Update app.py

app.py

@@ -12,6 +12,7 @@ from SuperGluePretrainedNetwork.models.matching import Matching
 from SuperGluePretrainedNetwork.models.utils import read_image
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
+from io import BytesIO
 
 # Set device
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -61,22 +62,27 @@ def stitch_images(images):
     return composite
 
 def unified_matching_plot2(image0, image1, kpts0, kpts1, mkpts0, mkpts1, color, text, path=None, show_keypoints=False, fast_viz=False, opencv_display=False, opencv_title='matches', small_text=[]):
+    # Resize images to have the same height
+    height = min(image0.shape[0], image1.shape[0])
+    image0_resized = cv2.resize(image0, (int(image0.shape[1] * height / image0.shape[0]), height))
+    image1_resized = cv2.resize(image1, (int(image1.shape[1] * height / image1.shape[0]), height))
+
     plt.figure(figsize=(15, 15))
     plt.subplot(1, 2, 1)
-    plt.imshow(image0)
+    plt.imshow(image0_resized)
     plt.scatter(kpts0[:, 0], kpts0[:, 1], color='r', s=1)
     plt.axis('off')
 
     plt.subplot(1, 2, 2)
-    plt.imshow(image1)
+    plt.imshow(image1_resized)
     plt.scatter(kpts1[:, 0], kpts1[:, 1], color='r', s=1)
     plt.axis('off')
 
     fig, ax = plt.subplots(figsize=(20, 20))
-    plt.plot([mkpts0[:, 0], mkpts1[:, 0] + image0.shape[1]], [mkpts0[:, 1], mkpts1[:, 1]], 'r', lw=0.5)
+    plt.plot([mkpts0[:, 0], mkpts1[:, 0] + image0_resized.shape[1]], [mkpts0[:, 1], mkpts1[:, 1]], 'r', lw=0.5)
     plt.scatter(mkpts0[:, 0], mkpts0[:, 1], s=2, marker='o', color='b')
-    plt.scatter(mkpts1[:, 0] + image0.shape[1], mkpts1[:, 1], s=2, marker='o', color='g')
-    plt.imshow(np.hstack([image0, image1]), aspect='auto')
+    plt.scatter(mkpts1[:, 0] + image0_resized.shape[1], mkpts1[:, 1], s=2, marker='o', color='g')
+    plt.imshow(np.hstack([image0_resized, image1_resized]), aspect='auto')
 
     plt.suptitle('\n'.join(text), fontsize=20, fontweight='bold')
     plt.tight_layout()