Graph Different ranklists sizes

1 files changed, 68 insertions, 34 deletions

diff --git a/part2.py b/part2.py
index b890837..1d6485c 100755
--- a/part2.py
+++ b/part2.py
@@ -32,18 +32,21 @@ from rerank import re_ranking
 
 parser = argparse.ArgumentParser()
 parser.add_argument("-t", "--test", help="Use test data instead of query", action='store_true')
-parser.add_argument("-cm", "--conf_mat", help="Show visual confusion matrix", action='store_true')
-parser.add_argument("-km", "--kmean", help="Perform Kmeans", action='store_true', default=0)
-parser.add_argument("-ma", "--mahalanobis", help="Perform Mahalanobis Distance metric", action='store_true', default=0)
+parser.add_argument("-c", "--conf_mat", help="Show visual confusion matrix", action='store_true')
+parser.add_argument("-k", "--kmean", help="Perform Kmeans", action='store_true', default=0)
+parser.add_argument("-m", "--mahalanobis", help="Perform Mahalanobis Distance metric", action='store_true', default=0)
 parser.add_argument("-e", "--euclidean", help="Standard euclidean", action='store_true', default=0)
 parser.add_argument("-r", "--rerank", help="Use k-reciprocal rernaking", action='store_true')
-parser.add_argument("-ka", "--reranka", help="Parameter 1 for Rerank", type=int, default = 20)
-parser.add_argument("-kb", "--rerankb", help="Parameter 2 for rerank", type=int, default = 6)
+parser.add_argument("-p", "--reranka", help="Parameter 1 for Rerank", type=int, default = 20)
+parser.add_argument("-q", "--rerankb", help="Parameter 2 for rerank", type=int, default = 6)
+parser.add_argument("-l", "--rerankl", help="Coefficient to combine distances", type=int, default = 0.3)
 parser.add_argument("-n", "--neighbors", help="Number of neighbors", type=int, default = 1)
 parser.add_argument("-v", "--verbose", help="Use verbose output", action='store_true')
 parser.add_argument("-s", "--showrank", help="Save ranklist pic id in a txt file", type=int, default = 0)
 parser.add_argument("-2", "--graphspace", help="Graph space", action='store_true', default=0)
 parser.add_argument("-1", "--norm", help="Normalized features", action='store_true', default=0)
+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)
 
 
 args = parser.parse_args()
@@ -52,7 +55,6 @@ def verbose(*text):
     if args.verbose:
         print(text)
 
-#prob query, gal train
 def draw_results(test_label, pred_label):
     acc_sc = accuracy_score(test_label, pred_label)
     cm = confusion_matrix(test_label, pred_label)
@@ -63,7 +65,7 @@ def draw_results(test_label, pred_label):
         plt.ylabel('Actual')
         plt.xlabel('Predicted')
         plt.show()
-    return
+    return acc_sc
 
 def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam, probe_cam, showfiles_train, showfiles_test, args):
     
@@ -81,38 +83,47 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
             distances = np.zeros((probe_label.size, gallery_label.size))
             for i in range(probe_label.size):
                 print('Mahalanobis step ', i, '/', probe_label.size)
-                distances [i] = cdist(probe_data[i].reshape((1,2048)), gallery_data, 'mahalanobis', VI = covmat)
+                distances[i] = cdist(probe_data[i].reshape((1,2048)), gallery_data, 'mahalanobis', VI = covmat)
         else:
             distances = cdist(probe_data, gallery_data, 'euclidean') 
 
     ranklist = np.argsort(distances, axis=1)
-
-    target_pred = np.zeros(ranklist.shape[0])
-    nneighbors = np.zeros((ranklist.shape[0],args.neighbors))
-    nnshowrank = (np.zeros((ranklist.shape[0],args.neighbors))).astype(object)
     
-    for probe_idx in range(probe_data.shape[0]):
-        row = ranklist[probe_idx]
-        n = 0
-        q = 0
-        while (q < args.neighbors):
-            while (probe_cam[probe_idx] == gallery_cam[row[n]] and
-              probe_label[probe_idx] == gallery_label[row[n]]):
+    test_table = np.arange(1, args.multrank+1)
+    target_pred = np.zeros((args.multrank, ranklist.shape[0]))
+    nsize = args.neighbors
+    if (args.multrank != 1):
+        nsize = test_table[args.multrank-1]
+    nneighbors = np.zeros((ranklist.shape[0],nsize))
+    nnshowrank = (np.zeros((ranklist.shape[0],nsize))).astype(object)
+    
+    for i in range(args.multrank):
+        args.neighbors = test_table[i]
+        for probe_idx in range(probe_data.shape[0]):
+            row = ranklist[probe_idx]
+            n = 0
+            q = 0
+            while (q < args.neighbors):
+                while (probe_cam[probe_idx] == gallery_cam[row[n]] and
+                  probe_label[probe_idx] == gallery_label[row[n]]):
+                    n += 1
+                nneighbors[probe_idx][q] = gallery_label[row[n]]
+                nnshowrank[probe_idx][q] = showfiles_train[row[n]] #
+                q += 1
                 n += 1
-            nneighbors[probe_idx][q] = gallery_label[row[n]]
-            nnshowrank[probe_idx][q] = showfiles_train[row[n]] #
-            q += 1
-            n += 1
-        if (args.neighbors) and (probe_label[probe_idx] in nneighbors[probe_idx]):
-                target_pred[probe_idx] = probe_label[probe_idx]
-        else:
-            target_pred[probe_idx] = nneighbors[probe_idx][0]
+                
+            if (args.neighbors) and (probe_label[probe_idx] in nneighbors[probe_idx]):
+                target_pred[i][probe_idx] = probe_label[probe_idx]
+            else:
+                target_pred[i][probe_idx] = nneighbors[probe_idx][0]
+        
         
         if (args.showrank): 
             with open("ranklist.txt", "w") as text_file:
                 text_file.write(np.array2string(nnshowrank[:args.showrank]))
             with open("query.txt", "w") as text_file:
                 text_file.write(np.array2string(showfiles_test[:args.showrank]))
+                
     if args.graphspace:
             # Colors for distinct individuals
             cols = ['#{:06x}'.format(randint(0, 0xffffff)) for i in range(1467)]
@@ -150,6 +161,10 @@ def main():
     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)
+
     if (args.norm):
         train_data = np.divide(train_data,LA.norm(train_data, axis=0))
         test_data = np.divide(test_data, LA.norm(test_data, axis=0))
@@ -211,13 +226,32 @@ def main():
         cluster = np.array(cl)
         clusterlabel = np.array(cllab)
         clustercam = np.array(clcam)
-
-        target_pred = test_model(cluster, test_data, clusterlabel, test_label, clustercam, test_cam, showfiles_train, showfiles_test, args)
-        draw_results(test_label, target_pred)
-    
-    else:    
-        target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args)
-        draw_results(test_label, target_pred)
+        
+        for q in range(args.comparison):
+            target_pred = test_model(cluster, test_data, clusterlabel, test_label, clustercam, test_cam, showfiles_train, showfiles_test, args)
+            for i in range(args.multrank):
+                accuracy[q][i] = draw_results(test_label, target_pred[i])   
+            args.rerank = True
+            args.neighbors = 1
+        
+    else:
+        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):
+                accuracy[q][i] = draw_results(test_label, target_pred[i])   
+            args.rerank = True
+            args.neighbors = 1
+            
+    if(args.multrank != 1):
+        plt.plot(test_table[:(args.multrank)], 100*accuracy[0])
+        if(args.comparison!=1):
+            plt.plot(test_table[:(args.multrank)], 100*accuracy[1])
+            plt.legend(['Baseline kNN', 'Improved metric'], loc='upper left')    
+        plt.xlabel('k rank')
+        plt.ylabel('Recognition Accuracy (%)')
+        plt.grid(True)
+        plt.show()
+            
 
 if __name__ == "__main__":
     main()