Delete app.py

app.py

@@ -1,64 +0,0 @@
-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")