def import_point_cloud(scene, cache, in_file, min_octree_depth=2):
batch.init_process("boxm2ImportPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, in_file)
batch.set_input_unsigned(3, min_octree_depth)
batch.run_process()
def batch_synoptic_function(self):
""" Create synoptic function (cubic function) at each voxel """
# write image identifiers to file
image_id_fname = self.model_dir + "/image_list.txt"
fd = open(image_id_fname, "w")
print >> fd, 2 * len(self.imgList)
for i, img in enumerate(self.imgList):
print >> fd, "img_%05d" % i
for i, img in enumerate(self.imgList):
print >> fd, "viewdir_%05d" % i
fd.close()
type_id_fname = self.model_dir + "/type_names_list.txt"
fd2 = open(type_id_fname, "w")
print >>fd2, 4
print >>fd2, "aux0"
print >>fd2, "aux1"
print >>fd2, "aux2"
print >>fd2, "aux3"
fd2.close()
# open the stream cache, this is a read-only cache
batch.init_process("boxm2OclBatchSynopticFunctionProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_unsigned(3, len(self.imgList))
batch.set_input_string(4, image_id_fname)
batch.set_input_float(5, 0.09)
batch.run_process()
def import_point_cloud(scene, cache, in_file, min_octree_depth=2):
batch.init_process("boxm2ImportPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, in_file)
batch.set_input_unsigned(3, min_octree_depth)
batch.run_process()
def batch_synoptic_function(self):
""" Create synoptic function (cubic function) at each voxel """
# write image identifiers to file
image_id_fname = self.model_dir + "/image_list.txt"
fd = open(image_id_fname, "w")
print >> fd, 2 * len(self.imgList)
for i, img in enumerate(self.imgList):
print >> fd, "img_%05d" % i
for i, img in enumerate(self.imgList):
print >> fd, "viewdir_%05d" % i
fd.close()
type_id_fname = self.model_dir + "/type_names_list.txt"
fd2 = open(type_id_fname, "w")
print >> fd2, 4
print >> fd2, "aux0"
print >> fd2, "aux1"
print >> fd2, "aux2"
print >> fd2, "aux3"
fd2.close()
# open the stream cache, this is a read-only cache
batch.init_process("boxm2OclBatchSynopticFunctionProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_unsigned(3, len(self.imgList))
batch.set_input_string(4, image_id_fname)
batch.set_input_float(5, 0.09)
batch.run_process()
def img_sum(img, plane_index=0):
boxm2_batch.init_process("vilImageSumProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_unsigned(1, plane_index)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
value = boxm2_batch.get_output_double(id)
return value
def img_sum(img, plane_index=0):
boxm2_batch.init_process("vilImageSumProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_unsigned(1,plane_index)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
value = boxm2_batch.get_output_double(id)
return value
def get_plane(img, plane_id):
boxm2_batch.init_process("vilGetPlaneProcess");
boxm2_batch.set_input_from_db(0, img);
boxm2_batch.set_input_unsigned(1, plane_id);
boxm2_batch.run_process();
(id, type) = boxm2_batch.commit_output(0);
img_plane = dbvalue(id, type);
return img_plane;
def get_plane(img, plane_id):
boxm2_batch.init_process("vilGetPlaneProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_unsigned(1, plane_id)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
img_plane = dbvalue(id, type)
return img_plane
def mask_image_using_id(img, id_img, input_id):
boxm2_batch.init_process("vilMaskImageUsingIDsProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_from_db(1, id_img)
boxm2_batch.set_input_unsigned(2, input_id)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
masked_img = dbvalue(id, type)
return masked_img
def mask_image_using_id(img, id_img, input_id):
boxm2_batch.init_process("vilMaskImageUsingIDsProcess");
boxm2_batch.set_input_from_db(0, img);
boxm2_batch.set_input_from_db(1, id_img);
boxm2_batch.set_input_unsigned(2, input_id);
boxm2_batch.run_process();
(id, type) = boxm2_batch.commit_output(0);
masked_img = dbvalue(id, type);
return masked_img;
def arf_seek_frame(rawStream, frame) :
boxm2_batch.init_process("bilArfSeekFrameProcess")
boxm2_batch.set_input_from_db(0,rawStream);
boxm2_batch.set_input_unsigned(1,frame);
boxm2_batch.run_process();
(id, type) = boxm2_batch.commit_output(0);
img = dbvalue(id,type);
(id, type) = boxm2_batch.commit_output(1);
time = boxm2_batch.get_output_unsigned(id);
return img, time
def arf_seek_frame(rawStream, frame) :
boxm2_batch.init_process("bilArfSeekFrameProcess")
boxm2_batch.set_input_from_db(0,rawStream);
boxm2_batch.set_input_unsigned(1,frame);
boxm2_batch.run_process();
(id, type) = boxm2_batch.commit_output(0);
img = dbvalue(id,type);
(id, type) = boxm2_batch.commit_output(1);
time = boxm2_batch.get_output_unsigned(id);
return img, time
def seek_frame(rawStream, frame):
boxm2_batch.init_process("bilSeekFrameProcess")
boxm2_batch.set_input_from_db(0, rawStream)
boxm2_batch.set_input_unsigned(1, frame)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
img = dbvalue(id, type)
#(id, type) = boxm2_batch.commit_output(1);
#time = boxm2_batch.get_output_unsigned(id);
return img
def apply_filters_cpp(scene, cpp_cache, prob_thres, filter_basename, filter_id, octree_lvl):
batch.init_process("boxm2CppFilterResponseProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cpp_cache)
batch.set_input_float(2, prob_thres)
batch.set_input_string(3, filter_basename)
# id kernel --> read the kernel from filter_basename + str(filter_id) +
# ".txt"
batch.set_input_unsigned(4, filter_id)
batch.set_input_unsigned(5, octree_lvl) # octree level to run kernel
batch.run_process()
def apply_filters_cpp(scene, cpp_cache, prob_thres, filter_basename, filter_id,
octree_lvl):
batch.init_process("boxm2CppFilterResponseProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cpp_cache)
batch.set_input_float(2, prob_thres)
batch.set_input_string(3, filter_basename)
# id kernel --> read the kernel from filter_basename + str(filter_id) +
# ".txt"
batch.set_input_unsigned(4, filter_id)
batch.set_input_unsigned(5, octree_lvl) # octree level to run kernel
batch.run_process()
def create_view_directions(self, cam, ni, nj, viewNum):
""" Create aux data for direction """
batch.init_process("boxm2OclAuxUpdateViewDirectionProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.cpu_cache)
batch.set_input_from_db(3, self.opencl_cache)
batch.set_input_from_db(4, cam)
batch.set_input_unsigned(5, ni)
batch.set_input_unsigned(6, nj)
batch.set_input_string(7, "viewdir_" + "%05d" % viewNum)
batch.set_input_string(8, "cartesian")
batch.run_process()
def batch_uncertainty(self, image_id_fname):
"""Calculate voxel uncertainty"""
# write image identifiers to file
# open the stream cache, this is a read-only cache
batch.init_process("boxm2OclBatchUncertaintyProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_unsigned(3, len(self.imgList))
batch.set_input_string(4, image_id_fname)
batch.run_process()
self.write_cache(True)
def create_view_directions(self, cam, ni, nj, viewNum):
""" Create aux data for direction """
batch.init_process("boxm2OclAuxUpdateViewDirectionProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.cpu_cache)
batch.set_input_from_db(3, self.opencl_cache)
batch.set_input_from_db(4, cam)
batch.set_input_unsigned(5, ni)
batch.set_input_unsigned(6, nj)
batch.set_input_string(7, "viewdir_" + "%05d" % viewNum)
batch.set_input_string(8, "cartesian")
batch.run_process()
def batch_uncertainty(self, image_id_fname):
"""Calculate voxel uncertainty"""
# write image identifiers to file
# open the stream cache, this is a read-only cache
batch.init_process("boxm2OclBatchUncertaintyProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_unsigned(3, len(self.imgList))
batch.set_input_string(4, image_id_fname)
batch.run_process()
self.write_cache(True)
def update_view_surface_density(device, scene, opencl_cache, cam, ni, nj, depth, std_depth, tnear=1000000, tfar=0.00001):
print("Updating View Surface Density")
batch.init_process("boxm2OclUpdateSurfaceDensityProcess")
batch.set_input_from_db(0, device)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, opencl_cache)
batch.set_input_from_db(3, cam)
batch.set_input_unsigned(4, ni)
batch.set_input_unsigned(5, nj)
batch.set_input_from_db(6, depth)
batch.set_input_from_db(7, std_depth)
batch.set_input_float(8, tnear)
batch.set_input_float(9, tfar)
return batch.run_process()
def update_max_vis(device, scene, opencl_cache, cam, ni, nj, mask_ptr, tnear=1000000, tfar=0.00001):
print("Updating Max Vis")
batch.init_process("boxm2OclUpdateMaxVisScoreProcess")
batch.set_input_from_db(0, device)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, opencl_cache)
batch.set_input_from_db(3, cam)
batch.set_input_unsigned(4, ni)
batch.set_input_unsigned(5, nj)
if(mask_ptr is not None):
batch.set_input_from_db(6, mask_ptr)
batch.set_input_float(7, tnear)
batch.set_input_float(8, tfar)
return batch.run_process()
def render_multi(scene, mcache, cam, ni=1280, nj=720, ident_string="", tnear=100000.0, tfar=100000.0):
batch.init_process("boxm2MultiRenderProcess")
batch.set_input_from_db(0, mcache)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, cam)
batch.set_input_unsigned(3, ni)
batch.set_input_unsigned(4, nj)
batch.set_input_string(5, ident_string)
batch.set_input_float(6, tnear)
batch.set_input_float(7, tfar)
batch.run_process()
(id, type) = batch.commit_output(0)
exp_image = dbvalue(id, type)
return exp_image
def gen_color_point_cloud(scene, cache, filename, thresh=0.3, ident="", depth=3):
batch.init_process("boxm2ExtractPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, thresh) # prob threshold
batch.set_input_unsigned(3, depth) # prob threshold
batch.run_process()
batch.init_process("boxm2ExportColorPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, filename)
batch.set_input_float(3, thresh)
batch.set_input_string(4, ident)
batch.run_process()
def gen_point_cloud(scene, cache, filename, thresh=0.3, vis_thresh=0.5, depth=3):
batch.init_process("boxm2ExtractPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, thresh) # prob threshold
batch.set_input_unsigned(3, depth) # prob threshold
batch.run_process()
batch.init_process("boxm2ExportOrientedPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, filename)
batch.set_input_float(4, vis_thresh)
batch.set_input_float(6, thresh)
batch.run_process()
def gen_error_point_cloud(scene, cache, filename, thresh=0.3, LE_thresh=-1.0, CE_thresh=-1.0, depth=3):
batch.init_process("boxm2ExtractPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, thresh) # prob threshold
batch.set_input_unsigned(3, depth) # prob threshold
batch.run_process()
batch.init_process("boxm2ExportErrorPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, filename) # ply filename
batch.set_input_float(3, thresh) # prob threshold
batch.set_input_float(4, LE_thresh) # LE threshold
batch.set_input_float(5, CE_thresh) # CE threshold
batch.run_process()
def render_expected_albedo_normal(self, camera, ni, nj):
batch.init_process("boxm2OclRenderExpectedAlbedoNormalProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_from_db(3, camera)
batch.set_input_unsigned(4, ni)
batch.set_input_unsigned(5, nj)
batch.run_process()
(id, type) = batch.commit_output(0)
exp_albedo = dbvalue(id, type)
(id, type) = batch.commit_output(1)
exp_normal = dbvalue(id, type)
(id, type) = batch.commit_output(2)
mask_image = dbvalue(id, type)
return(exp_albedo, exp_normal, mask_image)
def render_expected_albedo_normal(self, camera, ni, nj):
batch.init_process("boxm2OclRenderExpectedAlbedoNormalProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_from_db(3, camera)
batch.set_input_unsigned(4, ni)
batch.set_input_unsigned(5, nj)
batch.run_process()
(id, type) = batch.commit_output(0)
exp_albedo = dbvalue(id, type)
(id, type) = batch.commit_output(1)
exp_normal = dbvalue(id, type)
(id, type) = batch.commit_output(2)
mask_image = dbvalue(id, type)
return (exp_albedo, exp_normal, mask_image)
def render_expected_image_naa(self, camera, ni, nj, metadata,
atmospheric_params):
batch.init_process("boxm2OclRenderExpectedImageNAAProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_from_db(3, camera)
batch.set_input_unsigned(4, ni)
batch.set_input_unsigned(5, nj)
batch.set_input_from_db(6, metadata)
batch.set_input_from_db(7, atmospheric_params)
batch.run_process()
(id, type) = batch.commit_output(0)
exp_image = dbvalue(id, type)
(id, type) = batch.commit_output(1)
mask_image = dbvalue(id, type)
return (exp_image, mask_image)
def render_expected_image_naa(
self, camera, ni, nj, metadata, atmospheric_params):
batch.init_process("boxm2OclRenderExpectedImageNAAProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_from_db(3, camera)
batch.set_input_unsigned(4, ni)
batch.set_input_unsigned(5, nj)
batch.set_input_from_db(6, metadata)
batch.set_input_from_db(7, atmospheric_params)
batch.run_process()
(id, type) = batch.commit_output(0)
exp_image = dbvalue(id, type)
(id, type) = batch.commit_output(1)
mask_image = dbvalue(id, type)
return(exp_image, mask_image)
def render_uncertainty_map(self, ni, nj, ident="", cam_dir_1="", cam_dir_2=""):
""" Render Hemispherical Uncertainty Map """
# open the stream cache, this is a read-only cache
batch.init_process("boxm2OclRenderSceneUncertaintyMapProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_unsigned(3, ni)
batch.set_input_unsigned(4, nj)
batch.set_input_string(5, ident)
batch.set_input_string(6, cam_dir_1)
batch.set_input_string(7, cam_dir_2)
batch.run_process()
(id, type) = batch.commit_output(0)
exp_img = dbvalue(id, type)
(id, type) = batch.commit_output(1)
vis_img = dbvalue(id, type)
return exp_img, vis_img
def crop_image(img,i0,j0,ni,nj):
boxm2_batch.init_process("vilCropImageProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_unsigned(1,i0)
boxm2_batch.set_input_unsigned(2,j0)
boxm2_batch.set_input_unsigned(3,ni)
boxm2_batch.set_input_unsigned(4,nj)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
img_out = dbvalue(id,type)
return img_out
def crop_image(img, i0, j0, ni, nj):
boxm2_batch.init_process("vilCropImageProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_unsigned(1, i0)
boxm2_batch.set_input_unsigned(2, j0)
boxm2_batch.set_input_unsigned(3, ni)
boxm2_batch.set_input_unsigned(4, nj)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
img_out = dbvalue(id, type)
return img_out
def batch_synoptic_alpha_update(self):
""" Create synoptic function (cubic function) at each voxel """
image_id_fname = self.model_dir + "/image_list.txt"
fd = open(image_id_fname, "w")
print >> fd, len(self.imgList)
for i, img in enumerate(self.imgList):
print img
print >> fd, "img_%05d" % i
fd.close()
batch.init_process("boxm2OclSynopticUpdateAlphaProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_unsigned(3, len(self.imgList))
batch.set_input_string(4, image_id_fname)
batch.run_process()
self.write_cache()
def batch_synoptic_alpha_update(self):
""" Create synoptic function (cubic function) at each voxel """
image_id_fname = self.model_dir + "/image_list.txt"
fd = open(image_id_fname, "w")
print >> fd, len(self.imgList)
for i, img in enumerate(self.imgList):
print img
print >> fd, "img_%05d" % i
fd.close()
batch.init_process("boxm2OclSynopticUpdateAlphaProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_unsigned(3, len(self.imgList))
batch.set_input_string(4, image_id_fname)
batch.run_process()
self.write_cache()
def gen_color_point_cloud(scene,
cache,
filename,
thresh=0.3,
ident="",
depth=3):
batch.init_process("boxm2ExtractPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, thresh) # prob threshold
batch.set_input_unsigned(3, depth) # prob threshold
batch.run_process()
batch.init_process("boxm2ExportColorPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, filename)
batch.set_input_float(3, thresh)
batch.set_input_string(4, ident)
batch.run_process()
def gen_point_cloud(scene,
cache,
filename,
thresh=0.3,
vis_thresh=0.5,
depth=3):
batch.init_process("boxm2ExtractPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, thresh) # prob threshold
batch.set_input_unsigned(3, depth) # prob threshold
batch.run_process()
batch.init_process("boxm2ExportOrientedPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, filename)
batch.set_input_float(4, vis_thresh)
batch.set_input_float(6, thresh)
batch.run_process()
def render_multi(scene,
mcache,
cam,
ni=1280,
nj=720,
ident_string="",
tnear=100000.0,
tfar=100000.0):
batch.init_process("boxm2MultiRenderProcess")
batch.set_input_from_db(0, mcache)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, cam)
batch.set_input_unsigned(3, ni)
batch.set_input_unsigned(4, nj)
batch.set_input_string(5, ident_string)
batch.set_input_float(6, tnear)
batch.set_input_float(7, tfar)
batch.run_process()
(id, type) = batch.commit_output(0)
exp_image = dbvalue(id, type)
return exp_image
def gen_error_point_cloud(scene,
cache,
filename,
thresh=0.3,
LE_thresh=-1.0,
CE_thresh=-1.0,
depth=3):
batch.init_process("boxm2ExtractPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, thresh) # prob threshold
batch.set_input_unsigned(3, depth) # prob threshold
batch.run_process()
batch.init_process("boxm2ExportErrorPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, filename) # ply filename
batch.set_input_float(3, thresh) # prob threshold
batch.set_input_float(4, LE_thresh) # LE threshold
batch.set_input_float(5, CE_thresh) # CE threshold
batch.run_process()
def render_uncertainty_map(self,
ni,
nj,
ident="",
cam_dir_1="",
cam_dir_2=""):
""" Render Hemispherical Uncertainty Map """
# open the stream cache, this is a read-only cache
batch.init_process("boxm2OclRenderSceneUncertaintyMapProcess")
batch.set_input_from_db(0, self.device)
batch.set_input_from_db(1, self.scene)
batch.set_input_from_db(2, self.opencl_cache)
batch.set_input_unsigned(3, ni)
batch.set_input_unsigned(4, nj)
batch.set_input_string(5, ident)
batch.set_input_string(6, cam_dir_1)
batch.set_input_string(7, cam_dir_2)
batch.run_process()
(id, type) = batch.commit_output(0)
exp_img = dbvalue(id, type)
(id, type) = batch.commit_output(1)
vis_img = dbvalue(id, type)
return exp_img, vis_img
def get_info_along_ray(scene, cache, cam, u, v, prefix, identifier=""):
print("Ray Probe")
batch.init_process("boxm2CppRayProbeProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_from_db(2, cam)
batch.set_input_unsigned(3, u)
batch.set_input_unsigned(4, v)
batch.set_input_string(5, prefix)
batch.set_input_string(6, identifier)
batch.run_process()
(id, type) = batch.commit_output(0)
len_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(1)
alpha_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(2)
vis_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(3)
tabs_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(4)
res_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
if (prefix != ""):
(id, type) = batch.commit_output(5)
data_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(6)
nelems = batch.get_output_int(id)
batch.remove_data(id)
return len_array_1d, alpha_array_1d, vis_array_1d, tabs_array_1d, res_array_1d, data_array_1d, nelems
else:
return len_array_1d, alpha_array_1d, vis_array_1d, tabs_array_1d, res_array_1d
def get_info_along_ray(scene, cache, cam, u, v, prefix, identifier=""):
print("Ray Probe")
batch.init_process("boxm2CppRayProbeProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_from_db(2, cam)
batch.set_input_unsigned(3, u)
batch.set_input_unsigned(4, v)
batch.set_input_string(5, prefix)
batch.set_input_string(6, identifier)
batch.run_process()
(id, type) = batch.commit_output(0)
len_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(1)
alpha_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(2)
vis_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(3)
tabs_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(4)
res_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
if (prefix != ""):
(id, type) = batch.commit_output(5)
data_array_1d = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(6)
nelems = batch.get_output_int(id)
batch.remove_data(id)
return len_array_1d, alpha_array_1d, vis_array_1d, tabs_array_1d, res_array_1d, data_array_1d, nelems
else:
return len_array_1d, alpha_array_1d, vis_array_1d, tabs_array_1d, res_array_1d
def init_float_img(ni,nj,np,val):
boxm2_batch.init_process("vilInitFloatImageProcess")
boxm2_batch.set_input_unsigned(0,ni)
boxm2_batch.set_input_unsigned(1,nj)
boxm2_batch.set_input_unsigned(2,np)
boxm2_batch.set_input_float(3,val)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
img_out = dbvalue(id,type)
return img_out
def binary_edge_detection(img, max_size, min_size, threshold_id = 255):
boxm2_batch.init_process("vilBinaryEdgeDetectionProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_unsigned(1, max_size)
boxm2_batch.set_input_unsigned(2, min_size)
boxm2_batch.set_input_unsigned(3, threshold_id)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
edge_img = dbvalue(id,type)
return edge_img
def init_float_img(ni, nj, np, val):
boxm2_batch.init_process("vilInitFloatImageProcess")
boxm2_batch.set_input_unsigned(0, ni)
boxm2_batch.set_input_unsigned(1, nj)
boxm2_batch.set_input_unsigned(2, np)
boxm2_batch.set_input_float(3, val)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
img_out = dbvalue(id, type)
return img_out
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, openclcache)
boxm2_batch.set_input_from_db(3, cam)
boxm2_batch.set_input_from_db(4, img)
boxm2_batch.run_process()
print ("Refine")
boxm2_batch.init_process("boxm2OclRefineProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, openclcache)
boxm2_batch.set_input_float(3, 0.3)
boxm2_batch.run_process()
print ("Render")
boxm2_batch.init_process("boxm2OclRenderExpectedImageProcess")
boxm2_batch.set_input_from_db(0, device)
boxm2_batch.set_input_from_db(1, scene)
boxm2_batch.set_input_from_db(2, openclcache)
boxm2_batch.set_input_from_db(3, cam)
boxm2_batch.set_input_unsigned(4, 1280)
boxm2_batch.set_input_unsigned(5, 720)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
exp_img = dbvalue(id, type)
boxm2_batch.init_process("vilSaveImageViewProcess")
boxm2_batch.set_input_from_db(0, exp_img)
boxm2_batch.set_input_string(1, exp_fname)
boxm2_batch.run_process()
def writeSceneFromBox(data_path, resolution, min_pt, max_pt, ntrees_x=64,ntrees_y=64,ntrees_z=64, max_num_lvls=4, appearance_model1 = "boxm2_mog3_grey", appearance_model2 = "boxm2_num_obs", appearance_model3 = "boxm2_sum_log_msg_pos", p_init=0.001,max_data_size=1500.0):
"""A function that takes the minimum and maximum points of a bounding box for the scene in real world coordinates
and partitions the space into the appropriate number of boxes given a user specified number of trees.
min_pt : A python list specifying the 3d position of the minimum corner of the box.
This is interpreted by boxm2 as the 3d origin of the scene.
max-pt : A python list specifying the 3d position of the maximum corner of the box.
ntrees : Number of trees in all dimensions (the python interface only supports symmetric trees thus far)
max_num_lvls : Maximum number of levels in the trees (a tree will have pow(2,max_num_lvls-1) possible cells)
appearance_model : A string indicating desired appearance model
occupancy_model : A string indicating desired occupancy model
max_data_size : Maximum Sizer of a block in megabytes. Determined by GPU memory. Recomment 650MB for 1GB card and 1.1GB for 1.5GM card
maximum_data_size : Maximum memory allowable for a superblock. The max_data_size is determined by GPU memory size.
For a 1 GB card, a maximum data size of 650MB is recommended.
For a 1.5GB card, a 1.1G maximum data size of 1.1GB is recommended.
p_init : Initial occupancy probability."""
boxm2_batch.register_processes();
boxm2_batch.register_datatypes();
tree_size=resolution*pow(2,max_num_lvls-1);
block_size_x=ntrees_x*tree_size;
block_size_y=ntrees_y*tree_size;
block_size_z=ntrees_z*tree_size;
xsize=max_pt[0]-min_pt[0];
ysize=max_pt[1]-min_pt[1];
zsize=max_pt[2]-min_pt[2];
print "zsize: %f" % zsize
print "block size: %f" % block_size_z
nblocks_x=int(round(xsize/block_size_x));
nblocks_y=int(round(ysize/block_size_y));
nblocks_z=int(round(zsize/block_size_z));
print "nblocks_z : %f" % nblocks_z
if(nblocks_x<=0):nblocks_x=1;
if(nblocks_y<=0):nblocks_y=1;
if(nblocks_z<=0):nblocks_z=1;
print '\t Number of blocks in the x dimension: %d' % nblocks_x
print '\t Number of blocks in the y dimension: %d' % nblocks_y
print '\t Number of blocks in the z dimension: %d' % nblocks_z
print("\t CREATING THE BOXM2_SCENE_SPTR")
boxm2_batch.init_process("boxm2CreateSceneProcess");
boxm2_batch.set_input_string(0,data_path);
boxm2_batch.set_input_string(1,appearance_model1);
boxm2_batch.set_input_string(2,appearance_model2);
boxm2_batch.set_input_string(3,appearance_model3);
boxm2_batch.set_input_float(4,min_pt[0]);
boxm2_batch.set_input_float(5,min_pt[1]);
boxm2_batch.set_input_float(6,min_pt[2]);
boxm2_batch.run_process();
(id,type)=boxm2_batch.commit_output(0);
scene=dbvalue(id,type);
for k in range(nblocks_z):
for j in range(nblocks_y):
for i in range(nblocks_x):
block_origin_x=min_pt[0]+i*block_size_x;
block_origin_y=min_pt[1]+j*block_size_y;
block_origin_z=min_pt[2]+k*block_size_z;
print '\t \t Creating block with id (%d,%d,%d) at origin (%s,%s,%s)' % (i,j,k,block_origin_x, block_origin_y, block_origin_z)
boxm2_batch.init_process("boxm2AddBlockProcess");
boxm2_batch.set_input_from_db(0,scene);
boxm2_batch.set_input_int(1,i);
boxm2_batch.set_input_int(2,j);
boxm2_batch.set_input_int(3,k);
boxm2_batch.set_input_unsigned(4,ntrees_x);
boxm2_batch.set_input_unsigned(5,ntrees_y);
boxm2_batch.set_input_unsigned(6,ntrees_z);
boxm2_batch.set_input_unsigned(7,max_num_lvls);
boxm2_batch.set_input_float(8,block_origin_x);
boxm2_batch.set_input_float(9,block_origin_y);
boxm2_batch.set_input_float(10,block_origin_z);
boxm2_batch.set_input_float(11,tree_size);
boxm2_batch.set_input_float(12,max_data_size);
boxm2_batch.set_input_float(13,p_init);
boxm2_batch.set_input_unsigned(14,1);
boxm2_batch.run_process();
boxm2_batch.init_process("boxm2WriteSceneXMLProcess")
boxm2_batch.set_input_from_db(0,scene);
boxm2_batch.set_input_string(1,"scene");
boxm2_batch.run_process();
print("\t----------------------WE ARE REFINING!--------------------------");
boxm2_batch.init_process("boxm2OclRefineProcess");
boxm2_batch.set_input_from_db(0,device);
boxm2_batch.set_input_from_db(1,scene);
boxm2_batch.set_input_from_db(2,openclcache);
boxm2_batch.set_input_float(3, 0.3); #0.25 * (repeat+1) ); # originally set to .3
boxm2_batch.run_process();
print("Render");
boxm2_batch.init_process("boxm2OclRenderExpectedImageProcess");
boxm2_batch.set_input_from_db(0,device);
boxm2_batch.set_input_from_db(1,scene);
boxm2_batch.set_input_from_db(2,openclcache);
boxm2_batch.set_input_from_db(3,cam);
boxm2_batch.set_input_unsigned(4,NI);
boxm2_batch.set_input_unsigned(5,NJ);
boxm2_batch.run_process();
(id,type) = boxm2_batch.commit_output(0);
exp_img = dbvalue(id,type);
boxm2_batch.init_process("vilSaveImageViewProcess");
boxm2_batch.set_input_from_db(0,exp_img);
boxm2_batch.set_input_string(1,exp_fname);
boxm2_batch.run_process();
boxm2_batch.remove_data(exp_img.id)
boxm2_batch.remove_data(img.id)
boxm2_batch.remove_data(cam.id)
def boxm2WriteSceneXML(data_path, resolution, origin, nblocks_x, nblocks_y,nblocks_z,ntrees=64,max_num_lvls=4, appearance_model = "boxm2_mog3_grey", occupancy_model = "boxm2_num_obs", max_data_size=650.0,p_init=0.001):
"""This is boxm2WriteSceneXML.py
Function to create a boxm2 scene XML file
Author: Brandon Mayer
Date: 4/6/2011
data_path : A directory to which the xml file should be saved
origin : A python list specifying the 3d origin (x,y,z)
nblocks_x : Number of superblocks in the x dimension
nblocks_y : Number of superblocks in the y dimension
nblocks_z : Number of superblocks in the z dimension
ntrees : Number of trees in all dimensions (the python interface only supports symmetric trees thus far)
max_num_lvls : Maximum number of levels in the trees (a tree will have pow(2,max_num_lvls-1) possible cells)
appearance_model : A string indicating desired appearance model
occupancy_model : A string indicating desired occupancy model
maximum_data_size : Maximum memory allowable for a superblock. The max_data_size is determined by GPU memory size.
For a 1 GB card, a maximum data size of 650MB is recommended.
For a 1.5GB card, a 1.1G maximum data size of 1.1GB is recommended.
p_init : Initial occupancy probability."""
boxm2_batch.register_processes();
boxm2_batch.register_datatypes();
tree_size=resolution*pow(2,max_num_lvls-1);
block_size=ntrees*tree_size;
print("\t Creating the boxm2_scene_sptr")
boxm2_batch.init_process("boxm2CreateSceneProcess");
boxm2_batch.set_input_string(0,data_path);
boxm2_batch.set_input_string(1,appearance_model);
boxm2_batch.set_input_string(2,occupancy_model);
boxm2_batch.set_input_float(3,origin[0]);
boxm2_batch.set_input_float(4,origin[1]);
boxm2_batch.set_input_float(5,origin[2]);
boxm2_batch.run_process();
(id,type) = boxm2_batch.commit_output(0);
scene = dbvalue(id,type);
for i in range(nblocks_x):
for j in range(nblocks_y):
for k in range(nblocks_z):
block_origin_x=origin[0]+i*block_size;
block_origin_y=origin[1]+j*block_size;
block_origin_z=origin[2]+k*block_size;
print '\t Creating block with id (%d,%d,%d) at origin (%s,%s,%s)' % (i,j,k,block_origin_x, block_origin_y, block_origin_z)
boxm2_batch.init_process("boxm2AddBlockProcess");
boxm2_batch.set_input_from_db(0,scene);
boxm2_batch.set_input_unsigned(1,i);
boxm2_batch.set_input_unsigned(2,j);
boxm2_batch.set_input_unsigned(3,k);
boxm2_batch.set_input_unsigned(4,ntrees);
boxm2_batch.set_input_unsigned(5,ntrees);
boxm2_batch.set_input_unsigned(6,ntrees);
boxm2_batch.set_input_unsigned(7,max_num_lvls);
boxm2_batch.set_input_float(8,block_origin_x);
boxm2_batch.set_input_float(9,block_origin_y);
boxm2_batch.set_input_float(10,block_origin_z);
boxm2_batch.set_input_float(11,tree_size);
boxm2_batch.set_input_float(12,max_data_size);
boxm2_batch.set_input_float(13,p_init);
boxm2_batch.run_process();
boxm2_batch.init_process("boxm2WriteSceneXMLProcess")
boxm2_batch.set_input_from_db(0,scene)
boxm2_batch.run_process();
def writeSceneFromBox(data_path,
resolution,
min_pt,
max_pt,
ntrees_x=64,
ntrees_y=64,
ntrees_z=64,
max_num_lvls=4,
appearance_model1="boxm2_mog3_grey",
appearance_model2="boxm2_num_obs",
appearance_model3="boxm2_sum_log_msg_pos",
p_init=0.001,
max_data_size=1500.0):
"""A function that takes the minimum and maximum points of a bounding box for the scene in real world coordinates
and partitions the space into the appropriate number of boxes given a user specified number of trees.
min_pt : A python list specifying the 3d position of the minimum corner of the box.
This is interpreted by boxm2 as the 3d origin of the scene.
max-pt : A python list specifying the 3d position of the maximum corner of the box.
ntrees : Number of trees in all dimensions (the python interface only supports symmetric trees thus far)
max_num_lvls : Maximum number of levels in the trees (a tree will have pow(2,max_num_lvls-1) possible cells)
appearance_model : A string indicating desired appearance model
occupancy_model : A string indicating desired occupancy model
max_data_size : Maximum Sizer of a block in megabytes. Determined by GPU memory. Recomment 650MB for 1GB card and 1.1GB for 1.5GM card
maximum_data_size : Maximum memory allowable for a superblock. The max_data_size is determined by GPU memory size.
For a 1 GB card, a maximum data size of 650MB is recommended.
For a 1.5GB card, a 1.1G maximum data size of 1.1GB is recommended.
p_init : Initial occupancy probability."""
boxm2_batch.register_processes()
boxm2_batch.register_datatypes()
tree_size = resolution * pow(2, max_num_lvls - 1)
block_size_x = ntrees_x * tree_size
block_size_y = ntrees_y * tree_size
block_size_z = ntrees_z * tree_size
xsize = max_pt[0] - min_pt[0]
ysize = max_pt[1] - min_pt[1]
zsize = max_pt[2] - min_pt[2]
print "zsize: %f" % zsize
print "block size: %f" % block_size_z
nblocks_x = int(round(xsize / block_size_x))
nblocks_y = int(round(ysize / block_size_y))
nblocks_z = int(round(zsize / block_size_z))
print "nblocks_z : %f" % nblocks_z
if (nblocks_x <= 0): nblocks_x = 1
if (nblocks_y <= 0): nblocks_y = 1
if (nblocks_z <= 0): nblocks_z = 1
print '\t Number of blocks in the x dimension: %d' % nblocks_x
print '\t Number of blocks in the y dimension: %d' % nblocks_y
print '\t Number of blocks in the z dimension: %d' % nblocks_z
print("\t CREATING THE BOXM2_SCENE_SPTR")
boxm2_batch.init_process("boxm2CreateSceneProcess")
boxm2_batch.set_input_string(0, data_path)
boxm2_batch.set_input_string(1, appearance_model1)
boxm2_batch.set_input_string(2, appearance_model2)
boxm2_batch.set_input_string(3, appearance_model3)
boxm2_batch.set_input_float(4, min_pt[0])
boxm2_batch.set_input_float(5, min_pt[1])
boxm2_batch.set_input_float(6, min_pt[2])
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
scene = dbvalue(id, type)
for k in range(nblocks_z):
for j in range(nblocks_y):
for i in range(nblocks_x):
block_origin_x = min_pt[0] + i * block_size_x
block_origin_y = min_pt[1] + j * block_size_y
block_origin_z = min_pt[2] + k * block_size_z
print '\t \t Creating block with id (%d,%d,%d) at origin (%s,%s,%s)' % (
i, j, k, block_origin_x, block_origin_y, block_origin_z)
boxm2_batch.init_process("boxm2AddBlockProcess")
boxm2_batch.set_input_from_db(0, scene)
boxm2_batch.set_input_int(1, i)
boxm2_batch.set_input_int(2, j)
boxm2_batch.set_input_int(3, k)
boxm2_batch.set_input_unsigned(4, ntrees_x)
boxm2_batch.set_input_unsigned(5, ntrees_y)
boxm2_batch.set_input_unsigned(6, ntrees_z)
boxm2_batch.set_input_unsigned(7, max_num_lvls)
boxm2_batch.set_input_float(8, block_origin_x)
boxm2_batch.set_input_float(9, block_origin_y)
boxm2_batch.set_input_float(10, block_origin_z)
boxm2_batch.set_input_float(11, tree_size)
boxm2_batch.set_input_float(12, max_data_size)
boxm2_batch.set_input_float(13, p_init)
boxm2_batch.set_input_unsigned(14, 1)
boxm2_batch.run_process()
boxm2_batch.init_process("boxm2WriteSceneXMLProcess")
boxm2_batch.set_input_from_db(0, scene)
boxm2_batch.set_input_string(1, "scene")
boxm2_batch.run_process()
