updated experiments.py, triggers.py, configloader.py to match new release

Author: JensBlack

experiments/custom/experiments.py

@@ -37,7 +37,7 @@
 )
 
 
-from utils.configloader import THRESHOLD, POOL_SIZE
+from utils.configloader import THRESHOLD, POOL_SIZE, TRIGGER
 
 
 """ experimental classification experiment using Simba trained classifiers in a pool which are converted using the pure-predict package"""
@@ -62,7 +62,7 @@ def __init__(self):
             prob_threshold=THRESHOLD, class_process_pool=self._process_pool, debug=False
         )
         self._event = None
-        # is not fully utilized in this experiment but is usefull to keep for further adaptation
+        # is not fully utilized in this experiment but is useful to keep for further adaptation
         self._current_trial = None
         self._max_reps = 999
         self._trial_count = {trial: 0 for trial in self._trials}
@@ -173,7 +173,7 @@ class SimbaBehaviorPoolExperiment:
 
     def __init__(self):
         """Classifier process and initiation of behavior trigger"""
-        self.experiment_finished = False
+        self._process_experiment = ExampleProtocolProcess()
         self._process_pool = SimbaProcessPool(POOL_SIZE)
         # pass classifier to trigger, so that check_skeleton is the only function that passes skeleton
         # initiate in experiment, so that process can be started with start_experiment
@@ -223,14 +223,18 @@ def check_skeleton(self, frame, skeleton):
                     if self._current_trial == trial:
                         self._current_trial = None
                         self._trial_timers[trial].start()
+            self._process_experiment.set_trial(self._current_trial)
+        else:
+            pass
+        return result,response
 
     @property
     def _trials(self):
         """
         Defining the trials
         """
         trials = {
-            "SimBA1": dict(
+            "DLStream_test": dict(
                 trigger=self._behaviortrigger.check_skeleton, target_prob=None, count=0
             )
         }
@@ -249,6 +253,7 @@ def start_experiment(self):
         """
         Start the experiment
         """
+        self._process_experiment.start()
         self._process_pool.start()
         if not self.experiment_finished:
             self._exp_timer.start()
@@ -258,6 +263,7 @@ def stop_experiment(self):
         Stop the experiment and reset the timer
         """
         self.experiment_finished = True
+        self._process_experiment.end()
         self._process_pool.end()
         print("Experiment completed!")
         self._exp_timer.reset()
@@ -288,11 +294,12 @@ class BsoidBehaviorPoolExperiment:
     def __init__(self):
         """Classifier process and initiation of behavior trigger"""
         self.experiment_finished = False
+        self._process_experiment = ExampleProtocolProcess()
         self._process_pool = BsoidProcessPool(POOL_SIZE)
         # pass classifier to trigger, so that check_skeleton is the only function that passes skeleton
         # initiate in experiment, so that process can be started with start_experiment
         self._behaviortrigger = BsoidClassBehaviorPoolTrigger(
-            target_class=THRESHOLD, class_process_pool=self._process_pool
+            target_class=TRIGGER, class_process_pool=self._process_pool, debug= False
         )
         self._event = None
         # is not fully utilized in this experiment but is usefull to keep for further adaptation
@@ -337,14 +344,19 @@ def check_skeleton(self, frame, skeleton):
                     if self._current_trial == trial:
                         self._current_trial = None
                         self._trial_timers[trial].start()
+            self._process_experiment.set_trial(self._current_trial)
+        else:
+            pass
+        return result,response
 
     @property
     def _trials(self):
         """
         Defining the trials
+        Target class is the cluster of interest and can be changed with every call of check_skeleton
         """
         trials = {
-            "BSOID1": dict(
+            "DLStream_test": dict(
                 trigger=self._behaviortrigger.check_skeleton, target_class=None, count=0
             )
         }
@@ -364,6 +376,7 @@ def start_experiment(self):
         Start the experiment
         """
         self._process_pool.start()
+        self._process_experiment.start()
         if not self.experiment_finished:
             self._exp_timer.start()
 
@@ -372,6 +385,7 @@ def stop_experiment(self):
         Stop the experiment and reset the timer
         """
         self.experiment_finished = True
+        self._process_experiment.end()
         self._process_pool.end()
         print("Experiment completed!")
         self._exp_timer.reset()
@@ -388,6 +402,7 @@ def get_info(self):
         return info
 
 
+
 """Social or multiple animal experiments in combination with SLEAP or non-flattened maDLC pose estimation"""
 
 

experiments/custom/triggers.py

@@ -13,7 +13,7 @@
     EllipseROI,
     RectangleROI,
 )
-from utils.configloader import RESOLUTION, TIME_WINDOW
+from utils.configloader import RESOLUTION, TIME_WINDOW, FRAMERATE
 from collections import deque
 from experiments.custom.featureextraction import (
     SimbaFeatureExtractor,
@@ -804,12 +804,12 @@ def __init__(self, target_class: int, path_to_sav: str, debug: bool = False):
         self._trigger = target_class
         self._last_result = [0]
         self._center = None
-        self._debug = debug  # not used in this trigger
+        self._debug = debug
         self._skeleton = None
         self._classifier, self._time_window_len = self._init_classifier(
             path_to_sav
         )  # initialize classifier
-        self.feat_extractor = BsoidFeatureExtractor(self._time_window_len, fps=30)
+        self.feat_extractor = BsoidFeatureExtractor(self._time_window_len, fps=FRAMERATE)
         self._time_window = deque(maxlen=self._time_window_len)
 
     @staticmethod
@@ -818,7 +818,7 @@ def _init_classifier(path_to_sav):
 
         """Put your classifier of choice in here"""
         classifier = BsoidClassifier(path_to_clf=path_to_sav)
-        win_len = classifier.ge()
+        win_len = classifier.get_win_len()
         return classifier, win_len
 
     def fill_time_window(self, skeleton):
@@ -847,11 +847,12 @@ def check_skeleton(self, skeleton, trigger: float = None):
             start_time = time.time()
             f_extract_output = self.feat_extractor.extract_features(self._time_window)
             end_time = time.time()
-            print(
-                "Feature extraction time: {:.2f} msec".format(
-                    (end_time - start_time) * 1000
+            if self._debug:
+                print(
+                    "Feature extraction time: {:.2f} msec".format(
+                        (end_time - start_time) * 1000
+                    )
                 )
-            )
         if f_extract_output is not None:
             self._last_result, _, _ = self._classifier.classify(f_extract_output)
         else:
@@ -906,7 +907,7 @@ def __init__(self, target_class: int, class_process_pool, debug: bool = False):
         self._last_result = [0]
         self._feature_id = 0
         self._center = None
-        self._debug = debug  # not used in this trigger
+        self._debug = debug
         self._skeleton = None
         self._time_window_len = TIME_WINDOW
         self.feat_extractor = BsoidFeatureExtractor(self._time_window_len)
@@ -938,11 +939,12 @@ def check_skeleton(self, skeleton, target_class: int = None):
             start_time = time.time()
             f_extract_output = self.feat_extractor.extract_features(self._time_window)
             end_time = time.time()
-            print(
-                "Feature extraction time: {:.2f} msec".format(
-                    (end_time - start_time) * 1000
+            if self._debug:
+                print(
+                    "Feature extraction time: {:.2f} msec".format(
+                        (end_time - start_time) * 1000
+                    )
                 )
-            )
         if f_extract_output is not None:
             self._feature_id += 1
             self._process_pool.pass_features(

utils/configloader.py

@@ -78,6 +78,7 @@ def get_script_path():
 PATH_TO_CLASSIFIER = dsc_config["Classification"].get("PATH_TO_CLASSIFIER")
 PIXPERMM = dsc_config["Classification"].getfloat("PIXPERMM")
 THRESHOLD = dsc_config["Classification"].getfloat("THRESHOLD")
+TRIGGER = dsc_config["Classification"].getfloat("TRIGGER")
 POOL_SIZE = dsc_config["Classification"].getint("POOL_SIZE")
 TIME_WINDOW = dsc_config["Classification"].getint("TIME_WINDOW")