evaluation/totaltext/e2e/script.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# encoding=utf8
from collections import namedtuple
import rrc_evaluation_funcs_total_text as rrc_evaluation_funcs
import importlib
from prepare_results import prepare_results_for_evaluation

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,
            'WORD_SPOTTING' :False,
            'MIN_LENGTH_CARE_WORD' :3,
            '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. MAP and MAR will be calculated,
            'SPECIAL_CHARACTERS':'!?.:,*"()·[]/\'',
            'ONLY_REMOVE_FIRST_LAST_CHARACTER' : True
        }

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'],True,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().items():
        globals()[alias] = importlib.import_module(module)

    def polygon_from_points(points,correctOffset=False):
        """
        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,len(points)],dtype='int32')
        for i in range(int(len(points) / 2)):
            resBoxes[0, i] = int(points[2*i])
            resBoxes[0, int(len(points) / 2) + i] = int(points[2*i+1])

        pointMat = resBoxes[0].reshape([2,-1]).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 get_intersection(pD,pG):
        pInt = pD & pG
        if len(pInt) == 0:
            return 0
        return pInt.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  
    
    def transcription_match(transGt,transDet,specialCharacters='!?.:,*"()·[]/\'',onlyRemoveFirstLastCharacterGT=True):
        
        if onlyRemoveFirstLastCharacterGT:
            #special characters in GT are allowed only at initial or final position
            if (transGt==transDet):
                return True        

            if specialCharacters.find(transGt[0])>-1:
                if transGt[1:]==transDet:
                    return True

            if specialCharacters.find(transGt[-1])>-1:
                if transGt[0:len(transGt)-1]==transDet:
                    return True

            if specialCharacters.find(transGt[0])>-1 and specialCharacters.find(transGt[-1])>-1:
                if transGt[1:len(transGt)-1]==transDet:
                    return True
            return False
        else:
            #Special characters are removed from the begining and the end of both Detection and GroundTruth
            while len(transGt)>0 and specialCharacters.find(transGt[0])>-1:
                transGt = transGt[1:]
				
            while len(transDet)>0 and specialCharacters.find(transDet[0])>-1:
                transDet = transDet[1:]
                
            while len(transGt)>0 and specialCharacters.find(transGt[-1])>-1 :
                transGt = transGt[0:len(transGt)-1]
                
            while len(transDet)>0 and specialCharacters.find(transDet[-1])>-1:
                transDet = transDet[0:len(transDet)-1]
                
            return transGt == transDet
                    
    
    def include_in_dictionary(transcription):
        """
        Function used in Word Spotting that finds if the Ground Truth transcription meets the rules to enter into the dictionary. If not, the transcription will be cared as don't care
        """        
        #special case 's at final
        if transcription[len(transcription)-2:]=="'s" or transcription[len(transcription)-2:]=="'S":
            transcription = transcription[0:len(transcription)-2]
        
        #hypens at init or final of the word
        transcription = transcription.strip('-');
        
        specialCharacters = "'!?.:,*\"()·[]/";
        for character in specialCharacters:
            transcription = transcription.replace(character,' ')
        
        transcription = transcription.strip()
        
        if len(transcription) != len(transcription.replace(" ","")) :
            return False;
        
        if len(transcription) < evaluationParams['MIN_LENGTH_CARE_WORD']:
            return False;
        
        notAllowed = "×÷·";
        
        range1 = [ ord(u'a'), ord(u'z') ]
        range2 = [ ord(u'A'), ord(u'Z') ]
        range3 = [ ord(u'À'), ord(u'ƿ') ]
        range4 = [ ord(u'DŽ'), ord(u'ɿ') ]
        range5 = [ ord(u'Ά'), ord(u'Ͽ') ]
        range6 = [ ord(u'-'), ord(u'-') ]
        
        for char in transcription :
            charCode = ord(char)
            if(notAllowed.find(char) != -1):
                return False
            
            valid = ( charCode>=range1[0] and charCode<=range1[1] ) or ( charCode>=range2[0] and charCode<=range2[1] ) or ( charCode>=range3[0] and charCode<=range3[1] ) or ( charCode>=range4[0] and charCode<=range4[1] ) or ( charCode>=range5[0] and charCode<=range5[1] ) or ( charCode>=range6[0] and charCode<=range6[1] )
            if valid == False:
                return False
        
        return True
    
    def include_in_dictionary_transcription(transcription):
        """
        Function applied to the Ground Truth transcriptions used in Word Spotting. It removes special characters or terminations
        """
        #special case 's at final
        if transcription[len(transcription)-2:]=="'s" or transcription[len(transcription)-2:]=="'S":
            transcription = transcription[0:len(transcription)-2]
        
        #hypens at init or final of the word
        transcription = transcription.strip('-');            
        
        specialCharacters = "'!?.:,*\"()·[]/";
        for character in specialCharacters:
            transcription = transcription.replace(character,' ')
        
        transcription = transcription.strip()
        
        return transcription
    
    perSampleMetrics = {}
    
    matchedSum = 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 = [];

    for resFile in gt:
        
        gtFile = rrc_evaluation_funcs.decode_utf8(gt[resFile])
        if (gtFile is None) :
            raise Exception("The file %s is not UTF-8" %resFile)        

        recall = 0
        precision = 0
        hmean = 0    
        detCorrect = 0
        iouMat = np.empty([1,1])
        gtPols = []
        detPols = []
        gtTrans = []
        detTrans = []
        gtPolPoints = []
        detPolPoints = []  
        gtDontCarePolsNum = [] #Array of Ground Truth Polygons' keys marked as don't Care
        detDontCarePolsNum = [] #Array of Detected Polygons' matched with a don't Care GT
        detMatchedNums = []
        pairs = []
        
        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)

            #On word spotting we will filter some transcriptions with special characters
            if evaluationParams['WORD_SPOTTING'] :
                if dontCare == False : 
                    if include_in_dictionary(transcription) == False : 
                        dontCare = True
                    else:
                        transcription = include_in_dictionary_transcription(transcription)

            gtTrans.append(transcription)
            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,transcriptionsList = rrc_evaluation_funcs.get_tl_line_values_from_file_contents(detFile,evaluationParams['CRLF'],evaluationParams['LTRB'],True,evaluationParams['CONFIDENCES'])
            
            for n in range(len(pointsList)):
                points = pointsList[n]
                transcription = transcriptionsList[n]
                
                if evaluationParams['LTRB']:
                    detRect = Rectangle(*points)
                    detPol = rectangle_to_polygon(detRect)
                else:                    
                    detPol = polygon_from_points(points)
                detPols.append(detPol)
                detPolPoints.append(points)
                detTrans.append(transcription)

                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)
                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]>evaluationParams['IOU_CONSTRAINT']:
                                gtRectMat[gtNum] = 1
                                detRectMat[detNum] = 1
                                #detection matched only if transcription is equal
                                if evaluationParams['WORD_SPOTTING']:
                                    correct = gtTrans[gtNum].upper() == detTrans[detNum].upper()
                                else:
                                    correct = transcription_match(gtTrans[gtNum].upper(),detTrans[detNum].upper(),evaluationParams['SPECIAL_CHARACTERS'],evaluationParams['ONLY_REMOVE_FIRST_LAST_CHARACTER'])==True
                                detCorrect += (1 if correct else 0)
                                if correct:
                                    detMatchedNums.append(detNum)
                                pairs.append({'gt':gtNum,'det':detNum,'correct':correct})
                                evaluationLog += "Match GT #" + str(gtNum) + " with Det #" + str(detNum) + " trans. correct: " + str(correct) + "\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
        else:
            recall = float(detCorrect) / numGtCare
            precision = 0 if numDetCare==0 else float(detCorrect) / numDetCare
            if evaluationParams['CONFIDENCES']:
                sampleAP = compute_ap(arrSampleConfidences, arrSampleMatch, numGtCare )                    

        hmean = 0 if (precision + recall)==0 else 2.0 * precision * recall / (precision + recall)
            
        matchedSum += detCorrect
        numGlobalCareGt += numGtCare
        numGlobalCareDet += numDetCare

        perSampleMetrics[resFile] = {
                                        'precision':precision,
                                        'recall':recall,
                                        'hmean':hmean,
                                        'pairs':pairs,
                                        'AP':sampleAP,
                                        'iouMat':[] if len(detPols)>100 else iouMat.tolist(),
                                        'gtPolPoints':gtPolPoints,
                                        'detPolPoints':detPolPoints,
                                        'gtTrans':gtTrans,
                                        'detTrans':detTrans,
                                        'gtDontCare':gtDontCarePolsNum,
                                        'detDontCare':detDontCarePolsNum,
                                        'evaluationParams': evaluationParams,
                                        'evaluationLog': evaluationLog     
                                    }
        
    # Compute AP
    AP = 0
    if evaluationParams['CONFIDENCES']:
        AP = compute_ap(arrGlobalConfidences, arrGlobalMatches, numGlobalCareGt)

    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, 'AP': AP  }

    resDict = {'calculated':True,'Message':'','method': methodMetrics,'per_sample': perSampleMetrics}
    
    
    return resDict;



if __name__=='__main__':
    '''
    results_dir: result directory
    score_det: score of detection bounding box
    score_rec: score of the mask recognition branch
    score_rec_seq: score of the sequence recognition branch
    lexicon_type: 1 for generic; 2 for weak; 3 for strong
    '''
    results_dir = '../../../output/mixtrain/inference/total_text_test/model_0250000_1000_results/'
    score_det = 0.05
    score_rec = 0.5
    use_lexicon = False
    score_rec_seq = 0.9
    # use_lexicon = True
    # score_rec_seq = 0.8
    evaluate_result_path = prepare_results_for_evaluation(results_dir, 
        use_lexicon=use_lexicon, cache_dir='./cache_files', 
        score_det=score_det, score_rec=score_rec, score_rec_seq=score_rec_seq)
    p = {
        'g': "../gt.zip",  
        'o': "./cache_files",
        's': evaluate_result_path
    }
    rrc_evaluation_funcs.main_evaluation(p,default_evaluation_params,validate_data,evaluate_method)