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#!/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)
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