def extract_features_left(census,ncc,sobel,sad,
cens_sigma = 128.0,
ncc_sigma=0.02,
sad_sigma=20000.0,
sobel_sigma=20000.0,
disp_image = None):
# cost shape : [img_H, img_W, ndisp]
dims = census.shape
h,w,ndisp = dims[:]
## clip to remove RAND_MAX values + normalize in 0,1
#cost_tmp = census
#cost_tmp[np.isclose(cost_tmp, 2147483648.0)] = 1024.0
census = np.reshape(census, [h*w, ndisp])
ncc = np.reshape(ncc, [h*w,ndisp])
sobel = np.reshape(sobel, [h*w,ndisp])
sad = np.reshape(sad, [h*w,ndisp])
""" let channel number = 8, as the first dim, which makes
the following assignment and element access
in C contiguous arraya way, that is, a chunk of contiguous
memory in the C-like index order;
"""
features = np.empty((8, h, w, ndisp), order= 'C')
""" matcher cost : census , ncc, sobel, sad """
features[0, :,:,:]= np.reshape(np.clip(census, 0., 120.)/120. , [h, w, ndisp])
#features[1, :,:,:]= np.reshape(np.clip(ncc + 1.0, 0., 2.),[h, w, ndisp])
features[1, :,:,:]= np.reshape((1 + np.clip(ncc, -1., 1.))/2,[h, w, ndisp]) # Updated: change NCC to [0, 1] range!!!
features[2, :,:,:]= np.reshape(np.clip(sobel, 0., 2**13)/float(2**13), [h, w, ndisp])
features[3, :,:,:]= np.reshape(np.clip(sad, 0., 2**13) / float(2**13), [h, w, ndisp])
""" aml for left costs: census , ncc, sobel, sad """
# X ~ N(u, sigma^2), then aX ~ N(au, a^2*sigma^2);
# E(aX + b) = aE(x) + b;
# Var(aX + b) = a^2*Var(x);
#normlized_cens_var = cens_sigma / (120.0**2 *4)
#normalized_ncc_var = ncc_sigma/4.0
#normalized_sob_var = sad_sigma / float(2**26 *4)
#normalized_sad_var = sad_sigma / float(2**26 *16)
normlized_cens_var = cens_sigma
normalized_ncc_var = ncc_sigma
normalized_sob_var = sad_sigma
normalized_sad_var = sad_sigma
features[4,:,:,:]=np.reshape(fte.extract_likelihood(census, normlized_cens_var),[h,w,ndisp])
features[5,:,:,:]=np.reshape(fte.extract_likelihood(ncc, normalized_ncc_var), [h,w,ndisp])
features[6,:,:,:]=np.reshape(fte.extract_likelihood(sobel, normalized_sob_var), [h,w,ndisp])
features[7,:,:,:]=np.reshape(fte.extract_likelihood(sad, normalized_sad_var), [h,w,ndisp])
# in size [C, D, H, W]
features = features.transpose((0,3,1,2))
return features.astype(np.float32)
def extract_features_left_V2(census,ncc,sobel,sad,
cens_sigma = 128.0,
ncc_sigma=0.02,
sad_sigma=20000.0,
sobel_sigma=20000.0,
disp_image = None):
# cost shape : [img_H, img_W, ndisp]
dims = census.shape
h,w,ndisp = dims[:]
""" aml for left costs: census , ncc, sobel, sad """
# X ~ N(u, sigma^2), then aX ~ N(au, a^2*sigma^2);
# E(aX + b) = aE(x) + b;
# Var(aX + b) = a^2*Var(x);
#normlized_cens_var = cens_sigma / (120.0**2 *4)
#normalized_ncc_var = ncc_sigma/4.0
#normalized_sob_var = sad_sigma / float(2**26 *4)
#normalized_sad_var = sad_sigma / float(2**26 *16)
normlized_cens_var = cens_sigma
normalized_ncc_var = ncc_sigma
normalized_sob_var = sad_sigma
normalized_sad_var = sad_sigma
#census
census_aml = np.expand_dims(np.reshape(fte.extract_likelihood(np.reshape(census, [h*w,ndisp]), normlized_cens_var),[h,w,ndisp]), 0)
census = np.expand_dims(np.clip(census, 0., 120.)/120., 0)
# ncc
ncc_aml = np.expand_dims(np.reshape(fte.extract_likelihood(np.reshape(ncc,[h*w,ndisp]), normalized_ncc_var),[h,w, ndisp]), 0)
ncc = np.expand_dims(np.clip(ncc + 1.0, 0., 2.), 0)
# sobel
sobel_aml = np.expand_dims(np.reshape(fte.extract_likelihood(np.reshape(sobel, [h*w,ndisp]),normalized_sob_var),[h,w,ndisp]), 0)
sobel = np.expand_dims(np.clip(sobel, 0., 2**13) / float(2**13), 0)
# sad
sad_aml = np.expand_dims(np.reshape(fte.extract_likelihood(np.reshape(sad, [h*w, ndisp]), normalized_sad_var),[h,w,ndisp]), 0)
sad = np.expand_dims(np.clip(sad, 0., 2**13) / float(2**13), 0)
""" let channel number = 8, as the first dim, which makes
the following assignment and element access
in C contiguous arraya way, that is, a chunk of contiguous
memory in the C-like index order;
"""
features = np.concatenate((census, ncc, sobel, sad, census_aml, ncc_aml, sobel_aml, sad_aml), 0)
# in size [C, D, H, W]
features = features.transpose((0,3,1,2))
del census_aml
del ncc_aml
del sobel_aml
del sad_aml
return features.astype(np.float32)
def extract_features_lr(
census,ncc,sobel,sad,
cens_sigma = 128.0,
ncc_sigma=0.02,
sad_sigma=20000.0,
sobel_sigma=20000.0,
disp_image = None):
# cost shape : [img_H, img_W, ndisp]
h,w,ndisp = census.shape[:]
censusR = fte.get_right_cost(census)
#pfm.show(np.argmin(census, axis = 2).astype(np.float32), title='disp_census')
#pfm.show(np.argmin(censusR, axis = 2).astype(np.float32), title='R_disp_census')
nccR = fte.get_right_cost(ncc)
#pfm.show(np.argmin(ncc, axis = 2).astype(np.float32), title='disp_ncc')
#pfm.show(np.argmin(nccR, axis = 2).astype(np.float32), title='R_disp_ncc')
sadR = fte.get_right_cost(sad)
#pfm.show(np.argmin(sad, axis = 2).astype(np.float32), title='disp_sad')
#pfm.show(np.argmin(sadR, axis = 2).astype(np.float32), title='R_disp_sad')
sobelR = fte.get_right_cost(sobel)
#pfm.show(np.argmin(sobel, axis = 2).astype(np.float32), title='disp_sob')
#pfm.show(np.argmin(sobelR, axis = 2).astype(np.float32), title='R_disp_sob')
#cost_tmp = census
#cost_tmp[np.isclose(cost_tmp, 2147483648.0)] = 1024.0
census = np.reshape(census, [h*w, ndisp])
ncc = np.reshape(ncc, [h*w,ndisp])
sobel = np.reshape(sobel, [h*w,ndisp])
sad = np.reshape(sad, [h*w,ndisp])
censusR = np.reshape(censusR, [h*w, ndisp])
nccR = np.reshape(nccR, [h*w, ndisp])
sobelR = np.reshape(sobelR,[h*w,ndisp])
sadR = np.reshape(sadR, [h*w,ndisp])
"""
# just for debugging!!!
# left ones!
imgs_to_plot = []
d0 = 25
tmp_disp1 = disp_image.copy(order = 'C')
tmp_disp2 = disp_image.copy(order = 'C')
tmp_disp3 = disp_image.copy(order = 'C')
tmp_disp1[np.abs(tmp_disp1 - d0) > 3] = 0
tmp_disp2[np.abs(tmp_disp2 - d0) > 2] = 0
tmp_disp3[np.abs(tmp_disp3 - d0) > 1] = 0
# 0, 1, 2, 3;
imgs_to_plot.append(disp_image)
imgs_to_plot.append(tmp_disp1)
imgs_to_plot.append(tmp_disp2)
imgs_to_plot.append(tmp_disp3)
# 4, 5, 6, 7;
imgs_to_plot.append(np.reshape(np.reshape(fte.extract_likelihood(census, cens_sigma/ (120.0**2 *4)),[h,w,ndisp])[:,:,d0], [h,w]))
imgs_to_plot.append(np.reshape(np.reshape(fte.extract_likelihood(ncc, ncc_sigma / 4.0),[h,w,ndisp])[:,:,d0], [h,w]))
imgs_to_plot.append(np.reshape(np.reshape(fte.extract_likelihood(sobel, sobel_sigma / float (2**26*4)),[h,w,ndisp])[:,:,d0], [h,w]))
imgs_to_plot.append(np.reshape(np.reshape(fte.extract_likelihood(sad, sad_sigma / float (2**26*16)),[h,w,ndisp])[:,:,d0], [h,w]))
# 8, 9, 10, 11;
imgs_to_plot.append(np.reshape(np.reshape(fte.extract_likelihood(census, cens_sigma/ (120.0**2)),[h,w,ndisp])[:,:,d0], [h,w]))
imgs_to_plot.append(np.reshape(np.reshape(fte.extract_likelihood(ncc, ncc_sigma/ 1.0),[h,w,ndisp])[:,:,d0], [h,w]))
imgs_to_plot.append(np.reshape(np.reshape(fte.extract_likelihood(sobel, sobel_sigma/ float(2**26)),[h,w,ndisp])[:,:,d0], [h,w]))
imgs_to_plot.append(np.reshape(np.reshape(fte.extract_likelihood(sad, sad_sigma/ float(2**26)),[h,w,ndisp])[:,:,d0], [h,w]))
features_name = [
'censusL', 'nccL', 'sobelL', 'sadL',
'ratio_cenL', 'ratio_nccL', 'ratio_sobL', 'ratio_sadL',
'likly_cenL', 'likly_nccL', 'likly_sobL', 'likly_sadL',
'ratio_cenR', 'ratio_nccR', 'ratio_sobR', 'ratio_sadR',
'likly_cenR', 'likly_nccR', 'likly_sobR', 'likly_sadR',
]
show_4_imgs_3_row(imgs = imgs_to_plot,
img_names = ['slice_{}_d{}'.format(i, d0) for i in features_name[0:12]],
#cmap = ['inferno']*8 + ['gray'] * 4
#cmap = ['inferno']*12
cmap = ['gray']*12
)
# right ones
imgs_to_plot = []
features_name = [
'disp_censusL', 'disp_nccL', 'disp_sobelL', 'disp_sadL',
'disp_likly_cenL', 'disp_likly_nccL', 'disp_likly_sobL', 'disp_likly_sadL',
'disp_censusR', 'disp_nccR', 'disp_sobelR', 'disp_sadR',
'disp_likly_cenR', 'disp_likly_nccR', 'disp_likly_sobR', 'disp_likly_sadR',
]
imgs_to_plot.append(np.argmin(np.reshape(census, [h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmin(np.reshape(ncc, [h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmin(np.reshape(sobel, [h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmin(np.reshape(sad, [h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmax(np.reshape(fte.extract_likelihood(census, cens_sigma),[h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmax(np.reshape(fte.extract_likelihood(ncc, cens_sigma),[h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmax(np.reshape(fte.extract_likelihood(sobel, cens_sigma),[h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmax(np.reshape(fte.extract_likelihood(sad, cens_sigma),[h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmin(np.reshape(censusR, [h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmin(np.reshape(nccR, [h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmin(np.reshape(sobelR, [h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmin(np.reshape(sadR, [h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmax(np.reshape(fte.extract_likelihood(censusR, cens_sigma),[h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmax(np.reshape(fte.extract_likelihood(nccR, cens_sigma),[h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmax(np.reshape(fte.extract_likelihood(sobelR, cens_sigma),[h,w,ndisp]), axis=2))
imgs_to_plot.append(np.argmax(np.reshape(fte.extract_likelihood(sadR, cens_sigma),[h,w,ndisp]), axis=2))
show_4_imgs_4_row(imgs = imgs_to_plot,
img_names = features_name,
cmap = ['gray']*16
)
sys.exit()
"""
""" let channel number = 8, as the first dim, which makes
the following assignment and element access
in C contiguous arraya way, that is, a chunk of contiguous
memory in the C-like index order;
"""
features = np.empty((16, h, w, ndisp), order= 'C')
""" matcher cost : census , ncc, sobel, sad """
## clip to remove RAND_MAX values, and normalized to [0,1]
features[0, :,:,:]= np.reshape(np.clip(census, 0., 120.)/120. , [h, w, ndisp])
features[1, :,:,:]= np.reshape((1 + np.clip(ncc, -1., 1.))/2,[h, w, ndisp]) # Updated: change NCC to [0, 1] range!!!
features[2, :,:,:]= np.reshape(np.clip(sobel, 0., 2**13)/float(2**13), [h, w, ndisp])
features[3, :,:,:]= np.reshape(np.clip(sad, 0., 2**13) / float(2**13), [h, w, ndisp])
""" aml for left costs: census , ncc, sobel, sad """
# X ~ N(u, sigma^2), then aX ~ N(au, a^2*sigma^2);
# E(aX + b) = aE(x) + b;
# Var(aX + b) = a^2*Var(x);
#normlized_cens_var = cens_sigma / (120.0**2 *4)
#normalized_ncc_var = ncc_sigma/4.0
#normalized_sob_var = sad_sigma / float(2**26 *4)
#normalized_sad_var = sad_sigma / float(2**26 *16)
normlized_cens_var = cens_sigma
normalized_ncc_var = ncc_sigma
normalized_sob_var = sad_sigma
normalized_sad_var = sad_sigma
features[4,:,:,:]=np.reshape(fte.extract_likelihood(census, normlized_cens_var),[h,w,ndisp])
features[5,:,:,:]=np.reshape(fte.extract_likelihood(ncc, normalized_ncc_var), [h,w,ndisp])
features[6,:,:,:]=np.reshape(fte.extract_likelihood(sobel, normalized_sob_var), [h,w,ndisp])
features[7,:,:,:]=np.reshape(fte.extract_likelihood(sad, normalized_sad_var), [h,w,ndisp])
## Right features
features[8, :,:,:]= np.reshape(np.clip(censusR, 0., 120.)/120. , [h, w, ndisp])
features[9, :,:,:]= np.reshape( (1+np.clip(nccR, -1., 1.))/2,[h, w, ndisp]) # Updated: change NCC to [0, 1] range!!!
features[10, :,:,:]= np.reshape(np.clip(sobelR, 0., 2**13)/float(2**13), [h, w, ndisp])
features[11, :,:,:]= np.reshape(np.clip(sadR, 0., 2**13) / float(2**13), [h, w, ndisp])
features[12,:,:,:]=np.reshape(fte.extract_likelihood(censusR, normlized_cens_var),[h,w,ndisp])
features[13,:,:,:]=np.reshape(fte.extract_likelihood(nccR, normalized_ncc_var), [h,w,ndisp])
features[14,:,:,:]=np.reshape(fte.extract_likelihood(sobelR, normalized_sob_var), [h,w,ndisp])
features[15,:,:,:]=np.reshape(fte.extract_likelihood(sadR, normalized_sad_var), [h,w,ndisp])
#del census
#del ncc
#del sobel
#del sad
del censusR
del nccR
del sobelR
del sadR
# in size [C, D, H, W]
features = features.transpose((0,3,1,2))
return features.astype(np.float32)