添加DBnet代码

utils/cal_recall/__init__.py

+# -*- coding: utf-8 -*-
+# @Time    : 1/16/19 6:40 AM
+# @Author  : zhoujun
+from .script import  cal_recall_precison_f1
+__all__ = ['cal_recall_precison_f1']
\ No newline at end of file

utils/cal_recall/rrc_evaluation_funcs.py

+#!/usr/bin/env python2
+#encoding: UTF-8
+import json
+import sys;sys.path.append('./')
+import zipfile
+import re
+import sys
+import os
+import codecs
+import traceback
+import numpy as np
+from utils import order_points_clockwise
+
+def print_help():
+    sys.stdout.write('Usage: python %s.py -g=<gtFile> -s=<submFile> [-o=<outputFolder> -p=<jsonParams>]' %sys.argv[0])
+    sys.exit(2)
+    
+
+def load_zip_file_keys(file,fileNameRegExp=''):
+    """
+    Returns an array with the entries of the ZIP file that match with the regular expression.
+    The key's are the names or the file or the capturing group definied in the fileNameRegExp
+    """
+    try:
+        archive=zipfile.ZipFile(file, mode='r', allowZip64=True)
+    except :
+        raise Exception('Error loading the ZIP archive.')
+
+    pairs = []
+    
+    for name in archive.namelist():
+        addFile = True
+        keyName = name
+        if fileNameRegExp!="":
+            m = re.match(fileNameRegExp,name)
+            if m == None:
+                addFile = False
+            else:
+                if len(m.groups())>0:
+                    keyName = m.group(1)
+                    
+        if addFile:
+            pairs.append( keyName )
+                
+    return pairs
+    
+
+def load_zip_file(file,fileNameRegExp='',allEntries=False):
+    """
+    Returns an array with the contents (filtered by fileNameRegExp) of a ZIP file.
+    The key's are the names or the file or the capturing group definied in the fileNameRegExp
+    allEntries validates that all entries in the ZIP file pass the fileNameRegExp
+    """
+    try:
+        archive=zipfile.ZipFile(file, mode='r', allowZip64=True)
+    except :
+        raise Exception('Error loading the ZIP archive')    
+
+    pairs = []
+    for name in archive.namelist():
+        addFile = True
+        keyName = name
+        if fileNameRegExp!="":
+            m = re.match(fileNameRegExp,name)
+            if m == None:
+                addFile = False
+            else:
+                if len(m.groups())>0:
+                    keyName = m.group(1)
+        
+        if addFile:
+            pairs.append( [ keyName , archive.read(name)] )
+        else:
+            if allEntries:
+                raise Exception('ZIP entry not valid: %s' %name)             
+
+    return dict(pairs)
+
+
+def load_folder_file(file, fileNameRegExp='', allEntries=False):
+    """
+    Returns an array with the contents (filtered by fileNameRegExp) of a ZIP file.
+    The key's are the names or the file or the capturing group definied in the fileNameRegExp
+    allEntries validates that all entries in the ZIP file pass the fileNameRegExp
+    """
+    pairs = []
+    for name in os.listdir(file):
+        addFile = True
+        keyName = name
+        if fileNameRegExp != "":
+            m = re.match(fileNameRegExp, name)
+            if m == None:
+                addFile = False
+            else:
+                if len(m.groups()) > 0:
+                    keyName = m.group(1)
+
+        if addFile:
+            pairs.append([keyName, open(os.path.join(file,name)).read()])
+        else:
+            if allEntries:
+                raise Exception('ZIP entry not valid: %s' % name)
+
+    return dict(pairs)
+
+
+def decode_utf8(raw):
+    """
+    Returns a Unicode object on success, or None on failure
+    """
+    try:
+        raw = codecs.decode(raw,'utf-8', 'replace')
+        #extracts BOM if exists
+        raw = raw.encode('utf8')
+        if raw.startswith(codecs.BOM_UTF8):
+            raw = raw.replace(codecs.BOM_UTF8, '', 1)
+        return raw.decode('utf-8')
+    except:
+       return None
+   
+def validate_lines_in_file(fileName,file_contents,CRLF=True,LTRB=True,withTranscription=False,withConfidence=False,imWidth=0,imHeight=0):
+    """
+    This function validates that all lines of the file calling the Line validation function for each line
+    """
+    utf8File = decode_utf8(file_contents)
+    if (utf8File is None) :
+        raise Exception("The file %s is not UTF-8" %fileName)
+
+    lines = utf8File.split( "\r\n" if CRLF else "\n" )
+    for line in lines:
+        line = line.replace("\r","").replace("\n","")
+        if(line != ""):
+            try:
+                validate_tl_line(line,LTRB,withTranscription,withConfidence,imWidth,imHeight)
+            except Exception as e:
+                raise Exception(("Line in sample not valid. Sample: %s Line: %s Error: %s" %(fileName,line,str(e))).encode('utf-8', 'replace'))
+    
+   
+   
+def validate_tl_line(line,LTRB=True,withTranscription=True,withConfidence=True,imWidth=0,imHeight=0):
+    """
+    Validate the format of the line. If the line is not valid an exception will be raised.
+    If maxWidth and maxHeight are specified, all points must be inside the imgage bounds.
+    Posible values are:
+    LTRB=True: xmin,ymin,xmax,ymax[,confidence][,transcription] 
+    LTRB=False: x1,y1,x2,y2,x3,y3,x4,y4[,confidence][,transcription] 
+    """
+    get_tl_line_values(line,LTRB,withTranscription,withConfidence,imWidth,imHeight)
+    
+   
+def get_tl_line_values(line,LTRB=True,withTranscription=False,withConfidence=False,imWidth=0,imHeight=0):
+    """
+    Validate the format of the line. If the line is not valid an exception will be raised.
+    If maxWidth and maxHeight are specified, all points must be inside the imgage bounds.
+    Posible values are:
+    LTRB=True: xmin,ymin,xmax,ymax[,confidence][,transcription] 
+    LTRB=False: x1,y1,x2,y2,x3,y3,x4,y4[,confidence][,transcription] 
+    Returns values from a textline. Points , [Confidences], [Transcriptions]
+    """
+    confidence = 0.0
+    transcription = "";
+    points = []
+    
+    numPoints = 4;
+    
+    if LTRB:
+    
+        numPoints = 4;
+        
+        if withTranscription and withConfidence:
+            m = re.match(r'^\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-1].?[0-9]*)\s*,(.*)$',line)
+            if m == None :
+                m = re.match(r'^\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-1].?[0-9]*)\s*,(.*)$',line)
+                raise Exception("Format incorrect. Should be: xmin,ymin,xmax,ymax,confidence,transcription")
+        elif withConfidence:
+            m = re.match(r'^\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-1].?[0-9]*)\s*$',line)
+            if m == None :
+                raise Exception("Format incorrect. Should be: xmin,ymin,xmax,ymax,confidence")
+        elif withTranscription:
+            m = re.match(r'^\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*,(.*)$',line)
+            if m == None :
+                raise Exception("Format incorrect. Should be: xmin,ymin,xmax,ymax,transcription")
+        else:
+            m = re.match(r'^\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*([0-9]+)\s*,\s*([0-9]+)\s*,?\s*$',line)
+            if m == None :
+                raise Exception("Format incorrect. Should be: xmin,ymin,xmax,ymax")
+            
+        xmin = int(m.group(1))
+        ymin = int(m.group(2))
+        xmax = int(m.group(3))
+        ymax = int(m.group(4))
+        if(xmax<xmin):
+                raise Exception("Xmax value (%s) not valid (Xmax < Xmin)." %(xmax))
+        if(ymax<ymin):
+                raise Exception("Ymax value (%s)  not valid (Ymax < Ymin)." %(ymax))  
+
+        points = [ float(m.group(i)) for i in range(1, (numPoints+1) ) ]
+        
+        if (imWidth>0 and imHeight>0):
+            validate_point_inside_bounds(xmin,ymin,imWidth,imHeight);
+            validate_point_inside_bounds(xmax,ymax,imWidth,imHeight);
+
+    else:
+        
+        numPoints = 8;
+        
+        if withTranscription and withConfidence:
+            m = re.match(r'^\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*([0-1].?[0-9]*)\s*,(.*)$',line)
+            if m == None :
+                raise Exception("Format incorrect. Should be: x1,y1,x2,y2,x3,y3,x4,y4,confidence,transcription")
+        elif withConfidence:
+            m = re.match(r'^\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*([0-1].?[0-9]*)\s*$',line)
+            if m == None :
+                raise Exception("Format incorrect. Should be: x1,y1,x2,y2,x3,y3,x4,y4,confidence")
+        elif withTranscription:
+            m = re.match(r'^\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,(.*)$',line)
+            if m == None :
+                raise Exception("Format incorrect. Should be: x1,y1,x2,y2,x3,y3,x4,y4,transcription")
+        else:
+            m = re.match(r'^\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*,\s*(-?[0-9]+)\s*$',line)
+            if m == None :
+                raise Exception("Format incorrect. Should be: x1,y1,x2,y2,x3,y3,x4,y4")
+            
+        points = [ float(m.group(i)) for i in range(1, (numPoints+1) ) ]
+
+        points = order_points_clockwise(np.array(points).reshape(-1, 2)).reshape(-1)
+        validate_clockwise_points(points)
+        
+        if (imWidth>0 and imHeight>0):
+            validate_point_inside_bounds(points[0],points[1],imWidth,imHeight);
+            validate_point_inside_bounds(points[2],points[3],imWidth,imHeight);
+            validate_point_inside_bounds(points[4],points[5],imWidth,imHeight);
+            validate_point_inside_bounds(points[6],points[7],imWidth,imHeight);
+            
+    
+    if withConfidence:
+        try:
+            confidence = float(m.group(numPoints+1))
+        except ValueError:
+            raise Exception("Confidence value must be a float")       
+            
+    if withTranscription:
+        posTranscription = numPoints + (2 if withConfidence else 1)
+        transcription = m.group(posTranscription)
+        m2 = re.match(r'^\s*\"(.*)\"\s*$',transcription)
+        if m2 != None : #Transcription with double quotes, we extract the value and replace escaped characters
+            transcription = m2.group(1).replace("\\\\", "\\").replace("\\\"", "\"")
+    
+    return points,confidence,transcription
+    
+            
+def validate_point_inside_bounds(x,y,imWidth,imHeight):
+    if(x<0 or x>imWidth):
+            raise Exception("X value (%s) not valid. Image dimensions: (%s,%s)" %(xmin,imWidth,imHeight))
+    if(y<0 or y>imHeight):
+            raise Exception("Y value (%s)  not valid. Image dimensions: (%s,%s) Sample: %s Line:%s" %(ymin,imWidth,imHeight))
+
+def validate_clockwise_points(points):
+    """
+    Validates that the points that the 4 points that dlimite a polygon are in clockwise order.
+    """
+    
+    if len(points) != 8:
+        raise Exception("Points list not valid." + str(len(points)))
+    
+    point = [
+                [int(points[0]) , int(points[1])],
+                [int(points[2]) , int(points[3])],
+                [int(points[4]) , int(points[5])],
+                [int(points[6]) , int(points[7])]
+            ]
+    edge = [
+                ( point[1][0] - point[0][0])*( point[1][1] + point[0][1]),
+                ( point[2][0] - point[1][0])*( point[2][1] + point[1][1]),
+                ( point[3][0] - point[2][0])*( point[3][1] + point[2][1]),
+                ( point[0][0] - point[3][0])*( point[0][1] + point[3][1])
+    ]
+    
+    summatory = edge[0] + edge[1] + edge[2] + edge[3];
+    if summatory>0:
+        raise Exception("Points are not clockwise. The coordinates of bounding quadrilaterals have to be given in clockwise order. Regarding the correct interpretation of 'clockwise' remember that the image coordinate system used is the standard one, with the image origin at the upper left, the X axis extending to the right and Y axis extending downwards.")
+
+def get_tl_line_values_from_file_contents(content,CRLF=True,LTRB=True,withTranscription=False,withConfidence=False,imWidth=0,imHeight=0,sort_by_confidences=True):
+    """
+    Returns all points, confindences and transcriptions of a file in lists. Valid line formats:
+    xmin,ymin,xmax,ymax,[confidence],[transcription]
+    x1,y1,x2,y2,x3,y3,x4,y4,[confidence],[transcription]
+    """
+    pointsList = []
+    transcriptionsList = []
+    confidencesList = []
+    
+    lines = content.split( "\r\n" if CRLF else "\n" )
+    for line in lines:
+        line = line.replace("\r","").replace("\n","")
+        if(line != "") :
+            points, confidence, transcription = get_tl_line_values(line,LTRB,withTranscription,withConfidence,imWidth,imHeight);
+            pointsList.append(points)
+            transcriptionsList.append(transcription)
+            confidencesList.append(confidence)
+
+    if withConfidence and len(confidencesList)>0 and sort_by_confidences:
+        import numpy as np
+        sorted_ind = np.argsort(-np.array(confidencesList))
+        confidencesList = [confidencesList[i] for i in sorted_ind]
+        pointsList = [pointsList[i] for i in sorted_ind]
+        transcriptionsList = [transcriptionsList[i] for i in sorted_ind]        
+        
+    return pointsList,confidencesList,transcriptionsList
+
+def main_evaluation(p,default_evaluation_params_fn,validate_data_fn,evaluate_method_fn,show_result=True,per_sample=True):
+    """
+    This process validates a method, evaluates it and if it succed generates a ZIP file with a JSON entry for each sample.
+    Params:
+    p: Dictionary of parmeters with the GT/submission locations. If None is passed, the parameters send by the system are used.
+    default_evaluation_params_fn: points to a function that returns a dictionary with the default parameters used for the evaluation
+    validate_data_fn: points to a method that validates the corrct format of the submission
+    evaluate_method_fn: points to a function that evaluated the submission and return a Dictionary with the results
+    """
+    evalParams = default_evaluation_params_fn()
+    if 'p' in p.keys():
+        evalParams.update( p['p'] if isinstance(p['p'], dict) else json.loads(p['p'][1:-1]) )
+
+    resDict={'calculated':True,'Message':'','method':'{}','per_sample':'{}'}    
+    try:
+        # validate_data_fn(p['g'], p['s'], evalParams)
+        evalData = evaluate_method_fn(p['g'], p['s'], evalParams)
+        resDict.update(evalData)
+        
+    except Exception as e:
+        traceback.print_exc()
+        resDict['Message']= str(e)
+        resDict['calculated']=False
+
+    if 'o' in p:
+        if not os.path.exists(p['o']):
+            os.makedirs(p['o'])
+
+        resultsOutputname = p['o'] + '/results.zip'
+        outZip = zipfile.ZipFile(resultsOutputname, mode='w', allowZip64=True)
+
+        del resDict['per_sample']
+        if 'output_items' in resDict.keys():
+            del resDict['output_items']
+
+        outZip.writestr('method.json',json.dumps(resDict))
+        
+    if not resDict['calculated']:
+        if show_result:
+            sys.stderr.write('Error!\n'+ resDict['Message']+'\n\n')
+        if 'o' in p:
+            outZip.close()
+        return resDict
+    
+    if 'o' in p:
+        if per_sample == True:
+            for k,v in evalData['per_sample'].iteritems():
+                outZip.writestr( k + '.json',json.dumps(v)) 
+
+            if 'output_items' in evalData.keys():
+                for k, v in evalData['output_items'].iteritems():
+                    outZip.writestr( k,v) 
+
+        outZip.close()
+
+    if show_result:
+        sys.stdout.write("Calculated!")
+        sys.stdout.write(json.dumps(resDict['method']))
+    
+    return resDict
+
+
+def main_validation(default_evaluation_params_fn,validate_data_fn):
+    """
+    This process validates a method
+    Params:
+    default_evaluation_params_fn: points to a function that returns a dictionary with the default parameters used for the evaluation
+    validate_data_fn: points to a method that validates the corrct format of the submission
+    """    
+    try:
+        p = dict([s[1:].split('=') for s in sys.argv[1:]])
+        evalParams = default_evaluation_params_fn()
+        if 'p' in p.keys():
+            evalParams.update( p['p'] if isinstance(p['p'], dict) else json.loads(p['p'][1:-1]) )
+
+        validate_data_fn(p['g'], p['s'], evalParams)              
+        print('SUCCESS')
+        sys.exit(0)
+    except Exception as e:
+        print(str(e))
+        sys.exit(101)
\ No newline at end of file

utils/cal_recall/script.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+from collections import namedtuple
+from . import rrc_evaluation_funcs
+import Polygon as plg
+import numpy as 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_folder_file(gtFilePath, evaluationParams['GT_SAMPLE_NAME_2_ID'])
+
+    subm = rrc_evaluation_funcs.load_folder_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 }
+    """
+
+    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 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
+
+    perSampleMetrics = {}
+
+    matchedSum = 0
+
+    Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
+
+    gt = rrc_evaluation_funcs.load_folder_file(gtFilePath, evaluationParams['GT_SAMPLE_NAME_2_ID'])
+    subm = rrc_evaluation_funcs.load_folder_file(submFilePath, evaluationParams['DET_SAMPLE_NAME_2_ID'], True)
+
+    numGlobalCareGt = 0;
+    numGlobalCareDet = 0;
+
+    arrGlobalConfidences = [];
+    arrGlobalMatches = [];
+
+    for resFile in gt:
+
+        gtFile = gt[resFile]  # rrc_evaluation_funcs.decode_utf8(gt[resFile])
+        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 = [];
+        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 = subm[resFile]  # 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)
+                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
+                                detMatched += 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
+        else:
+            recall = float(detMatched) / numGtCare
+            precision = 0 if numDetCare == 0 else float(detMatched) / 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)
+
+        matchedSum += detMatched
+        numGlobalCareGt += numGtCare
+        numGlobalCareDet += numDetCare
+
+        if evaluationParams['PER_SAMPLE_RESULTS']:
+            perSampleMetrics[resFile] = {
+                'precision': precision,
+                'recall': recall,
+                'hmean': hmean,
+                'pairs': pairs,
+                'AP': sampleAP,
+                'iouMat': [] if len(detPols) > 100 else iouMat.tolist(),
+                'gtPolPoints': gtPolPoints,
+                'detPolPoints': detPolPoints,
+                'gtDontCare': gtDontCarePolsNum,
+                'detDontCare': detDontCarePolsNum,
+                'evaluationParams': evaluationParams,
+                'evaluationLog': evaluationLog
+            }
+
+    # Compute MAP and MAR
+    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;
+
+
+def cal_recall_precison_f1(gt_path, result_path, show_result=False):
+    p = {'g': gt_path, 's': result_path}
+    result = rrc_evaluation_funcs.main_evaluation(p, default_evaluation_params, validate_data, evaluate_method,
+                                                  show_result)
+    return result['method']
\ No newline at end of file

utils/compute_mean_std.py

+# -*- coding: utf-8 -*-
+# @Time    : 2019/12/7 14:46
+# @Author  : zhoujun
+
+import numpy as np
+import cv2
+import os
+import random
+from tqdm import tqdm
+# calculate means and std
+train_txt_path = './train_val_list.txt'
+
+CNum = 10000  # 挑选多少图片进行计算
+
+img_h, img_w = 640, 640
+imgs = np.zeros([img_w, img_h, 3, 1])
+means, stdevs = [], []
+
+with open(train_txt_path, 'r') as f:
+    lines = f.readlines()
+    random.shuffle(lines)  # shuffle , 随机挑选图片
+
+    for i in tqdm(range(CNum)):
+        img_path = lines[i].split('\t')[0]
+
+        img = cv2.imread(img_path)
+        img = cv2.resize(img, (img_h, img_w))
+        img = img[:, :, :, np.newaxis]
+
+        imgs = np.concatenate((imgs, img), axis=3)
+#         print(i)
+
+imgs = imgs.astype(np.float32) / 255.
+
+for i in tqdm(range(3)):
+    pixels = imgs[:, :, i, :].ravel()  # 拉成一行
+    means.append(np.mean(pixels))
+    stdevs.append(np.std(pixels))
+
+# cv2 读取的图像格式为BGR，PIL/Skimage读取到的都是RGB不用转
+means.reverse()  # BGR --> RGB
+stdevs.reverse()
+
+print("normMean = {}".format(means))
+print("normStd = {}".format(stdevs))
+print('transforms.Normalize(normMean = {}, normStd = {})'.format(means, stdevs))
\ No newline at end of file

utils/make_trainfile.py

+# -*- coding: utf-8 -*-
+# @Time    : 2019/8/24 12:06
+# @Author  : zhoujun
+import os
+import glob
+import pathlib
+
+data_path = r'test'
+# data_path/img 存放图片
+# data_path/gt 存放标签文件
+
+f_w = open(os.path.join(data_path, 'test.txt'), 'w', encoding='utf8')
+for img_path in glob.glob(data_path + '/img/*.jpg', recursive=True):
+    d = pathlib.Path(img_path)
+    label_path = os.path.join(data_path, 'gt', ('gt_' + str(d.stem) + '.txt'))
+    if os.path.exists(img_path) and os.path.exists(label_path):
+        print(img_path, label_path)
+    else:
+        print('不存在', img_path, label_path)
+    f_w.write('{}\t{}\n'.format(img_path, label_path))
+f_w.close()
\ No newline at end of file

utils/metrics.py

+# Adapted from score written by wkentaro
+# https://github.com/wkentaro/pytorch-fcn/blob/master/torchfcn/utils.py
+
+import numpy as np
+
+
+class runningScore(object):
+
+    def __init__(self, n_classes):
+        self.n_classes = n_classes
+        self.confusion_matrix = np.zeros((n_classes, n_classes))
+
+    def _fast_hist(self, label_true, label_pred, n_class):
+        mask = (label_true >= 0) & (label_true < n_class)
+
+        if np.sum((label_pred[mask] < 0)) > 0:
+            print(label_pred[label_pred < 0])
+        hist = np.bincount(n_class * label_true[mask].astype(int) +
+                           label_pred[mask], minlength=n_class ** 2).reshape(n_class, n_class)
+        return hist
+
+    def update(self, label_trues, label_preds):
+        # print label_trues.dtype, label_preds.dtype
+        for lt, lp in zip(label_trues, label_preds):
+            try:
+                self.confusion_matrix += self._fast_hist(lt.flatten(), lp.flatten(), self.n_classes)
+            except:
+                pass
+
+    def get_scores(self):
+        """Returns accuracy score evaluation result.
+            - overall accuracy
+            - mean accuracy
+            - mean IU
+            - fwavacc
+        """
+        hist = self.confusion_matrix
+        acc = np.diag(hist).sum() / (hist.sum() + 0.0001)
+        acc_cls = np.diag(hist) / (hist.sum(axis=1) + 0.0001)
+        acc_cls = np.nanmean(acc_cls)
+        iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist) + 0.0001)
+        mean_iu = np.nanmean(iu)
+        freq = hist.sum(axis=1) / (hist.sum() + 0.0001)
+        fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
+        cls_iu = dict(zip(range(self.n_classes), iu))
+
+        return {'Overall Acc': acc,
+                'Mean Acc': acc_cls,
+                'FreqW Acc': fwavacc,
+                'Mean IoU': mean_iu, }, cls_iu
+
+    def reset(self):
+        self.confusion_matrix = np.zeros((self.n_classes, self.n_classes))

utils/ocr_metric/__init__.py

+# -*- coding: utf-8 -*-
+# @Time    : 2019/12/5 15:36
+# @Author  : zhoujun
+from .icdar2015 import QuadMetric
+
+
+def get_metric(config):
+    try:
+        if 'args' not in config:
+            args = {}
+        else:
+            args = config['args']
+        if isinstance(args, dict):
+            cls = eval(config['type'])(**args)
+        else:
+            cls = eval(config['type'])(args)
+        return cls
+    except:
+        return None
\ No newline at end of file

utils/ocr_metric/icdar2015/__init__.py

+# -*- coding: utf-8 -*-
+# @Time    : 2019/12/5 15:36
+# @Author  : zhoujun
+
+from .quad_metric import QuadMetric
\ No newline at end of file