def fast_grid_search(pc, indices, model, shadow, img_file):
length, width, height = shadow.extents
split_size = max(length, width)
pc_data, ind = get_pc_data(pc, indices)
maxes = np.max(pc_data, axis=0)
mins = np.min(pc_data, axis=0)
bin_base = mins[2]
plane_normal = model[0:3]
di_temp = ci.project_to_image(pc)
vis2d.figure()
vis2d.imshow(di_temp)
vis2d.show()
plane_data = pc.data.T[indices]
#all_indices = np.where([(plane_data[::,2] > 0.795) & (plane_data[::,2] < 0.862)])
#all_indices = np.where((plane_data[::,1] < 0.16) & (plane_data[::,1] > -0.24) & (plane_data[::,0] > -0.3) & (plane_data[::,0] < 0.24))[0]
#plane_data = plane_data[all_indices]
plane_pc = PointCloud(plane_data.T, pc.frame)
di = ci.project_to_image(plane_pc)
bi = di.to_binary()
scene = Scene()
camera = VirtualCamera(ci, cp)
scene.camera = camera
# Get shadow depth img.
shadow_obj = SceneObject(shadow)
scene.add_object('shadow', shadow_obj)
orig_tow = shadow_obj.T_obj_world
scores = np.zeros((int(np.round((maxes[0] - mins[0]) / split_size)),
int(np.round((maxes[1] - mins[1]) / split_size))))
for i in range(int(np.round((maxes[0] - mins[0]) / split_size))):
x = mins[0] + i * split_size
for j in range(int(np.round((maxes[1] - mins[1]) / split_size))):
y = mins[1] + j * split_size
for tow in transforms(pc, pc_data, shadow, x, y, x + split_size,
y + split_size, 8):
shadow_obj.T_obj_world = tow
scores[i][j] = under_shadow(pc, pc_data, indices, model,
shadow, x, x + split_size, y,
y + split_size, scene, bi)
shadow_obj.T_obj_world = orig_tow
print("\nScores: \n" + str(scores))
best = best_cell(scores)
print("\nBest Cell: " + str(best) + ", with score = " +
str(scores[best[0]][best[1]]))
#-------
# Visualize best placement
vis3d.figure()
x = mins[0] + best[0] * split_size
y = mins[1] + best[1] * split_size
cell_indices = np.where((x < pc_data[:, 0])
& (pc_data[:, 0] < x + split_size)
& (y < pc_data[:, 1])
& (pc_data[:, 1] < y + split_size))[0]
points = pc_data[cell_indices]
rest = pc_data[np.setdiff1d(np.arange(len(pc_data)), cell_indices)]
vis3d.points(points, color=(0, 1, 1))
vis3d.points(rest, color=(1, 0, 1))
vis3d.show()
def quality(self, state, actions, params=None):
"""Given a suction point, compute a score based on a best-fit 3D plane of the neighboring points.
Parameters
----------
state : :obj:`RgbdImageState`
An RgbdImageState instance that encapsulates rgbd_im, camera_intr, segmask, full_observed.
action: :obj:`SuctionPoint2D`
A suction grasp in image space that encapsulates center, approach direction, depth, camera_intr.
params: dict
Stores params used in computing suction quality.
Returns
-------
:obj:`numpy.ndarray`
Array of the quality for each grasp
"""
qualities = []
# deproject points
point_cloud_image = state.camera_intr.deproject_to_image(
state.rgbd_im.depth)
# compute negative SSE from the best fit plane for each grasp
for i, action in enumerate(actions):
if not isinstance(action, SuctionPoint2D):
raise ValueError(
'This function can only be used to evaluate suction quality'
)
points = self._points_in_window(
point_cloud_image, action,
segmask=state.segmask) # x,y in matrix A and z is vector z.
A, b = self._points_to_matrices(points)
w = self._action_to_plane(
point_cloud_image,
action) # vector w w/ a bias term represents a best-fit plane.
sse = self._sum_of_squared_residuals(w, A, b)
if params is not None and params['vis']['plane']:
from visualization import Visualizer2D as vis2d
from visualization import Visualizer3D as vis3d
mid_i = A.shape[0] / 2
pred_z = A.dot(w)
p0 = np.array([A[mid_i, 0], A[mid_i, 1], pred_z[mid_i]])
n = np.array([w[0], w[1], -1])
n = n / np.linalg.norm(n)
tx = np.array([n[1], -n[0], 0])
tx = tx / np.linalg.norm(tx)
ty = np.cross(n, tx)
R = np.array([tx, ty, n]).T
c = state.camera_intr.deproject_pixel(action.depth,
action.center)
d = Point(c.data - 0.01 * action.axis, frame=c.frame)
T_table_world = RigidTransform(rotation=R,
translation=p0,
from_frame='patch',
to_frame='world')
vis3d.figure()
vis3d.points(point_cloud_image.to_point_cloud(),
scale=0.0025,
subsample=10,
random=True,
color=(0, 0, 1))
vis3d.points(PointCloud(points.T),
scale=0.0025,
color=(1, 0, 0))
vis3d.points(c, scale=0.005, color=(1, 1, 0))
vis3d.points(d, scale=0.005, color=(1, 1, 0))
vis3d.table(T_table_world, dim=0.01)
vis3d.show()
vis2d.figure()
vis2d.imshow(state.rgbd_im.depth)
vis2d.scatter(action.center.x, action.center.y, s=50, c='b')
vis2d.show()
quality = np.exp(
-sse
) # evaluate how well best-fit plane describles all points in window.
qualities.append(quality)
return np.array(qualities)
point_cloud_filtered, scale=0.001, color=(0, 0, 1), subsample=5
)
vis3d.show()
# convert to depth image
project_start = time.time()
point_cloud_cam = T_camera_world.inverse() * point_cloud_filtered
depth_im_filtered = small_camera_intr.project_to_image(point_cloud_cam)
noise_mask = depth_im_filtered.to_binary()
logging.info("Project took %.3f sec" % (time.time() - project_start))
depth_im_filtered = depth_im_filtered.inpaint(0.5)
filter_stop = time.time()
logging.info("Filtering took %.3f sec" % (filter_stop - filter_start))
if vis_final_images:
vis2d.figure()
vis2d.subplot(2, 2, 1)
vis2d.imshow(depth_im)
vis2d.title("Orig")
vis2d.subplot(2, 2, 2)
vis2d.imshow(depth_im_orig)
vis2d.title("Inpainted")
vis2d.subplot(2, 2, 3)
vis2d.imshow(noise_mask)
vis2d.title("Mask")
vis2d.subplot(2, 2, 4)
vis2d.imshow(depth_im_filtered)
vis2d.title("Filtered")
vis2d.show()
sensor.start()
camera_intr = sensor.ir_intrinsics
n = 15
frame_rates = []
for i in range(n):
logging.info("Reading frame %d of %d" % (i + 1, n))
read_start = time.time()
color_im, depth_im, _ = sensor.frames()
read_stop = time.time()
frame_rates.append(1.0 / (read_stop - read_start))
logging.info("Avg fps: %.3f" % (np.mean(frame_rates)))
color_im = color_im.inpaint(rescale_factor=0.5)
depth_im = depth_im.inpaint(rescale_factor=0.5)
point_cloud = camera_intr.deproject(depth_im)
vis3d.figure()
vis3d.points(point_cloud, subsample=15)
vis3d.show()
vis.figure()
vis.subplot(1, 2, 1)
vis.imshow(color_im)
vis.subplot(1, 2, 2)
vis.imshow(depth_im)
vis.show()
sensor.stop()
# optionally read a segmask
segmask = None
if segmask_filename is not None:
segmask = BinaryImage.open(segmask_filename)
valid_px_mask = depth_im.invalid_pixel_mask().inverse()
if segmask is None:
segmask = valid_px_mask
else:
segmask = segmask.mask_binary(valid_px_mask)
# inpaint
depth_im = depth_im.inpaint(rescale_factor=inpaint_rescale_factor)
if 'input_images' in policy_config['vis'].keys() and policy_config['vis']['input_images']:
vis.figure(size=(10,10))
num_plot = 1
if segmask is not None:
num_plot = 2
vis.subplot(1,num_plot,1)
vis.imshow(depth_im)
if segmask is not None:
vis.subplot(1,num_plot,2)
vis.imshow(segmask)
vis.show()
# create state
rgbd_im = RgbdImage.from_color_and_depth(color_im, depth_im)
state = RgbdImageState(rgbd_im, camera_intr, segmask=segmask)
# set input sizes for fully-convolutional policy
raise ValueError('Must provide mesh in Wavefront .OBJ format!')
orig_mesh = ObjFile(mesh_filename).read()
mesh = orig_mesh.subdivide(min_tri_length=0.01)
mesh.compute_vertex_normals()
stable_poses = mesh.stable_poses()
if vis_normals:
vis3d.figure()
vis3d.mesh(mesh)
vis3d.normals(NormalCloud(mesh.normals.T),
PointCloud(mesh.vertices.T),
subsample=10)
vis3d.show()
d = utils.sqrt_ceil(len(stable_poses))
vis.figure(size=(16, 16))
table_mesh = ObjFile('data/meshes/table.obj').read()
table_mesh = table_mesh.subdivide()
table_mesh.compute_vertex_normals()
table_mat_props = MaterialProperties(color=(0, 255, 0),
ambient=0.5,
diffuse=1.0,
specular=1,
shininess=0)
for k, stable_pose in enumerate(stable_poses):
logging.info('Rendering stable pose %d' % (k))
# set resting pose
T_obj_world = mesh.get_T_surface_obj(
# Read config.
config = YamlConfig(config_filename)
policy_config = config["policy"]
# Load test case.
state_path = os.path.join(test_case_path, "state")
action_path = os.path.join(test_case_path, "action")
state = RgbdImageState.load(state_path)
original_action = ParallelJawGrasp.load(action_path)
# Init policy.
policy = CrossEntropyRobustGraspingPolicy(policy_config)
if policy_config["vis"]["input_images"]:
vis2d.figure()
if state.segmask is None:
vis2d.subplot(1, 2, 1)
vis2d.imshow(state.rgbd_im.color)
vis2d.title("COLOR")
vis2d.subplot(1, 2, 2)
vis2d.imshow(state.rgbd_im.depth,
vmin=policy_config["vis"]["vmin"],
vmax=policy_config["vis"]["vmax"])
vis2d.title("DEPTH")
else:
vis2d.subplot(1, 3, 1)
vis2d.imshow(state.rgbd_im.color)
vis2d.title("COLOR")
vis2d.subplot(1, 3, 2)
vis2d.imshow(state.rgbd_im.depth,
def run_experiment():
""" Run the experiment """
if not config['robot_off']:
rospy.loginfo('Initializing YuMi')
robot, subscriber, left_arm, right_arm, home_pose = init_robot(config)
# create ROS CVBridge
cv_bridge = CvBridge()
# wait for Grasp Planning Service and create Service Proxy
rospy.wait_for_service('plan_gqcnn_grasp')
plan_grasp = rospy.ServiceProxy('plan_gqcnn_grasp', GQCNNGraspPlanner)
# get camera intrinsics
camera_intrinsics = sensor.ir_intrinsics
# setup experiment logger
experiment_logger = GraspIsolatedObjectExperimentLogger(
config['experiment_dir'],
config['supervisor'],
camera_intrinsics,
T_camera_world,
'/home/autolab/Workspace/vishal_working/catkin_ws/src/gqcnn/cfg/ros_nodes/yumi_control_node.yaml',
planner_type=config['planner_type'])
logging.info('Saving experiment to %s' %
(experiment_logger.experiment_path))
object_keys = config['test_object_keys']
trial_number = 1
re_try = False
logging.info('Beginning experiment')
while True:
if not re_try:
experiment_logger.start_trial()
obj = np.random.choice(object_keys, size=1)[0]
else:
re_try = False
rospy.loginfo('Please place object: ' + obj + ' on the workspace.')
raw_input("Press ENTER when ready ...")
# start the next trial
rospy.loginfo('Trial %d' % (trial_number))
# get the images from the sensor
color_image, depth_image, _ = sensor.frames()
# log some trial info
experiment_logger.update_trial_attribute('trial_num', trial_number)
experiment_logger.update_trial_attribute('color_im', color_image)
experiment_logger.update_trial_attribute('depth_im', depth_image)
# inpaint to remove holes
inpainted_color_image = color_image.inpaint(
rescale_factor=config['inpaint_rescale_factor'])
inpainted_depth_image = depth_image.inpaint(
rescale_factor=config['inpaint_rescale_factor'])
detector = RgbdDetectorFactory.detector('point_cloud_box')
detection = detector.detect(inpainted_color_image,
inpainted_depth_image,
detector_cfg,
camera_intrinsics,
T_camera_world,
vis_foreground=False,
vis_segmentation=False)[0]
if config['vis']['vis_detector_output']:
vis.figure()
vis.subplot(1, 2, 1)
vis.imshow(detection.color_thumbnail)
vis.subplot(1, 2, 2)
vis.imshow(detection.depth_thumbnail)
vis.show()
boundingBox = BoundingBox()
boundingBox.minY = detection.bounding_box.min_pt[0]
boundingBox.minX = detection.bounding_box.min_pt[1]
boundingBox.maxY = detection.bounding_box.max_pt[0]
boundingBox.maxX = detection.bounding_box.max_pt[1]
try:
start_time = time.time()
planned_grasp_data = plan_grasp(inpainted_color_image.rosmsg,
inpainted_depth_image.rosmsg,
camera_intrinsics.rosmsg,
boundingBox)
grasp_plan_time = time.time() - start_time
lift_gripper_width, T_gripper_world = process_GQCNNGrasp(
planned_grasp_data, robot, left_arm, right_arm, subscriber,
home_pose, config)
# get human label
human_input = raw_input('Grasp success, or grasp failure? [s/f] ')
while human_input.lower() != 's' and human_input.lower() != 'f':
logging.info(
'Did not understand input. Please answer \'s\' or \'f\'')
human_input = raw_input(
'Grasp success, or grasp failure? [s/f] ')
if human_input.lower() == 's':
experiment_logger.update_trial_attribute('human_label', 1)
else:
experiment_logger.update_trial_attribute('human_label', 0)
# log result
experiment_logger.update_trial_attribute('gripper_pose',
T_gripper_world)
experiment_logger.update_trial_attribute('planning_time',
grasp_plan_time)
experiment_logger.update_trial_attribute('gripper_width',
lift_gripper_width)
experiment_logger.update_trial_attribute('found_grasp', 1)
experiment_logger.update_trial_attribute('completed', True)
experiment_logger.update_trial_attribute('object_key', obj)
trial_number += 1
except rospy.ServiceException as e:
rospy.logerr("Service call failed: \n %s" % e)
experiment_logger.update_trial_attribute('found_grasp', 0)
experiment_logger.update_trial_attribute('completed', True)
experiment_logger.update_trial_attribute('object_key', obj)
trial_number += 1
except (YuMiCommException, YuMiControlException) as yce:
rospy.logerr(str(yce))
if sensor is not None:
sensor.stop()
if robot is not None:
robot.stop()
if subscriber is not None and subscriber._started:
subscriber.stop()
rospy.loginfo("Re-trying")
re_try = True
robot, subscriber, left_arm, right_arm, home_pose = init_robot(
config)
def generate_gqcnn_dataset(dataset_path, database, target_object_keys,
env_rv_params, gripper_name, config):
"""
Generates a GQ-CNN TensorDataset for training models with new grippers, quality metrics, objects, and cameras.
Parameters
----------
dataset_path : str
path to save the dataset to
database : :obj:`Hdf5Database`
Dex-Net database containing the 3D meshes, grasps, and grasp metrics
target_object_keys : :obj:`OrderedDict`
dictionary mapping dataset names to target objects (either 'all' or a list of specific object keys)
env_rv_params : :obj:`OrderedDict`
parameters of the camera and object random variables used in sampling (see meshpy.UniformPlanarWorksurfaceImageRandomVariable for more info)
gripper_name : str
name of the gripper to use
config : :obj:`autolab_core.YamlConfig`
other parameters for dataset generation
Notes
-----
Required parameters of config are specified in Other Parameters
Other Parameters
----------------
images_per_stable_pose : int
number of object and camera poses to sample for each stable pose
stable_pose_min_p : float
minimum probability of occurrence for a stable pose to be used in data generation (used to prune bad stable poses
gqcnn/crop_width : int
width, in pixels, of crop region around each grasp center, before resize (changes the size of the region seen by the GQ-CNN)
gqcnn/crop_height : int
height, in pixels, of crop region around each grasp center, before resize (changes the size of the region seen by the GQ-CNN)
gqcnn/final_width : int
width, in pixels, of final transformed grasp image for input to the GQ-CNN (defaults to 32)
gqcnn/final_height : int
height, in pixels, of final transformed grasp image for input to the GQ-CNN (defaults to 32)
table_alignment/max_approach_table_angle : float
max angle between the grasp axis and the table normal when the grasp approach is maximally aligned with the table normal
table_alignment/max_approach_offset : float
max deviation from perpendicular approach direction to use in grasp collision checking
table_alignment/num_approach_offset_samples : int
number of approach samples to use in collision checking
collision_checking/table_offset : float
max allowable interpenetration between the gripper and table to be considered collision free
collision_checking/table_mesh_filename : str
path to a table mesh for collision checking (default data/meshes/table.obj)
collision_checking/approach_dist : float
distance, in meters, between the approach pose and final grasp pose along the grasp axis
collision_checking/delta_approach : float
amount, in meters, to discretize the straight-line path from the gripper approach pose to the final grasp pose
tensors/datapoints_per_file : int
number of datapoints to store in each unique tensor file on disk
tensors/fields : :obj:`dict`
dictionary mapping field names to dictionaries specifying the data type, height, width, and number of channels for each tensor
debug : bool
True (or 1) if the random seed should be set to enforce deterministic behavior, False (0) otherwise
vis/candidate_grasps : bool
True (or 1) if the collision free candidate grasps should be displayed in 3D (for debugging)
vis/rendered_images : bool
True (or 1) if the rendered images for each stable pose should be displayed (for debugging)
vis/grasp_images : bool
True (or 1) if the transformed grasp images should be displayed (for debugging)
"""
# read data gen params
output_dir = dataset_path
gripper = RobotGripper.load(gripper_name)
image_samples_per_stable_pose = config['images_per_stable_pose']
stable_pose_min_p = config['stable_pose_min_p']
# read gqcnn params
gqcnn_params = config['gqcnn']
im_crop_height = gqcnn_params['crop_height']
im_crop_width = gqcnn_params['crop_width']
im_final_height = gqcnn_params['final_height']
im_final_width = gqcnn_params['final_width']
cx_crop = float(im_crop_width) / 2
cy_crop = float(im_crop_height) / 2
# open database
dataset_names = target_object_keys.keys()
datasets = [database.dataset(dn) for dn in dataset_names]
# set target objects
for dataset in datasets:
if target_object_keys[dataset.name] == 'all':
target_object_keys[dataset.name] = dataset.object_keys
# setup grasp params
table_alignment_params = config['table_alignment']
min_grasp_approach_offset = -np.deg2rad(
table_alignment_params['max_approach_offset'])
max_grasp_approach_offset = np.deg2rad(
table_alignment_params['max_approach_offset'])
max_grasp_approach_table_angle = np.deg2rad(
table_alignment_params['max_approach_table_angle'])
num_grasp_approach_samples = table_alignment_params[
'num_approach_offset_samples']
phi_offsets = []
if max_grasp_approach_offset == min_grasp_approach_offset:
phi_inc = 1
elif num_grasp_approach_samples == 1:
phi_inc = max_grasp_approach_offset - min_grasp_approach_offset + 1
else:
phi_inc = (max_grasp_approach_offset - min_grasp_approach_offset) / (
num_grasp_approach_samples - 1)
phi = min_grasp_approach_offset
while phi <= max_grasp_approach_offset:
phi_offsets.append(phi)
phi += phi_inc
# setup collision checking
coll_check_params = config['collision_checking']
approach_dist = coll_check_params['approach_dist']
delta_approach = coll_check_params['delta_approach']
table_offset = coll_check_params['table_offset']
table_mesh_filename = coll_check_params['table_mesh_filename']
if not os.path.isabs(table_mesh_filename):
table_mesh_filename = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..',
table_mesh_filename)
table_mesh = ObjFile(table_mesh_filename).read()
# set tensor dataset config
tensor_config = config['tensors']
tensor_config['fields']['depth_ims_tf_table']['height'] = im_final_height
tensor_config['fields']['depth_ims_tf_table']['width'] = im_final_width
tensor_config['fields']['color_ims_tf_table']['height'] = im_final_height
tensor_config['fields']['color_ims_tf_table']['width'] = im_final_height
tensor_config['fields']['obj_masks']['height'] = im_final_height
tensor_config['fields']['obj_masks']['width'] = im_final_width
# add available metrics (assuming same are computed for all objects)
metric_names = []
dataset = datasets[0]
obj_keys = dataset.object_keys
if len(obj_keys) == 0:
raise ValueError('No valid objects in dataset %s' % (dataset.name))
obj = dataset[obj_keys[0]]
grasps = dataset.grasps(obj.key, gripper=gripper.name)
grasp_metrics = dataset.grasp_metrics(obj.key,
grasps,
gripper=gripper.name)
metric_names = grasp_metrics[grasp_metrics.keys()[0]].keys()
for metric_name in metric_names:
tensor_config['fields'][metric_name] = {}
tensor_config['fields'][metric_name]['dtype'] = 'float32'
# init tensor dataset
tensor_dataset = TensorDataset(output_dir, tensor_config)
tensor_datapoint = tensor_dataset.datapoint_template
# setup log file
experiment_log_filename = os.path.join(output_dir,
'dataset_generation.log')
formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s')
hdlr = logging.FileHandler(experiment_log_filename)
hdlr.setFormatter(formatter)
logging.getLogger().addHandler(hdlr)
root_logger = logging.getLogger()
# copy config
out_config_filename = os.path.join(output_dir, 'dataset_generation.json')
ordered_dict_config = collections.OrderedDict()
for key in config.keys():
ordered_dict_config[key] = config[key]
with open(out_config_filename, 'w') as outfile:
json.dump(ordered_dict_config, outfile)
# collectImage = collect_image.CollectImage(MESH_PATH)
# 1. Precompute the set of valid grasps for each stable pose:
# i) Perpendicular to the table
# ii) Collision-free along the approach direction
# load grasps if they already exist
grasp_cache_filename = os.path.join(output_dir, CACHE_FILENAME)
if os.path.exists(grasp_cache_filename):
logging.info('Loading grasp candidates from file')
candidate_grasps_dict = pkl.load(open(grasp_cache_filename, 'rb'))
# otherwise re-compute by reading from the database and enforcing constraints
else:
# create grasps dict
candidate_grasps_dict = {}
# loop through datasets and objects
for dataset in datasets:
logging.info('Reading dataset %s' % (dataset.name))
for obj in dataset:
if obj.key not in target_object_keys[dataset.name]:
continue
obj.mesh.trimesh.export(MESH_PATH)
collectImage.load_new_object()
# init candidate grasp storage
candidate_grasps_dict[obj.key] = {}
# collision_checker.set_graspable_object(obj)
# read in the stable poses of the mesh
stable_poses = dataset.stable_poses(obj.key)
for i, stable_pose in enumerate(stable_poses):
# render images if stable pose is valid
if stable_pose.p > stable_pose_min_p:
candidate_grasps_dict[obj.key][stable_pose.id] = []
# setup table in collision checker
# T_obj_stp = stable_pose.T_obj_table.as_frames('obj', 'stp')
# T_obj_table = obj.mesh.get_T_surface_obj(T_obj_stp, delta=table_offset).as_frames('obj', 'table')
# T_table_obj = T_obj_table.inverse()
# collision_checker.set_table(table_mesh_filename, T_table_obj)
# read grasp and metrics
grasps = dataset.grasps(obj.key, gripper=gripper.name)
logging.info(
'Aligning %d grasps for object %s in stable %s' %
(len(grasps), obj.key, stable_pose.id))
T_obj_stp = stable_pose.T_obj_table.as_frames(
'obj', 'stp')
T_obj_stp = obj.mesh.get_T_surface_obj(T_obj_stp)
pose_matrix = np.eye(4, 4)
pose_matrix[:3, :3] = T_obj_stp.rotation
pose_matrix[:3, 3] = T_obj_stp.translation
collectImage.drop_object(pose_matrix)
# align grasps with the table
aligned_grasps = [
grasp.perpendicular_table(stable_pose)
for grasp in grasps
]
# check grasp validity
logging.info(
'Checking collisions for %d grasps for object %s in stable %s'
% (len(grasps), obj.key, stable_pose.id))
for aligned_grasp in aligned_grasps:
# check angle with table plane and skip unaligned grasps
_, grasp_approach_table_angle, _ = aligned_grasp.grasp_angles_from_stp_z(
stable_pose)
perpendicular_table = (
np.abs(grasp_approach_table_angle) <
max_grasp_approach_table_angle)
if not perpendicular_table:
continue
# check whether the aligned grasp is collision free and if it is successful
gripper_pose = np.eye(4, 4)
center_world = np.matmul(pose_matrix, [
aligned_grasp.center[0],
aligned_grasp.center[1],
aligned_grasp.center[2], 1
])
axis_world = np.matmul(pose_matrix, [
aligned_grasp.axis_[0], aligned_grasp.axis_[1],
aligned_grasp.axis_[2], 1
])
gripper_angle = math.atan2(axis_world[1],
axis_world[0])
gripper_pose[:3, 3] = center_world[:3]
cos_angle = math.cos(gripper_angle)
sin_angle = math.sin(gripper_angle)
gripper_pose[0, 0] = cos_angle
gripper_pose[0, 1] = -sin_angle
gripper_pose[1, 0] = sin_angle
gripper_pose[1, 1] = cos_angle
collision_free = not collectImage.check_collision(
gripper_pose)
# collision_free = True
# store if aligned to table
candidate_grasps_dict[obj.key][
stable_pose.id].append(
GraspInfo(aligned_grasp, collision_free))
# visualize if specified
if collision_free and config['vis'][
'candidate_grasps']:
logging.info('Grasp %d' % (aligned_grasp.id))
vis.figure()
vis.gripper_on_object(gripper, aligned_grasp,
obj,
stable_pose.T_obj_world)
vis.show()
# save to file
logging.info('Saving to file')
pkl.dump(candidate_grasps_dict, open(grasp_cache_filename, 'wb'))
# collectImage.stop()
# 2. Render a dataset of images and associate the gripper pose with image coordinates for each grasp in the Dex-Net database
# setup variables
obj_category_map = {}
pose_category_map = {}
cur_pose_label = 0
cur_obj_label = 0
cur_image_label = 0
# render images for each stable pose of each object in the dataset
render_modes = [
RenderMode.SEGMASK, RenderMode.DEPTH_SCENE, RenderMode.COLOR
]
for dataset in datasets:
logging.info('Generating data for dataset %s' % (dataset.name))
# iterate through all object keys
object_keys = dataset.object_keys
for obj_key in object_keys:
obj = dataset[obj_key]
if obj.key not in target_object_keys[dataset.name]:
continue
obj.mesh.trimesh.export(MESH_PATH)
# collectImage.load_new_object()
# read in the stable poses of the mesh
stable_poses = dataset.stable_poses(obj.key)
for i, stable_pose in enumerate(stable_poses):
# render images if stable pose is valid
if stable_pose.p > stable_pose_min_p:
# log progress
logging.info('Rendering images for object %s in %s' %
(obj.key, stable_pose.id))
# add to category maps
if obj.key not in obj_category_map.keys():
obj_category_map[obj.key] = cur_obj_label
pose_category_map['%s_%s' %
(obj.key,
stable_pose.id)] = cur_pose_label
# read in candidate grasps and metrics
candidate_grasp_info = candidate_grasps_dict[obj.key][
stable_pose.id]
candidate_grasps = [g.grasp for g in candidate_grasp_info]
grasp_metrics = dataset.grasp_metrics(obj.key,
candidate_grasps,
gripper=gripper.name)
# compute object pose relative to the table
T_obj_stp = stable_pose.T_obj_table.as_frames('obj', 'stp')
T_obj_stp = obj.mesh.get_T_surface_obj(T_obj_stp)
t_stable_pose = np.array(
[0, 0, -stable_pose.r.dot(stable_pose.x0)[2]])
T_stable_pose = RigidTransform(rotation=stable_pose.r,
translation=t_stable_pose,
from_frame='obj',
to_frame='stp')
# pose_matrix = np.eye(4,4)
# pose_matrix[:3,:3] = T_stable_pose.rotation
# pose_matrix[:3, 3] = T_stable_pose.translation
# collectImage.drop_object(pose_matrix)
collectImage = None
# sample images from random variable
T_table_obj = RigidTransform(from_frame='table',
to_frame='obj')
scene_objs = {
'table': SceneObject(table_mesh, T_table_obj)
}
urv = UniformPlanarWorksurfaceImageRandomVariable(
obj.mesh,
render_modes,
'camera',
env_rv_params,
stable_pose=stable_pose,
scene_objs=scene_objs,
sim=collectImage)
render_start = time.time()
render_samples = urv.rvs(
size=image_samples_per_stable_pose)
render_stop = time.time()
logging.info('Rendering images took %.3f sec' %
(render_stop - render_start))
# visualize
if config['vis']['rendered_images']:
d = int(np.ceil(
np.sqrt(image_samples_per_stable_pose)))
# binary
vis2d.figure()
for j, render_sample in enumerate(render_samples):
vis2d.subplot(d, d, j + 1)
vis2d.imshow(render_sample.renders[
RenderMode.SEGMASK].image)
# depth table
vis2d.figure()
for j, render_sample in enumerate(render_samples):
vis2d.subplot(d, d, j + 1)
vis2d.imshow(render_sample.renders[
RenderMode.DEPTH_SCENE].image)
vis2d.show()
# tally total amount of data
num_grasps = len(candidate_grasps)
num_images = image_samples_per_stable_pose
num_save = num_images * num_grasps
logging.info('Saving %d datapoints' % (num_save))
# for each candidate grasp on the object compute the projection
# of the grasp into image space
for render_sample in render_samples:
# read images
binary_im = render_sample.renders[
RenderMode.SEGMASK].image
depth_im_table = render_sample.renders[
RenderMode.DEPTH_SCENE].image
color_im_table = render_sample.renders[
RenderMode.COLOR].image
# read camera params
T_stp_camera = render_sample.camera.object_to_camera_pose
shifted_camera_intr = render_sample.camera.camera_intr
# read pixel offsets
cx = depth_im_table.center[1]
cy = depth_im_table.center[0]
# compute intrinsics for virtual camera of the final
# cropped and rescaled images
camera_intr_scale = float(im_final_height) / float(
im_crop_height)
cropped_camera_intr = shifted_camera_intr.crop(
im_crop_height, im_crop_width, cy, cx)
final_camera_intr = cropped_camera_intr.resize(
camera_intr_scale)
# create a thumbnail for each grasp
for grasp_info in candidate_grasp_info:
# read info
grasp = grasp_info.grasp
collision_free = grasp_info.collision_free
# get the gripper pose
T_obj_camera = T_stp_camera * T_obj_stp.as_frames(
'obj', T_stp_camera.from_frame)
grasp_2d = grasp.project_camera(
T_obj_camera, shifted_camera_intr)
# center images on the grasp, rotate to image x axis
dx = cx - grasp_2d.center.x
dy = cy - grasp_2d.center.y
translation = np.array([dy, dx])
binary_im_tf = binary_im.transform(
translation, grasp_2d.angle)
depth_im_tf_table = depth_im_table.transform(
translation, grasp_2d.angle)
color_im_tf_table = color_im_table.transform(
translation, grasp_2d.angle)
# crop to image size
binary_im_tf = binary_im_tf.crop(
im_crop_height, im_crop_width)
depth_im_tf_table = depth_im_tf_table.crop(
im_crop_height, im_crop_width)
color_im_tf_table = color_im_tf_table.crop(
im_crop_height, im_crop_width)
# resize to image size
binary_im_tf = binary_im_tf.resize(
(im_final_height, im_final_width),
interp='nearest')
depth_im_tf_table = depth_im_tf_table.resize(
(im_final_height, im_final_width))
color_im_tf_table = color_im_tf_table.resize(
(im_final_height, im_final_width))
# visualize the transformed images
if config['vis']['grasp_images']:
grasp_center = Point(
depth_im_tf_table.center,
frame=final_camera_intr.frame)
tf_grasp_2d = Grasp2D(
grasp_center,
0,
grasp_2d.depth,
width=gripper.max_width,
camera_intr=final_camera_intr)
# plot 2D grasp image
vis2d.figure()
vis2d.subplot(2, 2, 1)
vis2d.imshow(binary_im)
vis2d.grasp(grasp_2d)
vis2d.subplot(2, 2, 2)
vis2d.imshow(depth_im_table)
vis2d.grasp(grasp_2d)
vis2d.subplot(2, 2, 3)
vis2d.imshow(binary_im_tf)
vis2d.grasp(tf_grasp_2d)
vis2d.subplot(2, 2, 4)
vis2d.imshow(depth_im_tf_table)
vis2d.grasp(tf_grasp_2d)
vis2d.title('Coll Free? %d' %
(grasp_info.collision_free))
vis2d.show()
# plot 3D visualization
vis.figure()
T_obj_world = vis.mesh_stable_pose(
obj.mesh.trimesh,
stable_pose.T_obj_world,
style='surface',
dim=0.5)
vis.gripper(gripper,
grasp,
T_obj_world,
color=(0.3, 0.3, 0.3))
vis.show()
# form hand pose array
hand_pose = np.r_[grasp_2d.center.y,
grasp_2d.center.x,
grasp_2d.depth, grasp_2d.angle,
grasp_2d.center.y -
shifted_camera_intr.cy,
grasp_2d.center.x -
shifted_camera_intr.cx,
grasp_2d.width_px]
# store to data buffers
tensor_datapoint[
'depth_ims_tf_table'] = depth_im_tf_table.raw_data
tensor_datapoint[
'color_ims_tf_table'] = color_im_tf_table.raw_data
tensor_datapoint[
'obj_masks'] = binary_im_tf.raw_data
tensor_datapoint['hand_poses'] = hand_pose
tensor_datapoint['collision_free'] = collision_free
tensor_datapoint['obj_labels'] = cur_obj_label
tensor_datapoint['pose_labels'] = cur_pose_label
tensor_datapoint['image_labels'] = cur_image_label
for metric_name, metric_val in grasp_metrics[
grasp.id].iteritems():
coll_free_metric = (
1 * collision_free) * metric_val
tensor_datapoint[
metric_name] = coll_free_metric
tensor_dataset.add(tensor_datapoint)
# update image label
cur_image_label += 1
# update pose label
cur_pose_label += 1
# force clean up
gc.collect()
# update object label
cur_obj_label += 1
# force clean up
gc.collect()
# stop simulation
# collectImage.stop()
# save last file
tensor_dataset.flush()
# save category mappings
obj_cat_filename = os.path.join(output_dir, 'object_category_map.json')
json.dump(obj_category_map, open(obj_cat_filename, 'w'))
pose_cat_filename = os.path.join(output_dir, 'pose_category_map.json')
json.dump(pose_category_map, open(pose_cat_filename, 'w'))
def _plan_grasp(self,
color_im,
depth_im,
camera_intr,
bounding_box=None,
segmask=None):
""" Grasp planner request handler .
Parameters
---------
req: :obj:`ROS ServiceRequest`
ROS ServiceRequest for grasp planner service
"""
rospy.loginfo('Planning Grasp')
# inpaint images
color_im = color_im.inpaint(
rescale_factor=self.cfg['inpaint_rescale_factor'])
depth_im = depth_im.inpaint(
rescale_factor=self.cfg['inpaint_rescale_factor'])
# init segmask
if segmask is None:
segmask = BinaryImage(255 *
np.ones(depth_im.shape).astype(np.uint8),
frame=color_im.frame)
# visualize
if self.cfg['vis']['color_image']:
vis.imshow(color_im)
vis.show()
if self.cfg['vis']['depth_image']:
vis.imshow(depth_im)
vis.show()
if self.cfg['vis']['segmask'] and segmask is not None:
vis.imshow(segmask)
vis.show()
# aggregate color and depth images into a single perception rgbdimage
rgbd_im = RgbdImage.from_color_and_depth(color_im, depth_im)
# mask bounding box
if bounding_box is not None:
# calc bb parameters
min_x = bounding_box.minX
min_y = bounding_box.minY
max_x = bounding_box.maxX
max_y = bounding_box.maxY
# contain box to image->don't let it exceed image height/width bounds
if min_x < 0:
min_x = 0
if min_y < 0:
min_y = 0
if max_x > rgbd_im.width:
max_x = rgbd_im.width
if max_y > rgbd_im.height:
max_y = rgbd_im.height
# mask
bb_segmask_arr = np.zeros([rgbd_image.height, rgbd_image.width])
bb_segmask_arr[min_y:max_y, min_x:max_x] = 255
bb_segmask = BinaryImage(bb_segmask_arr.astype(np.uint8),
segmask.frame)
segmask = segmask.mask_binary(bb_segmask)
# visualize
if self.cfg['vis']['rgbd_state']:
masked_rgbd_im = rgbd_im.mask_binary(segmask)
vis.figure()
vis.subplot(1, 2, 1)
vis.imshow(masked_rgbd_im.color)
vis.subplot(1, 2, 2)
vis.imshow(masked_rgbd_im.depth)
vis.show()
# create an RGBDImageState with the cropped RGBDImage and CameraIntrinsics
rgbd_state = RgbdImageState(rgbd_im, camera_intr, segmask=segmask)
# execute policy
try:
return self.execute_policy(rgbd_state, self.grasping_policy,
self.grasp_pose_publisher,
camera_intr.frame)
except NoValidGraspsException:
rospy.logerr(
'While executing policy found no valid grasps from sampled antipodal point pairs. Aborting Policy!'
)
raise rospy.ServiceException(
'While executing policy found no valid grasps from sampled antipodal point pairs. Aborting Policy!'
)
def _plot(self, model_dir, model_output_dir, train_result, val_result):
"""Plot analysis curves."""
self.logger.info("Plotting")
_, model_name = os.path.split(model_output_dir)
# Set params.
colors = ["g", "b", "c", "y", "m", "r"]
styles = ["-", "--", "-.", ":", "-"]
# PR, ROC.
vis2d.clf()
train_result.precision_recall_curve(plot=True,
line_width=self.line_width,
color=colors[0],
style=styles[0],
label="TRAIN")
val_result.precision_recall_curve(plot=True,
line_width=self.line_width,
color=colors[1],
style=styles[1],
label="VAL")
vis2d.title("Precision Recall Curves", fontsize=self.font_size)
handles, labels = vis2d.gca().get_legend_handles_labels()
vis2d.legend(handles, labels, loc="best")
figname = os.path.join(model_output_dir, "precision_recall.png")
vis2d.savefig(figname, dpi=self.dpi)
vis2d.clf()
train_result.roc_curve(plot=True,
line_width=self.line_width,
color=colors[0],
style=styles[0],
label="TRAIN")
val_result.roc_curve(plot=True,
line_width=self.line_width,
color=colors[1],
style=styles[1],
label="VAL")
vis2d.title("Reciever Operating Characteristic",
fontsize=self.font_size)
handles, labels = vis2d.gca().get_legend_handles_labels()
vis2d.legend(handles, labels, loc="best")
figname = os.path.join(model_output_dir, "roc.png")
vis2d.savefig(figname, dpi=self.dpi)
# Plot histogram of prediction errors.
num_bins = min(self.num_bins, train_result.num_datapoints)
# Train positives.
pos_ind = np.where(train_result.labels == 1)[0]
diffs = np.abs(train_result.labels[pos_ind] -
train_result.pred_probs[pos_ind])
vis2d.figure()
utils.histogram(diffs,
num_bins,
bounds=(0, 1),
normalized=False,
plot=True)
vis2d.title("Error on Positive Training Examples",
fontsize=self.font_size)
vis2d.xlabel("Abs Prediction Error", fontsize=self.font_size)
vis2d.ylabel("Count", fontsize=self.font_size)
figname = os.path.join(model_output_dir,
"pos_train_errors_histogram.png")
vis2d.savefig(figname, dpi=self.dpi)
# Train negatives.
neg_ind = np.where(train_result.labels == 0)[0]
diffs = np.abs(train_result.labels[neg_ind] -
train_result.pred_probs[neg_ind])
vis2d.figure()
utils.histogram(diffs,
num_bins,
bounds=(0, 1),
normalized=False,
plot=True)
vis2d.title("Error on Negative Training Examples",
fontsize=self.font_size)
vis2d.xlabel("Abs Prediction Error", fontsize=self.font_size)
vis2d.ylabel("Count", fontsize=self.font_size)
figname = os.path.join(model_output_dir,
"neg_train_errors_histogram.png")
vis2d.savefig(figname, dpi=self.dpi)
# Histogram of validation errors.
num_bins = min(self.num_bins, val_result.num_datapoints)
# Val positives.
pos_ind = np.where(val_result.labels == 1)[0]
diffs = np.abs(val_result.labels[pos_ind] -
val_result.pred_probs[pos_ind])
vis2d.figure()
utils.histogram(diffs,
num_bins,
bounds=(0, 1),
normalized=False,
plot=True)
vis2d.title("Error on Positive Validation Examples",
fontsize=self.font_size)
vis2d.xlabel("Abs Prediction Error", fontsize=self.font_size)
vis2d.ylabel("Count", fontsize=self.font_size)
figname = os.path.join(model_output_dir,
"pos_val_errors_histogram.png")
vis2d.savefig(figname, dpi=self.dpi)
# Val negatives.
neg_ind = np.where(val_result.labels == 0)[0]
diffs = np.abs(val_result.labels[neg_ind] -
val_result.pred_probs[neg_ind])
vis2d.figure()
utils.histogram(diffs,
num_bins,
bounds=(0, 1),
normalized=False,
plot=True)
vis2d.title("Error on Negative Validation Examples",
fontsize=self.font_size)
vis2d.xlabel("Abs Prediction Error", fontsize=self.font_size)
vis2d.ylabel("Count", fontsize=self.font_size)
figname = os.path.join(model_output_dir,
"neg_val_errors_histogram.png")
vis2d.savefig(figname, dpi=self.dpi)
# Losses.
try:
train_errors_filename = os.path.join(model_dir,
GQCNNFilenames.TRAIN_ERRORS)
val_errors_filename = os.path.join(model_dir,
GQCNNFilenames.VAL_ERRORS)
val_iters_filename = os.path.join(model_dir,
GQCNNFilenames.VAL_ITERS)
pct_pos_val_filename = os.path.join(model_dir,
GQCNNFilenames.PCT_POS_VAL)
train_losses_filename = os.path.join(model_dir,
GQCNNFilenames.TRAIN_LOSSES)
raw_train_errors = np.load(train_errors_filename)
val_errors = np.load(val_errors_filename)
val_iters = np.load(val_iters_filename)
pct_pos_val = float(val_errors[0])
if os.path.exists(pct_pos_val_filename):
pct_pos_val = 100.0 * np.load(pct_pos_val_filename)
raw_train_losses = np.load(train_losses_filename)
val_errors = np.r_[pct_pos_val, val_errors]
val_iters = np.r_[0, val_iters]
# Window the training error.
i = 0
train_errors = []
train_losses = []
train_iters = []
while i < raw_train_errors.shape[0]:
train_errors.append(np.mean(raw_train_errors[i:i + WINDOW]))
train_losses.append(np.mean(raw_train_losses[i:i + WINDOW]))
train_iters.append(i)
i += WINDOW
train_errors = np.array(train_errors)
train_losses = np.array(train_losses)
train_iters = np.array(train_iters)
init_val_error = val_errors[0]
norm_train_errors = train_errors / init_val_error
norm_val_errors = val_errors / init_val_error
norm_final_val_error = val_result.error_rate / val_errors[0]
if pct_pos_val > 0:
norm_final_val_error = val_result.error_rate / pct_pos_val
vis2d.clf()
vis2d.plot(train_iters,
train_errors,
linewidth=self.line_width,
color="b")
vis2d.plot(val_iters,
val_errors,
linewidth=self.line_width,
color="g")
vis2d.ylim(0, 100)
vis2d.legend(("TRAIN (Minibatch)", "VAL"),
fontsize=self.font_size,
loc="best")
vis2d.xlabel("Iteration", fontsize=self.font_size)
vis2d.ylabel("Error Rate", fontsize=self.font_size)
vis2d.title("Error Rate vs Training Iteration",
fontsize=self.font_size)
figname = os.path.join(model_output_dir,
"training_error_rates.png")
vis2d.savefig(figname, dpi=self.dpi)
vis2d.clf()
vis2d.plot(train_iters, norm_train_errors, linewidth=4, color="b")
vis2d.plot(val_iters, norm_val_errors, linewidth=4, color="g")
vis2d.ylim(0, 2.0)
vis2d.legend(("TRAIN (Minibatch)", "VAL"),
fontsize=self.font_size,
loc="best")
vis2d.xlabel("Iteration", fontsize=self.font_size)
vis2d.ylabel("Normalized Error Rate", fontsize=self.font_size)
vis2d.title("Normalized Error Rate vs Training Iteration",
fontsize=self.font_size)
figname = os.path.join(model_output_dir,
"training_norm_error_rates.png")
vis2d.savefig(figname, dpi=self.dpi)
train_losses[train_losses > MAX_LOSS] = MAX_LOSS # CAP LOSSES.
vis2d.clf()
vis2d.plot(train_iters,
train_losses,
linewidth=self.line_width,
color="b")
vis2d.ylim(0, 2.0)
vis2d.xlabel("Iteration", fontsize=self.font_size)
vis2d.ylabel("Loss", fontsize=self.font_size)
vis2d.title("Training Loss vs Iteration", fontsize=self.font_size)
figname = os.path.join(model_output_dir, "training_losses.png")
vis2d.savefig(figname, dpi=self.dpi)
# Log.
self.logger.info("TRAIN")
self.logger.info("Original error: %.3f" % (train_errors[0]))
self.logger.info("Final error: %.3f" % (train_result.error_rate))
self.logger.info("Orig loss: %.3f" % (train_losses[0]))
self.logger.info("Final loss: %.3f" % (train_losses[-1]))
self.logger.info("VAL")
self.logger.info("Original error: %.3f" % (pct_pos_val))
self.logger.info("Final error: %.3f" % (val_result.error_rate))
self.logger.info("Normalized error: %.3f" % (norm_final_val_error))
return (train_errors[0], train_result.error_rate, train_losses[0],
train_losses[-1], pct_pos_val, val_result.error_rate,
norm_final_val_error)
except Exception as e:
self.logger.error("Failed to plot training curves!\n" + str(e))
def _run_prediction_single_model(self, model_dir, model_output_dir,
dataset_config):
"""Analyze the performance of a single model."""
# Read in model config.
model_config_filename = os.path.join(model_dir,
GQCNNFilenames.SAVED_CFG)
with open(model_config_filename) as data_file:
model_config = json.load(data_file)
# Load model.
self.logger.info("Loading model %s" % (model_dir))
log_file = None
for handler in self.logger.handlers:
if isinstance(handler, logging.FileHandler):
log_file = handler.baseFilename
gqcnn = get_gqcnn_model(verbose=self.verbose).load(
model_dir, verbose=self.verbose, log_file=log_file)
gqcnn.open_session()
gripper_mode = gqcnn.gripper_mode
angular_bins = gqcnn.angular_bins
# Read params from the config.
if dataset_config is None:
dataset_dir = model_config["dataset_dir"]
split_name = model_config["split_name"]
image_field_name = model_config["image_field_name"]
pose_field_name = model_config["pose_field_name"]
metric_name = model_config["target_metric_name"]
metric_thresh = model_config["metric_thresh"]
else:
dataset_dir = dataset_config["dataset_dir"]
split_name = dataset_config["split_name"]
image_field_name = dataset_config["image_field_name"]
pose_field_name = dataset_config["pose_field_name"]
metric_name = dataset_config["target_metric_name"]
metric_thresh = dataset_config["metric_thresh"]
gripper_mode = dataset_config["gripper_mode"]
self.logger.info("Loading dataset %s" % (dataset_dir))
dataset = TensorDataset.open(dataset_dir)
train_indices, val_indices, _ = dataset.split(split_name)
# Visualize conv filters.
conv1_filters = gqcnn.filters
num_filt = conv1_filters.shape[3]
d = utils.sqrt_ceil(num_filt)
vis2d.clf()
for k in range(num_filt):
filt = conv1_filters[:, :, 0, k]
vis2d.subplot(d, d, k + 1)
vis2d.imshow(DepthImage(filt))
figname = os.path.join(model_output_dir, "conv1_filters.pdf")
vis2d.savefig(figname, dpi=self.dpi)
# Aggregate training and validation true labels and predicted
# probabilities.
all_predictions = []
if angular_bins > 0:
all_predictions_raw = []
all_labels = []
for i in range(dataset.num_tensors):
# Log progress.
if i % self.log_rate == 0:
self.logger.info("Predicting tensor %d of %d" %
(i + 1, dataset.num_tensors))
# Read in data.
image_arr = dataset.tensor(image_field_name, i).arr
pose_arr = read_pose_data(
dataset.tensor(pose_field_name, i).arr, gripper_mode)
metric_arr = dataset.tensor(metric_name, i).arr
label_arr = 1 * (metric_arr > metric_thresh)
label_arr = label_arr.astype(np.uint8)
if angular_bins > 0:
# Form mask to extract predictions from ground-truth angular
# bins.
raw_poses = dataset.tensor(pose_field_name, i).arr
angles = raw_poses[:, 3]
neg_ind = np.where(angles < 0)
# TODO(vsatish): These should use the max angle instead.
angles = np.abs(angles) % GeneralConstants.PI
angles[neg_ind] *= -1
g_90 = np.where(angles > (GeneralConstants.PI / 2))
l_neg_90 = np.where(angles < (-1 * (GeneralConstants.PI / 2)))
angles[g_90] -= GeneralConstants.PI
angles[l_neg_90] += GeneralConstants.PI
# TODO(vsatish): Fix this along with the others.
angles *= -1 # Hack to fix reverse angle convention.
angles += (GeneralConstants.PI / 2)
pred_mask = np.zeros((raw_poses.shape[0], angular_bins * 2),
dtype=bool)
bin_width = GeneralConstants.PI / angular_bins
for i in range(angles.shape[0]):
pred_mask[i, int((angles[i] // bin_width) * 2)] = True
pred_mask[i, int((angles[i] // bin_width) * 2 + 1)] = True
# Predict with GQ-CNN.
predictions = gqcnn.predict(image_arr, pose_arr)
if angular_bins > 0:
raw_predictions = np.array(predictions)
predictions = predictions[pred_mask].reshape((-1, 2))
# Aggregate.
all_predictions.extend(predictions[:, 1].tolist())
if angular_bins > 0:
all_predictions_raw.extend(raw_predictions.tolist())
all_labels.extend(label_arr.tolist())
# Close session.
gqcnn.close_session()
# Create arrays.
all_predictions = np.array(all_predictions)
all_labels = np.array(all_labels)
train_predictions = all_predictions[train_indices]
val_predictions = all_predictions[val_indices]
train_labels = all_labels[train_indices]
val_labels = all_labels[val_indices]
if angular_bins > 0:
all_predictions_raw = np.array(all_predictions_raw)
train_predictions_raw = all_predictions_raw[train_indices]
val_predictions_raw = all_predictions_raw[val_indices]
# Aggregate results.
train_result = BinaryClassificationResult(train_predictions,
train_labels)
val_result = BinaryClassificationResult(val_predictions, val_labels)
train_result.save(os.path.join(model_output_dir, "train_result.cres"))
val_result.save(os.path.join(model_output_dir, "val_result.cres"))
# Get stats, plot curves.
self.logger.info("Model %s training error rate: %.3f" %
(model_dir, train_result.error_rate))
self.logger.info("Model %s validation error rate: %.3f" %
(model_dir, val_result.error_rate))
self.logger.info("Model %s training loss: %.3f" %
(model_dir, train_result.cross_entropy_loss))
self.logger.info("Model %s validation loss: %.3f" %
(model_dir, val_result.cross_entropy_loss))
# Save images.
vis2d.figure()
example_dir = os.path.join(model_output_dir, "examples")
if not os.path.exists(example_dir):
os.mkdir(example_dir)
# Train.
self.logger.info("Saving training examples")
train_example_dir = os.path.join(example_dir, "train")
if not os.path.exists(train_example_dir):
os.mkdir(train_example_dir)
# Train TP.
true_positive_indices = train_result.true_positive_indices
np.random.shuffle(true_positive_indices)
true_positive_indices = true_positive_indices[:self.num_vis]
for i, j in enumerate(true_positive_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
"Datapoint %d: Pred: %.3f Label: %.3f" %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
"true_positive_%03d.png" % (i)))
# Train FP.
false_positive_indices = train_result.false_positive_indices
np.random.shuffle(false_positive_indices)
false_positive_indices = false_positive_indices[:self.num_vis]
for i, j in enumerate(false_positive_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
"Datapoint %d: Pred: %.3f Label: %.3f" %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
"false_positive_%03d.png" % (i)))
# Train TN.
true_negative_indices = train_result.true_negative_indices
np.random.shuffle(true_negative_indices)
true_negative_indices = true_negative_indices[:self.num_vis]
for i, j in enumerate(true_negative_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
"Datapoint %d: Pred: %.3f Label: %.3f" %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
"true_negative_%03d.png" % (i)))
# Train TP.
false_negative_indices = train_result.false_negative_indices
np.random.shuffle(false_negative_indices)
false_negative_indices = false_negative_indices[:self.num_vis]
for i, j in enumerate(false_negative_indices):
k = train_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=train_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title(
"Datapoint %d: Pred: %.3f Label: %.3f" %
(k, train_result.pred_probs[j], train_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(train_example_dir,
"false_negative_%03d.png" % (i)))
# Val.
self.logger.info("Saving validation examples")
val_example_dir = os.path.join(example_dir, "val")
if not os.path.exists(val_example_dir):
os.mkdir(val_example_dir)
# Val TP.
true_positive_indices = val_result.true_positive_indices
np.random.shuffle(true_positive_indices)
true_positive_indices = true_positive_indices[:self.num_vis]
for i, j in enumerate(true_positive_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title("Datapoint %d: Pred: %.3f Label: %.3f" %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, "true_positive_%03d.png" % (i)))
# Val FP.
false_positive_indices = val_result.false_positive_indices
np.random.shuffle(false_positive_indices)
false_positive_indices = false_positive_indices[:self.num_vis]
for i, j in enumerate(false_positive_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title("Datapoint %d: Pred: %.3f Label: %.3f" %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, "false_positive_%03d.png" % (i)))
# Val TN.
true_negative_indices = val_result.true_negative_indices
np.random.shuffle(true_negative_indices)
true_negative_indices = true_negative_indices[:self.num_vis]
for i, j in enumerate(true_negative_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title("Datapoint %d: Pred: %.3f Label: %.3f" %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, "true_negative_%03d.png" % (i)))
# Val TP.
false_negative_indices = val_result.false_negative_indices
np.random.shuffle(false_negative_indices)
false_negative_indices = false_negative_indices[:self.num_vis]
for i, j in enumerate(false_negative_indices):
k = val_indices[j]
datapoint = dataset.datapoint(
k, field_names=[image_field_name, pose_field_name])
vis2d.clf()
if angular_bins > 0:
self._plot_grasp(datapoint,
image_field_name,
pose_field_name,
gripper_mode,
angular_preds=val_predictions_raw[j])
else:
self._plot_grasp(datapoint, image_field_name, pose_field_name,
gripper_mode)
vis2d.title("Datapoint %d: Pred: %.3f Label: %.3f" %
(k, val_result.pred_probs[j], val_result.labels[j]),
fontsize=self.font_size)
vis2d.savefig(
os.path.join(val_example_dir, "false_negative_%03d.png" % (i)))
# Save summary stats.
train_summary_stats = {
"error_rate": train_result.error_rate,
"ap_score": train_result.ap_score,
"auc_score": train_result.auc_score,
"loss": train_result.cross_entropy_loss
}
train_stats_filename = os.path.join(model_output_dir,
"train_stats.json")
json.dump(train_summary_stats,
open(train_stats_filename, "w"),
indent=JSON_INDENT,
sort_keys=True)
val_summary_stats = {
"error_rate": val_result.error_rate,
"ap_score": val_result.ap_score,
"auc_score": val_result.auc_score,
"loss": val_result.cross_entropy_loss
}
val_stats_filename = os.path.join(model_output_dir, "val_stats.json")
json.dump(val_summary_stats,
open(val_stats_filename, "w"),
indent=JSON_INDENT,
sort_keys=True)
return train_result, val_result
def _sample_suction_points(self,
depth_im,
camera_intr,
num_samples,
segmask=None,
visualize=False):
"""
Sample a set of 2D suction point candidates from a depth image by
choosing points on an object surface uniformly at random
and then sampling around the surface normal
Parameters
----------
depth_im : :obj:'perception.DepthImage'
Depth image to sample from
camera_intr : :obj:`perception.CameraIntrinsics`
intrinsics of the camera that captured the images
num_samples : int
number of grasps to sample
segmask : :obj:`perception.BinaryImage`
binary image segmenting out the object of interest
visualize : bool
whether or not to show intermediate samples (for debugging)
Returns
-------
:obj:`list` of :obj:`SuctionPoint2D`
list of 2D suction point candidates
"""
# compute edge pixels
filter_start = time()
depth_im_mask = depth_im.copy()
if segmask is not None:
depth_im_mask = depth_im.mask_binary(segmask)
logging.debug('Filtering took %.3f sec' % (time() - filter_start))
if visualize:
vis.figure()
vis.subplot(1, 2, 1)
vis.imshow(depth_im)
vis.subplot(1, 2, 2)
vis.imshow(depth_im_mask)
vis.show()
# project to get the point cloud
cloud_start = time()
point_cloud_im = camera_intr.deproject_to_image(depth_im_mask)
normal_cloud_im = point_cloud_im.normal_cloud_im()
nonzero_px = depth_im_mask.nonzero_pixels()
num_nonzero_px = nonzero_px.shape[0]
if num_nonzero_px == 0:
return []
logging.debug('Normal cloud took %.3f sec' % (time() - cloud_start))
# randomly sample points and add to image
sample_start = time()
suction_points = []
k = 0
sample_size = min(self._max_num_samples, num_nonzero_px)
indices = np.random.choice(num_nonzero_px,
size=sample_size,
replace=False)
while k < sample_size and len(suction_points) < num_samples:
# sample a point uniformly at random
ind = indices[k]
center_px = np.array([nonzero_px[ind, 1], nonzero_px[ind, 0]])
center = Point(center_px, frame=camera_intr.frame)
axis = -normal_cloud_im[center.y, center.x]
depth = point_cloud_im[center.y, center.x][2]
# update number of tries
k += 1
# check center px dist from boundary
if center_px[0] < self._min_dist_from_boundary or \
center_px[1] < self._min_dist_from_boundary or \
center_px[1] > depth_im.height - self._min_dist_from_boundary or \
center_px[0] > depth_im.width - self._min_dist_from_boundary:
continue
# perturb depth
delta_depth = self._depth_rv.rvs(size=1)[0]
depth = depth + delta_depth
# keep if the angle between the camera optical axis and the suction direction is less than a threshold
dot = max(min(axis.dot(np.array([0, 0, 1])), 1.0), -1.0)
psi = np.arccos(dot)
if psi < self._max_suction_dir_optical_axis_angle:
# check distance to ensure sample diversity
candidate = SuctionPoint2D(center,
axis,
depth,
camera_intr=camera_intr)
if visualize:
vis.figure()
vis.imshow(depth_im)
vis.scatter(center.x, center.y)
vis.show()
suction_points.append(candidate)
logging.debug('Loop took %.3f sec' % (time() - sample_start))
return suction_points
def _sample_antipodal_grasps(self,
depth_im,
camera_intr,
num_samples,
segmask=None,
visualize=False):
"""
Sample a set of 2D grasp candidates from a depth image by finding depth
edges, then uniformly sampling point pairs and keeping only antipodal
grasps with width less than the maximum allowable.
Parameters
----------
depth_im : :obj:'perception.DepthImage'
Depth image to sample from
camera_intr : :obj:`perception.CameraIntrinsics`
intrinsics of the camera that captured the images
num_samples : int
number of grasps to sample
segmask : :obj:`perception.BinaryImage`
binary image segmenting out the object of interest
visualize : bool
whether or not to show intermediate samples (for debugging)
Returns
-------
:obj:`list` of :obj:`Grasp2D`
list of 2D grasp candidates
"""
# compute edge pixels
edge_start = time()
depth_im = depth_im.apply(snf.gaussian_filter,
sigma=self._depth_grad_gaussian_sigma)
scale_factor = self._rescale_factor
depth_im_downsampled = depth_im.resize(scale_factor)
depth_im_threshed = depth_im_downsampled.threshold_gradients(
self._depth_grad_thresh)
edge_pixels = (1.0 / scale_factor) * depth_im_threshed.zero_pixels()
edge_pixels = edge_pixels.astype(np.int16)
depth_im_mask = depth_im.copy()
if segmask is not None:
edge_pixels = np.array(
[p for p in edge_pixels if np.any(segmask[p[0], p[1]] > 0)])
depth_im_mask = depth_im.mask_binary(segmask)
# re-threshold edges if there are too few
if edge_pixels.shape[0] < self._min_num_edge_pixels:
logging.info('Too few edge pixels!')
depth_im_threshed = depth_im.threshold_gradients(
self._depth_grad_thresh)
edge_pixels = depth_im_threshed.zero_pixels()
edge_pixels = edge_pixels.astype(np.int16)
depth_im_mask = depth_im.copy()
if segmask is not None:
edge_pixels = np.array([
p for p in edge_pixels if np.any(segmask[p[0], p[1]] > 0)
])
depth_im_mask = depth_im.mask_binary(segmask)
num_pixels = edge_pixels.shape[0]
logging.debug('Depth edge detection took %.3f sec' %
(time() - edge_start))
logging.debug('Found %d edge pixels' % (num_pixels))
# exit if no edge pixels
if num_pixels == 0:
return []
# compute_max_depth
min_depth = np.min(depth_im_mask.data[
depth_im_mask.data > 0]) + self._min_depth_offset
max_depth = np.max(depth_im_mask.data[
depth_im_mask.data > 0]) + self._max_depth_offset
# compute surface normals
normal_start = time()
edge_normals = self._surface_normals(depth_im, edge_pixels)
logging.debug('Normal computation took %.3f sec' %
(time() - normal_start))
if visualize:
vis.figure()
vis.subplot(1, 3, 1)
vis.imshow(depth_im)
if num_pixels > 0:
vis.scatter(edge_pixels[:, 1], edge_pixels[:, 0], s=10, c='b')
X = [pix[1] for pix in edge_pixels]
Y = [pix[0] for pix in edge_pixels]
U = [10 * pix[1] for pix in edge_normals]
V = [-10 * pix[0] for pix in edge_normals]
plt.quiver(X, Y, U, V, units='x', scale=0.5, zorder=2, color='g')
vis.title('Edge pixels and normals')
vis.subplot(1, 3, 2)
vis.imshow(depth_im_threshed)
vis.title('Edge map')
vis.subplot(1, 3, 3)
vis.imshow(segmask)
vis.title('Segmask')
vis.show()
# form set of valid candidate point pairs
pruning_start = time()
max_grasp_width_px = Grasp2D(Point(np.zeros(2)),
0.0,
min_depth,
width=self._gripper_width,
camera_intr=camera_intr).width_px
normal_ip = edge_normals.dot(edge_normals.T)
dists = ssd.squareform(ssd.pdist(edge_pixels))
valid_indices = np.where(
(normal_ip < -np.cos(np.arctan(self._friction_coef)))
& (dists < max_grasp_width_px) & (dists > 0.0))
valid_indices = np.c_[valid_indices[0], valid_indices[1]]
logging.debug('Normal pruning %.3f sec' % (time() - pruning_start))
# raise exception if no antipodal pairs
num_pairs = valid_indices.shape[0]
if num_pairs == 0:
return []
# prune out grasps
contact_points1 = edge_pixels[valid_indices[:, 0], :]
contact_points2 = edge_pixels[valid_indices[:, 1], :]
contact_normals1 = edge_normals[valid_indices[:, 0], :]
contact_normals2 = edge_normals[valid_indices[:, 1], :]
v = contact_points1 - contact_points2
v_norm = np.linalg.norm(v, axis=1)
v = v / np.tile(v_norm[:, np.newaxis], [1, 2])
ip1 = np.sum(contact_normals1 * v, axis=1)
ip2 = np.sum(contact_normals2 * (-v), axis=1)
ip1[ip1 > 1.0] = 1.0
ip1[ip1 < -1.0] = -1.0
ip2[ip2 > 1.0] = 1.0
ip2[ip2 < -1.0] = -1.0
beta1 = np.arccos(ip1)
beta2 = np.arccos(ip2)
alpha = np.arctan(self._friction_coef)
antipodal_indices = np.where((beta1 < alpha) & (beta2 < alpha))[0]
# raise exception if no antipodal pairs
num_pairs = antipodal_indices.shape[0]
if num_pairs == 0:
return []
sample_size = min(self._max_rejection_samples, num_pairs)
grasp_indices = np.random.choice(antipodal_indices,
size=sample_size,
replace=False)
logging.debug('Grasp comp took %.3f sec' % (time() - pruning_start))
# compute grasps
sample_start = time()
k = 0
grasps = []
while k < sample_size and len(grasps) < num_samples:
grasp_ind = grasp_indices[k]
p1 = contact_points1[grasp_ind, :]
p2 = contact_points2[grasp_ind, :]
n1 = contact_normals1[grasp_ind, :]
n2 = contact_normals2[grasp_ind, :]
width = np.linalg.norm(p1 - p2)
k += 1
# compute center and axis
grasp_center = (p1 + p2) / 2
grasp_axis = p2 - p1
grasp_axis = grasp_axis / np.linalg.norm(grasp_axis)
grasp_theta = np.pi / 2
if grasp_axis[1] != 0:
grasp_theta = np.arctan(grasp_axis[0] / grasp_axis[1])
grasp_center_pt = Point(
np.array([grasp_center[1], grasp_center[0]]))
# check center px dist from boundary
if grasp_center[0] < self._min_dist_from_boundary or \
grasp_center[1] < self._min_dist_from_boundary or \
grasp_center[0] > depth_im.height - self._min_dist_from_boundary or \
grasp_center[1] > depth_im.width - self._min_dist_from_boundary:
continue
# sample depths
for i in range(self._depth_samples_per_grasp):
# get depth in the neighborhood of the center pixel
depth_win = depth_im.data[grasp_center[0] -
self._h:grasp_center[0] + self._h,
grasp_center[1] -
self._w:grasp_center[1] + self._w]
center_depth = np.min(depth_win)
if center_depth == 0 or np.isnan(center_depth):
continue
# sample depth between the min and max
min_depth = np.min(center_depth) + self._min_depth_offset
max_depth = np.max(center_depth) + self._max_depth_offset
sample_depth = min_depth + (max_depth -
min_depth) * np.random.rand()
candidate_grasp = Grasp2D(grasp_center_pt,
grasp_theta,
sample_depth,
width=self._gripper_width,
camera_intr=camera_intr,
contact_points=[p1, p2],
contact_normals=[n1, n2])
if visualize:
vis.figure()
vis.imshow(depth_im)
vis.grasp(candidate_grasp)
vis.scatter(p1[1], p1[0], c='b', s=25)
vis.scatter(p2[1], p2[0], c='b', s=25)
vis.show()
grasps.append(candidate_grasp)
# return sampled grasps
logging.debug('Loop took %.3f sec' % (time() - sample_start))
return grasps
def _plan_grasp(self,
color_im,
depth_im,
camera_intr,
bounding_box=None,
segmask=None):
"""Grasp planner request handler.
Parameters
---------
req: :obj:`ROS ServiceRequest`
ROS `ServiceRequest` for grasp planner service.
"""
rospy.loginfo("Planning Grasp")
# Inpaint images.
color_im = color_im.inpaint(
rescale_factor=self.cfg["inpaint_rescale_factor"])
depth_im = depth_im.inpaint(
rescale_factor=self.cfg["inpaint_rescale_factor"])
# Init segmask.
if segmask is None:
segmask = BinaryImage(255 *
np.ones(depth_im.shape).astype(np.uint8),
frame=color_im.frame)
# Visualize.
if self.cfg["vis"]["color_image"]:
vis.imshow(color_im)
vis.show()
if self.cfg["vis"]["depth_image"]:
vis.imshow(depth_im)
vis.show()
if self.cfg["vis"]["segmask"] and segmask is not None:
vis.imshow(segmask)
vis.show()
# Aggregate color and depth images into a single
# BerkeleyAutomation/perception `RgbdImage`.
rgbd_im = RgbdImage.from_color_and_depth(color_im, depth_im)
# Mask bounding box.
if bounding_box is not None:
# Calc bb parameters.
min_x = bounding_box.minX
min_y = bounding_box.minY
max_x = bounding_box.maxX
max_y = bounding_box.maxY
# Contain box to image->don't let it exceed image height/width
# bounds.
if min_x < 0:
min_x = 0
if min_y < 0:
min_y = 0
if max_x > rgbd_im.width:
max_x = rgbd_im.width
if max_y > rgbd_im.height:
max_y = rgbd_im.height
# Mask.
bb_segmask_arr = np.zeros([rgbd_im.height, rgbd_im.width])
bb_segmask_arr[min_y:max_y, min_x:max_x] = 255
bb_segmask = BinaryImage(bb_segmask_arr.astype(np.uint8),
segmask.frame)
segmask = segmask.mask_binary(bb_segmask)
# Visualize.
if self.cfg["vis"]["rgbd_state"]:
masked_rgbd_im = rgbd_im.mask_binary(segmask)
vis.figure()
vis.subplot(1, 2, 1)
vis.imshow(masked_rgbd_im.color)
vis.subplot(1, 2, 2)
vis.imshow(masked_rgbd_im.depth)
vis.show()
# Create an `RgbdImageState` with the cropped `RgbdImage` and
# `CameraIntrinsics`.
rgbd_state = RgbdImageState(rgbd_im, camera_intr, segmask=segmask)
# Execute policy.
try:
return self.execute_policy(rgbd_state, self.grasping_policy,
self.grasp_pose_publisher,
camera_intr.frame)
except NoValidGraspsException:
rospy.logerr(
("While executing policy found no valid grasps from sampled"
" antipodal point pairs. Aborting Policy!"))
raise rospy.ServiceException(
("While executing policy found no valid grasps from sampled"
" antipodal point pairs. Aborting Policy!"))
