diff options
| -rwxr-xr-x | evaluate.py | 56 |
1 files changed, 38 insertions, 18 deletions
diff --git a/evaluate.py b/evaluate.py index c5528af..6561b81 100755 --- a/evaluate.py +++ b/evaluate.py @@ -52,6 +52,8 @@ parser.add_argument("-1", "--normalise", help="Normalized features", action='sto parser.add_argument("-M", "--multrank", help="Run for different ranklist sizes equal to M", type=int, default=1) parser.add_argument("-C", "--comparison", help="Set to 2 to obtain a comparison of baseline and Improved metric", type=int, default=1) parser.add_argument("--data", help="Data folder with features data", default='data') +parser.add_argument("-V", "--validation", help="Validation Mode", action='store_true') + args = parser.parse_args() @@ -147,22 +149,43 @@ def main(): train_idx = mat['train_idx'] - 1 with open(os.path.join(args.data,'feature_data.json'), 'r') as read_file: feature_vectors = np.array(json.load(read_file)) + if args.train: - query_idx = train_idx.reshape(train_idx.shape[0]) - gallery_idx = train_idx.reshape(train_idx.shape[0]) + cam = camId[train_idx] + cam = cam.reshape((cam.shape[0],1)) + labs = labels[train_idx].reshape((labels[train_idx].shape[0],1)) + tt = np.hstack((train_idx, cam)) + s_train, s_test, s_train_label, s_test_label = train_test_split(tt, labs, test_size=0.3, random_state=0) + train, test, train_label, test_label = train_test_split(s_train, s_train_label, test_size=0.3, random_state=0) + #to make it smaller we do a double split + del labs + del cam + train_data = feature_vectors[train[:,0]] + test_data = feature_vectors[test[:,0]] + train_cam = train[:,1] + test_cam = test[:,1] + showfiles_train = filelist[train[:,0]] + showfiles_test = filelist[train[:,0]] + del train + del test + del tt else: - query_idx = query_idx.reshape(query_idx.shape[0]) - gallery_idx = gallery_idx.reshape(gallery_idx.shape[0]) - camId = camId.reshape(camId.shape[0]) - - showfiles_train = filelist[gallery_idx] - showfiles_test = filelist[query_idx] - train_data = feature_vectors[gallery_idx] - test_data = feature_vectors[query_idx] - train_label = labels[gallery_idx] - test_label = labels[query_idx] - train_cam = camId[gallery_idx] - test_cam = camId[query_idx] + if args.validation: + query_idx = train_idx.reshape(train_idx.shape[0]) + gallery_idx = train_idx.reshape(train_idx.shape[0]) + else: + query_idx = query_idx.reshape(query_idx.shape[0]) + gallery_idx = gallery_idx.reshape(gallery_idx.shape[0]) + camId = camId.reshape(camId.shape[0]) + + showfiles_train = filelist[gallery_idx] + showfiles_test = filelist[query_idx] + train_data = feature_vectors[gallery_idx] + test_data = feature_vectors[query_idx] + train_label = labels[gallery_idx] + test_label = labels[query_idx] + train_cam = camId[gallery_idx] + test_cam = camId[query_idx] accuracy = np.zeros((2, args.multrank)) test_table = np.arange(1, args.multrank+1) @@ -173,10 +196,7 @@ def main(): test_data = np.divide(test_data, LA.norm(test_data, axis=0)) if(args.kmean): debug("Using Kmeans") - train_data, train_label, train_cam = create_kmean_clusters(feature_vectors, - labels, - gallery_idx, - camId) + train_data, train_label, train_cam = create_kmean_clusters(feature_vectors, labels,gallery_idx,camId) for q in range(args.comparison): target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args) for i in range(args.multrank): |