def read_CLIF07(indir,outdir,camnum,datatype="CLIF06") :
boxm2_batch.init_process("bilReadCLIF07DataProcess")
boxm2_batch.set_input_string(0,indir);
boxm2_batch.set_input_string(1,outdir);
boxm2_batch.set_input_int(2,camnum);
boxm2_batch.set_input_string(3,datatype);
boxm2_batch.run_process();
def read_CLIF07(indir, outdir, camnum, datatype="CLIF06"):
boxm2_batch.init_process("bilReadCLIF07DataProcess")
boxm2_batch.set_input_string(0, indir)
boxm2_batch.set_input_string(1, outdir)
boxm2_batch.set_input_int(2, camnum)
boxm2_batch.set_input_string(3, datatype)
boxm2_batch.run_process()
def median_filter_image(img, neighborhood_radius):
boxm2_batch.init_process("vilMedianFilterProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_int(1, neighborhood_radius)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
filt_img = dbvalue(id, type)
return filt_img
def median_filter_image(img, neighborhood_radius):
boxm2_batch.init_process("vilMedianFilterProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_int(1,neighborhood_radius)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
filt_img = dbvalue(id,type)
return filt_img
def undistort_image(img, param_file, iters):
boxm2_batch.init_process("vilUndistortImageProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_string(1, param_file)
boxm2_batch.set_input_int(2, iters)
boxm2_batch.run_process()
(o_id, o_type) = boxm2_batch.commit_output(0)
out_img = dbvalue(o_id, o_type)
return out_img
def undistort_image(img, param_file, iters) :
boxm2_batch.init_process("vilUndistortImageProcess");
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_string(1, param_file);
boxm2_batch.set_input_int(2, iters);
boxm2_batch.run_process();
(o_id,o_type) = boxm2_batch.commit_output(0);
out_img = dbvalue(o_id,o_type);
return out_img;
def pixel(img, point):
boxm2_batch.init_process("vilPixelValueProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_int(1, int(point[0]))
boxm2_batch.set_input_int(2, int(point[1]))
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
val = boxm2_batch.get_output_float(id)
return val
def pixel(img, point):
boxm2_batch.init_process("vilPixelValueProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_int(1, int(point[0]))
boxm2_batch.set_input_int(2, int(point[1]))
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
val = boxm2_batch.get_output_float(id)
return val
def resize(img, ni, nj, pixel="float"):
boxm2_batch.init_process("vilResampleProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_int(1, ni)
boxm2_batch.set_input_int(2, nj)
boxm2_batch.set_input_string(3, pixel)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
img = dbvalue(id, type)
return img
def resize(img, ni, nj, pixel="float"):
boxm2_batch.init_process("vilResampleProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_int(1, ni)
boxm2_batch.set_input_int(2, nj)
boxm2_batch.set_input_string(3, pixel);
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
img = dbvalue(id,type)
return img
def update_PusingQ(scene, device, opencl_cache, operation=2, view_ident=""):
#print("Init Manager");
batch.init_process("boxm2OclUpdateUsingQProcess")
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_int(3, operation)
batch.set_input_string(4, view_ident)
status = batch.run_process()
if(status != True):
print "Error in Running Aux Q "
def filter_scene_data(scene, cache, device, filters, filter_idx):
if cache.type == "boxm2_opencl_cache_sptr":
print("Filtering Scene Data")
batch.init_process("boxm2_ocl_filter_scene_data_process")
batch.set_input_from_db(0, device)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, cache)
batch.set_input_from_db(3, filters)
batch.set_input_int(4, filter_idx)
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
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 load_multi_cache(scene, numdevices=1):
###############################################################
# Create multi cache, opencl manager, device, and gpu cache
###############################################################
#print("Create Main Cache");
batch.init_process("boxm2CreateMultiCacheProcess")
batch.set_input_from_db(0, scene)
batch.set_input_int(1, numdevices)
batch.run_process()
(id, type) = batch.commit_output(0)
mcache = dbvalue(id, type)
return mcache
def load_multi_cache(scene, numdevices=1):
###############################################################
# Create multi cache, opencl manager, device, and gpu cache
###############################################################
#print("Create Main Cache");
batch.init_process("boxm2CreateMultiCacheProcess")
batch.set_input_from_db(0, scene)
batch.set_input_int(1, numdevices)
batch.run_process()
(id, type) = batch.commit_output(0)
mcache = dbvalue(id, type)
return mcache
def filter_scene_data(scene, cache, device, filters, filter_idx):
if cache.type == "boxm2_opencl_cache_sptr":
print("Filtering Scene Data")
batch.init_process("boxm2_ocl_filter_scene_data_process")
batch.set_input_from_db(0, device)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, cache)
batch.set_input_from_db(3, filters)
batch.set_input_int(4, filter_idx)
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def get_3d_point_from_index(scene, cache, block_index, index):
# Warning, you probably shouldn't be doing this!
batch.init_process("boxm2CppGet3dPointFromIndexProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_int(2, block_index[0])
batch.set_input_int(3, block_index[1])
batch.set_input_int(4, block_index[2])
batch.set_input_int(5, index)
batch.run_process()
(id, type) = batch.commit_output(0)
x = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(1)
y = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(2)
z = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(3)
xs = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(4)
ys = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(5)
zs = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(6)
leaf = batch.get_output_int(id)
batch.remove_data(id)
return ((x, y, z), (xs, ys, zs), leaf)
def get_3d_point_from_index(scene, cache, block_index, index):
# Warning, you probably shouldn't be doing this!
batch.init_process("boxm2CppGet3dPointFromIndexProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_int(2, block_index[0])
batch.set_input_int(3, block_index[1])
batch.set_input_int(4, block_index[2])
batch.set_input_int(5, index)
batch.run_process()
(id, type) = batch.commit_output(0)
x = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(1)
y = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(2)
z = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(3)
xs = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(4)
ys = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(5)
zs = batch.get_output_float(id)
batch.remove_data(id)
(id, type) = batch.commit_output(6)
leaf = batch.get_output_int(id)
batch.remove_data(id)
return ((x, y, z), (xs, ys, zs), leaf)
def createScene(self, stepNum):
bundleFile = os.path.abspath(self.config.NVMFileName)
imgDir = os.path.abspath(self.config.SRCImageDirName)
outDir = os.path.abspath(self.config.NVMOutputDirName)
if False == os.path.isfile(bundleFile):
log("Step %d - SceneCreation cannot begin because the NVM file %s is missing." %
(stepNum, bundleFile))
log("Processing will not proceed.")
return -stepNum
if False == os.path.isdir(imgDir):
log("Step %d - SceneCreation cannot beging because the image directory %s cannot be found." %
(stepNum, imgDir))
log("Processing will not proceed.")
return -stepNum
log("Creating scene with bundleFile %s." % (bundleFile))
log("Output will be written to %s." % (outDir))
boxm2_batch.not_verbose()
boxm2_batch.register_processes()
boxm2_batch.register_datatypes()
# class used for python/c++ pointers in database
class dbvalue:
def __init__(self, index, type):
self.id = index # unsigned integer
self.type = type # string
# run process
boxm2_batch.init_process("boxm2BundleToSceneProcess")
boxm2_batch.set_input_string(0, bundleFile)
boxm2_batch.set_input_string(1, imgDir)
boxm2_batch.set_input_string(2, self.config.NVMAppModel)
boxm2_batch.set_input_string(3, self.config.NVMNobsModel)
boxm2_batch.set_input_int(4, 8)
boxm2_batch.set_input_string(5, outDir)
boxm2_batch.run_process()
(scene_id, scene_type) = boxm2_batch.commit_output(0)
uscene = dbvalue(scene_id, scene_type)
(scene_id, scene_type) = boxm2_batch.commit_output(1)
rscene = dbvalue(scene_id, scene_type)
return 0
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 createScene(self, stepNum):
bundleFile = os.path.abspath(self.config.NVMFileName)
imgDir = os.path.abspath(self.config.SRCImageDirName)
outDir = os.path.abspath(self.config.NVMOutputDirName)
if False == os.path.isfile(bundleFile):
log("Step %d - SceneCreation cannot begin because the NVM file %s is missing."
% (stepNum, bundleFile))
log("Processing will not proceed.")
return -stepNum
if False == os.path.isdir(imgDir):
log("Step %d - SceneCreation cannot beging because the image directory %s cannot be found."
% (stepNum, imgDir))
log("Processing will not proceed.")
return -stepNum
log("Creating scene with bundleFile %s." % (bundleFile))
log("Output will be written to %s." % (outDir))
# run process
batch.init_process("boxm2BundleToSceneProcess")
batch.set_input_string(0, bundleFile)
batch.set_input_string(1, imgDir)
batch.set_input_string(2, self.config.NVMAppModel)
batch.set_input_string(3, self.config.NVMNobsModel)
batch.set_input_int(4, 8)
batch.set_input_string(5, outDir)
batch.run_process()
(scene_id, scene_type) = batch.commit_output(0)
uscene = dbvalue(scene_id, scene_type)
(scene_id, scene_type) = batch.commit_output(1)
rscene = dbvalue(scene_id, scene_type)
return 0
def bae_raw_stream(file_path, ni=0, nj=0, pixelsize=0):
boxm2_batch.init_process("bilCreateRawImageIstreamProcess")
boxm2_batch.set_input_string(0, file_path)
boxm2_batch.set_input_int(1, ni)
boxm2_batch.set_input_int(2, nj)
boxm2_batch.set_input_int(3, pixelsize)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
stream = dbvalue(id, type)
(id, type) = boxm2_batch.commit_output(1)
numImgs = boxm2_batch.get_output_int(id)
return stream, numImgs
def bae_raw_stream(file_path,ni=0,nj=0,pixelsize=0) :
boxm2_batch.init_process("bilCreateRawImageIstreamProcess")
boxm2_batch.set_input_string(0,file_path);
boxm2_batch.set_input_int(1,ni);
boxm2_batch.set_input_int(2,nj);
boxm2_batch.set_input_int(3,pixelsize);
boxm2_batch.run_process();
(id, type) = boxm2_batch.commit_output(0);
stream = dbvalue(id, type);
(id, type) = boxm2_batch.commit_output(1);
numImgs = boxm2_batch.get_output_int(id);
return stream, numImgs
def query_cell(scene, cache, point, model_name, model_type):
# Point should be 3 len, for a x, y, z coordinate OR
#4 in lenght, blk_i, blk_j, blk_k, index
if len(point) == 3:
(blk, index) = get_index_from_3d_point(scene, cache, point)
point = blk + (index,)
batch.init_process("boxm2CppQueryCellProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_int(2, point[0])
batch.set_input_int(3, point[1])
batch.set_input_int(4, point[2])
batch.set_input_int(5, point[3])
batch.set_input_string(6, model_name)
batch.set_input_string(7, model_type)
batch.run_process()
(id, type) = batch.commit_output(0)
data = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
return data
def query_cell(scene, cache, point, model_name, model_type):
# Point should be 3 len, for a x, y, z coordinate OR
#4 in lenght, blk_i, blk_j, blk_k, index
if len(point) == 3:
(blk, index) = get_index_from_3d_point(scene, cache, point)
point = blk + (index, )
batch.init_process("boxm2CppQueryCellProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_int(2, point[0])
batch.set_input_int(3, point[1])
batch.set_input_int(4, point[2])
batch.set_input_int(5, point[3])
batch.set_input_string(6, model_name)
batch.set_input_string(7, model_type)
batch.run_process()
(id, type) = batch.commit_output(0)
data = batch.get_bbas_1d_array_float(id)
batch.remove_data(id)
return data
import os
import shutil
import time
import sys
class dbvalue:
def __init__(self, index, type):
self.id = index # unsigned integer
self.type = type # string
print "create FS"
boxm2_batch.init_process("bhdfsCreateFSManagerProcess")
boxm2_batch.set_input_string(0, "default")
boxm2_batch.set_input_int(1, 0)
boxm2_batch.run_process()
file_list = glob.glob(sys.argv[1] + "/*" + sys.argv[2])
boxm2_batch.init_process("bhdfsCreateDirProcess")
boxm2_batch.set_input_string(0, sys.argv[3])
statuscode = boxm2_batch.run_process()
if statuscode != 1:
print "Unable to create folder: " + sys.argv[3] + " on HDFS, exiting!\n"
sys.exit(0)
for fname in file_list:
print fname
boxm2_batch.init_process("bhdfsCopyFileProcess")
boxm2_batch.set_input_string(0, fname)
import os
import shutil
import time
import sys
class dbvalue:
def __init__(self, index, type):
self.id = index # unsigned integer
self.type = type # string
print "create FS"
boxm2_batch.init_process("bhdfsCreateFSManagerProcess")
boxm2_batch.set_input_string(0, "default")
boxm2_batch.set_input_int(1, 0)
boxm2_batch.run_process()
boxm2_batch.init_process("bbasStringArrayProcess")
boxm2_batch.set_input_string(0, sys.argv[2])
boxm2_batch.run_process()
boxm2_batch.set_input_string(0, sys.argv[3])
boxm2_batch.run_process()
boxm2_batch.finish_process()
(arr_id, arr_type) = boxm2_batch.commit_output(0)
arr = dbvalue(arr_id, arr_type)
# generate an input file on the output hdfs folder, this file will be streamed to mapper processes
# a vector of strings are inputted which will be used to form globs to retrieve file names
# the output file will have <name1> <name2> ... <name n> names on each line depending on this vector's size
boxm2_batch.init_process("bhdfsGenerateStdInFileProcess")
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 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()
import glob
import random
import math
import os
import shutil
import time
import sys
import brl_init
import boxm2_batch as batch
dbvalue = brl_init.register_batch(batch)
print "create FS"
batch.init_process("bhdfsCreateFSManagerProcess")
batch.set_input_string(0, "default")
batch.set_input_int(1, 0)
batch.run_process()
batch.init_process("bbasStringArrayProcess")
batch.set_input_string(0, sys.argv[2])
batch.run_process()
batch.set_input_string(0, sys.argv[3])
batch.run_process()
batch.finish_process()
(arr_id, arr_type) = batch.commit_output(0)
arr = dbvalue(arr_id, arr_type)
# generate an input file on the output hdfs folder, this file will be streamed to mapper processes
# a vector of strings are inputted which will be used to form globs to retrieve file names
# the output file will have <name1> <name2> ... <name n> names on each
# line depending on this vector's size
import math
import os
import shutil
import time
import sys
import brl_init
import boxm2_batch as batch
dbvalue = brl_init.register_batch(batch)
mytime = time.clock()
print "create FS"
batch.init_process("bhdfsCreateFSManagerProcess")
batch.set_input_string(0, "default")
batch.set_input_int(1, 0)
batch.run_process()
# read the hadoop output folder from the configuration file
f = open("conf_mapper.txt", 'r')
dir_names = f.readlines()
f.close()
output_path = dir_names[0].strip()
print output_path
exh_search_rad = int(dir_names[1].strip())
print exh_search_rad
for line in sys.stdin:
line = line.strip()
words = line.split()
print words[0]
