def run_grasp(self, bbox, c_img, col_img, workspace_img, d_img):
print("grasping a " + cfg.labels[bbox.label])
#bbox has format [xmin, ymin, xmax, ymax]
fg, obj_w = bbox.to_mask(c_img, col_img)
# cv2.imwrite("debug_imgs/test.png", obj_w.data)
# cv2.imwrite("debug_imgs/test2.png", fg.data)
groups = get_cluster_info(fg)
curr_tol = cfg.COLOR_TOL
while len(groups) == 0 and curr_tol > 10:
curr_tol -= 5
#retry with lower tolerance- probably white object
fg, obj_w = bbox.to_mask(c_img, col_img, tol=curr_tol)
groups = get_cluster_info(fg)
if len(groups) == 0:
print("No object within bounding box")
return False
display_grasps(workspace_img, groups)
group = groups[0]
pose, rot = self.gm.compute_grasp(group.cm, group.dir, d_img)
grasp_pose = self.gripper.get_grasp_pose(pose[0],
pose[1],
pose[2],
rot,
c_img=workspace_img.data)
self.gm.execute_grasp(grasp_pose, class_num=bbox.label)
def to_group(self, c_img, col_img):
fg, obj_w = self.to_mask(c_img, col_img)
# cv2.imwrite("debug_imgs/test.png", obj_w.data)
# cv2.imwrite("debug_imgs/test2.png", fg.data)
groups = get_cluster_info(fg)
curr_tol = cfg.COLOR_TOL
while len(groups) == 0 and curr_tol > 10:
curr_tol -= 5
#retry with lower tolerance- probably white object
fg, obj_w = self.to_mask(c_img, col_img, tol=curr_tol)
groups = get_cluster_info(fg)
if len(groups) == 0:
print("No object within bounding box")
return False
return groups[0]
def run_grasp(self, bbox, c_img, col_img, workspace_img):
print("grasping a " + cfg.labels[bbox.label])
#bbox has format [xmin, ymin, xmax, ymax]
fg, obj_w = bbox.to_mask(c_img, col_img)
# cv2.imwrite("debug_imgs/test.png", obj_w.data)
# cv2.imwrite("debug_imgs/test2.png", fg.data)
groups = get_cluster_info(fg)
curr_tol = cfg.COLOR_TOL
while len(groups) == 0 and curr_tol > 10:
curr_tol -= 5
#retry with lower tolerance- probably white object
fg, obj_w = bbox.to_mask(c_img, col_img, tol=curr_tol)
groups = get_cluster_info(fg)
if len(groups) == 0:
print("No object within bounding box")
return False
display_grasps(workspace_img, groups)
def label_demo(self):
self.gm.position_head()
time.sleep(3) #making sure the robot is finished moving
c_img = self.cam.read_color_data()
d_img = self.cam.read_depth_data()
while not (c_img is None or d_img is None):
path = "/home/autolab/Workspaces/michael_working/siemens_challenge/debug_imgs/web.png"
cv2.imwrite(path, c_img)
time.sleep(2) #make sure new image is written before being read
# print "\n new iteration"
main_mask = crop_img(c_img, simple=True)
col_img = ColorImage(c_img)
workspace_img = col_img.mask_binary(main_mask)
labels = self.web.label_image(path)
obj = labels['objects'][0]
bbox = obj['box']
class_label = obj['class']
#bbox has format [xmin, ymin, xmax, ymax]
fg, obj_w = self.bbox_to_fg(bbox, c_img, col_img)
cv2.imwrite("debug_imgs/test.png", obj_w.data)
groups = get_cluster_info(fg)
display_grasps(workspace_img, groups)
group = groups[0]
pose, rot = self.gm.compute_grasp(group.cm, group.dir, d_img)
grasp_pose = self.gripper.get_grasp_pose(pose[0],
pose[1],
pose[2],
rot,
c_img=workspace_img.data)
self.gm.execute_grasp(grasp_pose, class_num=class_label)
#reset to start
self.whole_body.move_to_go()
self.gm.move_to_home()
self.gm.position_head()
time.sleep(3)
c_img = self.cam.read_color_data()
d_img = self.cam.read_depth_data()
def full_web_demo(self):
self.gm.position_head()
time.sleep(3) #making sure the robot is finished moving
c_img = self.cam.read_color_data()
d_img = self.cam.read_depth_data()
while not (c_img is None or d_img is None):
path = "/home/autolab/Workspaces/michael_working/siemens_challenge/debug_imgs/web.png"
cv2.imwrite(path, c_img)
time.sleep(2) #make sure new image is written before being read
# print "\n new iteration"
main_mask = crop_img(c_img, simple=True)
col_img = ColorImage(c_img)
workspace_img = col_img.mask_binary(main_mask)
labels = self.web.label_image(path)
objs = labels['objects']
bboxes = [Bbox(obj['box'], obj['class']) for obj in objs]
single_objs = find_isolated_objects(bboxes)
if len(single_objs) > 0:
to_grasp = select_first_obj(single_objs)
self.run_grasp(to_grasp, c_img, col_img, workspace_img, d_img)
else:
#for accurate singulation should have bboxes for all
fg_imgs = [box.to_mask(c_img, col_img) for box in bboxes]
groups = [get_cluster_info(fg[0])[0] for fg in fg_imgs]
groups = merge_groups(groups, cfg.DIST_TOL)
self.run_singulate(col_img, main_mask, groups, d_img)
#reset to start
self.whole_body.move_to_go()
self.gm.move_to_home()
self.gm.position_head()
time.sleep(3)
c_img = self.cam.read_color_data()
d_img = self.cam.read_depth_data()
def decision_demo(self):
time.sleep(3) #making sure the robot is finished moving
sample_data_paths = [
"debug_imgs/data_chris/test" + str(i) + ".png" for i in range(3)
]
img_ind = 0
c_img = cv2.imread(sample_data_paths[img_ind])
while not (c_img is None):
path = "/home/autolab/Workspaces/michael_working/siemens_challenge/debug_imgs/web.png"
cv2.imwrite(path, c_img)
time.sleep(2) #make sure new image is written before being read
# print "\n new iteration"
main_mask = crop_img(c_img, simple=True)
col_img = ColorImage(c_img)
workspace_img = col_img.mask_binary(main_mask)
labels = self.web.label_image(path)
objs = labels['objects']
bboxes = [Bbox(obj['box'], obj['class']) for obj in objs]
single_objs = find_isolated_objects(bboxes)
if len(single_objs) > 0:
to_grasp = select_first_obj(single_objs)
self.run_grasp(to_grasp, c_img, col_img, workspace_img)
else:
#for accurate singulation should have bboxes for all
fg_imgs = [box.to_mask(c_img, col_img) for box in bboxes]
groups = [get_cluster_info(fg[0])[0] for fg in fg_imgs]
groups = merge_groups(groups, cfg.DIST_TOL)
self.run_singulate(col_img, main_mask, groups)
img_ind += 1
c_img = cv2.imread(sample_data_paths[img_ind])
def label_demo(self):
""" Main method which executes the stuff we're interested in.
Should apply to both the physical and simulated HSRs. Call as `python
main/test_labeling.py`.
"""
self.gm.position_head()
time.sleep(3) #making sure the robot is finished moving
c_img = self.cam.read_color_data()
d_img = self.cam.read_depth_data()
path = "/home/ron/siemens_sim/siemens_challenge/debug_imgs/web.png"
cv2.imwrite(path, c_img)
time.sleep(2) #make sure new image is written before being read
# print "\n new iteration"
main_mask = crop_img(c_img, simple=True)
col_img = ColorImage(c_img)
workspace_img = col_img.mask_binary(main_mask)
labels = self.web.label_image(path)
obj = labels['objects'][0]
bbox = obj['box']
class_label = obj['class']
#bbox has format [xmin, ymin, xmax, ymax]
fg, obj_w = self.bbox_to_fg(bbox, c_img, col_img)
cv2.imwrite("debug_imgs/test.png", obj_w.data)
groups = get_cluster_info(fg)
display_grasps(workspace_img, groups)
group = groups[0]
print(d_img)
pose,rot = self.gm.compute_grasp(group.cm, group.dir, d_img)
pose = find_pose(pose)
if pose == None:
print("unable to find corresponding item.")
sys.exit()
a = tf.transformations.quaternion_from_euler(ai=-2.355,aj=-3.14,ak=0.0)
b = tf.transformations.quaternion_from_euler(ai=0.0,aj=0.0,ak=1.57)
base_rot = tf.transformations.quaternion_multiply(a,b)
print("now about to get grasp pose, w/pose: {}, rot: {}".format(pose, rot))
thread.start_new_thread(self.gripper.loop_broadcast,(pose,base_rot,rot))
time.sleep(5)
print("now calling execute_grasp w/grasp_pose: {}".format(grasp_pose))
# IPython.embed()
self.gm.execute_grasp("grasp_0")
self.whole_body.move_end_effector_pose(geometry.pose(),"temp_bin")
self.gripper.open_gripper()
#reset to start
self.whole_body.move_to_go()
# self.gm.move_to_home()
self.gm.position_head()
time.sleep(3)
def siemens_demo(self):
self.gm.position_head()
time.sleep(3) #making sure the robot is finished moving
c_img = self.cam.read_color_data()
d_img = self.cam.read_depth_data()
while not (c_img is None or d_img is None):
path = "/home/autolab/Workspaces/michael_working/siemens_challenge/debug_imgs/web.png"
cv2.imwrite(path, c_img)
time.sleep(2) #make sure new image is written before being read
# print "\n new iteration"
main_mask = crop_img(c_img, simple=True)
col_img = ColorImage(c_img)
workspace_img = col_img.mask_binary(main_mask)
bboxes, vis_util_image = self.get_bboxes(path, col_img)
if len(bboxes) == 0:
print("Cleared the workspace")
print("Add more objects, then resume")
IPython.embed()
else:
box_viz = draw_boxes(bboxes, c_img)
cv2.imwrite("debug_imgs/box.png", box_viz)
single_objs = find_isolated_objects_by_overlap(bboxes)
grasp_sucess = 1.0
if len(single_objs) == 0:
single_objs = find_isolated_objects_by_distance(
bboxes, col_img)
if len(single_objs) > 0:
to_grasp = select_first_obj(single_objs)
singulation_time = 0.0
self.run_grasp(to_grasp, c_img, col_img, workspace_img,
d_img)
grasp_sucess = self.dl.record_success(
"grasp", other_data=[c_img, vis_util_image, d_img])
else:
#for accurate singulation should have bboxes for all
fg_imgs = [box.to_mask(c_img, col_img) for box in bboxes]
groups = [get_cluster_info(fg[0])[0] for fg in fg_imgs]
groups = merge_groups(groups, cfg.DIST_TOL)
self.run_singulate(col_img, main_mask, groups, d_img)
sing_start = time.time()
singulation_success = self.dl.record_success(
"singulation",
other_data=[c_img, vis_util_image, d_img])
singulation_time = time.time() - sing_start
if cfg.EVALUATE:
reward = self.helper.get_reward(grasp_sucess,
singulation_time)
self.dl.record_reward(reward)
#reset to start
self.whole_body.move_to_go()
self.gm.move_to_home()
self.gm.position_head()
time.sleep(3)
c_img = self.cam.read_color_data()
d_img = self.cam.read_depth_data()