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random_forest_client_sample.py File Reference

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Namespaces

namespace  random_forest_client_sample

Variables

int random_forest_client_sample.answer = 1
tuple random_forest_client_sample.ax = fig.add_subplot(1, 1, 1)
tuple random_forest_client_sample.br = cv_bridge.CvBridge()
tuple random_forest_client_sample.circle = patches.Circle(xy=(0, 0), radius=1.0, fill=False, ec='g')
string random_forest_client_sample.ENDC = '\033[0m'
string random_forest_client_sample.FAIL = '\033[91m'
tuple random_forest_client_sample.fig = plt.figure()
string random_forest_client_sample.HEADER = '\033[95m'
tuple random_forest_client_sample.img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
tuple random_forest_client_sample.img_msg = br.cv2_to_imgmsg(img, 'rgb8')
string random_forest_client_sample.OKBLUE = '\033[94m'
string random_forest_client_sample.OKGREEN = '\033[92m'
list random_forest_client_sample.old_targets_fail = []
tuple random_forest_client_sample.old_targets_fail_nparr = np.array(old_targets_fail)
list random_forest_client_sample.old_targets_ok = []
tuple random_forest_client_sample.old_targets_ok_nparr = np.array(old_targets_ok)
tuple random_forest_client_sample.predict_data = rospy.ServiceProxy('predict', ClassifyData)
tuple random_forest_client_sample.pub_img = rospy.Publisher('~output/debug_image', Image, queue_size=1)
tuple random_forest_client_sample.req = ClassifyDataRequest()
tuple random_forest_client_sample.req_point = ClassDataPoint()
tuple random_forest_client_sample.response = predict_data(req)
tuple random_forest_client_sample.succeed = int(float(response.classifications[0]))
list random_forest_client_sample.target = [random.random(), random.random()]
string random_forest_client_sample.WARNING = '\033[93m'


jsk_perception
Author(s): Manabu Saito, Ryohei Ueda
autogenerated on Tue Jul 2 2019 19:41:08