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#!/usr/bin/env python | ||
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import numpy as np | ||
import sklearn | ||
from sklearn.preprocessing import LabelEncoder | ||
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import pickle | ||
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from obj_recognition.srv import GetNormals | ||
from obj_recognition.features import compute_color_histograms | ||
from obj_recognition.features import compute_normal_histograms | ||
from visualization_msgs.msg import Marker | ||
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from obj_recognition.marker_tools import * | ||
from obj_recognition.msg import DetectedObjectsArray | ||
from obj_recognition.msg import DetectedObject | ||
from obj_recognition.pcl_helper import * | ||
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def get_normals(cloud): | ||
get_normals_prox = rospy.ServiceProxy('/feature_extractor/get_normals', GetNormals) | ||
return get_normals_prox(cloud).cluster | ||
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# Callback function for your subscriber to the cluster array | ||
def pcl_callback(pcl_msg): | ||
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# TODO Classify the clusters! (loop through each detected cluster one at a time) | ||
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# Grab the points for the cluster | ||
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# Compute the associated feature vector | ||
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# Make the prediction | ||
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# Publish a label into RViz | ||
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# Add the detected object to the list of detected objects. | ||
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# Publish the list of detected objects | ||
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if __name__ == '__main__': | ||
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# TODO: ROS node initialization | ||
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# TODO: Create Subscribers | ||
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# TODO: Create Publishers | ||
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# TODO: Load Model From disk | ||
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# Initialize color_list | ||
get_color_list.color_list = [] | ||
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# TODO: Spin while node is not shutdown |