Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import laspy
+from sklearn.cluster import DBSCAN
+from sklearn.metrics import accuracy_score
+from scipy.spatial import ConvexHull
+from skimage.measure import profile_line
+
+st.title("Tree Analysis App")
+
+# Upload LAS file
+uploaded_file = st.file_uploader("Upload a LAS file", type=["las", "laz"])
+
+if uploaded_file is not None:
+    # Load the LAS file
+    las_file = laspy.read(uploaded_file)
+    height_filter = np.logical_and(las_file.z > 1, las_file.z < 30)
+    las_file = las_file[height_filter]
+    # Extract the x and y coordinates from the LAS file
+    x = las_file.x
+    y = las_file.y
+
+    # Combine the x and y coordinates into a feature matrix
+    feature_matrix = np.column_stack((x, y))
+
+    # Segment the trees using DBSCAN clustering with a specified distance threshold (e.g., 2 meters)
+    tree_labels = DBSCAN(eps=2, min_samples=10).fit_predict(feature_matrix)
+
+    # Count the number of trees
+    num_trees = len(set(tree_labels)) - (1 if -1 in tree_labels else 0)
+    st.write(f"Number of trees: {num_trees}")
+    for i in range(num_trees):
+        indices = np.where(tree_labels == i)[0]
+
+        tree_x = x[indices]
+        tree_y = y[indices]
+
+        tree_mid_x = np.mean(tree_x)
+        tree_mid_y = np.mean(tree_y)
+
+        st.write(f"Tree {i+1} middle point: ({tree_mid_x:.3f}, {tree_mid_y:.3f})")
+
+    def calculate_tree_data(points):
+        height = np.max(points.z) - np.min(points.z)
+        xy_points = np.column_stack((points.X, points.Y))
+        hull = ConvexHull(xy_points)
+        crown_spread = np.sqrt(hull.area / np.pi)/10
+        z_trunk = np.percentile(points.z, 20)  # assume trunk is the lowest 20% of the points
+        trunk_points = points[points.z < z_trunk]
+        dbh = 2 * np.mean(np.sqrt((trunk_points.X - np.mean(trunk_points.X)) ** 2 + (trunk_points.Y - np.mean(trunk_points.Y)) ** 2))
+        return height, crown_spread, dbh
+
+    tree_data = []
+    for tree_label in range(num_trees):
+        # Extract points for the current tree
+        tree_points = las_file.points[tree_labels == tree_label]
+        # Calculate tree data
+        data = calculate_tree_data(tree_points)
+        # Append data to list
+        tree_data.append(data)
+
+    # Print the data for each tree
+    for i, data in enumerate(tree_data):
+        st.write(f"Tree {i + 1} - Height: {data[0]:.3f} m, Crown Spread: {data[1]:.3f} m, DBH: {data[2]:.3f} mm")