Package: ukis-metrics
ukis-metricsA pure Numpy-based implementation of the most common performance metrics for semantic image segmentation.Installationpipinstallukis_metrics>>>importukis_metrics
>>>ukis_metrics.__version__'0.1.3'Why?Simply because we wanted a lightweight and fast alternative to scikit learn for tracking during training.execution_time.pycompares the execution time of ukis-metrics with sklearn. Here's an example output:Shape of array: (256, 256, 2)

                                        ### Metrics execution time [s] ###

                ukis-metrics                            sklearn metrics         speed gain
acc             0.001900                                0.007627                4.01
rec             0.001716                                0.024509                14.28
pre             0.001815                                0.025021                13.79
f1              0.001798                                0.024770                13.78
iou             0.001797                                0.024247                13.49
kap             0.001824                                0.034577                18.96Workings and included metricsIn a first step thetrue positivestp,true negativestn,false positivesfp,false negativesfnand the number of valid pixelsn_valid_pixelsare computed. These values are then used to compute the following
metrics:Accuracy [1]:acc = \frac{tp + tn}{tp + fn + fp + tn}Recall [1]:rec = \frac{tp}{tp + fn}Precision [1]:prec = \frac{tp}{tp + fp}F1-score [2]:F1 = \frac{2 * prec * rec}{prec + rec}IoU [3]:IoU = \frac{tp}{tp + fp + fn}Kappa: The computation of the Kappa-score incorporates several steps. Please refer to [4] for the full
documentationHow to useSimply pass a Numpy ndarray to get a dict containing thetpfptnfnand pass the dict tosegmentation_metrics(tpfptnfn):importukis_metrics.seg_metricsassegmimportnumpyasnp# ndarray containing the reference data, e.g.shape=(256,256,1)y_true=np.ones(shape)# ndarray containing the model predicions, e.g.y_pred=np.ones(shape)# get tp, fp, tn, fn an n_valid_pixeltpfptnfn=segm.tpfptnfn(y_true,y_pred,None)metrics=segm.segmentation_metrics(tpfptnfn)So far these metrics were only used for binary classification, although one should be able to use them for
multiclass segmentation too, if the slices for a given class are provided individually.There is ajupyter notebookshowing how ukis-metrics can be used and how it could be extended for multiclass problems.References[1]Sokolova and Lapalme 2009: A systematic analysis of performance measures for classification tasks[2]scikit-learn[3]Berman et al. 2018: The Lovász-Softmax loss: A tractable surrogate for the optimization of
the intersection-over-union measure in neural networks[4]Tang et al. 2015: Kappa coefficient: a popular measure of rater agreementContributorsThe UKIS team creates and adapts libraries which simplify the usage of satellite data. Our team includes (in alphabetical order):Fichtner, FlorianHelleis, MaxMartinis, SandroWieland, MarcGerman Aerospace Center (DLR)LicensesThis software is licensed under theApache 2.0 License.Copyright (c) 2021 German Aerospace Center (DLR) * German Remote Sensing Data Center * Department: Geo-Risks and Civil SecurityChangelogSeechangelog.What is UKIS?The DLR project Environmental and Crisis Information System (the German abbreviation is UKIS, standing forUmwelt- und Kriseninformationssystemeaims at harmonizing the development of information systems at the German Remote Sensing Data Center (DFD) and setting up a framework of modularized and generalized software components.UKIS is intended to ease and standardize the process of setting up specific information systems and thus bridging the gap from EO product generation and information fusion to the delivery of products and information to end users.Furthermore, the intention is to save and broaden know-how that was and is invested and earned in the development of information systems and components in several ongoing and future DFD projects.