Merge pull request #1426 from wkentaro/merge-sklearn-to-jsk-perception

Merge sklearn to jsk_perception

jsk_pcl_ros/CMakeLists.txt

@@ -29,7 +29,7 @@ find_package(catkin REQUIRED COMPONENTS
   ${PCL_MSGS} sensor_msgs geometry_msgs jsk_recognition_msgs
   eigen_conversions tf_conversions tf2_ros tf
   image_transport nodelet cv_bridge
-  sklearn jsk_topic_tools
+  jsk_topic_tools
   image_geometry
   jsk_footstep_msgs
   interactive_markers

jsk_pcl_ros/package.xml

@@ -38,7 +38,6 @@
   <build_depend>rosboost_cfg</build_depend>
   <build_depend>cv_bridge</build_depend>
   <build_depend>ml_classifiers</build_depend>
-  <build_depend>sklearn</build_depend>
   <build_depend>diagnostic_updater</build_depend>
   <build_depend>diagnostic_msgs</build_depend>
   <build_depend>jsk_topic_tools</build_depend>
@@ -82,7 +81,6 @@
   <run_depend>rosboost_cfg</run_depend>
   <run_depend>cv_bridge</run_depend>
   <run_depend>ml_classifiers</run_depend>
-  <run_depend>sklearn</run_depend>
   <run_depend>diagnostic_updater</run_depend>
   <run_depend>diagnostic_msgs</run_depend>
   <build_depend>python-sklearn</build_depend>
@@ -98,6 +96,7 @@
   <build_depend>jsk_recognition_msgs</build_depend>
   <run_depend>jsk_recognition_msgs</run_depend>
   <run_depend>jsk_footstep_msgs</run_depend>
+  <run_depend>jsk_perception</run_depend>
   <build_depend>interactive_markers</build_depend>
   <run_depend>interactive_markers</run_depend>
   <run_depend>jsk_recognition_utils</run_depend>

jsk_pcl_ros/sample/colorized_random_points_RF.launch

@@ -6,7 +6,7 @@
     <arg name="depth_registration" value="true"/>
   </include>
 
-  <node pkg="sklearn" type="random_forest_server.py" name="random_forest_cloth_classifier" output="screen">
+  <node pkg="jsk_perception" type="random_forest_server.py" name="random_forest_cloth_classifier" output="screen">
     <param name="random_forest_train_file" value="$(find jsk_data)/pkls/random_region_FPFH_Average/random_forest.pkl"/>
   </node>
 

jsk_pcl_ros/sample/colorized_segmented_RF.launch

@@ -6,7 +6,7 @@
     <arg name="depth_registration" value="true"/>
   </include>
 
-  <node pkg="sklearn" type="random_forest_server.py" name="random_forest_cloth_classifier" output="screen">
+  <node pkg="jsk_perception" type="random_forest_server.py" name="random_forest_cloth_classifier" output="screen">
     <param name="random_forest_train_file" value="$(find jsk_data)/pkls/random_region_FPFH_Average/random_forest.pkl"/>
   </node>
 

jsk_perception/node_scripts/random_forest_server.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+
+try:
+    from ml_classifiers.srv import *
+except:
+    import roslib;roslib.load_manifest("ml_classifiers")
+    from ml_classifiers.srv import *
+
+import rospy
+import numpy as np
+from sklearn.cross_validation import cross_val_score
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.externals import joblib
+
+class RandomForestServer:
+    def __init__(self, clf):
+        self.clf = clf
+        s = rospy.Service('predict', ClassifyData, self.classifyData)
+
+    @classmethod
+    def initWithData(cls, data_x, data_y):
+        if len(data_x) != len(data_y):
+            rospy.logerr("Lenght of datas are different")
+            exit()
+        rospy.loginfo("InitWithData please wait..")
+        clf = RandomForestClassifier(n_estimators=250, max_features=2, max_depth=29, min_samples_split=1, random_state=0)
+        clf.fit(data_x, data_y)
+        return cls(clf)
+
+    @classmethod
+    def initWithFileModel(cls, filename):
+        rospy.loginfo("InitWithFileModel with%s please wait.."%filename)
+        clf = joblib.load(filename)
+        return cls(clf)
+
+    #Return predict result
+    def classifyData(self, req):
+        ret = []
+        for data in req.data:
+            print data
+            ret.append(" ".join([str(predict_data) for predict_data in self.clf.predict(data.point)]))
+            rospy.loginfo("req : " + str(data.point) +  "-> answer : " + str(ret))
+        return ClassifyDataResponse(ret)
+
+    #Run random forest
+    def run(self):
+        rospy.loginfo("RandomForestServer is running!")
+        rospy.spin()
+
+
+if __name__ == "__main__":
+    rospy.init_node('random_forest_cloth_classifier')
+
+    try:
+        train_file = rospy.get_param('~random_forest_train_file')
+    except KeyError:
+        rospy.logerr("Train File is not Set. Set train_data file or tree model file as ~random_forest_train_file.")
+        exit()
+
+    if train_file.endswith("pkl"):
+        node = RandomForestServer.initWithFileModel(train_file)
+    else:
+        try:
+            class_file = rospy.get_param('~random_forest_train_class_file')
+
+            data_x = []
+            data_y = []
+            for l in open(train_file).readlines():
+                float_strings = l.split(",");
+                data_x.append(map(lambda x: float(x), float_strings))
+
+            for l in open(class_file).readlines():
+                data_y.append(float(l))
+
+            #build servece server
+            node = RandomForestServer.initWithData(np.array(data_x), np.array(data_y))
+
+        except KeyError:
+            rospy.logerr("Train Class File is not Set. Set train_data file or tree model file.")
+            rospy.logerr("Or Did you expect  Extension to be pkl?.")
+            exit()
+
+
+    #run
+    node.run()

jsk_perception/package.xml

@@ -62,11 +62,13 @@
   <run_depend>jsk_topic_tools</run_depend>
   <run_depend>message_runtime</run_depend>
   <run_depend>mk</run_depend>
+  <run_depend>ml_classifiers</run_depend>
   <run_depend>nodelet</run_depend>
   <run_depend>pcl_ros</run_depend>
   <run_depend>posedetection_msgs</run_depend>
   <run_depend>python-dlib</run_depend>
   <run_depend>python-gdown</run_depend>
+  <run_depend>python-sklearn</run_depend>
   <run_depend>rosbuild</run_depend>
   <run_depend>roscpp</run_depend>
   <run_depend>roseus</run_depend>

jsk_perception/sample/random_forest_client_sample.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+
+try:
+    from ml_classifiers.srv import *
+    from ml_classifiers.msg import *
+except:
+    import roslib;roslib.load_manifest("ml_classifiers")
+    from ml_classifiers.srv import *
+    from ml_classifiers.msg import *
+
+import rospy
+import random
+
+HEADER = '\033[95m'
+OKBLUE = '\033[94m'
+OKGREEN = '\033[92m'
+WARNING = '\033[93m'
+FAIL = '\033[91m'
+ENDC = '\033[0m'
+
+if __name__ == "__main__":
+    rospy.init_node("random_forest_client")
+
+    rospy.wait_for_service('predict')
+
+    rospy.loginfo("Start Request Service!!")
+
+    predict_data = rospy.ServiceProxy('predict', ClassifyData)
+
+    while not rospy.is_shutdown():
+        req = ClassifyDataRequest()
+        req_point = ClassDataPoint()
+        target = [random.random(), random.random()]
+        answer = 1
+        #Check if it is in the circle radius = 1?
+        if target[0] * target[0] + target[1] * target[1] > 1:
+            answer = 0
+        req_point.point = target
+        req.data.append(req_point)
+        print OKGREEN,"Send Request         ====================>         Answer",ENDC
+        print OKGREEN,"    ",req_point.point,"       : ",str(answer),ENDC
+        response = predict_data(req)
+        print WARNING,"Get the result : ",ENDC
+        print WARNING,response.classifications,ENDC
+        if response.classifications[0].find(str(answer)):
+            print OKBLUE,"Succeed!!!",ENDC
+        else:
+            print FAIL,"FAIL...",FAIL
+        print "--- --- --- ---"
+
+        rospy.sleep(1)

jsk_perception/sample/random_forest_sample.launch

@@ -0,0 +1,10 @@
+<launch>
+  <!-- <arg name="" value=""/> -->
+  <!-- <param name="depth_registered" value="true"/> -->
+  <node pkg="jsk_perception" name="random_server" type="random_forest_server.py" args="" output="screen" clear_params="true">
+    <param name="random_forest_train_file" value="$(find jsk_perception)/sample/random_forest_sample_data_x.txt" />
+    <param name="random_forest_train_class_file" value="$(find jsk_perception)/sample/random_forest_sample_data_y.txt" />
+  </node>
+
+  <node pkg="jsk_perception" name="random_client" type="random_forest_client_sample.py" launch-prefix="xterm -e"/>
+</launch>

jsk_perception/sample/random_forest_sample_data_x.txt

@@ -0,0 +1,100 @@
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+6.096653288013824668e-01,7.873620471643174579e-01
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