temp

app.py

@@ -47,8 +47,8 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
     im1_tensor = transform(im1).unsqueeze(0)
     im2_tensor = transform(im2).unsqueeze(0)
 
-    # im1_cv = cv2.imread(im1)
-    # im2_cv = cv2.imread(im2)
+    im1_cv = cv2.imread(im1)
+    im2_cv = cv2.imread(im2)
 
     # extract features
     with torch.no_grad():
@@ -83,7 +83,7 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
 
     
     fin_img = []
-    img1rsz = np.copy(im1)
+    img1rsz = np.copy(im1_cv)
     print(img1rsz.size)
     for j, att in enumerate(all_att_bin1):
         att = cv2.resize(att, im1.size, interpolation=cv2.INTER_NEAREST)
@@ -99,7 +99,7 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
             img1rsz[m,n, :] = col_[::-1]   
     fin_img.append(img1rsz)
             
-    img2rsz = np.copy(im2)
+    img2rsz = np.copy(im1_cv)
     for j, att in enumerate(all_att_bin2):
         att = cv2.resize(att, im2.size, interpolation=cv2.INTER_NEAREST)
         # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_CUBIC)
@@ -139,7 +139,8 @@ iface = gr.Interface(
         gr.inputs.Image(shape=(1024, 1024), type="pil"),
         gr.inputs.Slider(minimum=1, maximum=7, step=1, default=2, label="Scale"),
         gr.inputs.Slider(minimum=1, maximum=255, step=25, default=50, label="Binarizatio Threshold")],
-    outputs="plot",
+    # outputs="plot",
+    outputs=gr.outputs.Image(shape=(1024,2048), type="plot"),
     enable_queue=True,
     title=title,
     description=description,