def match(x0, x1):
# ad
ad_vol = main_.ad_vol(x0, x1)
if DEBUG:
pred = ad_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(
os.path.join(IMG_DIR, 'absdiff_vol.png'))
# census
x0c = main_.census_transform(x0)
x1c = main_.census_transform(x1)
census_vol = np.ones((disp_max, height, width)) * np.inf
for i in range(disp_max):
census_vol[i, :, i:] = np.sum(x0c[:, i:] != x1c[:, :width - i], 2)
census_vol /= 3
if DEBUG:
pred = census_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(
os.path.join(IMG_DIR, 'census_vol.png'))
# adcensus
def rho(c, lambda_):
return 1 - np.exp(-c / lambda_)
ad_vol_robust = rho(ad_vol, 10)
census_vol_robust = rho(census_vol, 30)
adcensus_vol = ad_vol_robust + census_vol_robust
if DEBUG:
pred = adcensus_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(
os.path.join(IMG_DIR, 'adcensus_vol.png'))
# cbca
x0c = main_.cross(x0)
x1c = main_.cross(x1)
for i in range(2):
adcensus_vol = main_.cbca(x0c, x1c, adcensus_vol, 0)
adcensus_vol = main_.cbca(x0c, x1c, adcensus_vol, 1)
if DEBUG:
pred = adcensus_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(
os.path.join(IMG_DIR, 'cbca_vol.png'))
# semi-global matching
c2_vol = main_.sgm(x0, x1, adcensus_vol)
if DEBUG:
pred = c2_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(
os.path.join(IMG_DIR, 'sgm_vol.png'))
return c2_vol
def match(x0, x1):
# ad
ad_vol = main_.ad_vol(x0, x1)
if DEBUG:
pred = ad_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(os.path.join(IMG_DIR, 'absdiff_vol.png'))
# census
x0c = main_.census_transform(x0)
x1c = main_.census_transform(x1)
census_vol = np.ones((disp_max, height, width)) * np.inf
for i in range(disp_max):
census_vol[i,:,i:] = np.sum(x0c[:,i:] != x1c[:,:width - i], 2)
census_vol /= 3
if DEBUG:
pred = census_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(os.path.join(IMG_DIR, 'census_vol.png'))
# adcensus
def rho(c, lambda_):
return 1 - np.exp(-c / lambda_)
ad_vol_robust = rho(ad_vol, 10)
census_vol_robust = rho(census_vol, 30)
adcensus_vol = ad_vol_robust + census_vol_robust
if DEBUG:
pred = adcensus_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(os.path.join(IMG_DIR, 'adcensus_vol.png'))
# cbca
x0c = main_.cross(x0)
x1c = main_.cross(x1)
for i in range(2):
adcensus_vol = main_.cbca(x0c, x1c, adcensus_vol, 0)
adcensus_vol = main_.cbca(x0c, x1c, adcensus_vol, 1)
if DEBUG:
pred = adcensus_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(os.path.join(IMG_DIR, 'cbca_vol.png'))
# semi-global matching
c2_vol = main_.sgm(x0, x1, adcensus_vol)
if DEBUG:
pred = c2_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(os.path.join(IMG_DIR, 'sgm_vol.png'))
return c2_vol
Image.fromarray(pred.astype(np.uint8)).save(os.path.join(IMG_DIR, 'census_vol.png'))
# adcensus
def rho(c, lambda_):
return 1 - np.exp(-c / lambda_)
ad_vol_robust = rho(ad_vol, 10)
census_vol_robust = rho(census_vol, 30)
adcensus_vol = ad_vol_robust + census_vol_robust
if DEBUG:
pred = adcensus_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(os.path.join(IMG_DIR, 'adcensus_vol.png'))
# cbca
x0c = main_.cross(x0)
x1c = main_.cross(x1)
for i in range(2):
adcensus_vol = main_.cbca(x0c, x1c, adcensus_vol, 0)
adcensus_vol = main_.cbca(x0c, x1c, adcensus_vol, 1)
if DEBUG:
pred = adcensus_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(os.path.join(IMG_DIR, 'cbca_vol.png'))
# sgm scanline
c2_vol = main_.sgm(x0, x1, adcensus_vol)
if DEBUG:
pred = c2_vol.argmin(0).astype(np.float64) * scale
Image.fromarray(pred.astype(np.uint8)).save(os.path.join(IMG_DIR, 'sgm_vol.png'))
return c2_vol
#stereo_pairs = [['cones', 60, 4]]
#stereo_pairs = [['tsukuba', 16, 16]]
stereo_pairs = [['teddy', 60, 4]]
stereo_pairs = [['tsukuba', 16, 16], ['venus', 20, 8], ['teddy', 60, 4], ['cones', 60, 4]]
for pair_name, disp_max, scale in stereo_pairs:
print(pair_name)
x0 = np.array(Image.open('data/stereo-pairs/%s/imL.png' % pair_name), dtype=np.float64)
x1 = np.array(Image.open('data/stereo-pairs/%s/imR.png' % pair_name), dtype=np.float64)
height = x0.shape[0]
width = x0.shape[1]
main_.init(height, width, disp_max)
x0c = main_.cross(x0)
x1c = main_.cross(x1)
c2_1 = match(x1[:,::-1], x0[:,::-1])[:,:,::-1]
c2_0 = match(x0, x1)
d0 = np.argmin(c2_0, 0)
d1 = np.argmin(c2_1, 0)
outlier = main_.outlier_detection(d0, d1)
if DEBUG:
img = x0.copy()
img[outlier != 0] = 0
img[outlier == 1, 0] = 255
img[outlier == 2, 1] = 255
