Add Gradio demo

app.py

@@ -0,0 +1,127 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+
+from maploc.demo import Demo
+from maploc.osm.tiling import TileManager
+from maploc.osm.viz import Colormap, GeoPlotter, plot_nodes
+from maploc.utils.viz_2d import features_to_RGB, plot_images
+from maploc.utils.viz_localization import (
+    add_circle_inset,
+    likelihood_overlay,
+    plot_dense_rotations,
+)
+
+
+def run(image, address, tile_size_meters, num_rotations):
+    image_path = image.name
+    demo = Demo(num_rotations=int(num_rotations))
+
+    try:
+        image, camera, gravity, proj, bbox = demo.read_input_image(
+            image_path,
+            prior_address=address or None,
+            tile_size_meters=int(tile_size_meters),
+        )
+    except ValueError as e:
+        raise gr.Error(str(e))
+
+    tiler = TileManager.from_bbox(proj, bbox + 10, demo.config.data.pixel_per_meter)
+    canvas = tiler.query(bbox)
+    map_viz = Colormap.apply(canvas.raster)
+
+    plot_images([image, map_viz], titles=["input image", "OpenStreetMap raster"], pad=2)
+    plot_nodes(1, canvas.raster[2], fontsize=6, size=10)
+    fig1 = plt.gcf()
+
+    # Run the inference
+    try:
+        uv, yaw, prob, neural_map, image_rectified = demo.localize(
+            image, camera, canvas, gravity=gravity
+        )
+    except RuntimeError as e:
+        raise gr.Error(str(e))
+
+    # Visualize the predictions
+    overlay = likelihood_overlay(prob.numpy().max(-1), map_viz.mean(-1, keepdims=True))
+    (neural_map_rgb,) = features_to_RGB(neural_map.numpy())
+    plot_images([overlay, neural_map_rgb], titles=["heatmap", "neural map"], pad=2)
+    ax = plt.gcf().axes[0]
+    ax.scatter(*canvas.to_uv(bbox.center), s=5, c="red")
+    plot_dense_rotations(ax, prob, w=0.005, s=1 / 25)
+    add_circle_inset(ax, uv)
+    fig2 = plt.gcf()
+
+    # Plot as interactive figure
+    latlon = proj.unproject(canvas.to_xy(uv))
+    bbox_latlon = proj.unproject(canvas.bbox)
+    plot = GeoPlotter(zoom=16.5)
+    plot.raster(map_viz, bbox_latlon, opacity=0.5)
+    plot.raster(likelihood_overlay(prob.numpy().max(-1)), proj.unproject(bbox))
+    plot.points(proj.latlonalt[:2], "red", name="location prior", size=10)
+    plot.points(latlon, "black", name="argmax", size=10, visible="legendonly")
+    plot.bbox(bbox_latlon, "blue", name="map tile")
+
+    coordinates = f"(latitude, longitude) = {tuple(latlon)}\nheading angle = {yaw:.2f}°"
+    return fig1, fig2, plot.fig, coordinates
+
+
+examples = [
+    ["assets/query_zurich_1.JPG", "ETH CAB Zurich", 128, 256],
+    ["assets/query_vancouver_1.JPG", "Vancouver Waterfront Station", 128, 256],
+    ["assets/query_vancouver_2.JPG", None, 128, 256],
+    ["assets/query_vancouver_3.JPG", None, 128, 256],
+]
+
+description = """
+<h1 align="center">
+  <ins>OrienterNet</ins>
+  <br>
+  Visual Localization in 2D Public Maps
+  <br>
+  with Neural Matching</h1>
+<h3 align="center">
+    <a href="https://psarlin.com/orienternet" target="_blank">Project Page</a> |
+    <a href="https://arxiv.org/pdf/2304.02009.pdf" target="_blank">Paper</a> |
+    <a href="https://github.com/facebookresearch/OrienterNet" target="_blank">Code</a> |
+    <a href="https://youtu.be/wglW8jnupSs" target="_blank">Video</a>
+</h3>
+<p align="center">
+OrienterNet finds the position and orientation of any image using OpenStreetMap.
+Click on one of the provided examples or upload your own image!
+</p>
+"""
+
+app = gr.Interface(
+    fn=run,
+    inputs=[
+        gr.File(file_types=["image"]),
+        gr.Textbox(
+            label="Prior location (optional)",
+            info="Required if the image metadata (EXIF) does not contain a GPS prior. "
+            "Enter an address or a street or building name.",
+        ),
+        gr.Radio(
+            [64, 128, 256, 512],
+            value=128,
+            label="Search radius (meters)",
+            info="Depends on how coarse the prior location is.",
+        ),
+        gr.Radio(
+            [64, 128, 256, 360],
+            value=256,
+            label="Number of rotations",
+            info="Reduce to scale to larger areas.",
+        ),
+    ],
+    outputs=[
+        gr.Plot(label="Inputs"),
+        gr.Plot(label="Outputs"),
+        gr.Plot(label="Interactive map"),
+        gr.Textbox(label="Predicted coordinates"),
+    ],
+    description=description,
+    examples=examples,
+    cache_examples=True,
+)
+
+app.launch(share=False)