Added functionality to plot cropped analysis points on cropped video;…

… expanded filters

deeplabcut/__init__.py

@@ -48,7 +48,8 @@
 if os.environ.get('Colab', default=False) == 'True':
     print("Project loaded in colab-mode. Apparently Colab has trouble loading statsmodels, so the smooting & outlier frame extraction is disabled. Sorry!")
 else:
-    from deeplabcut.refine_training_dataset import extract_outlier_frames, merge_datasets, filterpredictions
+    from deeplabcut.refine_training_dataset import extract_outlier_frames, merge_datasets
+    from deeplabcut.post_processing import filterpredictions
 
 #Direct import for convenience
 from deeplabcut.pose_estimation_tensorflow import train_network

deeplabcut/post_processing/__init__.py

@@ -0,0 +1,9 @@
+"""
+DeepLabCut2.0 Toolbox
+https://github.com/AlexEMG/DeepLabCut
+A Mathis, [email protected]
+T Nath, [email protected]
+M Mathis, [email protected]
+"""
+
+from deeplabcut.post_processing.filtering import *

deeplabcut/post_processing/filtering.py

@@ -0,0 +1,143 @@
+"""
+DeepLabCut2.0 Toolbox
+https://github.com/AlexEMG/DeepLabCut
+
+A Mathis, [email protected]
+T Nath, [email protected]
+M Mathis, [email protected]
+
+"""
+import numpy as np
+import os
+from pathlib import Path
+import pandas as pd
+
+from deeplabcut.utils import auxiliaryfunctions, visualization
+from deeplabcut.utils import frameselectiontools
+from deeplabcut.refine_training_dataset.outlier_frames import FitSARIMAXModel
+
+import argparse
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+from skimage.util import img_as_ubyte
+from scipy import signal
+
+
+def filterpredictions(config,video,videotype='avi',shuffle=1,trainingsetindex=0,filterype='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).
+
+    filterype: 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)
+    scorer=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 ARIMA model %s",video)
+            videofolder = str(Path(video).parents[0])
+            dataname = str(Path(video).stem)+scorer
+            filteredname=dataname.split('.h5')[0]+'filtered.h5'
+            try:
+                Dataframe = pd.read_hdf(os.path.join(videofolder,filteredname))
+                print("Video already filtered...")
+            except FileNotFoundError:
+                try:
+                    Dataframe = pd.read_hdf(os.path.join(videofolder,dataname+'.h5'))
+                    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 filterype=='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(os.path.join(videofolder,filteredname), 'df_with_missing', format='table', mode='w')
+                    if save_as_csv:
+                        print("Saving filtered csv poses!")
+                        data.to_csv(os.path.join(videofolder,filteredname.split('.h5')[0]+'.csv'))
+                except FileNotFoundError:
+                    print("Video not analyzed -- Run analyze_videos first.")
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('config')
+    parser.add_argument('videos')
+    cli_args = parser.parse_args()

deeplabcut/refine_training_dataset/outlier_frames.py

@@ -194,106 +194,6 @@ def extract_outlier_frames(config,videos,videotype='avi',shuffle=1,trainingsetin
           print("The video has not been analyzed yet!. You can only refine the labels, after the pose has been estimate. Please run 'analyze_video' first.")
 
 
-def filterpredictions(config,video,videotype='avi',shuffle=1,trainingsetindex=0,p_bound=.001,ARdegree=3,MAdegree=1,alpha=.01,save_as_csv=True,destfolder=None):
-    """
-    Fits frame-by-frame pose predictions with SARIMAX model.
-
-    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).
-
-    comparisonbodyparts: list of strings, optional
-        This select the body parts for which SARIMAX models are fit. Either ``all``, then all body parts
-        from config.yaml are used orr a list of strings that are a subset of the full list.
-        E.g. ['hand','Joystick'] for the demo Reaching-Mackenzie-2018-08-30/config.yaml to select only these two body parts.
-
-    p_bound: float between 0 and 1, optional
-        For outlieralgorithm 'uncertain' this parameter defines the likelihood below, 
-        below which a body part will be consided as missing data for filtering purposes.
-
-    ARdegree: int, optional
-        For outlieralgorithm 'fitting': Autoregressive degree of Sarimax model degree.
-        see https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html
-
-    MAdegree: int
-        For outlieralgorithm 'fitting': 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
-    --------
-    deeplabcut.filterpredictions('C:\\myproject\\reaching-task\\config.yaml',['C:\\myproject\\trailtracking-task\\test.mp4'],shuffle=3,ARdegree=5,MAdegree=2)
-    
-    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)
-    scorer=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 ARIMA model %s",video)
-            videofolder = str(Path(video).parents[0])
-            dataname = str(Path(video).stem)+scorer
-            filteredname=dataname.split('.h5')[0]+'filtered.h5'
-            try:
-                Dataframe = pd.read_hdf(os.path.join(videofolder,filteredname))
-                print("Video already filtered...")
-            except FileNotFoundError:
-                try:
-                    Dataframe = pd.read_hdf(os.path.join(videofolder,dataname+'.h5'))
-                    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
-                        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]
-
-                        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(os.path.join(videofolder,filteredname), 'df_with_missing', format='table', mode='w')
-                    if save_as_csv:
-                        print("Saving filtered csv poses!")
-                        data.to_csv(os.path.join(videofolder,filteredname.split('.h5')[0]+'.csv'))
-                except FileNotFoundError:
-                    print("Video not analyzed -- Run analyze_videos first.")
-
 def convertparms2start(pn):
     ''' Creating a start value for sarimax in case of an value error
     See: https://groups.google.com/forum/#!topic/pystatsmodels/S_Fo53F25Rk '''