def perturb(max_iters=100, mod=10):
global pnew, uv, err, points_range, rimg, range_image
# Apply a perturb to points_p
obj.RT = np.eye(4, dtype='f')
obj.RT[:3,3] = -obj.vertices[:,:3].mean(0)
obj.RT[:3,3] += [0,0,-3.0]
# Rotated object view
RT = obj.RT
rp = random_perturbation().astype('f')
obj.RT = np.dot(rp, obj.RT)
range_image = obj.range_render(camera.kinect_camera())
obj.RT = RT
points_range = range_image.point_model(True)
# Original object view
rimg = obj.range_render(camera.kinect_camera())
pnew = rimg.point_model()
# Estimate the transformation rp
for iters in range(max_iters):
npairs, pnew = model.align(range_image, pnew, rtmodel.RATE1, rtmodel.DIST1, 6)
#pnew, err, npairs, uv = fasticp.fast_icp(range_image, pnew, 0.1, dist=0.05)
if iters % mod == 0 or 1:
#print '%d iterations, [%d] RMS: %.3f' % (iters, npairs, np.sqrt(err))
window.Refresh()
pylab.waitforbuttonpress(0.02)
break
window.Refresh()
def animate_random(max_iters=1000, mod=100):
global pnew, points_range
# Apply a perturb to points_p
obj.RT = np.eye(4, dtype='f')
obj.RT[:3,3] = -obj.vertices[:,:3].mean(0)
obj.RT[:3,3] += [0,0,-3.0]
RT = obj.RT
prev_rimg = obj.range_render(camera.kinect_camera())
window.canvas.SetCurrent()
pnew = prev_rimg.point_model(True)
points_range = pnew
if 0:
obj.RT = np.dot(RT, M)
rimg = obj.range_render(camera.kinect_camera())
window.canvas.SetCurrent()
pm = rimg.point_model(True)
points_range = pm
for iters in range(max_iters):
pnew, err, npairs, uv = fasticp.fast_icp(rimg, pnew, 1000, dist=0.005)
if iters % mod == 0:
# print '%d iterations, [%d] RMS: %.3f' % (iters, npairs, np.sqrt(err))
window.Refresh()
pylab.waitforbuttonpress(0.02)
pnew = pm
window.Refresh()
pylab.waitforbuttonpress(0.02)
def metric_points(depth, points):
assert len(points) == 3
assert depth.dtype == np.uint16
rimg = RangeImage(depth, kinect_camera())
rimg.compute_points()
pts = []
for x,y in points:
pts.append(rimg.xyz[y,x,:])
pts = np.concatenate((pts,pts))
assert pts.shape == (6,3)
return pts
def once():
global range_image, points_range, RTold, RTguess
ts, M = seqiter.next()
# Take the image from an alternate camera location
obj.RT = np.dot(RTold, M)
range_image = obj.range_render(camera.kinect_camera())
points_range = range_image.point_model()
if RTguess is None:
range_image.camera.RT = np.eye(4, dtype='f')
else:
range_image.camera.RT = RTguess
p = model.add(range_image)
if p:
points_range = p
RTguess = p.RT
window.Refresh()
pylab.waitforbuttonpress(0.02)
def load_obj(name='blueghost'):
global obj, points, points_p, range_image, points_range, pnew
#obj = mesh.load_random()
window.canvas.SetCurrent()
obj = mesh.load(name)
obj.calculate_area_lookup()
obj.RT = np.eye(4, dtype='f')
obj.RT[:3,3] = -obj.vertices[:,:3].mean(0)
obj.RT[:3,3] += [0,0,-3.0]
points = obj.sample_point_cloud(10000)
# Range image of the original points
range_image = obj.range_render(camera.kinect_camera())
range_image.compute_normals()
points_range = range_image.point_model()
pnew = points_p = points
#range_image = obj.range_render(np.dot(camera.kinect_camera(), rp))
window.lookat = obj.RT[:3,3] + obj.vertices[:,:3].mean(0)
window.Refresh()
import numpy as np
import os
import shutil
import cv2
import datasetfull as dataset
import pylab
from wxpy3d import PointWindow
from OpenGL.GL import *
from rtmodel import pointmodel
from rtmodel.rangeimage import RangeImage
from rtmodel.camera import kinect_camera
cam = kinect_camera()
if not 'window' in globals():
window = PointWindow(size=(640,480))
def show_rgb(name, image):
cv2.imshow(name, np.ascontiguousarray(image[:,:,::-1]))
def show_depth(name, depth):
cv2.imshow(name, 1024./depth)
@window.event
def pre_draw():
glLightfv(GL_LIGHT0, GL_POSITION, (-40, 200, 100, 0.0))
glLightfv(GL_LIGHT0, GL_AMBIENT, (0.3, 0.3, 0.3, 0.0))
glLightfv(GL_LIGHT0, GL_DIFFUSE, (0.3, 0.3, 0.3, 0.0))
glEnable(GL_LIGHT0)
glEnable(GL_LIGHTING)
#glColorMaterial(GL_FRONT_AND_BACK, GL_AMBIENT_AND_DIFFUSE)
glEnable(GL_COLOR_MATERIAL)
def fwd_cam():
cam = camera.kinect_camera()
cam.RT[:3,3] += [0, 0, 3.0]
return cam
