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mixed_classifier.py
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mixed_classifier.py
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import os
import sys
import errno
import numpy as np
import classifiers as clfs
import class_objects as co
import cPickle as pickle
import logging
from numpy.linalg import inv
from CDBIMM import EnhancedDynamicClassifier
def makedir(path):
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
class CombinedGesturesClassifier(clfs.Classifier):
def __init__(self, dynamic_classifier=None,
passive_classifier=None, log_lev='INFO',
visualize=False, add_info=None,
*args, **kwargs):
self.enhanced_dyn = EnhancedDynamicClassifier(dynamic_classifier,
passive_classifier,
in_sync=True)
classifiers_used = 'Combined RDF'
clfs.Classifier.__init__(self, log_lev=log_lev, visualize=visualize,
masks_needed=False,
action_type='All',
classifiers_used=classifiers_used, name='',
descriptors=['Passive Cl:'
+ str(passive_classifier.classifier_folder),
'Dynamic Cl:' +
str(self.enhanced_dyn.classifier_folder)],
add_info=add_info, *args, **kwargs)
self.parameters['sub_classifiers'] = [
passive_classifier.classifier_folder,
dynamic_classifier.classifier_folder]
self.train_classes = np.hstack((self.passive_actions,
self.dynamic_actions)).tolist()
def single_run_training(self, action_path, action_name):
self.enhanced_dyn.testing_initialized = False
passive_scores, en_scores = self.enhanced_dyn.run_testing(
os.path.join(co.CONST['actions_path'],action_name),
online=False,
just_scores=True,
display_scores=False,
compute_perform=False,
construct_gt=False,
load=not
self.train_all)
traindata = np.concatenate((passive_scores, en_scores), axis=1)
ground_truth = co.gd_oper.construct_ground_truth(
data=action_path,
ground_truth_type='constant-' + action_name,
classes_namespace=self.train_classes)[0]
return traindata, ground_truth
def prepare_training_data(self, *args, **kwargs):
self.enhanced_dyn.run_training(load=not self.train_all)
traindata, ground_truth = self.apply_to_training(
self.single_run_training)
traindata = np.concatenate(traindata, axis=0)
ground_truth = np.concatenate(ground_truth, axis=0)
fmask = (np.isfinite(np.sum(traindata, axis=1)).astype(int) *
np.isfinite(ground_truth).astype(int)).astype(bool)
self.training_data = traindata[fmask, :]
self.train_ground_truth = ground_truth[fmask]
def offline_testdata_processing(self, data):
LOG.info('Processing test data..')
testdata = np.hstack(
self.enhanced_dyn.run_testing(data, online=False, just_scores=True,
compute_perform=False))
return testdata
def process_single_sample(self, data, img_count,
derot_angle=None, derot_center=None):
return self.enhanced_dyn.run_testing(data, img_count=img_count,
online=True,
derot_angle=derot_angle,
derot_center=derot_center)
def construct_mixed_classifier(testname='actions', train=False,
test=True, visualize=True,
dicts_retrain=False, hog_num=None,
name='actions', use_dicts=False,
des_dim=None, test_against_all=False,
train_all=False):
'''
Constructs a enhanced classifier
'''
dynamic_classifier = clfs.construct_dynamic_actions_classifier(
test=False, visualize=False, test_against_all=False,
descriptors=['GHOG', 'ZHOF'])
passive_classifier = clfs.construct_passive_actions_classifier(test=False,
visualize=False,
test_against_all=False)
mixed = CombinedGesturesClassifier(
dynamic_classifier=dynamic_classifier,
passive_classifier=passive_classifier)
mixed.run_training(load=not train, train_all=train_all)
if test or visualize:
if test_against_all:
iterat = mixed.available_tests
else:
iterat = [testname]
for name in iterat:
mixed.testing_initialized = False
if test:
mixed.run_testing(os.path.join(
co.CONST['test_save_path'], name),
ground_truth_type=os.path.join(
co.CONST['ground_truth_fold'],
name + '.csv'),
online=False, load=False)
else:
mixed.run_testing(os.path.join(
co.CONST['test_save_path'], name),
ground_truth_type=os.path.join(
co.CONST['ground_truth_fold'],
name + '.csv'),
online=False, load=False)
return mixed
def main():
from matplotlib import pyplot as plt
construct_mixed_classifier(
train=True,
test=False,
visualize=True,
test_against_all=True,
train_all=False)
plt.show()
LOG = logging.getLogger('__name__')
CH = logging.StreamHandler(sys.stderr)
CH.setFormatter(logging.Formatter(
'%(funcName)20s()(%(lineno)s)-%(levelname)s:%(message)s'))
LOG.handlers = []
LOG.addHandler(CH)
LOG.setLevel(logging.INFO)
if __name__ == '__main__':
# signal.signal(signal.SIGINT, signal_handler)
main()