ui

main.py

@@ -4,6 +4,15 @@ from generate_tree_images.generate_tree_images import generate_tree_images
 from classification.classification_predict import classify
 import os
 import json
+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
+
+
+
 
 def row_to_feature(row):
     feature = {
@@ -40,40 +49,122 @@ tif_file_name: the file name of the tif input. tif_input is the folder in which
 output_directory: the directory were all in-between and final files are stored
 
 generate_tree_images stores the cutout tree images in a separate folder
+
 """
 
-tif_file_name = "TreeCrownVectorDataset_761588_9673769_20_20_32720.tif"
-tif_input = "/Users/jonathanseele/ETH/Hackathons/EcoHackathon/WeCanopy/test/" + tif_file_name
 
-current_directory = os.getcwd()
+def greet(image_path: str):
+    current_directory = os.getcwd()
+
+    output_directory = os.path.join(current_directory, "outputs")
+    if not os.path.exists(output_directory):
+        os.makedirs(output_directory)
 
-output_directory = os.path.join(current_directory, "outputs")
-if not os.path.exists(output_directory):
-    os.makedirs(output_directory)
+    run_detectree2(image_path, store_path=output_directory)
 
-run_detectree2(tif_input, store_path=output_directory)
+    processed_output_df = postprocess(output_directory + '/detectree2_delin.geojson', output_directory + '/processed_delin')
 
-processed_output_df = postprocess(output_directory + '/detectree2_delin.geojson', output_directory + '/processed_delin')
+    processed_geojson = output_directory + '/processed_delin.geojson'
 
-processed_geojson = output_directory + '/processed_delin.geojson'
+    generate_tree_images(processed_geojson, image_path)
 
-generate_tree_images(processed_geojson, tif_input)
-output_folder = './tree_images'
+    output_folder = './tree_images'
 
-all_top_3_list = []  # Initialize an empty list to accumulate all top_3 lists
+    all_top_3_list = []  # Initialize an empty list to accumulate all top_3 lists
 
-for file_name in os.listdir(output_folder):
-    file_path = os.path.join(output_folder, file_name)
-    probs = classify(file_path)
-    top_3 = probs.head(3)
-    top_3_list = [[cls, prob] for cls, prob in top_3.items()]
+    for file_name in os.listdir(output_folder):
+        file_path = os.path.join(output_folder, file_name)
+        probs = classify(file_path)
+        top_3 = probs.head(3)
+        top_3_list = [[cls, prob] for cls, prob in top_3.items()]
     
-    # Accumulate the top_3_list for each file
-    all_top_3_list.append(top_3_list)
+        # Accumulate the top_3_list for each file
+        all_top_3_list.append(top_3_list)
 
 # Assign the accumulated top_3_list to the 'species' column of the dataframe
-processed_output_df['species'] = all_top_3_list
+    processed_output_df['species'] = all_top_3_list
+
+    final_output_path = 'result'
+    export_geojson(processed_output_df, final_output_path)
+
+    with rasterio.open(image_path) as src:
+        tif_image = src.read([1, 2, 3])  # Read the first three bands (RGB)
+        tif_transform = src.transform
+
+    # Read the GeoJSON file
+    geojson_data = gpd.read_file(final_output_path)
+
+    # Set the interactive backend to Qt5Agg
+    plt.switch_backend('Qt5Agg') # You have to install PyQt5
+
+    # Enable interactive mode
+    plt.ion()
+
+    # Plotting
+    fig, ax = plt.subplots(figsize=(10, 10))
+
+    # 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
+    cid = fig.canvas.mpl_connect('button_press_event', on_click)
+
+    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
+
+# File paths
+#tif_file_path = '/Users/taekim/ecohackathon/WeCanopy/test/TreeCrownVectorDataset_761588_9673769_20_20_32720.tif'
+#geojson_file_path = '/Users/taekim/ecohackathon/WeCanopy/test/result.geojson'
+
+# Read the TIF file
+
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=gr.File(type='filepath'),
+    outputs=gr.Plot(label="Tree Crowns")
+)
+
+demo.launch()
+
+
+
 
-final_output_path = 'result'
-export_geojson(processed_output_df, final_output_path)