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-rwxr-xr-xevaluate.py7
-rwxr-xr-xopt.py16
2 files changed, 11 insertions, 12 deletions
diff --git a/evaluate.py b/evaluate.py
index b178abc..a19a7a9 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -155,8 +155,11 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
AP[i] = sum(max_level_precision[i])/11
mAP = np.mean(AP)
print('mAP:',mAP)
- return target_pred, mAP
- return target_pred
+
+ if args.mAP:
+ return target_pred, mAP
+ else:
+ return target_pred
def main():
logging.debug("Verbose mode is on")
diff --git a/opt.py b/opt.py
index 28de96f..e29495e 100755
--- a/opt.py
+++ b/opt.py
@@ -99,18 +99,14 @@ def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
train_data=pca.transform(train_data)
test_data=pca.transform(test_data)
- accuracy = np.zeros((2, args.multrank))
- test_table = np.arange(1, args.multrank+1)
- for q in range(args.comparison+1):
- if args.mAP:
- return test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, train_model, args)
-
+ if args.mAP:
+ target_pred, mAP = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, train_model, args)
+ return mAP
+ else:
target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, train_model, args)
- for i in range(args.multrank):
- return draw_results(test_label, target_pred[i])
- args.rerank = True
- args.neighbors = 1
+ target_pred = target_pred.reshape(target_pred.shape[1])
+ return draw_results(test_label, target_pred)
def kopt(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args):
axis = 0