temp

app.py

@@ -15,6 +15,7 @@ import fire_network
 import numpy as np
 
 from PIL import Image
+from skimage.transform import resize
 
 # Possible Scales for multiscale inference
 scales = [2.0, 1.414, 1.0, 0.707, 0.5, 0.353, 0.25] 
@@ -37,10 +38,11 @@ transform = transforms.Compose([
 # which sf
 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)
@@ -82,11 +84,13 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
     
     fin_img = []
     img1rsz = np.copy(im1)
+    print(img1rsz.size)
     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(im1.shape)
+        att = att.resize(shape)
+        # att = resize(att, im1.size)
         mask2d = zip(*np.where(att==255))
         for m,n in mask2d:
             col_ = col.colors[j] if j < 7 else col.colors[j+1]
@@ -96,7 +100,6 @@ def generate_matching_superfeatures(im1, im2, scale_id=6, threshold=50):
     fin_img.append(img1rsz)
             
     img2rsz = np.copy(im2)
-    print(img2rsz.size)
     for j, att in enumerate(all_att_bin2):
         # 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)