Source Code for Module pyvision.analysis.FaceAnalysis.FaceRecognitionTest

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  3  # Copyright (c) 2006-2008 David S. Bolme 
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 33   
 34  from pyvision.analysis import Table 
 35  from pyvision.analysis.roc import ROC 
 36  from pyvision.analysis.stats import cibinom 
 37   
 38   
 39 -def firstFive(im_id): 
 40      '''  
 41      Parses the identity from the image id using the first five  
 42      charicters.  This is a very common scheme for many face datasets. 
 43       
 44      This will work for: FERET or CSU_Scraps 
 45      ''' 
 46      return im_id[:5] 
 47       
 48       
 49  # SCORE_LOW is for distance like measures where low scores  
 50  # indicate a better match 
 51  SCORE_TYPE_LOW  = 0 
 52  # SCORE_HIGH is for similarity like measures where high scores  
 53  # indicate a better match 
 54  SCORE_TYPE_HIGH = 1 
 55   
 56 -class FaceRecognitionTest: 
 57       
 58 -    def __init__(self, name=None, id_func=firstFive, score_type=SCORE_TYPE_LOW): 
 59          ''' 
 60          Create a face recognition test object. 
 61          ''' 
 62          self.name = name 
 63          self.id_func = id_func 
 64          self.score_type = score_type 
 65           
 66          self.probes_table   = Table.Table() 
 67          self.nonmatch_table = Table.Table() 
 68          self.rank_table    = Table.Table() 
 69           
 70          self.total_probes = 0 
 71          self.total_gallery = 0 
 72          self.rank1_success = 0 
 73           
 74          self.positives = [] 
 75          self.negatives = [] 
 76           
 77 -    def getROCAnalysis(self): 
 78          return ROC(self.positives,self.negatives) 
 79       
 80 -    def addSample(self,probe_id,scores): 
 81          ''' 
 82          Adds a sample to the test.  The similarity scores is a list of  
 83          tuples that contain tuples of (gallery_id, score)  
 84          ''' 
 85          if self.score_type == SCORE_TYPE_LOW: 
 86              scores.sort( lambda x,y: cmp(x[1],y[1]) )     
 87          elif self.score_type == SCORE_TYPE_HIGH:         
 88              scores.sort( lambda x,y: -cmp(x[1],y[1]) ) 
 89          else: 
 90              raise ValueError("Unknown score type: %s"%self.score_type) 
 91           
 92          best_match_id       = None 
 93          best_match_score    = None 
 94          best_match_rank     = None 
 95          best_nonmatch_id    = None 
 96          best_nonmatch_score = None 
 97          best_nonmatch_rank  = None 
 98           
 99          pid = self.id_func(probe_id) 
100          rank = 0 
101          for gallery_id,score in scores: 
102              if probe_id == gallery_id: 
103                  # Probe and gallery should not be the same image 
104                  continue 
105              gid = self.id_func(gallery_id) 
106              match = False 
107              if pid == gid: 
108                  match = True 
109               
110              if match and best_match_id == None: 
111                  best_match_id       = gallery_id 
112                  best_match_score    = score 
113                  best_match_rank     = rank 
114               
115              if not match and best_nonmatch_id == None: 
116                  best_nonmatch_id    = gallery_id 
117                  best_nonmatch_score = score 
118                  best_nonmatch_rank  = rank 
119               
120              if match: 
121                  self.positives.append(score) 
122              else: 
123                  self.negatives.append(score) 
124                   
125              rank += 1 
126               
127          self.total_probes  += 1 
128          self.total_gallery = max(self.total_gallery,rank) 
129   
130          if best_match_rank==0: 
131              self.rank1_success += 1 
132                   
133          self.probes_table.setElement(probe_id,'MatchId',best_match_id) 
134          self.probes_table.setElement(probe_id,'MatchScore',best_match_score) 
135          self.probes_table.setElement(probe_id,'MatchRank',best_match_rank) 
136          self.probes_table.setElement(probe_id,'Success',best_match_rank==0) 
137          self.probes_table.setElement(probe_id,'NonMatchId',best_nonmatch_id) 
138          self.probes_table.setElement(probe_id,'NonMatchScore',best_nonmatch_score) 
139          self.probes_table.setElement(probe_id,'NonMatchRank',best_nonmatch_rank) 
140               
141          self.rank1_rate = float(self.rank1_success)/float(self.total_probes) 
142          self.rank1_bounds = cibinom(self.total_probes,self.rank1_success) 
143