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JensBlackJensBlack
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inital commit for classifications
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‎experiments/custom/classifier.py‎

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‎experiments/custom/experiments.py‎

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from functools import partial
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from collections import Counter
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from experiments.custom.stimulus_process import ClassicProtocolProcess, SimpleProtocolProcess,Timer, ExampleProtocolProcess
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from experiments.custom.triggers import ScreenTrigger, RegionTrigger, OutsideTrigger, DirectionTrigger, SpeedTrigger
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from experiments.custom.triggers import ScreenTrigger, RegionTrigger, OutsideTrigger, DirectionTrigger, SpeedTrigger,\
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SimbaThresholdBehaviorPoolTrigger, BsoidClassBehaviorTriggerPool
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from utils.plotter import plot_triggers_response
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from utils.analysis import angle_between_vectors
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from experiments.custom.stimulation import show_visual_stim_img,laser_switch
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from experiments.custom.classifier import SimbaClassifier_Process, SimbaProcessPool, BsoidProcessPool
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from utils.configloader import THRESHOLD, POOL_SIZE
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""" experimental classification experiment using Simba trained classifiers in a pool"""
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class SimbaBehaviorExperimentPool:
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"""
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Test experiment for Simba classification
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Simple class to contain all of the experiment properties and includes classification
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Uses multiprocess to ensure the best possible performance and
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to showcase that it is possible to work with any type of equipment, even timer-dependant
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"""
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def __init__(self):
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"""Classifier process and initiation of behavior trigger"""
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self.experiment_finished = False
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self._process_pool = SimbaProcessPool(POOL_SIZE)
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#pass classifier to trigger, so that check_skeleton is the only function that passes skeleton
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#initiate in experiment, so that process can be started with start_experiment
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self._behaviortrigger = SimbaThresholdBehaviorPoolTrigger(prob_threshold= THRESHOLD,
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class_process_pool = self._process_pool)
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self._event = None
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#is not fully utilized in this experiment but is usefull to keep for further adaptation
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self._current_trial = None
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self._trial_count = {trial: 0 for trial in self._trials}
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self._trial_timers = {trial: Timer(10) for trial in self._trials}
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self._exp_timer = Timer(600)
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def check_skeleton(self, frame, skeleton):
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"""
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Checking each passed animal skeleton for a pre-defined set of conditions
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Outputting the visual representation, if exist
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Advancing trials according to inherent logic of an experiment
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:param frame: frame, on which animal skeleton was found
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:param skeleton: skeleton, consisting of multiple joints of an animal
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"""
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self.check_exp_timer() # checking if experiment is still on
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for trial in self._trial_count:
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# checking if any trial hit a predefined cap
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if self._trial_count[trial] >= 10:
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self.stop_experiment()
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if not self.experiment_finished:
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for trial in self._trials:
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# check for all trials if condition is met
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#this passes the skeleton to the trigger, where the feature extraction is done and the extracted features
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#are passed to the classifier process
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result, response = self._trials[trial]['trigger'](skeleton, target_prob = self._trials[trial]['target_prob'])
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plot_triggers_response(frame, response)
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#if the trigger is reporting back that the behavior is found: do something
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#currently nothing is done, expect counting the occurances
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if result:
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if self._current_trial is None:
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if not self._trial_timers[trial].check_timer():
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self._current_trial = trial
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self._trial_timers[trial].reset()
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self._trial_count[trial] += 1
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print(trial, self._trial_count[trial])
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else:
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if self._current_trial == trial:
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self._current_trial = None
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self._trial_timers[trial].start()
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@property
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def _trials(self):
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"""
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Defining the trials
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"""
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trials = {'SimBA1': dict(trigger=self._behaviortrigger.check_skeleton,
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target_prob = None,
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count=0)}
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return trials
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def check_exp_timer(self):
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"""
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Checking the experiment timer
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"""
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if not self._exp_timer.check_timer():
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print("Experiment is finished")
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print("Time ran out.")
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self.stop_experiment()
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def start_experiment(self):
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"""
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Start the experiment
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"""
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self._process_pool.start()
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if not self.experiment_finished:
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self._exp_timer.start()
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def stop_experiment(self):
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"""
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Stop the experiment and reset the timer
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"""
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self.experiment_finished = True
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self._process_pool.end()
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print('Experiment completed!')
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self._exp_timer.reset()
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def get_trial(self):
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"""
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Check which trial is going on right now
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"""
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return self._event
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def get_info(self):
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""" returns optional info"""
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info = self._behaviortrigger.get_last_prob()
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return info
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""" experimental classification experiment using BSOID trained classifiers in a pool"""
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class BsoidBehaviorExperimentPool:
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"""
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Test experiment for Simba classification
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Simple class to contain all of the experiment properties and includes classification
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Uses multiprocess to ensure the best possible performance and
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to showcase that it is possible to work with any type of equipment, even timer-dependant
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"""
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def __init__(self):
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"""Classifier process and initiation of behavior trigger"""
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self.experiment_finished = False
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self._process_pool = BsoidProcessPool(POOL_SIZE)
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#pass classifier to trigger, so that check_skeleton is the only function that passes skeleton
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#initiate in experiment, so that process can be started with start_experiment
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self._behaviortrigger = BsoidClassBehaviorTriggerPool(target_class= THRESHOLD,
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class_process_pool = self._process_pool)
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self._event = None
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#is not fully utilized in this experiment but is usefull to keep for further adaptation
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self._current_trial = None
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self._trial_count = {trial: 0 for trial in self._trials}
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self._trial_timers = {trial: Timer(10) for trial in self._trials}
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self._exp_timer = Timer(600)
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def check_skeleton(self, frame, skeleton):
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"""
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Checking each passed animal skeleton for a pre-defined set of conditions
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Outputting the visual representation, if exist
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Advancing trials according to inherent logic of an experiment
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:param frame: frame, on which animal skeleton was found
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:param skeleton: skeleton, consisting of multiple joints of an animal
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"""
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self.check_exp_timer() # checking if experiment is still on
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for trial in self._trial_count:
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# checking if any trial hit a predefined cap
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if self._trial_count[trial] >= 10:
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self.stop_experiment()
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if not self.experiment_finished:
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for trial in self._trials:
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# check for all trials if condition is met
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#this passes the skeleton to the trigger, where the feature extraction is done and the extracted features
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#are passed to the classifier process
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result, response = self._trials[trial]['trigger'](skeleton, target_class = self._trials[trial]['target_class'])
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plot_triggers_response(frame, response)
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#if the trigger is reporting back that the behavior is found: do something
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#currently nothing is done, expect counting the occurances
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if result:
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if self._current_trial is None:
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if not self._trial_timers[trial].check_timer():
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self._current_trial = trial
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self._trial_timers[trial].reset()
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self._trial_count[trial] += 1
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print(trial, self._trial_count[trial])
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else:
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if self._current_trial == trial:
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self._current_trial = None
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self._trial_timers[trial].start()
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@property
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def _trials(self):
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"""
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Defining the trials
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"""
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trials = {'BSOID1': dict(trigger=self._behaviortrigger.check_skeleton,
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target_class = None,
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count=0)}
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return trials
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def check_exp_timer(self):
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"""
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Checking the experiment timer
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"""
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if not self._exp_timer.check_timer():
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print("Experiment is finished")
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print("Time ran out.")
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self.stop_experiment()
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def start_experiment(self):
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"""
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Start the experiment
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"""
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self._process_pool.start()
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if not self.experiment_finished:
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self._exp_timer.start()
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def stop_experiment(self):
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"""
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Stop the experiment and reset the timer
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"""
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self.experiment_finished = True
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self._process_pool.end()
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print('Experiment completed!')
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self._exp_timer.reset()
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def get_trial(self):
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"""
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Check which trial is going on right now
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"""
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return self._event
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def get_info(self):
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""" returns optional info"""
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info = self._behaviortrigger.get_last_prob()
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return info
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class ExampleExperiment:

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