Esempio n. 1
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def test_cups():
    import rospy, itertools, glob
    from jds_utils.colorize import colorize
    from jds_image_proc.pcd_io import load_xyzrgb
    if rospy.get_name() == "/unnamed": rospy.init_node('test_registration_3d',disable_signals=True)
    data_dir = "/home/joschu/Data/cups"
    xyz1, rgb1 = load_xyzrgb(osp.join(data_dir,"cups1.pcd"))
    def preproc(xyz1):
        xyz1 = xyz1.reshape(-1,3)
        xyz1 = np.dot(xyz1, np.diag([1,-1,-1]))
        xyz1 = xyz1[(xyz1[:,2] > .02) & (xyz1[:,2] < .2) 
                    & (np.abs(xyz1[:,0]) < .15) & (np.abs(xyz1[:,1]) < .3)]
        xyz1 = voxel_downsample(xyz1, .015)
        return xyz1
    xyz1 = preproc(xyz1)
    
    #from mayavi import mlab
    #mlab.points3d(*xyz1.T,color=(1,1,1),scale_factor=.01)
    xyz2, rgb2 = load_xyzrgb(osp.join(data_dir,"cups4.pcd"))
    xyz2 = preproc(xyz2)
    f = registration.tps_rpm(3*xyz1, 3*xyz2, plotting=4,reg_init=1,reg_final=.05,n_iter=200, verbose=False)
Esempio n. 2
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#!/usr/bin/env python
import argparse, cv2, os

parser = argparse.ArgumentParser()
parser.add_argument("pcd_file")
parser.add_argument("label_file")
parser.add_argument("outfile")
parser.add_argument("--plotting", action="store_true")
args = parser.parse_args()

from jds_image_proc import pcd_io
from jds_image_proc.rope_initialization_lite import initialize_rope, RopeInitMessage

xyz, bgr = pcd_io.load_xyzrgb(args.pcd_file)

# get rope mask
label_img = cv2.imread(args.label_file, 0)
if label_img is None:
    raise Exception("could not read image %s" % args.label_file)

xyzs_unif, labels = initialize_rope(label_img,
                                    xyz,
                                    bgr,
                                    plotting=args.plotting)
if not os.path.exists(os.path.dirname(args.outfile)):
    os.mkdir(dirname(args.outfile))
print "writing", args.outfile
RopeInitMessage((xyzs_unif, labels)).toFiles(args.outfile, "/tmp/junk.json")
Esempio n. 3
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 def readDataFrom(self, fname):
     return pcd_io.load_xyzrgb(fname)
Esempio n. 4
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from jds_image_proc import pcd_io
import numpy as np
import cv2

comm_name = "pr2_rope1"

pcdfile = "/home/joschu/comm/%s/kinect/data000000000000.pcd" % comm_name
xyz, rgb = pcd_io.load_xyzrgb(pcdfile)
table_corners = np.loadtxt("/home/joschu/comm/%s/once/table_corners.txt" %
                           comm_name)
x_ax = table_corners[1] - table_corners[0]
y_ax = table_corners[2] - table_corners[1]
z_ax = np.cross(x_ax, y_ax)

zimg = (z_ax[None, None, :] * xyz).sum(axis=2)

table_height = np.dot(z_ax, table_corners[0])

tablemask = np.abs(zimg - table_height) <= .03

cv2.imshow('hi', (tablemask * 100).astype('uint8'))
cv2.waitKey(10)
Esempio n. 5
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from __future__ import division
from pylab import *
import scipy.spatial.distance as ssd
from jds_image_proc.clouds import voxel_downsample
from mayavi import mlab
from jds_image_proc.pcd_io import load_xyzrgb
from jds_image_proc.pcl_utils import xyz2uv, uv2xyz
import scipy.ndimage as ndi

xyz_orig, _ = load_xyzrgb("/home/joschu/Proj/shape_comp/cup.pcd")

us, vs = mgrid[:640, :480]

umin, umax = 220, 330
vmin, vmax = 230, 290
xres = yres = zres = .002

xyz = xyz_orig[umin:umax, vmin:vmax, :]
us = us[umin:umax, vmin:vmax]
vs = vs[umin:umax, vmin:vmax]
good_pts = xyz[np.isfinite(xyz[:, :, 2])]


def laplacian(F):
    Fxx = F[2:, 1:-1, 1:-1] + F[:-2, 1:-1, 1:-1] - 2 * F[1:-1, 1:-1, 1:-1]
    Fyy = F[1:-1, 2:, 1:-1] + F[1:-1, :-2, 1:-1] - 2 * F[1:-1, 1:-1, 1:-1]
    Fzz = F[1:-1, 1:-1, 2:] + F[1:-1, 1:-1, :-2] - 2 * F[1:-1, 1:-1, 1:-1]
    return Fxx + Fyy + Fzz


def curv_grad(F):
Esempio n. 6
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from jds_image_proc import pcl_utils, pcd_io, utils_images
import cPickle
from mlabraw_jds_image_processing import remove_holes
import cv2
import numpy as np
import scipy.ndimage as ndi
xyz, bgr = pcd_io.load_xyzrgb("/home/joschu/comm/pr2_knot/kinect/data000000000100.pcd")
bgr = bgr.copy()
mask = cv2.imread("/home/joschu/comm/pr2_knot/once/roi_mask.png")[:,:,0].copy()
with open("/home/joschu/comm/pr2_knot/human_labels/classifier.pkl","r") as fh: cls = cPickle.load(fh)
labels = cls.predict(bgr, mask=mask)


x,y,z = xyz[:,:,0], xyz[:,:,1], xyz[:,:,2]
depth = np.sqrt(x**2+y**2+z**2)
depth[np.isnan(depth)] = np.inf
bad = np.zeros(depth.shape,bool)
bad[1:] |= np.abs(depth[1:] - depth[:-1]) > .01
bad[:,1:] |= np.abs(depth[:,1:] - depth[:,:-1]) > .1
bad = ndi.binary_dilation(bad, utils_images.disk(2))
cv2.imshow('bad', bad.astype('uint8')*100)
cv2.imshow('labels',labels*100*~bad)
labels1 = labels*~bad
cv2.imshow('cleaned labels',labels1*100*~bad)
# make graph of reasonably nearby points
# make a spanning tree

from jds_image_proc import pcd_io
import scipy.spatial.distance as ssd, numpy as np
import networkx as nx

MAX_DIST = .03

(xyz,) , rgb = pcd_io.load_xyzrgb("/tmp/comm/rope_pts/data000000000093.pcd")
pdists = ssd.squareform(ssd.pdist(xyz))

G = nx.Graph()
for (i_from, row) in enumerate(pdists):
    to_inds = np.flatnonzero(row[:i_from] < MAX_DIST)
    for i_to in to_inds:
        G.add_edge(i_from, i_to, weight = pdists[i_from, i_to])

A = nx.floyd_warshall_numpy(G)
A[np.isinf(A)] = 0
(i_from_long, i_to_long) = np.unravel_index(A.argmax(), A.shape)
path = nx.shortest_path(G, source=i_from_long, target=i_to_long)

xyz_path = xyz[path,:]
import enthought.mayavi.mlab as mlab
mlab.clf()
mlab.plot3d(*xyz_path.T,tube_radius=.02)
mlab.points3d(*xyz.T, scale_factor=.025, color=(1,0,0))
Esempio n. 8
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#!/usr/bin/env python
import argparse, cv2, os

parser = argparse.ArgumentParser()
parser.add_argument("pcd_file")
parser.add_argument("label_file")
parser.add_argument("outfile")
parser.add_argument("--plotting",action="store_true")
args = parser.parse_args()

from jds_image_proc import pcd_io
from jds_image_proc.rope_initialization_lite import initialize_rope,RopeInitMessage


xyz,bgr = pcd_io.load_xyzrgb(args.pcd_file)

# get rope mask
label_img = cv2.imread(args.label_file,0)
if label_img is None: raise Exception("could not read image %s"%args.label_file)

xyzs_unif,labels = initialize_rope(label_img,xyz, bgr,plotting=args.plotting)
if not os.path.exists(os.path.dirname(args.outfile)): os.mkdir(dirname(args.outfile))
print "writing",args.outfile
RopeInitMessage((xyzs_unif,labels)).toFiles(args.outfile,"/tmp/junk.json")
Esempio n. 9
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from pylab import *
import scipy.spatial.distance as ssd
from jds_image_proc.clouds import voxel_downsample
from mayavi import mlab
from jds_image_proc.pcd_io import load_xyzrgb

xyz, bgr = load_xyzrgb("/home/joschu/Proj/shape_comp/cup.pcd")

xyz = xyz[200:330, 200:330, :]
good_pts = xyz[np.isfinite(xyz[:,:,2])]



ctrl_pts = []

for pt in good_pts:
    for s in [1,.95,.9]:
        ctrl_pts.append(s * pt)

#ctrl_pts.extend(good_pts)
ctrl_pts.append([0,0,0])
ctrl_pts = np.array(ctrl_pts)

def voxel_downsample(xyz,s, return_inds = False):
    xyz = xyz.reshape(-1,3)
    xyz = xyz[np.isfinite(xyz[:,0])]
    pts = []
    keys = set([])
    for (i,pt) in enumerate(xyz):
        x,y,z = pt
        key = (int(x//s),int(y//s),int(z//s))
Esempio n. 10
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 def readDataFrom(self,fname):
     return pcd_io.load_xyzrgb(fname)
Esempio n. 11
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from __future__ import division
from pylab import *
import scipy.spatial.distance as ssd
from jds_image_proc.clouds import voxel_downsample
from mayavi import mlab
from jds_image_proc.pcd_io import load_xyzrgb
from jds_image_proc.pcl_utils import xyz2uv, uv2xyz
import scipy.ndimage as ndi
xyz_orig, _ = load_xyzrgb("/home/joschu/Proj/shape_comp/cup.pcd")

us, vs = mgrid[:640, :480]

umin, umax = 220,330
vmin, vmax = 230,290
xres = yres = zres = .002

xyz = xyz_orig[umin:umax, vmin:vmax, :]
us = us[umin:umax, vmin:vmax]
vs = vs[umin:umax, vmin:vmax]
good_pts = xyz[np.isfinite(xyz[:,:,2])]


def laplacian(F):
    Fxx = F[2:,1:-1,1:-1] + F[:-2,1:-1,1:-1] - 2*F[1:-1,1:-1,1:-1]
    Fyy = F[1:-1,2:,1:-1] + F[1:-1,:-2,1:-1] - 2*F[1:-1,1:-1,1:-1]
    Fzz = F[1:-1,1:-1,2:] + F[1:-1,1:-1,:-2] - 2*F[1:-1,1:-1,1:-1]
    return Fxx + Fyy + Fzz

def curv_grad(F):
    return laplacian(laplacian(F))
def curv_cost(F):
Esempio n. 12
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from pylab import *
import scipy.spatial.distance as ssd
from jds_image_proc.clouds import voxel_downsample
from mayavi import mlab
from jds_image_proc.pcd_io import load_xyzrgb

xyz, bgr = load_xyzrgb("/home/joschu/Proj/shape_comp/cup.pcd")

xyz = xyz[200:330, 200:330, :]
good_pts = xyz[np.isfinite(xyz[:, :, 2])]

ctrl_pts = []

for pt in good_pts:
    for s in [1, .95, .9]:
        ctrl_pts.append(s * pt)

#ctrl_pts.extend(good_pts)
ctrl_pts.append([0, 0, 0])
ctrl_pts = np.array(ctrl_pts)


def voxel_downsample(xyz, s, return_inds=False):
    xyz = xyz.reshape(-1, 3)
    xyz = xyz[np.isfinite(xyz[:, 0])]
    pts = []
    keys = set([])
    for (i, pt) in enumerate(xyz):
        x, y, z = pt
        key = (int(x // s), int(y // s), int(z // s))
        if key not in keys:
Esempio n. 13
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from jds_image_proc import pcl_utils, pcd_io, utils_images
import cPickle
from mlabraw_jds_image_processing import remove_holes
import cv2
import numpy as np
import scipy.ndimage as ndi
xyz, bgr = pcd_io.load_xyzrgb(
    "/home/joschu/comm/pr2_knot/kinect/data000000000100.pcd")
bgr = bgr.copy()
mask = cv2.imread("/home/joschu/comm/pr2_knot/once/roi_mask.png")[:, :,
                                                                  0].copy()
with open("/home/joschu/comm/pr2_knot/human_labels/classifier.pkl", "r") as fh:
    cls = cPickle.load(fh)
labels = cls.predict(bgr, mask=mask)

x, y, z = xyz[:, :, 0], xyz[:, :, 1], xyz[:, :, 2]
depth = np.sqrt(x**2 + y**2 + z**2)
depth[np.isnan(depth)] = np.inf
bad = np.zeros(depth.shape, bool)
bad[1:] |= np.abs(depth[1:] - depth[:-1]) > .01
bad[:, 1:] |= np.abs(depth[:, 1:] - depth[:, :-1]) > .1
bad = ndi.binary_dilation(bad, utils_images.disk(2))
cv2.imshow('bad', bad.astype('uint8') * 100)
cv2.imshow('labels', labels * 100 * ~bad)
labels1 = labels * ~bad
cv2.imshow('cleaned labels', labels1 * 100 * ~bad)
Esempio n. 14
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from jds_image_proc import pcd_io
import numpy as np
import cv2

comm_name = "pr2_rope1"

pcdfile = "/home/joschu/comm/%s/kinect/data000000000000.pcd"%comm_name
xyz,rgb = pcd_io.load_xyzrgb(pcdfile)
table_corners = np.loadtxt("/home/joschu/comm/%s/once/table_corners.txt"%comm_name)
x_ax = table_corners[1] - table_corners[0]
y_ax = table_corners[2]-table_corners[1]
z_ax = np.cross(x_ax, y_ax)

zimg = (z_ax[None,None,:] * xyz).sum(axis=2)

table_height = np.dot(z_ax, table_corners[0])

tablemask =  np.abs(zimg-table_height) <= .03

cv2.imshow('hi',(tablemask*100).astype('uint8'))
cv2.waitKey(10)
Esempio n. 15
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# make graph of reasonably nearby points
# make a spanning tree

from jds_image_proc import pcd_io
import scipy.spatial.distance as ssd, numpy as np
import networkx as nx

MAX_DIST = .03

(xyz, ), rgb = pcd_io.load_xyzrgb("/tmp/comm/rope_pts/data000000000093.pcd")
pdists = ssd.squareform(ssd.pdist(xyz))

G = nx.Graph()
for (i_from, row) in enumerate(pdists):
    to_inds = np.flatnonzero(row[:i_from] < MAX_DIST)
    for i_to in to_inds:
        G.add_edge(i_from, i_to, weight=pdists[i_from, i_to])

A = nx.floyd_warshall_numpy(G)
A[np.isinf(A)] = 0
(i_from_long, i_to_long) = np.unravel_index(A.argmax(), A.shape)
path = nx.shortest_path(G, source=i_from_long, target=i_to_long)

xyz_path = xyz[path, :]
import enthought.mayavi.mlab as mlab
mlab.clf()
mlab.plot3d(*xyz_path.T, tube_radius=.02)
mlab.points3d(*xyz.T, scale_factor=.025, color=(1, 0, 0))