tmp

app.py

@@ -93,7 +93,7 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50, sf_ids='
     fin_img = []
     img1rsz = np.copy(im1_cv)
     print('im1:', im1.size)
-    print(img1rsz.shape)
+    print('img1rsz:', img1rsz.shape)
     for j, att in enumerate(all_att_bin1):
         att = cv2.resize(att, im1.size, interpolation=cv2.INTER_NEAREST)
         # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_CUBIC)
@@ -109,6 +109,8 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50, sf_ids='
     fin_img.append(img1rsz)
             
     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)
         # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_CUBIC)
@@ -171,8 +173,10 @@ css = ".input_image, .input_image {height: 600px !important; width: 600px !impor
 iface = gr.Interface(
     fn=generate_matching_superfeatures,
     inputs=[
-        gr.inputs.Image(shape=(1024, 1024), type="pil", label="First Image"),
-        gr.inputs.Image(shape=(1024, 1024), type="pil", label="Second Image"),
+#        gr.inputs.Image(shape=(1024, 1024), type="pil", label="First Image"),
+#        gr.inputs.Image(shape=(1024, 1024), type="pil", label="Second Image"),
+        gr.inputs.Image(type="pil", label="First Image"),
+        gr.inputs.Image(type="pil", label="Second Image"),
         gr.inputs.Slider(minimum=0, maximum=6, step=1, default=2, label="Scale"),
         gr.inputs.Slider(minimum=1, maximum=255, step=25, default=100, label="Binarization Threshold"),
         gr.inputs.Textbox(lines=1, default="", label="Super-feature IDs to show", optional=True)],