merge remote master

src/lhw_intelligence/lhw_intelligence/states/emotional_pepper.py

@@ -24,6 +24,7 @@ class EmotionalPepper(State):
         self.log = self.node.get_logger()
         self.machine = sm.Machine(model=self, states = EmotionalPepper.states, initial="dummy", send_event=True)
         self.to_look_for_persons()
+        self.known_persons = {}
         self.person_in_frame = False
         self.machine.add_transition("trigger_found_person", "look_for_persons", "recognize_person")
         self.machine.add_transition("trigger_greet_person", "recognize_person", "greet_person")
@@ -106,6 +107,14 @@ class EmotionalPepper(State):
     def __emotion_cb(self, emotions):
         self.log.info("Emotion CB!")
         self.log.info(str(emotions))
+        if (emotions.id in self.known_persons):
+            self.say(f"Hello {emotions.id}")
+            #if self.known_persons[emotions.id] == "unknown":
+            #    self.say("What's your name?")
+        #else:
+            #self.say("I don't know you yet. Look into the camera and I'll remember you")
+        self.known_persons[emotions.id] = "unknown"
+        self.log.info(f"Known perons: {self.known_persons}")
         self.trigger_analyze_emotion(emotion=emotions.dominant_emotion)
 
 

src/lhw_nlp/Dockerfile

@@ -3,9 +3,13 @@ ARG NO_GPU=0
 
 # Install pytorch
 RUN if [ "$NO_GPU" = 0 ]; then \
-    pip3 install torch torchvision; \
+    pip3 install torch torchvision deepspeech-gpu; \
     else \
-    pip3 install torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html; fi
+    pip3 install deepspeech torch==1.6.0+cpu torchvision==0.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html; fi
+
+# Get deepspeech pre-trained English model
+RUN curl -L -o src/lhw_nlp/lhw_nlp/deepspeech-0.9.3-models.pbmm https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/deepspeech-0.9.3-models.pbmm
+RUN curl -L -o src/lhw_nlp/lhw_nlp/deepspeech-0.9.3-models.scorer https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/deepspeech-0.9.3-models.scorer
 
 # Install hugging face transformers (for gpt-2)
 RUN pip3 install transformers

src/lhw_qi/src/speech_recognition_component.cpp

@@ -53,7 +53,6 @@ namespace lhw_qi
     asr_sub_ = almemory.call<qi::AnyObject>("subscriber", "WordRecognized");
     // MORE INFO AT http://doc.aldebaran.com/2-4/dev/libqi/api/cpp/type/anyobject.html
     asr_sub_.connect("signal", boost::function<void(qi::AnyValue)>(boost::bind(&SpeechRecognition::word_cb, this, boost::placeholders::_1)));
-
   }
   
 }  // namespace lhw_qi

src/lhw_vision/lhw_vision/tracker/kalman.py

-r""" Implementation of a Kalman filter for predicting the positions of objects in the image.
+""" Implementation of a Kalman filter for predicting the positions of objects in the image.
 We represent the "state" of a target by its position and velocity. Note that we only ever observe the position of objects.
 Note that the position is represented by the top-left and bottom-right corner of the target bounding box, hence 4 coordinates.