Plot Selection Scripts 🌳

This repository contains a set of scripts designed to help you work with segmented trees within a plot area. The scripts allow you to select areas of interest, extract coordinates, and filter files based on boundaries.

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Scripts Overview 📂

1. polyline.py

This script processes a plot boundary polyline to create polygon representations of the plot area, with options to expand or shrink the plot area using buffers.

Features

• Converts a LineString polyline to a Polygon.
• Expands or shrinks the polygon area by a specified buffer distance.
• Converts polygons back to polylines for visualization.
• Reprojects data to a specified CRS (EPSG:32755 - UTM Zone 55S, suitable for Queensland).
• Outputs results as GeoJSON files.

Inputs

• A GeoJSON file containing the polyline representing the plot boundary.
• expand_distance (float): Positive value to expand the plot area.
• shrink_distance (float): Negative value to shrink the plot area.

Outputs

GeoJSON files saved to the specified output directory:

• original_plot_polygon.geojson
• expanded_plot_polygon.geojson (if expansion is applied)
• shrunk_plot_polygon.geojson (if shrinking is applied)
• original_plot_polyline.geojson
• expanded_plot_polyline.geojson
• shrunk_plot_polyline.geojson

Example Usage

output_dict = process_plot(
    file_path=r"C:\path\to\your\Plot_circumference.geojson",
    shrink_distance=-5  # Shrink by 5m
)

output_dir = r"C:\path\to\output\directory"
save_to_geojson(output_dict, output_dir)

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2. name_cleaner.py

This script cleans and standardizes filenames in a directory by removing unwanted characters and ensuring consistent naming conventions.

Features

• Removes special characters and spaces from filenames.
• Standardizes filename format for consistent processing.
• Processes all files in a specified directory.
• Creates a backup log of original vs. cleaned filenames.

Inputs

• A directory containing files with inconsistent or problematic filenames.

Outputs

• Files with cleaned, standardized names in the same directory.
• A log file showing the mapping of original to cleaned filenames.

Example Usage

input_directory = r"C:\path\to\your\messy_files"
clean_filenames(input_directory)

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3. coordinate_extraction.py

This script extracts the coordinates of tree stems from .las files and saves them into a CSV file. It calculates the centroid of a defined cross-section of each tree.

Features

• Processes .las files in a specified folder.
• Filters points based on a cross-section height range (0.5m above the minimum Z value).
• Calculates the centroid (center_x, center_y) of points within the cross-section.
• Saves results to a CSV file.

Inputs

• A folder containing .las files of segmented trees.
• Cross-section height: 0.5m above the minimum Z value.

Outputs

• A CSV file named tree_locations.csv with the following columns:
  • filename: The name of the .las file processed.
  • center_x: X-coordinate of the tree center.
  • center_y: Y-coordinate of the tree center.

Example Usage

folder_path = r"C:\path\to\your\extracted_trees_folder"
output_csv = r"C:\path\to\output\directory\tree_locations.csv"

CSV Output Format

filename	center_x	center_y
tree_01_fixed.las	654321.123	1234567.456
tree_02_fixed.las	654328.321	1234573.789
...	...	...

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4. select_in_boundaries.py

This script checks which trees (based on their center coordinates) fall within a specified plot boundary and saves the valid filenames to a CSV file.

Features

• Loads a polygon plot boundary from a GeoJSON file.
• Loads tree center points from a CSV file generated by coordinate_extraction.py.
• Checks if points fall within the plot boundary.
• Saves filenames of valid points to a new CSV file.

Inputs

1. Plot boundary: A GeoJSON file containing a polygon or polyline plot boundary (e.g., shrunk_plot_polygon.geojson).
2. Tree center points: A CSV file containing filenames and their respective center_x and center_y coordinates (e.g., tree_locations.csv).
3. CRS: The CRS of the plot boundary (should match the CRS of the input points).

Outputs

• A CSV file named trees_within_plot.csv containing:
  • filename: The filenames of the trees within the plot boundary.

Example Usage

plot_geojson_path = r"C:\path\to\your\shrunk_plot_polygon.geojson"
csv_path = r"C:\path\to\your\tree_locations.csv"
output_csv_path = r"C:\path\to\output\directory\trees_within_plot.csv"

The output CSV will have a simple structure:

filename
tree_01_fixed.las
tree_02_fixed.las
...

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4. select_in_boundaries.py

This script checks which trees (based on their center coordinates) fall within a specified plot boundary and saves the valid filenames to a CSV file.

Features

• Loads a polygon plot boundary from a GeoJSON file.
• Loads tree center points from a CSV file generated by coordinate_extraction.py.
• Checks if points fall within the plot boundary.
• Saves filenames of valid points to a new CSV file.

Inputs

1. Plot boundary: A GeoJSON file containing a polygon or polyline plot boundary (e.g., shrunk_plot_polygon.geojson).
2. Tree center points: A CSV file containing filenames and their respective center_x and center_y coordinates (e.g., tree_locations.csv).
3. CRS: The CRS of the plot boundary (should match the CRS of the input points).

Outputs

• A CSV file named trees_within_plot.csv containing:
  • filename: The filenames of the trees within the plot boundary.

Example Usage

plot_geojson_path = r"C:\path\to\your\shrunk_plot_polygon.geojson"
csv_path = r"C:\path\to\your\tree_locations.csv"
output_csv_path = r"C:\path\to\output\directory\trees_within_plot.csv"

The output CSV will have a simple structure:

filename
tree_01_fixed.las
tree_02_fixed.las
...

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5. file_copy_new_folder.py

This script efficiently copies tree files based on a CSV selection list. It supports resumable operations and separates files into "selected" and "leftover" folders.

Features

• Resumable: Automatically skips files already processed if the script is interrupted.
• Dual sorting: Copies CSV-listed files to "selected" folder, others to "leftover" folder.
• Statistics tracking: Shows detailed progress and completion summary.

Inputs

1. CSV file: A CSV file containing a column named filename (e.g., trees_within_plot.csv).
2. Source folder: The folder where the original tree files are located.
3. Destination folders: Two folders - one for selected files, one for leftovers.

Outputs

• Files from CSV copied to the "selected" folder.
• All other files copied to the "leftover" folder.
• Progress tracking and completion statistics.

Example Usage

csv_path = r"C:\path\to\trees_within_plot.csv"
source_folder = r"C:\path\to\extracted_trees_folder"
destination_folder = r"C:\path\to\selected_trees"
leftover_folder = r"C:\path\to\leftover_trees"

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6. quick_leftovers.py

This is a simplified script for quickly copying only leftover files when you've already processed the selected files separately. Useful for completing interrupted operations from older versions of the copy script.

Features

• Simple and fast: Only processes leftover files (not in CSV selection).
• Resume-friendly: Skips files already in the leftover folder.
• Minimal overhead: Streamlined for quick execution when you only need leftovers.

Use Case

• When you've already copied selected files using an older script version.
• For completing interrupted operations where only leftovers remain.
• Quick one-time usage when you don't need the full functionality.

Example Usage

csv_path = r"C:\path\to\trees_within_plot.csv"
source_folder = r"C:\path\to\extracted_trees_folder"  
leftover_folder = r"C:\path\to\leftover_trees"

Output

Total files in source: 5000
Files in CSV (to skip): 150  
Already in leftover folder: 1200
Done! Copied 3650 new leftover files, skipped 1200 already processed

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Usage ⚙️

1. Modify paths and parameters within each script to suit your project.
2. Run scripts in order:
   • polyline.py → name_cleaner.py (if needed) → coordinate_extraction.py → select_in_boundaries.py → file_copy_new_folder.py
3. Alternative for leftovers only: Use quick_leftovers.py if you've already processed selected files.
4. Resume capability: All copy scripts can be safely restarted if interrupted.
5. Check your output directories to verify results.

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Requirements 📋

• This script was made and tested with Python 3.11
• Required libraries:
  • geopandas
  • shapely
  • laspy
  • numpy
  • pandas
  • datetime

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For questions, mail to geike.desloover@ugent.be

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