204 lines
8.2 KiB
Python
204 lines
8.2 KiB
Python
import pandas as pd
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import json
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from os.path import exists
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def read_derivedAttr(ar):
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derivedFilename = ar+"/derived_variables/derived_output.csv"
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if not exists(derivedFilename):
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return pd.DataFrame()
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derived_df = pd.read_csv(derivedFilename)
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facial_cols = [col for col in derived_df if "fac_" in col]
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acoustic_cols = [col for col in derived_df if "aco_" in col]
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movement_cols = [col for col in derived_df if "mov_" in col]
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nlp_cols = [col for col in derived_df if "nlp_" in col]
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derived_facial = derived_df.loc[:,derived_df.columns.isin(facial_cols)]
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derived_acoustic = derived_df.loc[:,derived_df.columns.isin(acoustic_cols)]
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derived_movement = derived_df.loc[:,derived_df.columns.isin(movement_cols)]
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derived_nlp = derived_df.loc[:,derived_df.columns.isin(nlp_cols)]
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ids = derived_df["Filename"].tolist()
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return {"ids": ids,
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"facialAttr": facial_cols, "acousticAttr": acoustic_cols, "movementAttr": movement_cols, "speechAttr": nlp_cols}
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def read_medatada(ar, ar2):
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metaData={}
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metdataFilename = ar+"/"+ar2
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if exists(metdataFilename):
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metadataDf = pd.read_csv(metdataFilename)
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metaData = metadataDf.to_json(orient="records")
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return metaData
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def read_derivedDf(ar):
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derivedFilename = ar+"/derived_variables/derived_output.csv"
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if not exists(derivedFilename):
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return pd.DataFrame()
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derived_df = pd.read_csv(derivedFilename)
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return derived_df
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def read_rawFacialDf(ar, id):
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skip_cols = ["frame", "face_id", "error_reason", "timestamp", "confidence", "success", "dbm_master_url", "error_reason", " confidence", " face_id", " success", " timestamp", "s_confidence", ]
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facial_asym_filename = ar + "/raw_variables/"+id+"/facial/face_asymmetry/"+id+"_facasym.csv"
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facial_au_filename = ar + "/raw_variables/"+id+"/facial/face_au/"+id+"_facau.csv"
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facial_expr_filename = ar + "/raw_variables/"+id+"/facial/face_expressivity/"+id+"_facemo.csv"
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landmark_filename = ar + "/raw_variables/"+id+"/facial/face_landmark/"+id+"_faclmk.csv"
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if not exists(facial_asym_filename) or not exists(facial_au_filename) or not exists(facial_expr_filename):
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return pd.DataFrame()
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facial_asym = pd.read_csv(facial_asym_filename)
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facial_asym_cols = [col for col in facial_asym if col not in skip_cols]
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facial_au = pd.read_csv(facial_au_filename)
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facial_au_cols = [col for col in facial_au if col not in skip_cols]
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facial_expr = pd.read_csv(facial_expr_filename)
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facial_expr_cols = [col for col in facial_expr if col not in skip_cols and "AU" not in col]
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landmark = pd.read_csv(landmark_filename)
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landmark_cols = [col for col in landmark if col not in skip_cols]
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face_df = landmark.loc[:, ~landmark.columns.isin(skip_cols)].copy()
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for el in facial_expr_cols:
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face_df[el] = facial_expr[el]
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for el in facial_au_cols:
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face_df[el] = facial_au[el]
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for el in facial_asym_cols:
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face_df[el] = facial_asym[el]
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return face_df[face_df.columns[::-1]].fillna(0)
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def read_rawMovementDf(ar, id):
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skip_cols = ["error_reason", "dbm_master_url", "Frames", " Frames", "vid_dur", "fps" ]
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gaze_filename = ar + "/raw_variables/"+id+"/movement/gaze/"+id+"_eyegaze.csv"
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head_movement_filename = ar + "/raw_variables/"+id+"/movement/head_movement/"+id+"_headmov.csv"
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head_pose_filename = ar + "/raw_variables/"+id+"/movement/head_pose/"+id+"_headpose.csv"
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blinks_filename = ar + "/raw_variables/"+id+"/movement/eye_blink/"+id+"_eyeblinks.csv"
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fac_tremor_filename = ar + "/raw_variables/"+id+"/movement/facial_tremor/"+id+"_fac_tremor.csv"
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voice_tremor_filename = ar + "/raw_variables/"+id+"/movement/voice_tremor/"+id+"_vtremor.csv"
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if not exists(gaze_filename) or not exists(head_movement_filename) or not exists(head_pose_filename):
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return pd.DataFrame()
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gaze = pd.read_csv(gaze_filename)
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gaze_cols = [col for col in gaze if col not in skip_cols]
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head_movement = pd.read_csv(head_movement_filename)
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head_movement_cols = [col for col in head_movement if col not in skip_cols]
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blinks = pd.read_csv(blinks_filename)
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blinks_cols = [col for col in blinks if col not in skip_cols]
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fac_tremor = pd.read_csv(fac_tremor_filename)
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fac_tremor_cols = [col for col in fac_tremor if col not in skip_cols]
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voice_tremor = pd.read_csv(voice_tremor_filename)
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voice_tremor_cols = [col for col in voice_tremor if col not in skip_cols]
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head_pose = pd.read_csv(head_pose_filename)
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head_pose_cols = [col for col in head_pose if col not in skip_cols]
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movement_df = head_pose.loc[:, ~head_pose.columns.isin(skip_cols)].copy()
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for el in head_movement_cols:
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movement_df[el] = head_movement[el]
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for el in gaze_cols:
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movement_df[el] = gaze[el]
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for el in blinks_cols:
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movement_df[el] = blinks[el]
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for el in fac_tremor_cols:
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movement_df[el] = fac_tremor[el]
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for el in voice_tremor_cols:
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movement_df[el] = voice_tremor[el]
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return movement_df.fillna(0)
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def read_rawAcousticDf(ar, id):
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skip_cols = ["error_reason", "dbm_master_url", "Frames", " Frames", "aco_voicelabel"]
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fm_filename = ar + "/raw_variables/"+id+"/acoustic/formant_freq/"+id+"_formant.csv"
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gne_filename = ar + "/raw_variables/"+id+"/acoustic/glottal_noise/"+id+"_gne.csv"
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hnr_filename = ar + "/raw_variables/"+id+"/acoustic/harmonic_noise/"+id+"_hnr.csv"
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intt_filename = ar + "/raw_variables/"+id+"/acoustic/intensity/"+id+"_intensity.csv"
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mfcc_filename = ar + "/raw_variables/"+id+"/acoustic/mfcc/"+id+"_mfcc.csv"
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pitch_filename = ar + "/raw_variables/"+id+"/acoustic/pitch/"+id+"_pitch.csv"
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jitter_filename = ar + "/raw_variables/"+id+"/acoustic/jitter/"+id+"_jitter.csv"
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pause_segment_filename = ar + "/raw_variables/"+id+"/acoustic/pause_segment/"+id+"_pausechar.csv"
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shimmer_filename = ar + "/raw_variables/"+id+"/acoustic/shimmer/"+id+"_shimmer.csv"
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voice_frame_score_filename = ar + "/raw_variables/"+id+"/acoustic/voice_frame_score/"+id+"_voiceprev.csv"
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filename_list = [fm_filename, gne_filename, hnr_filename, intt_filename,mfcc_filename, pitch_filename, pause_segment_filename, shimmer_filename, voice_frame_score_filename]
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if not exists(fm_filename) or not exists(gne_filename) or not exists(hnr_filename) or not exists(intt_filename) or not exists(mfcc_filename) or not exists(pitch_filename):
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return pd.DataFrame
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fm = pd.read_csv(fm_filename)
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fm_cols = [col for col in fm if col not in skip_cols]
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gne = pd.read_csv(gne_filename)
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gne_cols = [col for col in gne if col not in skip_cols]
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hnr = pd.read_csv(hnr_filename)
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hnr_cols = [col for col in hnr if col not in skip_cols]
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intt = pd.read_csv(intt_filename)
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intt_cols = [col for col in intt if col not in skip_cols]
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mfcc = pd.read_csv(mfcc_filename)
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mfcc_cols = [col for col in mfcc if col not in skip_cols]
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pitch = pd.read_csv(pitch_filename)
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pitch_cols = [col for col in pitch if col not in skip_cols]
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pause_segment = pd.read_csv(pause_segment_filename)
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pause_segment_cols = [col for col in pause_segment if col not in skip_cols]
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jitter = pd.read_csv(jitter_filename)
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jitter_cols = [col for col in jitter if col not in skip_cols]
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shimmer = pd.read_csv(shimmer_filename)
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shimmer_cols = [col for col in shimmer if col not in skip_cols]
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voice_frame_score = pd.read_csv(voice_frame_score_filename)
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voice_frame_score_cols = [col for col in voice_frame_score if col not in skip_cols]
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acoustic_df = fm.loc[:, ~fm.columns.isin(skip_cols)].copy()
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for el in gne_cols:
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acoustic_df[el] = gne[el]
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for el in hnr_cols:
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acoustic_df[el] = hnr[el]
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for el in intt_cols:
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acoustic_df[el] = intt[el]
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for el in mfcc_cols:
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acoustic_df[el] = mfcc[el]
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for el in pitch_cols:
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acoustic_df[el] = pitch[el]
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for el in pause_segment_cols:
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acoustic_df[el] = pause_segment[el]
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for el in jitter_cols:
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acoustic_df[el] = jitter[el]
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for el in shimmer_cols:
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acoustic_df[el] = shimmer[el]
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for el in voice_frame_score_cols:
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acoustic_df[el] = voice_frame_score[el]
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return acoustic_df.fillna(0)
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def load():
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global videoIds
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read_derivedAttr()
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read_derivedDf()
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if __name__=="__main__":
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load()
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