daccuracy 2021.4

Brief Description

DAccuracy 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.

Note that, with most image formats, ground-truth and detection cannot contain more than 255 objects. If they do, DAccuracy will complain about the image being incorrectly labeled. This constraint will be relaxed in a future version (it cannot be simply removed when using fixed-length representations of integers) by allowing to pass files of Numpy arrays and unlabeled images.

INSTALLATION

The DAccuracy project is published on the Python Package Index (PyPI) at: https://pypi.org/project/daccuracy. It requires version 3.8, or newer, 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: pip install daccuracy

  • Installation update: pip install --upgrade daccuracy

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

  • First installation: pip install --user daccuracy

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

Documentation

The following measures are computed (some measures are repeated under another name):

• Number of ground-truth objects

• Number of detected objects

• Number of correctly detected (true positives), missed (false negative), and invented (false positive) objects

• Number of true positives, false positives, and false negatives

• Precision, recall, and F1 score

• Free-response Receiver Operating Characteristic (FROC) curve sample: named froc_sample and corresponding to the tuple (false positives, true positive rate)

• Values for measure correctness checking: check_c_m_gt (correct + missed ?=? ground-truths) and check_c_i_dn (correct + invented ?=? detections)

Additionally, if the ground-truth has been passed as an image (as opposed to a CVS file), the mean, standard deviation, minimum, and maximum of the following measures are also computed:

• Ground-truth/detection overlap (as a percentage with respect to the smaller region among ground-truth and detection)

• Ground-truth/detection Jaccard index

• Pixel-wise precision, recall, and F1 score

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] [--no-usage-notice]

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)

Note that, WITH MOST IMAGE FORMATS, ground-truth and detection CANNOT CONTAIN MORE THAN 255 OBJECTS.
If they do, DAccuracy will complain about the image being incorrectly labeled. This constraint will be relaxed in a
future version (it cannot be simply removed when using fixed-length representations of integers) by allowing to pass
files of Numpy arrays and unlabeled 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
  --no-usage-notice     Silences usage notice about maximum number of objects

Thanks

The project is developed with PyCharm Community.

The development relies on several open-source packages (see install_requires in setup.py).

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

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