temp

app.py

@@ -41,9 +41,7 @@ sf_idx_ = [55, 14, 5, 4, 52, 57, 40, 9]
 col = plt.get_cmap('tab10')
 
 def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
-    print(im1.size)
-    return 
-    
+
     im1_tensor = transform(im1).unsqueeze(0)
     im2_tensor = transform(im2).unsqueeze(0)
 
@@ -80,17 +78,17 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
         att_heat = np.uint8(att_heat / np.max(att_heat[:]) * 255.0)
         att_heat_bin  = np.where(att_heat>threshold, 255, 0)
         all_att_bin2.append(att_heat_bin)
-
+    print(all_att_bin2[0].shape)
     
     fin_img = []
     img1rsz = np.copy(im1)
-    print(img1rsz.size)
+    print(img1rsz.shape)
     for j, att in enumerate(all_att_bin1):
         # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_NEAREST)
         # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_CUBIC)
         # att = cv2.resize(att, imgz[i].shape[:2][::-1])
-        att = att.resize(shape)
-        # att = resize(att, im1.size)
+        att = resize(att, im1.shape[:2])
+        print(att.shape)
         mask2d = zip(*np.where(att==255))
         for m,n in mask2d:
             col_ = col.colors[j] if j < 7 else col.colors[j+1]
@@ -104,8 +102,7 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
         # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_NEAREST)
         # att = cv2.resize(att, imgz[i].shape[:2][::-1], interpolation=cv2.INTER_CUBIC)
         # att = cv2.resize(att, imgz[i].shape[:2][::-1])
-        att = att.resize(im2.shape)
-        print('att:', att.shape)
+        att = resize(att, im2.shape[:2])
         mask2d = zip(*np.where(att==255))
         for m,n in mask2d:
             col_ = col.colors[j] if j < 7 else col.colors[j+1]
@@ -135,8 +132,8 @@ article = "<p style='text-align: center'><a href='https://github.com/naver/fire'
 iface = gr.Interface(
     fn=generate_matching_superfeatures,
     inputs=[
-        gr.inputs.Image(shape=(240, 240), type="pil"),
-        gr.inputs.Image(shape=(240, 240), type="pil"),
+        gr.inputs.Image(shape=(1024, 1024), type="numpy"),
+        gr.inputs.Image(shape=(1024, 1024), type="numpy"),
         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",

requirements.txt

@@ -1,6 +1,6 @@
+opencv-python
 numpy
 pyaml
 matplotlib
 torch==1.10.2
-torchvision==0.11.3
-scikit-image
+torchvision==0.11.3