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)