Updating of packages and confirmation of running on Mac OS

README.md

@@ -16,6 +16,13 @@ This may be improved in future versions, as it should be easily fixed with addit
 
 **A video showing the outputs of the tool is provided here: https://youtu.be/rej5Bu57AqM**
 
+## My contributions
+
+I have edited this code to update it and have solved some of the bugs that have cropped up for other users e.g. incompatible package installations/TypeErrors. This has been done on a Mac Studio M2 ultra and I used all 24 CPU cores for this on the CPU only setting. My TLS was also 74 million+ points a scan so I have not tested minimum numbers of points. The code now runs on python 3.11.7. 
+
+I might adapt the training data for European Arctic specific contexts, as per my research interests, as the original data relates to western Australia. I am collecting TLS data myself over summer 2024 in the European Arctic and may update the training data so that this is useable for large scale plot cleaning/metric extraction for forestry in these contexts. 
+
+The only slight bug is it looks for the tools and model.pth files in /x/x/x/FSCT/model or /tools. The way I got around this was to simply make another folder called FSCT and copy the file/folder in there so it would find it where it expected as the folder when I download from FSCT is called FSCT-main. I used VS code, pip3, and a conda environment.
 
 ## Installation
 
@@ -26,16 +33,14 @@ In Anaconda Prompt, type the following (replacing the path to FSCT and your desi
 
 ```shell
 cd PATH_TO_FSCT-MAIN_DIRECTORY
-conda create --name YOUR_ENVIRONMENT_NAME_HERE python==3.9
+conda create --name YOUR_ENVIRONMENT_NAME_HERE
 conda activate YOUR_ENVIRONMENT_NAME_HERE
 conda install pip
 pip install -r requirements.txt
 ```
 
 This should hopefully install all required packages for you.
 These are the instructions for Windows 10 and Linux.
-I have not tested this on Mac. If someone with a Mac tests this and 
-it works (or doesn't), please let me know!
 
 If you have any difficulties or find any bugs, please get in touch and I will try to help you get it going. 
 Suggestions for improvements are greatly appreciated.

requirements.txt

@@ -5,24 +5,24 @@ certifi==2021.10.8
 charset-normalizer==2.0.12
 colorama==0.4.4
 cycler==0.11.0
-Cython==0.29.28
+Cython==0.29.37
 fonttools==4.33.3
 googledrivedownloader==0.4
-hdbscan==0.8.28
+hdbscan==0.8.33
 idna==3.3
 imageio==2.19.0
 importlib-metadata==4.11.3
 isodate==0.6.1
 Jinja2==3.1.2
-joblib==1.1.0
+joblib==1.1.1
 kiwisolver==1.4.2
 laspy==2.1.2
 Markdown==3.3.7
 MarkupSafe==2.1.1
 matplotlib==3.5.2
 mdutils==1.3.1
 networkx==2.8
-numpy==1.22.3
+numpy==1.23.5
 packaging==21.3
 pandas==1.4.2
 Pillow==9.1.0
@@ -34,14 +34,14 @@ PyWavelets==1.3.0
 rdflib==6.1.1
 requests==2.27.1
 scikit-image==0.19.2
-scikit-learn==1.0.2
-scikit-spatial==6.4.0
-scipy==1.8.0
+scikit-learn==1.1.3
+scikit-spatial==6.4.1
+scipy==1.9.3
 six==1.16.0
 sklearn==0.0
 threadpoolctl==3.1.0
 tifffile==2022.5.4
-torch==1.9.0+cu111
+torch==2.1.0
 torch-cluster==1.5.9
 torch-geometric==1.7.2
 torch-scatter==2.0.8
@@ -50,4 +50,4 @@ torch-spline-conv==1.2.1
 tqdm==4.64.0
 typing_extensions==4.2.0
 urllib3==1.26.9
-zipp==3.8.0
+zipp==3.8.0

scripts/inference.py

@@ -1,4 +1,4 @@
-from abc import ABC
+from abc import ABC, abstractmethod
 import torch
 import torch_geometric
 from torch_geometric.data import Dataset, DataLoader, Data
@@ -20,9 +20,10 @@
 
 
 class TestingDataset(Dataset, ABC):
+
     def __init__(self, root_dir, points_per_box, device):
-        super().__init__()
-        self.filenames = glob.glob(root_dir + "*.npy")
+        super(). __init__()
+        self.filenames = glob.glob(os.path.join(root_dir, '*.npy'))
         self.device = device
         self.points_per_box = points_per_box
 
@@ -88,12 +89,11 @@ def __init__(self, parameters):
         self.filename = "working_point_cloud.las"
         self.directory = self.output_dir
         self.plot_summary = pd.read_csv(self.output_dir + "plot_summary.csv", index_col=None)
-        self.plot_centre = [[float(self.plot_summary["Plot Centre X"]), float(self.plot_summary["Plot Centre Y"])]]
+        self.plot_centre = [[float(self.plot_summary["Plot Centre X"].iloc[0]), float(self.plot_summary["Plot Centre Y"].iloc[0])]]
 
     def inference(self):
-        test_dataset = TestingDataset(
-            root_dir=self.working_dir, points_per_box=self.parameters["max_points_per_box"], device=self.device
-        )
+        test_dataset = TestingDataset(root_dir=self.working_dir, points_per_box=self.parameters["max_points_per_box"], 
+                                      device=self.device)
 
         test_loader = DataLoader(test_dataset, batch_size=self.parameters["batch_size"], shuffle=False, num_workers=0)
 

scripts/run.py

@@ -1,6 +1,8 @@
 from run_tools import FSCT, directory_mode, file_mode
 from other_parameters import other_parameters
 
+
+
 if __name__ == "__main__":
     """Choose one of the following or modify as needed.
     Directory mode will find all .las files within a directory and sub directories but will ignore any .las files in
@@ -14,22 +16,21 @@
     """
     # point_clouds_to_process = directory_mode()
     # point_clouds_to_process = ['full_path_to_your_point_cloud.las', 'full_path_to_your_second_point_cloud.las', etc.]
-    point_clouds_to_process = file_mode()
-
+    point_clouds_to_process = directory_mode()
     for point_cloud_filename in point_clouds_to_process:
         parameters = dict(
-            point_cloud_filename=point_cloud_filename,
+            point_cloud_filename = point_cloud_filename,
             # Adjust if needed
             plot_centre=None,  # [X, Y] Coordinates of the plot centre (metres). If "None", plot_centre is computed based on the point cloud bounding box.
             # Circular Plot options - Leave at 0 if not using.
-            plot_radius=0,  # If 0 m, the plot is not cropped. Otherwise, the plot is cylindrically cropped from the plot centre with plot_radius + plot_radius_buffer.
-            plot_radius_buffer=0,  # See README. If non-zero, this is used for "Tree Aware Plot Cropping Mode".
+            plot_radius=5,  # If 0 m, the plot is not cropped. Otherwise, the plot is cylindrically cropped from the plot centre with plot_radius + plot_radius_buffer.
+            plot_radius_buffer=5,  # See README. If non-zero, this is used for "Tree Aware Plot Cropping Mode".
             # Set these appropriately for your hardware.
             batch_size=2,  # You will get CUDA errors if this is too high, as you will run out of VRAM. This won't be an issue if running on CPU only. Must be >= 2.
             num_cpu_cores=0,  # Number of CPU cores you want to use. If you run out of RAM, lower this. 0 means ALL cores.
-            use_CPU_only=False,  # Set to True if you do not have an Nvidia GPU, or if you don't have enough vRAM.
+            use_CPU_only=True,  # Set to True if you do not have an Nvidia GPU, or if you don't have enough vRAM.
             # Optional settings - Generally leave as they are.
-            slice_thickness=0.15,  # If your point cloud resolution is a bit low (and only if the stem segmentation is still reasonably accurate), try increasing this to 0.2.
+            slice_thickness=0.10,  # If your point cloud resolution is a bit low (and only if the stem segmentation is still reasonably accurate), try increasing this to 0.2.
             # If your point cloud is really dense, you may get away with 0.1.
             slice_increment=0.05,  # The smaller this is, the better your results will be, however, this increases the run time.
             sort_stems=1,  # If you don't need the sorted stem points, turning this off speeds things up.
@@ -38,7 +39,7 @@
             tree_base_cutoff_height=5,  # A tree must have a cylinder measurement below this height above the DTM to be kept. This filters unsorted branches from being called individual trees.
             generate_output_point_cloud=1,  # Turn on if you would like a semantic and instance segmented point cloud. This mode will override the "sort_stems" setting if on.
             # If you activate "tree aware plot cropping mode", this function will use it.
-            ground_veg_cutoff_height=3,  # Any vegetation points below this height are considered to be understory and are not assigned to individual trees.
+            ground_veg_cutoff_height=1,  # Any vegetation points below this height are considered to be understory and are not assigned to individual trees.
             veg_sorting_range=1.5,  # Vegetation points can be, at most, this far away from a cylinder horizontally to be matched to a particular tree.
             stem_sorting_range=1,  # Stem points can be, at most, this far away from a cylinder in 3D to be matched to a particular tree.
             taper_measurement_height_min=0,  # Lowest height to measure diameter for taper output.

scripts/run_tools.py

@@ -7,6 +7,9 @@
 import tkinter as tk
 import tkinter.filedialog as fd
 import os
+import numpy
+numpy.float = numpy.float64
+numpy.int = numpy.int_
 
 
 def FSCT(

scripts/run_with_multiple_plot_centres.py

@@ -6,10 +6,7 @@
     """
     This script is an example of how to provide multiple different plot centres with your input point clouds.
     """
-    point_clouds_to_process = [
-        ["E:/your_point_cloud1.las", [your_plot_centre_X_coord, your_plot_centre_Y_coord], your_plot_radius],
-        ["E:/your_point_cloud2.las", [your_plot_centre_X_coord, your_plot_centre_Y_coord], your_plot_radius],
-    ]
+    point_clouds_to_process =  []
 
     for point_cloud_filename, plot_centre, plot_radius in point_clouds_to_process:
         parameters = dict(
@@ -19,7 +16,7 @@
             plot_radius_buffer=0,
             batch_size=18,
             num_cpu_cores=0,
-            use_CPU_only=False,
+            use_CPU_only=True,
             # Optional settings - Generally leave as they are.
             slice_thickness=0.15,
             # If your point cloud resolution is a bit low (and only if the stem segmentation is still reasonably accurate), try increasing this to 0.2.
@@ -35,7 +32,7 @@
             generate_output_point_cloud=1,
             # Turn on if you would like a semantic and instance segmented point cloud. This mode will override the "sort_stems" setting if on.
             # If you activate "tree aware plot cropping mode", this function will use it.
-            ground_veg_cutoff_height=3,
+            ground_veg_cutoff_height=2,
             # Any vegetation points below this height are considered to be understory and are not assigned to individual trees.
             veg_sorting_range=1.5,
             # Vegetation points can be, at most, this far away from a cylinder horizontally to be matched to a particular tree.