fixed ipc of vector as msgs iterator error

scripts/marker_generation.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+
+import numpy as np
+import sklearn
+from sklearn.preprocessing import LabelEncoder
+
+import pickle
+
+
+from obj_recognition.features import compute_color_histograms
+from obj_recognition.features import compute_normal_histograms
+from visualization_msgs.msg import Marker
+from obj_recognition.srv import GetNormals
+
+from obj_recognition.marker_tools import *
+from obj_recognition.msg import DetectedObjectsArray
+from obj_recognition.msg import DetectedObject
+from obj_recognition.msg import SegmentedClustersArray
+from obj_recognition.pcl_helper import *
+
+
+def get_normals(cloud):
+    get_normals_prox = rospy.ServiceProxy('/feature_extractor/get_normals', GetNormals)
+    return get_normals_prox(cloud).cluster
+
+# Callback function for your subscriber to the cluster array
+def pcl_callback(pcl_msg):
+
+
+    # TODO Classify the clusters!
+
+    # declare holders for the labels and objects
+    detected_objects_labels = []
+    detected_objects = []
+
+
+    for cloud in pcl_msg.clusters:
+
+        # Extract histogram features
+        chists = compute_color_histograms(cloud, using_hsv=True)
+        normals = get_normals(cloud)
+        nhists = compute_normal_histograms(normals)
+        feature = np.concatenate((chists, nhists))
+
+        prediction = clf.predict(scaler.transform(feature.reshape(1,-1)))
+        label = encoder.inverse_transform(prediction)[0]
+        detected_objects_labels.append(label)
+
+        # Publish a label into RViz
+        label_pos = list(white_cloud[pts_list[0]])
+        label_pos[2] += .4
+        object_markers_pub.publish(make_label(label,label_pos, index))
+
+        # Add the detected object to the list of detected objects.
+        do = DetectedObject()
+        do.label = label
+        do.cloud = ros_cluster
+        detected_objects.append(do)
+
+    rospy.loginfo('Detected {} objects: {}'.format(len(detected_objects_labels), detected_objects_labels))
+
+    # Publish the list of detected objects
+    # This is the output you'll need to complete the upcoming project!
+    detected_objects_pub.publish(detected_objects)
+
+
+if __name__ == '__main__':
+
+    rospy.init_node('obj_recognition_node')
+
+    # Create Subscribers
+    sub = rospy.Subscriber('obj_recognition/pcl_clusters' , SegmentedClustersArray, pcl_callback )
+    pub = rospy.Publisher('obj_recognition/detected_objects', DetectedObjectsArray , queue_size=1)
+
+    # Load Model From disk
+    model = pickle.load(open('model.sav', 'rb'))
+    clf = model['classifier']
+    encoder = LabelEncoder()
+    encoder.classes_ = model['classes']
+    scaler = model['scaler']
+
+    # Initialize color_list
+    get_color_list.color_list = []
+
+    while not rospy.is_shutdown():
+        rospy.spin()

src/segmentation.cpp

@@ -152,6 +152,7 @@ void segmentation::cloud_cb (const sensor_msgs::PointCloud2ConstPtr& cloud_msg)
   // declare an instance of the SegmentedClustersArray message
   obj_recognition::SegmentedClustersArray CloudClusters;
 
+  // declare the output variable instances
   sensor_msgs::PointCloud2 output;
   pcl::PCLPointCloud2 outputPCL;
 
@@ -197,8 +198,6 @@ int main (int argc, char** argv)
 
   segmentation segs(nh);
 
-
-
   while(ros::ok())
   ros::spin ();