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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from collections import namedtuple
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
Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
numGlobalCareGt = 0
numGlobalCareDet = 0
arrGlobalConfidences = []
arrGlobalMatches = []
recall = 0
precision = 0
hmean = 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 = []
arrSampleConfidences = []
arrSampleMatch = []
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:
recall = float(1)
precision = float(0) if numDetCare > 0 else float(1)
else:
recall = float(detMatched) / numGtCare
precision = 0 if numDetCare == 0 else float(detMatched) / numDetCare
hmean = 0 if (precision + recall) == 0 else 2.0 * \
precision * recall / (precision + recall)
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)
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