def histmain(images_path): train_data_set,entire_train_data_as_list,no_of_classes,test_data_set,count_of_dots_original_path,flag_for_testing=frame_work_v2.pre_process(images_path) # Calling the train_images_as_hists of train_hbs which returns the histogram of the trained images train_hist_list=train_hbs.train_images_as_hists(entire_train_data_as_list) ### Calling the testdb of test_hbs which does the comparison ## testdb takes some arguments and returns the efficiency of the database # arg_1 : test_data_set - this list contains test data set ,( one image per class ) # arg_2 : entire_train_data_as_list - entire train dataset # arg_3 : train_hist_list - histogram of the trained data # arg_4 : count_of_dots_original_path - number of dots in the original given path # arg_5 : flag_for_testing - This is the flag which says about the directory directory structure. # flag_for_testing = 1 , means the directory structure is flat, means modifications of path names is necessary to extract class names # flag_for_testing = 0 , means the directory structure is hierarchical which means modifcations is not necessary for extracting class names eff=test_hbs_L1.testdb(test_data_set,entire_train_data_as_list,train_hist_list,count_of_dots_original_path,flag_for_testing) print "efficiency" print eff return eff
def main_pca(images_path): #Calling the function pre_process of frame_work, this returns some values in the following order, using those return values the training part and testing part is called by passing the required #arguments #return order : train_data_set,entire_train_data_as_list,no_of_classes,no_of_images_per_class,test_data_set,flag_for_testing # # ret1: train_data_set = list of all training images as an 2-d array # ret2: entire_train_data_as_list = list of all training images as a single list # ret3: no_of_classes = total number of classes in the given input database # ret4: test_data_set=list of training images as a 1-array ( In case user wants, can be typecasted to list in his script using this return value ) # ret5: count_of_dots_original_path : contains number of dots the original path has, this +1 gives number of dots to be replaced to get modified names # ret6: flag_for_testing=This is the flag which says about the directory directory structure. # flag_for_testing = 1 , means the directory structure is flat, means modifications of path names is necessary to extract class names # flag_for_testing = 0 , means the directory structure is hierarchical which means modifcations is not necessary for extracting class names train_data_set,entire_train_data_as_list,no_of_classes,test_data_set,count_of_dots_original_path,flag_for_testing=frame_work_v2.pre_process(images_path) ######### Calling traindb in train_database which actually does the training part and it returns some values which actually is needed during the testing phase. ####### Input Argument : train_data_set ( set of tranining images ) ##### It returns 3 values #### (1) mean_img : contains the mean of all the images, its a 1-d array/list ### (2) eigen_selected : Usually only the major values of the eigen vector are taken, this contain those major eigen values only ## (3) signature_images_for_train_set : contains the signatures (mapped images / eigen images ) for the entire training dataset mean_img,eigen_selected,signature_images_for_train_set=train_database.traindb(train_data_set) #### to find number of images trained per class ## Thas obviously no_of_images_per_class - 1 because one image will be taken for testing part #no_images_trained_per_class=no_of_images_per_class-1 #Uncomment following to print signature of the trained images #print "Printing the signature/co-relation matrix of the trained image #print signature_images_for_train_set ####### Uncomment the following lines when any lengths or the types of the signature variable are to be checked #print "signature type" #print type(signature_images_for_train_set) #print "signature length" #print len(signature_images_for_train_set) # Calling the testdb in test_database.py which takes in quite a number of arguments, lets explore the arguments # arg_1 : signature_images_for_train_set : contains the signatures (mapped images / eigen images ) for the entire training dataset ( which is return by train_database ) # arg_2 : test_data_set : contains the list of test data images which is randomly selected, one from each class # arg_3 : entire_train_data_as_list : contains entire train data set ( removed test_data_set from original input ) # arg_4 : mean_img : contains the mean of all the images, its a 1-d array/list ( which is return by train_database ) # arg_5 : eigen_selected : Usually only the major values of the eigen vector are taken, this contain those major eigen values only ( which is return by train_database ) # arg_6 : count_of_dots_original_path : contains number of dots the original path has, this +1 gives number of dots to be replaced to get modified names # arg_7 : flag_for_tesing : flag which actually says if the given directory structure is flat or hierarchy; It sets the flag if the structure is flat r=test_database.testdb(signature_images_for_train_set,test_data_set,entire_train_data_as_list,mean_img,eigen_selected,count_of_dots_original_path,flag_for_testing) print "efficiency is " print r return r
