def pcl_callback(ros_point_cloud_msg):
# TODO: Convert ROS msg of type PointCloud2 to PCL data PointXYZRGB format
pcl_data = pclh.ros_to_pcl(ros_point_cloud_msg)
# TODO: Statistical filter
stat_inlier, stat_outlier = flt.statistical_outlier_filter(pcl_data)
# TODO: Voxel Grid Downsampling
vox_filt = flt.voxel_downsampling(stat_inlier)
# TODO: PassThrough Filter
pass_filt = flt.pass_through_filter(vox_filt)
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers and outliers
cloud_table, cloud_objects = flt.ransac(pass_filt)
# TODO: Euclidean Clustering
cluster_indices = flt.euclidean_clustering(cloud_objects)
# TODO: Create Cluster-Mask Point Cloud to visualize each cluster separately
cluster_cloud = flt.visualize_clusters(cloud_objects, cluster_indices)
# TODO: Convert PCL data to ROS messages
ros_table_cloud = pclh.pcl_to_ros(cloud_table)
ros_objects_cloud = pclh.pcl_to_ros(cloud_objects)
ros_cluster_cloud = pclh.pcl_to_ros(cluster_cloud)
# TODO: Publish ROS messages
pcl_table_pub.publish(ros_table_cloud)
pcl_objects_pub.publish(ros_objects_cloud)
pcl_cluster_pub.publish(ros_cluster_cloud)
def callback(input_ros_msg):
cloud = pcl_helper.ros_to_pcl(input_ros_msg)
#print("INPUT: ", cloud)
cloud = do_passthrough(cloud, "x", 0.0, 20.0)
cloud = do_passthrough(cloud, "y", -5.0, 5.0)
_, _, cloud = do_ransac_plane_normal_segmentation(cloud, 0.14)
#cloud = do_euclidian_clustering(cloud)
cloud_new = pcl_helper.pcl_to_ros(cloud)
pub.publish(cloud_new)
def pc2CB(self, msg):
self.cb_start_time = rospy.Time.now().to_sec()
print "--------------------------------------------"
# Convert ROS PointCloud2 msg to PCL data
self.cloud = pcl_helper.ros_to_pcl(msg)
print "CB_start_time", self.cb_start_time
print "CB_end_time", rospy.Time.now().to_sec()
def callback(input_ros_msg):
cloud = pcl_helper.ros_to_pcl(input_ros_msg)
# print("INPUT: ", cloud)
LEAF_SIZE = 0.1
cloud = do_voxel_grid_downsampling(cloud, LEAF_SIZE)
# print("OUTPUT: ", cloud)
# print("")
cloud_new = pcl_helper.pcl_to_ros(cloud)
pub.publish(cloud_new)
def callback(input_ros_msg):
cloud = pcl_helper.ros_to_pcl(input_ros_msg)
print("INPUT: ", cloud, type(cloud))
cloud = pcl_helper.XYZRGB_to_XYZ(cloud)
mean_k = 10
thresh = 0.001
cloud = do_statistical_outlier_filtering(cloud, mean_k, thresh)
color = pcl_helper.random_color_gen()
cloud = pcl_helper.XYZ_to_XYZRGB(cloud, color)
cloud_new = pcl_helper.pcl_to_ros(cloud)
print("OUTPUT: ", cloud, type(cloud))
pub.publish(cloud_new)
def calc_object_size(item):
points_array = pclh.ros_to_pcl(item.cloud).to_array()
centroid = np.mean(points_array, axis=0)[:3]
max_dim = np.max(points_array, axis=0)[:3]
min_dim = np.min(points_array, axis=0)[:3]
object_size = max_dim - min_dim
length, width, height = make_ros_pose(object_size[0], object_size[1],
object_size[3])
print(
"The dimensions of the detected item are: length=%f, width=%f, height=%f"
% (length.width, height))
return length, width, height, centroid
def CB_msgPCL(msgPCL):
"""
ROS "/sensor_stick/point_cloud" subscription Callback handler
Handle the PointCloud ROS msg received by the "/sensor_stick/point_cloud"
This function is almost entirely unpacking/packing ROS messages and publishing.
The the unpacked input pcl is processed by Process_rawPCL(pclpcRawIn)
which returns the values that need to be packed and published
:param msgPCL:
:return:
"""
global g_callBackCount
g_callBackCount += 1
if (g_callBackCount % g_callBackSkip != 0):
return;
print "\rCBCount= {:05d}".format(g_callBackCount),
sys.stdout.flush()
DebugDumpMsgPCL(msgPCL)
# Extract pcl Raw from Ros msgPCL
pclpcRawIn = pcl_helper.ros_to_pcl(msgPCL)
#------- PROCESS RAW PCL-------------------------
labelRecs, pclpcObjects, pclpcTable, pclpcClusters = Process_rawPCL(pclpcRawIn)
detected_objects_labels = [] # For ros loginfo only
detected_objects = [] # For publish - for PROJ3!
for (labelText, labelPos, labelIndex) in labelRecs:
detected_objects_labels.append(labelText)
g_object_markers_pub.publish(marker_tools.make_label(labelText, labelPos, labelIndex ))
# Add detected object to the list of detected objects.
do = DetectedObject()
do.label = labelText
do.cloud = pclpcClusters
detected_objects.append(do)
rospy.loginfo('Detected {} objects: {}'.format(len(detected_objects_labels), detected_objects_labels))
# Package Processed pcls into Ros msgPCL
msgPCLObjects = pcl_helper.pcl_to_ros(pclpcObjects)
msgPCLTable = pcl_helper.pcl_to_ros(pclpcTable)
msgPCLClusters = pcl_helper.pcl_to_ros(pclpcClusters)
# Publish everything
# This is the output you'll need to complete the upcoming project!
g_detected_objects_pub.publish(detected_objects) # THIS IS THE CRUCIAL STEP FOR PROJ3
g_pcl_objects_pub.publish(msgPCLObjects)
g_pcl_table_pub.publish(msgPCLTable)
g_pcl_cluster_pub.publish(msgPCLClusters)
def Process_msgPCL(msgPCL):
# Extract pcl Raw from Ros msgPCL
pclpcRawIn = pcl_helper.ros_to_pcl(msgPCL)
#------- PROCESS RAW PC-------------------------
pclpcObjects, pclpcTable, pclpcClusters = ProcessPCL(pclpcRawIn)
# Package Processed pcls into Ros msgPCL
msgPCLObjects = pcl_helper.pcl_to_ros(pclpcObjects)
msgPCLTable = pcl_helper.pcl_to_ros(pclpcTable)
msgPCLClusters = pcl_helper.pcl_to_ros(pclpcClusters)
return msgPCLObjects, msgPCLTable, msgPCLClusters
def pc2CB(self, msg):
self.start_time = rospy.Time.now().to_sec()
print "----------------------------------"
outer_pts = inner_pts = remaining_pts = []
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Passthrough Filter
fil_cloud = filtering_helper.do_passthrough_filter(
cloud, 'x', self.filter_xmin, self.filter_xmax)
fil_cloud = filtering_helper.do_passthrough_filter(
fil_cloud, 'y', self.filter_ymin, self.filter_ymax)
# Statistical Outlier Filter
fil = fil_cloud.make_statistical_outlier_filter()
fil.set_mean_k(10)
fil.set_std_dev_mul_thresh(0.02)
filtered_cloud = fil.filter()
# self.load_params_from_yaml(self.filepath)
# Setting Outer-Region and Inner-Region
self.outer_region, self.inner_region = self.getDetectionRegion(
self.fsm_state, self.lifter_status)
# Visualize Detection Region
self.visualize_region(self.outer_region, self.inner_region)
self.middle_time_1 = rospy.Time.now().to_sec()
# Grouping raw cloud into 2 groups of points
outer_pts, inner_pts = self.getPointsGroupedFromCloud(
filtered_cloud, self.outer_region, self.inner_region)
self.middle_time_2 = rospy.Time.now().to_sec()
remaining_pts = list(set(outer_pts) - set(inner_pts))
print "outer_pts", len(outer_pts)
print "inner_pts", len(inner_pts)
print "remaining_pts", len(remaining_pts)
if (len(remaining_pts) != 0):
print "Obstacle detected and stopping AGV"
self.stopping()
print "start_time", self.start_time
print "middle_time_1", self.middle_time_1
print "middle_time_2", self.middle_time_2
print "end_time", rospy.Time.now().to_sec()
def get_clusters(self, cloud):
#print("point cloud size = ",len(cloud.data), " bytes")
marker_array = MarkerArray()
pcl_cloud = ros_to_pcl(cloud)
#convert pointcloud_xyzrgb to pointcloud_xyz
# since pointcloud_xyzrgb does not support make_EuclideanClusterExtraction
xyz_pc = pcl.PointCloud()
xyz_pc.from_list( [ x[:3] for x in pcl_cloud.to_list() ] )
tree = xyz_pc.make_kdtree()
t1 = time.clock()
ec = xyz_pc.make_EuclideanClusterExtraction()
ec.set_ClusterTolerance (0.005)
ec.set_MinClusterSize (200)
ec.set_MaxClusterSize (5000)
ec.set_SearchMethod (tree)
cluster_indices = ec.Extract()
t2 = time.clock()
#print("ECE took ", t2-t1)
#for all j clusters
print("# clusters ", len(cluster_indices))
for j, indices in enumerate(cluster_indices):
print("Cluster", j ,": # points ", len(indices))
sum_x = 0
sum_y = 0
sum_z = 0
#for all points in the cluster j
for i, indice in enumerate(indices):
sum_x = sum_x + xyz_pc[indice][0]
sum_y = sum_y + xyz_pc[indice][1]
sum_z = sum_z + xyz_pc[indice][2]
x = sum_x/len(indices)
y = sum_y/len(indices)
z = sum_z/len(indices)
label = "obj_" + str(j)
marker_array.markers.append(get_marker(j, label, cloud.header.frame_id, x, y, z))
obj = pcl_cloud.extract(indices, negative=False)
pc_msg = pcl_to_ros(obj, cloud.header)
self.pub.publish(pc_msg)
self.track(marker_array)
def callback(input_ros_msg):
cloud = pcl_helper.ros_to_pcl(input_ros_msg)
filter_axis = 'x'
axis_min = 0.0
axis_max = 20.0
cloud = do_passthrough(cloud, filter_axis, axis_min, axis_max)
filter_axis = 'y'
axis_min = -5.0
axis_max = 5.0
cloud = do_passthrough(cloud, filter_axis, axis_min, axis_max)
cloud_new = pcl_helper.pcl_to_ros(cloud)
pub.publish(cloud_new)
def ground_filter(cloud):
pcl_cloud = ros_to_pcl(cloud)
t1 = time.clock()
'''
seg = pcl_cloud.make_segmenter_normals(ksearch=5)
seg.set_optimize_coefficients(True)
seg.set_model_type(pcl.SACMODEL_NORMAL_PLANE)
seg.set_normal_distance_weight(0.1)
seg.set_method_type(pcl.SAC_RANSAC)
seg.set_max_iterations(10)
seg.set_distance_threshold(0.03)
indices, model = seg.segment()
cnt = 0
i = 0
ind = []
for p in pc2.read_points(cloud, skip_nans=True):
d = distance_point_to_plane(p, model)
#0.005 is a fudge factor to correct for any error in the model
#to avoid having any parts of the ground to creep in
if d > 0.005:
cnt = cnt + 1
ind.append(i)
i = i + 1
'''
seg = pcl_cloud.make_segmenter()
seg.set_optimize_coefficients(True)
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
seg.set_distance_threshold(0.0075)
indices, model = seg.segment()
t2 = time.clock()
#print("RANSAC took ", t2-t1)
#print("model:", model)
print("org size", cloud.width * cloud.height, "ransac size",
cloud.width * cloud.height - len(indices))
#return pcl_cloud.extract(ind, negative=False)
return pcl_cloud.extract(indices, negative=True)
def callback(input_ros_msg):
cloud = pcl_helper.ros_to_pcl(input_ros_msg)
#cloud = pcl_helper.XYZRGB_to_XYZ(cloud)
#cloud_denoised = do_statistical_outlier_filtering(cloud)
#cloud_downsampled = do_voxel_grid_downsampling(cloud)
cloud_roi_x = do_passthrough(cloud, 'x', 0.0, 20.0)
cloud_roi_y = do_passthrough(cloud_roi_x, 'y', -5.0, 5.0)
_, _, cloud_ransaced = do_ransac_plane_normal_segmentation(
cloud_roi_y, 0.007)
cloud_clustered = do_euclidian_clustering(cloud_ransaced)
cloud_new = pcl_helper.pcl_to_ros(cloud_clustered)
pub.publish(cloud_new)
def pc2CB(self, msg):
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Filter the Cloud according limitation on x-axis and y-axis
fil_cloud = filtering_helper.do_passthrough_filter(
cloud, 'x', -0.5, 0.0)
fil_cloud = filtering_helper.do_passthrough_filter(
fil_cloud, 'y', -0.5, 0.5)
print fil_cloud.size
# Visualize the filtered cloud
outmsg = pcl_helper.pcl_to_ros(fil_cloud, "base_link")
outmsg.header = msg.header
self.pc2_pub.publish(outmsg)
# Save the cloud
if self.init:
pcl.save(fil_cloud, "/home/samuel/charger_unit.pcd", None, False)
self.init = False
print "record finish"
def pc2CB(self, msg):
self.start_time = rospy.Time.now().to_sec()
print "--------------------------------------------"
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# self.load_params_from_yaml(self.filepath)
print "fsm_state", self.fsm_state
# Setting Detection-Region for stopping
detection_region = self.getDetectionRegion(self.fsm_state,
self.lifter_status,
self.speed)
# Finding Min & Max of detection region
x_max, x_min, y_max, y_min = self.getMinMaxXYFromRegion(
detection_region)
# Passthrough Filter
fil_cloud = filtering_helper.do_passthrough_filter(
cloud, 'x', x_min, x_max)
fil_cloud = filtering_helper.do_passthrough_filter(
fil_cloud, 'y', y_min, y_max)
# Voxel Grid Filter
filtered_cloud = filtering_helper.do_voxel_grid_filter(fil_cloud, 0.01)
self.middle_time_1 = rospy.Time.now().to_sec()
# Use Euclidean clustering to filter out noise
clusters = self.getClusters(filtered_cloud, 0.05, 10, 9000) #0.05, 10
# Setting Front-region for snapshot
front_region = self.camera_region
# Visualize Detection Region
self.visualize_region(detection_region)
self.middle_time_2 = rospy.Time.now().to_sec()
# Grouping clusters into 2 groups of points
detected_pts = self.getPointsInRegionFromClusters(
clusters, filtered_cloud, detection_region)
# front_pts = self.getPointsInRegionFromPoints(detected_pts, front_region)
self.middle_time_3 = rospy.Time.now().to_sec()
print "detected_pts", len(detected_pts)
# If Obstacle detected, do following:
if (len(detected_pts) != 0):
self.stopping()
print "--------------------------------------"
print "| Obstacle detected and stopping AGV |"
print "--------------------------------------"
# Rosservice call the health_monitoring of obstacle blocking
if (rospy.Time.now().to_sec() - self.obs_timer >=
self.obs_timeout):
print "#######################################################"
print "########## calling health_monitoring ##########"
print "#######################################################"
self.obstacle_health_call()
self.obs_timer = rospy.Time.now().to_sec()
# Use for snapshot once per obstacle at front
if (self.fsm_state != "SUSPEND"):
self.error = 1
# Rosservice call to snapshot the front obstacle once
# print "front_pts", len(front_pts)
# if(len(front_pts)!=0 and self.fsm_state=="SUSPEND" and self.error==1):
# if(len(detected_pts)!=0 and self.fsm_state=="SUSPEND" and self.error==1):
if (len(detected_pts) != 0 and self.fsm_state == "SUSPEND"
and self.fsm_state != "ESTOP" and self.error == 1):
self.error = 0
print "######################################"
print "########## snapshot ##########"
print "######################################"
self.snapshot_call()
self.prev_detection_region = detection_region
print "start_time", self.start_time
print "middle_time_1", self.middle_time_1
print "middle_time_2", self.middle_time_2
print "middle_time_3", self.middle_time_3
print "end_time", rospy.Time.now().to_sec()
def callback(input_ros_msg):
cloud = pcl_helper.ros_to_pcl(input_ros_msg)
#color = pcl_helper.random_color_gen()
#white_cloud = pcl_helper.XYZ_to_XYZRGB(cloud, color)
cluster_cloud, cluster_indices = do_euclidian_clustering(cloud)
pub.publish(cluster_cloud)
def pcl_callback(ros_pcl_msg):
# Exercise-1 TODOs:
# TODO: Load point cloud data
# TODO: Voxel Grid Downsampling
# TODO: PassThrough Filter
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers and outliers
# TODO: Save the inliers in .pcd file
# TODO: Run the inliers.pcd file on terminal
# Exercise-2 TODOs:
# TODO: Convert ROS msg to PCL data
# TODO: Voxel Grid Downsampling
# TODO: PassThrough Filter
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers and outliers
# TODO: Euclidean Clustering
# TODO: Create Cluster-Mask Point Cloud to visualize each cluster separately
# TODO: Convert PCL data to ROS messages
# TODO: Publish ROS messages
# Exercise-3 TODOs:
# TODO: Classify the clusters! (loop through each detected cluster one at a time)
# TODO: Grab the points for the cluster
# TODO: Compute the associated feature vector
# TODO: Make the prediction
# TODO: Publish a label into RViz
# TODO: Add the detected object to the list of detected objects.
# TODO: Publish the list of detected objects
# TODO: Convert ROS msg of type PointCloud2 to PCL data PointXYZRGB format
pcl_data = pclh.ros_to_pcl(ros_pcl_msg)
# TODO: Statistical filter
stat_inlier, stat_outlier = flt.statistical_outlier_filter(pcl_data)
pcl_data = stat_inlier
# TODO: Voxel Grid Downsampling
vox_filt = flt.voxel_downsampling(pcl_data)
# TODO: PassThrough Filter
pass_filt = flt.pass_through_filter(vox_filt)
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers (objects) and outliers (table)
cloud_table, cloud_objects = flt.ransac(pass_filt)
# TODO: Euclidean Clustering
cluster_indices = flt.euclidean_clustering(cloud_objects)
# TODO: Create Cluster-Mask Point Cloud to visualize each cluster separately
cluster_cloud = flt.visualize_clusters(cloud_objects, cluster_indices)
# TODO: Convert PCL data to ROS messages
ros_table_cloud = pclh.pcl_to_ros(cloud_table)
ros_objects_cloud = pclh.pcl_to_ros(cloud_objects)
ros_cluster_cloud = pclh.pcl_to_ros(cluster_cloud)
# TODO: Publish ROS messages
pcl_table_pub.publish(ros_table_cloud)
pcl_objects_pub.publish(ros_objects_cloud)
pcl_cluster_pub.publish(ros_cluster_cloud)
# TODO: Classify the clusters!
detected_objects_labels = []
detected_objects = []
labeled_features = []
for index, pts_list in enumerate(cluster_indices):
# Grab the points for the cluster from the extracted outliers (cloud_objects)
pcl_cluster = cloud_objects.extract(pts_list)
# TODO: convert the cluster from pcl to ROS using helper function
ros_cluster = pclh.pcl_to_ros(pcl_cluster)
# Extract histogram features
# TODO: complete this step just as is covered in capture_features.py
# Extract histogram features
hists_color = compute_color_histograms(ros_cluster, using_hsv=True)
normals = get_normals(ros_cluster)
hists_normals = compute_normal_histograms(normals)
feature = np.concatenate((hists_color, hists_normals))
#labeled_features.append([feature, model_name])
# Make the prediction, retrieve the label for the result
# and add it to detected_objects_labels list
prediction = clf.predict(scaler.transform(feature.reshape(1,-1)))
label = encoder.inverse_transform(prediction)[0]
detected_objects_labels.append(label)
# Publish a label into RViz
white_cloud = pclh.XYZRGB_to_XYZ(cloud_objects)
label_pos = list(white_cloud[pts_list[0]])
label_pos[2] += .4
object_markers_pub.publish(make_label(label,label_pos, index))
# Add the detected object to the list of detected objects.
do = DetectedObject()
do.label = label
do.cloud = ros_cluster
detected_objects.append(do)
rospy.loginfo('Detected {} objects: {}'.format(len(detected_objects_labels), detected_objects_labels))
# Publish the list of detected objects
# This is the output you'll need to complete the upcoming project!
detected_objects_pub.publish(detected_objects)
def pc2CB(self, msg):
if self.init:
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Filter the Cloud
fil_cloud = filtering_helper.do_passthrough_filter(
cloud, 'x', self.x_min, self.x_max)
filtered_cloud = filtering_helper.do_passthrough_filter(
fil_cloud, 'y', self.y_min, self.y_max)
# Turn cloud into colorless cloud
colorless_cloud = pcl_helper.XYZRGB_to_XYZ(filtered_cloud)
# Get groups of cluster of points
# clusters = self.getClusters(colorless_cloud, 0.05, 20, 300) # Table on Top AGV - 60~300
clusters = self.getClusters(colorless_cloud, 0.05, 20, 350)
print "len(clusters)", len(clusters)
for i in range(0, len(clusters)):
print "len(clusters[", i, "]", len(clusters[i])
# Get mean points from clusters
mean_pts = []
for cluster in clusters:
mean_pts.append(self.getMeanPoint(cluster, filtered_cloud))
print "len(mean_pts)", len(mean_pts)
for i in range(0, len(mean_pts)):
print "mean_pts[", i, "]", mean_pts[i]
# Remove other points, leave the table points only
table_pts = []
table_pts = self.getTablePoints(mean_pts)
print "len(table_pts)", len(table_pts)
print table_pts
for i in range(len(table_pts)):
self.vizPoint(i, table_pts[i], ColorRGBA(1, 1, 1, 1),
"base_link")
# Get transform of "base_link" relative to "map"
trans = self.getTransform("map", "base_link")
# Finding 2 middle points from 4 table legs and use them as path_pts
path_pts = []
if (len(table_pts) == 4):
for p1 in table_pts:
for p2 in table_pts:
if (p1 == p2):
break
# Register 2 middle_pts of 4 table legs
if (0.73 < self.getDistance(p1, p2) < 0.83):
path_pts.append(self.getMiddlePoint(p1, p2))
break
print "len(path_pts)", len(path_pts)
print path_pts
# Turn path_pts into map frame
for i in range(len(path_pts)):
path_pts[i] = tf2_geometry_msgs.do_transform_pose(
self.toPoseStamped(path_pts[i], Quaternion(0, 0, 0, 1)),
trans).pose.position
path_pts[i] = Point(path_pts[i].x, path_pts[i].y, 0)
# Record the starting point and heading once
if self.record_once:
self.start_pt = trans.transform.translation
self.record_once = False
# Create a list with distance information between initial_pt and path_pts
distance_list = [
self.getDistance(p, self.start_pt) for p in path_pts
]
# Rearrange the path_pts to be in descending order
path_pts = self.getAscend(path_pts, distance_list)
# Duplicate the path_pts to prevent from being edited
table_pathpts = path_pts[:]
print "table_pathpts: ", len(table_pathpts)
if (len(table_pathpts) == 2):
# Get gradient of the Line of Best Fit
m1 = self.getGradientLineOfBestFit(table_pathpts)
# Insert another point on the line with d[m] away from the 2nd table_pathpts
path_pts.append(
self.getPointOnLine(m1, 1.0, table_pathpts[1],
self.start_pt))
# Visualize the path_pts
self.vizPoint(0, path_pts[0], ColorRGBA(1, 0, 0, 1),
"map") # Red
self.vizPoint(1, path_pts[1], ColorRGBA(1, 1, 0, 1),
"map") # Yellow
self.vizPoint(2, path_pts[2], ColorRGBA(1, 0, 1, 1),
"map") # Magenta
print len(path_pts)
# Find the heading of the table (last 2 path_pts)
heading_q = self.getOrientation(table_pathpts[0],
table_pathpts[1])
# Publish the path
self.route_pub.publish(self.getPath2Table(path_pts, heading_q))
print "-----------------"
def pc2CB(self, msg):
if self.init:
print "start_time", rospy.Time.now().to_sec()
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Filter the Cloud
fil_cloud = filtering_helper.do_passthrough_filter(cloud, 'x', self.x_min, self.x_max)
filtered_cloud = filtering_helper.do_passthrough_filter(fil_cloud, 'y', self.y_min, self.y_max)
# Turn cloud into colorless cloud
# colorless_cloud = pcl_helper.XYZRGB_to_XYZ(filtered_cloud)
colorless_cloud = pcl_helper.XYZI_to_XYZ(filtered_cloud)
# Get groups of cluster of points
# clusters = self.getClusters(colorless_cloud, 0.05, 20, 250) # Table on Top AGV - 60~230
clusters = self.getClusters(colorless_cloud, 0.05, 20, 350)
# print "len(clusters)", len(clusters)
# for i in range(0,len(clusters)):
# print "len(clusters[", i, "]", len(clusters[i])
# Get mean points from clusters
mean_pts = []
for cluster in clusters:
mean_pts.append(self.getMeanPoint(cluster, filtered_cloud))
# print "len(mean_pts)", len(mean_pts)
# for i in range(0,len(mean_pts)):
# print "mean_pts[", i, "]", mean_pts[i]
# Remove other points, leave the table points only
table_pts = []
table_pts = self.getTablePoints(mean_pts)
# print "len(table_pts)", len(table_pts)
# print table_pts
for i in range(len(table_pts)):
self.vizPoint(i, table_pts[i], ColorRGBA(1,1,1,1), "base_link")
# Get transform of "base_link" relative to "map"
trans = self.getTransform("map", "base_link")
# Finding 2 middle points from 4 table legs and use them as path_pts
path_pts = []
if(len(table_pts)==4):
for p1 in table_pts:
for p2 in table_pts:
if(p1==p2):
break
# Register 2 middle_pts of 4 table legs
if(0.73 < self.getDistance(p1, p2) < 0.83):
path_pts.append(self.getMiddlePoint(p1, p2))
break
# print "len(path_pts)", len(path_pts)
# print path_pts
# Path_pts that generated from between 2 legs of table must be fulfill below requirement, else return
if(len(path_pts)==2):
if not (0.33 < self.getDistance(path_pts[0], path_pts[1]) < 0.48):
return
# Turn path_pts into map frame
for i in range(len(path_pts)):
path_pts[i] = tf2_geometry_msgs.do_transform_pose(self.toPoseStamped(path_pts[i], Quaternion(0,0,0,1)), trans).pose.position
# Record the starting point once
if self.record_once:
self.start_pt = trans.transform.translation
self.record_once = False
# Create a list with distance information between initial_pt and path_pts
distance_list = [self.getDistance(p, self.start_pt) for p in path_pts]
# Rearrange the path_pts to be in ascending order
path_pts = self.getAscend(path_pts, distance_list)
# Duplicate the path_pts to prevent from being edited
table_pathpts = path_pts[:]
if(len(table_pathpts)==2):
# Get gradient of the Line of Best Fit
m1 = self.getGradientLineOfBestFit(table_pathpts)
# Insert another point on the line with d[m] away from the 1st table_pathpts
path_pts.insert(0, self.getPointOnLine(m1, 1.0, table_pathpts[0], self.start_pt))
# Insert a point ahead d[m] of table to the begin of path_pts
# ahead_pt = self.getPointAheadTable(table_pathpts, 1.0)
# path_pts.insert(0, ahead_pt)
# Insert a point ahead d[m] of table to the middle of table_pathpts
# midway_pt = self.getPointAheadTable(table_pathpts, -0.4)
# path_pts.insert(2, midway_pt)
# Insert another point on the line with d[m] away from the 1st table_pathpts
# path_pts.insert(2, self.getPointOnLine(m1, 0.05, table_pathpts[1], self.start_pt))
# Remove the last point of path_pts
# path_pts = path_pts[:len(path_pts)-1]
# Add the AGV start_pt to the begin of path_pts
# path_pts.insert(0, self.start_pt)
# Visualize the path_pts
# self.vizPoint(0, path_pts[0], ColorRGBA(1,0,0,1), "map") # Red
# self.vizPoint(1, path_pts[1], ColorRGBA(1,1,0,1), "map") # Yellow
# self.vizPoint(2, path_pts[2], ColorRGBA(0,0,1,1), "map") # Blue
# Find the heading of the table (last 2 path_pts)
heading_q = self.getOrientation(table_pathpts[0], table_pathpts[1])
# Publish the path
self.route_pub.publish(self.getPath2Table(path_pts, heading_q))
# print "-----------------"
print "end_time", rospy.Time.now().to_sec()
def pc2CB(self, msg):
if self.init:
self.start_time = rospy.Time.now().to_sec()
print "-----------------"
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Get transform of "base_link" relative to "map"
trans = self.getTransform("map", "base_link")
# Filter the Cloud
fil_cloud = filtering_helper.do_passthrough_filter(cloud, 'x', -2.5, 2.5)
fil_cloud = filtering_helper.do_passthrough_filter(fil_cloud, 'y', -1.5, 1.5)
# Turn cloud into colorless cloud
# colorless_cloud = pcl_helper.XYZRGB_to_XYZ(fil_cloud)
# Statistical outlier filter
# fil = colorless_cloud.make_statistical_outlier_filter()
fil = fil_cloud.make_statistical_outlier_filter()
fil.set_mean_k(30)
fil.set_std_dev_mul_thresh(0.1) #1.0 / 0.1
filtered_cloud = fil.filter()
# pc2_msg = pcl_helper.pcl_to_ros(filtered_cloud, "base_link")
# self.pc2_pub.publish(pc2_msg)
# Get groups of cluster of points
clusters = self.getClusters(filtered_cloud, 0.05, 20, 1000)
print "len(clusters)", len(clusters)
for i in range(len(clusters)):
print "clusters["+str(i)+"]", len(clusters[i])
# Not enough clusters to be identified as charger unit
if(len(clusters)<3):
return
self.middle_time_1 = rospy.Time.now().to_sec()
# Extract charger_wall & charger_pillars clusters
# - use for compute gradient of Line of Best Fit
# - use for compute middle_pt
wall_cluster, pillar_clusters = self.getChargerClusters(clusters, filtered_cloud)
self.middle_time_2 = rospy.Time.now().to_sec()
# Cannot find the charger_wall and charger_pillars
if(wall_cluster==None or pillar_clusters==[]):
print "wall cluster or pillar clusters not found"
return
print "wall_cluster", len(wall_cluster)
self.vizPoint(0, self.getMeanPointFromCluster(wall_cluster, filtered_cloud), ColorRGBA(0,1,0,1), "base_link")
for i in range(len(pillar_clusters)):
print "pillar_clusters["+str(i)+"]", len(pillar_clusters[i])
self.vizPoint(i+1, self.getMeanPointFromCluster(pillar_clusters[i], filtered_cloud), ColorRGBA(0,1,0,1), "base_link")
# Get Points of wall_cluster
wall_pts = self.getPointsFromCluster(wall_cluster, filtered_cloud)
# Turn wall_pts into map frame
for i in range(len(wall_cluster)):
wall_pts[i] = tf2_geometry_msgs.do_transform_pose(self.toPoseStamped(wall_pts[i], Quaternion(0,0,0,1)), trans).pose.position
# Get gradient of the Line of Best Fit
m1 = self.getGradientLineOfBestFit(wall_pts)
print "gradient", m1
pillar_pts = []
# Get mean points from pillar_clusters
for cluster in pillar_clusters:
pillar_pts.append(self.getMeanPointFromCluster(cluster, filtered_cloud))
# Finding 1 middle_pt from 2 charger_pillars and use it as path_pts
print pillar_pts
if(len(pillar_pts)==2):
middle_pt = None
for p1 in pillar_pts:
for p2 in pillar_pts:
if(p1==p2):
break
# Only register middle_pt of 2 charger_pillars points
if(0.5 < self.getDistance(p1, p2) < 0.65):
middle_pt = self.getMiddlePoint(p1, p2)
break
if(middle_pt==None):
"middle_pt not found"
return
# Turn middle_pt into map frame
middle_pt = tf2_geometry_msgs.do_transform_pose(self.toPoseStamped(middle_pt, Quaternion(0,0,0,1)), trans).pose.position
# Register the middle_pt as one of the path_pts
path_pts = []
path_pts.append(middle_pt)
# Record the starting point and heading once
if self.record_once:
self.start_pt = trans.transform.translation
self.record_once = False
# Insert another point on the normal line with d[m] away from middle_pt
path_pts.insert(0, self.getPointOnNormalLine(m1, 1.0, middle_pt, self.start_pt))
# Duplicate the path points to prevent from being edited
charger_pathpts = path_pts[:]
# Insert another point on the normal line with d[m] away from middle_pt
path_pts.insert(0, self.getPointOnNormalLine(m1, 0.3, middle_pt, self.start_pt))
# Create a list with distance information between init_pt and path_pts
distance_list = [self.getDistance(p, self.start_pt) for p in path_pts]
# Rearrange the path_pts to be in ascending order
path_pts = self.getAscend(path_pts, distance_list)
# Add the AGV start_pt to the begin of path_pts
# path_pts.insert(0, self.start_pt)
# Remove last point of path_pts (middle pt of 2 charger feature pts)
path_pts = path_pts[:len(path_pts)-1]
print "len(path_pts)", len(path_pts)
# # Visualize the path_pts
self.vizPoint(0, path_pts[0], ColorRGBA(1,0,0,1), "map") # Red
self.vizPoint(1, path_pts[1], ColorRGBA(1,1,0,1), "map") # Yellow
# self.vizPoint(2, path_pts[2], ColorRGBA(0,0,1,1), "map") # Blue
# Find the heading of the charger (last 2 path_pts)
heading_q = self.getOrientation(charger_pathpts[0], charger_pathpts[1])
# Publish the path
self.route_pub.publish(self.getPath2Charger(path_pts, heading_q))
print "start_time", self.start_time
print "middle_time_1", self.middle_time_1
print "middle_time_2", self.middle_time_2
print "end_time", rospy.Time.now().to_sec()
def pc2CB(self, msg):
if self.init:
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Get transform of "base_link" relative to "map"
trans = self.getTransform("map", "base_link")
# Filter the Cloud
fil_cloud = filtering_helper.do_passthrough_filter(
cloud, 'x', -3.0, 3.0)
fil_cloud = filtering_helper.do_passthrough_filter(
fil_cloud, 'y', -3.0, 3.0)
# Turn cloud into colorless cloud
colorless_cloud = pcl_helper.XYZRGB_to_XYZ(fil_cloud)
# source_cloud = colorless_cloud
# Get groups of cluster of points
clusters = self.getClusters(colorless_cloud, 0.05, 100, 450)
# print "len(clusters)", len(clusters)
source_cloud = self.getCloudFromCluster(clusters[0],
colorless_cloud)
middle_pt = self.getMeanPoint(clusters[0], colorless_cloud)
self.vizPoint(0, middle_pt, ColorRGBA(1, 1, 1, 1), "base_link")
icp = source_cloud.make_IterativeClosestPoint()
converged, transform, estimate, fitness = icp.icp(
source_cloud, self.target_cloud, 100)
# gicp = colorless_cloud.make_GeneralizedIterativeClosestPoint()
# converged, transform, estimate, fitness = gicp.gicp(colorless_cloud, self.target_cloud, 100)
# icp_nl = colorless_cloud.make_IterativeClosestPointNonLinear()
# converged, transform, estimate, fitness = icp_nl.icp_nl(self.target_cloud, colorless_cloud, 100)
# if not converged:
print "converged", converged
print "transform", transform
print "estimate", estimate
print "fitness", fitness
# new_transform = transform.inverse()
# print new_transform
translation = Vector3(transform[0, 3], transform[1, 3],
transform[2, 3])
q = tf.transformations.quaternion_from_matrix(transform)
rotation = Quaternion(q[0], q[1], q[2], q[3])
roll, pitch, yaw = tf.transformations.euler_from_matrix(transform)
print np.rad2deg(yaw)
# print "translation\n", translation
# print "rotation\n", rotation
# transformation = self.toTransformStamped(translation, rotation, "map", "base_link")
print "------------------------------------------------"
# Visualize the cloud
outmsg = pcl_helper.pcl_to_ros(estimate, "map")
outmsg.header = msg.header
self.pc2_pub.publish(outmsg)
def pcl_callback(ros_pcl_msg):
# Exercise-1 TODOs:
# TODO: Load point cloud data
# TODO: Statistical Outlier Filtering
# TODO: Voxel Grid Downsampling
# TODO: PassThrough Filter
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers and outliers
# TODO: Save the inliers in .pcd file
# TODO: Run the inliers.pcd file on terminal
# Exercise-2 TODOs:
# TODO: Convert ROS msg to PCL data
# TODO: Statistical Outlier Filtering
# TODO: Voxel Grid Downsampling
# TODO: PassThrough Filter
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers and outliers
# TODO: Euclidean Clustering
# TODO: Create Cluster-Mask Point Cloud to visualize each cluster separately
# TODO: Convert PCL data to ROS messages
# TODO: Publish ROS messages
# Exercise-3 TODOs:
# TODO: Classify the clusters! (loop through each detected cluster one at a time)
# TODO: Grab the points for the cluster
# TODO: Compute the associated feature vector
# TODO: Make the prediction
# TODO: Publish a label into RViz
# TODO: Add the detected object to the list of detected objects.
# TODO: Publish the list of detected objects
# TODO: Convert ROS msg of type PointCloud2 to PCL data PointXYZRGB format
pcl_data = pclh.ros_to_pcl(ros_pcl_msg)
# TODO: Statistical filter
stat_inlier, stat_outlier = flt.statistical_outlier_filter(pcl_data)
# TODO: Voxel Grid Downsampling
vox_filt = flt.voxel_downsampling(stat_inlier)
# TODO: PassThrough Filter
pass_filt_z = flt.pass_through_filter(vox_filt, 0.6, 1.2, 'z')
pass_filt_zy = flt.pass_through_filter(pass_filt_z, -0.5, 0.5, 'y')
pass_filt_zyx = flt.pass_through_filter(pass_filt_zy, 0.4, 1.2, 'x')
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers (objects) and outliers (table)
cloud_table, cloud_objects = flt.ransac(pass_filt_zyx)
# TODO: Euclidean Clustering
cluster_indices = flt.euclidean_clustering(cloud_objects)
# TODO: Create Cluster-Mask Point Cloud to visualize each cluster separately
cluster_cloud = flt.visualize_clusters(cloud_objects, cluster_indices)
# TODO: Convert PCL data to ROS messages
ros_table_cloud = pclh.pcl_to_ros(cloud_table)
ros_objects_cloud = pclh.pcl_to_ros(cloud_objects)
ros_cluster_cloud = pclh.pcl_to_ros(cluster_cloud)
# TODO: Publish ROS messages
pcl_table_pub.publish(ros_table_cloud)
pcl_objects_pub.publish(ros_objects_cloud)
pcl_cluster_pub.publish(ros_cluster_cloud)
# TODO: Classify the clusters and create list of detected objects
detected_objects, detected_objects_labels = detect_objects(
cloud_objects, cluster_indices)
# Suggested location for where to invoke your pr2_mover() function within pcl_callback()
# Could add some logic to determine whether or not your object detections are robust
# before calling pr2_mover()
detected_objects_pub.publish(detected_objects)
try:
pr2_mover(detected_objects)
except rospy.ROSInterruptException:
pass
def pc2CB(self, msg):
if self.init:
# Convert ROS PointCloud2 msg to PCL data
cloud = pcl_helper.ros_to_pcl(msg)
# Filter the Cloud
fil_cloud = filtering_helper.do_passthrough_filter(cloud, 'x', -2.5, 2.5)
filtered_cloud = filtering_helper.do_passthrough_filter(fil_cloud, 'y', -2.5, 2.5)
# Turn cloud into colorless cloud
colorless_cloud = pcl_helper.XYZRGB_to_XYZ(filtered_cloud)
# Get groups of cluster of points
clusters = self.getClusters(colorless_cloud, 0.05, 10, 1000)
print "len(clusters)", len(clusters)
# Extract charger wall cluster - use for compute Line of Best Fit
if(len(clusters) != 0):
size_clusters = []
for cluster in clusters:
size_clusters.append(len(cluster))
# Find the biggest cluster
max_id = size_clusters.index(max(size_clusters))
wall_cluster = clusters.pop(max_id)
print "len(wall_cluster)", len(wall_cluster)
feature_clusters = clusters
print "len(feature_clusters)", len(feature_clusters)
# Get transform of "base_link" relative to "map"
trans = self.getTransform("map", "base_link")
# Record the starting point and heading once
if self.record_once:
self.start_pt = trans.transform.translation
self.record_once = False
# Get Points of wall cluster
wall_pts = self.getPointsFromCluster(wall_cluster, filtered_cloud)
# Turn wall_pts into map frame
for i in range(len(wall_cluster)):
wall_pts[i] = tf2_geometry_msgs.do_transform_pose(self.toPoseStamped(wall_pts[i], Quaternion(0,0,0,1)), trans).pose.position
# Get gradient of the Line of Best Fit
m1 = self.getGradientLineOfBestFit(wall_pts)
print m1
# Get mean points from feature clusters
mean_pts = []
for cluster in feature_clusters:
mean_pts.append(self.getMeanPointFromCluster(cluster, filtered_cloud))
print "len(mean_pts)", len(mean_pts)
for i in range(len(mean_pts)):
self.vizPoint(i, mean_pts[i], ColorRGBA(1,1,1,1), "base_link")
# Finding 1 middle point from 2 charger feature columns and use it as path_pts
if(len(mean_pts)>=2):
for p1 in mean_pts:
for p2 in mean_pts:
if(p1==p2):
break
# Only register middle_pt of 2 charger feature points
if(0.5 < self.getDistance(p1, p2) < 0.65):
middle_pt = self.getMiddlePoint(p1, p2)
# Turn middle_pt into map frame
middle_pt = tf2_geometry_msgs.do_transform_pose(self.toPoseStamped(middle_pt, Quaternion(0,0,0,1)), trans).pose.position
# Register the middle_pt as one of the path_pts
path_pts = []
path_pts.append(middle_pt)
# Insert another point on the normal line with d[m] away from middle_pt
path_pts.insert(0, self.getPointOnNormalLine(m1, 1.0, middle_pt, self.start_pt))
# Duplicate the path points to prevent from being edited
feature_pathpts = path_pts[:]
# Insert another point on the normal line with d[m] away from middle_pt
path_pts.insert(1, self.getPointOnNormalLine(m1, 0.3, middle_pt, self.start_pt))
# Create a list with distance information between init_pt and path_pts
distance_list = [self.getDistance(p, self.start_pt) for p in path_pts]
# Rearrange the path_pts to be in ascending order
path_pts = self.getAscend(path_pts, distance_list)
print "len(path_pts)", len(path_pts)
# Add the AGV start_pt to the begin of path_pts
# path_pts.insert(0, self.start_pt)
# Remove last point of path_pts (middle pt of 2 charger feature pts)
path_pts = path_pts[:len(path_pts)-1]
# Visualize the path_pts
self.vizPoint(0, path_pts[0], ColorRGBA(1,0,0,1), "map") # Red
self.vizPoint(1, path_pts[1], ColorRGBA(1,1,0,1), "map") # Yellow
# self.vizPoint(2, path_pts[2], ColorRGBA(0,0,1,1), "map") # Blue
# Find the heading of the charger (last 2 path_pts)
heading_q = self.getOrientation(feature_pathpts[-2], feature_pathpts[-1])
# Publish the path
self.route_pub.publish(self.getPath2Charger(path_pts, heading_q))
print "-----------------"
def pcl_callback(pcl_msg):
# Convert ROS msg to PCL data
cloud = ros_to_pcl(pcl_msg)
# create a voxelgrid filter object for our input point cloud
vox = cloud.make_voxel_grid_filter()
# Choose a voxel (also known as leaf) size
LEAF_SIZE = 0.01
# set the voxel (or leaf)size
vox.set_leaf_size(LEAF_SIZE, LEAF_SIZE, LEAF_SIZE)
# Call the filter function to obtain the resultant downsampled point cloud
cloud_filtered = vox.filter()
#filename = 'voxel_downsampled.pcd'
#pcl.save(cloud_filtered, filename)
# PassThrough filter
# create pass through filter object
passthrough = cloud_filtered.make_passthrough_filter()
# assign axis and ranve to the passthrough filter object
filter_axis = 'z'
passthrough.set_filter_field_name(filter_axis)
axis_min = 0.6
axis_max = 1.1
passthrough.set_filter_limits(axis_min, axis_max)
# generate the resultant point cloud
cloud_filtered = passthrough.filter()
# RANSAC plane segmentation
# create the segmentation object
seg = cloud_filtered.make_segmenter()
# set the model to fit
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
# Max distance for a point to be considered fitting the model
max_distance = 0.01
seg.set_distance_threshold(max_distance)
# call the segment function to obtain set of inlier indices and model coefficients
inliers, coefficients = seg.segment()
# Extract table
cloud_table = cloud_filtered.extract(inliers, negative=False)
#filename = 'cloud_table.pcd'
#pcl.save(cloud_table, filename)
# Extract objects
#cloud_objects = cloud_filtered.extract(inliers, negative=True)
# Extract outliers
# create the filter objectd
objects = cloud_filtered.extract(inliers, negative=True)
outlier_filter = objects.make_statistical_outlier_filter()
# set the number of points to analyze for any given point
outlier_filter.set_mean_k(50)
# set threshold scale factor
x = 1.0
# any point with a mena distance large than the global (mean distance + x * std_dev)
# will be considered outlier
outlier_filter.set_std_dev_mul_thresh(x)
# call the filter function
cloud_objects = outlier_filter.filter()
filename = 'cloud_objects.pcd'
pcl.save(cloud_objects, filename)
# TODO: Euclidean Clustering
white_cloud = XYZRGB_to_XYZ(cloud_objects)
tree = white_cloud.make_kdtree()
# create cluster extraction
ec = white_cloud.make_EuclideanClusterExtraction()
# set the tolerances for
ec.set_ClusterTolerance(0.05)
ec.set_MinClusterSize(100)
ec.set_MaxClusterSize(2000)
ec.set_SearchMethod(tree)
cluster_indices = ec.Extract()
# TODO: Create Cluster-Mask Point Cloud to visualize each cluster separately
# asign colors to the individual point cloud indices for visualization
cluster_color = get_color_list(len(cluster_indices))
color_cluster_point_list = []
for j, indices in enumerate(cluster_indices):
for i, indice in enumerate(indices):
color_cluster_point_list.append([
white_cloud[indice][0], white_cloud[indice][1],
white_cloud[indice][2],
rgb_to_float(cluster_color[j])
])
# create new cloud containing all clusters, each with unique color
cluster_cloud = pcl.PointCloud_PointXYZRGB()
cluster_cloud.from_list(color_cluster_point_list)
# TODO: Convert PCL data to ROS messages
ros_cloud_objects = pcl_to_ros(cloud_objects)
ros_cloud_table = pcl_to_ros(cloud_table)
ros_cluster_cloud = pcl_to_ros(cluster_cloud)
# TODO: Publish ROS messages
pcl_objects_pub.publish(ros_cloud_objects)
pcl_table_pub.publish(ros_cloud_table)
pcl_cluster_pub.publish(ros_cluster_cloud)
def pcl_callback(ros_pcl_msg):
# Exercise-1 TODOs:
# TODO: Load point cloud data
# TODO: Voxel Grid Downsampling
# TODO: PassThrough Filter
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers and outliers
# TODO: Save the inliers in .pcd file
# TODO: Run the inliers.pcd file on terminal
# Exercise-2 TODOs:
# TODO: Convert ROS msg to PCL data
# TODO: Voxel Grid Downsampling
# TODO: PassThrough Filter
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers and outliers
# TODO: Euclidean Clustering
# TODO: Create Cluster-Mask Point Cloud to visualize each cluster separately
# TODO: Convert PCL data to ROS messages
# TODO: Publish ROS messages
# Exercise-3 TODOs:
# TODO: Classify the clusters! (loop through each detected cluster one at a time)
# TODO: Grab the points for the cluster
# TODO: Compute the associated feature vector
# TODO: Make the prediction
# TODO: Publish a label into RViz
# TODO: Add the detected object to the list of detected objects.
# TODO: Publish the list of detected objects
# TODO: Convert ROS msg of type PointCloud2 to PCL data PointXYZRGB format
pcl_data = pclh.ros_to_pcl(ros_pcl_msg)
# TODO: Statistical filter
stat_inlier, stat_outlier = flt.statistical_outlier_filter(pcl_data)
# TODO: Voxel Grid Downsampling
vox_filt = flt.voxel_downsampling(stat_inlier)
# TODO: PassThrough Filter
pass_filt = flt.pass_through_filter(vox_filt)
# TODO: RANSAC Plane Segmentation
# TODO: Extract inliers (objects) and outliers (table)
cloud_table, cloud_objects = flt.ransac(pass_filt)
# TODO: Euclidean Clustering
cluster_indices = flt.euclidean_clustering(cloud_objects)
# TODO: Create Cluster-Mask Point Cloud to visualize each cluster separately
cluster_cloud = flt.visualize_clusters(cloud_objects, cluster_indices)
# TODO: Classify the clusters and create list of detected objects
detected_objects, detected_objects_labels = detect_objects(cloud_objects, cluster_indices)
# TODO: Convert PCL data to ROS messages
ros_table_cloud = pclh.pcl_to_ros(cloud_table)
ros_objects_cloud = pclh.pcl_to_ros(cloud_objects)
ros_cluster_cloud = pclh.pcl_to_ros(cluster_cloud)
# TODO: Publish ROS messages
pcl_table_pub.publish(ros_table_cloud)
pcl_objects_pub.publish(ros_objects_cloud)
pcl_cluster_pub.publish(ros_cluster_cloud)
detected_objects_pub.publish(detected_objects)
def pr2_mover(detected_objects):
object_list = detected_objects
# TODO: Initialize variables
test_scene_num = msgStd.Int32()
arm_name = msgStd.String()
object_name = msgStd.String()
pick_pose = msgGeo.Pose()
place_pose = msgGeo.Pose()
test_scene_num.data = g_WORLD_NUM
detected = 0
dict_list = []
# TODO: Get/Read parameters
# get parameters
object_list_param = rospy.get_param('/object_list')
dropbox_param = rospy.get_param('/dropbox')
dictGroupColorToBoxParam = {}
for boxParam in dropbox_param:
groupColorStr = boxParam['group']
dictGroupColorToBoxParam[groupColorStr] = boxParam
# TODO: Loop through the pick list
for objIndex in range(0, len(object_list_param)):
# TODO: Parse parameters into individual variables
object_group = object_list_param[objIndex]['group']
# TODO: Get the PointCloud for a given object and obtain it's centroid
for object in object_list:
if object_list_param[objIndex]['name'] == object.label:
# labels.append(object.label)
points_arr = pcl_helper.ros_to_pcl(object.cloud).to_array()
centroids = np.mean(points_arr, axis=0)[:3]
pick_pose.position.x = np.asscalar(centroids[0])
pick_pose.position.y = np.asscalar(centroids[1])
pick_pose.position.z = np.asscalar(centroids[2])
object_name.data = object_list_param[objIndex]['name']
detected += 1
break
# TODO: Create 'place_pose' for the object
targetBox = dictGroupColorToBoxParam[object_group]
dropbox_pose = targetBox['position']
place_pose.position.x = dropbox_pose[0]
place_pose.position.y = dropbox_pose[1]
place_pose.position.z = dropbox_pose[2]
# TODO: Assign the arm to be used for pick_place
arm_name.data = targetBox['name']
# TODO: Create a list of dictionaries (made with make_yaml_dict()) for later output to yaml format
# Populate various ROS messages
yaml_dict = make_yaml_dict(test_scene_num, arm_name, object_name,
pick_pose, place_pose)
dict_list.append(yaml_dict)
# TODO: Rotate PR2 in place to capture side tables for the collision map
if g_doCommandRobot:
try:
rospy.wait_for_service('pick_place_routine')
#pick_place_routine = rospy.ServiceProxy('pick_place_routine', PickPlace)
# TODO: Insert your message variables to be sent as a service request
#resp = pick_place_routine(g_WORLD_NUM, OBJECT_NAME, WHICH_ARM, PICK_POSE, PLACE_POSE)
#print ("Response: ",resp.success)
except rospy.ServiceException, e:
print "Service call failed: %s" % e
def get_clusters(self, cloud):
print("point cloud size = ",len(cloud.data), " bytes")
marker_array = MarkerArray()
pcl_cloud = ros_to_pcl(cloud)
#convert pointcloud_xyzrgb to pointcloud_xyz
# since pointcloud_xyzrgb does not support make_EuclideanClusterExtraction
xyz_pc = pcl.PointCloud()
xyz_pc.from_list( [ x[:3] for x in pcl_cloud.to_list() ] )
tree = xyz_pc.make_kdtree()
ec = xyz_pc.make_EuclideanClusterExtraction()
ec.set_ClusterTolerance (0.01)
ec.set_MinClusterSize (100)
ec.set_MaxClusterSize (25000)
ec.set_SearchMethod (tree)
cluster_indices = ec.Extract()
indx = []
#for all j clusters
print("# clusters ", len(cluster_indices))
for j, indices in enumerate(cluster_indices):
tmp_indx = []
print("# points ", len(indices))
min_x = 999.9
max_x = -999.9
min_y = 999.9
max_y = -999.9
min_z = 999.9
max_z = -999.9
#for all points in the cluster j
for i, indice in enumerate(indices):
tmp_indx.append(indice)
if xyz_pc[indice][0] < min_x:
min_x = xyz_pc[indice][0]
elif xyz_pc[indice][0] > max_x:
max_x = xyz_pc[indice][0]
if xyz_pc[indice][1] < min_y:
min_y = xyz_pc[indice][1]
elif xyz_pc[indice][1] > max_y:
max_y = xyz_pc[indice][1]
if xyz_pc[indice][2] < min_z:
min_z = xyz_pc[indice][2]
elif xyz_pc[indice][2] > max_z:
max_z = xyz_pc[indice][2]
x = (min_x + max_x)/2
y = (min_y + max_y)/2
z = (min_z + max_z)/2
p = get_stamped_point(x, y, z, cloud.header.frame_id)
label = "obj_" + str(j)
marker_array.markers.append(get_marker(j, label, cloud.header.frame_id, x, y, z))
obj = pcl_cloud.extract(indices, negative=False)
pc_msg = pcl_to_ros(obj, stamp=cloud.header.stamp, frame_id=cloud.header.frame_id, seq=cloud.header.seq)
converter = PointCloudToImages('/home/phiggin1/deep_rgbd_v01.2/data/')
converter.convert(obj)
fuse_color_depth()
features = get_features()
self.pub.publish(pc_msg)
print("object at ", p, "\nwith features: ",features)
self.obj_markers_pub.publish(marker_array)
def create_pick_pose(item):
points_array = pclh.ros_to_pcl(item.cloud).to_array()
centroid = np.mean(points_array, axis=0)[:3]
return make_ros_pose(centroid[0], centroid[1], centroid[2])
