Generalized function

Author: sergiopaniego

behavior_metrics/brains/f1/brain_f1_keras_opencv_dataset.py

@@ -67,13 +67,23 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
             print("- Models path: " + PRETRAINED_MODELS)
             print("- Model: " + str(model))
 
-    def update_frame(self, frame_id, data):
+    def update_frame(self, frame_id, data, angular_speed=None):
         """Update the information to be shown in one of the GUI's frames.
 
         Arguments:
             frame_id {str} -- Id of the frame that will represent the data
             data {*} -- Data to be shown in the frame. Depending on the type of frame (rgbimage, laser, pose3d, etc)
         """
+        if angular_speed:
+            import math
+            x1, y1 = int(data.shape[:2][1] / 2), data.shape[:2][0]  # ancho, alto
+            length = 200
+            angle = (90 + int(math.degrees(-angular_speed))) * 3.14 / 180.0
+            x2 = int(x1 - length * math.cos(angle))
+            y2 = int(y1 - length * math.sin(angle))
+
+            line_thickness = 2
+            cv2.line(data, (x1, y1), (x2, y2), (0, 0, 0), thickness=line_thickness)
         self.handler.update_frame(frame_id, data)
 
     def execute(self):
@@ -130,17 +140,7 @@ def execute(self):
             self.previous_v = prediction[0][0]
             self.previous_w = prediction[0][1]
 
-            # show image in gui -> frame_0
-            import math
-            x1, y1 = int(base_image.shape[:2][1]/2), base_image.shape[:2][0] # ancho, alto
-            length = 200
-            angle = (90 + int(math.degrees(-prediction_w))) * 3.14 / 180.0
-            x2 = int(x1 - length * math.cos(angle))
-            y2 = int(y1 - length * math.sin(angle))
-
-            line_thickness = 2
-            cv2.line(base_image, (x1, y1), (x2, y2), (0, 0, 0), thickness=line_thickness)
-            self.update_frame('frame_0', base_image)
+            self.update_frame('frame_0', base_image, prediction_w)
 
             # GradCAM from image
             i = np.argmax(prediction[0])

behavior_metrics/brains/f1/brain_f1_keras_seq_3_opencv_dataset.py

@@ -72,13 +72,23 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
             print("- Models path: " + PRETRAINED_MODELS)
             print("- Model: " + str(model))
 
-    def update_frame(self, frame_id, data):
+    def update_frame(self, frame_id, data, angular_speed=None):
         """Update the information to be shown in one of the GUI's frames.
 
         Arguments:
             frame_id {str} -- Id of the frame that will represent the data
             data {*} -- Data to be shown in the frame. Depending on the type of frame (rgbimage, laser, pose3d, etc)
         """
+        if angular_speed:
+            import math
+            x1, y1 = int(data.shape[:2][1] / 2), data.shape[:2][0]  # ancho, alto
+            length = 200
+            angle = (90 + int(math.degrees(-angular_speed))) * 3.14 / 180.0
+            x2 = int(x1 - length * math.cos(angle))
+            y2 = int(y1 - length * math.sin(angle))
+
+            line_thickness = 2
+            cv2.line(data, (x1, y1), (x2, y2), (0, 0, 0), thickness=line_thickness)
         self.handler.update_frame(frame_id, data)
 
     def check_center(self, position_x):
@@ -181,17 +191,7 @@ def execute(self):
                     self.motors.sendV(prediction_v)
                     self.motors.sendW(prediction_w)
 
-                # show image in gui -> frame_0
-                import math
-                x1, y1 = int(base_image.shape[:2][1] / 2), base_image.shape[:2][0]  # ancho, alto
-                length = 200
-                angle = (90 + int(math.degrees(-prediction_w))) * 3.14 / 180.0
-                x2 = int(x1 - length * math.cos(angle))
-                y2 = int(y1 - length * math.sin(angle))
-
-                line_thickness = 2
-                cv2.line(base_image, (x1, y1), (x2, y2), (0, 0, 0), thickness=line_thickness)
-                self.update_frame('frame_0', base_image)
+                self.update_frame('frame_0', base_image, prediction_w)
 
                 if self.previous_v != None:
                     a = np.array((prediction[0][0], prediction[0][1]))

behavior_metrics/brains/f1/brain_f1_opencv.py

@@ -50,8 +50,8 @@ def __init__(self, sensors, actuators, handler, config=None):
         self.cont = 0
         self.iteration = 0
 
-        #self.previous_timestamp = 0
-        #self.previous_image = 0
+        # self.previous_timestamp = 0
+        # self.previous_image = 0
 
         self.previous_v = None
         self.previous_w = None
@@ -66,7 +66,23 @@ def __init__(self, sensors, actuators, handler, config=None):
         '''
         time.sleep(2)
 
-    def update_frame(self, frame_id, data):
+    def update_frame(self, frame_id, data, angular_speed=None):
+        """Update the information to be shown in one of the GUI's frames.
+
+        Arguments:
+            frame_id {str} -- Id of the frame that will represent the data
+            data {*} -- Data to be shown in the frame. Depending on the type of frame (rgbimage, laser, pose3d, etc)
+        """
+        if angular_speed:
+            import math
+            x1, y1 = int(data.shape[:2][1] / 2), data.shape[:2][0]  # ancho, alto
+            length = 200
+            angle = (90 + int(math.degrees(-angular_speed))) * 3.14 / 180.0
+            x2 = int(x1 - length * math.cos(angle))
+            y2 = int(y1 - length * math.sin(angle))
+
+            line_thickness = 2
+            cv2.line(data, (x1, y1), (x2, y2), (0, 0, 0), thickness=line_thickness)
         self.handler.update_frame(frame_id, data)
 
     def collinear3(self, x1, y1, x2, y2, x3, y3):
@@ -199,16 +215,7 @@ def execute(self):
         self.motors.sendW(w)
         self.motors.sendV(v)
 
-        # show image in gui -> frame_0
-        import math
-        x1, y1 = int(image.shape[:2][1] / 2), image.shape[:2][0]  # ancho, alto
-        length = 200
-        angle = (90 + int(math.degrees(-w))) * 3.14 / 180.0
-        x2 = int(x1 - length * math.cos(angle))
-        y2 = int(y1 - length * math.sin(angle))
-        line_thickness = 2
-        cv2.line(image, (x1, y1), (x2, y2), (0, 0, 0), thickness=line_thickness)
-        self.update_frame('frame_0', image)
+        self.update_frame('frame_0', image, w)
 
         v = np.interp(np.array([v]), (6.5, 24), (0, 1))[0]
         w = np.interp(np.array([w]), (-7.1, 7.1), (0, 1))[0]