OpenOCR/opendet/metrics/eval_det_iou.py

#!/usr/bin/env python
import numpy as np
from shapely.geometry import Polygon
"""
reference from :
https://github.com/MhLiao/DB/blob/3c32b808d4412680310d3d28eeb6a2d5bf1566c5/concern/icdar2015_eval/detection/iou.py#L8
"""


class DetectionIoUEvaluator(object):

    def __init__(self, iou_constraint=0.5, area_precision_constraint=0.5):
        self.iou_constraint = iou_constraint
        self.area_precision_constraint = area_precision_constraint

    def evaluate_image(self, gt, pred):

        def get_union(pD, pG):
            return Polygon(pD).union(Polygon(pG)).area

        def get_intersection_over_union(pD, pG):
            return get_intersection(pD, pG) / get_union(pD, pG)

        def get_intersection(pD, pG):
            return Polygon(pD).intersection(Polygon(pG)).area

        def compute_ap(confList, matchList, numGtCare):
            correct = 0
            AP = 0
            if len(confList) > 0:
                confList = np.array(confList)
                matchList = np.array(matchList)
                sorted_ind = np.argsort(-confList)
                confList = confList[sorted_ind]
                matchList = matchList[sorted_ind]
                for n in range(len(confList)):
                    match = matchList[n]
                    if match:
                        correct += 1
                        AP += float(correct) / (n + 1)

                if numGtCare > 0:
                    AP /= numGtCare

            return AP

        perSampleMetrics = {}

        matchedSum = 0

        numGlobalCareGt = 0
        numGlobalCareDet = 0

        precision = 0

        detMatched = 0

        iouMat = np.empty([1, 1])

        gtPols = []
        detPols = []

        gtPolPoints = []
        detPolPoints = []

        # Array of Ground Truth Polygons' keys marked as don't Care
        gtDontCarePolsNum = []
        # Array of Detected Polygons' matched with a don't Care GT
        detDontCarePolsNum = []

        pairs = []
        detMatchedNums = []

        evaluationLog = ''

        for n in range(len(gt)):
            points = gt[n]['points']
            dontCare = gt[n]['ignore']
            if not Polygon(points).is_valid:
                continue

            gtPol = points
            gtPols.append(gtPol)
            gtPolPoints.append(points)
            if dontCare:
                gtDontCarePolsNum.append(len(gtPols) - 1)

        evaluationLog += (
            'GT polygons: ' + str(len(gtPols)) +
            (' (' + str(len(gtDontCarePolsNum)) +
             " don't care)\n" if len(gtDontCarePolsNum) > 0 else '\n'))

        for n in range(len(pred)):
            points = pred[n]['points']
            if not Polygon(points).is_valid:
                continue

            detPol = points
            detPols.append(detPol)
            detPolPoints.append(points)
            if len(gtDontCarePolsNum) > 0:
                for dontCarePol in gtDontCarePolsNum:
                    dontCarePol = gtPols[dontCarePol]
                    intersected_area = get_intersection(dontCarePol, detPol)
                    pdDimensions = Polygon(detPol).area
                    precision = (0 if pdDimensions == 0 else intersected_area /
                                 pdDimensions)
                    if precision > self.area_precision_constraint:
                        detDontCarePolsNum.append(len(detPols) - 1)
                        break

        evaluationLog += (
            'DET polygons: ' + str(len(detPols)) +
            (' (' + str(len(detDontCarePolsNum)) +
             " don't care)\n" if len(detDontCarePolsNum) > 0 else '\n'))

        if len(gtPols) > 0 and len(detPols) > 0:
            # Calculate IoU and precision matrixs
            outputShape = [len(gtPols), len(detPols)]
            iouMat = np.empty(outputShape)
            gtRectMat = np.zeros(len(gtPols), np.int8)
            detRectMat = np.zeros(len(detPols), np.int8)
            for gtNum in range(len(gtPols)):
                for detNum in range(len(detPols)):
                    pG = gtPols[gtNum]
                    pD = detPols[detNum]
                    iouMat[gtNum, detNum] = get_intersection_over_union(pD, pG)

            for gtNum in range(len(gtPols)):
                for detNum in range(len(detPols)):
                    if (gtRectMat[gtNum] == 0 and detRectMat[detNum] == 0
                            and gtNum not in gtDontCarePolsNum
                            and detNum not in detDontCarePolsNum):
                        if iouMat[gtNum, detNum] > self.iou_constraint:
                            gtRectMat[gtNum] = 1
                            detRectMat[detNum] = 1
                            detMatched += 1
                            pairs.append({'gt': gtNum, 'det': detNum})
                            detMatchedNums.append(detNum)
                            evaluationLog += ('Match GT #' + str(gtNum) +
                                              ' with Det #' + str(detNum) +
                                              '\n')

        numGtCare = len(gtPols) - len(gtDontCarePolsNum)
        numDetCare = len(detPols) - len(detDontCarePolsNum)
        if numGtCare == 0:
            precision = float(0) if numDetCare > 0 else float(1)
        else:
            precision = 0 if numDetCare == 0 else float(
                detMatched) / numDetCare

        matchedSum += detMatched
        numGlobalCareGt += numGtCare
        numGlobalCareDet += numDetCare

        perSampleMetrics = {
            'gtCare': numGtCare,
            'detCare': numDetCare,
            'detMatched': detMatched,
        }
        return perSampleMetrics

    def combine_results(self, results):
        numGlobalCareGt = 0
        numGlobalCareDet = 0
        matchedSum = 0
        for result in results:
            numGlobalCareGt += result['gtCare']
            numGlobalCareDet += result['detCare']
            matchedSum += result['detMatched']

        methodRecall = (0 if numGlobalCareGt == 0 else float(matchedSum) /
                        numGlobalCareGt)
        methodPrecision = (0 if numGlobalCareDet == 0 else float(matchedSum) /
                           numGlobalCareDet)
        methodHmean = (0 if methodRecall + methodPrecision == 0 else 2 *
                       methodRecall * methodPrecision /
                       (methodRecall + methodPrecision))
        methodMetrics = {
            'precision': methodPrecision,
            'recall': methodRecall,
            'hmean': methodHmean,
        }

        return methodMetrics


if __name__ == '__main__':
    evaluator = DetectionIoUEvaluator()
    gts = [[
        {
            'points': [(0, 0), (1, 0), (1, 1), (0, 1)],
            'text': 1234,
            'ignore': False,
        },
        {
            'points': [(2, 2), (3, 2), (3, 3), (2, 3)],
            'text': 5678,
            'ignore': False,
        },
    ]]
    preds = [[{
        'points': [(0.1, 0.1), (1, 0), (1, 1), (0, 1)],
        'text': 123,
        'ignore': False,
    }]]
    results = []
    for gt, pred in zip(gts, preds):
        results.append(evaluator.evaluate_image(gt, pred))
    metrics = evaluator.combine_results(results)
    print(metrics)