positif_data_testing = 'testset/faces'
negatif_data_testing = 'testset/non-faces'
#define level cascade on list
level_cascade = [2,10,20,20,30,30,50,50,50,50,60,60,80,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100]
#load data training
print('Load data training positif...')
faces_data = ul.load_images(positif_data_training)
faces_ii_data = list(map(ii.to_integral_image,faces_data))
print(str(len(faces_ii_data))+' Has been loaded.\nLoad data training negatif...')
non_faces_data = ul.load_images(negatif_data_training)
non_faces_ii_data = list(map(ii.to_integral_image,non_faces_data))
print(str(len(non_faces_ii_data))+' Has been Loaded.')
img_height, img_width = faces_ii_data[0].shape
#create features
features = ab.create_features(24,24,min_feature_height=4,max_feature_height=10,min_feature_width=4,max_feature_width=10)
#cascade => stage of bunch classifiers, alpha => weights every classifier
cascade = cas.cascade_latih(faces_ii_data,non_faces_ii_data,features, level_cascade)
database = []
for casc in cascade:
for item in casc:
database.append(item)
with open('database.json','w') as f:
json.dump(database,f,default=dumper,indent=4)
