DB/concern/icdar2015_eval/detection/deteval.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
from collections import namedtuple
import numpy as np
from shapely.geometry import Polygon


class DetectionDetEvalEvaluator(object):
    def __init__(
        self,
        area_recall_constraint=0.8, area_precision_constraint=0.4,
        ev_param_ind_center_diff_thr=1,
        mtype_oo_o=1.0, mtype_om_o=0.8, mtype_om_m=1.0
    ):


        self.area_recall_constraint = area_recall_constraint
        self.area_precision_constraint = area_precision_constraint
        self.ev_param_ind_center_diff_thr = ev_param_ind_center_diff_thr
        self.mtype_oo_o = mtype_oo_o
        self.mtype_om_o = mtype_om_o
        self.mtype_om_m = mtype_om_m

    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 one_to_one_match(row, col):
            cont = 0
            for j in range(len(recallMat[0])):    
                if recallMat[row,j] >= self.area_recall_constraint and precisionMat[row,j] >= self.area_precision_constraint:
                    cont = cont +1
            if (cont != 1):
                return False
            cont = 0
            for i in range(len(recallMat)):    
                if recallMat[i,col] >= self.area_recall_constraint and precisionMat[i,col] >= self.area_precision_constraint:
                    cont = cont +1
            if (cont != 1):
                return False
            
            if recallMat[row,col] >= self.area_recall_constraint and precisionMat[row,col] >= self.area_precision_constraint:
                return True
            return False
        
        def num_overlaps_gt(gtNum):
            cont = 0
            for detNum in range(len(detRects)):
                if detNum not in detDontCareRectsNum:
                    if recallMat[gtNum,detNum] > 0 :
                        cont = cont +1
            return cont

        def num_overlaps_det(detNum):
            cont = 0
            for gtNum in range(len(recallMat)):    
                if gtNum not in gtDontCareRectsNum:
                    if recallMat[gtNum,detNum] > 0 :
                        cont = cont +1
            return cont
        
        def is_single_overlap(row, col):
            if num_overlaps_gt(row)==1 and num_overlaps_det(col)==1:
                return True
            else:
                return False
        
        def one_to_many_match(gtNum):
            many_sum = 0
            detRects = []
            for detNum in range(len(recallMat[0])):    
                if gtRectMat[gtNum] == 0 and detRectMat[detNum] == 0 and detNum not in detDontCareRectsNum:
                    if precisionMat[gtNum,detNum] >= self.area_precision_constraint:
                        many_sum += recallMat[gtNum,detNum]
                        detRects.append(detNum)
            if round(many_sum,4) >= self.area_recall_constraint:
                return True,detRects
            else:
                return False,[]         
        
        def many_to_one_match(detNum):
            many_sum = 0
            gtRects = []
            for gtNum in range(len(recallMat)):    
                if gtRectMat[gtNum] == 0 and detRectMat[detNum] == 0 and gtNum not in gtDontCareRectsNum:
                    if recallMat[gtNum,detNum] >= self.area_recall_constraint:
                        many_sum += precisionMat[gtNum,detNum]
                        gtRects.append(gtNum)
            if round(many_sum,4) >= self.area_precision_constraint:
                return True,gtRects
            else:
                return False,[]
        
        def center_distance(r1, r2):
            return ((np.mean(r1, axis=0) - np.mean(r2, axis=0)) ** 2).sum() ** 0.5
        
        def diag(r):
            r = np.array(r)
            return ((r[:, 0].max() - r[:, 0].min()) ** 2 + (r[:, 1].max() - r[:, 1].min()) ** 2) ** 0.5
        
        perSampleMetrics = {}
        
        recall = 0
        precision = 0
        hmean = 0        
        recallAccum = 0.
        precisionAccum = 0.
        gtRects = []
        detRects = []
        gtPolPoints = []
        detPolPoints = []
        gtDontCareRectsNum = []#Array of Ground Truth Rectangles' keys marked as don't Care
        detDontCareRectsNum = []#Array of Detected Rectangles' matched with a don't Care GT
        pairs = []
        evaluationLog = ""
        
        recallMat = np.empty([1,1])
        precisionMat = np.empty([1,1])              
        
        for n in range(len(gt)):
            points = gt[n]['points']
            # transcription = gt[n]['text']
            dontCare = gt[n]['ignore']

            if not Polygon(points).is_valid or not Polygon(points).is_simple:
                continue

            gtRects.append(points)
            gtPolPoints.append(points)
            if dontCare:
                gtDontCareRectsNum.append( len(gtRects)-1 )                 
        
        evaluationLog += "GT rectangles: " + str(len(gtRects)) + (" (" + str(len(gtDontCareRectsNum)) + " don't care)\n" if len(gtDontCareRectsNum)>0 else "\n")
        
        for n in range(len(pred)):
            points = pred[n]['points']

            if not Polygon(points).is_valid or not Polygon(points).is_simple:
                continue

            detRect = points
            detRects.append(detRect)
            detPolPoints.append(points)
            if len(gtDontCareRectsNum)>0 :
                for dontCareRectNum in gtDontCareRectsNum:
                    dontCareRect = gtRects[dontCareRectNum]
                    intersected_area = get_intersection(dontCareRect,detRect)
                    rdDimensions = Polygon(detRect).area
                    if (rdDimensions==0) :
                        precision = 0
                    else:
                        precision= intersected_area / rdDimensions
                    if (precision > self.area_precision_constraint):
                        detDontCareRectsNum.append( len(detRects)-1 )
                        break

        evaluationLog += "DET rectangles: " + str(len(detRects)) + (" (" + str(len(detDontCareRectsNum)) + " don't care)\n" if len(detDontCareRectsNum)>0 else "\n")

        if len(gtRects)==0:
            recall = 1
            precision = 0 if len(detRects)>0 else 1

        if len(detRects)>0:
            #Calculate recall and precision matrixs
            outputShape=[len(gtRects),len(detRects)]
            recallMat = np.empty(outputShape)
            precisionMat = np.empty(outputShape)
            gtRectMat = np.zeros(len(gtRects),np.int8)
            detRectMat = np.zeros(len(detRects),np.int8)
            for gtNum in range(len(gtRects)):
                for detNum in range(len(detRects)):
                    rG = gtRects[gtNum]
                    rD = detRects[detNum]
                    intersected_area = get_intersection(rG,rD)
                    rgDimensions = Polygon(rG).area
                    rdDimensions = Polygon(rD).area
                    recallMat[gtNum,detNum] = 0 if rgDimensions==0 else  intersected_area / rgDimensions
                    precisionMat[gtNum,detNum] = 0 if rdDimensions==0 else intersected_area / rdDimensions

            # Find one-to-one matches
            evaluationLog += "Find one-to-one matches\n"
            for gtNum in range(len(gtRects)):
                for detNum in range(len(detRects)):
                    if gtRectMat[gtNum] == 0 and detRectMat[detNum] == 0 and gtNum not in gtDontCareRectsNum and detNum not in detDontCareRectsNum :
                        match = one_to_one_match(gtNum, detNum)
                        if match is True :
                            #in deteval we have to make other validation before mark as one-to-one
                            if is_single_overlap(gtNum, detNum) is True :
                                rG = gtRects[gtNum]
                                rD = detRects[detNum]
                                normDist = center_distance(rG, rD);
                                normDist /= diag(rG) + diag(rD);
                                normDist *= 2.0;
                                if normDist < self.ev_param_ind_center_diff_thr:
                                    gtRectMat[gtNum] = 1
                                    detRectMat[detNum] = 1
                                    recallAccum += self.mtype_oo_o
                                    precisionAccum += self.mtype_oo_o
                                    pairs.append({'gt':gtNum,'det':detNum,'type':'OO'})
                                    evaluationLog += "Match GT #" + str(gtNum) + " with Det #" + str(detNum) + "\n"
                                else:
                                    evaluationLog += "Match Discarded GT #" + str(gtNum) + " with Det #" + str(detNum) + " normDist: " + str(normDist) + " \n"
                            else:
                                evaluationLog += "Match Discarded GT #" + str(gtNum) + " with Det #" + str(detNum) + " not single overlap\n"
            # Find one-to-many matches
            evaluationLog += "Find one-to-many matches\n"
            for gtNum in range(len(gtRects)):
                if gtNum not in gtDontCareRectsNum:
                    match,matchesDet = one_to_many_match(gtNum)
                    if match is True :
                        evaluationLog += "num_overlaps_gt=" + str(num_overlaps_gt(gtNum))
                        #in deteval we have to make other validation before mark as one-to-one
                        if num_overlaps_gt(gtNum)>=2 :
                            gtRectMat[gtNum] = 1
                            recallAccum += (self.mtype_oo_o if len(matchesDet)==1 else self.mtype_om_o)
                            precisionAccum += (self.mtype_oo_o if len(matchesDet)==1 else self.mtype_om_o*len(matchesDet))
                            pairs.append({'gt':gtNum,'det':matchesDet,'type': 'OO' if len(matchesDet)==1 else 'OM'})
                            for detNum in matchesDet :
                                detRectMat[detNum] = 1
                            evaluationLog += "Match GT #" + str(gtNum) + " with Det #" + str(matchesDet) + "\n"
                        else:
                            evaluationLog += "Match Discarded GT #" + str(gtNum) + " with Det #" + str(matchesDet) + " not single overlap\n"    

            # Find many-to-one matches
            evaluationLog += "Find many-to-one matches\n"
            for detNum in range(len(detRects)):
                if detNum not in detDontCareRectsNum:
                    match,matchesGt = many_to_one_match(detNum)
                    if match is True :
                        #in deteval we have to make other validation before mark as one-to-one
                        if num_overlaps_det(detNum)>=2 :                          
                            detRectMat[detNum] = 1
                            recallAccum += (self.mtype_oo_o if len(matchesGt)==1 else self.mtype_om_m*len(matchesGt))
                            precisionAccum += (self.mtype_oo_o if len(matchesGt)==1 else self.mtype_om_m)
                            pairs.append({'gt':matchesGt,'det':detNum,'type': 'OO' if len(matchesGt)==1 else 'MO'})
                            for gtNum in matchesGt :
                                gtRectMat[gtNum] = 1
                            evaluationLog += "Match GT #" + str(matchesGt) + " with Det #" + str(detNum) + "\n"
                        else:
                            evaluationLog += "Match Discarded GT #" + str(matchesGt) + " with Det #" + str(detNum) + " not single overlap\n"                                    

            numGtCare = (len(gtRects) - len(gtDontCareRectsNum))
            if numGtCare == 0:
                recall = float(1)
                precision = float(0) if len(detRects)>0 else float(1)
            else:
                recall = float(recallAccum) / numGtCare
                precision =  float(0) if (len(detRects) - len(detDontCareRectsNum))==0 else float(precisionAccum) / (len(detRects) - len(detDontCareRectsNum))
            hmean = 0 if (precision + recall)==0 else 2.0 * precision * recall / (precision + recall)  

        numGtCare = len(gtRects) - len(gtDontCareRectsNum)
        numDetCare = len(detRects) - len(detDontCareRectsNum)

        perSampleMetrics = {
            'precision':precision,
            'recall':recall,
            'hmean':hmean,
            'pairs':pairs,
            'recallMat':[] if len(detRects)>100 else recallMat.tolist(),
            'precisionMat':[] if len(detRects)>100 else precisionMat.tolist(),
            'gtPolPoints':gtPolPoints,
            'detPolPoints':detPolPoints,
            'gtCare': numGtCare,
            'detCare': numDetCare,
            'gtDontCare':gtDontCareRectsNum,
            'detDontCare':detDontCareRectsNum,
            'recallAccum':recallAccum,
            'precisionAccum':precisionAccum,
            'evaluationLog': evaluationLog
        }

        return perSampleMetrics

    def combine_results(self, results):
        numGt = 0
        numDet = 0
        methodRecallSum = 0
        methodPrecisionSum = 0

        for result in results:
            numGt += result['gtCare']
            numDet += result['detCare']
            methodRecallSum += result['recallAccum']
            methodPrecisionSum += result['precisionAccum']

        methodRecall = 0 if numGt==0 else methodRecallSum/numGt
        methodPrecision = 0 if numDet==0 else methodPrecisionSum/numDet
        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 = DetectionDetEvalEvaluator()
    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': True,
    }]]
    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)