def pile_scenario(n, vis, output, debug):
objects = YcbObjects('objects/ycb_objects',
mod_orn=['ChipsCan', 'MustardBottle',
'TomatoSoupCan'],
mod_stiffness=['Strawberry'],
exclude=['CrackerBox', 'Hammer'])
center_x, center_y = 0.05, -0.52
network_path = 'network/trained-models/cornell-randsplit-rgbd-grconvnet3-drop1-ch32/epoch_19_iou_0.98'
camera = Camera((center_x, center_y, 1.9), (center_x,
center_y, 0.785), 0.2, 2.0, (224, 224), 40)
env = Environment(camera, vis=vis, debug=debug, finger_length=0.06)
generator = GraspGenerator(network_path, camera, 5)
for i in range(n):
print(f'Trial {i}')
straight_fails = 0
objects.shuffle_objects()
env.move_away_arm()
info = objects.get_n_first_obj_info(5)
env.create_pile(info)
straight_fails = 0
while len(env.obj_ids) != 0 and straight_fails < 3:
env.move_away_arm()
env.reset_all_obj()
rgb, depth, _ = camera.get_cam_img()
grasps, save_name = generator.predict_grasp(
rgb, depth, n_grasps=3, show_output=output)
for i, grasp in enumerate(grasps):
x, y, z, roll, opening_len, obj_height = grasp
if vis:
debugID = p.addUserDebugLine(
[x, y, z], [x, y, 1.2], [0, 0, 1], lineWidth=3)
succes_grasp, succes_target = env.grasp(
(x, y, z), roll, opening_len, obj_height)
if vis:
p.removeUserDebugItem(debugID)
if succes_target:
straight_fails = 0
if save_name is not None:
os.rename(save_name + '.png', save_name +
f'_SUCCESS_grasp{i}.png')
break
else:
straight_fails += 1
if straight_fails == 3 or len(env.obj_ids) == 0:
break
env.reset_all_obj()
env.remove_all_obj()
def isolated_obj_scenario(n, vis, output, debug):
objects = YcbObjects(
'objects/ycb_objects',
mod_orn=['ChipsCan', 'MustardBottle', 'TomatoSoupCan'],
mod_stiffness=['Strawberry'])
data = IsolatedObjData(objects.obj_names, n, 'results')
center_x, center_y = 0.05, -0.52
network_path = 'network/trained-models/cornell-randsplit-rgbd-grconvnet3-drop1-ch32/epoch_19_iou_0.98'
camera = Camera((center_x, center_y, 1.9), (center_x, center_y, 0.785),
0.2, 2.0, (224, 224), 40)
env = Environment(camera, vis=vis, debug=debug)
generator = GraspGenerator(network_path, camera, 5)
for obj_name in objects.obj_names:
print(obj_name)
for _ in range(n):
path, mod_orn, mod_stiffness = objects.get_obj_info(obj_name)
env.load_isolated_obj(path, mod_orn, mod_stiffness)
env.move_away_arm()
rgb, depth, _ = camera.get_cam_img()
grasps, save_name = generator.predict_grasp(rgb,
depth,
n_grasps=3,
show_output=output)
for i, grasp in enumerate(grasps):
data.add_try(obj_name)
x, y, z, roll, opening_len, obj_height = grasp
if vis:
debug_id = p.addUserDebugLine([x, y, z], [x, y, 1.2],
[0, 0, 1])
succes_grasp, succes_target = env.grasp(
(x, y, z), roll, opening_len, obj_height)
if vis:
p.removeUserDebugItem(debug_id)
if succes_grasp:
data.add_succes_grasp(obj_name)
if succes_target:
data.add_succes_target(obj_name)
if save_name is not None:
os.rename(save_name + '.png',
save_name + f'_SUCCESS_grasp{i}.png')
break
env.reset_all_obj()
env.remove_all_obj()
data.write_json()
summarize(data.save_dir, n)