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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 = 0.0
precisionAccum = 0.0
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 matrixes
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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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
from collections import namedtuple
import numpy as np
from shapely.geometry import Polygon
class DetectionICDAR2013Evaluator(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 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 = 0.0
precisionAccum = 0.0
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 matrixes
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
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"
)
# 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)
)
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"
)
# 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:
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"
)
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 = DetectionICDAR2013Evaluator()
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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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from collections import namedtuple
import numpy as np
from shapely.geometry import Polygon
import cv2
def iou_rotate(box_a, box_b, method="union"):
rect_a = cv2.minAreaRect(box_a)
rect_b = cv2.minAreaRect(box_b)
r1 = cv2.rotatedRectangleIntersection(rect_a, rect_b)
if r1[0] == 0:
return 0
else:
inter_area = cv2.contourArea(r1[1])
area_a = cv2.contourArea(box_a)
area_b = cv2.contourArea(box_b)
union_area = area_a + area_b - inter_area
if union_area == 0 or inter_area == 0:
return 0
if method == "union":
iou = inter_area / union_area
elif method == "intersection":
iou = inter_area / min(area_a, area_b)
else:
raise NotImplementedError
return iou
class DetectionIoUEvaluator(object):
def __init__(
self, is_output_polygon=False, iou_constraint=0.5, area_precision_constraint=0.5
):
self.is_output_polygon = is_output_polygon
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"]
# transcription = gt[n]['text']
dontCare = gt[n]["ignore"]
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"]
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)
if self.is_output_polygon:
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)
else:
# gtPols = np.float32(gtPols)
# detPols = np.float32(detPols)
for gtNum in range(len(gtPols)):
for detNum in range(len(detPols)):
pG = np.float32(gtPols[gtNum])
pD = np.float32(detPols[detNum])
iouMat[gtNum, detNum] = iou_rotate(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)
)
methodMetrics = {
"precision": methodPrecision,
"recall": methodRecall,
"hmean": methodHmean,
}
return methodMetrics
if __name__ == "__main__":
evaluator = DetectionIoUEvaluator()
preds = [
[
{
"points": [(0.1, 0.1), (0.5, 0), (0.5, 1), (0, 1)],
"text": 1234,
"ignore": False,
},
{
"points": [(0.5, 0.1), (1, 0), (1, 1), (0.5, 1)],
"text": 5678,
"ignore": False,
},
]
]
gts = [
[
{
"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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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
from collections import namedtuple
import numpy as np
from shapely.geometry import Polygon
class DetectionMTWI2018Evaluator(object):
def __init__(
self,
area_recall_constraint=0.7,
area_precision_constraint=0.7,
ev_param_ind_center_diff_thr=1,
):
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
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 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 = 0.0
precisionAccum = 0.0
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 > 0.5:
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
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 += 1.0
precisionAccum += 1.0
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"
)
# 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:
gtRectMat[gtNum] = 1
recallAccum += 1.0
precisionAccum += len(matchesDet) / (
1 + math.log(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"
)
# 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:
detRectMat[detNum] = 1
recallAccum += len(matchesGt) / (1 + math.log(len(matchesGt)))
precisionAccum += 1.0
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"
)
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 = DetectionICDAR2013Evaluator()
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)