temp

app.py

@@ -51,10 +51,10 @@ transform = transforms.Compose([
 def match(query_feat, pos_feat, LoweRatioTh=0.9):
     # first perform reciprocal nn
     dist = torch.cdist(query_feat, pos_feat)
-    print('dist.size',dist.size())
+    # print('dist.size',dist.size())
     best1 = torch.argmin(dist, dim=1)
     best2 = torch.argmin(dist, dim=0)
-    print('best2.size',best2.size())
+    # print('best2.size',best2.size())
     arange = torch.arange(best2.size(0))
     reciprocal = best1[best2]==arange
     # check Lowe ratio test
@@ -84,8 +84,8 @@ def generate_matching_superfeatures(
         Imagenet_model=False, 
         scale_id=6, threshold=50, 
         random_mode=False, sf_ids=''): #, only_matching=True):
-    print('im1:', im1.size)
-    print('im2:', im2.size)
+    # print('im1:', im1.size)
+    # print('im2:', im2.size)
 
     clear_figures()
     col = plt.get_cmap('tab10')
@@ -159,8 +159,6 @@ def generate_matching_superfeatures(
             img1rsz[m,n, :] = col_[::-1]   
             
     img2rsz = np.copy(im2_cv)
-    print('im2:', im2.size)
-    print('img2rsz:', img2rsz.shape)
     for j, att in enumerate(all_att_bin2):
         att = cv2.resize(att, im2.size, interpolation=cv2.INTER_NEAREST)
         mask2d = zip(*np.where(att==255))