def test_transform_calls_sklearn_transform(self):
features = self.X['features'].copy()
svc = RbfSvc()
svc.run(self.X)
np.testing.assert_array_equal(SVC.decision_function.call_args[0][0],
features)
class IqmFacePad(FacePad):
def __init__(self, svm_model_path, threshold, name='IqmFacePad'):
self.features_extractor = IqmFeaturesExtractor()
self.classifier = RbfSvc(name='RbfSvc')
self.classifier.load(svm_model_path)
self.scores = []
self.threshold = threshold
self.finished = False
super(IqmFacePad, self).__init__(name, threshold)
def process(self, im):
dict_images = {'0': im}
dict_features = self.features_extractor.run(dict_images)
X = {'features': np.reshape(dict_features['0'], (1, dict_features['0'].shape[0]))}
X = self.classifier.run(X)
score = X['scores'][0]
self.scores.append(score)
self.finished = True
def is_finished(self):
return self.finished
def reset(self):
self.scores = []
self.finished = False
def get_decision(self):
average_score = np.mean(self.scores)
self.scores = []
if average_score < self.threshold:
return 'ATTACK', average_score
else:
return 'NO_ATTACK', average_score