filename for filename in os.listdir(training_output) if filename.endswith('.fisher.npz') ] testing = [ filename for filename in os.listdir(testing_output) if filename.endswith('.fisher.npz') ] training_dict = classify_library.toDict(training) testing_dict = classify_library.toDict(testing) #################################################################### #################################################################### ################################## Script starts X_train_vids = classify_library.limited_input1(training_dict, 1000) X_test_vids = classify_library.limited_input1(testing_dict, 1000) #GET THE TRAINING AND TESTING DATA. X_train, Y_train = classify_library.make_FV_matrix(X_train_vids, training_output, class_index) X_test, Y_test = classify_library.make_FV_matrix(X_test_vids, testing_output, class_index) X_total = np.concatenate((X_train, X_test), 0) Y_total = np.concatenate((Y_train, Y_test), 0) if not args.no_pca: #PCA reduction
if filename.endswith('.fisher.npz') ] testing = [ filename for filename in os.listdir(testing_output) if filename.endswith('.fisher.npz') ] print(training[:5]) print(testing[:5]) print(train_vid_class.keys()[:5]) training_dict = classify_library.toDict(training, train_vid_class) testing_dict = classify_library.toDict(testing, test_vid_class) #GET THE TRAINING AND TESTING DATA. X_train_vids = classify_library.limited_input1(training_dict, args.per_class_num) X_test_vids = classify_library.limited_input1(testing_dict, args.per_class_num) # X_train_vids, X_test_vids = classify_library.limited_input(training_dict, testing_dict, 101, 24) X_train, Y_train = classify_library.make_FV_matrix(X_train_vids, training_output, class_index, train_vid_class) X_test, Y_test = classify_library.make_FV_matrix(X_test_vids, testing_output, class_index, test_vid_class) # pdb.set_trace() training_PCA = classify_library.limited_input1(training_dict, 1) if not args.PCA_dim: X_train_PCA = X_train.tolist()
class_index_file = "./class_index.npz" class_index_file_loaded = np.load(class_index_file) class_index = class_index_file_loaded['class_index'][()] index_class = class_index_file_loaded['index_class'][()] training = [ filename for filename in os.listdir(training_output) if filename.endswith('.jpeg') ] testing = [ filename for filename in os.listdir(testing_output) if filename.endswith('.jpeg') ] training_dict = classify_library.toDict(training) training_PCA = classify_library.limited_input1(training_dict, 1) X_train, Y_train = make_frame_matrix(training, training_output, class_index) X_test, Y_test = make_frame_matrix(testing, testing_output, class_index) ### Reduced PCA dimension to 1000 # In[18]: X_PCA, _ = make_frame_matrix(training_PCA, training_output, class_index) pca = PCA(n_components=1000) pca.fit(X_PCA) X_train_PCA = pca.transform(X_train) X_test_PCA = pca.transform(X_test) # In[22]:
training = [filename for filename in os.listdir(training_output) if filename.endswith('.fisher.npz')] testing = [filename for filename in os.listdir(testing_output) if filename.endswith('.fisher.npz')] training_dict = classify_library.toDict(training) testing_dict = classify_library.toDict(testing) #################################################################### #################################################################### ################################## Script starts X_train_vids = classify_library.limited_input1(training_dict, 1000) X_test_vids = classify_library.limited_input1(testing_dict, 1000) #GET THE TRAINING AND TESTING DATA. X_train, Y_train = classify_library.make_FV_matrix(X_train_vids,training_output, class_index) X_test, Y_test = classify_library.make_FV_matrix(X_test_vids,testing_output, class_index) #PCA reduction training_PCA = classify_library.limited_input1(training_dict,40) X_PCA, _ = classify_library.make_FV_matrix(training_PCA,training_output, class_index) n_components = 1000 pca = PCA(n_components=n_components) pca.fit(X_PCA) X_train_PCA = pca.transform(X_train)
return (X,Y) # In[16]: class_index_file = "./class_index.npz" class_index_file_loaded = np.load(class_index_file) class_index = class_index_file_loaded['class_index'][()] index_class = class_index_file_loaded['index_class'][()] training = [filename for filename in os.listdir(training_output) if filename.endswith('.jpeg')] testing = [filename for filename in os.listdir(testing_output) if filename.endswith('.jpeg')] training_dict = classify_library.toDict(training) training_PCA = classify_library.limited_input1(training_dict,1) X_train, Y_train = make_frame_matrix(training,training_output,class_index) X_test, Y_test = make_frame_matrix(testing,testing_output,class_index) ### Reduced PCA dimension to 1000 # In[18]: X_PCA, _ = make_frame_matrix(training_PCA, training_output, class_index) pca = PCA(n_components=1000) pca.fit(X_PCA) X_train_PCA = pca.transform(X_train) X_test_PCA = pca.transform(X_test)