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-rwxr-xr-xevaluate.py15
-rw-r--r--lib/kmean.py2
-rw-r--r--lib/rerank.py5
3 files changed, 14 insertions, 8 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,
diff --git a/lib/kmean.py b/lib/kmean.py
index 58309bc..f041b19 100644
--- a/lib/kmean.py
+++ b/lib/kmean.py
@@ -1,3 +1,4 @@
+from logging import debug
import numpy as np
from sklearn.cluster import KMeans
@@ -39,6 +40,7 @@ def create_kmean_clusters(feature_vectors, labels, gallery_idx, camId):
cllab.append(km_labels[i][j])
train_data = np.array(cl)
+ debug("Kmean data has shape", train_data.shape)
train_label = np.array(cllab)
train_cam = np.array([clustercam[i] for i in range(2)]).reshape(train_label.shape[0])
return train_data, train_label, train_cam
diff --git a/lib/rerank.py b/lib/rerank.py
index 6b20f53..6fb5b7b 100644
--- a/lib/rerank.py
+++ b/lib/rerank.py
@@ -1,5 +1,6 @@
from scipy.spatial.distance import cdist
import numpy as np
+from logging import debug
def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch = 2000):
@@ -7,7 +8,7 @@ def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch
all_num = query_num + galFea.shape[0]
feat = np.append(probFea,galFea,axis = 0)
feat = feat.astype(np.float16)
- print('computing original distance')
+ debug('computing original distance')
if MemorySave:
original_dist = np.zeros(shape = [all_num,all_num],dtype = np.float16)
i = 0
@@ -29,7 +30,7 @@ def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch
initial_rank = np.argsort(original_dist).astype(np.int32)
- print('starting re_ranking')
+ debug('starting re_ranking')
for i in range(all_num):
# k-reciprocal neighbors
forward_k_neigh_index = initial_rank[i,:k1+1]