/
script.py
462 lines (383 loc) · 20.4 KB
/
script.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from collections import namedtuple
import rrc_evaluation_funcs
import importlib
import math
def evaluation_imports():
"""
evaluation_imports: Dictionary ( key = module name , value = alias ) with python modules used in the evaluation.
"""
return {
'Polygon':'plg',
'numpy':'np'
}
def default_evaluation_params():
"""
default_evaluation_params: Default parameters to use for the validation and evaluation.
"""
return {
'IOU_CONSTRAINT' :0.5,
'AREA_PRECISION_CONSTRAINT' :0.5,
'GT_SAMPLE_NAME_2_ID':'gt_img_([0-9]+).txt',
'DET_SAMPLE_NAME_2_ID':'res_img_([0-9]+).txt',
'LTRB':False, #LTRB:2points(left,top,right,bottom) or 4 points(x1,y1,x2,y2,x3,y3,x4,y4)
'CRLF':False, # Lines are delimited by Windows CRLF format
'CONFIDENCES':False, #Detections must include confidence value. AP will be calculated
'PER_SAMPLE_RESULTS':True #Generate per sample results and produce data for visualization
}
def validate_data(gtFilePath, submFilePath,evaluationParams):
"""
Method validate_data: validates that all files in the results folder are correct (have the correct name contents).
Validates also that there are no missing files in the folder.
If some error detected, the method raises the error
"""
gt = rrc_evaluation_funcs.load_zip_file(gtFilePath,evaluationParams['GT_SAMPLE_NAME_2_ID'])
subm = rrc_evaluation_funcs.load_zip_file(submFilePath,evaluationParams['DET_SAMPLE_NAME_2_ID'],True)
#Validate format of GroundTruth
for k in gt:
rrc_evaluation_funcs.validate_lines_in_file(k,gt[k],evaluationParams['CRLF'],evaluationParams['LTRB'],True)
#Validate format of results
for k in subm:
if (k in gt) == False :
raise Exception("The sample %s not present in GT" %k)
rrc_evaluation_funcs.validate_lines_in_file(k,subm[k],evaluationParams['CRLF'],evaluationParams['LTRB'],False,evaluationParams['CONFIDENCES'])
def evaluate_method(gtFilePath, submFilePath, evaluationParams):
"""
Method evaluate_method: evaluate method and returns the results
Results. Dictionary with the following values:
- method (required) Global method metrics. Ex: { 'Precision':0.8,'Recall':0.9 }
- samples (optional) Per sample metrics. Ex: {'sample1' : { 'Precision':0.8,'Recall':0.9 } , 'sample2' : { 'Precision':0.8,'Recall':0.9 }
"""
for module,alias in evaluation_imports().iteritems():
globals()[alias] = importlib.import_module(module)
def polygon_from_points(points):
"""
Returns a Polygon object to use with the Polygon2 class from a list of 8 points: x1,y1,x2,y2,x3,y3,x4,y4
"""
resBoxes=np.empty([1,8],dtype='int32')
resBoxes[0,0]=int(points[0])
resBoxes[0,4]=int(points[1])
resBoxes[0,1]=int(points[2])
resBoxes[0,5]=int(points[3])
resBoxes[0,2]=int(points[4])
resBoxes[0,6]=int(points[5])
resBoxes[0,3]=int(points[6])
resBoxes[0,7]=int(points[7])
pointMat = resBoxes[0].reshape([2,4]).T
return plg.Polygon( pointMat)
def rectangle_to_polygon(rect):
resBoxes=np.empty([1,8],dtype='int32')
resBoxes[0,0]=int(rect.xmin)
resBoxes[0,4]=int(rect.ymax)
resBoxes[0,1]=int(rect.xmin)
resBoxes[0,5]=int(rect.ymin)
resBoxes[0,2]=int(rect.xmax)
resBoxes[0,6]=int(rect.ymin)
resBoxes[0,3]=int(rect.xmax)
resBoxes[0,7]=int(rect.ymax)
pointMat = resBoxes[0].reshape([2,4]).T
return plg.Polygon( pointMat)
def rectangle_to_points(rect):
points = [int(rect.xmin), int(rect.ymax), int(rect.xmax), int(rect.ymax), int(rect.xmax), int(rect.ymin), int(rect.xmin), int(rect.ymin)]
return points
def get_union(pD,pG):
areaA = pD.area();
areaB = pG.area();
return areaA + areaB - get_intersection(pD, pG);
def get_intersection_over_union(pD,pG):
try:
return get_intersection(pD, pG) / get_union(pD, pG);
except:
return 0
def funcCt(x):
if x<=0.01:
return 1
else:
return 1-x
def get_text_intersection_over_union_recall(pD, pG):
'''
Ct (cut): Area of ground truth that is not covered by detection bounding box.
'''
try:
Ct = pG.area() - get_intersection(pD, pG)
assert(Ct>=0 and Ct<=pG.area()), 'Invalid Ct value'
assert(pG.area()>0), 'Invalid Gt'
return (get_intersection(pD, pG) * funcCt(Ct*1.0/pG.area())) / get_union(pD, pG);
except Exception as e:
return 0
def funcOt(x):
if x<=0.01:
return 1
else:
return 1-x
def get_text_intersection_over_union_precision(pD, pG, gtNum, gtPolys, gtDontCarePolsNum):
'''
Ot: Outlier gt area
'''
Ot = 0
try:
inside_pG = pD & pG
gt_union_inside_pD = None
gt_union_inside_pD_and_pG = None
count_initial = 0
for i in xrange(len(gtPolys)):
if i!= gtNum and gtNum not in gtDontCarePolsNum: # ignore don't care regions
if not get_intersection(pD, gtPolys[i]) == 0:
if count_initial == 0:
# initial
gt_union_inside_pD = gtPolys[i]
gt_union_inside_pD_and_pG = inside_pG & gtPolys[i]
count_initial = 1
continue
gt_union_inside_pD = gt_union_inside_pD | gtPolys[i]
inside_pG_i = inside_pG & gtPolys[i]
gt_union_inside_pD_and_pG = gt_union_inside_pD_and_pG | inside_pG_i
if not gt_union_inside_pD == None:
pD_union_with_other_gt = pD & gt_union_inside_pD
Ot = pD_union_with_other_gt.area() - gt_union_inside_pD_and_pG.area()
if Ot <=1.0e-10:
Ot = 0
else:
Ot = 0
assert(Ot>=0 and Ot<=pD.area()+0.001), 'Invalid Ot value: '+str(Ot)+' '+str(pD.area())
assert(pD.area()>0), 'Invalid pD: '+str(pD.area())
return (get_intersection(pD, pG) * funcOt(Ot*1.0/pD.area())) / get_union(pD, pG);
except Exception as e:
print(e)
return 0
def get_intersection(pD,pG):
pInt = pD & pG
if len(pInt) == 0:
return 0
return pInt.area()
def get_intersection_three(pD,pG,pGi):
pInt = pD & pG
pInt_3 = pInt & pGi
if len(pInt_3) == 0:
return 0
return pInt_3.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
matchedSum_iou = 0
matchedSum_tiouGt = 0
matchedSum_tiouDt = 0
matchedSum_cutGt = 0
matchedSum_coverOtherGt = 0
Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
gt = rrc_evaluation_funcs.load_zip_file(gtFilePath,evaluationParams['GT_SAMPLE_NAME_2_ID'])
subm = rrc_evaluation_funcs.load_zip_file(submFilePath,evaluationParams['DET_SAMPLE_NAME_2_ID'],True)
numGlobalCareGt = 0;
numGlobalCareDet = 0;
arrGlobalConfidences = [];
arrGlobalMatches = [];
totalNumGtPols = 0
totalNumDetPols = 0
fper_ = open('per_samle_result.txt', 'w')
for resFile in gt:
gtFile = rrc_evaluation_funcs.decode_utf8(gt[resFile])
recall = 0
precision = 0
hmean = 0
detMatched = 0
detMatched_iou = 0
detMatched_tiouGt = 0
detMatched_tiouDt = 0
detMatched_cutGt = 0
detMatched_coverOtherGt = 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 = [];
sampleAP = 0;
evaluationLog = ""
pointsList,_,transcriptionsList = rrc_evaluation_funcs.get_tl_line_values_from_file_contents(gtFile,evaluationParams['CRLF'],evaluationParams['LTRB'],True,False)
for n in range(len(pointsList)):
points = pointsList[n]
transcription = transcriptionsList[n]
dontCare = transcription == "###"
if evaluationParams['LTRB']:
gtRect = Rectangle(*points)
gtPol = rectangle_to_polygon(gtRect)
else:
gtPol = polygon_from_points(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")
if resFile in subm:
detFile = rrc_evaluation_funcs.decode_utf8(subm[resFile])
pointsList,confidencesList,_ = rrc_evaluation_funcs.get_tl_line_values_from_file_contents(detFile,evaluationParams['CRLF'],evaluationParams['LTRB'],False,evaluationParams['CONFIDENCES'])
for n in range(len(pointsList)):
points = pointsList[n]
if evaluationParams['LTRB']:
detRect = Rectangle(*points)
detPol = rectangle_to_polygon(detRect)
else:
detPol = polygon_from_points(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 = detPol.area()
precision = 0 if pdDimensions == 0 else intersected_area / pdDimensions
if (precision > evaluationParams['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)
tiouRecallMat = np.empty(outputShape)
tiouPrecisionMat = np.empty(outputShape)
tiouGtRectMat = np.zeros(len(gtPols),np.int8)
tiouDetRectMat = 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)
tiouRecallMat[gtNum,detNum] = get_text_intersection_over_union_recall(pD,pG)
tiouPrecisionMat[gtNum,detNum] = get_text_intersection_over_union_precision(pD, pG, gtNum, gtPols, gtDontCarePolsNum)
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]>evaluationParams['IOU_CONSTRAINT']:
gtRectMat[gtNum] = 1
detRectMat[detNum] = 1
detMatched += 1
detMatched_iou += iouMat[gtNum,detNum]
detMatched_tiouGt += tiouRecallMat[gtNum,detNum]
detMatched_tiouDt += tiouPrecisionMat[gtNum,detNum]
if iouMat[gtNum,detNum] != tiouRecallMat[gtNum,detNum]:
detMatched_cutGt +=1
if iouMat[gtNum,detNum] != tiouPrecisionMat[gtNum,detNum]:
detMatched_coverOtherGt +=1
pairs.append({'gt':gtNum,'det':detNum})
detMatchedNums.append(detNum)
evaluationLog += "Match GT #" + str(gtNum) + " with Det #" + str(detNum) + "\n"
if evaluationParams['CONFIDENCES']:
for detNum in range(len(detPols)):
if detNum not in detDontCarePolsNum :
#we exclude the don't care detections
match = detNum in detMatchedNums
arrSampleConfidences.append(confidencesList[detNum])
arrSampleMatch.append(match)
arrGlobalConfidences.append(confidencesList[detNum]);
arrGlobalMatches.append(match);
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)
sampleAP = precision
tiouRecall = float(1)
tiouPrecision = float(0) if numDetCare >0 else float(1)
else:
recall = float(detMatched) / numGtCare
precision = 0 if numDetCare==0 else float(detMatched) / numDetCare
iouRecall = float(detMatched_iou) / numGtCare
iouPrecision = 0 if numDetCare==0 else float(detMatched_iou) / numDetCare
tiouRecall = float(detMatched_tiouGt) / numGtCare
tiouPrecision = 0 if numDetCare==0 else float(detMatched_tiouDt) / numDetCare
if evaluationParams['CONFIDENCES'] and evaluationParams['PER_SAMPLE_RESULTS']:
sampleAP = compute_ap(arrSampleConfidences, arrSampleMatch, numGtCare )
hmean = 0 if (precision + recall)==0 else 2.0 * precision * recall / (precision + recall)
tiouHmean = 0 if (tiouPrecision + tiouRecall)==0 else 2.0 * tiouPrecision * tiouRecall / (tiouPrecision + tiouRecall)
iouHmean = 0 if (iouPrecision + iouRecall)==0 else 2.0 * iouPrecision * iouRecall / (iouPrecision + iouRecall)
matchedSum += detMatched
matchedSum_iou += detMatched_iou
matchedSum_tiouGt += detMatched_tiouGt
matchedSum_tiouDt += detMatched_tiouDt
matchedSum_cutGt += detMatched_cutGt
matchedSum_coverOtherGt += detMatched_coverOtherGt
numGlobalCareGt += numGtCare
numGlobalCareDet += numDetCare
if evaluationParams['PER_SAMPLE_RESULTS']:
perSampleMetrics[resFile] = {
'precision':precision,
'recall':recall,
'hmean':hmean,
'iouPrecision':iouPrecision,
'iouRecall':iouRecall,
'iouHmean':iouHmean,
'tiouPrecision':tiouPrecision,
'tiouRecall':tiouRecall,
'tiouHmean':tiouHmean,
'pairs':pairs,
'AP':sampleAP,
'iouMat':[] if len(detPols)>100 else iouMat.tolist(),
'gtPolPoints':gtPolPoints,
'detPolPoints':detPolPoints,
'gtDontCare':gtDontCarePolsNum,
'detDontCare':detDontCarePolsNum,
'evaluationParams': evaluationParams,
'evaluationLog': evaluationLog
}
fper_.writelines(resFile+'\t"IoU: (P: {:.3f}. R: {:.3f}. F: {:.3f})",\t"TIoU: (P: {:.3f}. R: {:.3f}. F: {:.3f})".\n'.format(precision, recall, hmean, tiouPrecision, tiouRecall, tiouHmean))
try:
totalNumGtPols += len(gtPols)
totalNumDetPols += len(detPols)
except Exception as e:
raise e
fper_.close()
# Compute MAP and MAR
AP = 0
if evaluationParams['CONFIDENCES']:
AP = compute_ap(arrGlobalConfidences, arrGlobalMatches, numGlobalCareGt)
print('num_gt, num_det: ', numGlobalCareGt, totalNumDetPols)
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)
methodRecall_iou = 0 if numGlobalCareGt == 0 else float(matchedSum_iou)/numGlobalCareGt
methodPrecision_iou = 0 if numGlobalCareDet == 0 else float(matchedSum_iou)/numGlobalCareDet
iouMethodHmean = 0 if methodRecall_iou + methodPrecision_iou==0 else 2* methodRecall_iou * methodPrecision_iou / (methodRecall_iou + methodPrecision_iou)
methodRecall_tiouGt = 0 if numGlobalCareGt == 0 else float(matchedSum_tiouGt)/numGlobalCareGt
methodPrecision_tiouDt = 0 if numGlobalCareDet == 0 else float(matchedSum_tiouDt)/numGlobalCareDet
tiouMethodHmean = 0 if methodRecall_tiouGt + methodPrecision_tiouDt==0 else 2* methodRecall_tiouGt * methodPrecision_tiouDt / (methodRecall_tiouGt + methodPrecision_tiouDt)
methodMetrics = {'precision':methodPrecision, 'recall':methodRecall,'hmean': methodHmean}
iouMethodMetrics = {'iouPrecision':methodPrecision_iou, 'iouRecall':methodRecall_iou,'iouHmean': iouMethodHmean }
tiouMethodMetrics = {'tiouPrecision':methodPrecision_tiouDt, 'tiouRecall':methodRecall_tiouGt,'tiouHmean': tiouMethodHmean }
# print('matchedSum: ', matchedSum, 'matchedSum_cutGt: ', matchedSum_cutGt, 'cut_Rate: ', round(matchedSum_cutGt*1.0/matchedSum, 3), 'matchedSum_coverOtherGt: ', matchedSum_coverOtherGt, 'cover_Outlier_Rate: ', round(matchedSum_coverOtherGt*1.0/matchedSum, 3))
print('Origin:')
print("recall: ", round(methodRecall,3), "precision: ", round(methodPrecision,3), "hmean: ", round(methodHmean,3))
print('SIoU-metric:')
print("iouRecall:", round(methodRecall_iou,3), "iouPrecision:", round(methodPrecision_iou,3), "iouHmean:", round(iouMethodHmean,3))
print('TIoU-metric:')
print("tiouRecall:", round(methodRecall_tiouGt,3), "tiouPrecision:", round(methodPrecision_tiouDt,3), "tiouHmean:", round(tiouMethodHmean,3))
resDict = {'calculated':True,'Message':'','method': methodMetrics,'per_sample': perSampleMetrics, 'iouMethod': iouMethodMetrics, 'tiouMethod': tiouMethodMetrics}
return resDict;
if __name__=='__main__':
rrc_evaluation_funcs.main_evaluation(None,default_evaluation_params,validate_data,evaluate_method)