diff options
Diffstat (limited to 'evaluate.py')
-rwxr-xr-x | evaluate.py | 13 |
1 files changed, 12 insertions, 1 deletions
diff --git a/evaluate.py b/evaluate.py index 9d41424..1f54b95 100755 --- a/evaluate.py +++ b/evaluate.py @@ -4,7 +4,7 @@ # # usage: evaluate.py [-h] [-t] [-c] [-k] [-m] [-e] [-r] [-a RERANKA] # [-b RERANKB] [-l RERANKL] [-n NEIGHBORS] [-v] -# [-s SHOWRANK] [-1] [-M MULTRANK] [-C] [DATA] +# [-s SHOWRANK] [-1] [-2] [-M MULTRANK] [-C] [DATA] # [-K KMEAN] [-A] [-P PCA] import matplotlib.pyplot as plt @@ -29,6 +29,7 @@ from rerank import re_ranking from kmean import create_kmean_clusters import logging from logging import debug +from sklearn.preprocessing import StandardScaler parser = argparse.ArgumentParser() parser.add_argument("-t", "--train", help="Use train data instead of query and gallery", action='store_true') @@ -44,6 +45,7 @@ parser.add_argument("-n", "--neighbors", help="Use customized ranklist size NEIG parser.add_argument("-v", "--verbose", help="Use verbose output", action='store_true') parser.add_argument("-s", "--showrank", help="Save ranklist pics id in a txt file for first SHOWRANK queries", type=int, default = 0) parser.add_argument("-1", "--normalise", help="Normalise features", action='store_true') +parser.add_argument("-2", "--standardise", help="Standardise features", action='store_true') parser.add_argument("-M", "--multrank", help="Run for different ranklist sizes equal to MULTRANK", type=int, default=1) parser.add_argument("-C", "--comparison", help="Compare baseline and improved metric", action='store_true') parser.add_argument("--data", help="Folder containing data", default='data') @@ -220,6 +222,15 @@ def main(): 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)) + train_model = np.divide(train_model, LA.norm(train_model,axis=0)) + + if (args.standardise): + debug("Standardising data") + scaler = StandardScaler() + train_data=scaler.fit_transform(train_data) + test_data=scaler.fit_transform(test_data) + train_model=scaler.fit_transform(train_model) + if(args.kmean_alt): debug("Using Kmeans") train_data, train_label, train_cam = create_kmean_clusters(feature_vectors, labels, gallery_idx, camId) |