def display_stable_poses(self, object_name, config=None):
"""Display an object's stable poses
Parameters
----------
object_name : :obj:`str`
Object to display.
config : :obj:`dict`
Configuration dict for visualization.
Parameters are in Other parameters. Values from self.default_config are used for keys not provided.
Other Parameters
----------------
animate
Whether or not to animate the displayed object
Raises
------
ValueError
invalid object name
RuntimeError
Database or dataset not opened.
"""
self._check_opens()
config = self._get_config(config)
if object_name not in self.dataset.object_keys:
raise ValueError(
"{} is not a valid object name".format(object_name))
logger.info('Displaying stable poses for'.format(object_name))
obj = self.dataset[object_name]
stable_poses = self.dataset.stable_poses(object_name)
for stable_pose in stable_poses:
print 'Stable pose %s with p=%.3f' % (stable_pose.id,
stable_pose.p)
vis.figure()
vis.mesh_stable_pose(obj.mesh,
stable_pose,
color=(0.5, 0.5, 0.5),
style='surface')
vis.pose(RigidTransform(), alpha=0.15)
vis.show(animate=config['animate'])
def save_samples(self, sample, grasps, T_obj_stp, collision_checker, grasp_metrics, stable_pose):
T_stp_camera = sample.camera.object_to_camera_pose
self.T_obj_camera = T_stp_camera * T_obj_stp.as_frames('obj', T_stp_camera.from_frame)
aligned_grasps = self.align_grasps_with_camera(grasps)
depth_im_table = sample.renders[RenderMode.DEPTH_SCENE].image
camera_pose = self.get_camera_pose(sample)
self.tensor_datapoint['camera_poses'] = camera_pose
shifted_camera_intr = sample.camera.camera_intr
for cnt, aligned_grasp in enumerate(aligned_grasps):
collision_free = self.is_grasp_collision_free(aligned_grasp, collision_checker)
# Project grasp coordinates in image
grasp_2d = aligned_grasp.project_camera(self.T_obj_camera, shifted_camera_intr)
grasp_point = aligned_grasp.close_fingers(self.obj, vis=False, check_approach=False)
if not grasp_point[0]:
Warning("Could not find contact points")
continue
# Get grasp width in pixel from endpoints
p_1 = np.dot(self.T_obj_camera.matrix, np.append(grasp_point[1][0].point, 1).T).T[:3]
p_2 = np.dot(self.T_obj_camera.matrix, np.append(grasp_point[1][1].point, 1).T).T[:3]
grasp_width_px = self.width_px(shifted_camera_intr, p_1, p_2)
grasp_width = aligned_grasp.width_from_endpoints(grasp_point[1][0].point, grasp_point[1][1].point)
depth_im_no_rot = self.prepare_images(depth_im_table, grasp_2d)
T_grasp_camera = aligned_grasp.gripper_pose(self.gripper).inverse() * self.T_obj_camera.inverse()
if VISUALISE_3D:
z_axis = T_grasp_camera.z_axis
theta = np.rad2deg(np.arccos((z_axis[2]) /
(np.sqrt((z_axis[0] ** 2 + z_axis[1] ** 2 + z_axis[2] ** 2)))))
vis.figure()
T_obj_world = vis.mesh_stable_pose(self.obj.mesh.trimesh,
stable_pose.T_obj_world, style='surface', dim=0.5, plot_table=True)
T_camera_world = T_obj_world * self.T_obj_camera.inverse()
vis.gripper(self.gripper, aligned_grasp, T_obj_world, color=(0.3, 0.3, 0.3),
T_camera_world=T_camera_world)
print(theta)
vis.show()
hand_pose = self.get_hand_pose(grasp_2d, T_grasp_camera, grasp_width, grasp_width_px)
self.add_datapoint(depth_im_no_rot, hand_pose,
collision_free, aligned_grasp, grasp_metrics)
self.cur_image_label += 1
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_berkeley.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']['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)
# 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
# init candidate grasp storage
candidate_grasps_dict[obj.key] = {}
# setup collision checker
collision_checker = GraspCollisionChecker(gripper)
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))
# 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 any valid approach directions are collision free
collision_free = False
for phi_offset in phi_offsets:
rotated_grasp = aligned_grasp.grasp_y_axis_offset(
phi_offset)
collides = collision_checker.collides_along_approach(
rotated_grasp, approach_dist,
delta_approach)
if not collides:
collision_free = True
break
# 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'))
# 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]
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
# 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)
# 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)
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
# 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)
# 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)
# 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))
# 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,
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[
'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()
# 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 save_samples(self, sample, grasps, T_obj_stp, collision_checker,
grasp_metrics, stable_pose):
self.tensor_datapoint['image_labels'] = self.cur_image_label
T_stp_camera = sample.camera.object_to_camera_pose
self.T_obj_camera = T_stp_camera * T_obj_stp.as_frames(
'obj', T_stp_camera.from_frame)
if UNALIGNED or STP_ALIGNED:
aligned_grasps = grasps
else:
aligned_grasps = self.align_grasps_with_camera(grasps)
depth_im_table = sample.renders[RenderMode.DEPTH_SCENE].image
self.tensor_datapoint['camera_poses'] = self.get_camera_pose(sample)
final_camera_intr = sample.camera.camera_intr.crop(
self.crop_height, self.crop_width, depth_im_table.center[0],
depth_im_table.center[1])
self.tensor_datapoint['camera_intrs'] = np.r_[final_camera_intr.fx,
final_camera_intr.fy,
final_camera_intr.cx,
final_camera_intr.cy]
if not IM_TENSOR:
self.save_image(depth_im_table)
else:
depth_im_tf = self._crop(depth_im_table, size=None)
for cnt, aligned_grasp in enumerate(aligned_grasps):
if UNALIGNED:
aligned_grasp = aligned_grasp.grasp_y_axis_offset(
np.random.uniform(0, 2 * np.pi))
T_stp_grasp = RigidTransform(
stable_pose.r, from_frame='obj',
to_frame='stp') * aligned_grasp.T_grasp_obj
if T_stp_grasp.x_axis[-1] > 0:
# Grasp from below table, rotate by 180 degrees
aligned_grasp = aligned_grasp.grasp_y_axis_offset(np.pi)
T_grasp_camera = aligned_grasp.gripper_pose(self.gripper).inverse() * \
self.T_obj_camera.inverse()
# Calculate psi, the angle between the grasp approach axis and the camera principal ray
z_axis = T_grasp_camera.z_axis
psi = np.arccos(
z_axis[2] /
np.sqrt(z_axis[0]**2 + z_axis[1]**2 + z_axis[2]**2))
# if np.rad2deg(psi) > 20:
# continue
# Get gamma, the angle between the grasp approach axis and the table normal
_, gamma, _ = aligned_grasp.grasp_angles_from_stp_z(stable_pose)
if LIMIT_BETA:
perpendicular_table = (np.abs(gamma) < np.deg2rad(5))
if not perpendicular_table:
continue
if VISUALISE_3D:
vis.figure()
T_obj_world = vis.mesh_stable_pose(self.obj.mesh.trimesh,
stable_pose.T_obj_world,
style='surface',
dim=0.5,
plot_table=True)
T_camera_world = T_obj_world * self.T_obj_camera.inverse()
vis.gripper(self.gripper,
aligned_grasp,
T_obj_world,
color=(0.3, 0.3, 0.3),
T_camera_world=T_camera_world)
# vis.gripper(self.gripper, grasps[cnt], T_obj_world, color=(0.3, 0.3, 0.3))
# print(psi)
vis.show()
collision_free = self.is_grasp_collision_free(
aligned_grasp, collision_checker)
# Project grasp coordinates in image
grasp_2d = aligned_grasp.project_camera(self.T_obj_camera,
final_camera_intr)
if IM_TENSOR:
im, new_grasp = self.random_im_crop(depth_im_tf, (32, 32),
grasp_2d.center.x,
grasp_2d.center.y)
self.tensor_datapoint['depth_ims_tf_table'] = im
hand_pose = self.get_hand_pose(grasp_2d,
T_grasp_camera.quaternion,
psi,
gamma,
new_grasp=new_grasp)
else:
hand_pose = self.get_hand_pose(grasp_2d,
T_grasp_camera.quaternion, psi,
gamma)
self.add_datapoint(hand_pose, collision_free, aligned_grasp,
grasp_metrics)
self.cur_image_label += 1
def save_samples(self, sample, grasps, T_obj_stp, collision_checker,
grasp_metrics, stable_pose):
self.tensor_datapoint['image_labels'] = self.cur_image_label
T_stp_camera = sample.camera.object_to_camera_pose
self.T_obj_camera = T_stp_camera * T_obj_stp.as_frames(
'obj', T_stp_camera.from_frame)
depth_im_table = sample.renders[RenderMode.DEPTH_SCENE].image
camera_pose = self.get_camera_pose(sample)
shifted_camera_intr = sample.camera.camera_intr
aligned_grasps = self.align_grasps_with_camera(grasps)
cx = depth_im_table.center[1]
cy = depth_im_table.center[0]
self.tensor_datapoint['camera_intrs'] = np.r_[shifted_camera_intr.fx,
shifted_camera_intr.fy,
shifted_camera_intr.cx,
shifted_camera_intr.cy]
for aligned_grasp in aligned_grasps:
if not self.is_grasp_aligned(aligned_grasp):
continue
collision_free = self.is_grasp_collision_free(
aligned_grasp, collision_checker)
grasp_2d = aligned_grasp.project_camera(self.T_obj_camera,
shifted_camera_intr)
depth_im_tf = self.prepare_images(depth_im_table, grasp_2d, cx, cy)
T_grasp_camera = aligned_grasp.gripper_pose(
self.gripper).inverse() * self.T_obj_camera.inverse()
if VISUALISE_3D:
z_axis = T_grasp_camera.z_axis
theta = np.rad2deg(
np.arccos((z_axis[2]) / (np.sqrt(
(z_axis[0]**2 + z_axis[1]**2 + z_axis[2]**2)))))
vis.figure()
T_obj_world = vis.mesh_stable_pose(self.obj.mesh.trimesh,
stable_pose.T_obj_world,
style='surface',
dim=0.5,
plot_table=True)
T_camera_world = T_obj_world * self.T_obj_camera.inverse()
vis.gripper(self.gripper,
aligned_grasp,
T_obj_world,
color=(0.3, 0.3, 0.3),
T_camera_world=T_camera_world)
vis.show()
theta = grasp_2d.angle
R = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0], [0, 0, 1]])
rot = RigidTransform(rotation=R,
to_frame='camera',
from_frame='camera')
new_quaternion = T_grasp_camera * rot
hand_pose = self.get_hand_pose(grasp_2d, new_quaternion.quaternion)
self.add_datapoint(depth_im_tf, hand_pose, collision_free,
camera_pose, aligned_grasp, grasp_metrics)
self.cur_image_label += 1
def save_samples(self, sample, grasps, T_obj_stp, collision_checker, grasp_metrics, stable_pose):
self.tensor_datapoint['image_labels'] = self.cur_image_label
T_stp_camera = sample.camera.object_to_camera_pose
self.T_obj_camera = T_stp_camera * T_obj_stp.as_frames('obj', T_stp_camera.from_frame)
aligned_grasps = self.align_grasps_with_camera(grasps)
depth_im_table = sample.renders[RenderMode.DEPTH_SCENE].image
cx = depth_im_table.center[1]
cy = depth_im_table.center[0]
camera_pose = self.get_camera_pose(sample)
self.tensor_datapoint['camera_poses'] = camera_pose
shifted_camera_intr = sample.camera.camera_intr
self.tensor_datapoint['camera_intrs'] = np.r_[shifted_camera_intr.fx,
shifted_camera_intr.fy,
shifted_camera_intr.cx,
shifted_camera_intr.cy]
self.save_orig_image(depth_im_table, camera_pose, stable_pose, shifted_camera_intr)
grid_space = 200
grids = 3
centre = np.empty((grids, grids, 2))
saved = np.ones((grids, grids)) * -1
depth_images = np.empty((grids, grids, 32, 32, 1))
# Crop images in a grid around the centre
for x_grid in range(grids):
x_pos = grid_space / 4 - x_grid * grid_space / (grids + 1)
for y_grid in range(grids):
y_pos = grid_space / 4 - y_grid * grid_space / (grids + 1)
depth_im = self.prepare_images(depth_im_table, x_pos, y_pos)
self._show_image(depth_im)
depth_images[x_grid, y_grid, :, :, :] = depth_im
centre[x_grid, y_grid, 0] = x_pos
centre[x_grid, y_grid, 1] = y_pos
for cnt, aligned_grasp in enumerate(aligned_grasps):
collision_free = self.is_grasp_collision_free(aligned_grasp, collision_checker)
# Project grasp coordinates in image
grasp_2d = aligned_grasp.project_camera(self.T_obj_camera, shifted_camera_intr)
grasp_point = aligned_grasp.close_fingers(self.obj, vis=False, check_approach=False)
if not grasp_point[0]:
Warning("Could not find contact points")
continue
# Get grasp width in pixel from endpoints
p_1 = np.dot(self.T_obj_camera.matrix, np.append(grasp_point[1][0].point, 1).T).T[:3]
p_2 = np.dot(self.T_obj_camera.matrix, np.append(grasp_point[1][1].point, 1).T).T[:3]
grasp_width_px = self.width_px(shifted_camera_intr, p_1, p_2)
grasp_width = aligned_grasp.width_from_endpoints(grasp_point[1][0].point, grasp_point[1][1].point)
pos_x = cx - grasp_2d.center.x
pos_y = cy - grasp_2d.center.y
distance = np.abs((centre[:, :, 0] - pos_x)**2 + (centre[:, :, 1] - pos_y)**2)
idx = np.where(distance == np.amin(distance))
idx_x = idx[0][0]
idx_y = idx[1][0]
grasp_center_x = (300 - grasp_2d.center.x - centre[idx_x, idx_y, 0])//3
grasp_center_y = (300 - grasp_2d.center.y - centre[idx_x, idx_y, 1])//3
if saved[idx_x, idx_y] == -1:
np.save(self.image_dir + "/depth_im_{:07d}.npy".format(self.cur_image_file),
depth_images[idx_x, idx_y, :, :, :])
saved[idx_x, idx_y] = self.cur_image_file
self.tensor_datapoint['image_files'] = self.cur_image_file
self.cur_image_file += 1
else:
self.tensor_datapoint['image_files'] = saved[idx_x, idx_y]
T_grasp_camera = aligned_grasp.gripper_pose(self.gripper).inverse() * self.T_obj_camera.inverse()
if VISUALISE_3D:
z_axis = T_grasp_camera.z_axis
theta = np.rad2deg(np.arccos((z_axis[2]) /
(np.sqrt((z_axis[0] ** 2 + z_axis[1] ** 2 + z_axis[2] ** 2)))))
vis.figure()
T_obj_world = vis.mesh_stable_pose(self.obj.mesh.trimesh,
stable_pose.T_obj_world, style='surface', dim=0.5, plot_table=True)
T_camera_world = T_obj_world * self.T_obj_camera.inverse()
vis.gripper(self.gripper, aligned_grasp, T_obj_world, color=(0.3, 0.3, 0.3),
T_camera_world=T_camera_world)
print(theta)
vis.show()
hand_pose = self.get_hand_pose(grasp_center_x, grasp_center_y, grasp_2d, T_grasp_camera,
grasp_width, grasp_width_px)
self.add_datapoint(hand_pose,
collision_free, aligned_grasp, grasp_metrics)
self.cur_image_label += 1
aligned_grasp.grasp_y_axis_offset(phi_offset)
collides = collision_checker.collides_along_approach(aligned_grasp, approach_dist,
delta_approach)
if not collides:
collision_free = True
break
# Load grasp metrics
grasp_metrics = dataset.grasp_metrics(obj.key, aligned_grasps, gripper=gripper.name)
# Project grasp coordinates in image
grasp_2d = aligned_grasp.project_camera(T_obj_camera, shifted_camera_intr)
if VISUALISE_3D:
vis.figure()
T_obj_world = vis.mesh_stable_pose(obj.mesh.trimesh,
stable_pose.T_obj_world, style='surface', dim=0.5)
T_camera_world = T_obj_world * T_obj_camera.inverse()
vis.gripper(gripper, aligned_grasp, T_obj_world, color=(0.3, 0.3, 0.3),
T_camera_world=T_camera_world)
vis.show()
# Get translation image distances to grasp
dx = cx - grasp_2d.center.x
dy = cy - grasp_2d.center.y
translation = np.array([dy, dx])
# Transform, crop and resize image
binary_im_tf = binary_im.transform(translation, grasp_2d.angle)
depth_im_tf_table = depth_im_table.transform(translation, grasp_2d.angle)
binary_im_tf = binary_im_tf.crop(96, 96)
def save_samples(self, sample, grasps, collisions, T_obj_stp,
grasp_metrics, stable_pose):
self.tensor_datapoint['image_labels'] = self.cur_image_label
T_stp_camera = sample.camera.object_to_camera_pose
self.T_obj_camera = T_stp_camera * T_obj_stp.as_frames(
'obj', T_stp_camera.from_frame)
depth_im_table = sample.renders[RenderMode.DEPTH_SCENE].image
camera_pose = self.get_camera_pose(sample)
final_camera_intr = sample.camera.camera_intr.crop(
self.crop_height, self.crop_width, depth_im_table.center[0],
depth_im_table.center[1])
self.tensor_datapoint['camera_intrs'] = np.r_[final_camera_intr.fx,
final_camera_intr.fy,
final_camera_intr.cx,
final_camera_intr.cy]
self.save_image(depth_im_table)
for aligned_grasp, collision_free in zip(grasps, collisions):
# Project grasp coordinates in image
grasp_point = aligned_grasp.close_fingers(self.obj,
vis=False,
check_approach=False)
if not grasp_point[0]:
Warning("Could not find contact points")
continue
# Get grasp width in pixel from endpoints
p_1 = np.dot(self.T_obj_camera.matrix,
np.append(grasp_point[1][0].point, 1).T).T[:3]
p_2 = np.dot(self.T_obj_camera.matrix,
np.append(grasp_point[1][1].point, 1).T).T[:3]
grasp_width_px = self.width_px(final_camera_intr, p_1, p_2)
grasp_width = aligned_grasp.width_from_endpoints(
grasp_point[1][0].point, grasp_point[1][1].point)
if VISUALISE_3D:
c = np.array((1, 0, 0))
vis.figure()
T_obj_world = vis.mesh_stable_pose(self.obj.mesh.trimesh,
stable_pose.T_obj_world,
style='surface',
dim=0.5,
plot_table=True)
points = np.array(
(np.append(grasp_point[1][0].point,
1), np.append(grasp_point[1][1].point, 1)))
vis.gripper(self.gripper,
aligned_grasp,
T_obj_world,
color=(0.3, 0.3, 0.3),
point=points,
point_color=c)
vis.show()
grasp_2d = aligned_grasp.project_camera(self.T_obj_camera,
final_camera_intr)
grasp_center_x = self.crop_width // 2 - grasp_2d.center.x
grasp_center_y = self.crop_height // 2 - grasp_2d.center.y
hand_pose = self.get_hand_pose(grasp_center_x, grasp_center_y,
grasp_2d, final_camera_intr,
grasp_width, grasp_width_px)
self.add_datapoint(hand_pose, collision_free, camera_pose,
aligned_grasp, grasp_metrics)
self.cur_image_label += 1
def save_samples(self, sample, grasps, collisions, T_obj_stp,
grasp_metrics, stable_pose):
self.tensor_datapoint['image_labels'] = self.cur_image_label
T_stp_camera = sample.camera.object_to_camera_pose
self.T_obj_camera = T_stp_camera * T_obj_stp.as_frames(
'obj', T_stp_camera.from_frame)
depth_im_table = sample.renders[RenderMode.DEPTH_SCENE].image
camera_pose = self.get_camera_pose(sample)
shifted_camera_intr = sample.camera.camera_intr
cx = depth_im_table.center[1]
cy = depth_im_table.center[0]
self.tensor_datapoint['camera_intrs'] = np.r_[shifted_camera_intr.fx,
shifted_camera_intr.fy,
shifted_camera_intr.cx,
shifted_camera_intr.cy]
for aligned_grasp, collision_free in zip(grasps, collisions):
# Project grasp coordinates in image
grasp_point = aligned_grasp.close_fingers(self.obj,
vis=False,
check_approach=False)
if not grasp_point[0]:
raise Warning("Could not find contact points")
continue
grasp_width = aligned_grasp.width_from_endpoints(
grasp_point[1][0].point, grasp_point[1][1].point)
if VISUALISE_3D:
c = np.array((1, 0, 0))
vis.figure()
T_obj_world = vis.mesh_stable_pose(self.obj.mesh.trimesh,
stable_pose.T_obj_world,
style='surface',
dim=0.5,
plot_table=True)
points = np.array(
(np.append(grasp_point[1][0].point,
1), np.append(grasp_point[1][1].point, 1)))
vis.gripper(self.gripper,
aligned_grasp,
T_obj_world,
color=(0.3, 0.3, 0.3),
point=points,
point_color=c)
vis.show()
grasp_2d = aligned_grasp.project_camera(self.T_obj_camera,
shifted_camera_intr)
depth_im_tf = self.prepare_images(depth_im_table, grasp_2d, cx, cy)
hand_pose = self.get_hand_pose(grasp_2d, shifted_camera_intr,
grasp_width)
self.add_datapoint(depth_im_tf, hand_pose, collision_free,
camera_pose, aligned_grasp, grasp_metrics)
self.cur_image_label += 1