def save_substance_grid(substance_id, outdir, show_progress=False):
try:
os.makedirs(outdir)
except OSError as e:
print e
substance = ShapeSubstance.objects.get(id=substance_id)
print 'substance: %s' % substance
qset = MaterialShape.objects.filter(substance_id=substance_id,
photo__inappropriate=False)
if not qset.exists():
print 'no shapes for %s' % substance
return
from common.utils import create_image_grid_qset
out = create_image_grid_qset(qset,
'image_square_300',
ncols=20,
size=300,
max_qset_size=10 * 20 * 16 / 9,
downsample_ratio=2,
show_progress=show_progress)
outname = os.path.join(
outdir,
substance.name.replace(' - ', '-').replace(' ', '-').replace(
'/', '-').replace("'", '') + '.png')
with open(outname, 'wb') as outfile:
save_image(out, outfile, format="PNG")
def save_substance_grid(substance_id, outdir, show_progress=False):
try:
os.makedirs(outdir)
except OSError as e:
print e
substance = ShapeSubstance.objects.get(id=substance_id)
print 'substance: %s' % substance
qset = MaterialShape.objects.filter(
substance_id=substance_id, photo__inappropriate=False)
if not qset.exists():
print 'no shapes for %s' % substance
return
from common.utils import create_image_grid_qset
out = create_image_grid_qset(qset, 'image_square_300',
ncols=20, size=300,
max_qset_size=10 * 20 * 16 / 9,
downsample_ratio=2,
show_progress=show_progress)
outname = os.path.join(outdir, substance.name
.replace(' - ', '-').replace(' ', '-')
.replace('/', '-').replace("'", '') + '.png')
with open(outname, 'wb') as outfile:
save_image(out, outfile, format="PNG")
def save_texture_grid(outdir, max_qset_size=None, ncols=25, show_progress=False, category=None):
try:
os.makedirs(outdir)
except OSError as e:
print e
qset = ShapeRectifiedNormalLabel.objects \
.filter(shape__photo__license__publishable=True,
shape__correct=True, shape__planar=True,
shape__rectified_normal_id=F('id')) \
.order_by('-shape__num_vertices')
if category:
qset = qset.filter(shape__substance__name=category)
if not qset.exists():
print 'no textures found'
return
if max_qset_size:
qset = qset[:max_qset_size]
from common.utils import create_image_grid_qset
out = create_image_grid_qset(qset, 'image_rectified_square_300',
ncols=ncols, size=256,
downsample_ratio=1,
show_progress=show_progress)
if category:
outname = os.path.join(outdir, '%s.png' % category.lower())
else:
outname = os.path.join(outdir, 'textures.png')
with open(outname, 'wb') as outfile:
save_image(out, outfile, format="PNG")
def save_texture_grid(outdir,
max_qset_size=None,
ncols=25,
show_progress=False,
category=None):
try:
os.makedirs(outdir)
except OSError as e:
print e
qset = ShapeRectifiedNormalLabel.objects \
.filter(shape__photo__license__publishable=True,
shape__correct=True, shape__planar=True,
shape__rectified_normal_id=F('id')) \
.order_by('-shape__num_vertices')
if category:
qset = qset.filter(shape__substance__name=category)
if not qset.exists():
print 'no textures found'
return
if max_qset_size:
qset = qset[:max_qset_size]
from common.utils import create_image_grid_qset
out = create_image_grid_qset(qset,
'image_rectified_square_300',
ncols=ncols,
size=256,
downsample_ratio=1,
show_progress=show_progress)
if category:
outname = os.path.join(outdir, '%s.png' % category.lower())
else:
outname = os.path.join(outdir, 'textures.png')
with open(outname, 'wb') as outfile:
save_image(out, outfile, format="PNG")
def save_substance_reflectance_grid(
outdir, initial_id=None, max_sequence_len=None, ncols=25, show_progress=False, substance=None
):
try:
os.makedirs(outdir)
except OSError as e:
print e
print "Fetching all BSDFs..."
# fetch all bsdfs
qset = ShapeBsdfLabel_wd.objects.filter(
color_correct=True,
gloss_correct=True,
shape__photo__inappropriate=False,
# color_correct_score__gte=0.1,
# gloss_correct_score__gte=0.1,
shape__photo__license__publishable=True,
)
if substance:
qset = qset.filter(shape__substance__name=substance)
bsdfs = list(
qset.extra(select={"correct_score": "color_correct_score + gloss_correct_score"}, order_by=("-correct_score",))
)
max_sequence_len = min(max_sequence_len, len(bsdfs))
print "Constructing sequence (%s items)..." % max_sequence_len
# start with a more interesting shape
bsdf = bsdfs[0]
for b in bsdfs:
if b.id == initial_id:
bsdf = b
break
bsdf_sequence = [bsdf]
shape_ids = set()
shape_ids.add(bsdf.shape_id)
try:
while True:
best_color_d = 1e10
best_gloss_b = None
best_gloss_d = 1e10
best_color_b = None
for b in bsdfs:
if b.shape_id in shape_ids:
continue
color_d = bsdf.color_distance(b)
if color_d <= best_color_d:
best_color_d = color_d
best_color_b = b
if color_d <= 2.3:
gloss_d = bsdf.gloss_distance(b)
if gloss_d < best_gloss_d:
best_gloss_d = gloss_d
best_gloss_b = b
best_b = best_gloss_b if best_gloss_b else best_color_b
if best_b:
print best_b.id
bsdf = best_b
bsdf_sequence.append(best_b)
shape_ids.add(best_b.shape_id)
if max_sequence_len and len(bsdf_sequence) >= max_sequence_len:
break
else:
break
if show_progress:
print len(bsdf_sequence)
except KeyboardInterrupt:
print "Search interrupted: using %s items" % len(bsdf_sequence)
print "Fetching shapes..."
image_pairs = [(b.image_blob, b.shape.image_square_300) for b in bsdf_sequence]
print "Constructing image..."
# save result
from common.utils import create_image_pair_grid_list
out = create_image_pair_grid_list(image_pairs, ncols=ncols, size=256, show_progress=True)
print "Saving result..."
if substance:
outname = os.path.join(outdir, "%s.png" % substance.replace("/", "-").lower())
else:
outname = os.path.join(outdir, "blob-sequence.png")
with open(outname, "wb") as outfile:
save_image(out, outfile, format="PNG")
def save_substance_reflectance_grid(
outdir, initial_id=None,
max_sequence_len=None, ncols=25,
show_progress=False, substance=None):
try:
os.makedirs(outdir)
except OSError as e:
print e
print 'Fetching all BSDFs...'
# fetch all bsdfs
qset = ShapeBsdfLabel_wd.objects.filter(
color_correct=True, gloss_correct=True, shape__photo__inappropriate=False,
#color_correct_score__gte=0.1,
#gloss_correct_score__gte=0.1,
shape__photo__license__publishable=True)
if substance:
qset = qset.filter(shape__substance__name=substance)
bsdfs = list(qset.extra(
select={'correct_score': 'color_correct_score + gloss_correct_score'},
order_by=('-correct_score',)
))
max_sequence_len = min(max_sequence_len, len(bsdfs))
print 'Constructing sequence (%s items)...' % max_sequence_len
# start with a more interesting shape
bsdf = bsdfs[0]
for b in bsdfs:
if b.id == initial_id:
bsdf = b
break
bsdf_sequence = [bsdf]
shape_ids = set()
shape_ids.add(bsdf.shape_id)
try:
while True:
best_color_d = 1e10
best_gloss_b = None
best_gloss_d = 1e10
best_color_b = None
for b in bsdfs:
if b.shape_id in shape_ids:
continue
color_d = bsdf.color_distance(b)
if color_d <= best_color_d:
best_color_d = color_d
best_color_b = b
if color_d <= 2.3:
gloss_d = bsdf.gloss_distance(b)
if gloss_d < best_gloss_d:
best_gloss_d = gloss_d
best_gloss_b = b
best_b = best_gloss_b if best_gloss_b else best_color_b
if best_b:
print best_b.id
bsdf = best_b
bsdf_sequence.append(best_b)
shape_ids.add(best_b.shape_id)
if max_sequence_len and len(bsdf_sequence) >= max_sequence_len:
break
else:
break
if show_progress:
print len(bsdf_sequence)
except KeyboardInterrupt:
print 'Search interrupted: using %s items' % len(bsdf_sequence)
print 'Fetching shapes...'
image_pairs = [
(b.image_blob, b.shape.image_square_300)
for b in bsdf_sequence
]
print 'Constructing image...'
# save result
from common.utils import create_image_pair_grid_list
out = create_image_pair_grid_list(
image_pairs, ncols=ncols, size=256, show_progress=True)
print 'Saving result...'
if substance:
outname = os.path.join(
outdir, '%s.png' % substance.replace('/', '-').lower())
else:
outname = os.path.join(outdir, 'blob-sequence.png')
with open(outname, 'wb') as outfile:
save_image(out, outfile, format="PNG")
def detect_vanishing_points_impl(photo, image, save=True):
# algorithm parameters
max_em_iter = 0 # if 0, don't do EM
min_cluster_size = 10
min_line_len2 = 4.0
residual_stdev = 0.75
max_clusters = 8
outlier_weight = 0.2
weight_clamp = 0.1
lambda_perp = 1.0
verbose = False
width, height = image.size
print 'size: %s x %s' % (width, height)
vpdetection_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, 'opt',
'vpdetection', 'matlab'))
tmpdir = tempfile.mkdtemp()
try:
# save image to local tmpdir
localname = os.path.join(tmpdir, 'image.jpg')
with open(tmpdir + '/image.jpg', 'wb') as target:
save_image(image, target, format='JPEG', options={'quality': 90})
# detect line segments using LSD (Grompone, G., Jakubowicz, J., Morel,
# J. and Randall, G. (2010). LSD: A Fast Line Segment Detector with a
# False Detection Control. IEEE Transactions on Pattern Analysis and
# Machine Intelligence, 32, 722.)
linesname = os.path.join(tmpdir, 'lines.txt')
matlab_command = ";".join([
"try",
"addpath('../lsd-1.5/')",
"lines = lsd(double(rgb2gray(imread('%s'))))" % localname,
"save('%s', 'lines', '-ascii', '-tabs')" % linesname,
"catch",
"end",
"quit",
])
print 'matlab command: %s' % matlab_command
subprocess.check_call(args=[
'matlab', '-nodesktop', '-nosplash', '-nodisplay', '-r',
matlab_command
],
cwd=vpdetection_dir)
# cluster lines using J-linkage (Toldo, R. and Fusiello, A. (2008).
# Robust multiple structures estimation with J-Linkage. European
# Conference on Computer Vision(ECCV), 2008.)
# and (Tardif J.-P., Non-iterative Approach for Fast and Accurate
# Vanishing Point Detection, 12th IEEE International Conference on
# Computer Vision, Kyoto, Japan, September 27 - October 4, 2009.)
clustername = os.path.join(tmpdir, 'clusters.txt')
subprocess.check_call(args=['./vpdetection', linesname, clustername],
cwd=vpdetection_dir)
# collect line clusters
clusters_dict = {}
all_lines = []
for row in open(clustername, 'r').readlines():
cols = row.split()
idx = int(cols[4])
line = [float(f) for f in cols[0:4]]
# discard small lines
x1, y1, x2, y2 = line
len2 = (x1 - x2)**2 + (y2 - y1)**2
if len2 < min_line_len2:
continue
if idx in clusters_dict:
clusters_dict[idx].append(line)
all_lines.append(line)
else:
clusters_dict[idx] = [line]
finally:
shutil.rmtree(tmpdir)
# discard invalid clusters and sort by cluster length
thresh = 3 if max_em_iter else min_cluster_size
clusters = filter(lambda x: len(x) >= thresh, clusters_dict.values())
clusters.sort(key=line_cluster_length, reverse=True)
if max_em_iter and len(clusters) > max_clusters:
clusters = clusters[:max_clusters]
print "Using %s clusters and %s lines" % (len(clusters), len(all_lines))
if not clusters:
print "Not enough clusters"
return
# Solve for optimal vanishing point using V_GS in 5.2 section of
# (http://www-etud.iro.umontreal.ca/~tardif/fichiers/Tardif_ICCV2009.pdf).
# where "optimal" minimizes algebraic error.
vectors = []
for lines in clusters:
# Minimize 'algebraic' error to get an initial solution
A = np.zeros((len(lines), 3))
for i in xrange(0, len(lines)):
x1, y1, x2, y2 = lines[i]
A[i, :] = [y1 - y2, x2 - x1, x1 * y2 - y1 * x2]
__, __, VT = np.linalg.svd(A, full_matrices=False, compute_uv=True)
if VT.shape != (3, 3):
raise ValueError("Invalid SVD shape (%s)" % VT.size)
x, y, w = VT[2, :]
p = [x / w, y / w]
v = photo.vanishing_point_to_vector((p[0] / width, p[1] / height))
vectors.append(v)
# EM
if max_em_iter:
# complete orthonormal system
if len(vectors) >= 2:
vectors.append(normalized_cross(vectors[0], vectors[1]))
### EM refinement ###
x0 = None
x_opt = None
exp_coeff = 0.5 / (residual_stdev**2)
num_weights_nnz = 0
num_weights = 0
for em_iter in xrange(max_em_iter):
### E STEP ###
# convert back to vanishing points
points = vectors_to_points(photo, image, vectors)
# last column is the outlier cluster
weights = np.zeros((len(all_lines), len(vectors) + 1))
# estimate weights (assume uniform prior)
for i_p, p in enumerate(points):
weights[:, i_p] = [line_residual(l, p) for l in all_lines]
weights = np.exp(-exp_coeff * np.square(weights))
# outlier weight
weights[:, len(points)] = outlier_weight
# normalize each row (each line segment) to have unit sum
weights_row_sum = weights.sum(axis=1)
weights /= weights_row_sum[:, np.newaxis]
# add sparsity
weights[weights < weight_clamp] = 0
num_weights += weights.size
num_weights_nnz += np.count_nonzero(weights)
# check convergence
if (em_iter >= 10 and len(x0) == len(x_opt) and
np.linalg.norm(np.array(x0) - np.array(x_opt)) <= 1e-5):
break
# sort by weight
if len(vectors) > 1:
vectors_weights = [(v, weights[:, i_v].sum())
for i_v, v in enumerate(vectors)]
vectors_weights.sort(key=lambda x: x[1], reverse=True)
vectors = [x[0] for x in vectors_weights]
### M STEP ###
# objective function to minimize
def objective_function(x, *args):
cur_vectors = unpack_x(x)
cur_points = vectors_to_points(photo, image, cur_vectors)
# line-segment errors
residuals = [
weights[i_l, i_p] * line_residual(all_lines[i_l], p)
for i_p, p in enumerate(cur_points)
for i_l in np.flatnonzero(weights[:, i_p])
]
# penalize deviations from 45 or 90 degree angles
if lambda_perp:
residuals += [
lambda_perp * math.sin(4 * math.acos(abs_dot(v, w)))
for i_v, v in enumerate(cur_vectors)
for w in cur_vectors[:i_v]
]
return residuals
# slowly vary parameters
t = min(1.0, em_iter / 20.0)
# vary tol from 1e-2 to 1e-6
tol = math.exp(math.log(1e-2) * (1 - t) + math.log(1e-6) * t)
from scipy.optimize import leastsq
x0 = pack_x(vectors)
x_opt, __ = leastsq(objective_function, x0, ftol=tol, xtol=tol)
vectors = unpack_x(x_opt)
### BETWEEN ITERATIONS ###
if verbose:
print 'EM: %s iters, %s clusters, weight sparsity: %s%%' % (
em_iter, len(vectors),
100.0 * num_weights_nnz / num_weights)
print 'residual: %s' % sum(y**2
for y in objective_function(x_opt))
# complete orthonormal system if missing
if len(vectors) == 2:
vectors.append(normalized_cross(vectors[0], vectors[1]))
# merge similar clusters
cluster_merge_dot = math.cos(math.radians(t * 20.0))
vectors_merged = []
for v in vectors:
if (not vectors_merged or all(
abs_dot(v, w) < cluster_merge_dot
for w in vectors_merged)):
vectors_merged.append(v)
if verbose and len(vectors) != len(vectors_merged):
print 'Merging %s --> %s vectors' % (len(vectors),
len(vectors_merged))
vectors = vectors_merged
residual = sum(r**2 for r in objective_function(x_opt))
print 'EM: %s iters, residual: %s, %s clusters, weight sparsity: %s%%' % (
em_iter, residual, len(vectors),
100.0 * num_weights_nnz / num_weights)
# final points
points = vectors_to_points(photo, image, vectors)
# sanity checks
assert len(vectors) == len(points)
# re-assign clusters
clusters_points = [([], p) for p in points]
line_map_cluster = np.argmax(weights, axis=1)
for i_l, l in enumerate(all_lines):
i_c = line_map_cluster[i_l]
if i_c < len(points):
clusters_points[i_c][0].append(l)
# throw away small clusters
clusters_points = filter(lambda x: len(x[0]) >= min_cluster_size,
clusters_points)
# reverse sort by cluster length
clusters_points.sort(key=lambda x: line_cluster_length(x[0]),
reverse=True)
# split into two parallel arrays
clusters = [cp[0] for cp in clusters_points]
points = [cp[1] for cp in clusters_points]
else: # no EM
for i_v, lines in enumerate(clusters):
def objective_function(x, *args):
p = vectors_to_points(photo, image, unpack_x(x))[0]
return [line_residual(l, p) for l in lines]
from scipy.optimize import leastsq
x0 = pack_x([vectors[i_v]])
x_opt, __ = leastsq(objective_function, x0)
vectors[i_v] = unpack_x(x_opt)[0]
# delete similar vectors
cluster_merge_dot = math.cos(math.radians(20.0))
vectors_merged = []
clusters_merged = []
for i_v, v in enumerate(vectors):
if (not vectors_merged or all(
abs_dot(v, w) < cluster_merge_dot
for w in vectors_merged)):
vectors_merged.append(v)
clusters_merged.append(clusters[i_v])
vectors = vectors_merged
clusters = clusters_merged
# clamp number of vectors
if len(clusters) > max_clusters:
vectors = vectors[:max_clusters]
clusters = clusters[:max_clusters]
points = vectors_to_points(photo, image, vectors)
# normalize to [0, 0], [1, 1]
clusters_normalized = [[[
l[0] / width, l[1] / height, l[2] / width, l[3] / height
] for l in lines] for lines in clusters]
points_normalized = [(x / width, y / height) for (x, y) in points]
# save result
photo.vanishing_lines = json.dumps(clusters_normalized)
photo.vanishing_points = json.dumps(points_normalized)
photo.vanishing_length = sum(
line_cluster_length(c) for c in clusters_normalized)
if save:
photo.save()
def detect_vanishing_points_impl(photo, image, save=True):
# algorithm parameters
max_em_iter = 0 # if 0, don't do EM
min_cluster_size = 10
min_line_len2 = 4.0
residual_stdev = 0.75
max_clusters = 8
outlier_weight = 0.2
weight_clamp = 0.1
lambda_perp = 1.0
verbose = False
width, height = image.size
print 'size: %s x %s' % (width, height)
vpdetection_dir = os.path.abspath(os.path.join(
os.path.dirname(__file__), os.pardir, os.pardir, 'opt', 'vpdetection', 'matlab'
))
tmpdir = tempfile.mkdtemp()
try:
# save image to local tmpdir
localname = os.path.join(tmpdir, 'image.jpg')
with open(tmpdir + '/image.jpg', 'wb') as target:
save_image(image, target, format='JPEG', options={'quality': 90})
# detect line segments using LSD (Grompone, G., Jakubowicz, J., Morel,
# J. and Randall, G. (2010). LSD: A Fast Line Segment Detector with a
# False Detection Control. IEEE Transactions on Pattern Analysis and
# Machine Intelligence, 32, 722.)
linesname = os.path.join(tmpdir, 'lines.txt')
matlab_command = ";".join([
"try",
"addpath('../lsd-1.5/')",
"lines = lsd(double(rgb2gray(imread('%s'))))" % localname,
"save('%s', 'lines', '-ascii', '-tabs')" % linesname,
"catch",
"end",
"quit",
])
print 'matlab command: %s' % matlab_command
subprocess.check_call(args=[
'matlab', '-nodesktop', '-nosplash', '-nodisplay',
'-r', matlab_command
], cwd=vpdetection_dir)
# cluster lines using J-linkage (Toldo, R. and Fusiello, A. (2008).
# Robust multiple structures estimation with J-Linkage. European
# Conference on Computer Vision(ECCV), 2008.)
# and (Tardif J.-P., Non-iterative Approach for Fast and Accurate
# Vanishing Point Detection, 12th IEEE International Conference on
# Computer Vision, Kyoto, Japan, September 27 - October 4, 2009.)
clustername = os.path.join(tmpdir, 'clusters.txt')
subprocess.check_call(
args=['./vpdetection', linesname, clustername],
cwd=vpdetection_dir)
# collect line clusters
clusters_dict = {}
all_lines = []
for row in open(clustername, 'r').readlines():
cols = row.split()
idx = int(cols[4])
line = [float(f) for f in cols[0:4]]
# discard small lines
x1, y1, x2, y2 = line
len2 = (x1 - x2) ** 2 + (y2 - y1) ** 2
if len2 < min_line_len2:
continue
if idx in clusters_dict:
clusters_dict[idx].append(line)
all_lines.append(line)
else:
clusters_dict[idx] = [line]
finally:
shutil.rmtree(tmpdir)
# discard invalid clusters and sort by cluster length
thresh = 3 if max_em_iter else min_cluster_size
clusters = filter(lambda x: len(x) >= thresh, clusters_dict.values())
clusters.sort(key=line_cluster_length, reverse=True)
if max_em_iter and len(clusters) > max_clusters:
clusters = clusters[:max_clusters]
print "Using %s clusters and %s lines" % (len(clusters), len(all_lines))
if not clusters:
print "Not enough clusters"
return
# Solve for optimal vanishing point using V_GS in 5.2 section of
# (http://www-etud.iro.umontreal.ca/~tardif/fichiers/Tardif_ICCV2009.pdf).
# where "optimal" minimizes algebraic error.
vectors = []
for lines in clusters:
# Minimize 'algebraic' error to get an initial solution
A = np.zeros((len(lines), 3))
for i in xrange(0, len(lines)):
x1, y1, x2, y2 = lines[i]
A[i, :] = [y1 - y2, x2 - x1, x1 * y2 - y1 * x2]
__, __, VT = np.linalg.svd(A, full_matrices=False, compute_uv=True)
if VT.shape != (3, 3):
raise ValueError("Invalid SVD shape (%s)" % VT.size)
x, y, w = VT[2, :]
p = [x / w, y / w]
v = photo.vanishing_point_to_vector(
(p[0] / width, p[1] / height)
)
vectors.append(v)
# EM
if max_em_iter:
# complete orthonormal system
if len(vectors) >= 2:
vectors.append(normalized_cross(vectors[0], vectors[1]))
### EM refinement ###
x0 = None
x_opt = None
exp_coeff = 0.5 / (residual_stdev ** 2)
num_weights_nnz = 0
num_weights = 0
for em_iter in xrange(max_em_iter):
### E STEP ###
# convert back to vanishing points
points = vectors_to_points(photo, image, vectors)
# last column is the outlier cluster
weights = np.zeros((len(all_lines), len(vectors) + 1))
# estimate weights (assume uniform prior)
for i_p, p in enumerate(points):
weights[:, i_p] = [line_residual(l, p) for l in all_lines]
weights = np.exp(-exp_coeff * np.square(weights))
# outlier weight
weights[:, len(points)] = outlier_weight
# normalize each row (each line segment) to have unit sum
weights_row_sum = weights.sum(axis=1)
weights /= weights_row_sum[:, np.newaxis]
# add sparsity
weights[weights < weight_clamp] = 0
num_weights += weights.size
num_weights_nnz += np.count_nonzero(weights)
# check convergence
if (em_iter >= 10 and len(x0) == len(x_opt) and
np.linalg.norm(np.array(x0) - np.array(x_opt)) <= 1e-5):
break
# sort by weight
if len(vectors) > 1:
vectors_weights = [
(v, weights[:, i_v].sum()) for i_v, v in enumerate(vectors)
]
vectors_weights.sort(key=lambda x: x[1], reverse=True)
vectors = [x[0] for x in vectors_weights]
### M STEP ###
# objective function to minimize
def objective_function(x, *args):
cur_vectors = unpack_x(x)
cur_points = vectors_to_points(photo, image, cur_vectors)
# line-segment errors
residuals = [
weights[i_l, i_p] * line_residual(all_lines[i_l], p)
for i_p, p in enumerate(cur_points)
for i_l in np.flatnonzero(weights[:, i_p])
]
# penalize deviations from 45 or 90 degree angles
if lambda_perp:
residuals += [
lambda_perp * math.sin(4 * math.acos(abs_dot(v, w)))
for i_v, v in enumerate(cur_vectors)
for w in cur_vectors[:i_v]
]
return residuals
# slowly vary parameters
t = min(1.0, em_iter / 20.0)
# vary tol from 1e-2 to 1e-6
tol = math.exp(math.log(1e-2) * (1 - t) + math.log(1e-6) * t)
from scipy.optimize import leastsq
x0 = pack_x(vectors)
x_opt, __ = leastsq(objective_function, x0, ftol=tol, xtol=tol)
vectors = unpack_x(x_opt)
### BETWEEN ITERATIONS ###
if verbose:
print 'EM: %s iters, %s clusters, weight sparsity: %s%%' % (
em_iter, len(vectors), 100.0 * num_weights_nnz / num_weights)
print 'residual: %s' % sum(y ** 2 for y in objective_function(x_opt))
# complete orthonormal system if missing
if len(vectors) == 2:
vectors.append(normalized_cross(vectors[0], vectors[1]))
# merge similar clusters
cluster_merge_dot = math.cos(math.radians(t * 20.0))
vectors_merged = []
for v in vectors:
if (not vectors_merged or
all(abs_dot(v, w) < cluster_merge_dot for w in vectors_merged)):
vectors_merged.append(v)
if verbose and len(vectors) != len(vectors_merged):
print 'Merging %s --> %s vectors' % (len(vectors), len(vectors_merged))
vectors = vectors_merged
residual = sum(r ** 2 for r in objective_function(x_opt))
print 'EM: %s iters, residual: %s, %s clusters, weight sparsity: %s%%' % (
em_iter, residual, len(vectors), 100.0 * num_weights_nnz / num_weights)
# final points
points = vectors_to_points(photo, image, vectors)
# sanity checks
assert len(vectors) == len(points)
# re-assign clusters
clusters_points = [([], p) for p in points]
line_map_cluster = np.argmax(weights, axis=1)
for i_l, l in enumerate(all_lines):
i_c = line_map_cluster[i_l]
if i_c < len(points):
clusters_points[i_c][0].append(l)
# throw away small clusters
clusters_points = filter(
lambda x: len(x[0]) >= min_cluster_size, clusters_points)
# reverse sort by cluster length
clusters_points.sort(
key=lambda x: line_cluster_length(x[0]), reverse=True)
# split into two parallel arrays
clusters = [cp[0] for cp in clusters_points]
points = [cp[1] for cp in clusters_points]
else: # no EM
for i_v, lines in enumerate(clusters):
def objective_function(x, *args):
p = vectors_to_points(photo, image, unpack_x(x))[0]
return [line_residual(l, p) for l in lines]
from scipy.optimize import leastsq
x0 = pack_x([vectors[i_v]])
x_opt, __ = leastsq(objective_function, x0)
vectors[i_v] = unpack_x(x_opt)[0]
# delete similar vectors
cluster_merge_dot = math.cos(math.radians(20.0))
vectors_merged = []
clusters_merged = []
for i_v, v in enumerate(vectors):
if (not vectors_merged or
all(abs_dot(v, w) < cluster_merge_dot for w in vectors_merged)):
vectors_merged.append(v)
clusters_merged.append(clusters[i_v])
vectors = vectors_merged
clusters = clusters_merged
# clamp number of vectors
if len(clusters) > max_clusters:
vectors = vectors[:max_clusters]
clusters = clusters[:max_clusters]
points = vectors_to_points(photo, image, vectors)
# normalize to [0, 0], [1, 1]
clusters_normalized = [[
[l[0] / width, l[1] / height, l[2] / width, l[3] / height]
for l in lines
] for lines in clusters]
points_normalized = [
(x / width, y / height) for (x, y) in points
]
# save result
photo.vanishing_lines = json.dumps(clusters_normalized)
photo.vanishing_points = json.dumps(points_normalized)
photo.vanishing_length = sum(line_cluster_length(c)
for c in clusters_normalized)
if save:
photo.save()
