TypeError: 'numpy.float64' object cannot be interpreted as an integer #38
Description
I ran in Windows 10 Anaconda. try to ues las data from Test data
TypeError: 'numpy.float64' object cannot be interpreted as an integer
Do you have any suggestion ?
FSCT-main) C:\FSCT\scripts>python run.py
Current point cloud being processed: C:/FSCT/data/test/example.las
Using default number of CPU cores (all of them).
Processing using 16 / 16 CPU cores.
Loading file... C:/FSCT/data/test/example.las
Saving file: C:\FSCT\data\test/example_FSCT_output/working_point_cloud.las
Saved.
Pre-processing point cloud...
Preprocessing took 4.8439555168151855 s
Preprocessing done
Is CUDA available? True
Performing inference on device: cuda
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Loading file... C:/FSCT/data/test/example_FSCT_output/working_point_cloud.las
Choosing most confident labels...
Saving file: C:/FSCT/data/test/example_FSCT_output/segmented.las
Saved.
Semantic segmentation took 37.000956535339355 s
Semantic segmentation done
Loading segmented point cloud...
Loading file... C:/FSCT/data/test/example_FSCT_output/segmented.las
Making DTM...
DTM Done
Saving file: C:/FSCT/data/test/example_FSCT_output/DTM.las
Saved.
Plot area is approximately 0.003600000000003154 ha
Getting heights above DTM...
Saving file: C:/FSCT/data/test/example_FSCT_output/terrain_points.las
Saved.
Saving file: C:/FSCT/data/test/example_FSCT_output/stem_points.las
Saved.
Saving file: C:/FSCT/data/test/example_FSCT_output/vegetation_points.las
Saved.
Saving file: C:/FSCT/data/test/example_FSCT_output/cwd_points.las
Saved.
Saving file: C:/FSCT/data/test/example_FSCT_output/segmented_cleaned.las
Saved.
Post-processing took 1.8837203979492188 seconds
Post processing done.
Loading file... C:/FSCT/data/test/example_FSCT_output/stem_points.las
stempoints ['x', 'y', 'z', 'red', 'green', 'blue', 'label', 'height_above_DTM']
Loading file... C:/FSCT/data/test/example_FSCT_output/DTM.las
Loading file... C:/FSCT/data/test/example_FSCT_output/terrain_points.las
Loading file... C:/FSCT/data/test/example_FSCT_output/vegetation_points.las
Saving file: C:/FSCT/data/test/example_FSCT_output/ground_veg.las
Saved.
Loading file... C:/FSCT/data/test/example_FSCT_output/cwd_points.las
Canopy Cover Fraction: 0.7836691410392365
Understory Veg Fraction: 0.8886532343584306
Coarse Woody Debris Fraction: 0.40402969247083775
Making and clustering slices...
0 / 485Traceback (most recent call last):
File "C:\FSCT\scripts\run.py", line 54, in
FSCT(
File "C:\FSCT\scripts\run_tools.py", line 44, in FSCT
measure1.run_measurement_extraction()
File "C:\FSCT\scripts\measure.py", line 833, in run_measurement_extraction
cluster, skel = MeasureTree.slice_clustering(new_slice, self.parameters["min_cluster_size"])
File "C:\FSCT\scripts\measure.py", line 778, in slice_clustering
new_slice = cluster_hdbscan(new_slice[:, :3], min_cluster_size)
File "C:\FSCT\scripts\tools.py", line 218, in cluster_hdbscan
cluster_labels = hdbscan.HDBSCAN(min_cluster_size=min_cluster_size).fit_predict(points[:, :3])
File "C:\Users\Boonlue\anaconda3\envs\FSCT-main\lib\site-packages\hdbscan\hdbscan_.py", line 1243, in fit_predict
self.fit(X)
File "C:\Users\Boonlue\anaconda3\envs\FSCT-main\lib\site-packages\hdbscan\hdbscan_.py", line 1205, in fit
) = hdbscan(clean_data, **kwargs)
File "C:\Users\Boonlue\anaconda3\envs\FSCT-main\lib\site-packages\hdbscan\hdbscan_.py", line 884, in hdbscan
tree_to_labels(
File "C:\Users\Boonlue\anaconda3\envs\FSCT-main\lib\site-packages\hdbscan\hdbscan.py", line 78, in _tree_to_labels
condensed_tree = condense_tree(single_linkage_tree, min_cluster_size)
File "hdbscan\_hdbscan_tree.pyx", line 43, in hdbscan._hdbscan_tree.condense_tree
File "hdbscan\_hdbscan_tree.pyx", line 109, in hdbscan._hdbscan_tree.condense_tree
TypeError: 'numpy.float64' object cannot be interpreted as an integer
(FSCT-main) C:\FSCT\scripts>