Update readme, requirements, and add Redis cache support

README.md

@@ -33,6 +33,13 @@ The vision system has main packages, helpers and 3rd helpers.
 - [libfreenect2](https://github.com/butia-bots/libfreenect2);
 - [yolov5](https://github.com/butia-bots/yolov5).
 
+## Workspace directory ( If there's none )
+Create a workspace
+```
+mkdir -p ~/butia_ws/src
+cd ~/butia_ws/src
+```
+
 ## Clone
 
 Clone this repository using the follow command:
@@ -74,5 +81,17 @@ source ~/.zshrc
 Run catkin_make
 
 ```
+cd ~/butia_ws/
 catkin_make
 ```
+
+Source setup
+
+For bash users:
+```
+source ~/butia_ws/devel/setup.bash
+```
+For zsh users:
+```
+source ~/butia_ws/devel/setup.zsh
+```

butia_recognition/launch/face_rec.launch

@@ -8,5 +8,6 @@
   <node pkg="butia_recognition" type="face_recog.py" output="$(arg output)" name="face_recognition_node" machine="$(arg machine)">
     <rosparam command="load" file="$(find butia_recognition)/config/face_rec.yaml"/>
   </node>
-
+  <include file="$(find butia_world)/launch/redis_cache_reader.launch" />
+  <include file="$(find butia_world)/launch/redis_cache_writer.launch" />
 </launch>

butia_recognition/scripts/butia_recognition/face_recognition/face_recog.py

@@ -18,10 +18,13 @@
 
 from std_msgs.msg import Header
 from sensor_msgs.msg import Image
-from butia_vision_msgs.msg import Description2D, Recognitions2D
+from butia_vision_msgs.msg import Description2D, Recognitions2D, FaceEncoding, FaceDescription
 from butia_vision_msgs.srv import PeopleIntroducing, PeopleIntroducingResponse
 from geometry_msgs.msg import Vector3
 
+from butia_world_msgs.srv import RedisCacheReaderSrv, RedisCacheWriterSrv
+from std_msgs.msg import Empty
+
 PACK_DIR = rospkg.RosPack().get_path('butia_recognition')
 class FaceRecognition(BaseRecognition):
     def __init__(self, state=True):
@@ -33,8 +36,10 @@ def __init__(self, state=True):
 
         self.initRosComm()
 
+        self.cache = self.getCache()
         known_faces_dict = self.loadVar('features')
         self.know_faces = self.flatten(known_faces_dict)
+        #self.encode_faces()
 
     def initRosComm(self):
         self.debug_publisher = rospy.Publisher(self.debug_topic, Image, queue_size=self.debug_qs)
@@ -76,14 +81,14 @@ def encode_faces(self):
         except:
             encoded_face = {}
         train_dir = os.listdir(self.dataset_dir)
-
         for person in train_dir:
-            if person not in self.know_faces[0]:   
+            if person not in self.know_faces[0] or person not in self.cache[0]:   
                 pix = os.listdir(self.dataset_dir + person)
-
+                
                 for person_img in pix:
                     face = face_recognition.load_image_file(self.dataset_dir + person + "/" + person_img)
-                    face_bounding_boxes = face_recognition.face_locations(face, model = 'yolov8')
+                    shape = face.shape
+                    face_bounding_boxes = face_recognition.face_locations(face, model='yolov8')
 
                     M_face = None
                     M_area = -float('inf')
@@ -96,6 +101,14 @@ def encode_faces(self):
                     if M_face is not None:
                         face_enc = face_recognition.face_encodings(face, known_face_locations=[M_face])[0]
                         encodings.append(face_enc)
+
+                        # Database saving service
+                        data = [person, list(face_enc)]
+                        try:
+                            if person not in self.cache[0]:
+                                self.saveToDB(data)
+                        except rospy.ServiceException as e:
+                            rospy.logerr('Service call failed: %s' % e)
 
                         if person not in names:
                             names.append(person)
@@ -109,11 +122,79 @@ def encode_faces(self):
                 pass
         self.saveVar(encoded_face, 'features')             
 
+    def _encapsulateDataRequest(self, data):
+        h = Header()
+        h.stamp = rospy.Time.now()
+        h.seq = self.seq
+
+        description = FaceDescription()
+        description.header = h
+
+        description.label = data[0]
+        description.encoding = data[1]
+
+        request = FaceEncoding()
+        request.header = h
+        request.descriptions.append(description)
+
+        return request
+
+    def saveToDB(self, data):
+        rospy.wait_for_service(self.cache_writer_servername)
+        try:
+            self.cache_writer_service = rospy.ServiceProxy(self.cache_writer_servername, RedisCacheWriterSrv)
+            request = self._encapsulateDataRequest(data)
+            response = self.cache_writer_service(request)
+            if response.response:
+                rospy.loginfo('New face was saved in database.')
+                self.getCache()
+
+            else:
+                rospy.logerr('New face was not saved in database.')
+        except rospy.ServiceException as e:
+            rospy.logerr("Service call failed: %s" % e)
+
+    def getCache(self):
+        rospy.wait_for_service(self.cache_reader_servername)
+        try:
+            self.cache_reader = rospy.ServiceProxy(self.cache_reader_servername, RedisCacheReaderSrv)
+            cache = self._saveCache(self.cache_reader(Empty()))
+        except rospy.ServiceException as e:
+            cache = {}
+            rospy.logerr("Service call failed: %s" % e)
+
+        return cache
+
+    def _saveCache(self, response):
+        cache = {}
+        cache_global_id = []
+
+        data = response.response
+        data_header = data.header
+        descriptions = data.descriptions
+
+        for item in descriptions:
+            h = item.header
+            if item.label not in cache.keys():
+                cache[item.label] = []
+            cache[item.label].append(list(item.encoding))
+            cache_global_id.append(item.global_id)
+        if cache != {}:
+            rospy.loginfo('Cache was saved and is not empty.')
+        #print(cache.keys())
+        formated_cache = self.flatten(cache)
+        formated_cache_final = formated_cache + tuple(cache_global_id)
+        return formated_cache_final
+
     def PeopleIntroducing(self, ros_srv):
 
         name = ros_srv.name
         num_images = ros_srv.num_images
         NAME_DIR = os.path.join(self.dataset_dir, name)
+
+        if os.path.exists(NAME_DIR):
+            NAME_DIR = os.path.join(self.dataset_dir, name + '_1')
+
         os.makedirs(NAME_DIR, exist_ok=True)
         os.makedirs(self.features_dir, exist_ok=True)
         image_type = '.jpg'
@@ -160,9 +241,9 @@ def PeopleIntroducing(self, ros_srv):
             face_locations = face_recognition.face_locations(ros_image, model='yolov8')
 
             if len(face_locations) > 0:
-                ros_image = cv2.cvtColor(ros_image, cv2.COLOR_BGR2RGB)
-                cv2.imwrite(os.path.join(NAME_DIR, add_image_labels[i]), ros_image)
                 rospy.logwarn('Picture ' + add_image_labels[i] + ' was  saved.')
+                cv2.imwrite(os.path.join(NAME_DIR, add_image_labels[i]), ros_image)
+                ros_image = cv2.cvtColor(ros_image, cv2.COLOR_BGR2RGB)
                 i+= 1
             else:
                 rospy.logerr("The face was not detected.")
@@ -172,7 +253,6 @@ def PeopleIntroducing(self, ros_srv):
         response.response = True
 
         self.encode_faces()
-
         known_faces_dict = self.loadVar('features')
         self.know_faces = self.flatten(known_faces_dict)
         return response
@@ -211,7 +291,18 @@ def callback(self, *args):
             top, right, bottom, left = current_faces[idx]
             description = Description2D()
             name = 'unknown'
-            if(len(self.know_faces[0]) > 0):
+            global_id = ""
+            if(len(self.cache[0]) > 0):
+                #rospy.loginfo('Using cache')
+                face_distances = np.linalg.norm(self.cache[1] - current_encoding, axis = 1)
+                #print(self.cache[0])
+                min_distance_idx = np.argmin(face_distances)
+                min_distance = face_distances[min_distance_idx]
+                if min_distance < thold:
+                    name = (self.cache[0][min_distance_idx])
+                    global_id = self.cache[2][min_distance_idx]
+            elif(len(self.know_faces[0]) > 0):
+                #rospy.loginfo('Using pkl file')
                 face_distances = np.linalg.norm(self.know_faces[1] - current_encoding, axis = 1)
                 min_distance_idx = np.argmin(face_distances)
                 min_distance = face_distances[min_distance_idx]
@@ -224,6 +315,7 @@ def callback(self, *args):
             description_header = img.header
             description_header.seq = 0
             description.header = copy(description_header)
+            description.global_id = global_id
             description.type = Description2D.DETECTION
             description.id = description.header.seq
             description.score = 1
@@ -255,6 +347,10 @@ def readParameters(self):
         self.face_recognition_qs = rospy.get_param("~publishers/face_recognition/queue_size", 1)
 
         self.introduct_person_servername = rospy.get_param("~servers/introduct_person/servername", "/butia_vision/br/introduct_person")
+
+        self.cache_writer_servername = "redis_cache_writer_srv"
+
+        self.cache_reader_servername = "redis_cache_reader_srv"
 
         super().readParameters()
         rospy.loginfo('foi 1')

requirements.txt

@@ -1,5 +1,4 @@
 base
-numpy==1.21.6
 tensorflow==2.8.0
 protobuf==3.19.4
 tensorboard==2.10.0
@@ -13,6 +12,12 @@ idna==3.3
 pyparsing==3.0.9
 props
 librosa==0.9.2
-multipledispatch==0.6.0
 yolov5==7.0.9
-open3d==0.15.2
+open3d
+face_recognition
+ultralytics
+torch 
+torchvision
+multipledispatch
+numpy==1.22.2
+rosnumpy