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 scale_and_offset_values(img, scale, offset):
boxm2_batch.init_process("vilScaleAndOffsetValuesProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, scale)
boxm2_batch.set_input_float(2, offset)
boxm2_batch.run_process()
return
def create_stream_cache(self, imgs, interval=1, types="", max_gb=6.0):
# write image identifiers to file
# imgRange = range(0, len(imgs), interval);
# num_imgs = len(imgRange);
image_id_fname = self.model_dir + "/image_list.txt"
fd = open(image_id_fname, "w")
print >> fd, len(imgs)
# for i in imgRange:
# print >>fd, "img_%05d"%i
for img in imgs:
fname, fextension = os.path.splitext(img)
bname = os.path.basename(fname)
print >> fd, bname
fd.close()
# write type identifiers into file
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("boxm2CreateStreamCacheProcess")
batch.set_input_from_db(0, self.scene)
batch.set_input_string(1, type_id_fname)
batch.set_input_string(2, image_id_fname)
batch.set_input_float(3, max_gb)
batch.run_process()
(cache_id, cache_type) = batch.commit_output(0)
self.str_cache = dbvalue(cache_id, cache_type)
def create_stream_cache(self, imgs, interval=1, types="", max_gb=6.0):
# write image identifiers to file
# imgRange = range(0, len(imgs), interval);
# num_imgs = len(imgRange);
image_id_fname = self.model_dir + "/image_list.txt"
fd = open(image_id_fname, "w")
print >> fd, len(imgs)
# for i in imgRange:
# print >>fd, "img_%05d"%i
for img in imgs:
fname, fextension = os.path.splitext(img)
bname = os.path.basename(fname)
print >> fd, bname
fd.close()
# write type identifiers into file
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("boxm2CreateStreamCacheProcess")
batch.set_input_from_db(0, self.scene)
batch.set_input_string(1, type_id_fname)
batch.set_input_string(2, image_id_fname)
batch.set_input_float(3, max_gb)
batch.run_process()
(cache_id, cache_type) = batch.commit_output(0)
self.str_cache = dbvalue(cache_id, cache_type)
def import_triangle_mesh(scene, cache, ply_filename, occupied_prob=0.99):
batch.init_process("boxm2ImportTriangleMeshProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, ply_filename)
batch.set_input_float(3, occupied_prob)
batch.run_process()
def scale_and_offset_values(img,scale,offset):
boxm2_batch.init_process("vilScaleAndOffsetValuesProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_float(1,scale)
boxm2_batch.set_input_float(2,offset)
boxm2_batch.run_process()
return
def import_triangle_mesh(scene, cache, ply_filename, occupied_prob=0.99):
batch.init_process("boxm2ImportTriangleMeshProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, ply_filename)
batch.set_input_float(3, occupied_prob)
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 detect_shadow_rgb(img, threshold):
boxm2_batch.init_process("vilShadowDetectionProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, threshold)
boxm2_batch.run_process()
(o_id, o_type) = boxm2_batch.commit_output(0)
region_img = dbvalue(o_id, o_type)
return region_img
def max_threshold_image(img, threshold):
boxm2_batch.init_process("vilThresholdMaxImageProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, threshold)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
mask = dbvalue(id, type)
return mask
def max_threshold_image(img, threshold):
boxm2_batch.init_process("vilThresholdMaxImageProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_float(1,threshold)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
mask = dbvalue(id,type)
return mask
def detect_shadow_rgb(img,threshold) :
boxm2_batch.init_process("vilShadowDetectionProcess");
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_float(1, threshold);
boxm2_batch.run_process();
(o_id,o_type) = boxm2_batch.commit_output(0);
region_img = dbvalue(o_id,o_type);
return region_img;
def threshold_image(img, value, threshold_above=True):
boxm2_batch.init_process("vilThresholdImageProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, value)
boxm2_batch.set_input_bool(2, threshold_above)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
mask = dbvalue(id, type)
return mask
def truncate_image(img, min_value, max_value):
boxm2_batch.init_process("vilTruncateImageProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, min_value)
boxm2_batch.set_input_float(2, max_value)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
img_out = dbvalue(id, type)
return img_out
def truncate_image(img,min_value,max_value):
boxm2_batch.init_process("vilTruncateImageProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_float(1,min_value)
boxm2_batch.set_input_float(2,max_value)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
img_out = dbvalue(id,type)
return img_out
def threshold_image(img, value, threshold_above=True):
boxm2_batch.init_process("vilThresholdImageProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_float(1,value)
boxm2_batch.set_input_bool(2,threshold_above)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
mask = dbvalue(id,type)
return mask
def threshold_image_inside(img, min_thres, max_thres, threshold_inside=True):
boxm2_batch.init_process("vilThresholdImageInsideProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, min_thres)
boxm2_batch.set_input_float(2, max_thres)
boxm2_batch.set_input_bool(3, threshold_inside)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
mask = dbvalue(id, type)
return mask
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 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 stretch_image(img, min_value, max_value, output_type_str='float'):
boxm2_batch.init_process("vilStretchImageProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_float(1,min_value)
boxm2_batch.set_input_float(2,max_value)
boxm2_batch.set_input_string(3,output_type_str)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
img_out = dbvalue(id,type)
return img_out
def extract_cell_centers(scene, cache, prob_thresh=0.0):
if cache.type == "boxm2_cache_sptr":
batch.init_process("boxm2ExtractPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, prob_thresh) # prob t
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def stretch_image(img, min_value, max_value, output_type_str='float'):
boxm2_batch.init_process("vilStretchImageProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_float(1, min_value)
boxm2_batch.set_input_float(2, max_value)
boxm2_batch.set_input_string(3, output_type_str)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
img_out = dbvalue(id, type)
return img_out
def extract_cell_centers(scene, cache, prob_thresh=0.0):
if cache.type == "boxm2_cache_sptr":
batch.init_process("boxm2ExtractPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, prob_thresh) # prob t
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
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 cpu_batch_paint(self, imgs, cams):
if (self.str_cache is None):
self.create_stream_cache(imgs)
# sigma norm table?
under_estimation_probability = 0.2
batch.init_process("bstaSigmaNormTableProcess")
batch.set_input_float(0, under_estimation_probability)
batch.run_process()
(id, type) = batch.commit_output(0)
n_table = dbvalue(id, type)
# loop over images creating aux data
for idx in range(0, len(imgs)):
# load cam/img
img, ni, nj = vil_adaptor.load_image(imgs[idx])
pcam = vpgl_adaptor.load_perspective_camera(cams[idx])
gcam = vpgl_adaptor.persp2gen(pcam, ni, nj)
# create norm intensity (num rays...)
batch.init_process("boxm2CppCreateNormIntensitiesProcess")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, gcam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, "img_" + "%05d" % idx)
batch.run_process()
# create aux
batch.init_process("boxm2CppCreateAuxDataOPT2Process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, gcam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, "img_" + "%05d" % idx)
batch.run_process()
self.write_cache(True)
batch.init_process("boxm2CppBatchUpdateOPT2Process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, self.str_cache)
batch.set_input_from_db(3, n_table)
batch.run_process()
# close the files so that they can be reloaded after the next iteration
batch.init_process("boxm2StreamCacheCloseFilesProcess")
batch.set_input_from_db(0, self.str_cache)
batch.run_process()
self.write_cache()
def cpu_batch_paint(self, imgs, cams):
if (self.str_cache is None):
self.create_stream_cache(imgs)
# sigma norm table?
under_estimation_probability = 0.2
batch.init_process("bstaSigmaNormTableProcess")
batch.set_input_float(0, under_estimation_probability)
batch.run_process()
(id, type) = batch.commit_output(0)
n_table = dbvalue(id, type)
# loop over images creating aux data
for idx in range(0, len(imgs)):
# load cam/img
img, ni, nj = vil_adaptor.load_image(imgs[idx])
pcam = vpgl_adaptor.load_perspective_camera(cams[idx])
gcam = vpgl_adaptor.persp2gen(pcam, ni, nj)
# create norm intensity (num rays...)
batch.init_process("boxm2CppCreateNormIntensitiesProcess")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, gcam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, "img_" + "%05d" % idx)
batch.run_process()
# create aux
batch.init_process("boxm2CppCreateAuxDataOPT2Process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, gcam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, "img_" + "%05d" % idx)
batch.run_process()
self.write_cache(True)
batch.init_process("boxm2CppBatchUpdateOPT2Process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, self.str_cache)
batch.set_input_from_db(3, n_table)
batch.run_process()
# close the files so that they can be reloaded after the next iteration
batch.init_process("boxm2StreamCacheCloseFilesProcess")
batch.set_input_from_db(0, self.str_cache)
batch.run_process()
self.write_cache()
def detect_shadow_ridge(region_img, blob_size_t, sun_angle):
boxm2_batch.init_process("vilShadowRidgeDetectionProcess")
boxm2_batch.set_input_from_db(0, region_img)
boxm2_batch.set_input_int(1, blob_size_t)
boxm2_batch.set_input_float(2, sun_angle)
boxm2_batch.run_process()
(o_id, o_type) = boxm2_batch.commit_output(0)
region_img = dbvalue(o_id, o_type)
(o_id, o_type) = boxm2_batch.commit_output(1)
out_img = dbvalue(o_id, o_type)
(o_id, o_type) = boxm2_batch.commit_output(2)
dist_img = dbvalue(o_id, o_type)
return region_img, out_img, dist_img
def detect_shadow_ridge(region_img,blob_size_t, sun_angle) :
boxm2_batch.init_process("vilShadowRidgeDetectionProcess");
boxm2_batch.set_input_from_db(0,region_img)
boxm2_batch.set_input_int(1, blob_size_t);
boxm2_batch.set_input_float(2, sun_angle);
boxm2_batch.run_process();
(o_id,o_type) = boxm2_batch.commit_output(0);
region_img = dbvalue(o_id,o_type);
(o_id,o_type) = boxm2_batch.commit_output(1);
out_img = dbvalue(o_id,o_type);
(o_id,o_type) = boxm2_batch.commit_output(2);
dist_img = dbvalue(o_id,o_type);
return region_img, out_img, dist_img;
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 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 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 refine_scene_around_geometry(self, filter_v, n_times, p_thresh,
use_gpu):
if self.opencl_cache.type == "boxm2_opencl_cache_sptr":
print("Refining around surface geometry")
batch.init_process(
"boxm2_ocl_refine_scene_around_geometry_process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.opencl_cache)
batch.set_input_from_db(2, self.device)
batch.set_input_from_db(3, filter_v)
batch.set_input_int(4, n_times)
# use negative value to refine all
batch.set_input_float(5, p_thresh)
batch.set_input_bool(6, use_gpu)
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", self.opencl_cache.type
return False
def refine_scene_around_geometry(
self, filter_v, n_times, p_thresh, use_gpu):
if self.opencl_cache.type == "boxm2_opencl_cache_sptr":
print("Refining around surface geometry")
batch.init_process(
"boxm2_ocl_refine_scene_around_geometry_process")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.opencl_cache)
batch.set_input_from_db(2, self.device)
batch.set_input_from_db(3, filter_v)
batch.set_input_int(4, n_times)
# use negative value to refine all
batch.set_input_float(5, p_thresh)
batch.set_input_bool(6, use_gpu)
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", self.opencl_cache.type
return False
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 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 export_points_and_normals(scene,
cache,
file_out,
save_aux=True,
prob_thresh=0.0,
vis_thresh=0.0,
nmag_thresh=0.0,
exp_thresh=0.0,
bbox_file=""):
if cache.type == "boxm2_cache_sptr":
print("Exporting to oriented point cloud")
batch.init_process("boxm2ExportOrientedPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, file_out)
# output aux (prob, vis, normal magnitdue)
batch.set_input_bool(3, save_aux)
batch.set_input_float(4, vis_thresh) # vis threshold
batch.set_input_float(5, nmag_thresh) # nmag threshold
batch.set_input_float(6, prob_thresh) # prob threshold
# batch.set_input_float(7,exp_thresh); #exp threshold
# batch.set_input_string(8, bbox_file); #bb filename
batch.set_input_string(7, bbox_file) # bb filename
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def query_cell_data(scene, cache, point):
batch.init_process("boxm2CppQueryCellDataProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, point[0])
batch.set_input_float(3, point[1])
batch.set_input_float(4, point[2])
batch.run_process()
def query_cell_data(scene, cache, point):
batch.init_process("boxm2CppQueryCellDataProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, point[0])
batch.set_input_float(3, point[1])
batch.set_input_float(4, point[2])
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 batch_paint(self, imgs, cams, device_string=""):
# verify stream cache
if (self.str_cache is None):
self.create_stream_cache(imgs)
# sigma norm table?
under_estimation_probability = 0.2
batch.init_process("bstaSigmaNormTableProcess")
batch.set_input_float(0, under_estimation_probability)
batch.run_process()
(id, type) = batch.commit_output(0)
n_table = dbvalue(id, type)
# call batch paint process
if device_string == "":
batch.init_process("boxm2OclPaintBatchProcess")
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, self.str_cache)
batch.set_input_from_db(4, n_table)
batch.run_process()
elif device_string == "cpu":
batch.init_process("boxm2CppBatchUpdateAppProcess")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, self.str_cache)
batch.set_input_from_db(3, n_table)
batch.run_process()
# close the files so that they can be reloaded after the next iteration
batch.init_process("boxm2StreamCacheCloseFilesProcess")
batch.set_input_from_db(0, self.str_cache)
batch.run_process()
# write out afterwards
self.write_cache()
def query_cell_brdf(scene, cache, point, model_type):
batch.init_process("boxm2CppQueryCellBrdfProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, point[0])
batch.set_input_float(3, point[1])
batch.set_input_float(4, point[2])
batch.set_input_string(5, model_type)
batch.run_process()
def query_cell_brdf(scene, cache, point, model_type):
batch.init_process("boxm2CppQueryCellBrdfProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, point[0])
batch.set_input_float(3, point[1])
batch.set_input_float(4, point[2])
batch.set_input_string(5, model_type)
batch.run_process()
def batch_paint(self, imgs, cams, device_string=""):
# verify stream cache
if (self.str_cache is None):
self.create_stream_cache(imgs)
# sigma norm table?
under_estimation_probability = 0.2
batch.init_process("bstaSigmaNormTableProcess")
batch.set_input_float(0, under_estimation_probability)
batch.run_process()
(id, type) = batch.commit_output(0)
n_table = dbvalue(id, type)
# call batch paint process
if device_string == "":
batch.init_process("boxm2OclPaintBatchProcess")
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, self.str_cache)
batch.set_input_from_db(4, n_table)
batch.run_process()
elif device_string == "cpu":
batch.init_process("boxm2CppBatchUpdateAppProcess")
batch.set_input_from_db(0, self.scene)
batch.set_input_from_db(1, self.cpu_cache)
batch.set_input_from_db(2, self.str_cache)
batch.set_input_from_db(3, n_table)
batch.run_process()
# close the files so that they can be reloaded after the next iteration
batch.init_process("boxm2StreamCacheCloseFilesProcess")
batch.set_input_from_db(0, self.str_cache)
batch.run_process()
# write out afterwards
self.write_cache()
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 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 update_multi(scene, mcache, cam, img, ident="", update_alpha=True, var=-1.0, update_app=True, tnear=100000.0, tfar=0.000001):
# If no device is passed in, do cpu update
print("boxm2_batch multi GPU update")
batch.init_process("boxm2MultiUpdateProcess")
batch.set_input_from_db(0, mcache)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, cam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, ident)
batch.set_input_bool(5, update_alpha)
batch.set_input_float(6, var)
batch.set_input_bool(7, update_app)
batch.set_input_float(8, tnear)
batch.set_input_float(9, tfar)
return batch.run_process()
def probe_intensities(scene, cpu_cache, str_cache, point):
batch.init_process("boxm2CppBatchProbeIntensitiesProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cpu_cache)
batch.set_input_from_db(2, str_cache)
batch.set_input_float(3, point[0])
batch.set_input_float(4, point[1])
batch.set_input_float(5, point[2])
batch.run_process()
(id, type) = batch.commit_output(0)
intensities = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(1)
visibilities = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
return intensities, visibilities
def probe_intensities(scene, cpu_cache, str_cache, point):
batch.init_process("boxm2CppBatchProbeIntensitiesProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cpu_cache)
batch.set_input_from_db(2, str_cache)
batch.set_input_float(3, point[0])
batch.set_input_float(4, point[1])
batch.set_input_float(5, point[2])
batch.run_process()
(id, type) = batch.commit_output(0)
intensities = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(1)
visibilities = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
return intensities, visibilities
def export_points_and_normals(scene, cache, file_out, save_aux=True, prob_thresh=0.0, vis_thresh=0.0, nmag_thresh=0.0, exp_thresh=0.0, bbox_file=""):
if cache.type == "boxm2_cache_sptr":
print("Exporting to oriented point cloud")
batch.init_process("boxm2ExportOrientedPointCloudProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, file_out)
# output aux (prob, vis, normal magnitdue)
batch.set_input_bool(3, save_aux)
batch.set_input_float(4, vis_thresh) # vis threshold
batch.set_input_float(5, nmag_thresh) # nmag threshold
batch.set_input_float(6, prob_thresh) # prob threshold
# batch.set_input_float(7,exp_thresh); #exp threshold
# batch.set_input_string(8, bbox_file); #bb filename
batch.set_input_string(7, bbox_file) # bb filename
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
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 get_index_from_3d_point(scene, cache, point):
batch.init_process("boxm2CppGetIndexFrom3dPointProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, point[0])
batch.set_input_float(3, point[1])
batch.set_input_float(4, point[2])
batch.run_process()
(id, type) = batch.commit_output(0)
blk_i = batch.get_output_int(id)
batch.remove_data(id)
(id, type) = batch.commit_output(1)
blk_j = batch.get_output_int(id)
batch.remove_data(id)
(id, type) = batch.commit_output(2)
blk_k = batch.get_output_int(id)
batch.remove_data(id)
(id, type) = batch.commit_output(3)
index = batch.get_output_int(id)
batch.remove_data(id)
return ((blk_i, blk_j, blk_k), index)
def get_index_from_3d_point(scene, cache, point):
batch.init_process("boxm2CppGetIndexFrom3dPointProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_float(2, point[0])
batch.set_input_float(3, point[1])
batch.set_input_float(4, point[2])
batch.run_process()
(id, type) = batch.commit_output(0)
blk_i = batch.get_output_int(id)
batch.remove_data(id)
(id, type) = batch.commit_output(1)
blk_j = batch.get_output_int(id)
batch.remove_data(id)
(id, type) = batch.commit_output(2)
blk_k = batch.get_output_int(id)
batch.remove_data(id)
(id, type) = batch.commit_output(3)
index = batch.get_output_int(id)
batch.remove_data(id)
return ((blk_i, blk_j, blk_k), index)
def update_multi(scene,
mcache,
cam,
img,
ident="",
update_alpha=True,
var=-1.0,
update_app=True,
tnear=100000.0,
tfar=0.000001):
# If no device is passed in, do cpu update
print("boxm2_batch multi GPU update")
batch.init_process("boxm2MultiUpdateProcess")
batch.set_input_from_db(0, mcache)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, cam)
batch.set_input_from_db(3, img)
batch.set_input_string(4, ident)
batch.set_input_bool(5, update_alpha)
batch.set_input_float(6, var)
batch.set_input_bool(7, update_app)
batch.set_input_float(8, tnear)
batch.set_input_float(9, tfar)
return 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();
def create_kernel_vector(factory_name, dir_type, dim_x, dim_y, dim_z, supp_x,
supp_y, supp_z):
print("Creating Filtering Kernels")
batch.init_process("bvpl_create_generic_kernel_vector_process")
batch.set_input_float(0, dim_x)
batch.set_input_float(1, dim_y)
batch.set_input_float(2, dim_z)
batch.set_input_float(3, supp_x)
batch.set_input_float(4, supp_y)
batch.set_input_float(5, supp_z)
batch.set_input_string(6, factory_name)
batch.set_input_string(7, dir_type)
batch.run_process()
(id, type) = batch.commit_output(0)
filters = dbvalue(id, type)
return filters
def transform(self, tx, ty, tz, rx, ry, rz, scale):
batch.init_process("boxm2TransformModelProcess")
batch.set_input_from_db(0, self.scene)
batch.set_input_float(1, tx)
batch.set_input_float(2, ty)
batch.set_input_float(3, tz)
batch.set_input_float(4, rx)
batch.set_input_float(5, ry)
batch.set_input_float(6, rz)
batch.set_input_float(7, scale)
batch.run_process()
return