Add FPS calculations and added License and description to setup.cfg

object_detection/config/params.yaml

@@ -7,6 +7,6 @@ object_detection:
     model_params:
       detector_type: YOLOv5
       model_dir_path: /root/percep_ws/models/yolov5
-      weight_file_name: yolov5.onnx
+      weight_file_name: yolov5s.pt
       confidence_threshold : 0.5
       show_fps : 1

object_detection/object_detection/ObjectDetection.py

@@ -16,6 +16,7 @@
 
 import importlib
 import os
+import time
 
 import cv2
 
@@ -77,6 +78,12 @@ def __init__(self):
 
         self.bridge = CvBridge()
 
+        if self.show_fps:
+            self.start_time = time.time()
+            self.frame_count = 0
+            self.total_elapsed_time = 0
+            self.average_fps = 0
+
     def discover_detectors(self):
         curr_dir = os.path.dirname(__file__)
         dir_contents = os.listdir(curr_dir + "/Detectors")
@@ -101,6 +108,8 @@ def load_detector(self):
     def detection_cb(self, img_msg):
         cv_image = self.bridge.imgmsg_to_cv2(img_msg, "bgr8")
 
+        start_time = time.time()
+
         predictions = self.detector.get_predictions(cv_image=cv_image)
 
         if predictions is None:
@@ -120,6 +129,22 @@ def detection_cb(self, img_msg):
                 cv_image = cv2.putText(cv_image, label, (x1, y1 - 5),
                                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
 
+            if self.show_fps:
+                elapsed_time = time.time() - start_time
+                self.frame_count += 1
+                self.total_elapsed_time += elapsed_time
+
+                # Write FPS on the frame
+                cv_image = cv2.putText(cv_image, f"FPS: {self.average_fps:.2f}", (10, 30),
+                                       cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+
+                if time.time() - self.start_time >= 1.0:
+                    self.average_fps = self.frame_count / self.total_elapsed_time
+
+                    self.frame_count = 0
+                    self.total_elapsed_time = 0
+                    self.start_time = time.time()
+
             output = self.bridge.cv2_to_imgmsg(cv_image, "bgr8")
             self.img_pub.publish(output)
 

object_detection/package.xml

@@ -3,7 +3,7 @@
 <package format="3">
   <name>object_detection</name>
   <version>0.0.0</version>
-  <description>This is a ROS 2 package aimed at providing an interfacing between Deep Learning models and the ROS architecture using a plug-and-play modular arhitecture</description>
+  <description>This is a ROS 2 package aimed at providing an interfacing between Deep Learning models and the ROS architecture using a plug-and-play modular architecture</description>
   <maintainer email="[email protected]">singh</maintainer>
   <license>Apache 2.0</license>
 

object_detection/setup.py

@@ -21,8 +21,8 @@
     zip_safe=True,
     maintainer='singh',
     maintainer_email='[email protected]',
-    description='TODO: Package description',
-    license='TODO: License declaration',
+    description='Plug-and-Play ROS 2 package for Perception in Robotics',
+    license='Apache 2.0',
     tests_require=['pytest'],
     entry_points={
         'console_scripts': [