Implement LightGlue image matching app with Gradio interface and necessary dependencies

app.py

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+import gradio as gr
+import numpy as np
+from transformers import AutoImageProcessor, AutoModel
+from transformers.image_utils import to_numpy_array
+import torch
+import plotly.graph_objects as go
+from PIL import Image
+
+
+def process_images(image1, image2):
+    """
+    Process two images and return a plot of the matching keypoints.
+    """
+    if image1 is None or image2 is None:
+        return None
+
+    images = [image1, image2]
+    processor = AutoImageProcessor.from_pretrained("ETH-CVG/lightglue_superpoint")
+    model = AutoModel.from_pretrained("ETH-CVG/lightglue_superpoint")
+
+    inputs = processor(images, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    image_sizes = [[(image.height, image.width) for image in images]]
+    outputs = processor.post_process_keypoint_matching(
+        outputs, image_sizes, threshold=0.2
+    )
+    output = outputs[0]
+
+    image1 = to_numpy_array(image1)
+    image2 = to_numpy_array(image2)
+
+    height0, width0 = image1.shape[:2]
+    height1, width1 = image2.shape[:2]
+
+    # Create PIL image from numpy array
+    pil_img = Image.fromarray((image1 / 255.0 * 255).astype(np.uint8))
+    pil_img2 = Image.fromarray((image2 / 255.0 * 255).astype(np.uint8))
+
+    # Create Plotly figure
+    fig = go.Figure()
+
+    # Get keypoints
+    keypoints0_x, keypoints0_y = output["keypoints0"].unbind(1)
+    keypoints1_x, keypoints1_y = output["keypoints1"].unbind(1)
+
+    # Add a separate trace for each match (line + markers) to enable highlighting
+    for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
+        keypoints0_x,
+        keypoints0_y,
+        keypoints1_x,
+        keypoints1_y,
+        output["matching_scores"],
+    ):
+        color_val = matching_score.item()
+        color = f"rgba({int(255 * (1 - color_val))}, {int(255 * color_val)}, 0, 0.7)"
+
+        hover_text = (
+            f"Score: {matching_score.item():.2f}<br>"
+            f"Point 1: ({keypoint0_x.item():.1f}, {keypoint0_y.item():.1f})<br>"
+            f"Point 2: ({keypoint1_x.item():.1f}, {keypoint1_y.item():.1f})"
+        )
+
+        fig.add_trace(
+            go.Scatter(
+                x=[keypoint0_x.item(), keypoint1_x.item() + width0],
+                y=[keypoint0_y.item(), keypoint1_y.item()],
+                mode="lines+markers",
+                line=dict(color=color, width=2),
+                marker=dict(color=color, size=5, opacity=0.8),
+                hoverinfo="text",
+                hovertext=hover_text,
+                showlegend=False,
+            )
+        )
+
+    # Update layout to use images as background
+    fig.update_layout(
+        title="LightGlue Keypoint Matching",
+        xaxis=dict(
+            range=[0, width0 + width1],
+            showgrid=False,
+            zeroline=False,
+            showticklabels=False,
+        ),
+        yaxis=dict(
+            range=[max(height0, height1), 0],
+            showgrid=False,
+            zeroline=False,
+            showticklabels=False,
+            scaleanchor="x",
+            scaleratio=1,
+        ),
+        margin=dict(l=0, r=0, t=50, b=0),
+        height=max(height0, height1),
+        width=width0 + width1,
+        images=[
+            dict(
+                source=pil_img,
+                xref="x",
+                yref="y",
+                x=0,
+                y=0,
+                sizex=width0,
+                sizey=height0,
+                sizing="stretch",
+                opacity=1,
+                layer="below",
+            ),
+            dict(
+                source=pil_img2,
+                xref="x",
+                yref="y",
+                x=width0,
+                y=0,
+                sizex=width1,
+                sizey=height1,
+                sizing="stretch",
+                opacity=1,
+                layer="below",
+            ),
+        ],
+    )
+
+    return fig
+
+
+# Create the Gradio interface
+with gr.Blocks(title="LightGlue Matching Demo") as demo:
+    gr.Markdown("# LightGlue Matching Demo")
+    gr.Markdown(
+        "Upload two images and get a side-by-side matching of your images using LightGlue."
+    )
+    gr.Markdown("""
+        ## How to use:
+        1. Upload two images using the file uploaders above
+        2. Click the 'Match Images' button
+        3. View the matched output image below
+        
+        The app will create a side-by-side matching of your images using LightGlue.
+        You can also select an example image pair from the dataset.
+    """)
+
+    with gr.Row():
+        # Input images on the same row
+        image1 = gr.Image(label="First Image", type="pil")
+        image2 = gr.Image(label="Second Image", type="pil")
+
+    # Process button
+    process_btn = gr.Button("Match Images", variant="primary")
+
+    # Output plot
+    output_plot = gr.Plot(label="Matching Results")
+
+    # Connect the function
+    process_btn.click(fn=process_images, inputs=[image1, image2], outputs=output_plot)
+
+    # Add some example usage
+
+    examples = gr.Dataset(
+        components=[image1, image2],
+        label="Example Image Pairs",
+        samples=[
+            [
+                "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg",
+                "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg",
+            ],
+        ],
+    )
+
+    examples.select(lambda x: (x[0], x[1]), [examples], [image1, image2])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=5.34.2
+Pillow>=10.0.0
+numpy>=1.24.0 
+transformers
+matplotlib
+torch
+plotly