Merge pull request #4 from gbr1/foxy-dev

0.0.4 - fixes centroids and center visualization

Author: gbr1

README.md

@@ -2,6 +2,8 @@
 
 ROS2 wrapper for Edge Impulse on Linux.
 
+![ezgif-4-3cda6ac695](https://user-images.githubusercontent.com/9216366/193153736-fccd9451-2277-42f2-8141-6027a10f0245.gif)
+
 
 ## 1. Topics
 
@@ -15,6 +17,7 @@ ROS2 wrapper for Edge Impulse on Linux.
 - `frame_id` (**string**), _"base_link"_, frame id of output topics
 - `model.filepath` (**string**), _""_, absolute filepath to .eim file
 - `show.overlay` (**bool**), _true_, show bounding boxes on output image
+- `show.center` (**bool**), _false_, show centroids on output image
 - `show.labels` (**bool**), _true_, show labels on bounding boxes,
 - `show.classification_info` (**bool**), _true_, show the attendibility (0-1) of the prediction
 

edgeimpulse_ros/__pycache__/image_classification.cpython-38.pyc

@@ -81,6 +81,10 @@ def init_parameters(self):
         self.declare_parameter('show.overlay', True)
         self.show_overlay = self.get_parameter('show.overlay').get_parameter_value().bool_value
 
+        self.declare_parameter('show.center', False)
+        self.show_center = self.get_parameter('show.center').get_parameter_value().bool_value
+
+
         self.declare_parameter('show.labels',True)
         self.show_labels_on_image = self.get_parameter('show.labels').get_parameter_value().bool_value
 
@@ -94,6 +98,7 @@ def parameters_callback(self):
         self.show_labels_on_image = self.get_parameter('show.labels').get_parameter_value().bool_value
         self.show_extra_classification_info = self.get_parameter('show.classification_info').get_parameter_value().bool_value
         self.show_overlay = self.get_parameter('show.overlay').get_parameter_value().bool_value
+        self.show_center= self.get_parameter('show.center').get_parameter_value().bool_value
 
 
 
@@ -141,6 +146,11 @@ def classify_callback(self):
                 self.get_logger().info('Found %d bounding boxes (%d ms.)' % (len(res["result"]["bounding_boxes"]), res['timing']['dsp'] + res['timing']['classification']))
 
             for bb in res["result"]["bounding_boxes"]:
+
+                centerX=bb['x']+int(bb['width']/2)
+                centerY=bb['y']+int(bb['height']/2)
+                center=(centerX, centerY)
+
                 result_msg = Detection2D()
                 result_msg.header.stamp = time_now
                 result_msg.header.frame_id = self.frame_id
@@ -149,13 +159,13 @@ def classify_callback(self):
                 obj_hyp = ObjectHypothesisWithPose()
                 obj_hyp.id = bb['label'] #str(self.ei_classifier.labels.index(bb['label']))
                 obj_hyp.score = bb['value']
-                obj_hyp.pose.pose.position.x = float(bb['x'])
-                obj_hyp.pose.pose.position.y = float(bb['y'])
+                obj_hyp.pose.pose.position.x = float(centerX)
+                obj_hyp.pose.pose.position.y = float(centerY)
                 result_msg.results.append(obj_hyp)
 
                 # bounding box
-                result_msg.bbox.center.x = float(bb['x'])
-                result_msg.bbox.center.y = float(bb['y'])
+                result_msg.bbox.center.x = float(centerX)
+                result_msg.bbox.center.y = float(centerY)
                 result_msg.bbox.size_x = float(bb['width'])
                 result_msg.bbox.size_y = float(bb['height'])
 
@@ -170,6 +180,11 @@ def classify_callback(self):
                     if self.show_labels_on_image:
                         composed_label = bb['label']+' '+str(round(bb['value'],2))
                         img_res = cv2.putText(img_res, composed_label, (bb['x'], bb['y']-5), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255,0,0),1)
+                    if self.show_center:
+                        img_res = cv2.circle(img_res, center, 3, (255, 0, 0), 1)
+                        composed_center = '('+str(centerX)+','+str(centerY)+')'
+                        img_res = cv2.putText(img_res, composed_center, (centerX, centerY-5), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255,0,0),1)
+
 
         cropped=cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR)
 

edgeimpulse_ros/image_classification.py

@@ -2,7 +2,7 @@
 <?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
 <package format="3">
   <name>edgeimpulse_ros</name>
-  <version>0.0.3</version>
+  <version>0.0.4</version>
   <description>ROS2 wrapper for Edge Impulse</description>
   <maintainer email="[email protected]">gbr1</maintainer>
   <license>Apache 2.0</license>

package.xml

@@ -4,7 +4,7 @@
 submodules = 'edgeimpulse_ros/submodules'
 setup(
     name=package_name,
-    version='0.0.3',
+    version='0.0.4',
     packages=[package_name, submodules],
     data_files=[
         ('share/ament_index/resource_index/packages',