def fill_in_bbox_task(shape):
""" Helper to fill in the potentially empty image_bbox field """
image_bbox = mask_complex_polygon(
image=open_image(shape.photo.image_orig),
vertices=shape.vertices,
triangles=shape.triangles,
bbox_only=True)
save_obj_attr_image(shape, attr='image_bbox',
img=image_bbox,
format='jpg', save=True)
def fill_in_bbox_task(shape):
""" Helper to fill in the potentially empty image_bbox field """
image_bbox = mask_complex_polygon(image=open_image(shape.photo.image_orig),
vertices=shape.vertices,
triangles=shape.triangles,
bbox_only=True)
save_obj_attr_image(shape,
attr='image_bbox',
img=image_bbox,
format='jpg',
save=True)
def handle(self, *args, **options):
photos = Photo.objects.filter(id__in=[95686, 97532, 116625, 85877, 69122, 104870])
for p in photos:
decomp = IntrinsicImagesDecomposition.objects.get(photo_id=p.id, algorithm_id=1141)
img_i = p.open_image(width='orig')
img_r = open_image(decomp.reflectance_image)
img_s = open_image(decomp.shading_image)
if not os.path.exists('example-intrinsic-segments/%s' % p.id):
os.makedirs('example-intrinsic-segments/%s' % p.id)
img_i.save('example-intrinsic-segments/%s/image.jpg' % p.id)
img_r.save('example-intrinsic-segments/%s/reflectance.png' % p.id)
img_s.save('example-intrinsic-segments/%s/shading.png' % p.id)
for s in progress_bar(p.material_shapes.all()):
mask_complex_polygon(img_i, s.vertices, s.triangles)[0].save('example-intrinsic-segments/%s/shape-%s-image.png' % (p.id, s.id))
mask_complex_polygon(img_r, s.vertices, s.triangles)[0].save('example-intrinsic-segments/%s/shape-%s-reflectance.png' % (p.id, s.id))
mask_complex_polygon(img_s, s.vertices, s.triangles)[0].save('example-intrinsic-segments/%s/shape-%s-shading.png' % (p.id, s.id))
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