def filterpredictions(config,
video,
videotype='avi',
shuffle=1,
trainingsetindex=0,
filtertype='median',
windowlength=5,
p_bound=.001,
ARdegree=3,
MAdegree=1,
alpha=.01,
save_as_csv=True,
destfolder=None):
"""
Fits frame-by-frame pose predictions with ARIMA model (filtertype='arima') or median filter (default).
Parameter
----------
config : string
Full path of the config.yaml file as a string.
video : string
Full path of the video to extract the frame from. Make sure that this video is already analyzed.
shuffle : int, optional
The shufle index of training dataset. The extracted frames will be stored in the labeled-dataset for
the corresponding shuffle of training dataset. Default is set to 1
trainingsetindex: int, optional
Integer specifying which TrainingsetFraction to use. By default the first (note that TrainingFraction is a list in config.yaml).
filtertype: string
Select which filter, 'arima' or 'median' filter.
windowlength: int
For filtertype='median' filters the input array using a local window-size given by windowlength. The array will automatically be zero-padded.
https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.medfilt.html The windowlenght should be an odd number.
p_bound: float between 0 and 1, optional
For filtertype 'arima' this parameter defines the likelihood below,
below which a body part will be consided as missing data for filtering purposes.
ARdegree: int, optional
For filtertype 'arima' Autoregressive degree of Sarimax model degree.
see https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html
MAdegree: int
For filtertype 'arima' Moving Avarage degree of Sarimax model degree.
See https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html
alpha: float
Significance level for detecting outliers based on confidence interval of fitted SARIMAX model.
save_as_csv: bool, optional
Saves the predictions in a .csv file. The default is ``False``; if provided it must be either ``True`` or ``False``
destfolder: string, optional
Specifies the destination folder for analysis data (default is the path of the video). Note that for subsequent analysis this
folder also needs to be passed.
Example
--------
Arima model:
deeplabcut.filterpredictions('C:\\myproject\\reaching-task\\config.yaml',['C:\\myproject\\trailtracking-task\\test.mp4'],shuffle=3,filterype='arima',ARdegree=5,MAdegree=2)
Use median filter over 10bins:
deeplabcut.filterpredictions('C:\\myproject\\reaching-task\\config.yaml',['C:\\myproject\\trailtracking-task\\test.mp4'],shuffle=3,windowlength=10)
One can then use the filtered rather than the frame-by-frame predictions by calling:
deeplabcut.plot_trajectories('C:\\myproject\\reaching-task\\config.yaml',['C:\\myproject\\trailtracking-task\\test.mp4'],shuffle=3,filtered=True)
deeplabcut.create_labeled_video('C:\\myproject\\reaching-task\\config.yaml',['C:\\myproject\\trailtracking-task\\test.mp4'],shuffle=3,filtered=True)
--------
Returns filtered pandas array with the same structure as normal output of network.
"""
cfg = auxiliaryfunctions.read_config(config)
DLCscorer, DLCscorerlegacy = auxiliaryfunctions.GetScorerName(
cfg, shuffle, trainFraction=cfg['TrainingFraction'][trainingsetindex])
Videos = auxiliaryfunctions.Getlistofvideos(video, videotype)
if len(Videos) > 0:
for video in Videos:
if destfolder is None:
destfolder = str(Path(video).parents[0])
print("Filtering with %s model %s" % (filtertype, video))
videofolder = destfolder
vname = Path(video).stem
notanalyzed, outdataname, sourcedataname, scorer = auxiliaryfunctions.CheckifPostProcessing(
destfolder,
vname,
DLCscorer,
DLCscorerlegacy,
suffix='filtered')
if notanalyzed:
Dataframe = pd.read_hdf(sourcedataname, 'df_with_missing')
for bpindex, bp in tqdm(enumerate(cfg['bodyparts'])):
pdindex = pd.MultiIndex.from_product(
[[scorer], [bp], ['x', 'y', 'likelihood']],
names=['scorer', 'bodyparts', 'coords'])
x, y, p = Dataframe[scorer][bp]['x'].values, Dataframe[
scorer][bp]['y'].values, Dataframe[scorer][bp][
'likelihood'].values
if filtertype == 'arima':
meanx, CIx = FitSARIMAXModel(x, p, p_bound, alpha,
ARdegree, MAdegree, False)
meany, CIy = FitSARIMAXModel(y, p, p_bound, alpha,
ARdegree, MAdegree, False)
meanx[0] = x[0]
meany[0] = y[0]
else:
meanx = signal.medfilt(x, kernel_size=windowlength)
meany = signal.medfilt(y, kernel_size=windowlength)
if bpindex == 0:
data = pd.DataFrame(np.hstack([
np.expand_dims(meanx, axis=1),
np.expand_dims(meany, axis=1),
np.expand_dims(p, axis=1)
]),
columns=pdindex)
else:
item = pd.DataFrame(np.hstack([
np.expand_dims(meanx, axis=1),
np.expand_dims(meany, axis=1),
np.expand_dims(p, axis=1)
]),
columns=pdindex)
data = pd.concat([data.T, item.T]).T
data.to_hdf(outdataname,
'df_with_missing',
format='table',
mode='w')
if save_as_csv:
print("Saving filtered csv poses!")
data.to_csv(outdataname.split('.h5')[0] + '.csv')
def filterpredictions(
config,
video,
videotype="avi",
shuffle=1,
trainingsetindex=0,
filtertype="median",
windowlength=5,
p_bound=0.001,
ARdegree=3,
MAdegree=1,
alpha=0.01,
save_as_csv=True,
destfolder=None,
modelprefix="",
track_method="",
):
"""
Fits frame-by-frame pose predictions with ARIMA model (filtertype='arima') or median filter (default).
Parameter
----------
config : string
Full path of the config.yaml file as a string.
video : string
Full path of the video to extract the frame from. Make sure that this video is already analyzed.
shuffle : int, optional
The shufle index of training dataset. The extracted frames will be stored in the labeled-dataset for
the corresponding shuffle of training dataset. Default is set to 1
trainingsetindex: int, optional
Integer specifying which TrainingsetFraction to use. By default the first (note that TrainingFraction is a list in config.yaml).
filtertype: string
Select which filter, 'arima', 'median' or 'spline'.
windowlength: int
For filtertype='median' filters the input array using a local window-size given by windowlength. The array will automatically be zero-padded.
https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.medfilt.html The windowlenght should be an odd number.
If filtertype='spline', windowlength is the maximal gap size to fill.
p_bound: float between 0 and 1, optional
For filtertype 'arima' this parameter defines the likelihood below,
below which a body part will be consided as missing data for filtering purposes.
ARdegree: int, optional
For filtertype 'arima' Autoregressive degree of Sarimax model degree.
see https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html
MAdegree: int
For filtertype 'arima' Moving Avarage degree of Sarimax model degree.
See https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html
alpha: float
Significance level for detecting outliers based on confidence interval of fitted SARIMAX model.
save_as_csv: bool, optional
Saves the predictions in a .csv file. The default is ``False``; if provided it must be either ``True`` or ``False``
destfolder: string, optional
Specifies the destination folder for analysis data (default is the path of the video). Note that for subsequent analysis this
folder also needs to be passed.
Example
--------
Arima model:
deeplabcut.filterpredictions('C:\\myproject\\reaching-task\\config.yaml',['C:\\myproject\\trailtracking-task\\test.mp4'],shuffle=3,filterype='arima',ARdegree=5,MAdegree=2)
Use median filter over 10bins:
deeplabcut.filterpredictions('C:\\myproject\\reaching-task\\config.yaml',['C:\\myproject\\trailtracking-task\\test.mp4'],shuffle=3,windowlength=10)
One can then use the filtered rather than the frame-by-frame predictions by calling:
deeplabcut.plot_trajectories('C:\\myproject\\reaching-task\\config.yaml',['C:\\myproject\\trailtracking-task\\test.mp4'],shuffle=3,filtered=True)
deeplabcut.create_labeled_video('C:\\myproject\\reaching-task\\config.yaml',['C:\\myproject\\trailtracking-task\\test.mp4'],shuffle=3,filtered=True)
--------
Returns filtered pandas array with the same structure as normal output of network.
"""
cfg = auxiliaryfunctions.read_config(config)
DLCscorer, DLCscorerlegacy = auxiliaryfunctions.GetScorerName(
cfg,
shuffle,
trainFraction=cfg["TrainingFraction"][trainingsetindex],
modelprefix=modelprefix,
)
Videos = auxiliaryfunctions.Getlistofvideos(video, videotype)
if not len(Videos):
print(
"No video(s) were found. Please check your paths and/or 'video_type'."
)
return
for video in Videos:
if destfolder is None:
destfolder = str(Path(video).parents[0])
print("Filtering with %s model %s" % (filtertype, video))
vname = Path(video).stem
try:
_ = auxiliaryfunctions.load_analyzed_data(destfolder, vname,
DLCscorer, True,
track_method)
print(f"Data from {vname} were already filtered. Skipping...")
except FileNotFoundError: # Data haven't been filtered yet
try:
df, filepath, _, _ = auxiliaryfunctions.load_analyzed_data(
destfolder, vname, DLCscorer, track_method=track_method)
nrows = df.shape[0]
if filtertype == "arima":
temp = df.values.reshape((nrows, -1, 3))
placeholder = np.empty_like(temp)
for i in range(temp.shape[1]):
x, y, p = temp[:, i].T
meanx, _ = FitSARIMAXModel(x, p, p_bound, alpha,
ARdegree, MAdegree, False)
meany, _ = FitSARIMAXModel(y, p, p_bound, alpha,
ARdegree, MAdegree, False)
meanx[0] = x[0]
meany[0] = y[0]
placeholder[:, i] = np.c_[meanx, meany, p]
data = pd.DataFrame(
placeholder.reshape((nrows, -1)),
columns=df.columns,
index=df.index,
)
elif filtertype == "median":
data = df.copy()
mask = data.columns.get_level_values(
"coords") != "likelihood"
data.loc[:,
mask] = df.loc[:,
mask].apply(signal.medfilt,
args=(windowlength, ),
axis=0)
elif filtertype == "spline":
data = df.copy()
mask_data = data.columns.get_level_values("coords").isin(
("x", "y"))
xy = data.loc[:, mask_data].values
prob = data.loc[:, ~mask_data].values
missing = np.isnan(xy)
xy_filled = columnwise_spline_interp(xy, windowlength)
filled = ~np.isnan(xy_filled)
xy[filled] = xy_filled[filled]
inds = np.argwhere(missing & filled)
if inds.size:
# Retrieve original individual label indices
inds[:, 1] //= 2
inds = np.unique(inds, axis=0)
prob[inds[:, 0], inds[:, 1]] = 0.01
data.loc[:, ~mask_data] = prob
data.loc[:, mask_data] = xy
else:
raise ValueError(f"Unknown filter type {filtertype}")
outdataname = filepath.replace(".h5", "_filtered.h5")
data.to_hdf(outdataname,
"df_with_missing",
format="table",
mode="w")
if save_as_csv:
print("Saving filtered csv poses!")
data.to_csv(outdataname.split(".h5")[0] + ".csv")
except FileNotFoundError as e:
print(e)
continue