def agg_movement_intervals(
file_name,
has_labels=False,
train_on=['training1', 'training2', 'training3', 'training4']):
from models import movement_interval
df = skeletion_from_archive_cached(file_name)
df = preprocessed_skeleton(file_name,
demain=True,
keep_only_top_40=False,
train_id=False,
drop_lower_joints=True,
dummy_gesture=False)
df_interval = movement_interval(train_on=train_on)
df_merge = pd.merge(df,
df_interval[['sample_id', 'frame', 'movement']],
on=['frame', 'sample_id'],
how='left')
df_merge.sort(['sample_id', 'frame'], inplace=True)
df_merge.fillna(method='ffill', inplace=True)
df_merge.fillna(method='bfill', inplace=True)
df_merge['interval'] = (df_merge.movement.shift(1) !=
df_merge.movement).astype(int).cumsum()
# delete break frames tagged with gesture
df_merge = df_merge[(df_merge.movement != 0)]
# unify gestures in interval to most frequent one
def smooth_gesture(df):
df['gesture'] = df.gesture.value_counts().index[0]
return df
agg_functions = ['median', 'var', 'min', 'max']
if has_labels:
df_merge = df_merge.groupby(['interval']).apply(smooth_gesture)
# drop intervalls without movement or if labelt as break
df_merge = df_merge[(df_merge['gesture'] != 'break')
& (df_merge['movement'] == 1)]
y_gest = df_merge[['sample_id', 'gesture',
'interval']].drop_duplicates()
y_gest['gesture'] = np.array(
[gesture_to_id[g] for g in y_gest.gesture])
df_merge = df_merge.drop(['movement', 'frame'], axis=1)
df_agg = df_merge.groupby(['sample_id', 'JointType', 'interval'
]).agg(agg_functions).unstack('JointType')
df_agg['_count'] = df_merge.groupby(['sample_id', 'JointType',
'interval']).agg({
'x_p': 'count'
}).unstack('JointType')[('x_p',
'HandRight')]
if has_labels:
return df_agg, y_gest
return df_agg, None
def train_movement_model_and_merge_on_audio_interval(train_on, predict_on,
path_to_audio_intervals, path_to_movement_model_with_audio_interval):
df_out = movement_interval(train_on=train_on, predict_on=predict_on)
df_out = df_out.groupby('sample_id').apply(pad_smooth, window_len=20)
middle = pd.read_csv(path_to_audio_intervals, header=None, skiprows=1)
middle = middle.ix[:, [0, 2]]
middle.columns = ['sample_id', 'frame']
middle_probs = pd.merge(middle, df_out, how='left', on=['sample_id', 'frame'])
middle_probs = middle_probs.drop(['sample_id', 'frame', 0, 'movement'], axis=1)
middle_probs.to_csv(path_to_movement_model_with_audio_interval, index=False)
def agg_movement_intervals(file_name, has_labels=False,
train_on=['training1','training2', 'training3', 'training4']):
from models import movement_interval
df = skeletion_from_archive_cached(file_name)
df = preprocessed_skeleton(file_name, demain=True, keep_only_top_40=False,
train_id=False, drop_lower_joints=True, dummy_gesture=False)
df_interval = movement_interval(train_on=train_on)
df_merge = pd.merge(df, df_interval[['sample_id', 'frame', 'movement']], on=['frame', 'sample_id'], how='left')
df_merge.sort(['sample_id', 'frame'], inplace=True)
df_merge.fillna(method='ffill', inplace=True)
df_merge.fillna(method='bfill', inplace=True)
df_merge['interval'] = (df_merge.movement.shift(1) != df_merge.movement).astype(int).cumsum()
# delete break frames tagged with gesture
df_merge = df_merge[(df_merge.movement != 0)]
# unify gestures in interval to most frequent one
def smooth_gesture(df):
df['gesture'] = df.gesture.value_counts().index[0]
return df
agg_functions=['median', 'var', 'min', 'max']
if has_labels:
df_merge = df_merge.groupby(['interval']).apply(smooth_gesture)
# drop intervalls without movement or if labelt as break
df_merge = df_merge[(df_merge['gesture'] != 'break') & (df_merge['movement'] == 1)]
y_gest = df_merge[['sample_id', 'gesture', 'interval']].drop_duplicates()
y_gest['gesture'] = np.array([gesture_to_id[g] for g in y_gest.gesture])
df_merge = df_merge.drop(['movement', 'frame'], axis=1)
df_agg = df_merge.groupby(['sample_id', 'JointType', 'interval']).agg(agg_functions).unstack('JointType')
df_agg['_count'] = df_merge.groupby(['sample_id', 'JointType', 'interval']).agg({'x_p': 'count'}).unstack('JointType')[('x_p', 'HandRight')]
if has_labels:
return df_agg, y_gest
return df_agg, None
#window_length=30, retrain=False)
#eval_gesture_model(window_shift=5,
#window_length=20, retrain=False)
#leaderboard_model(retrain=True)
#leaderboard_model(window_shift=1)
#leaderboard_model(window_shift=5)
#eval_seq_model(retrain=True)
#eval_seq_model(retrain=False, window_shift=1)
#eval_seq_model(retrain=False, window_shift=5)
#eval_gesture_model(retrain=True)
#eval_gesture_model(window_shift=1)
#eval_gesture_model(window_shift=5)
from models import movement_interval, agg_movement_intervals, collect_movement_intervalls
movement_interval(
train_on=['training1', 'training2', 'training3', 'training4'],
predict_on=['validation1_lab', 'validation2_lab', 'validation3_lab'])
movement_interval(train_on=[
'training1', 'training3', 'training4', 'validation1_lab',
'validation3_lab'
],
predict_on=['training2', 'validation2_lab'])
movement_interval(train_on=[
'training2', 'training3', 'training4', 'validation2_lab',
'validation3_lab'
],
predict_on=['training1', 'validation1_lab'])
movement_interval(
#window_length=40, retrain=False)
#eval_gesture_model(window_shift=5,
#window_length=30, retrain=False)
#eval_gesture_model(window_shift=5,
#window_length=20, retrain=False)
#leaderboard_model(retrain=True)
#leaderboard_model(window_shift=1)
#leaderboard_model(window_shift=5)
#eval_seq_model(retrain=True)
#eval_seq_model(retrain=False, window_shift=1)
#eval_seq_model(retrain=False, window_shift=5)
#eval_gesture_model(retrain=True)
#eval_gesture_model(window_shift=1)
#eval_gesture_model(window_shift=5)
from models import movement_interval, agg_movement_intervals, collect_movement_intervalls
movement_interval(train_on=['training1','training2','training3', 'training4'],
predict_on=['validation1_lab', 'validation2_lab', 'validation3_lab'])
movement_interval(train_on=['training1','training3', 'training4', 'validation1_lab',
'validation3_lab'],
predict_on=['training2', 'validation2_lab'])
movement_interval(train_on=['training2','training3', 'training4', 'validation2_lab',
'validation3_lab'],
predict_on=['training1', 'validation1_lab'])
movement_interval(train_on=['training1','training2','training3', 'training4',
'validation1_lab', 'validation2_lab', 'validation3_lab'],
predict_on=['test1', 'test2', 'test3', 'test4', 'test5', 'test6'])