitter_frames = [np.NaN] * len(voice_seg[0])
|
||||
jitter_segment_frames = segment_jitter(
|
||||
voice_seg[0], voice_seg[1], voice_seg[2], jitter_frames, audio_file
|
||||
)
|
||||
|
||||
df_jitter = pd.DataFrame(jitter_segment_frames, columns=[r_config.aco_jitter])
|
||||
df_jitter[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
|
||||
df_jitter["Frames"] = df_jitter.index
|
||||
df_jitter["dbm_master_url"] = video_uri
|
||||
if save:
|
||||
logger.info("Processing Output file {} ".format(out_loc))
|
||||
ut.save_output(df_jitter, out_loc, fl_name, jitter_dir, csv_ext)
|
||||
df = df_jitter
|
||||
else:
|
||||
error_txt = "error: fundamental freq not available"
|
||||
df = empty_jitter(video_uri, out_loc, fl_name, r_config, error_txt, save=save)
|
||||
return df
|
||||
|
||||
|
||||
def run_jitter(video_uri, out_dir, r_config, save=True, ff_df=None):
|
||||
"""
|
||||
Processing all patient's videos for fetching jitter
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.064sec".format(audio_file)
|
||||
)
|
||||
|
||||
error_txt = "error: length less than 0.064"
|
||||
df = empty_jitter(
|
||||
video_uri, out_loc, fl_name, r_config, error_txt, save=save
|
||||
)
|
||||
else:
|
||||
df = calc_jitter(
|
||||
video_uri,
|
||||
audio_file,
|
||||
out_loc,
|
||||
fl_name,
|
||||
r_config,
|
||||
save=save,
|
||||
ff_df=ff_df,
|
||||
)
|
||||
return df
|
||||
except Exception as e:
|
||||
logger.error("Error in jitter: {}".format(e))
|
||||
logger.error("Failed to process audio file")
|
||||
147
opendbm/dbm_lib/dbm_features/raw_features/audio/mfcc.py
Normal file
147
opendbm/dbm_lib/dbm_features/raw_features/audio/mfcc.py
Normal file
@@ -0,0 +1,147 @@
|
||||
"""
|
||||
file_name: mfcc
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
mfcc_dir = "acoustic/mfcc"
|
||||
csv_ext = "_mfcc.csv"
|
||||
error_txt = "error: length less than 0.064"
|
||||
|
||||
|
||||
def empty_mfcc(video_uri, out_loc, fl_name, r_config, save=True):
|
||||
|
||||
"""
|
||||
Preparing empty empty_mfcc matrix if something fails
|
||||
"""
|
||||
cols = [
|
||||
"Frames",
|
||||
r_config.aco_mfcc1,
|
||||
r_config.aco_mfcc2,
|
||||
r_config.aco_mfcc3,
|
||||
r_config.aco_mfcc4,
|
||||
r_config.aco_mfcc5,
|
||||
r_config.aco_mfcc6,
|
||||
r_config.aco_mfcc7,
|
||||
r_config.aco_mfcc8,
|
||||
r_config.aco_mfcc9,
|
||||
r_config.aco_mfcc10,
|
||||
r_config.aco_mfcc11,
|
||||
r_config.aco_mfcc12,
|
||||
r_config.err_reason,
|
||||
]
|
||||
out_val = [
|
||||
[
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
np.nan,
|
||||
error_txt,
|
||||
]
|
||||
]
|
||||
df_mfcc = pd.DataFrame(out_val, columns=cols)
|
||||
df_mfcc["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_mfcc, out_loc, fl_name, mfcc_dir, csv_ext)
|
||||
|
||||
return df_mfcc
|
||||
|
||||
|
||||
def audio_mfcc(path):
|
||||
"""
|
||||
Using parselmouth library fetching mfccs
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of mfccs for each voice frame
|
||||
"""
|
||||
sound = parselmouth.Sound(path)
|
||||
mfcc_object = sound.to_mfcc(time_step=0.001, number_of_coefficients=12)
|
||||
mfccs = mfcc_object.to_array()
|
||||
mfccs = np.delete(mfccs, (0), axis=0)
|
||||
return mfccs
|
||||
|
||||
|
||||
def calc_mfcc(video_uri, audio_file, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Preparing mfcc matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: output location to save csv
|
||||
fl_name: (str) name of audio file
|
||||
r_config: config.config_raw_feature.pyConfigFeatureNmReader object
|
||||
"""
|
||||
dict_ = {}
|
||||
mfccs = audio_mfcc(audio_file)
|
||||
|
||||
for i in range(1, 13):
|
||||
conf_str = r_config.base_raw["raw_feature"]
|
||||
dict_[conf_str["aco_mfcc" + str(i)]] = mfccs[i - 1, :]
|
||||
|
||||
df = pd.DataFrame(dict_)
|
||||
df["Frames"] = df.index
|
||||
|
||||
df[r_config.err_reason] = "Pass" # may replace based on threshold in future release
|
||||
df["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df, out_loc, fl_name, mfcc_dir, csv_ext)
|
||||
return df
|
||||
|
||||
|
||||
def run_mfcc(video_uri, out_dir, r_config, save=True):
|
||||
"""
|
||||
Processing all patients to fetch mfccs
|
||||
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.064sec".format(audio_file)
|
||||
)
|
||||
|
||||
return empty_mfcc(video_uri, out_loc, fl_name, r_config, save=save)
|
||||
|
||||
return calc_mfcc(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, save=save
|
||||
)
|
||||
except Exception as e:
|
||||
e
|
||||
logger.error("Failed to process audio file")
|
||||
@@ -4,24 +4,25 @@ project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import os
|
||||
import glob
|
||||
from pydub import AudioSegment
|
||||
import librosa
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import webrtcvad
|
||||
from os.path import join
|
||||
import logging
|
||||
import os
|
||||
from os.path import join
|
||||
|
||||
from dbm_lib.dbm_features.raw_features.util import vad_utilities as vu
|
||||
from dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import webrtcvad
|
||||
from pydub import AudioSegment
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import vad_utilities as vu
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger=logging.getLogger()
|
||||
logger = logging.getLogger()
|
||||
|
||||
pause_seg_dir = "acoustic/pause_segment"
|
||||
csv_ext = "_pausechar.csv"
|
||||
|
||||
pause_seg_dir = 'acoustic/pause_segment'
|
||||
csv_ext = '_pausechar.csv'
|
||||
|
||||
def get_timing_cues(seg_starts_sec, seg_ends_sec, r_config):
|
||||
"""
|
||||
@@ -38,23 +39,25 @@ def get_timing_cues(seg_starts_sec, seg_ends_sec, r_config):
|
||||
pause_len = np.zeros(num_pauses)
|
||||
|
||||
for p in range(num_pauses):
|
||||
pause_len[p] = seg_starts_sec[p+1] - seg_ends_sec[p]
|
||||
pause_len[p] = seg_starts_sec[p + 1] - seg_ends_sec[p]
|
||||
|
||||
if len(pause_len)>0:
|
||||
pause_len_mean = np.mean(pause_len)
|
||||
pause_len_std = np.std(pause_len)
|
||||
if len(pause_len) > 0:
|
||||
pause_time = np.sum(pause_len)
|
||||
|
||||
else:
|
||||
pause_len_mean = 0
|
||||
pause_len_std = 0
|
||||
pause_time = 0
|
||||
|
||||
pause_frac = pause_time / total_time
|
||||
timing_dict = {r_config.aco_totaltime: total_time, r_config.aco_speakingtime: speaking_time,
|
||||
r_config.aco_numpauses: num_pauses, r_config.aco_pausetime: pause_time, r_config.aco_pausefrac: pause_frac}
|
||||
timing_dict = {
|
||||
r_config.aco_totaltime: total_time,
|
||||
r_config.aco_speakingtime: speaking_time,
|
||||
r_config.aco_numpauses: num_pauses,
|
||||
r_config.aco_pausetime: pause_time,
|
||||
r_config.aco_pausefrac: pause_frac,
|
||||
}
|
||||
return timing_dict
|
||||
|
||||
|
||||
def process_silence(audio_file, r_config):
|
||||
"""
|
||||
Returns dataframe for pause between words using voice activity detection
|
||||
@@ -70,59 +73,75 @@ def process_silence(audio_file, r_config):
|
||||
aggressiveness = 3
|
||||
frame_dur_ms = 20
|
||||
|
||||
#pause segment(long & short pad)
|
||||
# pause segment(long & short pad)
|
||||
long_pad_around_voice_ms = 200
|
||||
short_pad_around_voice_ms = 100
|
||||
|
||||
if len(y)>0:
|
||||
if len(y) > 0:
|
||||
vad = webrtcvad.Vad(aggressiveness)
|
||||
|
||||
frames = vu.frame_generator(frame_dur_ms, y, sr)
|
||||
frames = list(frames)
|
||||
|
||||
#longer pad time screens out little blips, but misses short silences
|
||||
long_seg_starts, long_seg_ends = vu.vad_get_segment_times(sr, frame_dur_ms, long_pad_around_voice_ms, vad, frames)
|
||||
# longer pad time screens out little blips, but misses short silences
|
||||
long_seg_starts, long_seg_ends = vu.vad_get_segment_times(
|
||||
sr, frame_dur_ms, long_pad_around_voice_ms, vad, frames
|
||||
)
|
||||
|
||||
#Logic to handle blank audio file
|
||||
# Logic to handle blank audio file
|
||||
if len(long_seg_starts) == 0 or len(long_seg_ends) == 0:
|
||||
return ''
|
||||
return ""
|
||||
|
||||
t_start = long_seg_starts[0]
|
||||
t_end = long_seg_ends[-1]
|
||||
# shorter pad time captures short silences (but misfires on little blips)
|
||||
short_seg_starts, short_seg_ends = vu.vad_get_segment_times(sr, frame_dur_ms, short_pad_around_voice_ms, vad, frames)
|
||||
short_seg_starts, short_seg_ends = vu.vad_get_segment_times(
|
||||
sr, frame_dur_ms, short_pad_around_voice_ms, vad, frames
|
||||
)
|
||||
|
||||
seg_starts = []
|
||||
seg_ends = []
|
||||
for k in range(len(short_seg_starts)): # logic to clean up some typical misfires
|
||||
if (short_seg_starts[k] >=t_start) and (short_seg_starts[k] <= t_end):
|
||||
for k in range(
|
||||
len(short_seg_starts)
|
||||
): # logic to clean up some typical misfires
|
||||
if (short_seg_starts[k] >= t_start) and (short_seg_starts[k] <= t_end):
|
||||
|
||||
seg_starts.append(short_seg_starts[k])
|
||||
seg_ends.append(short_seg_ends[k])
|
||||
if len(seg_starts) == 0 or len(seg_ends) == 0:
|
||||
return ''
|
||||
return ""
|
||||
|
||||
timing_dict = get_timing_cues(seg_starts, seg_ends, r_config)
|
||||
feat_dict_list.append(timing_dict)
|
||||
|
||||
df = pd.DataFrame(feat_dict_list)
|
||||
df[r_config.err_reason] = 'Pass'# will replace with threshold in future release
|
||||
df[r_config.err_reason] = "Pass" # will replace with threshold in future release
|
||||
return df
|
||||
|
||||
def empty_pause_segment(video_uri, out_loc, fl_name, r_config, error_txt):
|
||||
|
||||
def empty_pause_segment(video_uri, out_loc, fl_name, r_config, error_txt, save=True):
|
||||
"""
|
||||
Preparing empty Pause Segment matrix if something fails
|
||||
"""
|
||||
cols = [r_config.aco_totaltime, r_config.aco_speakingtime, r_config.aco_numpauses, r_config.aco_pausetime,
|
||||
r_config.aco_pausefrac, r_config.err_reason]
|
||||
cols = [
|
||||
r_config.aco_totaltime,
|
||||
r_config.aco_speakingtime,
|
||||
r_config.aco_numpauses,
|
||||
r_config.aco_pausetime,
|
||||
r_config.aco_pausefrac,
|
||||
r_config.err_reason,
|
||||
]
|
||||
out_val = [[np.nan, np.nan, np.nan, np.nan, np.nan, error_txt]]
|
||||
df_pause = pd.DataFrame(out_val, columns = cols)
|
||||
df_pause['dbm_master_url'] = video_uri
|
||||
df_pause = pd.DataFrame(out_val, columns=cols)
|
||||
df_pause["dbm_master_url"] = video_uri
|
||||
|
||||
logger.info('Saving Output file {} '.format(out_loc))
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_pause, out_loc, fl_name, pause_seg_dir, csv_ext)
|
||||
return df_pause
|
||||
|
||||
def run_pause_segment(video_uri, out_dir, r_config):
|
||||
|
||||
def run_pause_segment(video_uri, out_dir, r_config, save=True):
|
||||
"""
|
||||
Processing all patient's for getting Pause Segment
|
||||
---------------
|
||||
@@ -134,39 +153,48 @@ def run_pause_segment(video_uri, out_dir, r_config):
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
|
||||
if len(aud_filter)>0:
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = librosa.get_duration(filename=audio_file)
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.064sec".format(audio_file)
|
||||
)
|
||||
|
||||
error_txt = 'error: length less than 0.064'
|
||||
error_txt = "error: length less than 0.064"
|
||||
empty_pause_segment(video_uri, out_loc, fl_name, r_config, error_txt)
|
||||
return
|
||||
|
||||
logger.info('Converting stereo sound to mono-lD')
|
||||
logger.info("Converting stereo sound to mono-lD")
|
||||
sound_mono = AudioSegment.from_wav(audio_file)
|
||||
sound_mono = sound_mono.set_channels(1)
|
||||
sound_mono = sound_mono.set_frame_rate(48000)
|
||||
|
||||
mono_wav = os.path.join(input_loc, fl_name + '_mono.wav')
|
||||
mono_wav = os.path.join(input_loc, fl_name + "_mono.wav")
|
||||
sound_mono.export(mono_wav, format="wav")
|
||||
|
||||
df_pause_seg = process_silence(mono_wav, r_config)
|
||||
os.remove(mono_wav)#removing mono wav file
|
||||
os.remove(mono_wav) # removing mono wav file
|
||||
|
||||
if isinstance(df_pause_seg, pd.DataFrame) and len(df_pause_seg)>0:
|
||||
logger.info('Processing Output file {} '.format(out_loc))
|
||||
|
||||
df_pause_seg['dbm_master_url'] = video_uri
|
||||
ut.save_output(df_pause_seg, out_loc, fl_name, pause_seg_dir, csv_ext)
|
||||
if isinstance(df_pause_seg, pd.DataFrame) and len(df_pause_seg) > 0:
|
||||
df_pause_seg["dbm_master_url"] = video_uri
|
||||
if save:
|
||||
logger.info("Processing Output file {} ".format(out_loc))
|
||||
ut.save_output(
|
||||
df_pause_seg, out_loc, fl_name, pause_seg_dir, csv_ext
|
||||
)
|
||||
df = df_pause_seg
|
||||
|
||||
else:
|
||||
error_txt = 'error: webrtcvad returns no segment'
|
||||
empty_pause_segment(video_uri, out_loc, fl_name, r_config, error_txt)
|
||||
error_txt = "error: webrtcvad returns no segment"
|
||||
df = empty_pause_segment(
|
||||
video_uri, out_loc, fl_name, r_config, error_txt, save=save
|
||||
)
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
logger.error('Failed to process audio file')
|
||||
e
|
||||
logger.error("Failed to process audio file", str(e))
|
||||
139
opendbm/dbm_lib/dbm_features/raw_features/audio/pitch_freq.py
Normal file
139
opendbm/dbm_lib/dbm_features/raw_features/audio/pitch_freq.py
Normal file
@@ -0,0 +1,139 @@
|
||||
"""
|
||||
file_name: pitch_freq
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
ff_dir = "acoustic/pitch"
|
||||
csv_ext = "_pitch.csv"
|
||||
error_txt = "error: length less than 0.064"
|
||||
|
||||
|
||||
def audio_pitch(path):
|
||||
"""
|
||||
Using parselmouth library fetching pitch/fundamental frequency
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(list) list of pitch/fundamental frequency for each voice frame
|
||||
"""
|
||||
sound_pat = parselmouth.Sound(path)
|
||||
pitch = sound_pat.to_pitch(time_step=0.001)
|
||||
pitch_values = pitch.selected_array["frequency"]
|
||||
|
||||
return list(pitch_values)
|
||||
|
||||
|
||||
def label_speech(row, fd_freq):
|
||||
"""
|
||||
identify whether frame is voiced or not
|
||||
Args:
|
||||
row: (item) pitch frequency value
|
||||
Returns:
|
||||
(str) yes or no indicator for voice
|
||||
"""
|
||||
if row[fd_freq] > 0:
|
||||
return "yes"
|
||||
else:
|
||||
return "no"
|
||||
|
||||
|
||||
def calc_pitch(video_uri, audio_file, out_loc, fl_name, r_config, save=True):
|
||||
|
||||
"""
|
||||
Preparing pitch frequency matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
row: (dataframe) subject details from master csv
|
||||
new_out_base_dir: (str) Output directory for csv
|
||||
"""
|
||||
|
||||
ff_frames = audio_pitch(audio_file)
|
||||
df_ffreq = pd.DataFrame(ff_frames, columns=[r_config.aco_ff])
|
||||
|
||||
df_ffreq["Frames"] = df_ffreq.index
|
||||
df_ffreq[r_config.aco_voiceLabel] = df_ffreq.apply(
|
||||
lambda row: label_speech(row, r_config.aco_ff), axis=1
|
||||
)
|
||||
|
||||
df_ffreq[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
df_ffreq["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Processing Output file {} ".format(out_loc))
|
||||
ut.save_output(df_ffreq, out_loc, fl_name, ff_dir, csv_ext)
|
||||
return df_ffreq
|
||||
|
||||
|
||||
def empty_pitch(video_uri, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Preparing empty pitch frequency matrix if something fails
|
||||
"""
|
||||
|
||||
df_ffreq = pd.DataFrame(
|
||||
[[np.nan, np.nan, "no", error_txt]],
|
||||
columns=[
|
||||
"Frames",
|
||||
r_config.aco_ff,
|
||||
r_config.aco_voiceLabel,
|
||||
r_config.err_reason,
|
||||
],
|
||||
)
|
||||
df_ffreq["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_ffreq, out_loc, fl_name, ff_dir, csv_ext)
|
||||
return df_ffreq
|
||||
|
||||
|
||||
def run_pitch(video_uri, out_dir, r_config, save=True):
|
||||
|
||||
"""
|
||||
Processing audio for fetching pitch
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.064sec".format(audio_file)
|
||||
)
|
||||
|
||||
df = empty_pitch(video_uri, out_loc, fl_name, r_config, save=save)
|
||||
else:
|
||||
df = calc_pitch(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, save=save
|
||||
)
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
e
|
||||
logger.error("Failed to process audio file")
|
||||
166
opendbm/dbm_lib/dbm_features/raw_features/audio/shimmer.py
Normal file
166
opendbm/dbm_lib/dbm_features/raw_features/audio/shimmer.py
Normal file
@@ -0,0 +1,166 @@
|
||||
"""
|
||||
file_name: shimmer_processing
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
from os.path import join
|
||||
|
||||
import more_itertools as mit
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
shimmer_dir = "acoustic/shimmer"
|
||||
ff_dir = "acoustic/pitch"
|
||||
csv_ext = "_shimmer.csv"
|
||||
|
||||
|
||||
def audio_shimmer(sound):
|
||||
"""
|
||||
Using parselmouth library fetching shimmer
|
||||
Args:
|
||||
sound: parselmouth object
|
||||
Returns:
|
||||
(list) list of shimmers for each voice frame
|
||||
"""
|
||||
pointProcess = parselmouth.praat.call(
|
||||
sound, "To PointProcess (periodic, cc)...", 80, 500
|
||||
)
|
||||
shimmer = parselmouth.praat.call(
|
||||
[sound, pointProcess], "Get shimmer (local)", 0, 0, 0.0001, 0.02, 1.3, 1.6
|
||||
)
|
||||
return shimmer
|
||||
|
||||
|
||||
def empty_shimmer(video_uri, out_loc, fl_name, r_config, error_txt, save=True):
|
||||
"""
|
||||
Preparing empty shimmer matrix if something fails
|
||||
"""
|
||||
cols = ["Frames", r_config.aco_shimmer, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_shimmer = pd.DataFrame(out_val, columns=cols)
|
||||
df_shimmer["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_shimmer, out_loc, fl_name, shimmer_dir, csv_ext)
|
||||
return df_shimmer
|
||||
|
||||
|
||||
def segment_shimmer(com_speech_sort, voiced_yes, voiced_no, shimmer_frames, audio_file):
|
||||
"""
|
||||
calculating shimmer for each voice segment
|
||||
"""
|
||||
snd = parselmouth.Sound(audio_file)
|
||||
pitch = snd.to_pitch(time_step=0.001)
|
||||
|
||||
for idx, vs in enumerate(com_speech_sort):
|
||||
try:
|
||||
|
||||
shimmer = np.NaN
|
||||
if vs in voiced_yes and len(vs) > 1:
|
||||
|
||||
start_time = pitch.get_time_from_frame_number(vs[0])
|
||||
end_time = pitch.get_time_from_frame_number(vs[-1])
|
||||
|
||||
snd_start = int(snd.get_frame_number_from_time(start_time))
|
||||
snd_end = int(snd.get_frame_number_from_time(end_time))
|
||||
|
||||
samples = parselmouth.Sound(snd.as_array()[0][snd_start:snd_end])
|
||||
shimmer = audio_shimmer(samples)
|
||||
except:
|
||||
pass
|
||||
|
||||
shimmer_frames[idx] = shimmer
|
||||
return shimmer_frames
|
||||
|
||||
|
||||
def calc_shimmer(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, save=True, ff_df=None
|
||||
):
|
||||
"""
|
||||
Preparing shimmer matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file
|
||||
out_loc: (str) Output directory for csv
|
||||
r_config: config.config_raw_feature.pyConfigFeatureNmReader object
|
||||
"""
|
||||
dir_path = os.path.join(out_loc, ff_dir)
|
||||
if os.path.isdir(dir_path) or ff_df is not None:
|
||||
if ff_df is not None:
|
||||
voice_seg = ut.process_segment_pitch(ff_df, r_config)
|
||||
else:
|
||||
voice_seg = ut.segment_pitch(dir_path, r_config, ff_df=ff_df)
|
||||
|
||||
shimmer_frames = [np.NaN] * len(voice_seg[0])
|
||||
shimmer_segment_frames = segment_shimmer(
|
||||
voice_seg[0], voice_seg[1], voice_seg[2], shimmer_frames, audio_file
|
||||
)
|
||||
|
||||
df_shimmer = pd.DataFrame(
|
||||
shimmer_segment_frames, columns=[r_config.aco_shimmer]
|
||||
)
|
||||
df_shimmer[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
|
||||
df_shimmer["Frames"] = df_shimmer.index
|
||||
df_shimmer["dbm_master_url"] = video_uri
|
||||
if save:
|
||||
logger.info("Processing Output file {} ".format(out_loc))
|
||||
ut.save_output(df_shimmer, out_loc, fl_name, shimmer_dir, csv_ext)
|
||||
df = df_shimmer
|
||||
else:
|
||||
error_txt = "error: fundamental freq not available"
|
||||
df = empty_shimmer(video_uri, out_loc, fl_name, r_config, error_txt, save=save)
|
||||
return df
|
||||
|
||||
|
||||
def run_shimmer(video_uri, out_dir, r_config, save=True, ff_df=None):
|
||||
"""
|
||||
Processing all patients to fetch shimmer
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
# try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info("Output file {} size is less than 0.064sec".format(audio_file))
|
||||
|
||||
error_txt = "error: length less than 0.064"
|
||||
df = empty_shimmer(
|
||||
video_uri, out_loc, fl_name, r_config, error_txt, save=save
|
||||
)
|
||||
else:
|
||||
df = calc_shimmer(
|
||||
video_uri,
|
||||
audio_file,
|
||||
out_loc,
|
||||
fl_name,
|
||||
r_config,
|
||||
save=save,
|
||||
ff_df=ff_df,
|
||||
)
|
||||
return df
|
||||
# except Exception as e:
|
||||
# logger.error('Error in shimmer: {}'.format(e))
|
||||
# logger.error('Failed to process audio file')
|
||||
@@ -0,0 +1,131 @@
|
||||
"""
|
||||
file_name: voice_frame_score
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
vfs_dir = "acoustic/voice_frame_score"
|
||||
csv_ext = "_voiceprev.csv"
|
||||
error_txt = "error: length less than 0.064"
|
||||
|
||||
|
||||
def audio_pitch_frame(pitch):
|
||||
"""
|
||||
Computing total number of speech and participant voiced frames
|
||||
Args:
|
||||
pitch: speech pitch
|
||||
Returns:
|
||||
(float) total voice frames and participant voiced frames
|
||||
"""
|
||||
total_frames = pitch.get_number_of_frames()
|
||||
voiced_frames = pitch.count_voiced_frames()
|
||||
return total_frames, voiced_frames
|
||||
|
||||
|
||||
def voice_segment(path):
|
||||
"""
|
||||
Using parselmouth library for fundamental frequency
|
||||
Args:
|
||||
path: (.wav) audio file location
|
||||
Returns:
|
||||
(float) total voice frames, participant voiced frames and voiced frames percentage
|
||||
"""
|
||||
sound_pat = parselmouth.Sound(path)
|
||||
pitch = sound_pat.to_pitch()
|
||||
total_frames, voiced_frames = audio_pitch_frame(pitch)
|
||||
|
||||
voiced_percentage = (voiced_frames / total_frames) * 100
|
||||
return voiced_percentage, voiced_frames, total_frames
|
||||
|
||||
|
||||
def calc_vfs(video_uri, audio_file, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
creating dataframe matrix for voice frame score
|
||||
Args:
|
||||
audio_file: Audio file path
|
||||
new_out_base_dir: AWS instance output base directory path
|
||||
f_nm_config: Config file object
|
||||
"""
|
||||
|
||||
voice_percentage, voiced_frames, total_frames = voice_segment(audio_file)
|
||||
df_vfs = pd.DataFrame([voiced_frames], columns=[r_config.aco_voiceFrame])
|
||||
|
||||
df_vfs[r_config.aco_totVoiceFrame] = [total_frames]
|
||||
df_vfs[r_config.aco_voicePct] = [voice_percentage]
|
||||
df_vfs[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
|
||||
df_vfs["Frames"] = df_vfs.index
|
||||
df_vfs["dbm_master_url"] = video_uri
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_vfs, out_loc, fl_name, vfs_dir, csv_ext)
|
||||
return df_vfs
|
||||
|
||||
|
||||
def empty_vfs(video_uri, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Preparing empty VFS matrix if something fails
|
||||
"""
|
||||
cols = [
|
||||
"Frames",
|
||||
r_config.aco_voiceFrame,
|
||||
r_config.aco_totVoiceFrame,
|
||||
r_config.aco_voicePct,
|
||||
r_config.err_reason,
|
||||
]
|
||||
out_val = [[np.nan, np.nan, np.nan, np.nan, error_txt]]
|
||||
df_vfs = pd.DataFrame(out_val, columns=cols)
|
||||
df_vfs["dbm_master_url"] = video_uri
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_vfs, out_loc, fl_name, vfs_dir, csv_ext)
|
||||
return df_vfs
|
||||
|
||||
|
||||
def run_vfs(video_uri, out_dir, r_config, save=True):
|
||||
"""
|
||||
Processing all participants for fetching voice frame score
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.064:
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.064sec".format(audio_file)
|
||||
)
|
||||
|
||||
df = empty_vfs(video_uri, out_loc, fl_name, r_config, save=save)
|
||||
else:
|
||||
df = calc_vfs(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, save=save
|
||||
)
|
||||
return df
|
||||
except Exception as e:
|
||||
e
|
||||
logger.error("Failed to process audio file")
|
||||
@@ -0,0 +1,12 @@
|
||||
"""
|
||||
file_name: init
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
from .eye_blink import run_eye_blink
|
||||
from .eye_gaze import run_eye_gaze
|
||||
from .facial_tremor import fac_tremor_process
|
||||
from .head_motion import run_head_movement
|
||||
195
opendbm/dbm_lib/dbm_features/raw_features/movement/eye_blink.py
Normal file
195
opendbm/dbm_lib/dbm_features/raw_features/movement/eye_blink.py
Normal file
@@ -0,0 +1,195 @@
|
||||
"""
|
||||
file_name: eye_blink
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
import cv2
|
||||
import dlib
|
||||
import imutils
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from imutils import face_utils
|
||||
from imutils.video import FileVideoStream
|
||||
from scipy.signal import find_peaks
|
||||
from scipy.spatial import distance as dist
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
movement_expr_dir = "movement/eye_blink"
|
||||
csv_ext = "_eyeblinks.csv"
|
||||
|
||||
|
||||
def get_length(filename):
|
||||
result = subprocess.run(
|
||||
[
|
||||
"ffprobe",
|
||||
"-v",
|
||||
"error",
|
||||
"-show_entries",
|
||||
"format=duration",
|
||||
"-of",
|
||||
"default=noprint_wrappers=1:nokey=1",
|
||||
filename,
|
||||
],
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.DEVNULL,
|
||||
)
|
||||
return float(result.stdout)
|
||||
|
||||
|
||||
def eye_aspect_ratio(eye):
|
||||
"""
|
||||
Computing eye aspect ratio for an individual frame
|
||||
Args:
|
||||
eye: Eye landmarks
|
||||
Return:
|
||||
Eye aspect ratio for a frame
|
||||
"""
|
||||
# euclidean distance for vertical eye landmarks
|
||||
dist_cor1 = dist.euclidean(eye[1], eye[5])
|
||||
dist_cor2 = dist.euclidean(eye[2], eye[4])
|
||||
|
||||
# euclidean distance for horizontal eye landmark
|
||||
dist_cor3 = dist.euclidean(eye[0], eye[3])
|
||||
|
||||
ear = (dist_cor1 + dist_cor2) / (2.0 * dist_cor3)
|
||||
return ear
|
||||
|
||||
|
||||
def blink_detection(video_path, facial_landmarks, raw_config):
|
||||
"""
|
||||
Blink detection for each frame
|
||||
Args:
|
||||
video_path: MP4 file location
|
||||
facial_landmarks: Facial landmark pre-trained model path
|
||||
raw_config: Raw configuration file object
|
||||
Return:
|
||||
Dataframe with blink informatiom like blink frame, duration etc.
|
||||
"""
|
||||
tot_frame = 1
|
||||
blink_frame = []
|
||||
ear_frame = []
|
||||
|
||||
# clip = VideoFileClip(video_path, has_mask=True)
|
||||
vid_length = get_length(video_path)
|
||||
|
||||
identifier = dlib.get_frontal_face_detector() # dlib's face detector (HOG-based)
|
||||
forecaster = dlib.shape_predictor(facial_landmarks) # the facial landmark predictor
|
||||
|
||||
# left and right eye landmarks
|
||||
(left_beg, left_end) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
|
||||
(right_beg, right_end) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
|
||||
|
||||
f_stream = True
|
||||
vid_stream = FileVideoStream(video_path).start()
|
||||
|
||||
while True:
|
||||
try:
|
||||
# check if stream/frame available in video
|
||||
if f_stream and not vid_stream.more():
|
||||
break
|
||||
|
||||
# reading & converting frame into grayscale
|
||||
vid_frame = vid_stream.read()
|
||||
vid_frame = imutils.resize(vid_frame, width=450)
|
||||
gray = cv2.cvtColor(vid_frame, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
# detecting face
|
||||
rects = identifier(gray, 0)
|
||||
for rect in rects:
|
||||
lmk = forecaster(gray, rect)
|
||||
lmk = face_utils.shape_to_np(lmk)
|
||||
|
||||
l_eye = lmk[left_beg:left_end] # Extracting left eye ratio
|
||||
r_eye = lmk[right_beg:right_end] # Extracting right eye ratio
|
||||
l_ear = eye_aspect_ratio(l_eye) # eye aspect ratio for left eye
|
||||
r_ear = eye_aspect_ratio(r_eye) # eye aspect ratio for right eye
|
||||
|
||||
ear = (l_ear + r_ear) / 2.0 # average the eye aspect ratio
|
||||
blink_frame.append(tot_frame)
|
||||
ear_frame.append(ear)
|
||||
|
||||
tot_frame += 1
|
||||
except Exception as e:
|
||||
e
|
||||
logger.info(
|
||||
"blink detection processing finished in frame: {}".format(tot_frame - 1)
|
||||
)
|
||||
continue
|
||||
vid_stream.stop()
|
||||
blink_df = pd.DataFrame(ear_frame, columns=[raw_config.mov_blink_ear])
|
||||
blink_df[raw_config.vid_dur] = vid_length
|
||||
blink_df[raw_config.fps] = int(tot_frame / vid_length)
|
||||
blink_df[raw_config.mov_blinkframes] = blink_frame
|
||||
|
||||
peaks, _ = find_peaks(
|
||||
blink_df[raw_config.mov_blink_ear] * -1, prominence=0.1
|
||||
) # prominence = 0.1 based on tuning
|
||||
final_blink_df = blink_df.iloc[peaks, :].reset_index(drop=True)
|
||||
|
||||
u_blink_df = blink_dur(final_blink_df, raw_config)
|
||||
u_blink_df["dbm_master_url"] = video_path
|
||||
return u_blink_df
|
||||
|
||||
|
||||
def blink_dur(blink_df, raw_config):
|
||||
"""
|
||||
Computing blink duration between each blink
|
||||
Args:
|
||||
blink_df : Dataframe with blink informatiom like blink frame
|
||||
raw_config: Raw configuration file object
|
||||
Returns:
|
||||
Updated dataframe with blink duration
|
||||
"""
|
||||
if len(blink_df) > 0:
|
||||
blink_df[raw_config.mov_blinkdur] = (
|
||||
blink_df[raw_config.mov_blinkframes]
|
||||
.diff()
|
||||
.fillna(blink_df[raw_config.mov_blinkframes])
|
||||
)
|
||||
else:
|
||||
blink_df[raw_config.mov_blinkdur] = np.nan
|
||||
blink_df[raw_config.mov_blinkdur] = (
|
||||
blink_df[raw_config.mov_blinkdur] / blink_df[raw_config.fps]
|
||||
)
|
||||
return blink_df
|
||||
|
||||
|
||||
def run_eye_blink(video_uri, out_dir, r_config, facial_landmarks, save=True):
|
||||
"""
|
||||
Processing all patient's for getting eye blink artifacts
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; input_dir : input directory for video's
|
||||
out_dir: (str) Output directory for processed output;
|
||||
r_config: raw variable config object;
|
||||
facial_landmarks: landmark model path
|
||||
save: whether to save in csv or not
|
||||
"""
|
||||
try:
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
|
||||
vid_file_path = os.path.exists(video_uri)
|
||||
if vid_file_path:
|
||||
|
||||
logger.info(
|
||||
"Processing Output file {} ".format(os.path.join(out_loc, fl_name))
|
||||
)
|
||||
df_blink = blink_detection(video_uri, facial_landmarks, r_config)
|
||||
if save:
|
||||
ut.save_output(df_blink, out_loc, fl_name, movement_expr_dir, csv_ext)
|
||||
|
||||
return df_blink
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to process video file: {e}")
|
||||
182
opendbm/dbm_lib/dbm_features/raw_features/movement/eye_gaze.py
Normal file
182
opendbm/dbm_lib/dbm_features/raw_features/movement/eye_gaze.py
Normal file
@@ -0,0 +1,182 @@
|
||||
"""
|
||||
file_name: eye_gaze
|
||||
project_name: DBM
|
||||
created: 2020-30-11
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from scipy.spatial import distance
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
eye_pose_dir = "movement/gaze"
|
||||
eye_pose_ext = "_eyegaze.csv"
|
||||
|
||||
|
||||
def eye_motion_df(l_disp, r_disp, error_list, r_config):
|
||||
"""
|
||||
Generating eye movement dataframe
|
||||
|
||||
Args:
|
||||
error_list:
|
||||
l_disp: displacement list(left eye);
|
||||
r_disp: displacement list(right eye)
|
||||
r_config: raw variable config file object
|
||||
|
||||
Reutrns:
|
||||
Final eye displacement dataframe
|
||||
"""
|
||||
df_eye_left = pd.DataFrame(l_disp, columns=[r_config.mov_eleft_disp])
|
||||
df_eye_right = pd.DataFrame(r_disp, columns=[r_config.mov_eright_disp])
|
||||
|
||||
df_eye_motion = pd.concat([df_eye_left, df_eye_right], axis=1, sort=False)
|
||||
df_eye_motion[r_config.err_reason] = error_list
|
||||
return df_eye_motion
|
||||
|
||||
|
||||
def filter_motion(df_of, df_disp, col_l, col_r, r_config):
|
||||
"""
|
||||
Filtering final eye movement dataframe
|
||||
|
||||
Args:
|
||||
df_of: Openface raw out dataframe;
|
||||
df_disp: displacement dataframe
|
||||
col_r: right eye column
|
||||
col_l: left eye column;
|
||||
r_config: raw variable config file object
|
||||
"""
|
||||
|
||||
df_of = df_of[col_l + col_r + [" confidence"]].copy()
|
||||
df_of.loc[(df_of[" confidence"].astype(float) < 0.8), col_l + col_r] = np.nan
|
||||
|
||||
df_filter = df_of[col_l + col_r]
|
||||
df_filter.columns = [
|
||||
r_config.mov_leye_x,
|
||||
r_config.mov_leye_y,
|
||||
r_config.mov_leye_z,
|
||||
r_config.mov_reye_x,
|
||||
r_config.mov_reye_y,
|
||||
r_config.mov_reye_z,
|
||||
]
|
||||
|
||||
df_motion = pd.concat([df_filter, df_disp], axis=1, sort=False)
|
||||
return df_motion
|
||||
|
||||
|
||||
def eye_disp(of_results, col, r_config):
|
||||
"""
|
||||
Computing head velocity frame by frame
|
||||
|
||||
Args:
|
||||
of_results: Openface raw out dataframe
|
||||
col: col of eye_disp
|
||||
r_config: Face config file object
|
||||
|
||||
Reutrns:
|
||||
Final head velocity frame by frame output
|
||||
"""
|
||||
distance_list = []
|
||||
error_list = []
|
||||
|
||||
of_results = of_results[col + [" confidence"]]
|
||||
for index, row in of_results.iterrows():
|
||||
dst = np.nan
|
||||
|
||||
if index == 0 or float(row[" confidence"]) < 0.8: # Threshold < 0.8
|
||||
distance_list.append(dst)
|
||||
|
||||
if float(row[" confidence"]) < 0.8:
|
||||
error_list.append("confidence less than 80%")
|
||||
|
||||
else:
|
||||
error_list.append("Pass")
|
||||
continue
|
||||
|
||||
if index > 0:
|
||||
|
||||
point_x = (
|
||||
of_results[col[0]][index - 1],
|
||||
of_results[col[1]][index - 1],
|
||||
of_results[col[2]][index - 1],
|
||||
)
|
||||
point_y = (row[col[0]], row[col[1]], row[col[2]])
|
||||
try:
|
||||
dst = distance.euclidean(point_x, point_y)
|
||||
except Exception as e:
|
||||
logger.info("Exception on eye_disp method", e)
|
||||
pass
|
||||
|
||||
distance_list.append(abs(dst))
|
||||
error_list.append("Pass")
|
||||
|
||||
return distance_list, error_list
|
||||
|
||||
|
||||
def calc_eye_mov(video_uri, df_of, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Computing eye motion variables
|
||||
Args:
|
||||
video_uri: self explanatory
|
||||
df_of: Openface dataframe
|
||||
out_loc: Output path for saving output csv's
|
||||
fl_name: file name for output csv
|
||||
r_config: raw variable config file object
|
||||
save: whether to save result to csv or not
|
||||
|
||||
"""
|
||||
|
||||
col_l = [" gaze_0_x", " gaze_0_y", " gaze_0_z"]
|
||||
col_r = [" gaze_1_x", " gaze_1_y", " gaze_1_z"]
|
||||
|
||||
gazel_disp, err_l = eye_disp(df_of, col_l, r_config)
|
||||
gazer_disp, err_r = eye_disp(df_of, col_r, r_config)
|
||||
|
||||
df_disp = eye_motion_df(gazel_disp, gazer_disp, err_l, r_config)
|
||||
df_disp["dbm_master_url"] = video_uri
|
||||
|
||||
df_motion = filter_motion(df_of, df_disp, col_l, col_r, r_config)
|
||||
if save:
|
||||
ut.save_output(df_motion, out_loc, fl_name, eye_pose_dir, eye_pose_ext)
|
||||
return df_motion
|
||||
|
||||
|
||||
def run_eye_gaze(video_uri, out_dir, r_config, save=True):
|
||||
"""
|
||||
Processing all patient's for getting eye movement artifacts
|
||||
--------------------------------
|
||||
--------------------------------
|
||||
Args:
|
||||
video_uri: video path; input_dir : input directory for video's
|
||||
out_dir: (str) Output directory for processed output;
|
||||
r_config: raw variable config object
|
||||
save: whether to save result to csv or not
|
||||
"""
|
||||
try:
|
||||
|
||||
# filtering path to generate input & output path
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + "_openface/*.csv"))
|
||||
|
||||
if len(of_csv_path) > 0:
|
||||
of_csv = of_csv_path[0]
|
||||
df_of = pd.read_csv(of_csv)
|
||||
|
||||
logger.info(
|
||||
"Processing Output file {} ".format(os.path.join(out_loc, fl_name))
|
||||
)
|
||||
df_motion = calc_eye_mov(
|
||||
video_uri, df_of, out_loc, fl_name, r_config, save=save
|
||||
)
|
||||
return df_motion
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Failed to process video file", e)
|
||||
@@ -0,0 +1,194 @@
|
||||
import glob
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import pickle
|
||||
import re
|
||||
import sys
|
||||
from os.path import join
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import numpy.ma as ma
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
from ..util.math_util import calc_displacement_vec
|
||||
|
||||
DBMLIB_PATH = os.path.dirname(__file__)
|
||||
|
||||
DBMLIB_FTREMOR_CONFIG = os.path.abspath(
|
||||
os.path.join(DBMLIB_PATH, "../../../../resources/features/facial/config.json")
|
||||
)
|
||||
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
ft_dir = "movement/facial_tremor"
|
||||
csv_ext = "_fac_tremor.csv"
|
||||
model_ext = "_fac_model.csv"
|
||||
fac_features_ext = "_fac_features.csv"
|
||||
|
||||
|
||||
def compute_features(out_dir, df_of, r_config):
|
||||
"""Computes features
|
||||
|
||||
Returns: features in vector format
|
||||
"""
|
||||
config = json.loads(open(DBMLIB_FTREMOR_CONFIG, "r").read())
|
||||
|
||||
pattern_x = re.compile(r"l\d+_x")
|
||||
pattern_y = re.compile(r"l\d+_y")
|
||||
|
||||
# assumption: distance of face to camera remains at roughly static
|
||||
|
||||
# logic break
|
||||
landmark_columns = []
|
||||
for col in df_of.columns:
|
||||
if pattern_x.match(col) or pattern_y.match(col):
|
||||
landmark_columns.append(col)
|
||||
|
||||
df_of = df_of[(df_of[landmark_columns] != 0).any(axis=1)]
|
||||
df_of.reset_index(inplace=True)
|
||||
|
||||
num_frames = len(df_of)
|
||||
logger.info("Number of frames to be processed: {}".format(str(num_frames)))
|
||||
landmarks = config["landmarks"]
|
||||
|
||||
try:
|
||||
if num_frames == 0:
|
||||
error_reason = "No frames with visible face."
|
||||
logger.error(error_reason)
|
||||
return empty_frame(landmarks, r_config, error_reason)
|
||||
|
||||
# if num_frames < 60:
|
||||
# error_reason = 'Number of frames with visible face < 60. Video too short'
|
||||
# logger.error(error_reason)
|
||||
# return empty_frame(landmarks, f_cfg, error_reason)
|
||||
|
||||
first_row = df_of.iloc[0]
|
||||
|
||||
facew = abs(
|
||||
first_row[config["face_width_left"]] - first_row[config["face_width_right"]]
|
||||
)
|
||||
faceh = abs(
|
||||
first_row[config["face_height_left"]]
|
||||
- first_row[config["face_height_right"]]
|
||||
)
|
||||
|
||||
if facew == 0 or faceh == 0:
|
||||
error_reason = "face width or height = 0. Check landmark values"
|
||||
logger.error(error_reason)
|
||||
return empty_frame(landmarks, r_config)
|
||||
|
||||
fac_disp = calc_displacement_vec(df_of, landmarks, num_frames)
|
||||
|
||||
# if verbose:
|
||||
# logger.info("Displacement output: {}".format(str(fac_disp)))
|
||||
|
||||
fac_disp_median = np.median(fac_disp, axis=1)
|
||||
fac_disp_mean = np.mean(fac_disp, axis=1)
|
||||
|
||||
if len(fac_disp.shape) != 2:
|
||||
error_reason = "fac_disp is not 2D. smth went wrong with disp calc"
|
||||
logger.error(error_reason)
|
||||
return empty_frame(landmarks, r_config, error_reason)
|
||||
|
||||
if len(fac_disp[0]) <= 1:
|
||||
error_reason = "Video too short. smth went wrong with disp calc"
|
||||
logger.error(error_reason)
|
||||
return empty_frame(landmarks, r_config, error_reason)
|
||||
|
||||
fac_corr_mat = np.corrcoef(fac_disp, rowvar=True)
|
||||
# extract relevant row from cov matrix
|
||||
ref_lmk_index = [
|
||||
i for i, lmk in enumerate(landmarks) if config["ref_lmk"] == lmk
|
||||
]
|
||||
fac_corr = fac_corr_mat[ref_lmk_index][0]
|
||||
|
||||
fac_area = config["ref_area"] / (facew * faceh)
|
||||
|
||||
# if verbose:
|
||||
# logger.info("Face area: {}".format(fac_area))
|
||||
# logger.info("Face Displacement Median: {}".format(str(fac_disp_median)))
|
||||
# logger.info("Face Displacement Mean: {}".format(str(fac_disp_mean)))
|
||||
|
||||
fac_features1 = np.multiply(fac_area * fac_disp_median, (1.0 - fac_corr))
|
||||
fac_features2 = np.multiply(fac_area * fac_disp_mean, (1.0 - fac_corr))
|
||||
|
||||
# base_fac_features = np.dot(fac_area * fac_disp_median, (1. - fac_corr))
|
||||
|
||||
fac_features_dict = {}
|
||||
for i, landmark in enumerate(landmarks):
|
||||
fac_features_dict["fac_features_mean_{}".format(landmark)] = [
|
||||
fac_features2[i]
|
||||
]
|
||||
raw_variable_map = "fac_tremor_median_{}".format(landmark)
|
||||
fac_features_dict[r_config.base_raw["raw_feature"][raw_variable_map]] = [
|
||||
fac_features1[i]
|
||||
]
|
||||
|
||||
fac_features_dict["fac_disp_median_{}".format(landmark)] = [
|
||||
fac_disp_median[i]
|
||||
]
|
||||
fac_features_dict["fac_corr_{}".format(landmark)] = [fac_corr[i]]
|
||||
|
||||
fac_features_dict[r_config.err_reason] = [""]
|
||||
data = pd.DataFrame.from_dict(fac_features_dict)
|
||||
logger.info("Concluded computing tremor features")
|
||||
|
||||
return data
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Error computing tremor features: {}".format(str(e)))
|
||||
return empty_frame(landmarks, r_config, str(e))
|
||||
|
||||
|
||||
def empty_frame(landmarks, r_config, error_reason):
|
||||
fac_features_dict = {}
|
||||
for i, landmark in enumerate(landmarks):
|
||||
raw_variable_map = "fac_tremor_median_{}".format(landmark)
|
||||
fac_features_dict[r_config.base_raw["raw_feature"][raw_variable_map]] = [np.nan]
|
||||
|
||||
fac_features_dict["fac_features_mean_{}".format(landmark)] = [np.nan]
|
||||
fac_features_dict["fac_disp_median_{}".format(landmark)] = [np.nan]
|
||||
fac_features_dict["fac_corr_{}".format(landmark)] = [np.nan]
|
||||
|
||||
fac_features_dict[r_config.err_reason] = [error_reason]
|
||||
empty_frame = pd.DataFrame.from_dict(fac_features_dict)
|
||||
return empty_frame
|
||||
|
||||
|
||||
def fac_tremor_process(video_uri, out_dir, r_config, model_output=False, save=True):
|
||||
"""
|
||||
processing input videos
|
||||
|
||||
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + "_openface_lmk/*_output.csv"))
|
||||
if len(of_csv_path) > 0:
|
||||
of_csv = of_csv_path[0]
|
||||
df_of = pd.read_csv(of_csv)
|
||||
|
||||
logger.info(
|
||||
"Processing Output file for facial_tremor {} ".format(
|
||||
os.path.join(out_loc, fl_name)
|
||||
)
|
||||
)
|
||||
|
||||
feats = compute_features(of_csv_path, df_of, r_config)
|
||||
|
||||
# if model_output:
|
||||
# result = score(feats, r_config)
|
||||
# feats = pd.concat([feats, result], axis=1)
|
||||
if save:
|
||||
ut.save_output(feats, out_loc, fl_name, ft_dir, csv_ext)
|
||||
return feats
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Failed to process video file for facial_tremor", str(e))
|
||||
@@ -0,0 +1,240 @@
|
||||
"""
|
||||
file_name: head_mov
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from scipy.spatial import distance
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
h_mov_dir = "movement/head_movement"
|
||||
h_pose_dir = "movement/head_pose"
|
||||
h_mov_ext = "_headmov.csv"
|
||||
h_pose_ext = "_headpose.csv"
|
||||
|
||||
|
||||
def head_pose_dist(of_results):
|
||||
"""
|
||||
Computing head pose distance frame by frame
|
||||
|
||||
Args:
|
||||
of_results: Openface raw out dataframe
|
||||
|
||||
Returns:
|
||||
Final head pose distance frame by frame output
|
||||
"""
|
||||
distance_list = []
|
||||
error_list = []
|
||||
for index, row in of_results.iterrows():
|
||||
dst = np.nan
|
||||
|
||||
if index == 0 or float(row[" confidence"]) < 0.2: # Threshold < 0.2
|
||||
distance_list.append(dst)
|
||||
|
||||
if float(row[" confidence"]) < 0.2:
|
||||
error_list.append("confidence less than 20%")
|
||||
|
||||
else:
|
||||
error_list.append("Pass")
|
||||
continue
|
||||
|
||||
if index > 0:
|
||||
|
||||
point_x = (
|
||||
of_results[" pose_Rx"][index - 1],
|
||||
of_results[" pose_Ry"][index - 1],
|
||||
of_results[" pose_Rz"][index - 1],
|
||||
)
|
||||
point_y = (row[" pose_Rx"], row[" pose_Ry"], row[" pose_Rz"])
|
||||
try:
|
||||
dst = distance.euclidean(point_x, point_y)
|
||||
except Exception as e:
|
||||
logger.info("Exception met on head_pose_dist method", e)
|
||||
pass
|
||||
distance_list.append(abs(dst))
|
||||
error_list.append("Pass")
|
||||
return distance_list, error_list
|
||||
|
||||
|
||||
def head_pose(of_results, r_config):
|
||||
"""
|
||||
Generating head pose estimation dataframe
|
||||
|
||||
Args:
|
||||
of_results: openface results as dataframe
|
||||
r_config: raw variable config file object
|
||||
|
||||
Returns:
|
||||
Final head pose estimation dataframe
|
||||
"""
|
||||
pose_dist_list, error_list = head_pose_dist(of_results)
|
||||
of_results = of_results.copy()
|
||||
of_results.loc[
|
||||
(of_results[" confidence"].astype(float) < 0.2),
|
||||
[" pose_Rx", " pose_Ry", " pose_Rz"],
|
||||
] = np.nan
|
||||
pose_of = of_results[[" pose_Rx", " pose_Ry", " pose_Rz"]]
|
||||
pose_of.columns = [
|
||||
r_config.mov_Hpose_Pitch,
|
||||
r_config.mov_Hpose_Yaw,
|
||||
r_config.mov_Hpose_Roll,
|
||||
]
|
||||
pose_of = pose_of.copy()
|
||||
pose_of[r_config.mov_Hpose_Dist] = pose_dist_list
|
||||
pose_of[r_config.err_reason] = error_list
|
||||
|
||||
return pose_of
|
||||
|
||||
|
||||
def head_motion_df(distance_val, error_list, r_config):
|
||||
"""
|
||||
Generating head movement dataframe
|
||||
|
||||
Args:
|
||||
distance_val: distance list
|
||||
error_list: Error reason
|
||||
r_config: raw variable config file object
|
||||
|
||||
Returns:
|
||||
Final head velocity dataframe
|
||||
"""
|
||||
head_motion = r_config.head_vel
|
||||
df_head_motion = pd.DataFrame(distance_val, columns=[head_motion])
|
||||
df_head_motion["Frames"] = df_head_motion.index
|
||||
|
||||
new_df_intensity = df_head_motion[["Frames", head_motion]].copy()
|
||||
new_df_intensity[r_config.err_reason] = error_list
|
||||
|
||||
return new_df_intensity
|
||||
|
||||
|
||||
def head_vel(of_results, r_config):
|
||||
"""
|
||||
Computing head velocity frame by frame
|
||||
|
||||
Args:
|
||||
of_results: Openface raw out dataframe
|
||||
r_config: Face config file object
|
||||
|
||||
Returns:
|
||||
Final head velocity frame by frame output
|
||||
"""
|
||||
distance_list = []
|
||||
error_list = []
|
||||
for index, row in of_results.iterrows():
|
||||
dst = np.nan
|
||||
|
||||
if index == 0 or float(row[" confidence"]) < 0.2: # Threshold < 0.2
|
||||
distance_list.append(dst)
|
||||
|
||||
if float(row[" confidence"]) < 0.2:
|
||||
error_list.append("confidence less than 20%")
|
||||
|
||||
else:
|
||||
error_list.append("Pass")
|
||||
continue
|
||||
|
||||
if index > 0:
|
||||
|
||||
point_x = (
|
||||
of_results[" pose_Tx"][index - 1],
|
||||
of_results[" pose_Ty"][index - 1],
|
||||
of_results[" pose_Tz"][index - 1],
|
||||
)
|
||||
point_y = (row[" pose_Tx"], row[" pose_Ty"], row[" pose_Tz"])
|
||||
try:
|
||||
dst = distance.euclidean(point_x, point_y)
|
||||
except Exception as e:
|
||||
logger.info("Exception met on head_vel method", e)
|
||||
pass
|
||||
|
||||
if abs(dst) > 200:
|
||||
dst = np.nan
|
||||
error_list.append("Out of range")
|
||||
|
||||
else:
|
||||
error_list.append("Pass")
|
||||
distance_list.append(dst)
|
||||
df_velocity = head_motion_df(distance_list, error_list, r_config)
|
||||
|
||||
return df_velocity
|
||||
|
||||
|
||||
def calc_head_mov(video_uri, df_of, out_loc, fl_name, r_config, save=True):
|
||||
"""
|
||||
Computing head motion and head pose variables
|
||||
Args:
|
||||
video_uri: video path
|
||||
df_of: Openface dataframe
|
||||
out_loc: Output path for saving output csv's
|
||||
fl_name: file name for output csv
|
||||
r_config: raw variable config file object
|
||||
save: whether to save result to csv or not
|
||||
|
||||
"""
|
||||
|
||||
col = [
|
||||
" confidence",
|
||||
" pose_Rx",
|
||||
" pose_Ry",
|
||||
" pose_Rz",
|
||||
" pose_Tx",
|
||||
" pose_Ty",
|
||||
" pose_Tz",
|
||||
]
|
||||
df_of = df_of[col]
|
||||
|
||||
df_hmotion = head_vel(df_of, r_config)
|
||||
df_hmotion["dbm_master_url"] = video_uri
|
||||
|
||||
df_pose = head_pose(df_of, r_config)
|
||||
df_pose["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
ut.save_output(df_hmotion, out_loc, fl_name, h_mov_dir, h_mov_ext)
|
||||
ut.save_output(df_pose, out_loc, fl_name, h_pose_dir, h_pose_ext)
|
||||
|
||||
df_mot = pd.concat([df_hmotion[["Frames", "mov_headvel"]], df_pose], axis=1)
|
||||
return df_mot
|
||||
|
||||
|
||||
def run_head_movement(video_uri, out_dir, r_config):
|
||||
"""
|
||||
Processing all patient's for getting movement artifacts for cdx_analysis workflow
|
||||
--------------------------------
|
||||
--------------------------------
|
||||
Args:
|
||||
video_uri: video path; input_dir : input directory for video's
|
||||
out_dir: (str) Output directory for processed output;
|
||||
r_config: raw variable config object
|
||||
"""
|
||||
try:
|
||||
|
||||
# filtering path to generate input & output path
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
of_csv_path = glob.glob(join(out_loc, fl_name + "_openface/*.csv"))
|
||||
|
||||
if len(of_csv_path) > 0:
|
||||
of_csv = of_csv_path[0]
|
||||
df_of = pd.read_csv(of_csv)
|
||||
|
||||
logger.info(
|
||||
"Processing Output file {} ".format(os.path.join(out_loc, fl_name))
|
||||
)
|
||||
|
||||
df_mot = calc_head_mov(video_uri, df_of, out_loc, fl_name, r_config)
|
||||
return df_mot
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Failed to process video file", e)
|
||||
@@ -0,0 +1,136 @@
|
||||
import glob
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import parselmouth
|
||||
from parselmouth.praat import run_file
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
vt_dir = "movement/voice_tremor"
|
||||
csv_ext = "_vtremor.csv"
|
||||
|
||||
DBMLIB_PATH = os.path.dirname(__file__)
|
||||
DBMLIB_VTREMOR_LIB = os.path.abspath(
|
||||
os.path.join(DBMLIB_PATH, "../../../../resources/libraries/voice_tremor.praat")
|
||||
)
|
||||
|
||||
|
||||
# Executing praat script using parselmouth function
|
||||
def tremor_praat(snd_file, r_cfg):
|
||||
"""
|
||||
Generating Voice tremor endpoint dataframe
|
||||
Args:
|
||||
snd_file: (.wav) parsed audio file
|
||||
r_cfg: Raw variable configuration file
|
||||
Returns tremor endpoint dataframe
|
||||
"""
|
||||
snd = parselmouth.Sound(snd_file)
|
||||
tremor_var = run_file(snd, DBMLIB_VTREMOR_LIB, capture_output=True)
|
||||
new_tremor_var = re.sub("--undefined--", "0", tremor_var[1])
|
||||
res = json.loads(new_tremor_var)
|
||||
tremor_df = pd.DataFrame(
|
||||
res,
|
||||
index=[
|
||||
"0",
|
||||
],
|
||||
)
|
||||
tremor_df.columns = [
|
||||
r_cfg.mov_freq_trem_freq,
|
||||
r_cfg.mov_amp_trem_freq,
|
||||
r_cfg.mov_freq_trem_index,
|
||||
r_cfg.mov_amp_trem_index,
|
||||
r_cfg.mov_freq_trem_pindex,
|
||||
r_cfg.mov_amp_trem_pindex,
|
||||
]
|
||||
return tremor_df
|
||||
|
||||
|
||||
def prepare_vtrem_output(audio_file, out_loc, r_config, fl_name, save=True):
|
||||
"""
|
||||
Preparing voice tremor matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file ; r_config: raw config object
|
||||
out_loc: (str) Output directory for csv ; fl_name: file name
|
||||
r_config: Raw variable configuration file
|
||||
fl_name: base filepath
|
||||
save: whether to write results to csv or not
|
||||
"""
|
||||
df_tremor = tremor_praat(audio_file, r_config)
|
||||
df_tremor[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
|
||||
if save:
|
||||
logger.info("Processing Output file {} ".format(os.path.join(out_loc, fl_name)))
|
||||
ut.save_output(df_tremor, out_loc, fl_name, vt_dir, csv_ext)
|
||||
return df_tremor
|
||||
|
||||
|
||||
def prepare_empty_vt(out_loc, fl_name, r_config, error_txt, save=True):
|
||||
"""
|
||||
Preparing empty voice tremor matrix
|
||||
"""
|
||||
cols = [
|
||||
r_config.mov_freq_trem_freq,
|
||||
r_config.mov_amp_trem_freq,
|
||||
r_config.mov_freq_trem_index,
|
||||
r_config.mov_amp_trem_index,
|
||||
r_config.mov_freq_trem_pindex,
|
||||
r_config.mov_amp_trem_pindex,
|
||||
r_config.err_reason,
|
||||
]
|
||||
|
||||
out_val = [[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, error_txt]]
|
||||
df_tremor = pd.DataFrame(out_val, columns=cols)
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(os.path.join(out_loc, fl_name)))
|
||||
ut.save_output(df_tremor, out_loc, fl_name, vt_dir, csv_ext)
|
||||
return df_tremor
|
||||
|
||||
|
||||
def run_vtremor(video_uri, out_dir, r_config, save=True):
|
||||
"""
|
||||
Processing all patient's for fetching Formant freq
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
r_config: Raw variable configuration file
|
||||
save: whether to write results to csv or not
|
||||
"""
|
||||
try:
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
|
||||
if float(aud_dur) < 0.5:
|
||||
logger.info(
|
||||
"Output file {} size is less than 0.5sec".format(audio_file)
|
||||
)
|
||||
|
||||
error_txt = "error: length less than 0.5 sec"
|
||||
df_trem = prepare_empty_vt(video_uri, out_loc, fl_name, error_txt, save)
|
||||
else:
|
||||
df_trem = prepare_vtrem_output(
|
||||
audio_file, out_loc, r_config, fl_name, save
|
||||
)
|
||||
|
||||
return df_trem
|
||||
except Exception as e:
|
||||
logger.error("Failed to compute Voice Tremor {} for {}".format(e, video_uri))
|
||||
prepare_empty_vt(out_loc, fl_name, r_config, e, save)
|
||||
@@ -0,0 +1,2 @@
|
||||
from .speech_features import run_speech_feature
|
||||
from .transcribe import run_transcribe
|
||||
@@ -0,0 +1,58 @@
|
||||
"""
|
||||
file_name: speech_features
|
||||
project_name: DBM
|
||||
created: 2020-13-11
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
import os
|
||||
import shutil
|
||||
from os.path import join
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import nlp_util as n_util
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
speech_dir = "speech/speech_feature"
|
||||
speech_ext = "_nlp.csv"
|
||||
transcribe_ext = "speech/deepspeech/*_transcribe.csv"
|
||||
|
||||
|
||||
def run_speech_feature(video_uri, out_dir, r_config, tran_tog, save=True):
|
||||
"""
|
||||
Processing all patient's for fetching nlp features
|
||||
-------------------
|
||||
-------------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output
|
||||
"""
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
|
||||
transcribe_path = glob.glob(join(out_loc, transcribe_ext))
|
||||
transcribe_df = pd.read_csv(transcribe_path[0])
|
||||
df_speech = n_util.process_speech(transcribe_df, r_config)
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
logger.info("filename {} ".format(fl_name))
|
||||
ut.save_output(df_speech, out_loc, fl_name, speech_dir, speech_ext)
|
||||
|
||||
if (tran_tog is None) or (tran_tog != "on"):
|
||||
if os.getcwd() == "/app": # docker version
|
||||
shutil.rmtree(os.path.dirname(transcribe_path[0]))
|
||||
else: # api_lib version
|
||||
if fl_name.endswith("mp4"):
|
||||
shutil.rmtree((out_dir + "/" + fl_name).replace("//", "/"))
|
||||
else:
|
||||
shutil.rmtree(
|
||||
(out_dir + "/" + fl_name.strip(".mp4")).replace("//", "/")
|
||||
)
|
||||
|
||||
return df_speech
|
||||
94
opendbm/dbm_lib/dbm_features/raw_features/nlp/transcribe.py
Normal file
94
opendbm/dbm_lib/dbm_features/raw_features/nlp/transcribe.py
Normal file
@@ -0,0 +1,94 @@
|
||||
"""
|
||||
file_name: transcribe
|
||||
project_name: DBM
|
||||
created: 2020-10-11
|
||||
"""
|
||||
|
||||
import glob
|
||||
import logging
|
||||
from os.path import join
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import nlp_util as n_util
|
||||
from opendbm.dbm_lib.dbm_features.raw_features.util import util as ut
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
formant_dir = "speech/deepspeech"
|
||||
csv_ext = "_transcribe.csv"
|
||||
error_txt = "error: length less than 0.1"
|
||||
|
||||
|
||||
def calc_transcribe(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, deep_path, aud_dur, save=True
|
||||
):
|
||||
"""
|
||||
Preparing Formant freq matrix
|
||||
Args:
|
||||
audio_file: (.wav) parsed audio file; fl_name: input file name
|
||||
out_loc: (str) Output directory; r_config: raw variable config
|
||||
"""
|
||||
|
||||
text = n_util.process_deepspeech(audio_file, deep_path)
|
||||
df_formant = pd.DataFrame([text], columns=[r_config.nlp_transcribe])
|
||||
|
||||
df_formant.replace("", np.nan, regex=True, inplace=True)
|
||||
df_formant[r_config.nlp_totalTime] = aud_dur
|
||||
df_formant[
|
||||
r_config.err_reason
|
||||
] = "Pass" # will replace with threshold in future release
|
||||
df_formant["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_formant, out_loc, fl_name, formant_dir, csv_ext)
|
||||
return df_formant
|
||||
|
||||
|
||||
def empty_transcribe(video_uri, out_loc, fl_name, r_config, save=True):
|
||||
|
||||
"""
|
||||
Preparing empty formant frequency matrix if something fails
|
||||
"""
|
||||
cols = [r_config.nlp_transcribe, r_config.nlp_totalTime, r_config.err_reason]
|
||||
out_val = [[np.nan, np.nan, error_txt]]
|
||||
df_fm = pd.DataFrame(out_val, columns=cols)
|
||||
df_fm["dbm_master_url"] = video_uri
|
||||
|
||||
if save:
|
||||
logger.info("Saving Output file {} ".format(out_loc))
|
||||
ut.save_output(df_fm, out_loc, fl_name, formant_dir, csv_ext)
|
||||
return df_fm
|
||||
|
||||
|
||||
def run_transcribe(video_uri, out_dir, r_config, deep_path, save=True):
|
||||
|
||||
"""
|
||||
Processing all patient's for fetching Formant freq
|
||||
---------------
|
||||
---------------
|
||||
Args:
|
||||
video_uri: video path; r_config: raw variable config object
|
||||
out_dir: (str) Output directory for processed output;
|
||||
deep_path: deepspeech build path
|
||||
"""
|
||||
|
||||
input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
|
||||
aud_filter = glob.glob(join(input_loc, fl_name + ".wav"))
|
||||
if len(aud_filter) > 0:
|
||||
|
||||
audio_file = aud_filter[0]
|
||||
aud_dur = ut.get_length(audio_file)
|
||||
if float(aud_dur) < 0.1:
|
||||
logger.info("Output file {} size is less than 0.1 sec".format(audio_file))
|
||||
|
||||
df = empty_transcribe(video_uri, out_loc, fl_name, r_config)
|
||||
return df
|
||||
|
||||
df = calc_transcribe(
|
||||
video_uri, audio_file, out_loc, fl_name, r_config, deep_path, aud_dur
|
||||
)
|
||||
return df
|
||||
@@ -4,9 +4,13 @@ project_name: cdx_analysis
|
||||
created: 2019-03-16
|
||||
author: Deshana Desai
|
||||
"""
|
||||
import sys, os, glob, cv2
|
||||
import pandas as pd
|
||||
import glob
|
||||
import os
|
||||
import sys
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def euclidean_distance(point1, point2):
|
||||
@@ -14,7 +18,7 @@ def euclidean_distance(point1, point2):
|
||||
Compute euclidean distance between points
|
||||
"""
|
||||
|
||||
return np.sqrt((point1[0] - point2[0])**2 + (point1[1] - point2[1])**2)
|
||||
return np.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2)
|
||||
|
||||
|
||||
# def detect_peaks()
|
||||
@@ -25,8 +29,7 @@ def expand_landmarks(landmarks):
|
||||
util method to expand landmark list:
|
||||
eg: [1,2] -> [['l1_x', 'l1_y'], ['l2_x', 'l2_y']]
|
||||
"""
|
||||
return [['l{}_x'.format(l), 'l{}_y'.format(l)] for l in landmarks]
|
||||
|
||||
return [["l{}_x".format(point), "l{}_y".format(point)] for point in landmarks]
|
||||
|
||||
|
||||
def calc_displacement_vec(df, landmarks, num_frames):
|
||||
@@ -44,13 +47,12 @@ def calc_displacement_vec(df, landmarks, num_frames):
|
||||
first_row = df.iloc[0]
|
||||
prev_point[j] = (first_row[pair[0]], first_row[pair[1]])
|
||||
|
||||
|
||||
for i in range(num_frames):
|
||||
frame_row = df.iloc[i]
|
||||
for j, pair in enumerate(landmarks):
|
||||
x, y = pair[0], pair[1]
|
||||
current = (frame_row[x], frame_row[y])
|
||||
deviation = euclidean_distance( current, prev_point[j])
|
||||
deviation = euclidean_distance(current, prev_point[j])
|
||||
disp_vec[j][i] = deviation
|
||||
prev_point[j] = current
|
||||
|
||||
273
opendbm/dbm_lib/dbm_features/raw_features/util/nlp_util.py
Normal file
273
opendbm/dbm_lib/dbm_features/raw_features/util/nlp_util.py
Normal file
@@ -0,0 +1,273 @@
|
||||
"""
|
||||
file_name: nlp_util
|
||||
project_name: DBM
|
||||
created: 2020-10-11
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import subprocess
|
||||
|
||||
import nltk
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from lexicalrichness import LexicalRichness
|
||||
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger()
|
||||
|
||||
# Speech to text using Deepspeech 0.9.1
|
||||
def deepspeech(AUDIO_FILE, deep_path):
|
||||
"""
|
||||
Extracting text from audio using Deep Speech neural network trained model
|
||||
Returns:
|
||||
Text: text which is extracted from audio
|
||||
"""
|
||||
api = "deepspeech"
|
||||
arg_speech0 = "--model"
|
||||
arg_speech_path0 = os.path.join(deep_path, "deepspeech-0.9.1-models.pbmm")
|
||||
arg_speech1 = "--scorer"
|
||||
arg_speech_path1 = os.path.join(deep_path, "deepspeech-0.9.1-models.scorer")
|
||||
arg_audio = "--audio"
|
||||
|
||||
out = subprocess.Popen(
|
||||
[
|
||||
api,
|
||||
arg_speech0,
|
||||
arg_speech_path0,
|
||||
arg_speech1,
|
||||
arg_speech_path1,
|
||||
arg_audio,
|
||||
AUDIO_FILE,
|
||||
],
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
)
|
||||
logger.info("Deepspeech output...... {}".format(out))
|
||||
try:
|
||||
stdout, stderr = out.communicate()
|
||||
except:
|
||||
return "error", "error"
|
||||
# print(stderr)
|
||||
return stdout, stderr
|
||||
|
||||
|
||||
def deep_speech_output_clean(result):
|
||||
"""
|
||||
Parsing deep speech output(text)
|
||||
Return:
|
||||
Text from speech
|
||||
"""
|
||||
text = ""
|
||||
if len(result) > 0:
|
||||
res_split = str(result[0]).split("\\n")
|
||||
|
||||
if len(res_split) > 0:
|
||||
for i in range(len(res_split)):
|
||||
if "Inference took" in res_split[i]:
|
||||
text = res_split[i + 1]
|
||||
return text
|
||||
return text
|
||||
|
||||
|
||||
def process_deepspeech(audio_file, deep_path):
|
||||
"""
|
||||
Transcribing audio to extract text from speech
|
||||
"""
|
||||
deep_output = deepspeech(audio_file, deep_path)
|
||||
deep_text = deep_speech_output_clean(deep_output)
|
||||
|
||||
return deep_text
|
||||
|
||||
|
||||
def nltk_download():
|
||||
|
||||
try:
|
||||
nltk.data.find("tokenizers/punkt")
|
||||
|
||||
except LookupError:
|
||||
logger.info("punkt is not available")
|
||||
nltk.download("punkt")
|
||||
|
||||
try:
|
||||
nltk.data.find("averaged_perceptron_tagger")
|
||||
|
||||
except LookupError:
|
||||
logger.info("averaged_perceptron_tagger is not available")
|
||||
nltk.download("averaged_perceptron_tagger")
|
||||
|
||||
|
||||
def empty_speech(r_config, master_url, error_txt):
|
||||
"""
|
||||
Preparing empty speech matrix with error
|
||||
Args:
|
||||
r_config: raw config file object
|
||||
error_txt: Error message during transcription
|
||||
|
||||
Returns:
|
||||
Empty dataframe for speech features with error
|
||||
"""
|
||||
|
||||
col = [
|
||||
r_config.nlp_numSentences,
|
||||
r_config.nlp_singPronPerAns,
|
||||
r_config.nlp_singPronPerSen,
|
||||
r_config.nlp_pastTensePerAns,
|
||||
r_config.nlp_pastTensePerSen,
|
||||
r_config.nlp_pronounsPerAns,
|
||||
r_config.nlp_pronounsPerSen,
|
||||
r_config.nlp_verbsPerAns,
|
||||
r_config.nlp_verbsPerSen,
|
||||
r_config.nlp_adjectivesPerAns,
|
||||
r_config.nlp_adjectivesPerSen,
|
||||
r_config.nlp_nounsPerAns,
|
||||
r_config.nlp_nounsPerSen,
|
||||
r_config.nlp_sentiment_mean,
|
||||
r_config.nlp_mattr,
|
||||
r_config.nlp_wordsPerMin,
|
||||
r_config.nlp_totalTime,
|
||||
r_config.err_reason,
|
||||
]
|
||||
|
||||
df_speech = pd.DataFrame([[np.nan] * len(col) + [error_txt]], columns=col)
|
||||
df_speech["dbm_master_url"] = master_url
|
||||
|
||||
return df_speech
|
||||
|
||||
|
||||
def divide_var(speech_var1, spech_var2):
|
||||
"""
|
||||
divide variables
|
||||
"""
|
||||
speech_var = np.nan
|
||||
if spech_var2 != 0:
|
||||
speech_var = speech_var1 / spech_var2
|
||||
return speech_var
|
||||
|
||||
|
||||
def process_speech(transcribe_df, r_config):
|
||||
"""
|
||||
Preparing speech features
|
||||
Args:
|
||||
transcribe_df: Transcribed dataframe
|
||||
r_config: raw config file object
|
||||
Returns:
|
||||
Dataframe for speech features
|
||||
"""
|
||||
transcribe_df = transcribe_df.replace(np.nan, "", regex=True)
|
||||
err_transcribe = transcribe_df[r_config.err_reason].iloc[0]
|
||||
transcribe = transcribe_df[r_config.nlp_transcribe].iloc[0]
|
||||
total_time = transcribe_df[r_config.nlp_totalTime].iloc[0]
|
||||
master_url = transcribe_df["dbm_master_url"].iloc[0]
|
||||
|
||||
# clean transcribe
|
||||
transcribe = transcribe.replace(",", "")
|
||||
transcribe = " ".join(re.findall(r"[\w']+|[.!?]", transcribe))
|
||||
|
||||
if err_transcribe != "Pass":
|
||||
df_speech = empty_speech(r_config, master_url, "error")
|
||||
|
||||
return df_speech
|
||||
|
||||
speech_dict = {}
|
||||
nltk_download()
|
||||
|
||||
sentences = nltk.tokenize.sent_tokenize(transcribe)
|
||||
words_all = nltk.tokenize.word_tokenize(transcribe)
|
||||
num_sentences = len(sentences)
|
||||
|
||||
speech_dict[r_config.nlp_numSentences] = num_sentences
|
||||
|
||||
# nlp_singPron
|
||||
i_s = transcribe.count("I")
|
||||
me_s = transcribe.count("me")
|
||||
my_s = transcribe.count("my")
|
||||
sing_count = i_s + me_s + my_s
|
||||
|
||||
speech_dict[r_config.nlp_singPronPerAns] = (
|
||||
sing_count if len(words_all) > 0 else np.nan
|
||||
)
|
||||
speech_dict[r_config.nlp_singPronPerSen] = divide_var(
|
||||
speech_dict[r_config.nlp_singPronPerAns], num_sentences
|
||||
)
|
||||
|
||||
tagged = nltk.pos_tag(transcribe.split())
|
||||
tagged_df = pd.DataFrame(tagged, columns=["word", "pos_tag"])
|
||||
|
||||
# Past tense per answer
|
||||
all_POSs = tagged_df["pos_tag"].tolist()
|
||||
speech_dict[r_config.nlp_pastTensePerAns] = (
|
||||
all_POSs.count("VBD") if len(words_all) > 0 else np.nan
|
||||
)
|
||||
speech_dict[r_config.nlp_pastTensePerSen] = divide_var(
|
||||
speech_dict[r_config.nlp_pastTensePerAns], num_sentences
|
||||
)
|
||||
|
||||
# Pronoun per answer
|
||||
pronounsPerAns = all_POSs.count("PRP") + all_POSs.count("PRP$")
|
||||
speech_dict[r_config.nlp_pronounsPerAns] = (
|
||||
pronounsPerAns if len(words_all) > 0 else np.nan
|
||||
)
|
||||
speech_dict[r_config.nlp_pronounsPerSen] = divide_var(
|
||||
speech_dict[r_config.nlp_pronounsPerAns], num_sentences
|
||||
)
|
||||
|
||||
# Verb per answer
|
||||
verbPerAns = (
|
||||
all_POSs.count("VB")
|
||||
+ all_POSs.count("VBD")
|
||||
+ all_POSs.count("VBG")
|
||||
+ all_POSs.count("VBN")
|
||||
+ all_POSs.count("VBP")
|
||||
+ all_POSs.count("VBZ")
|
||||
)
|
||||
speech_dict[r_config.nlp_verbsPerAns] = verbPerAns if len(words_all) > 0 else np.nan
|
||||
speech_dict[r_config.nlp_verbsPerSen] = divide_var(
|
||||
speech_dict[r_config.nlp_verbsPerAns], num_sentences
|
||||
)
|
||||
|
||||
# Adjective per answer
|
||||
adjectivesAns = all_POSs.count("JJ") + all_POSs.count("JJR") + all_POSs.count("JJS")
|
||||
speech_dict[r_config.nlp_adjectivesPerAns] = (
|
||||
adjectivesAns if len(words_all) > 0 else np.nan
|
||||
)
|
||||
speech_dict[r_config.nlp_adjectivesPerSen] = divide_var(
|
||||
speech_dict[r_config.nlp_adjectivesPerAns], num_sentences
|
||||
)
|
||||
|
||||
# Noun per answer
|
||||
nounsAns = all_POSs.count("NN") + all_POSs.count("NNP") + all_POSs.count("NNS")
|
||||
speech_dict[r_config.nlp_nounsPerAns] = nounsAns if len(words_all) > 0 else np.nan
|
||||
speech_dict[r_config.nlp_nounsPerSen] = divide_var(
|
||||
speech_dict[r_config.nlp_nounsPerAns], num_sentences
|
||||
)
|
||||
|
||||
# Sentiment analysis
|
||||
vader = SentimentIntensityAnalyzer()
|
||||
sentence_valences = []
|
||||
|
||||
for s in sentences:
|
||||
sentiment_dict = vader.polarity_scores(s)
|
||||
sentence_valences.append(sentiment_dict["compound"])
|
||||
|
||||
speech_dict[r_config.nlp_sentiment_mean] = (
|
||||
np.mean(sentence_valences) if len(sentence_valences) > 0 else np.nan
|
||||
)
|
||||
non_punc = list(value for value in words_all if value not in [".", "!", "?"])
|
||||
|
||||
non_punc_as_str = " ".join(str(non_punc))
|
||||
lex = LexicalRichness(non_punc_as_str)
|
||||
speech_dict[r_config.nlp_mattr] = (
|
||||
lex.mattr(window_size=lex.words) if lex.words > 0 else np.nan
|
||||
)
|
||||
|
||||
# Number of words per minute
|
||||
speech_dict[r_config.nlp_wordsPerMin] = divide_var(len(non_punc), total_time) * 60
|
||||
speech_dict[r_config.nlp_totalTime] = total_time
|
||||
speech_dict["dbm_master_url"] = master_url
|
||||
|
||||
df_speech = pd.DataFrame([speech_dict])
|
||||
return df_speech
|
||||
183
opendbm/dbm_lib/dbm_features/raw_features/util/util.py
Normal file
183
opendbm/dbm_lib/dbm_features/raw_features/util/util.py
Normal file
@@ -0,0 +1,183 @@
|
||||
"""
|
||||
file_name: util
|
||||
project_name: DBM
|
||||
created: 2020-20-07
|
||||
"""
|
||||
|
||||
import glob
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
import more_itertools as mit
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def get_length(filename):
|
||||
result = subprocess.run(
|
||||
[
|
||||
"ffprobe",
|
||||
"-v",
|
||||
"error",
|
||||
"-show_entries",
|
||||
"format=duration",
|
||||
"-of",
|
||||
"default=noprint_wrappers=1:nokey=1",
|
||||
filename,
|
||||
],
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
stdin=subprocess.DEVNULL,
|
||||
)
|
||||
return float(result.stdout)
|
||||
|
||||
|
||||
def process_segment_pitch(ff_df, r_config):
|
||||
voice_label = ff_df[r_config.aco_voiceLabel]
|
||||
|
||||
indices_yes = [i for i, x in enumerate(voice_label) if x == "yes"]
|
||||
voiced_yes = [list(group) for group in mit.consecutive_groups(indices_yes)]
|
||||
|
||||
indices_no = [i for i, x in enumerate(voice_label) if x == "no"]
|
||||
voiced_no = [list(group) for group in mit.consecutive_groups(indices_no)]
|
||||
|
||||
com_speech = voiced_yes + voiced_no
|
||||
com_speech_sort = sorted(com_speech, key=lambda x: x[0])
|
||||
return com_speech_sort, voiced_yes, voiced_no
|
||||
|
||||
|
||||
def segment_pitch(dir_path, r_config, ff_df=None):
|
||||
"""
|
||||
segmenting pitch freq for each voice segment
|
||||
"""
|
||||
com_speech_sort, voiced_yes, voiced_no = ([],) * 3
|
||||
|
||||
for file in os.listdir(dir_path):
|
||||
try:
|
||||
if file.endswith("_pitch.csv") and ff_df is None:
|
||||
ff_df = pd.read_csv((dir_path + "/" + file))
|
||||
com_speech_sort, voiced_yes, voiced_no
|
||||
except:
|
||||
pass
|
||||
|
||||
return com_speech_sort, voiced_yes, voiced_no
|
||||
|
||||
|
||||
def filter_path(video_url, out_dir):
|
||||
|
||||
"""
|
||||
Filtering video uri path to prepare input and ouptut location
|
||||
|
||||
Args:
|
||||
video_url: S3 bucket path for video
|
||||
out_dir: Output directory path
|
||||
|
||||
"""
|
||||
|
||||
fl_name, _ = os.path.splitext(os.path.basename(video_url))
|
||||
input_loc = os.path.dirname(video_url)
|
||||
out_loc = os.path.join(out_dir, fl_name)
|
||||
return input_loc, out_loc, fl_name
|
||||
|
||||
|
||||
def save_output(df, out_loc, fl_name, f_dir, f_ext):
|
||||
"""
|
||||
creating output directory for Audio features
|
||||
Args:
|
||||
df: (dataframe) feature dataframe[ex: Formant freq, pitch]
|
||||
out_loc: (dir) Output location where we want to save raw output
|
||||
fl_name: file name
|
||||
f_dir: directory name for a feature
|
||||
f_ext: extension for a feature [ex: '_pose.csv']
|
||||
"""
|
||||
full_f_name = fl_name + f_ext
|
||||
dir_path = os.path.join(out_loc, f_dir)
|
||||
|
||||
if not os.path.exists(dir_path):
|
||||
os.makedirs(dir_path)
|
||||
|
||||
sav_path = os.path.join(dir_path, full_f_name)
|
||||
df.to_csv(sav_path, index=False)
|
||||
|
||||
|
||||
def audio_process(base_dir, video_url):
|
||||
"""
|
||||
Parsing cleaned audio files(Audio files without IMA voice)
|
||||
Args:
|
||||
base_dir: Base path for raw data
|
||||
video_url: Raw video file path
|
||||
"""
|
||||
new_video_url = base_dir + "/".join(video_url[2:])
|
||||
split_val = new_video_url.split("/")
|
||||
wav_path = "/".join(split_val[0 : len(split_val) - 1])
|
||||
audio_split_check = glob.glob(wav_path + "/*_split.wav")
|
||||
return audio_split_check
|
||||
|
||||
|
||||
def compute_open_face_features(
|
||||
input_filepath,
|
||||
output_directory,
|
||||
open_face_executable,
|
||||
au_static=False,
|
||||
tracked_visualization=False,
|
||||
clobber=False,
|
||||
verbose=True,
|
||||
):
|
||||
"""
|
||||
Runs OpenFace on an input video.
|
||||
See https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments
|
||||
Args:
|
||||
input_filepath:
|
||||
output_directory:
|
||||
au_static:
|
||||
tracked_visualization:
|
||||
open_face_executable:
|
||||
clobber: (bool) if True existing files will be overwritten
|
||||
verbose:
|
||||
Returns:
|
||||
(str) path to output csv file
|
||||
Raises:
|
||||
IOError if OpenFace executable is missing
|
||||
"""
|
||||
|
||||
if not os.path.isfile(open_face_executable):
|
||||
raise IOError(
|
||||
"OpenFace executable {} could not be found.".format(open_face_executable)
|
||||
)
|
||||
|
||||
bn, _ = os.path.splitext(os.path.basename(input_filepath))
|
||||
if not output_directory:
|
||||
output_directory = os.path.join(
|
||||
os.path.dirname(input_filepath), bn + "_openface"
|
||||
)
|
||||
|
||||
output_csv = os.path.join(output_directory, bn + ".csv")
|
||||
if not os.path.isfile(output_csv) or clobber:
|
||||
call = [
|
||||
open_face_executable,
|
||||
]
|
||||
if au_static:
|
||||
call += [
|
||||
"-au_static",
|
||||
]
|
||||
|
||||
if tracked_visualization:
|
||||
call += [
|
||||
"-tracked",
|
||||
]
|
||||
|
||||
call += ["-q", "-2Dfp", "-3Dfp", "-pdmparams", "-pose", "-aus", "-gaze"]
|
||||
call += ["-f", input_filepath, "-out_dir", output_directory]
|
||||
|
||||
if verbose:
|
||||
print(
|
||||
"Computing OpenFace features {} from video file".format(input_filepath)
|
||||
)
|
||||
subprocess.check_output(call)
|
||||
if verbose:
|
||||
print("OpenFace features saved to {}".format(output_directory))
|
||||
else:
|
||||
if verbose:
|
||||
print("Output file {} already exists".format(output_csv))
|
||||
|
||||
return os.path.join(output_directory, bn + ".csv")
|
||||
@@ -10,11 +10,12 @@ import contextlib
|
||||
import sys
|
||||
import wave
|
||||
|
||||
|
||||
def read_wave(path):
|
||||
"""Reads a .wav file.
|
||||
Takes the path, and returns (PCM audio data, sample rate).
|
||||
"""
|
||||
with contextlib.closing(wave.open(path, 'rb')) as wf:
|
||||
with contextlib.closing(wave.open(path, "rb")) as wf:
|
||||
num_channels = wf.getnchannels()
|
||||
assert num_channels == 1
|
||||
sample_width = wf.getsampwidth()
|
||||
@@ -27,11 +28,13 @@ def read_wave(path):
|
||||
|
||||
class Frame(object):
|
||||
"""Represents a "frame" of audio data."""
|
||||
|
||||
def __init__(self, bytes, timestamp, duration):
|
||||
self.bytes = bytes
|
||||
self.timestamp = timestamp
|
||||
self.duration = duration
|
||||
|
||||
|
||||
def frame_generator(frame_duration_ms, audio, sample_rate):
|
||||
"""Generates audio frames from PCM audio data.
|
||||
Takes the desired frame duration in milliseconds, the PCM data, and
|
||||
@@ -43,13 +46,12 @@ def frame_generator(frame_duration_ms, audio, sample_rate):
|
||||
timestamp = 0.0
|
||||
duration = (float(n) / sample_rate) / 2.0
|
||||
while offset + n < len(audio):
|
||||
yield Frame(audio[offset:offset + n], timestamp, duration)
|
||||
yield Frame(audio[offset : offset + n], timestamp, duration)
|
||||
timestamp += duration
|
||||
offset += n
|
||||
|
||||
|
||||
def vad_collector(sample_rate, frame_duration_ms,
|
||||
padding_duration_ms, vad, frames):
|
||||
def vad_collector(sample_rate, frame_duration_ms, padding_duration_ms, vad, frames):
|
||||
"""Filters out non-voiced audio frames.
|
||||
Given a webrtcvad.Vad and a source of audio frames, yields only
|
||||
the voiced audio.
|
||||
@@ -80,7 +82,7 @@ def vad_collector(sample_rate, frame_duration_ms,
|
||||
for frame in frames:
|
||||
is_speech = vad.is_speech(frame.bytes, sample_rate)
|
||||
|
||||
sys.stdout.write('1' if is_speech else '0')
|
||||
sys.stdout.write("1" if is_speech else "0")
|
||||
if not triggered:
|
||||
ring_buffer.append((frame, is_speech))
|
||||
num_voiced = len([f for f, speech in ring_buffer if speech])
|
||||
@@ -89,7 +91,7 @@ def vad_collector(sample_rate, frame_duration_ms,
|
||||
# TRIGGERED state.
|
||||
if num_voiced > 0.9 * ring_buffer.maxlen:
|
||||
triggered = True
|
||||
sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
|
||||
sys.stdout.write("+(%s)" % (ring_buffer[0][0].timestamp,))
|
||||
# We want to yield all the audio we see from now until
|
||||
# we are NOTTRIGGERED, but we have to start with the
|
||||
# audio that's already in the ring buffer.
|
||||
@@ -106,23 +108,23 @@ def vad_collector(sample_rate, frame_duration_ms,
|
||||
# unvoiced, then enter NOTTRIGGERED and yield whatever
|
||||
# audio we've collected.
|
||||
if num_unvoiced > 0.9 * ring_buffer.maxlen:
|
||||
sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
|
||||
sys.stdout.write("-(%s)" % (frame.timestamp + frame.duration))
|
||||
triggered = False
|
||||
yield b''.join([f.bytes for f in voiced_frames])
|
||||
yield b"".join([f.bytes for f in voiced_frames])
|
||||
ring_buffer.clear()
|
||||
voiced_frames = []
|
||||
if triggered: # BT if were in triggered state at end of signal, set output time
|
||||
sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
|
||||
sys.stdout.write('\n')
|
||||
sys.stdout.write("-(%s)" % (frame.timestamp + frame.duration))
|
||||
sys.stdout.write("\n")
|
||||
# If we have any leftover voiced audio when we run out of input,
|
||||
# yield it.
|
||||
if voiced_frames:
|
||||
yield b''.join([f.bytes for f in voiced_frames])
|
||||
yield b"".join([f.bytes for f in voiced_frames])
|
||||
|
||||
|
||||
|
||||
def vad_get_segment_times(sample_rate, frame_duration_ms,
|
||||
padding_duration_ms, vad, frames):
|
||||
def vad_get_segment_times(
|
||||
sample_rate, frame_duration_ms, padding_duration_ms, vad, frames
|
||||
):
|
||||
"""Filters out non-voiced audio frames.
|
||||
BT: based on vad_collector, but returns start and end times for voiced segs
|
||||
|
||||
@@ -158,7 +160,7 @@ def vad_get_segment_times(sample_rate, frame_duration_ms,
|
||||
for frame in frames:
|
||||
is_speech = vad.is_speech(frame.bytes, sample_rate)
|
||||
|
||||
#sys.stdout.write('1' if is_speech else '0')
|
||||
# sys.stdout.write('1' if is_speech else '0')
|
||||
if not triggered:
|
||||
ring_buffer.append((frame, is_speech))
|
||||
num_voiced = len([f for f, speech in ring_buffer if speech])
|
||||
@@ -167,7 +169,7 @@ def vad_get_segment_times(sample_rate, frame_duration_ms,
|
||||
# TRIGGERED state.
|
||||
if num_voiced > 0.9 * ring_buffer.maxlen:
|
||||
triggered = True
|
||||
#sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
|
||||
# sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
|
||||
start_times.append(ring_buffer[0][0].timestamp) # BT
|
||||
ring_buffer.clear()
|
||||
else:
|
||||
@@ -179,23 +181,23 @@ def vad_get_segment_times(sample_rate, frame_duration_ms,
|
||||
# unvoiced, then enter NOTTRIGGERED and yield whatever
|
||||
# audio we've collected.
|
||||
if num_unvoiced > 0.9 * ring_buffer.maxlen:
|
||||
#sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
|
||||
# sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
|
||||
end_times.append(ring_buffer[0][0].timestamp + frame.duration) # BT
|
||||
triggered = False
|
||||
|
||||
if triggered: # BT if were in triggered state at end of signal, set output time
|
||||
#sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
|
||||
if len(ring_buffer)>0:
|
||||
end_times.append(ring_buffer[0][0].timestamp ) # BT
|
||||
# sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
|
||||
if len(ring_buffer) > 0:
|
||||
end_times.append(ring_buffer[0][0].timestamp) # BT
|
||||
else:
|
||||
# only get here in very rare case that we triggered on 2nd-to-last frame
|
||||
end_times.append(frame.timestamp + frame.duration)
|
||||
#sys.stdout.write('\n')
|
||||
# sys.stdout.write('\n')
|
||||
|
||||
return(start_times, end_times)
|
||||
return (start_times, end_times)
|
||||
|
||||
|
||||
def filter_seg_times(seg_starts, seg_ends, pad_at_start = 0.5, len_to_keep=2.5 ):
|
||||
def filter_seg_times(seg_starts, seg_ends, pad_at_start=0.5, len_to_keep=2.5):
|
||||
"""
|
||||
do some filtering on the segments found to select part for analysis
|
||||
rule: find the first segment that is at least (pad_at_start+len_to_keep sec long.
|
||||
@@ -210,12 +212,14 @@ def filter_seg_times(seg_starts, seg_ends, pad_at_start = 0.5, len_to_keep=2.5 )
|
||||
|
||||
not_found = True
|
||||
for iseg in range(len(seg_starts)):
|
||||
seg_dur = seg_ends[iseg]-seg_starts[iseg]
|
||||
if (not_found & (seg_dur > (pad_at_start + len_to_keep))):
|
||||
seg_dur = seg_ends[iseg] - seg_starts[iseg]
|
||||
if not_found & (seg_dur > (pad_at_start + len_to_keep)):
|
||||
t_start = seg_starts[iseg] + pad_at_start
|
||||
sel_start.append(t_start)
|
||||
sel_end.append(t_start + len_to_keep)
|
||||
sel_end_longer.append(max(t_start + len_to_keep, seg_ends[iseg]-pad_at_start))
|
||||
sel_end_longer.append(
|
||||
max(t_start + len_to_keep, seg_ends[iseg] - pad_at_start)
|
||||
)
|
||||
not_found = False
|
||||
|
||||
return sel_start, sel_end, sel_end_longer
|
||||
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Reference in New Issue
Block a user