def main():
rospy.init_node(name='grasping', anonymous=True)
pnp = BaxterAPI(args.limb, args.hover_distance,
args.reach_distance) # pick and place
pnp.move_to_start(initial_pose)
image_tools = ImageTools(img_id=args.img_id)
while not rospy.is_shutdown():
utils.listener(args.topic_name, image_tools.callback)
def main():
rospy.init_node(name='grasping', anonymous=True)
pnp = BaxterAPI(args.limb, args.hover_distance,
args.reach_distance) # pick and place
pnp.move_to_start(initial_pose)
image_tools = ImageTools(img_id=args.img_id)
images_t = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224, 3])
images_t = images_t - [123.68, 116.779, 103.939]
with slim.arg_scope(model_arg_scope()):
net, end_points = model(inputs=images_t,
num_classes=num_classes,
is_training=False,
dropout_keep_prob=1.0,
scope='target_only')
# with slim.arg_scope(model_arg_scope()):
# net, end_points = model(inputs=images_t,
# num_classes=num_classes,
# is_training=False,
# dropout_keep_prob=1.0,
# reuse=None,
# scope='domain_adapt',
# adapt_scope='target_adapt_layer',
# adapt_dims=128)
angle_index_t = tf.argmin(net, axis=1)
saver = tf.train.Saver()
session_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
session_config.gpu_options.allow_growth = True
sess = tf.Session(config=session_config)
saver.restore(sess, tf.train.latest_checkpoint(args.checkpoint_dir))
print 'Successfully loading model.'
while not rospy.is_shutdown():
flag = 0
while flag != 1:
c_x, c_y = utils.get_center_coordinate_from_kinect(
args.center_coor, args.limb)
d_x = float(c_x) / 1000.0
d_y = float(c_y) / 1000.0
x = origin[0] + d_x
y = origin[1] + d_y
z = origin[2]
flag = pnp.approach(
Pose(position=Point(x, y, z), orientation=initial_orientation))
rospy.sleep(0.7)
utils.listener(args.topic_name, image_tools)
coors, images = image_tools.sampling_image(args.patch_size,
args.num_patches)
scores = sess.run(net, feed_dict={images_t: images})
patch, coor, new_coors = image_tools.resampling_image(
scores, coors, args.patch_size)
angle_index, scores = sess.run([angle_index_t, net],
feed_dict={images_t: patch})
angle_index = angle_index[0]
print angle_index
print scores
grasp_angle = (angle_index * 10 - 90) * 1.0 / 180 * pi
x -= float(coor[0] - 160) * 0.6 / 1000
y -= float(coor[1] - 320) * 0.5 / 1000
z = z
pose = Pose(position=Point(x, y, z), orientation=initial_orientation)
success = pnp.pick(pose, image_tools, args.output_path, new_coors,
coor, args.patch_size, grasp_angle)
pnp.move_to_start(initial_pose)