realtime-emotion.py 文件源码

def predict_emotion(self,feature):
        """
        Get arousal and valence class from feature.
        Input: Feature (standard deviasion and mean) from all frequency bands and channels with dimesion 1 x M (number of feature).
        Output: Class of emotion between 1 to 3 from each arousal and valence. 1 denotes low category, 2 denotes normal category, and 3 denotes high category.
        """
        #Compute canberra with arousal training data
        distance_ar = map(lambda x:ss.distance.canberra(x,feature),self.train_arousal)

        #Compute canberra with valence training data
        distance_va = map(lambda x:ss.distance.canberra(x,feature),self.train_valence)

        #Compute 3 nearest index and distance value from arousal
        idx_nearest_ar = np.array(np.argsort(distance_ar)[:3])
        val_nearest_ar = np.array(np.sort(distance_ar)[:3])

        #Compute 3 nearest index and distance value from arousal
        idx_nearest_va = np.array(np.argsort(distance_va)[:3])
        val_nearest_va = np.array(np.sort(distance_va)[:3])

        #Compute comparation from first nearest and second nearest distance. If comparation less or equal than 0.7, then take class from the first nearest distance. Else take frequently class.
        #Arousal
        comp_ar = val_nearest_ar[0]/val_nearest_ar[1]
        if comp_ar<=0.97:
            result_ar = self.class_arousal[0,idx_nearest_ar[0]]
        else:
            result_ar = sst.mode(self.class_arousal[0,idx_nearest_ar])
            result_ar = float(result_ar[0])

        #Valence
        comp_va = val_nearest_va[0]/val_nearest_va[1]
        if comp_va<=0.97:
            result_va = self.class_valence[0,idx_nearest_va[0]]
        else:
            result_va = sst.mode(self.class_valence[0,idx_nearest_va])
            result_va = float(result_va[0])

        return result_ar,result_va