def main():
u_data, v_data, fg_imgs, original_imgs, abnormal_fg_imgs = load_data(
) #load data from ref_data
weight = Weight_matrix().get_weight_matrix(
) #use normalization method for feature correction
thisFeatureExtractor = Feature_extractor(original_imgs, fg_imgs,
abnormal_fg_imgs, u_data, v_data,
weight)
train_data, train_labels = thisFeatureExtractor.get_features_and_labels(
80, 140) #training frames
########################## To see the train data features distribution, uncomment next line##################################
#uvPlot(train_data[:,0],train_data[:,1],train_labels,False)
#############################################################################################################################
classifiers = Classifiers(train_data, train_labels)
test_data, test_labels = thisFeatureExtractor.get_features_and_labels(
140, 199) #testing frames
for name, model in classifiers.models.items(): #get each classifier
for ind, original_img in enumerate(
original_imgs[:-1]): #get each frame
pos, thisImg, _, _ = thisFeatureExtractor.getPosition(
fg_imgs, ind) #get the position of each person in this frame
features, _ = thisFeatureExtractor.get_features_and_labels(
ind, ind + 1,
False) #get the features for each person in the frame
labels = classifiers.models[name].predict(features) #predict label
plot(pos, labels, thisImg, name) #show
classifiers.prediction_metrics(
test_data, test_labels,
name) #metrics for each classifier based on the test data
def main(self):
pre = Preprocessing.Preprocessing(self.features_to_consider)
if self.normalise:
pre.scale() # TODO different scaling options
if self.classification_model == 'logisticRegression':
logReg_predict = Classifiers.Classifiers(pre.features_dataframe,
self.features_to_consider)
if self.splitByDate:
logReg_predict.splitDataset(self.trainingDateStart,
self.trainingDateEnd,
self.testDateStart,
self.testDateEnd)
logReg_predict.LogRegTrainModel()
logReg_predict.LogRegTestModel()
else:
print("other model")
def main():
directory = '/xxxx/features'
feature_names = [name for name in os.listdir(directory) if name == '200']
feature_names.sort(reverse=True)
for feature_name in feature_names:
# K=dictKvalue[feature_name]
K = 40
data_dir = os.path.join(directory, feature_name)
dataset = load_dataset('sigactcuboid', data_dir)
classifiers = Classifiers(dataset.get_data(), dataset.feature_name,
dataset.model_dir, K)
for name, model in classifiers.models.items(): #get each classifier
model_directory = os.path.join(dataset.model_dir)
modname = os.path.join(
model_directory,
name + '_' + 'train' + '_' + feature_name + '.pkl')
model_data = load_model(modname)
labels, scores, test_time = model_data.predict()
timefile = os.path.join(
model_directory,
name + '_' + 'test' + '_' + feature_name + '.json')
times2json(test_time, timefile)
