def planned_place(self):
# Place approx. at the center bin
binId = 0
drop_pose = get_params_yaml('bin{}_pose'.format(binId))
drop_pose[1] = drop_pose[1] + 0.03
# Place depending on the position of the COM of the contact patch
image_list = self.capture_images()
img0, aux, contact = crop_contact(self.back_img_list[0],
image_list[0],
gel_id=1,
is_zeros=True)
img1, aux, contact = crop_contact(self.back_img_list[1],
image_list[1],
gel_id=2,
is_zeros=True)
img0 = scipy.misc.imresize(img0, (224, 224, 3))
img1 = scipy.misc.imresize(img1, (224, 224, 3))
pos_pixel_img0 = get_center_of_mass(img0)
pos_pixel_img1 = get_center_of_mass(img1)
pos_pixel_avg = np.mean(np.array([pos_pixel_img0, pos_pixel_img1]),
axis=0)
# Compute actual motion for the robot and check if there is collision (x in pixel frame -> y in hand frame y in pixel frame -> -z in hand frame)
pos_world_avg = np.nan_to_num(
convert_image2world([225 / 2 - pos_pixel_avg[1],
0])) #np.array([y,-z])
print('Pixel positions: ', pos_pixel_avg, '. World positions: ',
pos_world_avg)
drop_pose[1] = drop_pose[1] + pos_world_avg[0]
print(drop_pose)
# Place object using graspingc
self.rel_pose, self.BoxBody = vision_transform_precise_placing_with_visualization(
self.bbox_info, viz_pub=self.viz_array_pub, listener=self.listener)
place(listener=self.listener,
br=self.br,
isExecute=self.isExecute,
binId=binId,
withPause=self.withPause,
rel_pose=self.rel_pose,
BoxBody=self.BoxBody,
place_pose=drop_pose,
viz_pub=self.viz_array_pub,
is_drop=False,
recorder=self.gdr)
def flip_image2(data, topic):
#flip image using opencv
bridge = CvBridge()
cv_image = bridge.imgmsg_to_cv2(data, "bgr8")
# flip_cv_image = cv2.flip(cv_image,1)
flip_cv_image = cv2.flip(cv_image, 0)
outImage = flip_cv_image
# cv2.imwrite('/home/mcube/background_1.png',outImage)
if rospy.get_param('/is_high_viz', True):
back_cv_image = cv2.imread('/home/mcube/background_1.png', 1)
patch_cv_image, initial_img, contact_img = crop_contact(back_cv_image,
flip_cv_image,
gel_id=2,
is_zeros=True)
COM_pos = get_center_of_mass(contact_img)
COM_pos = np.nan_to_num(COM_pos)
print(COM_pos)
#publish flipped image
# Convert uint8 to float
foreground = initial_img
#foreground = foreground[:-140,55:-70] #im_crop_grasp = img_grasp[0:-120,135:-135]
img = contact_img * 255
# threshold image
ret, threshed_img = cv2.threshold(
cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 127, 255, cv2.THRESH_BINARY)
# find contours and get the external one
image, contours, hier = cv2.findContours(threshed_img, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# with each contour, draw boundingRect in green
# a minAreaRect in red and
# a minEnclosingCircle in blue
biggest_area = 0
biggest_rect = None
for c in contours:
# get the min area rect
rect = cv2.minAreaRect(c)
area = float(rect[1][0]) * float(rect[1][1])
if biggest_area < area:
biggest_rect = rect
biggest_area = area
if biggest_rect:
box = cv2.boxPoints(biggest_rect)
# convert all coordinates floating point values to int
box = np.int0(box)
# draw a red 'nghien' rectangle
cv2.drawContours(img, [box], 0, (0, 0, 255), 2)
cv2.rectangle(
img,
(int(biggest_rect[0][0] - 10), int(biggest_rect[0][1] - 10)),
(int(biggest_rect[0][0] + 10), int(biggest_rect[0][1] + 10)),
(0, 255, 0), -1)
cv2.rectangle(img, (int(COM_pos[1] - 10), int(COM_pos[0] - 10)),
(int(COM_pos[1] + 10), int(COM_pos[0] + 10)),
(0, 0, 255), -1)
outimg = cv2.drawContours(img, contours, -1, (255, 255, 0), 2)
# Normalize the alpha mask to keep intensity between 0 and 1
alpha = 0.8
#img = cv2.resize(img, (foreground.shape[1], foreground.shape[0]))
# Add the masked foreground and background.
outImage = cv2.addWeighted(foreground, alpha, img, 1 - alpha, 0)
#rotation angle in degree
rotated = cv2.pyrDown(
cv2.imread('/home/mcube/vertical_nut.png',
cv2.IMREAD_UNCHANGED))
rows = rotated.shape[1]
cols = rotated.shape[1]
if (gripper.getGripperopening() > 0.03):
if biggest_rect[1][0] > biggest_rect[1][1]:
M = cv2.getRotationMatrix2D((cols / 2, rows / 2),
rect[2] + 90, 1)
else:
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), rect[2],
1)
rotated = cv2.warpAffine(rotated, M, (cols, rows))
else:
rotated = cv2.imread('/home/mcube/hor2_nut.png',
cv2.IMREAD_UNCHANGED)
rotated = cv2.resize(rotated,
(outImage.shape[1], outImage.shape[0]))
outImage = np.concatenate([outImage, rotated], axis=1)
else:
outImage = foreground
flip_image_pub = rospy.Publisher(topic,
sensor_msgs.msg.Image,
queue_size=10)
flip_image_pub.publish(bridge.cv2_to_imgmsg(outImage, "bgr8"))
def use_center_patch(model, image_list, x_validation, rgb_img, out_dict,
tcp_pose, binId):
# Crop and Resize
img0 = scipy.misc.imresize(image_list[0], (224, 224, 3))
img1 = scipy.misc.imresize(image_list[1], (224, 224, 3))
img0 = scipy.misc.imresize(
crop_gelsight(img0,
bottom_edge=40,
top_edge=0,
left_edge=15,
right_edge=18), (224, 224, 3))
img1 = scipy.misc.imresize(
crop_gelsight(img1,
bottom_edge=25,
top_edge=10,
left_edge=37,
right_edge=25), (224, 224, 3))
pos_pixel_img0 = get_center_of_mass(img0)
pos_pixel_img1 = get_center_of_mass(img1)
pos_pixel_avg = np.mean(np.array([pos_pixel_img0, pos_pixel_img1]),
axis=0) #TODO: check axis
# Compute actual motion for the robot and check if there is collision
pos_world_0 = convert_image2world(pos_pixel_img0) #np.array([y,-z])
delta_pos_0 = np.array([0, pos_world_0[0], -pos_world_0[1]])
is_collision_0 = check_collision(tcp_pose, delta_pos_0, self.listener,
self.br, binId)
pos_world_1 = convert_image2world(pos_pixel_img0) #np.array([y,-z])
delta_pos_1 = np.array([0, pos_world_1[0], -pos_world_1[1]])
is_collision_1 = check_collision(tcp_pose, delta_pos_1, self.listener,
self.br, binId)
pos_world_avg = convert_image2world(pos_pixel_img0) #np.array([y,-z])
delta_pos_avg = np.array([0, pos_world_avg[0], -pos_world_avg[1]])
is_collision_avg = check_collision(tcp_pose, delta_pos_avg,
self.listener, self.br, binId)
# Translate in all the possible options
img0_0 = translate_image(img0, 225 / 2 - pos_pixel_img0[1],
0) #, borderValue=0#img0.mean(1).mean(0))
img1_0 = translate_image(img1, 225 / 2 - pos_pixel_img0[1],
0) #, borderValue=0#img1.mean(1).mean(0))
img0_1 = translate_image(img0, 225 / 2 - pos_pixel_img1[1],
0) #, borderValue=0#img0.mean(1).mean(0))
img1_1 = translate_image(img1, 225 / 2 - pos_pixel_img1[1],
0) #, borderValue=0#img1.mean(1).mean(0))
img0_avg = translate_image(img0, 225 / 2 - pos_pixel_avg[1],
0) #, borderValue=0#img0.mean(1).mean(0))
img1_avg = translate_image(img1, 225 / 2 - pos_pixel_avg[1],
0) #, borderValue=0#img1.mean(1).mean(0))
# Preporcess all the images
img0_0 = preprocess_image(img0_0)
img1_0 = preprocess_image(img1_0)
img0_1 = preprocess_image(img0_1)
img1_1 = preprocess_image(img1_1)
img0_avg = preprocess_image(img0_avg)
img1_avg = preprocess_image(img1_avg)
img0 = preprocess_image(img0)
img1 = preprocess_image(img1)
# Evaluate the score of each image
score_0 = model.predict(
[np.expand_dims(img0_0, axis=0),
np.expand_dims(img1_0, axis=0)])[0][0]
score_1 = model.predict(
[np.expand_dims(img0_1, axis=0),
np.expand_dims(img1_1, axis=0)])[0][0]
score_avg = model.predict([
np.expand_dims(img0_avg, axis=0),
np.expand_dims(img1_avg, axis=0)
])[0][0]
score_inital = model.predict(
[np.expand_dims(img0, axis=0),
np.expand_dims(img1, axis=0)])[0][0]
# Find best way use CoM motion
best_score = max([
score_initial, score_0 * (1 - int(is_collision_0)),
score_1 * (1 - int(is_collision_1)),
score_avg * (1 - int(is_collision_avg))
])
if best_score == score_0:
out_dict['images'].append(img0_0)
out_dict['images2'].append(img1_0)
out_dict['delta_pos'].append(delta_pos_0)
print('Moving to the CoM of Gelsight 0')
elif best_score == score_1:
out_dict['images'].append(img0_1)
out_dict['images2'].append(img1_1)
out_dict['delta_pos'].append(delta_pos_1)
print('Moving to the CoM of Gelsight 1')
elif best_score == score_avg:
out_dict['images'].append(img0_avg)
out_dict['images2'].append(img1_avg)
out_dict['delta_pos'].append(delta_pos_avg)
print('Moving to the CoM of the average')
else: #Do nothing
# TODO: add the patch detection if needed !!!
img0 = preprocess_image(img0)
img1 = preprocess_image(img1)
out_dict['images'].append(img0)
out_dict['images2'].append(img1)
out_dict['delta_pos'].append([0, 0, 0])
return out_dict
def use_center_patch(self, model, image_list, out_dict):
# Crop and Resize
img0 = scipy.misc.imresize(image_list[0], (224, 224, 3))
img1 = scipy.misc.imresize(image_list[1], (224, 224, 3))
# img0 = scipy.misc.imresize(crop_gelsight(img0, bottom_edge = 40, top_edge = 0, left_edge = 15, right_edge = 18), (224,224,3))
# img1 = scipy.misc.imresize(crop_gelsight(img1,bottom_edge = 25, top_edge = 10, left_edge = 37, right_edge = 25), (224,224,3))
pos_pixel_img0 = get_center_of_mass(img0)
pos_pixel_img1 = get_center_of_mass(img1)
pos_pixel_avg = np.mean(np.array([pos_pixel_img0, pos_pixel_img1]),
axis=0) #TODO: check axis
#x in pixel frame -> y in hand frame
#y in pixel frame -> -z in hand frame
# Compute actual motion for the robot and check if there is collision
pos_world_0 = convert_image2world([225 / 2 - pos_pixel_img0[1],
0]) #np.array([y,-z])
delta_pos_0 = np.array([0, -pos_world_0[0], 0])
pos_world_1 = convert_image2world([225 / 2 - pos_pixel_img1[1],
0]) #np.array([y,-z])
delta_pos_1 = np.array([0, -pos_world_1[0], 0])
pos_world_avg = convert_image2world([225 / 2 - pos_pixel_avg[1],
0]) #np.array([y,-z])
delta_pos_avg = np.array([0, -pos_world_avg[0], 0])
print('Pixel positions')
print(pos_pixel_img0)
print(pos_pixel_img1)
print(pos_pixel_avg)
print('World positions')
print(pos_world_avg)
out_dict['images_initial'].append(img0)
out_dict['images2_initial'].append(img1)
# Translate in all the possible options
img0_0 = translate_image(img0, 225 / 2 - pos_pixel_img0[1],
0) #, borderValue=0#img0.mean(1).mean(0))
img1_0 = translate_image(img1, 225 / 2 - pos_pixel_img0[1],
0) #, borderValue=0#img1.mean(1).mean(0))
img0_1 = translate_image(img0, 225 / 2 - pos_pixel_img1[1],
0) #, borderValue=0#img0.mean(1).mean(0))
img1_1 = translate_image(img1, 225 / 2 - pos_pixel_img1[1],
0) #, borderValue=0#img1.mean(1).mean(0))
img0_avg = translate_image(img0, 225 / 2 - pos_pixel_avg[1],
0) #, borderValue=0#img0.mean(1).mean(0))
img1_avg = translate_image(img1, 225 / 2 - pos_pixel_avg[1],
0) #, borderValue=0#img1.mean(1).mean(0))
out_dict['images_before'].append(img0_avg)
out_dict['images2_before'].append(img1_avg)
# Preporcess all the images
img0_0 = preprocess_image(img0_0)
img1_0 = preprocess_image(img1_0)
img0_1 = preprocess_image(img0_1)
img1_1 = preprocess_image(img1_1)
img0_avg = preprocess_image(img0_avg)
img1_avg = preprocess_image(img1_avg)
img0 = preprocess_image(img0)
img1 = preprocess_image(img1)
out_dict['images'].append(img0_avg)
out_dict['images2'].append(img1_avg)
out_dict['delta_pos'].append(delta_pos_avg)
print('Moving to the CoM of the average')
best_score = 0
'''
# Evaluate the score of each image
score_0 = model.predict([np.expand_dims(img0_0, axis=0), np.expand_dims(img1_0, axis=0)])[0][0]
score_1 = model.predict([np.expand_dims(img0_1, axis=0), np.expand_dims(img1_1, axis=0)])[0][0]
score_avg = model.predict([np.expand_dims(img0_avg, axis=0), np.expand_dims(img1_avg, axis=0)])[0][0]
score_initial = model.predict([np.expand_dims(img0, axis=0), np.expand_dims(img1, axis=0)])[0][0]
# Find best way use CoM motion
best_score = score_avg #max([score_initial, score_0,score_1,score_avg])
if best_score == score_0:
out_dict['images'].append(img0_0)
out_dict['images2'].append(img1_0)
out_dict['delta_pos'].append(delta_pos_0)
print('Moving to the CoM of Gelsight 0')
elif best_score == score_1:
out_dict['images'].append(img0_1)
out_dict['images2'].append(img1_1)
out_dict['delta_pos'].append(delta_pos_1)
print('Moving to the CoM of Gelsight 1')
elif best_score == score_avg:
out_dict['images'].append(img0_avg)
out_dict['images2'].append(img1_avg)
out_dict['delta_pos'].append(delta_pos_avg)
print('Moving to the CoM of the average')
else: #Do nothing
# TODO: add the patch detection if needed !!!
img0 =preprocess_image(img0)
img1 =preprocess_image(img1)
out_dict['images'].append(img0)
out_dict['images2'].append(img1)
out_dict['delta_pos'].append([0,0,0])
'''
return out_dict, best_score
def planned_place(self):
if len(self.get_objects()) == 1:
fixed_container = [self.tote_ID]
else:
fixed_container = [
1 - self.tote_ID
] #TODO_M: planner only accepts bins 1,2,3 and names them as 0,1,2
if self.PlacingPlanner.visionType == 'real': #Update HM
self.PlacingPlanner.update_real_height_map(fixed_container[0])
#Get obj dimensions:
try:
asking_for = '/obj/' + self.obj_ID
aux_obj = rospy.get_param(asking_for)
obj_dim = aux_obj['dimensions']
except:
print('Could not get the object dimensions')
obj_dim = [0.12, 0.12, 0.12]
drop_pose = self.PlacingPlanner.place_object_local_best(
obj_dim=obj_dim, containers=fixed_container
) #TODO_M : change placing to return drop_pose
print('drop_pose', drop_pose)
#~frank hack: drop pose
drop_pose = get_params_yaml('bin' + str(fixed_container[0]) + '_pose')
drop_pose[1] = drop_pose[1] + 0.03
## TODO add here a correction depending on the com of the contact patch of the object?
image_list = self.capture_images()
image_list[0] = crop_contact(self.background_images[0],
image_list[0],
gel_id=1,
is_zeros=use_COM)
image_list[1] = crop_contact(self.background_images[1],
image_list[1],
gel_id=2,
is_zeros=use_COM)
img0 = scipy.misc.imresize(image_list[0], (224, 224, 3))
img1 = scipy.misc.imresize(image_list[1], (224, 224, 3))
pos_pixel_img0 = get_center_of_mass(img0)
pos_pixel_img1 = get_center_of_mass(img1)
pos_pixel_avg = np.mean(np.array([pos_pixel_img0, pos_pixel_img1]),
axis=0) #TODO: check axis
#x in pixel frame -> y in hand frame
#y in pixel frame -> -z in hand frame
# Compute actual motion for the robot and check if there is collision
pos_world_avg = convert_image2world([225 / 2 - pos_pixel_avg[1],
0]) #np.array([y,-z])
delta_pos_avg = np.array([0, -pos_world_avg[0], 0])
print('Pixel positions: ', pos_pixel_avg, '. World positions: ',
pos_world_avg)
drop_pose[1] = drop_pose[1] + pos_world_avg[0]
# Place object using graspingc
self.rel_pose, self.BoxBody = vision_transform_precise_placing_with_visualization(
self.bbox_info, viz_pub=self.viz_array_pub, listener=self.listener)
place(listener=self.listener,
br=self.br,
isExecute=self.isExecute,
binId=fixed_container[0],
withPause=self.withPause,
rel_pose=self.rel_pose,
BoxBody=self.BoxBody,
place_pose=drop_pose,
viz_pub=self.viz_array_pub,
is_drop=False,
recorder=self.gdr)