Add mAP (still needs revision)

1 files changed, 21 insertions, 12 deletions

diff --git a/evaluate.py b/evaluate.py
index caed403..ca6c8db 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -48,13 +48,13 @@ parser.add_argument("-l", "--rerankl", help="Coefficient to combine distances",
 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", "--normalise", 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)
 parser.add_argument("--data", help="Data folder with features data", default='data')
 parser.add_argument("-V", "--validation", help="Validation Mode", action='store_true')
-parser.add_argument("-K", "--kmean_alt", help="Validation Mode", type=int, default=0)
+parser.add_argument("-K", "--kmean_alt", help="Kmean", type=int, default=0)
+parser.add_argument("-P", "--mAP", help="Mean Average Precision", action='store_true')
 
 
 
@@ -131,16 +131,25 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
                 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)]
-            gallery_label_tmp = np.subtract(gallery_label, 1)
-            pltCol = [cols[int(k)] for k in gallery_label_tmp]
-            fig = plt.figure()
-            ax = fig.add_subplot(111, projection='3d')
-            ax.scatter(gallery_data[:, 0], gallery_data[:, 1], gallery_data[:, 2], marker='o', color=pltCol)
-            plt.show()
+        
+        if args.mAP:
+            precision = np.zeros((probe_label.shape[0], args.neighbors))
+            mAP = np.zeros(probe_label.shape[0])
+            truths = precision
+            for i in range(probe_label.shape[0]):
+                truth_count=0
+                false_count=0
+                for j in range(args.neighbors):
+                    if probe_label[i] == nneighbors[i][j]:
+                        truths[i][j] = 1
+                        truth_count+=1
+                        precision[i][j] = truth_count/(j+1)
+                        mAP[i] += precision[i][j]
+                if truth_count!=0:
+                    mAP[i] = mAP[i]/truth_count
+                    #print(mAP[i])
+            print('mAP:',np.mean(mAP))
+                    
     return target_pred
 
 def main():