Update README.md

README.md

@@ -81,10 +81,10 @@ import torch
 from PIL import Image
 import requests
 
-url = "https://github.com/magicleap/SuperGluePretrainedNetwork/blob/master/assets/phototourism_sample_images/london_bridge_78916675_4568141288.jpg?raw=true"
-im1 = Image.open(requests.get(url, stream=True).raw)
-url = "https://github.com/magicleap/SuperGluePretrainedNetwork/blob/master/assets/phototourism_sample_images/london_bridge_19481797_2295892421.jpg?raw=true"
-im2 = Image.open(requests.get(url, stream=True).raw)
+url_image1 = "https://github.com/magicleap/SuperGluePretrainedNetwork/blob/master/assets/scannet_sample_images/scene0758_00_frame-000510.jpg?raw=true"
+image1 = Image.open(requests.get(url_image1, stream=True).raw)
+url_image2 = "https://github.com/magicleap/SuperGluePretrainedNetwork/blob/master/assets/scannet_sample_images/scene0758_00_frame-000165.jpg?raw=true"
+image2 = Image.open(requests.get(url_image2, stream=True).raw)
 images = [im1, im2]
 
 processor = AutoImageProcessor.from_pretrained("stevenbucaille/superglue_indoor")
@@ -103,10 +103,10 @@ import torch
 from PIL import Image
 import requests
 
-url_image_1 = "https://github.com/cvg/LightGlue/blob/main/assets/sacre_coeur1.jpg?raw=true"
-image_1 = Image.open(requests.get(url_image_1, stream=True).raw)
-url_image_2 = "https://github.com/cvg/LightGlue/blob/main/assets/sacre_coeur2.jpg?raw=true"
-image_2 = Image.open(requests.get(url_image_2, stream=True).raw)
+url_image1 = "https://github.com/magicleap/SuperGluePretrainedNetwork/blob/master/assets/scannet_sample_images/scene0758_00_frame-000510.jpg?raw=true"
+image1 = Image.open(requests.get(url_image1, stream=True).raw)
+url_image2 = "https://github.com/magicleap/SuperGluePretrainedNetwork/blob/master/assets/scannet_sample_images/scene0758_00_frame-000165.jpg?raw=true"
+image2 = Image.open(requests.get(url_image2, stream=True).raw)
 
 images = [image_1, image_2]
 
@@ -132,7 +132,7 @@ image1_matching_scores = outputs.matching_scores[0, 1][image1_indices]
 
 You can use the `post_process_keypoint_matching` method from the `SuperGlueImageProcessor` to get the keypoints and matches in a more readable format:
 ```python
-image_sizes = [(image.height, image.width) for image in images]
+image_sizes = [[(image.height, image.width) for image in images]]
 outputs = processor.post_process_keypoint_matching(outputs, image_sizes)
 for i, output in enumerate(outputs):
     print("For the image pair", i)
@@ -182,7 +182,7 @@ plt.savefig("matched_image.png", dpi=300, bbox_inches='tight')
 plt.close()
 ```
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/01ZYaLB1NL5XdA8u7yCo4.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/OrOJQ19s3R8LuG11uzH1R.png)
 
 ## Training Details