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 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 paint_mesh(scene, cache, in_file, out_file):
batch.init_process("boxm2PaintMeshProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, in_file)
batch.set_input_string(3, out_file)
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 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 load_mesh(filename):
batch.init_process("boxm2LoadMeshProcess")
batch.set_input_string(0, filename)
batch.run_process()
(id, type) = batch.commit_output(0)
mesh = dbvalue(id, type)
return mesh
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 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 export_stack(scene, cache, outdir, identifier):
batch.init_process("boxm2ExportStackImagesProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, outdir)
batch.set_input_string(3, identifier)
batch.run_process()
def load_mesh(filename):
batch.init_process("boxm2LoadMeshProcess")
batch.set_input_string(0, filename)
batch.run_process()
(id, type) = batch.commit_output(0)
mesh = dbvalue(id, type)
return mesh
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 paint_mesh(scene, cache, in_file, out_file):
batch.init_process("boxm2PaintMeshProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, in_file)
batch.set_input_string(3, out_file)
batch.run_process()
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 export_stack(scene, cache, outdir, identifier):
batch.init_process("boxm2ExportStackImagesProcess")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, outdir)
batch.set_input_string(3, identifier)
batch.run_process()
def convert_image(img, type="byte"):
boxm2_batch.init_process("vilConvertPixelTypeProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_string(1, type)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
cimg = dbvalue(id, type)
return cimg
def convert_image(img, type="byte") :
boxm2_batch.init_process("vilConvertPixelTypeProcess");
boxm2_batch.set_input_from_db(0, img);
boxm2_batch.set_input_string(1, type);
boxm2_batch.run_process();
(id,type) = boxm2_batch.commit_output(0);
cimg = dbvalue(id,type);
return cimg;
def binary_img_op(img1, img2, operation="sum"):
boxm2_batch.init_process("vilBinaryImageOpProcess")
boxm2_batch.set_input_from_db(0,img1)
boxm2_batch.set_input_from_db(1,img2)
boxm2_batch.set_input_string(2,operation)
boxm2_batch.run_process()
(id,type) = boxm2_batch.commit_output(0)
out = dbvalue(id, type);
return out
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 compute_dispersion(device, scene, opencl_cache, filename, coordinate_type="Cartesian"):
print("Fuse two models using Surface Density")
batch.init_process("boxm2OclComputeExpectationViewDirectionProcess")
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_string(3, filename)
batch.set_input_string(4, coordinate_type)
return batch.run_process()
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 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 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 arf_stream(file_path):
boxm2_batch.init_process("bilCreateArfImageIstreamProcess")
boxm2_batch.set_input_string(0, file_path)
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 binary_img_op(img1, img2, operation="sum"):
boxm2_batch.init_process("vilBinaryImageOpProcess")
boxm2_batch.set_input_from_db(0, img1)
boxm2_batch.set_input_from_db(1, img2)
boxm2_batch.set_input_string(2, operation)
boxm2_batch.run_process()
(id, type) = boxm2_batch.commit_output(0)
out = dbvalue(id, type)
return out
def arf_stream(file_path) :
boxm2_batch.init_process("bilCreateArfImageIstreamProcess")
boxm2_batch.set_input_string(0,file_path);
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 gen_mesh(z_img, x_img, y_img, out_dir):
batch.init_process("boxm2ExportMeshProcess")
batch.set_input_from_db(0, z_img)
batch.set_input_from_db(1, x_img)
batch.set_input_from_db(2, y_img)
batch.set_input_string(3, out_dir)
batch.run_process()
(id, type) = batch.commit_output(0)
mesh = dbvalue(id, type)
return mesh
def update_alpha_with_cubic(self, pcam, img):
""" Update occupancy (alpha) values using the cubic appearances """
batch.init_process("boxm2OclUpdateAlphaWcubicProcess")
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, pcam)
batch.set_input_from_db(4, img)
batch.set_input_string(5, "cubic_model")
batch.run_process()
def uncertainty_per_view(self, cam, img, viewNum):
"""Store uncertainty aux data per image for this model """
batch.init_process("boxm2OclUncertaintyPerImageProcess")
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, cam)
batch.set_input_from_db(4, img)
batch.set_input_string(5, "uncertain_" + "%05d" % viewNum)
batch.run_process()
def update_alpha_with_cubic(self, pcam, img):
""" Update occupancy (alpha) values using the cubic appearances """
batch.init_process("boxm2OclUpdateAlphaWcubicProcess")
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, pcam)
batch.set_input_from_db(4, img)
batch.set_input_string(5, "cubic_model")
batch.run_process()
def gen_mesh(z_img, x_img, y_img, out_dir):
batch.init_process("boxm2ExportMeshProcess")
batch.set_input_from_db(0, z_img)
batch.set_input_from_db(1, x_img)
batch.set_input_from_db(2, y_img)
batch.set_input_string(3, out_dir)
batch.run_process()
(id, type) = batch.commit_output(0)
mesh = dbvalue(id, type)
return mesh
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 create_aux_data(self, cam, img, viewNum):
""" Computes aux for view num (visibility, seg len, app) """
batch.init_process("boxm2OclUpdateAuxPerViewProcess")
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, cam)
batch.set_input_from_db(4, img)
batch.set_input_string(5, "img_" + "%05d" % viewNum)
batch.run_process()
def uncertainty_per_view(self, cam, img, viewNum):
"""Store uncertainty aux data per image for this model """
batch.init_process("boxm2OclUncertaintyPerImageProcess")
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, cam)
batch.set_input_from_db(4, img)
batch.set_input_string(5, "uncertain_" + "%05d" % viewNum)
batch.run_process()
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 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 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 create_aux_data(self, cam, img, viewNum):
""" Computes aux for view num (visibility, seg len, app) """
batch.init_process("boxm2OclUpdateAuxPerViewProcess")
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, cam)
batch.set_input_from_db(4, img)
batch.set_input_string(5, "img_" + "%05d" % viewNum)
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 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 grey_to_rgb(img, color_txt):
boxm2_batch.init_process("vilGreyToRGBProcess")
boxm2_batch.set_input_from_db(0,img)
boxm2_batch.set_input_string(1,color_txt)
result = boxm2_batch.run_process()
if result:
(id, type) = boxm2_batch.commit_output(0)
outimg = dbvalue(id, type);
else:
outimg = 0
return outimg
def grey_to_rgb(img, color_txt):
boxm2_batch.init_process("vilGreyToRGBProcess")
boxm2_batch.set_input_from_db(0, img)
boxm2_batch.set_input_string(1, color_txt)
result = boxm2_batch.run_process()
if result:
(id, type) = boxm2_batch.commit_output(0)
outimg = dbvalue(id, type)
else:
outimg = 0
return outimg
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 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 create_coarse_model(scene, cache, device, model_dir):
if cache.type == "boxm2_opencl_cache_sptr":
print("Create a Coarser Scene")
batch.init_process("boxm2OclCreateCoarserSceneProcess")
batch.set_input_from_db(0, device)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, cache)
batch.set_input_string(3, model_dir)
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def create_coarse_model(scene, cache, device, model_dir):
if cache.type == "boxm2_opencl_cache_sptr":
print("Create a Coarser Scene")
batch.init_process("boxm2OclCreateCoarserSceneProcess")
batch.set_input_from_db(0, device)
batch.set_input_from_db(1, scene)
batch.set_input_from_db(2, cache)
batch.set_input_string(3, model_dir)
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
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 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 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 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 add_aux_info_to_ply(scene, cache, file_in, file_out):
if cache.type == "boxm2_cache_sptr":
print("Adding aux info to ply points")
batch.init_process("boxm2_add_aux_info_to_ply_process")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, file_in)
# output aux (prob, vis, normal magnitdue)
batch.set_input_string(3, file_out)
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def update_auxQ(scene, device, opencl_cache, cam, img, ident="", view_ident=""):
#print("Init Manager");
batch.init_process("boxm2OclUpdateAuxQPerViewProcess")
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_from_db(4, img)
batch.set_input_string(5, ident)
batch.set_input_string(6, view_ident)
status = batch.run_process()
if(status != True):
print "Error in Running Aux Q "
def add_aux_info_to_ply(scene, cache, file_in, file_out):
if cache.type == "boxm2_cache_sptr":
print("Adding aux info to ply points")
batch.init_process("boxm2_add_aux_info_to_ply_process")
batch.set_input_from_db(0, scene)
batch.set_input_from_db(1, cache)
batch.set_input_string(2, file_in)
# output aux (prob, vis, normal magnitdue)
batch.set_input_string(3, file_out)
return batch.run_process()
else:
print "ERROR: Cache type not recognized: ", cache.type
return False
def load_image(file_path) :
boxm2_batch.init_process("vilLoadImageViewProcess");
boxm2_batch.set_input_string(0, file_path);
boxm2_batch.run_process();
(id,type) = boxm2_batch.commit_output(0);
(ni_id, ni_type) = boxm2_batch.commit_output(1);
(nj_id, nj_type) = boxm2_batch.commit_output(2);
ni = boxm2_batch.get_output_unsigned(ni_id);
nj = boxm2_batch.get_output_unsigned(nj_id);
img = dbvalue(id,type);
boxm2_batch.remove_data(ni_id)
boxm2_batch.remove_data(nj_id)
return img, ni, nj;
