daccuracy 2021.3

Detection and Segmentation Accuracy Measures

Purpose of DAccuracy

In a Few Words

The DAccuracy project allows to compute some accuracy measures of an N-dimensional detection or segmentation image when the ground-truth is represented by a CSV file or an image. It works in 3 contexts:

• one-to-one: single ground-truth, single segmentation;

• one-to-many: unique ground-truth, several segmentations (typically obtained with several methods);

• many-to-many: set of ground-truth/segmentation pairs.

Illustration

daccuracy --gt ground-truth.csv --dn detection.png -t 5 -s --format csv

INSTALLATION

The DAccuracy project is published on the Python Package Index (PyPI) at: https://pypi.org/project/daccuracy. It requires version 3.8, or higher, of the interpreter. It should be installable from Python distribution platforms or Integrated Development Environments (IDEs). Otherwise, it can be installed from a command-line console:

• For all users, after acquiring administrative rights:

  • First installation: pip3 install daccuracy

  • Installation update: pip3 install --upgrade daccuracy

• For the current user (no administrative rights required):

  • First installation: pip3 install --user daccuracy

  • Installation update: pip3 install --user --upgrade daccuracy

Note

The command pip3 was mentioned above to emphasize that DAccuracy requires major version 3 of Python. If pip defaults to this version, it can of course be used instead.

Documentation

Usage Help:

usage: daccuracy [-h] --gt ground_truth --dn detection [--shifts Dn_shift Dn_shift] [-e] [-t TOLERANCE] [-f {csv,nev}] [-o Output file] [-s]

3 modes:
    - one-to-one: one ground-truth (image or csv) vs. one detection (image)
    - one-to-many: one ground-truth (image or csv) vs. several detections (folder of images)
    - many-to-many: several ground-truths (folder of images and/or csv's) vs. corresponding detections (folder of images)

optional arguments:
  -h, --help            show this help message and exit
  --gt ground_truth     Ground-truth labeled image or CSV file of centers, or ground-truth folder; If CSV, --rAcB or --xAyB can be passed additionally
                        to indicate which columns contain the centers' rows and cols or x's and y's respectively
  --dn detection        Detection labeled image, or detection folder
  --shifts Dn_shift Dn_shift
                        Vertical (row) and horizontal (col) shifts to apply to detection
  -e, --exclude-border  If present, this option instructs to discard objects touching image border, both in ground-truth and detection
  -t TOLERANCE, --tol TOLERANCE, --tolerance TOLERANCE
                        Max ground-truth-to-detection distance to count as a hit (meant to be used when ground-truth is a CSV file of centers)
  -f {csv,nev}, --format {csv,nev}
                        nev: one "Name = Value"-row per measure; csv: one CSV-row per ground-truth/detection pairs
  -o Output file        Name-Value or CSV file to store the computed measures, or "-" for console output
  -s, --show-image      If present, this option instructs to show an image superimposing ground-truth onto detection

Thanks

The project is developed with PyCharm Community.

The code is formatted by Black, The Uncompromising Code Formatter.

The imports are ordered by isort… your imports, so you don’t have to.