new updated

contrast/event_test.py

@@ -116,15 +116,19 @@ def cluster(data, thresh=0.15):
     return clt_center
 
 def calc_simil(event, stdfeat):
+    '''事件与标准库的对比策略
+       该比对策略是否可以拓展到事件与事件的比对？
+    '''
     
-    def calsiml(feat1, feat2):
+    
+    def calsiml(feat1, feat2, topkp=75, cluth=0.15):
         '''轨迹样本和标准特征集样本相似度的选择策略'''
         matrix = 1 - cdist(feat1, feat2, 'cosine')
         simi_max = []
         for i in range(len(matrix)):
-            sim = np.mean(get_topk_percent(matrix[i, :], 75))
+            sim = np.mean(get_topk_percent(matrix[i, :], topkp))
             simi_max.append(sim)
-        cltc_max = cluster(simi_max)
+        cltc_max = cluster(simi_max, cluth)
         Simi = max(cltc_max) 
         
         ## cltc_max为空属于编程考虑不周，应予以排查解决 
@@ -193,8 +197,6 @@ def simi_matrix():
         histpath = os.path.join(resultPath, "simi_hist")
         if not os.path.exists(histpath):
             os.makedirs(histpath)
-            
-            
 
         mean_values, max_values = [], []
         cameras = ('front', 'back')

contrast/stdfeat_analys.py

@@ -57,7 +57,13 @@ def save_imgpairs(barcode, imgpaths, matrix, savepath, thresh=(0.4, 0.6), ctype=
         
 
 
-def feat_analysis(featpath):    
+def feat_analysis(featpath):   
+    '''
+    标准特征集中样本类内、类间相似度分布
+    '''
+    
+    
+    
     savepath = r"D:\exhibition\result\stdfeat"
 
     InterThresh = (0.4, 0.6)