print('Elapsed time: {:.2f} s'.format(time.time() - start)) temp = time.time() print('Creating codebook with {} visual words'.format(K)) codebook = bovw.create_codebook(D, codebook_name='default_codebook') print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() print('Getting visual words from training set...') vis_words, labels = bovw.visual_words(D, L, I, codebook, spatial_pyramid=True) print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() # Train Linear SVM classifier print('Training the SVM classifier...') pyramid_svm, std_scaler, pca = classification.train_pyramid_svm(vis_words, labels, C=0.0004, dim_reduction=None, model_name='svm_pyramid_dense' ) print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() # Read the test set test_images_filenames, test_labels = io.load_test_set() print('Loaded {} test images.'.format(len(test_images_filenames))) # Feature extraction with sift, prediction with SVM and aggregation to obtain final class print('Predicting test data...') test_results = joblib.Parallel(n_jobs=N_JOBS, backend='threading')( joblib.delayed(parallel_testing)(test_image, test_label, codebook, pyramid_svm, std_scaler, pca) for test_image, test_label in
print('Creating codebook with {} visual words'.format(settings.codebook_size)) #gmm = bovw.create_gmm(D, codebook_name='gmm_{}_dense'.format(settings.codebook_size)) codebook = bovw.create_codebook(D) print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() print('Getting visual words from training set...') #fisher, labels = bovw.fisher_vectors(D, L, I, gmm) pyramid, labels = bovw.visual_words(D, L, I, codebook, spatial_pyramid=True) print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() # Train Linear SVM classifier print('Training the SVM classifier...') lin_svm, std_scaler, _ = classification.train_pyramid_svm(pyramid, train_labels, C=0.0009, dim_reduction=None) print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() # Read the test set test_images_filenames, test_labels = io.load_test_set() print('Loaded {} test images.'.format(len(test_images_filenames))) # Feature extraction with sift, prediction with SVM and aggregation to obtain final class print('Predicting test data...') test_results = joblib.Parallel(n_jobs=settings.n_jobs, backend='threading')( joblib.delayed(parallel_testing)(test_image, test_label, lin_svm, std_scaler) for test_image, test_label in zip(test_images_filenames, test_labels)) pred_results = [x[0] for x in test_results]
codebook = bovw.create_codebook(D) #, codebook_name='default_codebook' print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() print('Getting visual words from training set...') vis_words, labels = bovw.visual_words(D, L, I, codebook, spatial_pyramid=True) print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() # Train Linear SVM classifier print('Training the SVM classifier...') pyramid_svm, std_scaler, pca = classification.train_pyramid_svm( vis_words, labels, C=0.0009, dim_reduction=None) print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() # Read the test set test_images_filenames, test_labels = io.load_test_set() print('Loaded {} test images.'.format(len(test_images_filenames))) # Feature extraction with sift, prediction with SVM and aggregation to obtain final class print('Predicting test data...') test_results = joblib.Parallel( n_jobs=settings.n_jobs, backend='threading')(joblib.delayed(parallel_testing)( test_image, test_label, codebook, pyramid_svm, std_scaler, pca) for test_image, test_label in zip(
print('Getting visual words from training set...') vis_words, labels = bovw.visual_words(D, L, I, codebook, spatial_pyramid=True, normalization='l1') print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() # Train Linear SVM classifier print('Training the SVM classifier...') pyramid_svm, std_scaler, pca = classification.train_pyramid_svm( vis_words, labels, C=840.42, dim_reduction=None, model_name='svm_pyramid_l1') print('Elapsed time: {:.2f} s'.format(time.time() - temp)) temp = time.time() # Read the test set test_images_filenames, test_labels = io.load_test_set() print('Loaded {} test images.'.format(len(test_images_filenames))) # Feature extraction with sift, prediction with SVM and aggregation to obtain final class print('Predicting test data...') test_results = joblib.Parallel(n_jobs=N_JOBS, backend='threading')( joblib.delayed(parallel_testing)(test_image, test_label, codebook,