from pylab import *
#Fig5-10
# im_l = array(Image.open('../data/cones/im0.ppm').convert('L'),'f')
# im_r = array(Image.open('../data/cones/im1.ppm').convert('L'),'f')
# #Fig5-11
im_l = array(Image.open('../data/cones/im0.ppm').convert('L'), 'f')
im_r = array(Image.open('../data/cones/im1.ppm').convert('L'), 'f')
# 开始偏移，并设置步长
steps = 12
start = 4
# ncc 的宽度
wid = 9
res1 = stereo.plane_sweep_ncc(im_l, im_r, start, steps, wid)
scipy.misc.imsave('../images/ch05/depth_ncc.png', res1)
res2 = stereo.plane_sweep_gauss(im_l, im_r, start, steps, wid)
scipy.misc.imsave('../images/ch05/depth_gauss.png', res2)
# 添加中文字体支持
from matplotlib.font_manager import FontProperties
font = FontProperties(fname="/System/Library/Fonts/PingFang.ttc", size=14)
figure()
subplot(221)
imshow(im_l)
import pickle
#reload(stereo)
im_l = np.array(Image.open('scene1.row3.col3.ppm').convert('L'),'f')
im_r = np.array(Image.open('scene1.row3.col4.ppm').convert('L'),'f')
#視差の開始値と調べる範囲
start = 4
steps = 12
#相互相関を調べるパッチの幅
wid = 9
res = stereo.plane_sweep_ncc(im_l,im_r,start,steps,wid)
import scipy.misc
scipy.misc.imsave('depth.png',res)
#ガウシアンフィルタ
im_l = np.array(Image.open('scene1.row3.col3.ppm').convert('L'),'f')
im_r = np.array(Image.open('scene1.row3.col4.ppm').convert('L'),'f')
#視差の開始値と調べる範囲
start = 4
steps = 12
from PIL import Image
import numpy
import stereo
im_l = numpy.array(Image.open('./data/ppm/scene1.row3.col3.ppm').convert('L'), 'f')
im_r = numpy.array(Image.open('./data/ppm/scene1.row3.col4.ppm').convert('L'), 'f')
steps = 12
start = 4
wid = 9
res1 = stereo.plane_sweep_ncc(im_l, im_r, start, steps, wid)
res2 = steps - 1 - stereo.plane_sweep_ncc(im_r, im_l, -start - steps + 1,
steps, wid)
import scipy.misc
scipy.misc.imsave('./output/depth_twosided.png', res1)
scipy.misc.imsave('./output/depth_twosided2.png', res2)
res = (res1 == res2) * res1
scipy.misc.imsave('./output/depth_twosided_c_sparse.png', res)
# Use first disparity image to align second disparity with first.
ir, ic = numpy.mgrid[:res1.shape[0], :res1.shape[1]]
mv = res2[ir, (ic + res1) % res1.shape[1]]
scipy.misc.imsave('./output/tmp.png', mv)
res = (res1 == mv) * res1
scipy.misc.imsave('./output/depth_twosided_c.png', res)
from PIL import Image
import numpy
import stereo
im_l = numpy.array(Image.open('out_stereo1.ppm').convert('L'), 'f')
im_r = numpy.array(Image.open('out_stereo2.ppm').convert('L'), 'f')
steps = 12
start = 4
wid = 9
res1 = stereo.plane_sweep_ncc(im_l, im_r, start, steps, wid)
res2 = steps - 1 - stereo.plane_sweep_ncc(im_r, im_l, -start - steps + 1,
steps, wid)
import scipy.misc
scipy.misc.imsave('out_depth_twosided.png', res1)
scipy.misc.imsave('out_depth_twosided2.png', res2)
res = (res1 == res2) * res1
scipy.misc.imsave('out_depth_twosided_c_sparse.png', res)
# Use first disparity image to align second disparity with first.
ir, ic = numpy.mgrid[:res1.shape[0], :res1.shape[1]]
mv = res2[ir, (ic + res1) % res1.shape[1]]
scipy.misc.imsave('out_tmp.png', mv)
res = (res1 == mv) * res1
scipy.misc.imsave('out_depth_twosided_c.png', res)
