ellishape-cli
A command-line tool for shape analysis using Elliptic Fourier Descriptors (EFD).
Description
Process grayscale images (with white as foreground) to calculate shape descriptors or contour coordinates. Supports single image processing or batch processing via file lists. Outputs results in CSV format and optionally generates visualization images.
Installation
pip install ellishape_cli
- Recommended use the program in Linux.
Usage
Basic Command
python3 -m ellishape_cli -i input.png -out results.csv
Process Multiple Files
python3 -m ellishape_cli -I file_list.txt -method chain_code -out batch_results.csv
Command Line Options
| Option | Description | Default | Require |
|---|---|---|---|
| -i, -input | Input grayscale image (white=foreground) | - | Yes* |
| -I, -input_list | Text file containing list of input paths (one per line) | - | Yes* |
| -n, -n_order | Number of Elliptic Fourier Descriptor orders to calculate | 64 | No |
| -N, -n_dots | Number of contour points to output | 512 | No |
| -method | Calculate method: chain code or directly pixel dots (faster but may rough) | dots | No |
| -skip_normalize | Disable automatic normalization of EFD coefficients | False | No |
| -out | Output CSV file path | - | Yes |
| -out_image | Generate output visualization image | False | No |
- Either -i or -I must be provided
Output
CSV File Format
Contour coordinates (x,y pairs)and normalized elliptic fourier descriptor coefficients
Visualization Image
When using -out_image, outputs an image showing original contour and reconstructed shape from EFD coefficients.
Examples
Basic analysis with visualization
python3 -m ellishape_cli -i cell.png -n 32 -out cell_analysis.csv -out_image
Batch processing with chain codes
python3 -m ellishape_cli -I shapes.txt -method chain_code -N 256 -out output.csv
High-precision EFD calculation
python3 -m ellishape_cli -i specimen.tiff -n 128 -skip_normalize -out highres_efd.csv
Tree module
Calculate distance between samples with several methods and build neighbor-joining tree.
Usage
Calculate Minimum Euclidean Distance and build trees.
python3 -m ellishape_cli.tree -i dots.csv -min_dist -o result
Command Line Options
Core Parameters
| Option | Description | Default | Required |
|---|---|---|---|
| -i, -input | Input CSV matrix with feature values (first column = sample names) | - | Yes |
| -kind | Metadata CSV specifying sample categories (name,kind columns) | - | No |
| -o, -output | Prefix for output files | out | No |
Distance Metrics
| Option | Description |
|---|---|
| -min_dist | Minimum Euclidean distance with rotational/shift alignment |
| -h_dist | Hausdorff distance |
| -s_dist | Shape context distance |
Advanced Options
-pca: Generate PCA plot of shape features
-no_factor: Disable normalization factor in distance calculations
- If no one distance metric (-min_dist/-h_dist/-s_dist) is specified, by default the program calculate standard Euclidean distance.
Input Requirements
Feature Matrix (-i)
CSV format with:
First column: Sample names
Subsequent columns: Feature values (e.g., dots coordinates)
sample,feature1,feature2,...
cell1,0.12,0.45,...
cell2,0.08,0.39,...
Metadata File (-kind)
CSV format with:
First column: Sample names
Second column: Category labels
sample,category
cell1,typeA
cell2,typeB
Examples
Basic Distance Analysis
python3 -m ellishape_cli.tree -i shapes.csv -min_dist -h_dist -o cell_comparison
Category-based Analysis
python3 -m ellishape_cli.tree -i features.csv -kind cell_types.csv -s_dist -pca -o typed_analysis
1 maintainer
Metadata
1 maintainer
Release files for ellishape-cli 1.23.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ellishape_cli-1.23.0-py3-none-any.whl | Python 3 | none | any | Details |
Release files / ellishape_cli-1.23.0-py3-none-any.whl
| Download URL | ellishape_cli-1.23.0-py3-none-any.whl |
|---|---|
| Size | 29.9 kB |
| Tags | Python 3 |
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