cls->clf

demos/classifier.py

@@ -96,35 +96,35 @@ def train(args):
     print("Training for {} classes.".format(nClasses))
 
     if args.classifier == 'LinearSvm':
-        cls = SVC(C=1, kernel='linear', probability=True)
+        clf = SVC(C=1, kernel='linear', probability=True)
     elif args.classifier == 'GMM':
-        cls = GMM(n_components=nClasses)
+        clf = GMM(n_components=nClasses)
 
-    cls.fit(embeddings, labelsNum)
+    clf.fit(embeddings, labelsNum)
 
     fName = "{}/classifier.pkl".format(args.workDir)
     print("Saving classifier to '{}'".format(fName))
     with open(fName, 'w') as f:
-        pickle.dump((le, cls), f)
+        pickle.dump((le, clf), f)
 
 
 def infer(args):
     with open(args.classifierModel, 'r') as f:
-        (le, cls) = pickle.load(f)
+        (le, clf) = pickle.load(f)
 
     for img in args.imgs:
         print("\n=== {} ===".format(img))
         rep = getRep(img).reshape(1, -1)
         start = time.time()
-        predictions = cls.predict_proba(rep).ravel()
+        predictions = clf.predict_proba(rep).ravel()
         maxI = np.argmax(predictions)
         person = le.inverse_transform(maxI)
         confidence = predictions[maxI]
         if args.verbose:
             print("Prediction took {} seconds.".format(time.time() - start))
         print("Predict {} with {:.2f} confidence.".format(person, confidence))
-        if isinstance(cls, GMM):
-            dist = np.linalg.norm(rep - cls.means_[maxI])
+        if isinstance(clf, GMM):
+            dist = np.linalg.norm(rep - clf.means_[maxI])
             print("  + Distance from the mean: {}".format(dist))