Revert

Dockerfile

@@ -37,7 +37,6 @@ RUN python -m pip install git+https://github.com/PatBall1/detectree2.git
 RUN python -m pip install opencv-python
 RUN python -m pip install requests
 
-RUN python -m pip install plotly
 RUN python -m pip install gradio
 
 COPY --chown=user . $HOME/app

main.py

@@ -8,8 +8,8 @@ import gradio as gr
 import rasterio
 from rasterio.plot import show
 import geopandas as gpd
+import matplotlib.pyplot as plt
 from shapely.geometry import Point
-import plotly.graph_objects as go
 
 
 
@@ -97,53 +97,54 @@ def greet(image_path: str):
     # Set the interactive backend to Qt5Agg
     # plt.switch_backend('Qt5Agg') # You have to install PyQt5
 
+    # Enable interactive mode
+    plt.ion()
+
     # Plotting
-    fig = go.Figure()
-    fig.add_trace(go.Image(z=tif_image.transpose((1, 2, 0))))
-    for idx, row in geojson_data.iterrows():
-        geojson_dict = json.loads(row['geometry'].to_json())
-        fig.add_trace(go.Scattergeo(
-            lon=[coord[0] for coord in geojson_dict['coordinates'][0]],
-            lat=[coord[1] for coord in geojson_dict['coordinates'][0]],
-            mode='lines',
-            line=dict(color='red'),
-            name=f'Polygon {idx}'
-        ))
+    fig, ax = plt.subplots(figsize=(10, 10))
 
-    # Set plot title
-    fig.update_layout(
-        title='TIF Image with Tree Crowns Overlay',
-        geo=dict(
-            projection_scale=1,
-            center=dict(
-                lon=tif_transform.c + (tif_transform.a * tif_image.width / 2),
-                lat=tif_transform.f + (tif_transform.e * tif_image.height / 2)
-            )
-        )
-    )
-
-    # Function to handle click events
-    def on_click(trace, points, state):
-        if points.point_inds:
-            idx = points.point_inds[0]
-            row = geojson_data.iloc[idx]
-            species_info = row['species']
-            confidence_score = row['Confidence_score']
-            text = f"Polygon {idx}\n\nConfidence:\n{confidence_score}\n\nSpecies and their probability:\n{species_info}"
-            fig.update_layout(annotations=[dict(
-                x=points.xs[0],
-                y=points.ys[0],
-                text=text,
-                showarrow=True,
-                arrowhead=7
-            )])
+    # Plot the RGB TIF image
+    show(tif_image, transform=tif_transform, ax=ax)
+
+    # Plot the GeoJSON polygons
+    geojson_data.plot(ax=ax, facecolor='none', edgecolor='red')
 
+    # Set plot title
+    ax.set_title('TIF Image with Tree Crowns Overlay')
+
+    # Create an annotation box
+    annot = ax.annotate("", xy=(0, 0), xytext=(20, 20),
+                    textcoords="offset points",
+                    bbox=dict(boxstyle="round", fc="w"),
+                    arrowprops=dict(arrowstyle="->"))
+    annot.set_visible(False)
+
+    # Create a function to handle mouse clicks
+    def on_click(event):
+        if event.inaxes is not None:
+            # Get the coordinates of the click
+            click_point = Point(event.xdata, event.ydata)
+            # Check if the click is within any of the polygons
+            for idx, row in geojson_data.iterrows():
+                if row['geometry'].contains(click_point):
+                    # Access the properties dictionarㅋ
+                    # Extract species and confidence score
+                    species_info = row['species']
+                    confidence_score = row['Confidence_score']
+                    # Display information about the clicked polygon
+                    annot.xy = (event.xdata, event.ydata)
+                    text = f"Polygon {idx}\n\nConfidence:\n{confidence_score}\n\nSpecies and their probability:\n{species_info}"
+                    annot.set_text(text)
+                    annot.set_visible(True)
+                    fig.canvas.draw()
+                    break
+    
     # Connect the click event to the handler function
-    for trace in fig.data:
-        trace.on_click(on_click)
+    # cid = fig.canvas.mpl_connect('button_press_event', on_click)
 
-    return fig
+    figure = plt.figure()
 
+    return figure
 
 #tif_file_name = "TreeCrownVectorDataset_761588_9673769_20_20_32720.tif"
 #tif_input = "/Users/jonathanseele/ETH/Hackathons/EcoHackathon/WeCanopy/test/" + tif_file_name