236 lines
8.0 KiB
Python
236 lines
8.0 KiB
Python
#!/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 = ""
|
|
|
|
# print(len(gt))
|
|
for n in range(len(gt)):
|
|
points = gt[n]['points']
|
|
# transcription = gt[n]['text']
|
|
dontCare = gt[n]['ignore']
|
|
# points = Polygon(points)
|
|
# points = points.buffer(0)
|
|
if not Polygon(points).is_valid or not Polygon(points).is_simple:
|
|
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']
|
|
# points = Polygon(points)
|
|
# points = points.buffer(0)
|
|
if not Polygon(points).is_valid or not Polygon(points).is_simple:
|
|
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 = {
|
|
'precision': precision,
|
|
'recall': recall,
|
|
'hmean': hmean,
|
|
'pairs': pairs,
|
|
'iouMat': [] if len(detPols) > 100 else iouMat.tolist(),
|
|
'gtPolPoints': gtPolPoints,
|
|
'detPolPoints': detPolPoints,
|
|
'gtCare': numGtCare,
|
|
'detCare': numDetCare,
|
|
'gtDontCare': gtDontCarePolsNum,
|
|
'detDontCare': detDontCarePolsNum,
|
|
'detMatched': detMatched,
|
|
'evaluationLog': evaluationLog
|
|
}
|
|
|
|
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)
|
|
# print(methodRecall, methodPrecision, methodHmean)
|
|
# sys.exit(-1)
|
|
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)
|