fixed vision_msgs

README.md

@@ -1,8 +1,25 @@
 # edgeimpulse_ros
-ROS2 wrapper for Edge Impulse 
 
+ROS2 wrapper for Edge Impulse on Linux.
 
-## How to install
+
+## 1. Topics
+
+- `/detection/input/image`, image topic to analyze
+- `/detection/output/image`, image with bounding boxes
+- `/detection/output/info`, VisionInfo message
+- `/detection/output/results`, results as text
+
+## 2. Parameters
+
+- `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.labels` (**bool**), _true_, show labels on bounding boxes,
+- `show.classification_info` (**bool**), _true_, show the attendibility (0-1) of the prediction
+
+
+## 3. How to install
 
 1. install edge_impulse_linux: <br>
     `pip3 install edge_impulse_linux`
@@ -30,12 +47,23 @@ ROS2 wrapper for Edge Impulse
     `source install/setup.bash` <br>
 
 
-## How to run
+## 4.  How to run
 
 Launch the node: <br>
     `ros2 run edgeimpulse_ros image_classification --ros-args -p model.filepath:="</absolute/path/to/your/eim/file.eim>" -r /detection/input/image:="/your_image_topic"`
 ` <br>
 
+## 5. Models
+
+Here you find some prebuilt models: [https://github.com/gbr1/edgeimpulse_example_models](https://github.com/gbr1/edgeimpulse_example_models)
+
+## 6. Known issues
+
+- this wrapper works on foxy, galactic and humble are coming soon (incompatibility on vision msgs by ros-perception)
+- if you use a classification model, topic results is empty
+- you cannot change color of bounding boxes (coming soon)
+- other types (imu and sound based ml) are unavailable
+
 
 
 ***Copyright © 2022 Giovanni di Dio Bruno - gbr1.github.io***

edgeimpulse_ros/image_classification.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 
+from distutils.log import info
 from unittest import result
 from .submodules import device_patches
 import cv2
@@ -27,7 +28,7 @@
 from vision_msgs.msg import Detection2DArray
 from vision_msgs.msg import Detection2D
 from vision_msgs.msg import ObjectHypothesisWithPose
-#from vision_msgs.msg import VisionInfo
+from vision_msgs.msg import VisionInfo
 
 import os
 import time
@@ -44,16 +45,23 @@ def __init__(self):
 
         self.occupied = False
         self.img = None
+        self.info_msg = VisionInfo()
         self.cv_bridge = CvBridge()
 
         super().__init__('ei_image_classifier_node')
         self.init_parameters()
         self.ei_classifier = self.EI_Classifier(self.modelfile, self.get_logger())
 
+        self.info_msg.header.frame_id = self.frame_id
+        self.info_msg.method = self.ei_classifier.model_info['model_parameters']['model_type']
+        self.info_msg.database_location = self.ei_classifier.model_info['project']['name']+' / '+self.ei_classifier.model_info['project']['owner']
+        self.info_msg.database_version = self.ei_classifier.model_info['project']['deploy_version']
+
         self.timer_parameter = self.create_timer(2,self.parameters_callback)
 
         self.image_publisher = self.create_publisher(Image,'/detection/output/image',1)
         self.results_publisher = self.create_publisher(Detection2DArray,'/detection/output/results',1)
+        self.info_publisher = self.create_publisher(VisionInfo, '/detection/output/info',1)
 
         self.timer_classify = self.create_timer(0.01,self.classify_callback)
         self.timer_classify.cancel()
@@ -137,7 +145,7 @@ def classify_callback(self):
 
                 # object with hypthothesis
                 obj_hyp = ObjectHypothesisWithPose()
-                #obj_hyp.id = 
+                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'])
@@ -166,6 +174,8 @@ def classify_callback(self):
         # publish message
         self.image_publisher.publish(self.cv_bridge.cv2_to_imgmsg(cropped,"bgr8"))
         self.results_publisher.publish(results_msg)
+        self.info_msg.header.stamp = time_now
+        self.info_publisher.publish(self.info_msg)
 
         self.occupied= False
         self.timer_classify.cancel()