Module `datatap.metrics.iou`

Expand source code

from datatap.metrics.confusion_matrix import ConfusionMatrix
from typing import Sequence

from ..droplet import ImageAnnotation
from ..template import ImageAnnotationTemplate
from .precision_recall_curve import PrecisionRecallCurve


def generate_pr_curve(ground_truths: Sequence[ImageAnnotation], predictions: Sequence[ImageAnnotation], iou_threshold: float) -> PrecisionRecallCurve:
        """
        Returns a precision-recall curve for the given ground truth and prediction annotation lists evaluated with the given
        IOU threshold.

        Note: this handles instances only; multi-instances are ignored.
        """
        precision_recall_curve = PrecisionRecallCurve()
        precision_recall_curve.batch_add_annotation(ground_truths, predictions, iou_threshold)
        return precision_recall_curve

def generate_confusion_matrix(
        template: ImageAnnotationTemplate,
        ground_truths: Sequence[ImageAnnotation],
        predictions: Sequence[ImageAnnotation],
        iou_threshold: float,
        confidence_threshold: float
) -> ConfusionMatrix:
        """
        Returns a confusion matrix for the given ground truth and prediction annotation lists evaluated with the given IOU
        threshold.

        Note: this handles instances only; multi-instances are ignored.
        """
        confusion_matrix = ConfusionMatrix(sorted(template.classes.keys()))
        confusion_matrix.batch_add_annotation(ground_truths, predictions, iou_threshold, confidence_threshold)
        return confusion_matrix

Functions

def generate_confusion_matrix(template: ImageAnnotationTemplate, ground_truths: Sequence[ImageAnnotation], predictions: Sequence[ImageAnnotation], iou_threshold: float, confidence_threshold: float) ‑> ConfusionMatrix

Returns a confusion matrix for the given ground truth and prediction annotation lists evaluated with the given IOU threshold.

Note: this handles instances only; multi-instances are ignored.

Expand source code

def generate_confusion_matrix(
        template: ImageAnnotationTemplate,
        ground_truths: Sequence[ImageAnnotation],
        predictions: Sequence[ImageAnnotation],
        iou_threshold: float,
        confidence_threshold: float
) -> ConfusionMatrix:
        """
        Returns a confusion matrix for the given ground truth and prediction annotation lists evaluated with the given IOU
        threshold.

        Note: this handles instances only; multi-instances are ignored.
        """
        confusion_matrix = ConfusionMatrix(sorted(template.classes.keys()))
        confusion_matrix.batch_add_annotation(ground_truths, predictions, iou_threshold, confidence_threshold)
        return confusion_matrix

def generate_pr_curve(ground_truths: Sequence[ImageAnnotation], predictions: Sequence[ImageAnnotation], iou_threshold: float) ‑> PrecisionRecallCurve

Returns a precision-recall curve for the given ground truth and prediction annotation lists evaluated with the given IOU threshold.

Note: this handles instances only; multi-instances are ignored.

Expand source code

def generate_pr_curve(ground_truths: Sequence[ImageAnnotation], predictions: Sequence[ImageAnnotation], iou_threshold: float) -> PrecisionRecallCurve:
        """
        Returns a precision-recall curve for the given ground truth and prediction annotation lists evaluated with the given
        IOU threshold.

        Note: this handles instances only; multi-instances are ignored.
        """
        precision_recall_curve = PrecisionRecallCurve()
        precision_recall_curve.batch_add_annotation(ground_truths, predictions, iou_threshold)
        return precision_recall_curve