Replace verbose with logging

1 files changed, 9 insertions, 6 deletions

diff --git a/evaluate.py b/evaluate.py
index ace647f..76ee472 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -31,6 +31,8 @@ from scipy.spatial.distance import cdist
 sys.path.append('lib')
 from rerank import re_ranking
 from kmean import create_kmean_clusters
+import logging
+from logging import debug
 
 parser = argparse.ArgumentParser()
 parser.add_argument("-t", "--train", help="Use test data instead of query", action='store_true')
@@ -53,10 +55,8 @@ parser.add_argument("--data", help="Data folder with features data", default='da
 
 
 args = parser.parse_args()
-
-def verbose(*text):
-    if args.verbose:
-        print(text)
+if args.verbose:
+    logging.basicConfig(level=logging.DEBUG)
 
 def draw_results(test_label, pred_label):
     acc_sc = accuracy_score(test_label, pred_label)
@@ -72,8 +72,8 @@ def draw_results(test_label, pred_label):
 
 def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam, probe_cam, showfiles_train, showfiles_test, args):
 
-    verbose("probe shape:", probe_data.shape)
-    verbose("gallery shape:", gallery_data.shape)
+    debug("probe shape:", probe_data.shape)
+    debug("gallery shape:", gallery_data.shape)
 
     if args.rerank:
         distances = re_ranking(probe_data, gallery_data,
@@ -137,6 +137,7 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
     return target_pred
 
 def main():
+    logging.debug("Verbose mode is on")
     mat = scipy.io.loadmat(os.path.join(args.data,'cuhk03_new_protocol_config_labeled.mat'))
     camId = mat['camId']
     filelist = mat['filelist']
@@ -167,9 +168,11 @@ def main():
     test_table = np.arange(1, args.multrank+1)
 
     if (args.normalise):
+        debug("Normalising data")
         train_data = np.divide(train_data,LA.norm(train_data, axis=0))
         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,