# Import PCL module
import pcl
# Load Point Cloud file
cloud = pcl.load_XYZRGB('tabletop.pcd')
################################################
# Voxel Grid filter
# Create a VoxelGrid filter object for our input point cloud
vox = cloud.make_voxel_grid_filter()
# Choose a voxel (also known as leaf) size
# Note: this (1) is a poor choice of leaf size
# Experiment and find the appropriate 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 a PassThrough filter object.
passthrough = cloud_filtered.make_passthrough_filter()
def setUp(self):
self.p = pcl.load_XYZRGB("tests" + os.path.sep + "flydracyl.pcd")
from __future__ import print_function
import numpy as np
import pcl
import pcl.pcl_visualization
# from pcl.pcl_registration import icp, gicp, icp_nl
cloud = pcl.load_XYZRGB(
'./examples/pcldata/tutorials/table_scene_mug_stereo_textured.pcd')
visual = pcl.pcl_visualization.CloudViewing()
# PointXYZ
# visual.ShowMonochromeCloud(cloud)
# visual.ShowGrayCloud(cloud, b'cloud')
visual.ShowColorCloud(cloud, b'cloud')
# visual.ShowColorACloud(cloud, b'cloud')
# while True:
# visual.WasStopped()
# end
flag = True
while flag:
flag != visual.WasStopped()
end
def random_color_gen():
""" Generates a random color
Args: None
Returns:
list: 3 elements, R, G, and B
"""
r = randint(0, 255)
g = randint(0, 255)
b = randint(0, 255)
return [r, g, b]
# Load Point Cloud file
cloud = pcl.load_XYZRGB('camera.pcd')
outlier_filter = cloud.make_statistical_outlier_filter()
outlier_filter.set_mean_k(20)
x = 0.05
outlier_filter.set_std_dev_mul_thresh(x)
cloud = outlier_filter.filter()
### Voxel Grid filter
vox = cloud.make_voxel_grid_filter()
LEAF_SIZE = 0.007
vox.set_leaf_size(LEAF_SIZE, LEAF_SIZE, LEAF_SIZE)
cloud = vox.filter()
### PassThrough filter
passthrough = cloud.make_passthrough_filter()
filter_axis = 'z'
def setUp(self):
self.p = pcl.load_XYZRGB("tests" + os.path.sep +
"table_scene_mug_stereo_textured_noplane.pcd")
self.tmpdir = tempfile.mkdtemp(suffix='pcl-test')
# -*- coding: utf-8 -*-
from __future__ import print_function
import numpy as np
import pcl
from pcl import pcl_visualization
# from pcl.pcl_registration import icp, gicp, icp_nl
#p = pcl.PointCloud()
#p.from_file("office.pcd")
cloud = pcl.load_XYZRGB('office.pcd')
visual = pcl.pcl_visualization.CloudViewing()
# PointXYZ
# visual.ShowMonochromeCloud(cloud)
# visual.ShowGrayCloud(cloud, b'cloud')
visual.ShowColorCloud(cloud, b'cloud')
# visual.ShowColorACloud(cloud, b'cloud')
# while True:
# visual.WasStopped()
# end
flag = True
while flag:
flag != visual.WasStopped()
end
# -*- coding: utf-8 -*-
import pcl
# Cargar la nube de puntos
cloud = pcl.load_XYZRGB('../data/mesa.pcd')
# Crear un filtro VoxelGrid para la nube de puntos
fvox = cloud.make_voxel_grid_filter()
# Tamaño de voxel
VOXEL_SIZE = 1
# Asignar el tamaño del voxel al filtro
fvox.set_leaf_size(VOXEL_SIZE, VOXEL_SIZE, VOXEL_SIZE)
# Ejecutar el filtro
cloud_filtered = fvox.filter()
# Grabar el resultado en disco
filename = 'mesa_downsampled.pcd'
pcl.save(cloud_filtered, filename)
"""lets create a working code"""
from sklearn.cluster import KMeans
import pcl
from pcl import pcl_visualization
cloud3 = pcl.PointCloud_PointXYZRGB()
point_cloud = pcl.PointCloud()
import numpy as np
import matplotlib.pyplot as plt
#cloud = pcl.load_XYZRGB('/home/ribin/Downloads/object_02/test58.pcd')
#path = "/home/ribin/Desktop/pcdfiles"
cloud = pcl.load_XYZRGB(
'/media/ribin/DATA/addverb/binpicking/pcd_files/object_pcd/biscut/2.pcd')
print(cloud)
# print(cloud.shape)
cloud2 = np.zeros((307201, 4), dtype=np.float32)
#cloud2 = np.array(cloud2)
def do_passthrough_filter(point_cloud,
name_axis='z',
min_axis=0.01,
max_axis=1):
pass_filter = point_cloud.make_passthrough_filter()
pass_filter.set_filter_field_name(name_axis)
pass_filter.set_filter_limits(min_axis, max_axis)
return pass_filter.filter()
segmenter.set_model_type(pcl.SACMODEL_PLANE)
segmenter.set_method_type(pcl.SAC_RANSAC)
segmenter.set_distance_threshold(max_distance)
#obtain inlier indices and model coefficients
inlier_indices, coefficients = segmenter.segment()
inliers = point_cloud.extract(inlier_indices, negative=False)
outliers = point_cloud.extract(inlier_indices, negative=True)
return inliers, outliers
##################################################################################
# This pipeline separates the objects in the table from the given scene
# Load the point cloud in memory
cloud = pcl.load_XYZRGB('point_clouds/tabletop.pcd')
# Get only information in our region of interest, as we don't care about the other parts
filtered_cloud = do_passthrough_filter(point_cloud=cloud,
name_axis='z',
min_axis=0.6,
max_axis=1.1)
# Separate the table from everything else
table_cloud, objects_cloud = do_ransac_plane_segmentation(filtered_cloud,
max_distance=0.01)
pcl.save(objects_cloud, 'objects.pcd')
def DBScan():
# Load Point Cloud file
cloud = pcl.load_XYZRGB('./test_rgb.pcd')
# Voxel Grid Downsampling filter
################################
# Create a VoxelGrid filter object for our input point cloud
vox = cloud.make_voxel_grid_filter()
# Choose a voxel (also known as leaf) size
# Note: this (1) means 1mx1mx1m is a poor choice of leaf size
# Experiment and find the appropriate size!
#LEAF_SIZE = 0.01
LEAF_SIZE =45
# 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 = './pcd_out/voxel_downsampled.pcd'
pcl.save(cloud_filtered, filename)
# PassThrough filter
################################
# Create a PassThrough filter object.
passthrough = cloud_filtered.make_passthrough_filter()
# Assign axis and range to the passthrough filter object.
filter_axis = 'z'
passthrough.set_filter_field_name(filter_axis)
axis_min = 0
axis_max = 100
passthrough.set_filter_limits(axis_min, axis_max)
# Finally use the filter function to obtain the resultant point cloud.
cloud_filtered = passthrough.filter()
filename = './pcd_out/pass_through_filtered.pcd'
pcl.save(cloud_filtered, filename)
# RANSAC plane segmentation
################################
# Create the segmentation object
seg = cloud_filtered.make_segmenter()
# Set the model you wish 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
# Experiment with different values for max_distance
# for segmenting the table
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 outliers
# Save pcd for tabletop objects
################################
extracted_outliers = cloud_filtered.extract(inliers, negative=True)
e=np.asarray(extracted_outliers)
#print e[:,:-1]
filename = './pcd_out/extracted_outliers.pcd'
pcl.save(extracted_outliers, filename)
# Generate some clusters!
data = e[:,:-1]
return(data)
# -*- coding: utf-8 -*-
from __future__ import print_function
import numpy as np
import pcl
import pcl.pcl_visualization
# from pcl.pcl_registration import icp, gicp, icp_nl
cloud = pcl.load_XYZRGB(
'/home/dllab/kitti_object/data_object_velodyne/pcl/1.pcd')
visual = pcl.pcl_visualization.CloudViewing()
# PointXYZ
# visual.ShowMonochromeCloud(cloud)
# visual.ShowGrayCloud(cloud, b'cloud')
visual.ShowColorCloud(cloud, b'cloud')
# visual.ShowColorACloud(cloud, b'cloud')
# while True:
# visual.WasStopped()
# end
flag = True
while flag:
flag != visual.WasStopped()
end
# print(type(make_label))
# print("label", label)
# print("label pos",label_pos)
# print("index",index)
# print(make_label(label,label_pos, index))
# 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)
# Publish the list of detected objects
# 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()
# try:
# pr2_mover(detected_objects_list)
# except rospy.ROSInterruptException:
# pass
if __name__ == '__main__':
cloud = pcl.load_XYZRGB('sample_pcd_files/left_cloud.pcd')
get_color_list.color_list = []
pcl_callback(cloud)
#!/usr/bin/env python
# Import modules
from pcl_helper import *
import pcl
#rospy.init_node('clustering', anonymous=True)
get_color_list.color_list =[]
# Load Point Cloud file
cloud = pcl.load_XYZRGB('tabletop.pcd') # <type 'pcl._pcl.PointCloud_PointXYZRGB'>
#print cloud.size # 202627, each row is a 4-element tuple
# TODO: Voxel Grid Downsampling
vox = cloud.make_voxel_grid_filter()
LEAF_SIZE = 0.01
vox.set_leaf_size(LEAF_SIZE, LEAF_SIZE, LEAF_SIZE)
cloud_filtered = vox.filter()
# TODO: PassThrough Filter
passthrough = cloud_filtered.make_passthrough_filter()
filter_axis = 'z'
passthrough.set_filter_field_name(filter_axis)
axis_min = 0.5
axis_max = 1.1
passthrough.set_filter_limits(axis_min, axis_max)
cloud_filtered = passthrough.filter()
# TODO: RANSAC Plane Segmentation
seg = cloud_filtered.make_segmenter()
#Import PCL
import pcl
#load Point Cloud File
cloud = pcl.load_XYZRGB('filename.pcd')
#Voxel Grid filter
#a VoxelGrid filter object for the input cloud
vox = cloud.make_voxel_grid_filter()
########################################
#Tune Voxel Parameters
#voxel size
n = 0.01
leaf_size = n
########################################
#set the voxel with the assigned leaf_size
vox.set_leaf_size(leaf_size, leaf_size, leaf_size)
#call filter function to obtain the resultant downsampled point cloud
cloud_filtered = vox.filter()
import pcl
import numpy as np
from sensor_stick.pcl_helper import *
# p_base
p_robot = np.array([[0.27265, -0.65591, 0.16760, 1],
[0.35291, -0.65217, 0.13476, 1],
[0.33833, -0.71059, 0.14230, 1],
[0.27451, -0.71246, 0.13478, 1]])
p_robot = p_robot.transpose()
p_camera = []
# process point cloud
for i in range(4):
# load obtained point cloud
cloud = pcl.load_XYZRGB('waypoint%s.ply' % (i + 1))
# TODO: PassThrough Filter
# roughly slicing for the point cloud
passthrough = cloud.make_passthrough_filter()
# filtering in z axis from 380 to 435
filter_axis = 'z'
passthrough.set_filter_field_name(filter_axis)
axis_min = 380
axis_max = 435
passthrough.set_filter_limits(axis_min, axis_max)
cloud_filtered = passthrough.filter()
# TODO: Statistical outlier filter
outlier_filter = cloud_filtered.make_statistical_outlier_filter()
outlier_filter.set_mean_k(50)
outlier_filter.set_std_dev_mul_thresh(1.0)
#this is a working program for cylinder segmentation
import pcl
from pcl import pcl_visualization
cloud = pcl.load("/home/ribin/Downloads/object_02/test58.pcd")
cloud1 = pcl.load_XYZRGB('/home/ribin/Downloads/object_02/test58.pcd')
print('PointCloud has: ' + str(cloud.size) + ' data points.')
"""
passthrough = cloud.make_passthrough_filter()
passthrough.set_filter_field_name ('z')
passthrough.set_filter_limits (0, 1.5)
cloud_filtered = passthrough.filter()
print('PointCloud has: ' + str(cloud_filtered.size) + ' data points.')
"""
# Estimate point normals
# ne.setSearchMethod (tree);
# ne.setInputCloud (cloud_filtered);
# ne.setKSearch (50);
# ne.compute (*cloud_normals);
#ne = cloud_filtered.make_NormalEstimation()
#tree = cloud_filtered.make_kdtree()
ne = cloud.make_NormalEstimation()
tree = cloud.make_kdtree()
ne.set_SearchMethod(tree)
ne.set_KSearch(50)
# cloud_normals = ne.compute ()
seg = cloud.make_segmenter_normals(ksearch=50)
# Import PCL module
import pcl
# Load Point Cloud file
cloud = pcl.load_XYZRGB("tabletop.pcd")
# STEP 1 : Voxel Grid filter
# Create a VoxelGrid filter object for our input point cloud
vox = cloud.make_voxel_grid_filter()
# Choose a voxel (also known as leaf) size
# Note: this (0.01) is a decent choice of 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()
# Save the Downsampled Point Cloud
filename = 'voxel_downsampled.pcd'
pcl.save(cloud_filtered, filename)
# STEP 2 : PassThrough filter
# Create a PassThrough filter object.
passthrough = cloud_filtered.make_passthrough_filter()
# Assign axis and range 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)
# Finally use the filter function to obtain the resultant point cloud.
# Import PCL module
import pcl
# Load Point Cloud file
cloud = pcl.load_XYZRGB('pr2_view.pcd')
# Voxel Grid filter
# Create a VoxelGrid filter object for our input point cloud
vox = cloud.make_voxel_grid_filter()
# Choose a voxel (also known as leaf) size
# Note: this (1) is a poor choice of leaf size
# Experiment and find the appropriate size!
LEAF_SIZE = 0.003 #reasonable voxel(leaf) size
# 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 a PassThrough filter object.
passthrough = cloud_filtered.make_passthrough_filter()
# Assign axis and range to the passthrough filter object.
filter_axis = 'y'
passthrough.set_filter_field_name(filter_axis)
axis_min = -0.5
import pcl
cloud = pcl.load_XYZRGB('Aubo_tool0_exposure10.ply')
# TODO: PassThrough Filter
passthrough = cloud.make_passthrough_filter()
filter_axis = 'z'
passthrough.set_filter_field_name(filter_axis)
axis_min = 0.4
axis_max = 0.435
passthrough.set_filter_limits(axis_min, axis_max)
cloud_filtered = passthrough.filter()
# TODO: Statistical outlier filter
outlier_filter = cloud.make_statistical_outlier_filter()
outlier_filter.set_mean_k(50)
outlier_filter.set_std_dev_mul_thresh(1.0)
cloud_filtered = outlier_filter.filter()
# TODO: RANSAC Plane Segmentation
seg = cloud_filtered.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
max_distance = 0.01
seg.set_distance_threshold(max_distance)
inliers, coefficients = seg.segment()
# TODO: Extract inliers and outliers
cloud_table = cloud_filtered.extract(inliers, negative=False)
cloud_objects = cloud_filtered.extract(inliers, negative=True)
# Import PCL module
import pcl
# Import modules
from pcl_helper import *
# Load Point Cloud file
cloud_filtered = pcl.load_XYZRGB('voxel_downsampled.pcd')
"""
###################
# Voxel Grid filter
###################
#Create a VoxelGrid filter object four input point cloud
vox=cloud.make_voxel_grid_filter()
# Choose a voxel (also known as leaf) size
# Note: this (1) is a poor choice of leaf size
# Experiment and find the appropriate size!
LEAF_SIZE = 0.01
#Set the voxel (of leaf) size
vox.set_leaf_size(LEAF_SIZE,LEAF_SIZE,LEAF_SIZE)
#Call the filiter function to obtain the resultant downsampled point cloud
cloud_filtered = vox.filter()
filename = 'voxel_downsampled.pcd'
pcl.save(cloud_filtered, filename)
"""
#####################
# PassThrough filter
import numpy.core.multiarray
import pcl
from pcl import pcl_visualization
cloud3 = pcl.PointCloud_PointXYZRGB()
point_cloud = pcl.PointCloud()
import numpy as np
#import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#import matplotlib.pyplot as plt
#from mpl_toolkits.mplot3d import Axes3D
#cloud = pcl.load_XYZRGB('/home/ribin/Downloads/object_02/test58.pcd')
cloud = pcl.load_XYZRGB('/home/ribin/Downloads/mug.pcd')
# print(cloud.shape)
cloud2 = np.zeros((307201, 4), dtype=np.float32)
#cloud2 = np.array(cloud2)
def do_passthrough_filter(point_cloud,
name_axis='z',
min_axis=0.01,
max_axis=1):
pass_filter = point_cloud.make_passthrough_filter()
pass_filter.set_filter_field_name(name_axis)
pass_filter.set_filter_limits(min_axis, max_axis)
return pass_filter.filter()
#!/usr/bin/python
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
import cv2
from sklearn.cluster import DBSCAN
from extra_functions import cluster_gen
import pcl
import numpy as np
import matplotlib.cm as cm
import data_image_2
import itertools
# get pcd file
data_image_2.plot()
# Load Point Cloud file
cloud = pcl.load_XYZRGB('./test_rgb.pcd')
# Voxel Grid Downsampling filter
################################
# Create a VoxelGrid filter object for our input point cloud
vox = cloud.make_voxel_grid_filter()
# Choose a voxel (also known as leaf) size
# Note: this (1) means 1mx1mx1m is a poor choice of leaf size
# Experiment and find the appropriate size!
#LEAF_SIZE = 0.01
LEAF_SIZE =45
# Set the voxel (or leaf) size
vox.set_leaf_size(LEAF_SIZE, LEAF_SIZE, LEAF_SIZE)
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 11 15:20:49 2018
@author: bimta
"""
import pcl
cloud = pcl.load_XYZRGB('./Data/poles.pcd')
print(cloud.size)
def subtractGround(pointcloud):
seg = pointcloud.make_segmenter_normals(ksearch=50)
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(10000)
seg.set_distance_threshold(0.3)
indices, model = seg.segment()
#print(model)
#cloud_plane = pointcloud.extract(indices, negative=False)
cloud_without_plane = pointcloud.extract(indices, negative=True)
return cloud_without_plane
#TODO: filter for cylinder axes with certain angle
def findCylinder(pointcloud):
seg = pointcloud.make_segmenter_normals(ksearch=50)
plt.xlabel('Predicted label')
if __name__ == "__main__":
rospy.init_node('train_from_file_node')
pcl_objects_pub = rospy.Publisher("/pr2/pcl_objects",
PointCloud2,
queue_size=1)
# Features
labeled_features = []
for model_name in MODELS_3:
print "Features: %s..." % model_name
for c_url in glob.glob(MODELS_URL + model_name + "/*.pcd"):
cloud = pcl.load_XYZRGB(c_url)
cloud_r = pcl_to_ros(cloud)
pcl_objects_pub.publish(cloud_r)
# chists = compute_color_histograms(cloud_r, using_hsv=True)
# nhists = compute_normal_histograms(get_normals(cloud_r))
# bounds = compute_size(cloud_r)
# feature = np.concatenate((chists, nhists, bounds))
feature = compute_all_features(cloud_r, get_normals(cloud_r))
labeled_features.append([feature, model_name])
pickle.dump(labeled_features, open('training_set.sav', 'wb'))
# Train SVM
# Format the features and labels for use with scikit learn
def testSave(self):
for ext in ["pcd", "ply"]:
d = os.path.join(self.tmpdir, "foo." + ext)
pcl.save(self.p, d)
p = pcl.load_XYZRGB(d)
self.assertEqual(self.p.size, p.size)
#import pandas as pd
import matplotlib.pyplot as plt
import os
from mpl_toolkits.mplot3d import Axes3D
#import matplotlib.pyplot as plt
#from mpl_toolkits.mplot3d import Axes3D
#cloud = pcl.load_XYZRGB('/home/ribin/Downloads/object_02/test58.pcd')
#path = "/home/ribin/Desktop/pcdfiles"
cloud = pcl.load_XYZRGB('/media/ribin/DATA/bagfiles/pcdfiles/Zed_new_HQ/135.pcd')
print(cloud)
# print(cloud.shape)
cloud2 = np.zeros((307201,4),dtype=np.float32)
#cloud2 = np.array(cloud2)
def do_passthrough_filter(point_cloud, name_axis = 'y', min_axis = 0.01, max_axis = 1):
pass_filter = point_cloud.make_passthrough_filter()
pass_filter.set_filter_field_name(name_axis);
pass_filter.set_filter_limits(min_axis, max_axis)
return pass_filter.filter()
def do_voxel_grid_filter(point_cloud, LEAF_SIZE):
voxel_filter = point_cloud.make_voxel_grid_filter()
def setUp(self):
self.p = pcl.load_XYZRGB(
"tests/table_scene_mug_stereo_textured_noplane.pcd")
#!/usr/bin/env python
import pcl
import numpy as np
cloud = pcl.load_XYZRGB("pass_through_filtered.pcd")
#pcl.getMinMax3D(cloud, min, max)
#print(cloud[0])
cloud_array = np.array(cloud)
#print(type(cloud_array))
print('min: ', np.amin(cloud_array, axis=0))
print('max: ', np.amax(cloud_array, axis=0))
def DBScan():
# Load Point Cloud file
cloud = pcl.load_XYZRGB('./test_rgb.pcd')
# Voxel Grid Downsampling filter
################################
# Create a VoxelGrid filter object for our input point cloud
vox = cloud.make_voxel_grid_filter()
# Choose a voxel (also known as leaf) size
# Note: this (1) means 1mx1mx1m is a poor choice of leaf size
# Experiment and find the appropriate size!
#LEAF_SIZE = 0.01
LEAF_SIZE = 45
# 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 = './pcd_out/voxel_downsampled.pcd'
pcl.save(cloud_filtered, filename)
# PassThrough filter
################################
# Create a PassThrough filter object.
passthrough = cloud_filtered.make_passthrough_filter()
# Assign axis and range to the passthrough filter object.
filter_axis = 'z'
passthrough.set_filter_field_name(filter_axis)
axis_min = 0
axis_max = 100
passthrough.set_filter_limits(axis_min, axis_max)
# Finally use the filter function to obtain the resultant point cloud.
cloud_filtered = passthrough.filter()
filename = './pcd_out/pass_through_filtered.pcd'
pcl.save(cloud_filtered, filename)
# RANSAC plane segmentation
################################
# Create the segmentation object
seg = cloud_filtered.make_segmenter()
# Set the model you wish 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
# Experiment with different values for max_distance
# for segmenting the table
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 outliers
# Save pcd for tabletop objects
################################
extracted_outliers = cloud_filtered.extract(inliers, negative=True)
e = np.asarray(extracted_outliers)
#print e[:,:-1]
filename = './pcd_out/extracted_outliers.pcd'
pcl.save(extracted_outliers, filename)
# Generate some clusters!
data = e[:, :-1]
#print data
#file=open("data.txt","w")
#file.write(data)
#print type(data)
# Define max_distance (eps parameter in DBSCAN())
max_distance = 175
#max_distance = 1000
db = DBSCAN(eps=max_distance, min_samples=10).fit(data)
# Extract a mask of core cluster members
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
# Extract labels (-1 is used for outliers)
labels = db.labels_
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
unique_labels = set(labels)
#print unique_labels
# Plot up the results!
# The following is just a fancy way of plotting core, edge and outliers
colors = cm.rainbow(np.linspace(0, 1, len(unique_labels)))
array_x = []
array_y = []
array_z = []
for k, col in zip(unique_labels, colors):
#ax.clear()
if k == -1:
# Black used for noise.
#col = [0, 0, 0, 1]
col = (0, 0, 0, 1)
class_member_mask = (labels == k)
#print len(class_member_mask)
xy2 = data[class_member_mask & core_samples_mask]
array_x.extend(xy2[:, 0])
array_y.extend(xy2[:, 1])
array_z.extend(xy2[:, 2])
stack_array = np.vstack((array_x, array_y, array_z))
#print xy2
return stack_array
def random_color_gen():
""" Generates a random color
Args: None
Returns:
list: 3 elements, R, G, and B
"""
r = randint(0, 255)
g = randint(0, 255)
b = randint(0, 255)
return [r, g, b]
# Load Point Cloud file
cloud = pcl.load_XYZRGB('camera_right.pcd')
outlier_filter = cloud.make_statistical_outlier_filter()
outlier_filter.set_mean_k(20)
x = 0.05
outlier_filter.set_std_dev_mul_thresh(x)
cloud = outlier_filter.filter()
### Voxel Grid filter
vox = cloud.make_voxel_grid_filter()
LEAF_SIZE = 0.01
vox.set_leaf_size(LEAF_SIZE, LEAF_SIZE, LEAF_SIZE)
cloud = vox.filter()
### 1st PassThrough filter in z-axis
passthrough = cloud.make_passthrough_filter()
filter_axis = 'z'
# Import PCL module
import pcl
# Load Point Cloud file
cloud = pcl.load_XYZRGB('tabletop.pcd')
# Voxel Grid filter
vox = cloud.make_voxel_grid_filter()
LEAF_SIZE = 0.01
vox.set_leaf_size(LEAF_SIZE, LEAF_SIZE, LEAF_SIZE)
cloud_filtered = vox.filter()
filename = 'voxel_downsampled.pcd'
pcl.save(cloud_filtered, filename)
# PassThrough filter
passthrough = cloud_filtered.make_passthrough_filter()
# Assign axis and range 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)
# Finally use the filter function to obtain the resultant point cloud.
cloud_filtered = passthrough.filter()
filename = 'pass_through_filtered.pcd'
pcl.save(cloud_filtered, filename)
# -*- coding: utf-8 -*-
# http://virtuemarket-lab.blogspot.jp/2015/03/harris.html
from __future__ import print_function
import numpy as np
import pcl
import pcl.pcl_visualization
# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
# pcl::io::loadPCDFile<pcl::PointXYZ> (argv[1], *cloud);
cloud = pcl.load_XYZRGB('G:\\tmp\\PCL\\extendlibrary\\python-pcl\\examples\\pcldata\\tutorials\\table_scene_mug_stereo_textured.pcd')
# pcl::HarrisKeypoint3D<pcl::PointXYZ,pcl::PointXYZI> detector;
# detector.setNonMaxSupression (true);
# detector.setRadius (0.01);
# //detector.setRadiusSearch (100);
# detector.setInputCloud(cloud);
detector = cloud.make_HarrisKeypoint3D()
# pcl::PointCloud<pcl::PointXYZI>::Ptr keypoints(new pcl::PointCloud<pcl::PointXYZI>());
# detector.compute(*keypoints);
keypoints = detector.compute()
# std::cout << "keypoints detected: " << keypoints->size() << std::endl;
# pcl::PointCloud<pcl::PointXYZ>::Ptr keypoints3D(new pcl::PointCloud<pcl::PointXYZ>());
# pcl::PointXYZ tmp;
# double max = 0,min=0;
#