def triangles_segments_intersections_only(
vertices, segments, triangles, query_segments):
"""
Return the indices of the query segments that intersect any of the triangles,
but none of the segments.
vertices are a list of point tuples, segments are lists of tuples of indices into vertices,
and triangles are lists of tuples of indices into vertices.
query_segments are a list of point tuples [((x1, y1), (x2, y2)), ...].
"""
from shapes.utils import bbox_vertices
bbox = bbox_vertices(vertices)
ret = []
for (idx, (a, b)) in enumerate(query_segments):
if not bbox_segment_intersects(bbox, a, b):
continue
# intersect with each segment
for (s0, s1) in segments:
if segment_segment_intersects(
vertices[s0], vertices[s1], a, b):
continue
# intersect with each triangle
for (t0, t1, t2) in triangles:
if triangle_segment_intersects(
vertices[t0], vertices[t1], vertices[t2], a, b):
ret.append(idx)
return ret
def estimate_uvnb_from_vanishing_points(shape, try_fully_automatic=False):
""" Return (uvnb, num_vanishing_points) """
print 'estimate_uvnb for shape_id: %s' % shape.id
# local import to avoid cyclic dependencies
from shapes.utils import parse_vertices, parse_triangles, \
parse_segments, bbox_vertices
# load photo
photo = shape.photo
if not photo.vanishing_lines or not photo.vanishing_points:
raise ValueError("Vanishing points not computed")
if not photo.focal_y:
raise ValueError("Photo does not have focal_y")
vlines = json.loads(photo.vanishing_lines)
vpoints = json.loads(photo.vanishing_points)
vvectors = copy.copy(photo.vanishing_vectors())
if len(vlines) != len(vpoints):
raise ValueError("Invalid vanishing points data structure")
# add any missing vanishing points
vvectors = complete_vector_triplets(vvectors, tolerance_dot=0.75)
# find vanishing lines inside shape
vertices = parse_vertices(shape.vertices)
segments = parse_segments(shape.triangles)
triangles = parse_triangles(shape.triangles)
# intersect shapes with segments
counts = []
for idx in xrange(len(vlines)):
# re-pack for geom routines
query_segments = [((l[0], l[1]), (l[2], l[3])) for l in vlines[idx]]
from common.geom import triangles_segments_intersections_only
n = len(
triangles_segments_intersections_only(vertices, segments,
triangles, query_segments))
if n >= 5:
counts.append((n, idx))
counts.sort(key=lambda x: x[0], reverse=True)
# function to judge normals: its vanishing line can't intersect the shape.
def auto_normal_acceptable(n):
sign = None
for (x, y) in vertices:
# vanishing line
line = (n[0], n[1], n[2] * photo.focal_y)
# signed distance
d = ((x - 0.5) * photo.aspect_ratio * line[0] +
(0.5 - y) * line[1] + # flip y
line[2])
if abs(d) < 0.05:
return False
elif sign is None:
sign = (d > 0)
else:
if sign != (d > 0):
return False
return True
# find coordinate frame
best_n = None
best_u = None
method = None
num_vanishing_lines = 0
# make sure shape has label
if not shape.label_pos_x or not shape.label_pos_y:
from shapes.utils import update_shape_label_pos
update_shape_label_pos(shape)
# place label in 3D
b_z = -(photo.focal_y / photo.aspect_ratio) / 0.1
b = [(shape.label_pos_x - 0.5) * photo.aspect_ratio * (-b_z) /
photo.focal_y, (0.5 - shape.label_pos_y) * (-b_z) / photo.focal_y,
b_z]
# estimate closest human normal
human_labels = list(
ShapeRectifiedNormalLabel.objects.filter(
shape=shape,
automatic=False,
correct_score__isnull=False,
).order_by('-correct_score'))
human_labels += list(
ShapeRectifiedNormalLabel.objects.filter(shape=shape,
automatic=False,
correct_score__isnull=True))
if human_labels:
for label in human_labels:
human_u = label.u()
human_n = label.n()
b = list(label.uvnb_numpy()[0:3, 3].flat)
# find best normal
best_n_dot = 0.9
best_n = None
for n in vvectors:
d = abs_dot(human_n, n)
if d > best_n_dot:
best_n_dot = d
best_n = n
method = 'S'
# if there is a match find u and quit
if best_n is not None:
# find best u
best_u_dot = 0
best_u = None
for u in vvectors:
if abs_dot(u, best_n) < 0.1:
d = abs_dot(human_u, u)
if d > best_u_dot:
best_u_dot = d
best_u = u
break
# try using object label
if best_n is None and shape.name:
if shape.name.name.lower() in ('floor', 'carpet/rug', 'ceiling'):
best_y = 0.9
for v in vvectors[0:3]:
if abs(v[1]) > best_y:
best_y = abs(v[1])
best_n = v
method = 'O'
# try fully automatic method if human normals are not good enough
if (try_fully_automatic and best_n is None and len(vpoints) >= 3
and len(counts) >= 2 and
(shape.substance is None or shape.substance.name != 'Painted')):
# choose two dominant vanishing points
best_u = vvectors[counts[0][1]]
best_v = vvectors[counts[1][1]]
# don't try and auto-rectify frontal surfaces
if auto_normal_acceptable(normalized_cross(best_u, best_v)):
num_vanishing_lines = counts[0][0] + counts[1][0]
uv_dot = abs_dot(best_u, best_v)
print 'u dot v = %s' % uv_dot
else:
best_u, best_v = None, None
uv_dot = None
# make sure these vectors are accurate
if not uv_dot or uv_dot > 0.05:
# try and find two other orthogonal vanishing points
best_dot = 0.05
best_u = None
best_v = None
for c1, i1 in counts:
for c2, i2 in counts[:i1]:
d = abs_dot(vvectors[i1], vvectors[i2])
if d < best_dot:
# don't try and auto-rectify frontal surfaces
if auto_normal_acceptable(
normalized_cross(vvectors[i1], vvectors[i2])):
best_dot = d
if c1 > c2:
best_u = vvectors[i1]
best_v = vvectors[i2]
else:
best_u = vvectors[i2]
best_v = vvectors[i1]
num_vanishing_lines = c1 + c2
if best_u is not None and best_v is not None:
best_n = normalized_cross(best_u, best_v)
method = 'A'
# give up for some classes of objects
if shape.name:
name = shape.name.name.lower()
if ((abs(best_n[1]) > 0.5
and name in ('wall', 'door', 'window'))
or (abs(best_n[1]) < 0.5
and name in ('floor', 'ceiling', 'table',
'worktop/countertop', 'carpet/rug'))):
method = best_u = best_v = best_n = None
num_vanishing_lines = 0
# for walls that touch the edge of the photo, try using bbox center as a
# vanishing point (i.e. assume top/bottom shapes are horizontal, side
# shapes are vertical)
if (try_fully_automatic and best_n is None and shape.name and
(shape.substance is None or shape.substance.name != 'Painted')):
if shape.name.name.lower() == 'wall':
bbox = bbox_vertices(parse_vertices(shape.vertices))
if ((bbox[0] < 0.05 and bbox[2] < 0.50)
or (bbox[0] > 0.50 and bbox[2] > 0.95)):
bbox_n = photo.vanishing_point_to_vector(
(0.5 + 10 * (bbox[0] + bbox[2] - 1), 0.5))
# find normal that best matches this fake bbox normal
best_n_dot = 0.9
best_n = None
for n in vvectors:
if auto_normal_acceptable(n):
d = abs_dot(bbox_n, n)
if d > best_n_dot:
best_n_dot = d
best_n = n
method = 'O'
# find best u vector if not already found
if best_n is not None and best_u is None:
# first check if any in-shape vanishing points
# are perpendicular to the normal
best_u = most_orthogonal_vector(best_n,
[vvectors[i] for __, i in counts],
tolerance_dot=0.05)
# else, find the best u from all vectors
if best_u is None:
best_u = most_orthogonal_vector(best_n, vvectors)
# failure
if best_u is None or best_n is None:
return (None, None, 0)
# ortho-normalize system
uvn = construct_uvn_frame(best_n, best_u, b, flip_to_match_image=True)
# form uvnb matrix, column major
uvnb = (uvn[0, 0], uvn[1, 0], uvn[2, 0], 0, uvn[0, 1], uvn[1, 1],
uvn[2, 1], 0, uvn[0, 2], uvn[1, 2], uvn[2,
2], 0, b[0], b[1], b[2], 1)
return uvnb, method, num_vanishing_lines
def estimate_uvnb_from_vanishing_points(shape, try_fully_automatic=False):
""" Return (uvnb, num_vanishing_points) """
print 'estimate_uvnb for shape_id: %s' % shape.id
# local import to avoid cyclic dependencies
from shapes.utils import parse_vertices, parse_triangles, \
parse_segments, bbox_vertices
# load photo
photo = shape.photo
if not photo.vanishing_lines or not photo.vanishing_points:
raise ValueError("Vanishing points not computed")
if not photo.focal_y:
raise ValueError("Photo does not have focal_y")
vlines = json.loads(photo.vanishing_lines)
vpoints = json.loads(photo.vanishing_points)
vvectors = copy.copy(photo.vanishing_vectors())
if len(vlines) != len(vpoints):
raise ValueError("Invalid vanishing points data structure")
# add any missing vanishing points
vvectors = complete_vector_triplets(vvectors, tolerance_dot=0.75)
# find vanishing lines inside shape
vertices = parse_vertices(shape.vertices)
segments = parse_segments(shape.triangles)
triangles = parse_triangles(shape.triangles)
# intersect shapes with segments
counts = []
for idx in xrange(len(vlines)):
# re-pack for geom routines
query_segments = [((l[0], l[1]), (l[2], l[3])) for l in vlines[idx]]
from common.geom import triangles_segments_intersections_only
n = len(triangles_segments_intersections_only(
vertices, segments, triangles, query_segments))
if n >= 5:
counts.append((n, idx))
counts.sort(key=lambda x: x[0], reverse=True)
# function to judge normals: its vanishing line can't intersect the shape.
def auto_normal_acceptable(n):
sign = None
for (x, y) in vertices:
# vanishing line
line = (n[0], n[1], n[2] * photo.focal_y)
# signed distance
d = (
(x - 0.5) * photo.aspect_ratio * line[0] +
(0.5 - y) * line[1] + # flip y
line[2]
)
if abs(d) < 0.05:
return False
elif sign is None:
sign = (d > 0)
else:
if sign != (d > 0):
return False
return True
# find coordinate frame
best_n = None
best_u = None
method = None
num_vanishing_lines = 0
# make sure shape has label
if not shape.label_pos_x or not shape.label_pos_y:
from shapes.utils import update_shape_label_pos
update_shape_label_pos(shape)
# place label in 3D
b_z = -(photo.focal_y / photo.aspect_ratio) / 0.1
b = [
(shape.label_pos_x - 0.5) * photo.aspect_ratio * (-b_z) / photo.focal_y,
(0.5 - shape.label_pos_y) * (-b_z) / photo.focal_y,
b_z
]
# estimate closest human normal
human_labels = list(ShapeRectifiedNormalLabel.objects.filter(
shape=shape, automatic=False, correct_score__isnull=False,
).order_by('-correct_score'))
human_labels += list(ShapeRectifiedNormalLabel.objects.filter(
shape=shape, automatic=False, correct_score__isnull=True))
if human_labels:
for label in human_labels:
human_u = label.u()
human_n = label.n()
b = list(label.uvnb_numpy()[0:3, 3].flat)
# find best normal
best_n_dot = 0.9
best_n = None
for n in vvectors:
d = abs_dot(human_n, n)
if d > best_n_dot:
best_n_dot = d
best_n = n
method = 'S'
# if there is a match find u and quit
if best_n is not None:
# find best u
best_u_dot = 0
best_u = None
for u in vvectors:
if abs_dot(u, best_n) < 0.1:
d = abs_dot(human_u, u)
if d > best_u_dot:
best_u_dot = d
best_u = u
break
# try using object label
if best_n is None and shape.name:
if shape.name.name.lower() in ('floor', 'carpet/rug', 'ceiling'):
best_y = 0.9
for v in vvectors[0:3]:
if abs(v[1]) > best_y:
best_y = abs(v[1])
best_n = v
method = 'O'
# try fully automatic method if human normals are not good enough
if (try_fully_automatic and best_n is None and len(vpoints) >= 3 and len(counts) >= 2 and
(shape.substance is None or shape.substance.name != 'Painted')):
# choose two dominant vanishing points
best_u = vvectors[counts[0][1]]
best_v = vvectors[counts[1][1]]
# don't try and auto-rectify frontal surfaces
if auto_normal_acceptable(normalized_cross(best_u, best_v)):
num_vanishing_lines = counts[0][0] + counts[1][0]
uv_dot = abs_dot(best_u, best_v)
print 'u dot v = %s' % uv_dot
else:
best_u, best_v = None, None
uv_dot = None
# make sure these vectors are accurate
if not uv_dot or uv_dot > 0.05:
# try and find two other orthogonal vanishing points
best_dot = 0.05
best_u = None
best_v = None
for c1, i1 in counts:
for c2, i2 in counts[:i1]:
d = abs_dot(vvectors[i1], vvectors[i2])
if d < best_dot:
# don't try and auto-rectify frontal surfaces
if auto_normal_acceptable(normalized_cross(
vvectors[i1], vvectors[i2])):
best_dot = d
if c1 > c2:
best_u = vvectors[i1]
best_v = vvectors[i2]
else:
best_u = vvectors[i2]
best_v = vvectors[i1]
num_vanishing_lines = c1 + c2
if best_u is not None and best_v is not None:
best_n = normalized_cross(best_u, best_v)
method = 'A'
# give up for some classes of objects
if shape.name:
name = shape.name.name.lower()
if ((abs(best_n[1]) > 0.5 and name in (
'wall', 'door', 'window')) or
(abs(best_n[1]) < 0.5 and name in (
'floor', 'ceiling', 'table',
'worktop/countertop', 'carpet/rug'))):
method = best_u = best_v = best_n = None
num_vanishing_lines = 0
# for walls that touch the edge of the photo, try using bbox center as a
# vanishing point (i.e. assume top/bottom shapes are horizontal, side
# shapes are vertical)
if (try_fully_automatic and best_n is None and shape.name and
(shape.substance is None or shape.substance.name != 'Painted')):
if shape.name.name.lower() == 'wall':
bbox = bbox_vertices(parse_vertices(shape.vertices))
if ((bbox[0] < 0.05 and bbox[2] < 0.50) or
(bbox[0] > 0.50 and bbox[2] > 0.95)):
bbox_n = photo.vanishing_point_to_vector((
0.5 + 10 * (bbox[0] + bbox[2] - 1), 0.5
))
# find normal that best matches this fake bbox normal
best_n_dot = 0.9
best_n = None
for n in vvectors:
if auto_normal_acceptable(n):
d = abs_dot(bbox_n, n)
if d > best_n_dot:
best_n_dot = d
best_n = n
method = 'O'
# find best u vector if not already found
if best_n is not None and best_u is None:
# first check if any in-shape vanishing points
# are perpendicular to the normal
best_u = most_orthogonal_vector(
best_n, [vvectors[i] for __, i in counts],
tolerance_dot=0.05)
# else, find the best u from all vectors
if best_u is None:
best_u = most_orthogonal_vector(best_n, vvectors)
# failure
if best_u is None or best_n is None:
return (None, None, 0)
# ortho-normalize system
uvn = construct_uvn_frame(
best_n, best_u, b, flip_to_match_image=True)
# form uvnb matrix, column major
uvnb = (
uvn[0, 0], uvn[1, 0], uvn[2, 0], 0,
uvn[0, 1], uvn[1, 1], uvn[2, 1], 0,
uvn[0, 2], uvn[1, 2], uvn[2, 2], 0,
b[0], b[1], b[2], 1
)
return uvnb, method, num_vanishing_lines
def rectify_shape_from_uvnb(shape, rectified_normal, max_dim=None):
"""
Returns the rectified PIL image
shape: MaterialShape instance
rectified_normal: ShapeRectifiedNormalLabel instance
pq: original pixel coordinates with y down
xy: centered pixel coordinates with y up
uv: in-plane coordinates (arbitrary) with y up
st: rescaled and shifted plane coordinates (fits inside [0,1]x[0,1] but
with correct aspect ratio) with y down
ij: scaled final pixel plane coordinates with y down
"""
# helper function that applies a homography
def transform(H, points):
proj = projection_function(H)
return [proj(p) for p in points]
# grab original photo info
w = shape.photo.image_orig.width
h = shape.photo.image_orig.height
focal_pixels = 0.5 * max(w, h) / math.tan(
math.radians(0.5 * shape.photo.fov))
# uvnb: [u v n b] matrix arranged in column-major order
uvnb = [float(f) for f in json.loads(rectified_normal.uvnb)]
# mapping from plane coords to image plane
M_uv_to_xy = np.matrix([
[focal_pixels, 0, 0], [0, focal_pixels, 0], [0, 0, -1]
]) * np.matrix([[uvnb[0], uvnb[4], uvnb[12]], [uvnb[1], uvnb[5], uvnb[13]],
[uvnb[2], uvnb[6], uvnb[14]]])
M_xy_to_uv = linalg.inv(M_uv_to_xy)
M_pq_to_xy = np.matrix([
[1, 0, -0.5 * w],
[0, -1, 0.5 * h],
[0, 0, 1],
])
verts_pq = [(v[0] * w, v[1] * h) for v in parse_vertices(shape.vertices)]
#print 'verts_pq:', verts_pq
# estimate rough resolution from original bbox
if not max_dim:
min_p, min_q, max_p, max_q = bbox_vertices(verts_pq)
max_dim = max(max_p - min_p, max_q - min_q)
#print 'max_dim:', max_dim
# transform
verts_xy = transform(M_pq_to_xy, verts_pq)
#print 'verts_xy:', verts_pq
verts_uv = transform(M_xy_to_uv, verts_xy)
#print 'verts_uv:', verts_uv
# compute bbox in uv plane
min_u, min_v, max_u, max_v = bbox_vertices(verts_uv)
max_uv_range = float(max(max_u - min_u, max_v - min_v))
#print 'max_uv_range:', max_uv_range
# scale so that st fits inside [0, 1] x [0, 1]
# (but with the correct aspect ratio)
M_uv_to_st = np.matrix([[1, 0, -min_u], [0, -1, max_v],
[0, 0, max_uv_range]])
verts_st = transform(M_uv_to_st, verts_uv)
#print 'verts_st:', verts_st
M_st_to_ij = np.matrix([[max_dim, 0, 0], [0, max_dim, 0], [0, 0, 1]])
verts_ij = transform(M_st_to_ij, verts_st)
#print 'verts_ij:', verts_ij
# find final bbox
min_i, min_j, max_i, max_j = bbox_vertices(verts_ij)
size = (int(math.ceil(max_i)), int(math.ceil(max_j)))
#print 'i: %s to %s, j: %s to %s' % (min_i, max_i, min_j, max_j)
#print 'size:', size
# homography for final pixels to original pixels (ij --> pq)
M_pq_to_ij = M_st_to_ij * M_uv_to_st * M_xy_to_uv * M_pq_to_xy
M_ij_to_pq = linalg.inv(M_pq_to_ij)
M_ij_to_pq /= M_ij_to_pq[2, 2] # NORMALIZE!
data = M_ij_to_pq.ravel().tolist()[0]
image = open_image(shape.photo.image_orig)
rectified = image.transform(size=size,
method=Image.PERSPECTIVE,
data=data,
resample=Image.BICUBIC)
# crop to polygon
verts_ij_normalized = [(v[0] / size[0], v[1] / size[1]) for v in verts_ij]
image_crop, image_bbox = mask_complex_polygon(rectified,
verts_ij_normalized,
shape.triangles)
return image_crop
def rectify_shape_from_uvnb(shape, rectified_normal, max_dim=None):
"""
Returns the rectified PIL image
shape: MaterialShape instance
rectified_normal: ShapeRectifiedNormalLabel instance
pq: original pixel coordinates with y down
xy: centered pixel coordinates with y up
uv: in-plane coordinates (arbitrary) with y up
st: rescaled and shifted plane coordinates (fits inside [0,1]x[0,1] but
with correct aspect ratio) with y down
ij: scaled final pixel plane coordinates with y down
"""
# helper function that applies a homography
def transform(H, points):
proj = projection_function(H)
return [proj(p) for p in points]
# grab original photo info
w = shape.photo.image_orig.width
h = shape.photo.image_orig.height
focal_pixels = 0.5 * max(w, h) / math.tan(math.radians(
0.5 * shape.photo.fov))
# uvnb: [u v n b] matrix arranged in column-major order
uvnb = [float(f) for f in json.loads(rectified_normal.uvnb)]
# mapping from plane coords to image plane
M_uv_to_xy = np.matrix([
[focal_pixels, 0, 0],
[0, focal_pixels, 0],
[0, 0, -1]
]) * np.matrix([
[uvnb[0], uvnb[4], uvnb[12]],
[uvnb[1], uvnb[5], uvnb[13]],
[uvnb[2], uvnb[6], uvnb[14]]
])
M_xy_to_uv = linalg.inv(M_uv_to_xy)
M_pq_to_xy = np.matrix([
[1, 0, -0.5 * w],
[0, -1, 0.5 * h],
[0, 0, 1],
])
verts_pq = [(v[0] * w, v[1] * h) for v in parse_vertices(shape.vertices)]
#print 'verts_pq:', verts_pq
# estimate rough resolution from original bbox
if not max_dim:
min_p, min_q, max_p, max_q = bbox_vertices(verts_pq)
max_dim = max(max_p - min_p, max_q - min_q)
#print 'max_dim:', max_dim
# transform
verts_xy = transform(M_pq_to_xy, verts_pq)
#print 'verts_xy:', verts_pq
verts_uv = transform(M_xy_to_uv, verts_xy)
#print 'verts_uv:', verts_uv
# compute bbox in uv plane
min_u, min_v, max_u, max_v = bbox_vertices(verts_uv)
max_uv_range = float(max(max_u - min_u, max_v - min_v))
#print 'max_uv_range:', max_uv_range
# scale so that st fits inside [0, 1] x [0, 1]
# (but with the correct aspect ratio)
M_uv_to_st = np.matrix([
[1, 0, -min_u],
[0, -1, max_v],
[0, 0, max_uv_range]
])
verts_st = transform(M_uv_to_st, verts_uv)
#print 'verts_st:', verts_st
M_st_to_ij = np.matrix([
[max_dim, 0, 0],
[0, max_dim, 0],
[0, 0, 1]
])
verts_ij = transform(M_st_to_ij, verts_st)
#print 'verts_ij:', verts_ij
# find final bbox
min_i, min_j, max_i, max_j = bbox_vertices(verts_ij)
size = (int(math.ceil(max_i)), int(math.ceil(max_j)))
#print 'i: %s to %s, j: %s to %s' % (min_i, max_i, min_j, max_j)
#print 'size:', size
# homography for final pixels to original pixels (ij --> pq)
M_pq_to_ij = M_st_to_ij * M_uv_to_st * M_xy_to_uv * M_pq_to_xy
M_ij_to_pq = linalg.inv(M_pq_to_ij)
M_ij_to_pq /= M_ij_to_pq[2, 2] # NORMALIZE!
data = M_ij_to_pq.ravel().tolist()[0]
image = open_image(shape.photo.image_orig)
rectified = image.transform(size=size, method=Image.PERSPECTIVE,
data=data, resample=Image.BICUBIC)
# crop to polygon
verts_ij_normalized = [(v[0] / size[0], v[1] / size[1]) for v in verts_ij]
image_crop, image_bbox = mask_complex_polygon(
rectified, verts_ij_normalized, shape.triangles)
return image_crop
