Cluster the profiles using multidirectional 3D profile analysis method

Question

I am having a problem with the clustering process in the multidirectional profile analysis method used for clustering with airborne LiDAR data. Since the profile analysis is done in one dimension and the clustering process is done in at least two dimensions, I am getting errors in coding. I would be happy if you help.

def multidirectional_profile_analysis(las_file_path, directions=[0, 45, 90, 135, 180, 225, 270, 315]):
    
    profile_data = []

   
    for direction in directions:
       
        radian = np.radians(direction)
        cos_angle = np.cos(radian)
        sin_angle = np.sin(radian)

        
        profile_x = x * cos_angle + y * sin_angle
        profile_y = x * -sin_angle + y * cos_angle
        profile_z = z  

        profile_data.append((profile_x, profile_y, profile_z))

    return profile_data

a = np.asarray(profile_data)

# Cluster the profiles using 3D profile analysis method
# This method takes into account the shape of the tree crown, which can help to improve the accuracy of the segmentation.
cluster_centers = []
for i in range(len(a)):
    # Find the minimum and maximum values in the profile
    min_val = np.min(a[i])
    max_val = np.max(a[i])

    # Find the points in the profile that are within the minimum and maximum values
    in_range_points = np.asarray(a[i][(a[i] >= min_val) & (a[i] <= max_val)])

    # If there are at least 10 points in the range, then cluster them
    if len(in_range_points) >= 10:
        kmeans = KMeans(n_clusters=8)
        cluster_centers.append(kmeans.fit_predict(in_range_points))

ValueError Traceback (most recent call last)

<ipython-input-45-a206a3d15e5f> in <cell line: 4>()
     13     if len(in_range_points) >= 10:
     14         kmeans = KMeans(n_clusters=8)
---> 15         cluster_centers.append(kmeans.fit_predict(in_range_points))

3 frames
/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py in fit_predict(self, X, y, sample_weight)
   1031             Index of the cluster each sample belongs to.
   1032         """
-> 1033         return self.fit(X, sample_weight=sample_weight).labels_
   1034 
   1035     def predict(self, X, sample_weight=None):

/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py in fit(self, X, y, sample_weight)
   1415         self._validate_params()
   1416 
-> 1417         X = self._validate_data(
   1418             X,
   1419             accept_sparse="csr",

/usr/local/lib/python3.10/dist-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
    563             raise ValueError("Validation should be done on X, y or both.")
    564         elif not no_val_X and no_val_y:
--> 565             X = check_array(X, input_name="X", **check_params)
    566             out = X
    567         elif no_val_X and not no_val_y:

/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py in

check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name) 900 # If input is 1D raise error 901 if array.ndim == 1: --> 902 raise ValueError( 903 "Expected 2D array, got 1D array instead:\narray={}.\n" 904 "Reshape your data either using array.reshape(-1, 1) if "

ValueError: Expected 2D array, got 1D array instead:
array=[491883.2864686 491883.2924686 491883.2934686 ...   1463.268
   1463.365       1463.454    ].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single

sample.